GroupMorph A Group Collaboration Mode Approach to Shared 3D Virtual Environments for Produc
219486447_罗沙司他在慢性肾脏病患者肾性贫血治疗中的效果评价
系统医学 2023 年 3 月第 8 卷第 5期罗沙司他在慢性肾脏病患者肾性贫血治疗中的效果评价钱娜,刘晓东,何传梅,李慧连云港市第二人民医院肾内科,江苏连云港222000[摘要]目的探讨在慢性肾脏病患者肾性贫血治疗中采用罗沙司他的效果。
方法选择2022年1月—2023年1月连云港市第二人民医院收治的慢性肾脏病合肾性贫血患者80例,按随机数表法分为对照组(40例,重组人促红素治疗)与研究组(40例,罗沙司他治疗),比较两组治疗前后贫血指标、铁代谢指标、血脂代谢指标及不良反应情况。
结果与治疗前相比,治疗后两组Hb、Hct、RBC水平均升高,且研究组Hb、Hct、RBC水平为(117.35±9.62)g/L、(38.07±3.51)%、(3.04±0.56)×1012/L,均高于对照组的(106.42±8.46)g/L、(30.63±3.16)%、(2.36±0.47)×1012/L,差异有统计学意义(t=5.396、9.963、5.883,P<0.05)。
与治疗前相比,两组治疗后TRF、SF、TSAT水平均升高,Hepc水平则下降,且研究组改善幅度高于对照组,差异有统计学意义(P<0.05)。
与治疗前相比,两组治疗后TCH、LDL水平均下降,且研究组TCH、LDL水平均低于对照组,差异有统计学意义(P< 0.05)。
与对照组相比,研究组不良反应发生率更低,差异有统计学意义(P<0.05)。
结论在慢性肾脏病合并肾性贫血患者治疗中采用罗沙司他的效果理想,能够改善机体贫血状态,并调节机体铁代谢与血脂代谢水平,且不良反应少,值得推广。
[关键词]慢性肾脏病;肾性贫血;罗沙司他;铁代谢;血脂代谢;不良反应[中图分类号]R4 [文献标识码]A [文章编号]2096-1782(2023)03(a)-0101-05Evaluation of the Effect of Roxadustat in the Treatment of Renal Anemia in Patients with Chronic Kidney DiseaseQIAN Na, LIU Xiaodong, HE Chuanmei, LI HuiDepartment of Nephrology, Lianyungang Second People's Hospital, Lianyungang, Jiangsu Province, 222000 China[Abstract] Objective To investigate the effect of using roxadustat in the treatment of renal anemia in patients with chronic kidney disease. Methods Eighty patients with chronic kidney disease combined with renal anemia admitted to the Second People's Hospital of Lianyungang City from January 2022 to January 2023 were divided into control group (40 cases treated with recombinant human erythropoietin) and study group (40 cases treated with rosalrestat) according to random number table method. Anemia indexes, iron metabolism indexes, lipid metabolism indexes before and after treatment and adverse reactions of the two groups were compared. Results Compared with before treatment, the Hb, Hct and RBC levels in both groups increased after treatment, and the Hb, Hct and RBC levels in the study group were (117.35±9.62) g/L, (38.07±3.51) %, (3.04±0.56)×1012/L, respectively, which were higher than those in the control group (106.42±8.46) g/L, (30.63±3.16) %, (2.36±0.47)×1012/L,the difference was statistically significant(t=5.396, 9.963, 5.883,P<0.05). Compared with before treatment, TRF, SF and TSAT levels increased in both groups after treat‐ment, while Hepc levels decreased, and the improvement was higher in the study group than in the control group, the difference was statistically significant (P<0.05). Compared with before treatment, TCH and LDL levels decreased in both groups after treatment, and TCH and LDL levels in the study group were lower than those in the control group, the difference was statistically significant (P<0.05). Adverse reaction rate were lower in the study group compared with those in the control group, the difference was statistically significant (P<0.05). Conclusion The use of roxadustat in DOI:10.19368/ki.2096-1782.2023.05.101[作者简介] 钱娜(1985-),女,本科,主治医师,研究方向为IgA肾病的发病机制和治疗。
Collaborative plans for group activities
C o l l a b o r a t i v e Plans for G r o u p A c t i v i t i e s *B a r b a r a G r o s zDivision of Applied SciencesHarvard UniversityCambridge, MA 02138 USA grosz@A b s t r a c tThe original formulation of SharedPlans [Grosz and Sidner, 1990] was developed to provide a model of collaborative planning in which it was not necessary for one agent to have in-tentions toward an act of a different agent.This formulation provided for two agents tocoordinate their activities without introducing any notion of jointly held intentions (or, 'we-intentions'). However, it only treated activities that directly decomposed into single agents ac-tions. I n this paper we provide a revised and expanded version of SharedPlans that accom-modates actions involving groups of agents as well as complex actions that decompose into multi-agent actions. The new definitions also allow for contracting out certain actions, and provide a model with the features required in Bratman's account of shared cooperative activ-ity [Bratman, 1992]. A reformulation of the model of individual plans that meshes with the definition of SharedPlans is also provided.1 In t r o d u c t i o nCollaboration in planning and acting is an essential in-gredient of multi-agent cooperative problem solving. In this paper we present a model of collaborative planning that supports cooperative problem solving by teams con-sisting of humans and computer systems. The model deals more completely with collaboration than previous theories did in meeting two criteria. First, collabora-tive planning and activity cannot be analyzed simply in terms of the plans of individual agents, but require an integrated treatment of the beliefs and intentions of the different collaborating agents. Second, collaborative planning is a refinement process; a partial plan descrip-tion is modified over the course of planning by the mul-* We thank Joyce Friedman for many provoking questions, and Karen Lochbaum for the same and for helpful comments on many drafts. This research was initiated when the first author was a Harold Perlman Visiting Professor, Hebrew Uni-versity, Jerusalem. Partial support for the first author was provided by U S WEST Advanced Technologies. The second author was supported by NSF Grant No. R -9123460.S a r i t K r a u sDept. of Mathematics and Computer ScienceBar I lan University, Ramat Gan, 52900 I srael sarit@bimacs.cs.biu.ac.iltiple collaborating agents. This model grew out of an attempt to provide an adequate treatment of the in-tentional component of discourse structure [Grosz and Sidner, 1986]. However, many multi-agent situations re-quire that agents have an ability to plan and act to-gether; merely avoiding conflicting actions or situations is not sufficient. Thus, the model is applicable not only to natural language processing, but also to the general problem of the design of computer-based collaborating agents.The original formulation of the SharedPlan model of collaborative planning [Grosz and Sidner, 1990] extended Pollack's mental state model of plans [Pollack, 1990] to the situation in which two agents jointly have a plan to perform some action requiring actions by both agents. Pollack s definition of the individual plan of an individ-ual agent to do an action a includes four constituent mental attitudes: (1) belief that performance of certain actions would entail performance of we will refer to the as constituting "a recipe for " (2) belief that the agent could perform each of the (3) intentions to do each of the (4) an intention to do a by do-ing the To define SharedPlans, Grosz and Sidner modified these components to provide a specification of the set of beliefs and intentions required for collabora-tive action. In subsequent work [Lochbaum et al., 1990; Lochbaum, 1991], algorithms were provided for con-structing and augmenting SharedPlans in the context of a dialogue.Although this formulation overcame several problems of previous models of planning for discourse (e.g. the treatment of intentions of one agent toward another agents actions in applications of speech act theory [Allen and Perrault, 1980]), it had several problems that emerged when we attempted to apply it [Lochbaum et al., 1990; Lochbaum, 1991]. First, the original model presumed that every multi-agent action decomposed di-rectly into single agent actions, a similar assumption underlies several alternative models (e.g. [Cohen and Levesque, 1990]). As a result, the model did not ad-equately provide for complex activities involving joint activity at multiple levels or for meshing of individual plans for individual action with collaborative plans for joint activity. Second, the model did not account for the commitment of an agent to the success of a collaborative partner's actions. This omission combined with the firstGrosz and Kraus 367so t h a t the model accepted some plans as collaborative t h a t were not.1 T h i r d , the agents who undertake the development of a collaborative plan often do not know a complete recipe for accomplishing their j o i n t action; the model d i d not provide a sufficient means of describing the mental state of agents in this situation.2 Each of these problems is addressed in this paper.Collaborative a c t i v i t y must rest eventually on the ac-tions of i n d i v i d u a l agents; thus, SharedPlans must i n -clude as constituents at some levels the i n d i v i d u a l plans of i n d i v i d u a l agents. B u t these i n d i v i d u a l plans may bemore complex t h a n those accounted for by Pollack's for-m u l a t i o n in two ways: the recipes require different typesof relations [Balkanski, 1990]3, and an agent may not i n i t i a l l y know a complete recipe. Hence, we also provide a revised definition of the plans of an i n d i v i d u a l agent. We begin the description of the revised model w i t h an overview of different types of intentional attitudes that play a role in collaborative planning. T h e n we provide adefinition of i n d i v i d u a l plans t h a t accommodates morecomplex recipes b u t still requires complete knowledge of the recipe. To describe these f u l l individual plans re-quires a specification of certain properties of intention, so we define the n t.T o operator at this point. Next, we show how to ease the complete knowledge requirement, yielding a definition of p a r t i a l individual plan. We then define SharedPlans recursively in terms of f u l l and par-t i a l SharedPlans. A f u l l SharedPlan is the collaborative correlate of a full i n d i v i d u a l plan and includes full in-d i v i d u a l plans as constituents. To describe interactions among the intentions of the different agents requires that we introduce the notion of an agent intending that some proposition hold so we explain the I nt. Th operator in this section. Finally, we provide a definition of p a r t i a l ShartdPlan. At each stage we discuss those aspects of the resulting theory t h a t address the problems described above to provide a more adequate model of collaborativeactivity.2 A t t i t u d e s o f I n t e n t i o nT h e definitions of i n d i v i d u a l and SharedPlans w i l l use a first-order logic augmented w i t h several modal oper-ators. We introduce four different intention operators. T w o of these, I nt. To and I nt. Th, represent intentions t h a t have been adopted by an agent. T h e other two, Pot.Int. To and Pot. Int. Th, are variations of the first two t h a t are used to represent potential intentions, i.e. inten-tions an agent is considering adopting but to which it is not yet c o m m i t t e d. I n t. To and Pot.Int. To are action-directed whereas I n t. Th and P o t.n t. Th are proposition-directed.1For example, joint activity like that in Searle's MBA counterexample [Searle, 1990], but involving actions that de-composed at multiple levels would have been inaccurately characterized.2The notion of a partial SharedPlan, SharedPlan*, was intended to represent this kind of partiality, but was never specified in any detail.3ln Pollack's simple plans the and a were related only by the action relation of generation.An Int. To c o m m i t s an agent to means-ends reason-ing [B r a t m a n , 1987] whereas an I n t.T h does not directly engender such behavior. In t.T h 's f o r m the basis for meshing subplans, helping one's collaborator, and co-o r d i n a t i n g status updates [Cohen and Leveque, 1991]. An nt. Th may, however, lead to adoption of an I nt. To and thus indirectly to means-ends reasoning. Potential intentions are used to account for an agent's need to weigh different possible courses of actions [B r a t m a n et al/., 1988]; they typically arise in the course of means-ends reasoning. A t t i t u d e s of P o t.n t. To stem f r o m an agent's deliberations about how to do some action it is c o m m i t t e d to p e r f o r m. P o t.In t.T h 's arise in the course of collaborative planning and are needed to insure t h a t agents' i n d i v i d u a l plans mesh correctly [B r a t m a n , 1992]. T h e difference among these operators can be illus-trated w i t h an example we w i l l use t h r o u g h o u t the pa-per. T w o agents, Jan and Sandy, have agreed to make dinner together. T h e i r collaborative plan consists of Jan m a k i n g an appetizer, Sandy the m a i n course, and the two of them together m a k i n g the dessert. Their Shared-Plan to make dinner includes Jan having an intention to [Int. To] make the appetizer (and an i n d i v i d u a l plan fordoing so), Sandy having an intention to [Int.To] make the m a i n course (and an i n d i v i d u a l plan for doing so), and their having a SharedPlan to make the dessert. T h e SharedPlan for m a k i n g dinner also includes Sandy's in-tention that [In t.T h ] Jan 4can m a k e ' the appetizer, and Jan's intention t h a t [n t.T h ] Sandy 'can m a k e ' the m a i n course.Jan may have decided to make cheese puffs for the appetizer, but not yet have chosen a recipe for doing so. If so, his i n d i v i d u a l plan w i l l be p a r t i a l. It will include anInt. To get a recipe for cheese puffs and a full i n d i v i d u a l plan for doing so.4 I n a d d i t i o n , he believes that he can perform all of the actions in the recipe once he gets it. As he determines the recipe and thus the actions he needs to perform (according to t h a t recipe), he adopts potential intentions to [P o t.In t. To] perform these actions. T h e potential intentions w i l l become part of a deliberation process [B r a t m a n et a/., 1988] and through t h a t processmay become nt. TVs.368 Distributed AIthat the agent either can do the action at the time using the recipe 5 or can get another agent to do it; CBAG isthe analogous group operator. holds when G (either a group or a single agent) has done a overtime interval using the recipeIn addition, we will use to denote a recipe for i.e. a specification of a group of actions, which we will refer toas , the doing of which under appropriate constraints, , will constitute performance of [Pollack, 1990; Balkanski, 1990; Lochbaum et a/., 1990]. A recipe may include uninstantiated variables (e.g. for the agent or time of an action) and constraints on these variables. Weassume each agent has a library of recipes that it collectsand updates over time. Agents' libraries may differ, and the successful completion of a SharedPlan may require integrating recipes from different libraries, i.e. from dif-ferent agents.3 I n d i v i d u a l Plans and I ntending To3.1 F u l l I n d i v i d u a l PlansThe definition of a full individual plan, FI P, is given in Figure 1. t specifies those conditions under which an in-dividual agent G can be said to have a plan P, at time to do action at time using recipe in the con-text . Full plans are distinguished by the requirementthat the agent know a complete recipe for doing the ac-tion; as a result is a parameter of the operator. Afull individual plan for a represents the mental state ofan agent after he has completely determined the means by which he will do and has full-fledged intentions to do the actions in . Most, typically an agent will not have a full plan until after he has done some or all of the actions in ; thus, most often agents have only partial plans. However, it is useful to understand the limiting case of the full plan before examining the partial version. We will illustrate the F P by showing its use in de-scribing Sandy's individual plan for making the main course in the meals example. According to Clause (0), Sandy believes that a particular recipe, say his mother's recipe for lasagne, is a recipe for making a main course The remaining clauses provide a specification of certain attitudes Sandy must hold with respect to the individual constituents of this recipe. I n particular, for each action in the recipe (e.g. making noodles, preparing sauce), he must either intend to do the action (1) or believe that he can get someone else to do the action (2). We will refer to the first case as the "core case 1' of the individual plan, and the second case as the "contracting case." In the core case, the agent must believe either that the action is basic level and he can bring it about (la), or that he has a recipe that will enable him to do the action and a full individual plan to carry out the act ions in that recipe (l b ). The Int. To operator in Clause (1) includes a context parameter, that is used in any replan-n i g involving . For the purposes of this paper, the important element of the context encoded in this param-eter is a representation (using the Contributes relation *If the action is basic level, this reduces to Pollack's EXEC operator. The connective that we use in all the definitions is really exclusive or (XOR).Grosz and Kraus 3693.2 P a r t i a l I n d i v i d u a l PlansIndividual plans may be partial in several ways. Each of these will lead to some Int. To in which Clause (2b) of the definition in Figure 2 applies The Achieve function in this clause maps a proposition to a generalized action [Pollack, 1990].8 A typical way in which partial individ-ual plans differ from full plans is in allowing an agent to have only a partial recipe for an action. As can be seen in the definition in Figure 3, the minimal requirement for the partial plan is that the agent believe there is a recipe for a (Clause (0)), believe that it is able to determine any constituents of that recipe not already known (lc), in-tend to obtain that recipe (l b) and have a full individual plan for doing so (I d); a procedure associated with the GET operator will add potential intentions to do all the actions in this plan.9 For example, if an agent is assem-bling a bicycle from a kit, he must believe that the ac-companying instructions are complete, that he can read the instructions, and that he can perform each of them at the requisite time. While reading the instructions, the agent will adopt potential intentions to do the actions.Clause (2) represents the attitudes of the agent to-This function may be seen as connected to an agenda of tasks maintained by the agent. Discussion of this component of the model is beyond the scope of this paper. Briefly, be-cause the agent must be committed to completing his partial plan for a for the Int. To operator to hold, the agenda must include tasks for establishing any of the beliefs and intentions needed in a full plan but absent in the partial plan.9W C B A is a weaker version of the can bring about oper-ator, one in which the agent may only believe it can find a recipe that it can use to do the action; W C B A G is a corre-sponding group operator.370 Distributed Al4 S haredPlans and Intending T h a tBoth the belief and the intention components of collab-orative plans are more complex than those of individual plans. The collaborating agents must establish mutual belief of the ways in which they will perform their joint activity and must agree on the agent or agents who will do each action. Actions requiring multiple agents engen-der subsidiary S haredPlans of groups of agents; those re-quiring only a single agent lead to subsidiary individual plans. The agents also need to establish mutual belief of their individual intentions to act.There are several important properties of these belief and intention components that are captured in the def-initions that follow. First, agents do not need to know recipes for any actions that they are not personally com-mitted to doing. In our meals example, Jan and S andy need to establish mutual belief of the recipe for making dinner, namely that this will comprise Jan's making the appetizer, S andy the main course, and the two of them together making the dessert. Only Jan needs to know the recipe for the appetizer; but S andy needs to share mutual belief that Jan has such a recipe and can carry it out. The analogous case holds for S andy and a recipe for the main course. In contrast, S andy and Jan needmutual belief of the recipe for making dessert. Second, an agent only has Int. 7Vs to acts of which it is the agent. However, it has Int.Th's that the actions of other agents be successfully done. More generally, the ways in which the belief and intention operators are used differ. In the following definitions we presume the usual definition of mutual belief [Kraus and Lehmann, 1988] which requires infinite nestings of individual beliefs, but utilizes only a single belief operator, BEL. In contrast, to handle the intentions that arise in S haredPlans, we need two opera-tors Int. To and Int. Th but there is no need for infinite embeddings of these operators (either in themselves or within one another). However, both operators may be embedded within the mutual belief operator, MB.The S haredPlan operator, representing that a group of agents G has a plan to collaboratively perform someactiona, is defined recursively in terms of full and partialSharedPlans as follows:A group of agents will be said to have a S haredPlan just in case either (1) they have a full S haredPlan for doing a or (2) they have a partial S haredPlan, and a SharedPlan to complete that partial plan As will be seen from the definitions of these two types of S hared-Plans, each of these possibilities leads eventually to in-dividual intentions to do actions, including actions of elaborating or extending partial plans. 4.1 F u l l S haredPlansWe will use the formula to represent the situation in which a group of agents G has a full shared plan P at time to do action a atGrosz and Kraus 371372 Distributed Al5 Conclusions and Future WorkTo provide an account of collaborative activity, Searle [Searle, 1990] introduced the notion of 'we-intention.' Grosz and Sidner [Grosz and Sidner, 1990] argued that such a notion should not be necessary and their initial formulation of SharedPlans avoids use of one. How-ever, the definitions provided in that formulation could only accommodate group activity that directly decom-posed into actions of individual agents. Subsequent work in A Ion formalizing the plans and intentions of mul-tiple agents has, like Searle's proposal, included some notion of joint intention [Cohen and Levesque, 1990; Rao et al., 1992]. In this paper, we have provided a formulation that again avoids the need for a notion of joint intention. n this work, SharedPlans serve two ma-jor roles. They summarize the set of beliefs and inten-tions needed for collaborative activity, and also provide the rationale for the process of revising beliefs and in-tentions; consequently, they motivate the collaborative correlate of means-ends reasoning in the plans of an in-dividual agent. SharedPlans ground out in the individ-ual intentions of individual agents and the individual plans that they engender. This formulation accommo-dates the properties of shared cooperative activity pro-posed by Bratman [Bratman, 1992]. Intentions to do constituent actions form the basis of each individual'sactions. Intentions-that directed toward other agents abilities to act and success in acting, as well as toward the success of the joint activity, ensure cooperation in subplans and helpful behavior.References[Allen and Perrault, 1980] J F. Allen and C.R. Perrault. Analyzing intention in utterances. Artificial I ntelli-gence, 15(3):143 178, 1980. [Balkanski, 1990] C. Balkanski. Modelling act-type rela-tions in collaborative activity. Tr-23-90, Harvard Uni-versity, 1990.[Bratman et al., 1988] M. Bratman, D. Israel, and M. Pollack. Plans and resource-bounded practical rea-soning. Computational I ntelligence, 4:349-355, 1988. [Bratman, 1987] Michael E. Bratman. I ntention, Plans, and Practical Reason. Harvard University Press, Cam-bridge, Massachusetts and London, England, 1987. [Bratman, 1992] M E . Bratman. Shared cooperative ac-tivity. The Philosophical Review, 101:327-341, 1992. [Cohen and Leveque, 1991] P. Cohen and H. Leveque. Teamwork. Tr 503, SRI I nternational, 1991. [Cohen and Levesque, 1990] P. R. Cohen and Hector Levesque. Rational interaction as the basis for com-munication. I n Cohen et al. 1990. [Cohen el al., 1990] P. R. Cohen, J. Morgan, and M. E. Pollack (editors). I ntentions in Communica-tion. MI T Press, 1990. [Grosz and Sidner, 1986] B J. Grosz and C.L. Sidner. Attention, intentions, and the structure of discourse. Computational Linguistics, 12(3), 1986.[Grosz and Sidner, 1990] B.J. Grosz and C.L. Sidner. Plans for discourse. n Cohen et al. 1990. [Kraus and Lehmann, 1988] S. Kraus and D. Lehmann. Knowledge, belief and time. Theoretical Computer Science, 58:155-174, 1988. [Kraus, 1993] S. Kraus. Agents contracting tasks in non-collaborative environments. I n Proc. of AAAI -98, Washington, D C , 1993. [Lochbaum et al., 1990] K.E. Lochbaum, B.J. Grosz, and C.L. Sidner. Models of plans to support communi-cation: An initial report. In Proceedings of AAAI-90, Boston, MA, 1990. [Lochbaum, 1991] K. E. Lochbaum. An algorithm for plan recognition in collaborative discourse. I n Proc. ACL-91, Berkeley, CA, 1991. [Pollack, 1990] M. Pollack. Plans as complex mental at-titudes. n Cohen et al. 1990. [Rao et al., 1992] A. S. Rao, M. P. Georgeff, and E. A Sonenberg. Social plans. In Decentralized Artificial Intelligence, Volume 3. Elsevier Science Pub., 1992. [Searle, 1990] J.R. Searle. Collective intentionality. I n Cohen et al. 1990.Grosz and Kraus 373。
Collaborative Governance in Theory and Practice
JPART18:543–571 Collaborative Governance in Theoryand PracticeChris AnsellAlison GashUniversity of California,BerkeleyABSTRACTOver the past few decades,a new form of governance has emerged to replace adversarial and managerial modes of policy making and implementation.Collaborative governance,as it has come to be known,brings public and private stakeholders together in collective forums with public agencies to engage in consensus-oriented decision making.In this article, we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a contingency model of collaborative governance.After review-ing137cases of collaborative governance across a range of policy sectors,we identify critical variables that will influence whether or not this mode of governance will produce successful collaboration.These variables include the prior history of conflict or cooperation, the incentives for stakeholders to participate,power and resources imbalances,leadership, and institutional design.We also identify a series of factors that are crucial within the collaborative process itself.These factors include face-to-face dialogue,trust building,and the development of commitment and shared understanding.We found that a virtuous cycle of collaboration tends to develop when collaborative forums focus on‘‘small wins’’that deepen trust,commitment,and shared understanding.The article concludes with a discus-sion of the implications of our contingency model for practitioners and for future research on collaborative governance.Over the last two decades,a new strategy of governing called‘‘collaborative governance’’has developed.This mode of governance brings multiple stakeholders together in common forums with public agencies to engage in consensus-oriented decision making.In this article,we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a general model of collaborative governance. The ultimate goal is to develop a contingency approach to collaboration that can highlight conditions under which collaborative governance will be more or less effective as an Early versions of this article were presented at the Conference on Democratic Network Governance,Copenhagen,the Interdisciplinary Committee on Organizations at the University of California,Irvine,and the Berkeley graduate seminar Perspectives on Governance.We thank the participants of these events for their useful suggestions and Martha Feldman,in particular,for her encouragement.We also thank two anonymous reviewers for their thoughtful and useful comments.Address correspondence to the author at cansell@ or aligash@.doi:10.1093/jopart/mum032Advance Access publication on November13,2007ªThe Author2007.Published by Oxford University Press on behalf of the Journal of Public Administration Researchand Theory,Inc.All rights reserved.For permissions,please e-mail:journals.permissions@approach to policy making and public management.1In conducting this meta-analytic study,we adopted a strategy we call ‘‘successive approximation’’:we used a sample of the literature to develop a common language for analyzing collaborative governance and then successively ‘‘tested’’this language against additional cases,refining and elaborating our model of collaborative governance as we evaluated additional cases.Although collaborative governance may now have a fashionable management cache´,the untidy character of the literature on collaboration reflects the way it has bubbled up from many local experiments,often in reaction to previous governance failures.Collabo-rative governance has emerged as a response to the failures of downstream implementation and to the high cost and politicization of regulation.It has developed as an alternative to the adversarialism of interest group pluralism and to the accountability failures of manageri-alism (especially as the authority of experts is challenged).More positively,one might argue that trends toward collaboration also arise from the growth of knowledge and in-stitutional capacity.As knowledge becomes increasingly specialized and distributed and as institutional infrastructures become more complex and interdependent,the demand for collaboration increases.The common metric for all these factors may be,as Gray (1989)has pointed out,the increasing ‘‘turbulence’’faced by policy makers and managers.Although Susskind and Cruikshank (1987),Gray (1989),and Fung and Wright (2001,2003)have suggested more general theoretical accounts of collaborative governance,much of the literature is focused on the species rather than the genus .The bulk of the collaborative governance literature is composed of single-case case studies focused on sector-specific governance issues like site-based management of schools,community po-licing,watershed councils,regulatory negotiation,collaborative planning,community health partnerships,and natural resource comanagement (the species).2Moreover,a num-ber of the most influential theoretical accounts of this phenomenon are focused on specific types of collaborative governance.Healey (1996,2003)and Innes and Booher (1999a,1999b),for example,provide foundational accounts of collaborative planning,as Freeman (1997)does for regulation and administrative law and Wondolleck and Yaffee (2000)do for natural resources management.Our goal is to build on the findings of this rich literature,but also to derive theoretical and empirical claims about the genus of collaborative governance—about the common mode of governing.DEFINING COLLABORATIVE GOVERNANCEWe define collaborative governance as follows:A governing arrangement where one or more public agencies directly engage non-state stakeholders in a collective decision-making process that is formal,consensus-oriented,and deliberative and that aims to make or implement public policy or manage publicprograms or assets.This definition stresses six important criteria:(1)the forum is initiated by public agencies or institutions,(2)participants in the forum include nonstate actors,(3)participants engage directly in decision making and are not merely ‘‘consulted’’by public agencies,(4)the 1Thomas (1995)develops a contingency perspective on public participation,though it aims more broadly and is developed from the perspective of public managers.2A smaller group of studies evaluates specific types of collaborative governance at a more aggregated level (for example,see Beierle [2000],Langbein [2002],and Leach,Pelkey,and Sabatier [2002]).Journal of Public Administration Research and Theory544Ansell and Gash Collaborative Governance in Theory and Practice545 forum is formally organized and meets collectively,(5)the forum aims to make decisionsby consensus(even if consensus is not achieved in practice),and(6)the focus of collab-oration is on public policy or public management.This is a more restrictive definition thanis sometimes found in the literature.However,the wide-ranging use of the term has,as Imperial notes,been a barrier to theory building(Imperial2005,286).Since our goal is to compare apples with apples(to the extent possible),we have defined the term restrictivelyso as to increase the comparability of our cases.One critical component of the term collaborative governance is‘‘governance.’’Much research has been devoted to establishing a workable definition of governance that is bounded and falsifiable,yet comprehensive.For instance,Lynn,Heinrich,and Hill (2001,7)construe governance broadly as‘‘regimes of laws,rules,judicial decisions,and administrative practices that constrain,prescribe,and enable the provision of publicly supported goods and services.’’This definition provides room for traditional governmental structures as well as emerging forms of public/private decision-making bodies.Stoker,onthe other hand,argues:As a baseline definition it can be taken that governance refers to the rules and forms that guidecollective decision-making.That the focus is on decision-making in the collective impliesthat governance is not about one individual making a decision but rather about groups ofindividuals or organisations or systems of organisations making decisions(2004,3).He also suggests that among the various interpretations of the term,there is‘‘baseline agreement that governance refers to the development of governing styles in which bound-aries between and within public and private sectors have become blurred’’(Stoker1998, 17).We opt for a combined approach to conceptualize governance.We agree with Lynn, Heinrich,and Hill that governance applies to laws and rules that pertain to the provision ofpublic goods.However,we adopt Stoker’s claim that governance is also about collective decision making—and specifically about collective decision making that includes bothpublic and private actors.Collaborative governance is therefore a type of governance inwhich public and private actors work collectively in distinctive ways,using particular processes,to establish laws and rules for the provision of public goods.Although there are many forms of collaboration involving strictly nonstate actors,ourdefinition stipulates a specific role for public agencies.By using the term‘‘public agency,’’our intention is to include public institutions such as bureaucracies,courts,legislatures,andother governmental bodies at the local,state,or federal level.But the typical public in-stitution among our cases is,in fact,an executive branch agency,and therefore,the term‘‘public agency’’is apt.Such public agencies may initiate collaborative forums either tofulfill their own purposes or to comply with a mandate,including court orders,legislation,or rules governing the allocation of federal funds.For example,the Workforce InvestmentAct of1998stipulates that all states and localities receiving federal workforce develop-ment funds must convene a workforce investment board that comprised public and privateactors in order to develop and oversee policies at the state and local level concerning job training,under-and unemployment.According to our definition,these workforce invest-ments boards are mandated to engage in collaborative governance.Although public agencies are typically the initiators or instigators of collaborative governance,our definition requires participation by nonstate stakeholders.Some scholars describe interagency coordination as collaborative governance.Although there is nothing inherently wrong with using the term in this way,much of the literature on collaborativegovernance uses this term to signal a different kind of relationship between public agencies and nonstate stakeholders.Smith (1998,61),for example,argues that collaboratives in-volve ‘‘representation by key interest groups.’’Connick and Innes (2003,180)define collaborative governance as including ‘‘representatives of all relevant interests.’’Reilly (1998,115)describes collaborative efforts as a type of problem solving that involves the ‘‘shared pursuit of government agencies and concerned citizens.’’We use the term ‘‘stakeholder’’to refer both to the participation of citizens as indi-viduals and to the participation of organized groups.For convenience,we will also here-after use the term ‘‘stakeholder’’to refer to both public agencies and nonstate stakeholders,though we believe that public agencies have a distinctive leadership role in collaborative governance.Our definition of collaborative governance also sets standards for the type of participation of nonstate stakeholders.We believe that collaborative governance is never merely consultative.3Collaboration implies two-way communication and influence be-tween agencies and stakeholders and also opportunities for stakeholders to talk with each other.Agencies and stakeholders must meet together in a deliberative and multilateral process.In other words,as described above,the process must be collective .Consultative techniques,such as stakeholder surveys or focus groups,although possibly very useful management tools,are not collaborative in the sense implied here because they do not permit two-way flows of communication or multilateral deliberation.Collaboration also implies that nonstate stakeholders will have real responsibility for policy outcomes.Therefore,we impose the condition that stakeholders must be directly engaged in decision making.This criterion is implicit in much of the collaborative gov-ernance literature.Freeman (1997,22),for example,argues that stakeholders participate ‘‘in all stages of the decisionmaking process.’’The watershed partnerships studied by Leach,Pelkey,and Sabatier (2002,648)make policy and implementation decisions on a range of ongoing water management issues regarding streams,rivers,and watersheds.Ultimate authority may lie with the public agency (as with regulatory negotiation),but stakeholders must directly participate in the decision-making process.Thus,advisory committees may be a form of collaborative governance if their advice is closely linked to decision-making outcomes.In practice (and by design),however,advisory committees are often far removed from actual decision making.We impose the criteria of formal collaboration to distinguish collaborative gover-nance from more casual and conventional forms of agency-interest group interaction.For example,the term collaborative governance might be thought to describe the informal relationships that agencies and interest groups have always cultivated.Surely,interest groups and public agencies have always engaged in two-way flows of influence.The difference between our definition of collaborative governance and conventional interest group influence is that the former implies an explicit and public strategy of organizing this influence.Walter and Petr (2000,495),for example,describe collaborative governance as a formal activity that ‘‘involves joint activities,joint structures and shared resources,’’and Padilla and Daigle (1998,74)prescribe the development of a ‘‘structured arrangement.’’This formal arrangement implies organization and structure.Decisions in collaborative forums are consensus oriented (Connick and Innes 2003;Seidenfeld 2000).Although public agencies may have the ultimate authority to make 3See Beierle and Long (1999)for an example of collaboration as consultation.Journal of Public Administration Research and Theory546Ansell and Gash Collaborative Governance in Theory and Practice547 a decision,the goal of collaboration is typically to achieve some degree of consensus among stakeholders.We use the term consensus oriented because collaborative forumsoften do not succeed in reaching consensus.However,the premise of meeting together ina deliberative,multilateral,and formal forum is to strive toward consensus or,at least,tostrive to discover areas of agreement.Finally,collaborative governance focuses on public policies and issues.The focuson public issues distinguishes collaborative governance from other forms of consensus decision making,such as alternative dispute resolution or transformative mediation. Although agencies may pursue dispute resolution or mediation to reduce social or politicalconflict,these techniques are often used to deal with strictly private conflicts.Moreover,public dispute resolution or mediation may be designed merely to resolve private disputes.While acknowledging the ambiguity of the boundary between public and private,we restrict the use of the term‘‘collaborative governance’’to the governance of public affairs.Our definition of collaborative governance is meant to distinguish collaborative gov-ernance from two alternative patterns of policy making:adversarialism and managerialism (Busenberg1999;Futrell2003;Williams and Matheny1995).By contrast with decisionsmade adversarially,collaborative governance is not a‘‘winner-take-all’’form of interest intermediation.In collaborative governance,stakeholders will often have an adversarial relationship to one another,but the goal is to transform adversarial relationships into more cooperative ones.In adversarial politics,groups may engage in positive-sum bargainingand develop cooperative alliances.However,this cooperation is ad hoc,and adversarial politics does not explicitly seek to transform conflict into cooperation.In managerialism,public agencies make decisions unilaterally or through closed de-cision processes,typically relying on agency experts to make decisions(Futrell2003; Williams and Matheny1995).Although managerial agencies may take account of stake-holder perspectives in their decision making and may even go so far as to consult directlywith stakeholders,collaborative governance requires that stakeholders be directly includedin the decision-making process.A number of synonyms for collaborative governance may cause confusion.For ex-ample,‘‘corporatism’’is certainly a form of collaborative governance as we define it. Classic definitions of corporatism(like Schmitter’s)emphasize tripartite bargaining be-tween peak associations of labor and capital and the state.Typically,these peak associa-tions have a representational monopoly in their sector(they are‘‘encompassing’’).If westart with this narrower definition of corporatism,collaborative governance is the broader term.Collaborative governance often implies the inclusion of a broader range of stake-holders than corporatism,and the stakeholders often lack a representational monopoly overtheir sector.The term‘‘associational governance’’is sometimes used to refer to the more generic mode of governing with associations,but collaborative governance may not even include formal associations.The Porte Alegre project,for example,is a form of collabo-rative governance that includes individual citizens in budgetary decision making(Fung and Wright2001).Sometimes the term‘‘policy network’’is used to describe more pluralistic forms ofstate-society cooperation.A policy network may include both public agencies and stake-holder groups.Moreover,policy networks typically imply cooperative modes of deliber-ation or decision making among actors within the network.Thus,the terms policy networkand collaborative governance can refer to similar phenomena.However,collaborative governance refers to an explicit and formal strategy of incorporating stakeholders intomultilateral and consensus-oriented decision-making processes.By contrast,the co-operation inherent in policy networks may be informal and remain largely implicit (e.g.,unacknowledged,unstated,nondesigned).Moreover,it may operate through infor-mal patterns of brokerage and shuttle diplomacy rather than through formal multilateral processes.Collaborative governance and public-private partnership can also sometimes refer to the same phenomenon.Public-private partnerships typically require collaboration to func-tion,but their goal is often to achieve coordination rather than to achieve decision-making consensus per se.A public-private partnership may simply represent an agreement between public and private actors to deliver certain services or perform certain tasks.Collective decision making is therefore secondary to the definition of public-private partnership.By contrast,the institutionalization of a collective decision-making process is central to the definition of collaborative governance.Finally,a range of terms are often used interchangeably with collaborative gover-nance.Such terms include participatory management,interactive policy making,stake-holder governance,and collaborative management.We prefer the term governance to management because it is broader and encompasses various aspects of the governing process,including planning,policy making,and management.The term collaborative is also more indicative of the deliberative and consensus-oriented approach that we contrast with adversarialism or managerialism than terms like participatory or interactive.A MODEL OF COLLABORATIVE GOVERNANCEArmed with a working definition of collaborative governance,we collected a wide range of case studies from the literature.We did this in the typical fashion:we systematically reviewed journals across a wide range of disciplines,including specialist journals in public health,education,social welfare,international relations,etc.We also conducted key word electronic searches using a wide variety of search terms,including those described above and many more (e.g.,‘‘comanagement,’’‘‘public participation,’’‘‘alternative dispute res-olution’’).Of course,we also followed up on the literature cited in the cases we discovered.Ultimately,our model is built on an analysis of 137cases.Although international in scope,our search was restricted to literature in English,and thus,American cases are overrepre-sented.Even a cursory examination of our cases also suggests that natural resource man-agement cases are overrepresented.This is not due to any sampling bias on our part but rather reflects the importance of collaborative strategies for managing contentious local resource disputes.Most of the studies we reviewed were case studies of an attempt to implement collaborative governance in a particular sector.As you might imagine,the universe of cases we collected was quite diverse and the cases differed in quality,methodology,and intent.Although our definition was restrictive so as to facilitate comparison of apples with apples,representing this diversity was also one of our goals.We perceived experiments with collaborative governance bubbling up in many different policy sectors,with little sense that they were engaged in a similar governance strategy.Surely,we felt,these diverse experiments could learn from each other.Yet this diversity proved a challenge.Our original intention to treat these cases as a large-N data set subject to quasi-experimental statistical evaluation was not successful.Since it is useful for both scholarsJournal of Public Administration Research and Theory548Ansell and Gash Collaborative Governance in Theory and Practice549 and practitioners to understand how we arrived at our conclusions,we briefly report on the problems we encountered in conducting our meta-analysis.Early attempts at systematic coding were frustrating,and we soon developed an understanding of our dilemma.Although scholars studying collaborative governancehad already made some important theoretical statements,the language used to describewhat was happening was far from standardized.We found ourselves groping tofinda common language of description and evaluation even as we were trying to‘‘code’’studies.Add to this challenge a severe problem of‘‘missing data’’—a reflection of thehighly varied motivations of the researchers—and we concluded that a quasi-experimental approach was ill advised.Ultimately,we moved toward a meta-analytic strategy that wecall successive approximation.We selected a subset of our cases and used them to developa common‘‘model’’of collaborative governance.4We then randomly selected additional subsets of case studies.The second subset was used to‘‘test’’the model developed in thefirst round and then to further‘‘refine’’the model.A third sample of cases was used to testthe second-round model,and so on.The appendix provides a list of the studies evaluated ineach of four successive rounds of evaluation.Successive approximation has the advantage of both refining the conceptual modelwhile providing some of the evaluative‘‘discipline’’of a quasi-experimental study.How-ever,we are under no illusion that this process yielded‘‘the one’’model of collaborative governance.There was a large element of art involved in both specifying and evaluatingour model.As we proceeded,we were overwhelmed by the complexity of the collaborative process.Variables and causal relationships proliferated beyond what we felt would ulti-mately be useful for policy makers and practitioners.Therefore,our model representsa conscious attempt to simplify as much as possible the representation of key variablesand their relationships.This goal of simplification led us to stress common and frequentfindings across cases.This approach strengthens the generality of ourfindings but dis-counts less universal or frequently mentionedfindings from the literature.Toward the endof our analysis,we were ourselves in disagreement about how to represent key relations.We used thefinal round of case analysis to settle these differences.One other important clarification needs to be made before we introduce ourfindings.Our survey of the cases quickly disabused us of the notion that we could use our analysis to answer the question:‘‘Is collaborative governance more effective than adversarial or managerial governance?’’Very few of the studies we reviewed actually evaluated gover-nance outcomes.This is not to say that the comparison between collaborative,adversarial,and managerial governance is not relevant to these studies.Experiments with collaborative governance were typically driven by earlier failures with adversarial or managerial approaches.But systematic comparisons were rarely explicitly made.What most studiesdid try to do was understand the conditions under which stakeholders acted collaboratively.Did they engage in good faith negotiation?Did they pursue mutual gains?Did they achieve consensus?Were they satisfied with the process?In other words,most studies in the collaborative governance literature evaluate‘‘process outcomes’’rather than policy or management outcomes.Figure1provides a visual representation of our centralfindings.The model has fourbroad variables—starting conditions,institutional design,leadership,and collaborative4To avoid recreating the wheel,ourfirst subset was not randomly selected but included many of the most prominent theoretical statements about collaborative governance.process.Each of these broad variables can be disaggregated into more fine-grained vari-ables.Collaborative process variables are treated as the core of our model,with starting conditions,institutional design,and leadership variables represented as either critical contributions to or context for the collaborative process.Starting conditions set the basic level of trust,conflict,and social capital that become resources or liabilities during col-laboration.Institutional design sets the basic ground rules under which collaboration takes place.And,leadership provides essential mediation and facilitation for the collaborative process.The collaborative process itself is highly iterative and nonlinear,and thus,we represent it (with considerable simplification)as a cycle.The remainder of the article describes each of these variables in more detail and draws out their implications for a contingency model of collaborative governance.STARTING CONDITIONSThe literature is clear that conditions present at the outset of collaboration can either facilitate or discourage cooperation among stakeholders and between agencies and stake-holders.Imagine two very different starting points.In one,the stakeholders have a history of bitter division over some emotionally charged local issue and have come to regard each other as unscrupulous enemies.In the other,the stakeholders have a shared vision for what they would like to achieve through collaboration and a history of past cooperation and mutual respect.In both cases,collaboration may be difficult,but the first case must over-come problems of distrust,disrespect,and outright antagonism.We narrowed the critical starting conditions down to three broad variables:imbalances between the resources orFigure 1A Model of Collaborative GovernanceJournal of Public Administration Research and Theory550。
Native Instruments MASCHINE MK3 用户手册说明书
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All rights reserved.Table of Contents1Welcome to MASCHINE (25)1.1MASCHINE Documentation (26)1.2Document Conventions (27)1.3New Features in MASCHINE 2.8 (29)1.4New Features in MASCHINE 2.7.10 (31)1.5New Features in MASCHINE 2.7.8 (31)1.6New Features in MASCHINE 2.7.7 (32)1.7New Features in MASCHINE 2.7.4 (33)1.8New Features in MASCHINE 2.7.3 (36)2Quick Reference (38)2.1Using Your Controller (38)2.1.1Controller Modes and Mode Pinning (38)2.1.2Controlling the Software Views from Your Controller (40)2.2MASCHINE Project Overview (43)2.2.1Sound Content (44)2.2.2Arrangement (45)2.3MASCHINE Hardware Overview (48)2.3.1MASCHINE Hardware Overview (48)2.3.1.1Control Section (50)2.3.1.2Edit Section (53)2.3.1.3Performance Section (54)2.3.1.4Group Section (56)2.3.1.5Transport Section (56)2.3.1.6Pad Section (58)2.3.1.7Rear Panel (63)2.4MASCHINE Software Overview (65)2.4.1Header (66)2.4.2Browser (68)2.4.3Arranger (70)2.4.4Control Area (73)2.4.5Pattern Editor (74)3Basic Concepts (76)3.1Important Names and Concepts (76)3.2Adjusting the MASCHINE User Interface (79)3.2.1Adjusting the Size of the Interface (79)3.2.2Switching between Ideas View and Song View (80)3.2.3Showing/Hiding the Browser (81)3.2.4Showing/Hiding the Control Lane (81)3.3Common Operations (82)3.3.1Using the 4-Directional Push Encoder (82)3.3.2Pinning a Mode on the Controller (83)3.3.3Adjusting Volume, Swing, and Tempo (84)3.3.4Undo/Redo (87)3.3.5List Overlay for Selectors (89)3.3.6Zoom and Scroll Overlays (90)3.3.7Focusing on a Group or a Sound (91)3.3.8Switching Between the Master, Group, and Sound Level (96)3.3.9Navigating Channel Properties, Plug-ins, and Parameter Pages in the Control Area.973.3.9.1Extended Navigate Mode on Your Controller (102)3.3.10Navigating the Software Using the Controller (105)3.3.11Using Two or More Hardware Controllers (106)3.3.12Touch Auto-Write Option (108)3.4Native Kontrol Standard (110)3.5Stand-Alone and Plug-in Mode (111)3.5.1Differences between Stand-Alone and Plug-in Mode (112)3.5.2Switching Instances (113)3.5.3Controlling Various Instances with Different Controllers (114)3.6Host Integration (114)3.6.1Setting up Host Integration (115)3.6.1.1Setting up Ableton Live (macOS) (115)3.6.1.2Setting up Ableton Live (Windows) (116)3.6.1.3Setting up Apple Logic Pro X (116)3.6.2Integration with Ableton Live (117)3.6.3Integration with Apple Logic Pro X (119)3.7Preferences (120)3.7.1Preferences – General Page (121)3.7.2Preferences – Audio Page (126)3.7.3Preferences – MIDI Page (130)3.7.4Preferences – Default Page (133)3.7.5Preferences – Library Page (137)3.7.6Preferences – Plug-ins Page (145)3.7.7Preferences – Hardware Page (150)3.7.8Preferences – Colors Page (154)3.8Integrating MASCHINE into a MIDI Setup (156)3.8.1Connecting External MIDI Equipment (156)3.8.2Sync to External MIDI Clock (157)3.8.3Send MIDI Clock (158)3.9Syncing MASCHINE using Ableton Link (159)3.9.1Connecting to a Network (159)3.9.2Joining and Leaving a Link Session (159)3.10Using a Pedal with the MASCHINE Controller (160)3.11File Management on the MASCHINE Controller (161)4Browser (163)4.1Browser Basics (163)4.1.1The MASCHINE Library (163)4.1.2Browsing the Library vs. Browsing Your Hard Disks (164)4.2Searching and Loading Files from the Library (165)4.2.1Overview of the Library Pane (165)4.2.2Selecting or Loading a Product and Selecting a Bank from the Browser (170)4.2.2.1[MK3] Browsing by Product Category Using the Controller (174)4.2.2.2[MK3] Browsing by Product Vendor Using the Controller (174)4.2.3Selecting a Product Category, a Product, a Bank, and a Sub-Bank (175)4.2.3.1Selecting a Product Category, a Product, a Bank, and a Sub-Bank on theController (179)4.2.4Selecting a File Type (180)4.2.5Choosing Between Factory and User Content (181)4.2.6Selecting Type and Character Tags (182)4.2.7List and Tag Overlays in the Browser (186)4.2.8Performing a Text Search (188)4.2.9Loading a File from the Result List (188)4.3Additional Browsing Tools (193)4.3.1Loading the Selected Files Automatically (193)4.3.2Auditioning Instrument Presets (195)4.3.3Auditioning Samples (196)4.3.4Loading Groups with Patterns (197)4.3.5Loading Groups with Routing (198)4.3.6Displaying File Information (198)4.4Using Favorites in the Browser (199)4.5Editing the Files’ Tags and Properties (203)4.5.1Attribute Editor Basics (203)4.5.2The Bank Page (205)4.5.3The Types and Characters Pages (205)4.5.4The Properties Page (208)4.6Loading and Importing Files from Your File System (209)4.6.1Overview of the FILES Pane (209)4.6.2Using Favorites (211)4.6.3Using the Location Bar (212)4.6.4Navigating to Recent Locations (213)4.6.5Using the Result List (214)4.6.6Importing Files to the MASCHINE Library (217)4.7Locating Missing Samples (219)4.8Using Quick Browse (221)5Managing Sounds, Groups, and Your Project (225)5.1Overview of the Sounds, Groups, and Master (225)5.1.1The Sound, Group, and Master Channels (226)5.1.2Similarities and Differences in Handling Sounds and Groups (227)5.1.3Selecting Multiple Sounds or Groups (228)5.2Managing Sounds (233)5.2.1Loading Sounds (235)5.2.2Pre-listening to Sounds (236)5.2.3Renaming Sound Slots (237)5.2.4Changing the Sound’s Color (237)5.2.5Saving Sounds (239)5.2.6Copying and Pasting Sounds (241)5.2.7Moving Sounds (244)5.2.8Resetting Sound Slots (245)5.3Managing Groups (247)5.3.1Creating Groups (248)5.3.2Loading Groups (249)5.3.3Renaming Groups (251)5.3.4Changing the Group’s Color (251)5.3.5Saving Groups (253)5.3.6Copying and Pasting Groups (255)5.3.7Reordering Groups (258)5.3.8Deleting Groups (259)5.4Exporting MASCHINE Objects and Audio (260)5.4.1Saving a Group with its Samples (261)5.4.2Saving a Project with its Samples (262)5.4.3Exporting Audio (264)5.5Importing Third-Party File Formats (270)5.5.1Loading REX Files into Sound Slots (270)5.5.2Importing MPC Programs to Groups (271)6Playing on the Controller (275)6.1Adjusting the Pads (275)6.1.1The Pad View in the Software (275)6.1.2Choosing a Pad Input Mode (277)6.1.3Adjusting the Base Key (280)6.1.4Using Choke Groups (282)6.1.5Using Link Groups (284)6.2Adjusting the Key, Choke, and Link Parameters for Multiple Sounds (286)6.3Playing Tools (287)6.3.1Mute and Solo (288)6.3.2Choke All Notes (292)6.3.3Groove (293)6.3.4Level, Tempo, Tune, and Groove Shortcuts on Your Controller (295)6.3.5Tap Tempo (299)6.4Performance Features (300)6.4.1Overview of the Perform Features (300)6.4.2Selecting a Scale and Creating Chords (303)6.4.3Scale and Chord Parameters (303)6.4.4Creating Arpeggios and Repeated Notes (316)6.4.5Swing on Note Repeat / Arp Output (321)6.5Using Lock Snapshots (322)6.5.1Creating a Lock Snapshot (322)6.5.2Using Extended Lock (323)6.5.3Updating a Lock Snapshot (323)6.5.4Recalling a Lock Snapshot (324)6.5.5Morphing Between Lock Snapshots (324)6.5.6Deleting a Lock Snapshot (325)6.5.7Triggering Lock Snapshots via MIDI (326)6.6Using the Smart Strip (327)6.6.1Pitch Mode (328)6.6.2Modulation Mode (328)6.6.3Perform Mode (328)6.6.4Notes Mode (329)7Working with Plug-ins (330)7.1Plug-in Overview (330)7.1.1Plug-in Basics (330)7.1.2First Plug-in Slot of Sounds: Choosing the Sound’s Role (334)7.1.3Loading, Removing, and Replacing a Plug-in (335)7.1.3.1Browser Plug-in Slot Selection (341)7.1.4Adjusting the Plug-in Parameters (344)7.1.5Bypassing Plug-in Slots (344)7.1.6Using Side-Chain (346)7.1.7Moving Plug-ins (346)7.1.8Alternative: the Plug-in Strip (348)7.1.9Saving and Recalling Plug-in Presets (348)7.1.9.1Saving Plug-in Presets (349)7.1.9.2Recalling Plug-in Presets (350)7.1.9.3Removing a Default Plug-in Preset (351)7.2The Sampler Plug-in (352)7.2.1Page 1: Voice Settings / Engine (354)7.2.2Page 2: Pitch / Envelope (356)7.2.3Page 3: FX / Filter (359)7.2.4Page 4: Modulation (361)7.2.5Page 5: LFO (363)7.2.6Page 6: Velocity / Modwheel (365)7.3Using Native Instruments and External Plug-ins (367)7.3.1Opening/Closing Plug-in Windows (367)7.3.2Using the VST/AU Plug-in Parameters (370)7.3.3Setting Up Your Own Parameter Pages (371)7.3.4Using VST/AU Plug-in Presets (376)7.3.5Multiple-Output Plug-ins and Multitimbral Plug-ins (378)8Using the Audio Plug-in (380)8.1Loading a Loop into the Audio Plug-in (384)8.2Editing Audio in the Audio Plug-in (385)8.3Using Loop Mode (386)8.4Using Gate Mode (388)9Using the Drumsynths (390)9.1Drumsynths – General Handling (391)9.1.1Engines: Many Different Drums per Drumsynth (391)9.1.2Common Parameter Organization (391)9.1.3Shared Parameters (394)9.1.4Various Velocity Responses (394)9.1.5Pitch Range, Tuning, and MIDI Notes (394)9.2The Kicks (395)9.2.1Kick – Sub (397)9.2.2Kick – Tronic (399)9.2.3Kick – Dusty (402)9.2.4Kick – Grit (403)9.2.5Kick – Rasper (406)9.2.6Kick – Snappy (407)9.2.7Kick – Bold (409)9.2.8Kick – Maple (411)9.2.9Kick – Push (412)9.3The Snares (414)9.3.1Snare – Volt (416)9.3.2Snare – Bit (418)9.3.3Snare – Pow (420)9.3.4Snare – Sharp (421)9.3.5Snare – Airy (423)9.3.6Snare – Vintage (425)9.3.7Snare – Chrome (427)9.3.8Snare – Iron (429)9.3.9Snare – Clap (431)9.3.10Snare – Breaker (433)9.4The Hi-hats (435)9.4.1Hi-hat – Silver (436)9.4.2Hi-hat – Circuit (438)9.4.3Hi-hat – Memory (440)9.4.4Hi-hat – Hybrid (442)9.4.5Creating a Pattern with Closed and Open Hi-hats (444)9.5The Toms (445)9.5.1Tom – Tronic (447)9.5.2Tom – Fractal (449)9.5.3Tom – Floor (453)9.5.4Tom – High (455)9.6The Percussions (456)9.6.1Percussion – Fractal (458)9.6.2Percussion – Kettle (461)9.6.3Percussion – Shaker (463)9.7The Cymbals (467)9.7.1Cymbal – Crash (469)9.7.2Cymbal – Ride (471)10Using the Bass Synth (474)10.1Bass Synth – General Handling (475)10.1.1Parameter Organization (475)10.1.2Bass Synth Parameters (477)11Working with Patterns (479)11.1Pattern Basics (479)11.1.1Pattern Editor Overview (480)11.1.2Navigating the Event Area (486)11.1.3Following the Playback Position in the Pattern (488)11.1.4Jumping to Another Playback Position in the Pattern (489)11.1.5Group View and Keyboard View (491)11.1.6Adjusting the Arrange Grid and the Pattern Length (493)11.1.7Adjusting the Step Grid and the Nudge Grid (497)11.2Recording Patterns in Real Time (501)11.2.1Recording Your Patterns Live (501)11.2.2The Record Prepare Mode (504)11.2.3Using the Metronome (505)11.2.4Recording with Count-in (506)11.2.5Quantizing while Recording (508)11.3Recording Patterns with the Step Sequencer (508)11.3.1Step Mode Basics (508)11.3.2Editing Events in Step Mode (511)11.3.3Recording Modulation in Step Mode (513)11.4Editing Events (514)11.4.1Editing Events with the Mouse: an Overview (514)11.4.2Creating Events/Notes (517)11.4.3Selecting Events/Notes (518)11.4.4Editing Selected Events/Notes (526)11.4.5Deleting Events/Notes (532)11.4.6Cut, Copy, and Paste Events/Notes (535)11.4.7Quantizing Events/Notes (538)11.4.8Quantization While Playing (540)11.4.9Doubling a Pattern (541)11.4.10Adding Variation to Patterns (541)11.5Recording and Editing Modulation (546)11.5.1Which Parameters Are Modulatable? (547)11.5.2Recording Modulation (548)11.5.3Creating and Editing Modulation in the Control Lane (550)11.6Creating MIDI Tracks from Scratch in MASCHINE (555)11.7Managing Patterns (557)11.7.1The Pattern Manager and Pattern Mode (558)11.7.2Selecting Patterns and Pattern Banks (560)11.7.3Creating Patterns (563)11.7.4Deleting Patterns (565)11.7.5Creating and Deleting Pattern Banks (566)11.7.6Naming Patterns (568)11.7.7Changing the Pattern’s Color (570)11.7.8Duplicating, Copying, and Pasting Patterns (571)11.7.9Moving Patterns (574)11.7.10Adjusting Pattern Length in Fine Increments (575)11.8Importing/Exporting Audio and MIDI to/from Patterns (576)11.8.1Exporting Audio from Patterns (576)11.8.2Exporting MIDI from Patterns (577)11.8.3Importing MIDI to Patterns (580)12Audio Routing, Remote Control, and Macro Controls (589)12.1Audio Routing in MASCHINE (590)12.1.1Sending External Audio to Sounds (591)12.1.2Configuring the Main Output of Sounds and Groups (596)12.1.3Setting Up Auxiliary Outputs for Sounds and Groups (601)12.1.4Configuring the Master and Cue Outputs of MASCHINE (605)12.1.5Mono Audio Inputs (610)12.1.5.1Configuring External Inputs for Sounds in Mix View (611)12.2Using MIDI Control and Host Automation (614)12.2.1Triggering Sounds via MIDI Notes (615)12.2.2Triggering Scenes via MIDI (622)12.2.3Controlling Parameters via MIDI and Host Automation (623)12.2.4Selecting VST/AU Plug-in Presets via MIDI Program Change (631)12.2.5Sending MIDI from Sounds (632)12.3Creating Custom Sets of Parameters with the Macro Controls (636)12.3.1Macro Control Overview (637)12.3.2Assigning Macro Controls Using the Software (638)12.3.3Assigning Macro Controls Using the Controller (644)13Controlling Your Mix (646)13.1Mix View Basics (646)13.1.1Switching between Arrange View and Mix View (646)13.1.2Mix View Elements (647)13.2The Mixer (649)13.2.1Displaying Groups vs. Displaying Sounds (650)13.2.2Adjusting the Mixer Layout (652)13.2.3Selecting Channel Strips (653)13.2.4Managing Your Channels in the Mixer (654)13.2.5Adjusting Settings in the Channel Strips (656)13.2.6Using the Cue Bus (660)13.3The Plug-in Chain (662)13.4The Plug-in Strip (663)13.4.1The Plug-in Header (665)13.4.2Panels for Drumsynths and Internal Effects (667)13.4.3Panel for the Sampler (668)13.4.4Custom Panels for Native Instruments Plug-ins (671)13.4.5Undocking a Plug-in Panel (Native Instruments and External Plug-ins Only) (675)13.5Controlling Your Mix from the Controller (677)13.5.1Navigating Your Channels in Mix Mode (678)13.5.2Adjusting the Level and Pan in Mix Mode (679)13.5.3Mute and Solo in Mix Mode (680)13.5.4Plug-in Icons in Mix Mode (680)14Using Effects (681)14.1Applying Effects to a Sound, a Group or the Master (681)14.1.1Adding an Effect (681)14.1.2Other Operations on Effects (690)14.1.3Using the Side-Chain Input (692)14.2Applying Effects to External Audio (695)14.2.1Step 1: Configure MASCHINE Audio Inputs (695)14.2.2Step 2: Set up a Sound to Receive the External Input (698)14.2.3Step 3: Load an Effect to Process an Input (700)14.3Creating a Send Effect (701)14.3.1Step 1: Set Up a Sound or Group as Send Effect (702)14.3.2Step 2: Route Audio to the Send Effect (706)14.3.3 A Few Notes on Send Effects (708)14.4Creating Multi-Effects (709)15Effect Reference (712)15.1Dynamics (713)15.1.1Compressor (713)15.1.2Gate (717)15.1.3Transient Master (721)15.1.4Limiter (723)15.1.5Maximizer (727)15.2Filtering Effects (730)15.2.1EQ (730)15.2.2Filter (733)15.2.3Cabinet (737)15.3Modulation Effects (738)15.3.1Chorus (738)15.3.2Flanger (740)15.3.3FM (742)15.3.4Freq Shifter (743)15.3.5Phaser (745)15.4Spatial and Reverb Effects (747)15.4.1Ice (747)15.4.2Metaverb (749)15.4.3Reflex (750)15.4.4Reverb (Legacy) (752)15.4.5Reverb (754)15.4.5.1Reverb Room (754)15.4.5.2Reverb Hall (757)15.4.5.3Plate Reverb (760)15.5Delays (762)15.5.1Beat Delay (762)15.5.2Grain Delay (765)15.5.3Grain Stretch (767)15.5.4Resochord (769)15.6Distortion Effects (771)15.6.1Distortion (771)15.6.2Lofi (774)15.6.3Saturator (775)15.7Perform FX (779)15.7.1Filter (780)15.7.2Flanger (782)15.7.3Burst Echo (785)15.7.4Reso Echo (787)15.7.5Ring (790)15.7.6Stutter (792)15.7.7Tremolo (795)15.7.8Scratcher (798)16Working with the Arranger (801)16.1Arranger Basics (801)16.1.1Navigating Song View (804)16.1.2Following the Playback Position in Your Project (806)16.1.3Performing with Scenes and Sections using the Pads (807)16.2Using Ideas View (811)16.2.1Scene Overview (811)16.2.2Creating Scenes (813)16.2.3Assigning and Removing Patterns (813)16.2.4Selecting Scenes (817)16.2.5Deleting Scenes (818)16.2.6Creating and Deleting Scene Banks (820)16.2.7Clearing Scenes (820)16.2.8Duplicating Scenes (821)16.2.9Reordering Scenes (822)16.2.10Making Scenes Unique (824)16.2.11Appending Scenes to Arrangement (825)16.2.12Naming Scenes (826)16.2.13Changing the Color of a Scene (827)16.3Using Song View (828)16.3.1Section Management Overview (828)16.3.2Creating Sections (833)16.3.3Assigning a Scene to a Section (834)16.3.4Selecting Sections and Section Banks (835)16.3.5Reorganizing Sections (839)16.3.6Adjusting the Length of a Section (840)16.3.6.1Adjusting the Length of a Section Using the Software (841)16.3.6.2Adjusting the Length of a Section Using the Controller (843)16.3.7Clearing a Pattern in Song View (843)16.3.8Duplicating Sections (844)16.3.8.1Making Sections Unique (845)16.3.9Removing Sections (846)16.3.10Renaming Scenes (848)16.3.11Clearing Sections (849)16.3.12Creating and Deleting Section Banks (850)16.3.13Working with Patterns in Song view (850)16.3.13.1Creating a Pattern in Song View (850)16.3.13.2Selecting a Pattern in Song View (850)16.3.13.3Clearing a Pattern in Song View (851)16.3.13.4Renaming a Pattern in Song View (851)16.3.13.5Coloring a Pattern in Song View (851)16.3.13.6Removing a Pattern in Song View (852)16.3.13.7Duplicating a Pattern in Song View (852)16.3.14Enabling Auto Length (852)16.3.15Looping (853)16.3.15.1Setting the Loop Range in the Software (854)16.4Playing with Sections (855)16.4.1Jumping to another Playback Position in Your Project (855)16.5Triggering Sections or Scenes via MIDI (856)16.6The Arrange Grid (858)16.7Quick Grid (860)17Sampling and Sample Mapping (862)17.1Opening the Sample Editor (862)17.2Recording Audio (863)17.2.1Opening the Record Page (863)17.2.2Selecting the Source and the Recording Mode (865)17.2.3Arming, Starting, and Stopping the Recording (868)17.2.5Using the Footswitch for Recording Audio (871)17.2.6Checking Your Recordings (872)17.2.7Location and Name of Your Recorded Samples (876)17.3Editing a Sample (876)17.3.1Using the Edit Page (877)17.3.2Audio Editing Functions (882)17.4Slicing a Sample (890)17.4.1Opening the Slice Page (891)17.4.2Adjusting the Slicing Settings (893)17.4.3Live Slicing (898)17.4.3.1Live Slicing Using the Controller (898)17.4.3.2Delete All Slices (899)17.4.4Manually Adjusting Your Slices (899)17.4.5Applying the Slicing (906)17.5Mapping Samples to Zones (912)17.5.1Opening the Zone Page (912)17.5.2Zone Page Overview (913)17.5.3Selecting and Managing Zones in the Zone List (915)17.5.4Selecting and Editing Zones in the Map View (920)17.5.5Editing Zones in the Sample View (924)17.5.6Adjusting the Zone Settings (927)17.5.7Adding Samples to the Sample Map (934)18Appendix: Tips for Playing Live (937)18.1Preparations (937)18.1.1Focus on the Hardware (937)18.1.2Customize the Pads of the Hardware (937)18.1.3Check Your CPU Power Before Playing (937)18.1.4Name and Color Your Groups, Patterns, Sounds and Scenes (938)18.1.5Consider Using a Limiter on Your Master (938)18.1.6Hook Up Your Other Gear and Sync It with MIDI Clock (938)18.1.7Improvise (938)18.2Basic Techniques (938)18.2.1Use Mute and Solo (938)18.2.2Use Scene Mode and Tweak the Loop Range (939)18.2.3Create Variations of Your Drum Patterns in the Step Sequencer (939)18.2.4Use Note Repeat (939)18.2.5Set Up Your Own Multi-effect Groups and Automate Them (939)18.3Special Tricks (940)18.3.1Changing Pattern Length for Variation (940)18.3.2Using Loops to Cycle Through Samples (940)18.3.3Using Loops to Cycle Through Samples (940)18.3.4Load Long Audio Files and Play with the Start Point (940)19Troubleshooting (941)19.1Knowledge Base (941)19.2Technical Support (941)19.3Registration Support (942)19.4User Forum (942)20Glossary (943)Index (951)1Welcome to MASCHINEThank you for buying MASCHINE!MASCHINE is a groove production studio that implements the familiar working style of classi-cal groove boxes along with the advantages of a computer based system. MASCHINE is ideal for making music live, as well as in the studio. It’s the hands-on aspect of a dedicated instru-ment, the MASCHINE hardware controller, united with the advanced editing features of the MASCHINE software.Creating beats is often not very intuitive with a computer, but using the MASCHINE hardware controller to do it makes it easy and fun. You can tap in freely with the pads or use Note Re-peat to jam along. Alternatively, build your beats using the step sequencer just as in classic drum machines.Patterns can be intuitively combined and rearranged on the fly to form larger ideas. You can try out several different versions of a song without ever having to stop the music.Since you can integrate it into any sequencer that supports VST, AU, or AAX plug-ins, you can reap the benefits in almost any software setup, or use it as a stand-alone application. You can sample your own material, slice loops and rearrange them easily.However, MASCHINE is a lot more than an ordinary groovebox or sampler: it comes with an inspiring 7-gigabyte library, and a sophisticated, yet easy to use tag-based Browser to give you instant access to the sounds you are looking for.What’s more, MASCHINE provides lots of options for manipulating your sounds via internal ef-fects and other sound-shaping possibilities. You can also control external MIDI hardware and 3rd-party software with the MASCHINE hardware controller, while customizing the functions of the pads, knobs and buttons according to your needs utilizing the included Controller Editor application. We hope you enjoy this fantastic instrument as much as we do. Now let’s get go-ing!—The MASCHINE team at Native Instruments.MASCHINE Documentation1.1MASCHINE DocumentationNative Instruments provide many information sources regarding MASCHINE. The main docu-ments should be read in the following sequence:1.MASCHINE Getting Started: This document provides a practical approach to MASCHINE viaa set of tutorials covering easy and more advanced tasks in order to help you familiarizeyourself with MASCHINE.2.MASCHINE Manual (this document): The MASCHINE Manual provides you with a compre-hensive description of all MASCHINE software and hardware features.Additional documentation sources provide you with details on more specific topics:▪Controller Editor Manual: Besides using your MASCHINE hardware controller together withits dedicated MASCHINE software, you can also use it as a powerful and highly versatileMIDI controller to pilot any other MIDI-capable application or device. This is made possibleby the Controller Editor software, an application that allows you to precisely define all MIDIassignments for your MASCHINE controller. The Controller Editor was installed during theMASCHINE installation procedure. For more information on this, please refer to the Con-troller Editor Manual available as a PDF file via the Help menu of Controller Editor.▪Online Support Videos: You can find a number of support videos on The Official Native In-struments Support Channel under the following URL: https:///NIsupport-EN. We recommend that you follow along with these instructions while the respective ap-plication is running on your computer.Other Online Resources:If you are experiencing problems related to your Native Instruments product that the supplied documentation does not cover, there are several ways of getting help:▪Knowledge Base▪User Forum▪Technical Support▪Registration SupportYou will find more information on these subjects in the chapter Troubleshooting.1.2Document ConventionsThis section introduces you to the signage and text highlighting used in this manual. This man-ual uses particular formatting to point out special facts and to warn you of potential issues. The icons introducing these notes let you see what kind of information is to be expected:This document uses particular formatting to point out special facts and to warn you of poten-tial issues. The icons introducing the following notes let you see what kind of information can be expected:Furthermore, the following formatting is used:▪Text appearing in (drop-down) menus (such as Open…, Save as… etc.) in the software and paths to locations on your hard disk or other storage devices is printed in italics.▪Text appearing elsewhere (labels of buttons, controls, text next to checkboxes etc.) in the software is printed in blue. Whenever you see this formatting applied, you will find the same text appearing somewhere on the screen.▪Text appearing on the displays of the controller is printed in light grey. Whenever you see this formatting applied, you will find the same text on a controller display.▪Text appearing on labels of the hardware controller is printed in orange. Whenever you see this formatting applied, you will find the same text on the controller.▪Important names and concepts are printed in bold.▪References to keys on your computer’s keyboard you’ll find put in square brackets (e.g.,“Press [Shift] + [Enter]”).►Single instructions are introduced by this play button type arrow.→Results of actions are introduced by this smaller arrow.Naming ConventionThroughout the documentation we will refer to MASCHINE controller (or just controller) as the hardware controller and MASCHINE software as the software installed on your computer.The term “effect” will sometimes be abbreviated as “FX” when referring to elements in the MA-SCHINE software and hardware. These terms have the same meaning.Button Combinations and Shortcuts on Your ControllerMost instructions will use the “+” sign to indicate buttons (or buttons and pads) that must be pressed simultaneously, starting with the button indicated first. E.g., an instruction such as:“Press SHIFT + PLAY”means:1.Press and hold SHIFT.2.While holding SHIFT, press PLAY and release it.3.Release SHIFT.Unlabeled Buttons on the ControllerThe buttons and knobs above and below the displays on your MASCHINE controller do not have labels.。
低分子肝素联合硫酸镁治疗胎儿生长受限的效果分析及对母婴结局的影响
低分子肝素联合硫酸镁治疗胎儿生长受限的效果分析及对母婴结局的影响*宋晖① 【摘要】 目的:探讨低分子肝素联合硫酸镁治疗胎儿生长受限的效果及对母婴结局的影响。
方法:选取2017年1月-2020年1月本院收治的胎儿生长受限孕妇120例作为研究对象。
按照随机数字表法将其分为观察组和对照组,每组60例。
对照组进行低分子肝素治疗,观察组进行低分子肝素联合硫酸镁治疗。
比较两组治疗前、妊娠结束前的超声检测指标,包括胎儿的脐动脉收缩期最大血流与舒张末期血流速度的比值(S/D)、脐动脉搏动指数(PI)及阻力指数(RI)。
比较两组平均每周宫高、头围、腹围、双顶径、股骨长的增长值。
比较两组母婴结局情况,包括母婴不良事件发生情况、出生5 min的Apgar评分、新生儿体重及新生儿胎龄。
结果:妊娠结束前,两组S/D、PI及RI均低于治疗前,且观察组均低于对照组(P<0.05)。
观察组平均每周宫高、头围、腹围、双顶径及股骨长的增长值均高于对照组(P<0.05)。
观察组母婴不良事件发生率低于对照组,出生5 min的Apgar评分、新生儿体重及新生儿胎龄均高于对照组(P<0.05)。
结论:低分子肝素联合硫酸镁治疗胎儿生长受限的效果较好,改善胎儿各项生长指标,且母婴结局较好,不良事件发生率较低,临床应用价值较高。
【关键词】 低分子肝素 硫酸镁 胎儿生长受限 母婴结局 Effect of Low Molecular Weight Heparin Combined with Magnesium Sulfate in the Treatment of FetalGrowth Restriction and Its Effect on Maternal and Infant Outcomes/SONG Hui. //Medical Innovation ofChina, 2021, 18(10): 005-008 [Abstract] Objective: To investigate the effect of Low Molecular Weight Heparin combined with MagnesiumSulfate on fetal growth restriction and its effect on maternal and infant outcomes. Method: A total of 120 pregnantwomen with fetal growth restriction admitted to our hospital from January 2017 to January 2020 were selected asthe research objects. According to the random number table method, they were divided into observation group andcontrol group, with 60 cases in each group. The control group was treated with Low Molecular Weight Heparinand the observation group was treated with Low Molecular Weight Heparin combined with Magnesium Sulfate.The ultrasonographic indicators including the ratio of fetal umbilical artery maximum systolic blood flow to end-diastolic blood flow velocity (S/D), umbilical artery pulse index (PI) and resistance index (RI) of the two groups werecompared before treatment and before the end of pregnancy. The average weekly increase values of uterine height,head circumference, abdominal circumference, biparietal diameter and femur length were compared between thetwo groups. The maternal and infant outcomes including the incidence of maternal and infant adverse events, Apgarscore 5 min after birth, neonatal weight and gestational age of the two groups were compared. Result: Before the endof pregnancy, S/D, PI and RI in both groups were lower than those before treatment, and the observation group werelower than those in the control group (P<0.05). The average weekly increase of uterine height, head circumference,abdominal circumference, biparietal diameter and femur length in the observation group were higher than those inthe control group (P<0.05). The incidence of maternal and infant adverse events in the observation group was lowerthan that in the control group, and the Apgar score 5 min after birth, neonatal weight and gestational age of neonatesin the observation group were higher than those in the control group (P<0.05). Conclusion: Low Molecular WeightHeparin combined with Magnesium Sulfate in the treatment of fetal growth restriction has a good effect, improvingfetal growth indicators, maternal and infant outcomes are good, and the incidence of adverse events is low. Theclinical application value is high.*基金项目:黑龙江省医药卫生科研课题立项(2019-453)①黑龙江省佳木斯市妇幼保健院 黑龙江 佳木斯 154002通信作者:宋晖- 5 - 胎儿生长受限是指胎儿应有的生长潜力受损,胎儿大小异常,是一种妊娠期较为严重的疾病,对于胎儿后续生长发育及母婴健康均产生严重的影响[1-3]。
分子生物学名词解释大全
分子生物学名词解释大全AAbundance (mRNA 丰度):指每个细胞中mRNA 分子的数目。
Abundant mRNA(高丰度mRNA):由少量不同种类mRNA组成,每一种在细胞中出现大量拷贝。
Acceptor splicing site (受体剪切位点):内含子右末端和相邻外显子左末端的边界。
Acentric fragment(无着丝粒片段):(由打断产生的)染色体无着丝粒片段缺少中心粒,从而在细胞分化中被丢失。
Active site(活性位点):蛋白质上一个底物结合的有限区域。
Allele(等位基因):在染色体上占据给定位点基因的不同形式。
Allelic exclusion(等位基因排斥):形容在特殊淋巴细胞中只有一个等位基因来表达编码的免疫球蛋白质。
Allosteric control(别构调控):指蛋白质一个位点上的反应能够影响另一个位点活性的能力。
Alu-equivalent family(Alu 相当序列基因):哺乳动物基因组上一组序列,它们与人类Alu家族相关。
Alu family (Alu家族):人类基因组中一系列分散的相关序列,每个约300bp长。
每个成员其两端有Alu 切割位点(名字的由来)。
α-Amanitin(鹅膏覃碱):是来自毒蘑菇Amanita phalloides 二环八肽,能抑制真核RNA聚合酶,特别是聚合酶II 转录。
Amber codon (琥珀MM子):核苷酸三联体UAG,引起蛋白质合成终止的三个MM子之一。
Amber mutation (琥珀突变):指代表蛋白质中氨基酸MM子占据的位点上突变成琥珀MM子的任何DNA 改变。
Amber suppressors (琥珀抑制子):编码tRNA的基因突变使其反MM子被改变,从而能识别UAG MM子和之前的MM子。
Aminoacyl-tRNA (氨酰-tRNA):是携带氨基酸的转运RNA,共价连接位在氨基酸的NH2基团和tRNA 终止碱基的3¢或者2¢-OH 基团上。
协同过滤算法英语作文
协同过滤算法英语作文Title: The Application and Advancements of Collaborative Filtering Algorithm。
Collaborative filtering algorithm, a cornerstone in the field of recommender systems, has garnered widespread attention for its ability to predict user preferences and provide personalized recommendations. In recent years, with the exponential growth of online platforms and the increasing volume of data generated by users, collaborative filtering algorithms have become indispensable tools for businesses seeking to enhance user experience and drive engagement. This essay explores the principles, applications, and advancements of collaborative filtering algorithms, shedding light on their significance in today's digital landscape.At its core, collaborative filtering relies on the principle of leveraging collective user behavior to make predictions about the interests of individual users. Byanalyzing user interactions, such as ratings, purchases, and preferences, collaborative filtering algorithmsidentify patterns and similarities among users to generate recommendations. There are two main approaches to collaborative filtering: memory-based and model-based.Memory-based collaborative filtering, also known as neighborhood-based collaborative filtering, operates by calculating similarities between users or items based on their historical interactions. One of the most widely used techniques in this approach is cosine similarity, which measures the cosine of the angle between two vectors representing user preferences. By identifying users with similar preferences, memory-based collaborative filtering generates recommendations based on items liked or purchased by similar users.On the other hand, model-based collaborative filtering involves building a mathematical model based on the user-item interaction data. Techniques such as matrix factorization and singular value decomposition (SVD) are commonly employed to decompose the user-item matrix intolatent factors representing user preferences and item characteristics. By learning these latent factors, model-based collaborative filtering can make accurate predictions even in the presence of sparse data.The applications of collaborative filtering algorithms are manifold, spanning across various industries including e-commerce, media streaming, social networking, and more.E-commerce platforms utilize collaborative filtering to recommend products based on the browsing and purchasing history of users, thereby increasing sales and customer satisfaction. Similarly, media streaming services leverage collaborative filtering to suggest movies, TV shows, or music based on users' past viewing or listening behavior, enhancing user engagement and retention.Furthermore, social networking platforms employ collaborative filtering to recommend friends, groups, or content tailored to the interests and preferences of users. By analyzing the social graph and user interactions, these platforms can foster connections and facilitate content discovery, thereby enriching the user experience.Additionally, collaborative filtering algorithms are usedin content-based filtering and hybrid recommender systems, combining multiple approaches to generate more accurate and diverse recommendations.Despite its effectiveness, collaborative filtering algorithms are not without limitations. One of the primary challenges is the cold start problem, which occurs when new users or items have limited interaction data, making it difficult to generate accurate recommendations. To address this issue, techniques such as demographic filtering, content-based filtering, and hybrid approaches are employed to supplement collaborative filtering and improve recommendation quality.Moreover, collaborative filtering algorithms may suffer from the problem of popularity bias, wherein popular items tend to receive more recommendations, leading to a lack of diversity in recommendations. To mitigate this bias, techniques such as diversity-aware recommendation and serendipity enhancement are employed to ensure that users are exposed to a variety of items across differentcategories.In recent years, significant advancements have been made in collaborative filtering research, driven by innovations in machine learning, deep learning, and data mining techniques. Deep learning models, such as neural collaborative filtering (NCF) and recurrent neural networks (RNNs), have shown promising results in capturing complex patterns and dependencies in user-item interactions, thereby improving recommendation accuracy and scalability.Furthermore, the integration of contextual information, such as temporal dynamics, location-based factors, and social influence, has enhanced the capabilities of collaborative filtering algorithms to provide context-aware recommendations. By considering contextual factors, such as time of day, user location, or social connections, collaborative filtering algorithms can adapt recommendations to better suit the preferences and situational needs of users.In conclusion, collaborative filtering algorithms playa crucial role in the era of big data and personalized recommendation systems. By harnessing the collective wisdom of users, collaborative filtering enables businesses to deliver tailored recommendations that enhance user experience, drive engagement, and foster loyalty. With ongoing research and advancements in machine learning and data science, collaborative filtering algorithms are poised to remain at the forefront of recommender systems, shaping the future of digital commerce and content consumption.。
COLLABORATION
The Impact of Culture on Collaborative TechnologiesBedoor K. AlShebli, Karrie KarahaliousUniversity of Illinois at Urbana-Champaign{alshebli, kkarahal}@ABSTRACTThe increase in globalization,international trading,and outsourcing in the world’s economy has lead to an increase in the demand for cross-cultural anizations,today, frequently consist of individuals with diverse cultural backgrounds and skills.This creates a pressing need to better understand how the interplay of culture and collaboration in technology can influence productivity and outcomes. In this paper we'll discuss the cultural constraints affecting collaborative technologies, provide insight to help increase the understanding of cultural issues in collaborative technologies, distribute research findings in the domain, and provide guidelines to follow when designing cross-cultural collaborative tools.1. CULTURAL CONSTRAINTS AFFECTING COLLABORATIONThere are certain factors and characteristics of behavior that are common within certain populations depending their culture. Knowing these social and cultural constraints is important when developing collaborative systems.We mention three main constraints to consider when developing interactive systems. 1.1 LanguageLanguage is one of the biggest constraints faced in cross-cultural collaboration. There are cases, such as the 1977 Tenerife disaster, where linguistic differences have led to fatalities.There are language characteristics that make the understanding of certain behaviors easier or more difficult than in the case of other languages. Sakuma and Yaguchi presented a questionnaire based on Smith's, [10], investigating the strength of cultural stereotypes. One of the questions can be used as to show the extent of the differences that can arise on the basis of linguistics is: “Working with a fire crew the hose-man calls 'Pressure High!' What should be done?Raise the pressure or lower the pressure?”They presented the question to four different cultural groups. Most of the Americans answered “lower”, while all the Dutch answered “raise”; even though most of the Dutch speak fluent English. 1.2Population/Cultural StereotypesThere are characteristic patterns of behavior that are common within large populations.Populations from different cultures respond to stimuli differently. One classical example is the light switches. In the U.S., for instance, flipping the switch upwards would turn it on, while in Europe such an action would turn off the lights. In Japan, on the other hand, the light switch is from side-to-side, where a right flip would turn on the lights.As the growth of globalization proceeds,and as military equipment is shared among multi-national forces,taking population stereotypes into consideration becomes extremely important. Violation of population stereotypes could be a source of human error, especially since a lot of equipment nowadays is made up of components supplied from different cultural backgrounds.Something as simple as flipping a switch in the wrong direction, especially in an emergency where the response is automatic and dominates performance,could lead to dire consequences. This is confirmed by Jost's Law (1897) [11] that states that the stereotypical habit acquired over many years in one population will from time to time interfere with performance even after much practice,and particularly in an emergency when a rapid “skill-based” reaction is required.1.3 AnthropometricsFactors as simple as differences in anatomical dimensions can be considered cultural as well. Fernandez et al. [7] studied Korean factory female workers,and found that while there was little difference between Korean and Western workers'anatomical characteristics on many measures,there were some very significant differences. They found that the difference in the eye height is significant enough to cause difficulties if Asian women have to look over a high control panel when operating equipment manufactured in the West.Therefore, when designing tools, whether or not for collaborative purposes, body dimensions should be scaled and the tool designed to be specialized for populations with the same culture.2. DESIGNING CULTURALLY-ORIENTED COLLABORATIVE TECHNOLOGIESThe question that arises now is, are these previous constraints all that we need to take into account when designing a collaborative system?What yet needs to be explored?How can we create culturally-oriented collaborative systems?The global interaction between different cultures involves sharing the knowledge of all interacting users and sometimes they define their own “communication culture” to interact. Bourges-Waldegg [1] put it nicely when he said,“...Design changes culture and at the same time is shaped by it. In the same way, globalization is a social phenomenon both influencing and influenced by design and therefore, by culture..., both globalization and technology have an effect on culture, and play a role in shaping them.”Therefore, we need to look into teams in today's workforce and the impact of cultural diversity on them. What can be learned from previous research in that area to help in the design culturally-oriented collaborative systems?2.1 Affect of Cultural Diversity on TeamsThe increase of globalization has increased the opportunities for workers of different cultures to interact and work together. In addition to this exchange amongst workers from different countries,the increasing proportion of minority workers in American companies has resulted in a culturally diverse workplace. As a result, work groups in many U.S organizations are receiving more attention from researchers; because a thorough understanding of these groups and their performance can improve overall company productivity.Cox, Lobel, and McLeod [3] conducted an empirical study where they compared the performance of teams from four ethnic groups, Anglo-American,African-American,Asian-American,andHispanic-Americans, in performing the “prisoner's dilemma” task. They concluded that organizations with an ethnically diverse work force may be better suited for intra-team cooperation than those with teams made up exclusively of inherently less cooperative workers (i.e. Anglo-American). They further noted that there was significantly more research needed in this area by stating that “there is a need for studies addressing the differences between homogeneity and heterogeneity more generally.”However, Gersick, [8], looks at it from a different perspective and argues that while it may be true that culturally heterogeneous teams might perform better, it is only in the later stages of team development that this happens. He argues that process losses occur due to the lack of a common set of language, norms, and expectations. Such losses can be damaging to performance in the first stages of team development; commonly known as “forming”and “storming”.2.2 Designing Collaborative Systems for Culturally Diverse TeamsIn an age of globalization, culture orientation is one essential component for successful user-centered designs. Therefore, the culture has the same importance as other factors such as the user's profession, choice of operating systems, learning style and other elements.There exists the need for communication that goes beyond the borders of countries and cultures. The global interaction between different cultures involves sharing the values of both interaction partners.The key problem of inter-cultural design is how the designer can get his message across to the user of another culture. This is not simply a question of language. The most important fact is that the designer and the users of different cultures agree on the information meaning and its interpretation.There must be a significant element of shared meaning between the user and the developer.Therefore,based on our discussion and previous research, we propose some ground rules to take into consideration when designing your system:1.Identify and classify the kind of system you are designing.Röse, [9], mentioned two established approaches for inter-cultural design: Internationalization and Localization. Internationalization describes a basic structure with the consideration of future integration of culture-specific requirements. This design concept takes into account some general culture specifics (like language, format, etc.) and is often designed for flexible switching between different user cultures. Localization, on the other hand, focuses on one specific user culture. (In application areas like the aerospace and car industry, a third approach called global design is used.)2.Know the users' and cultural requirementsUser requirements include the analysis of user preferences for specific tasks,products and cultures.Resulting from this,a culture-oriented design is not possible without the empirical analysis of the user requirements in each culture, and the product to be developed for the respective markets.Cultural requirements for the targeted market, such as language, cultural stereotypes,and anthropometrics,should be well addressed as well. There are still questions about how the system designers could go about determining these requirements based on the analysis of the targeted culture and then create a basis for his or her design. A good understanding of culture could provide the designers with clues to answering their questions. 3.Look at existing work,especially when designing culture-specific user interfaces, before creating your own design.del Galdo[4]and Fernandes[6]work,for instance,included colors,icons,symbols,date formats,time formats,number formats,language translations and more for different cultures. Other design issues such as menu direction, interface structure, information flow, etc. have also been addressed by Choong [2], and Dong and Salvendy [5].4.Do not neglect other “hidden” cultural constraints that couldstill affect collaboration when designing; such as attitudes, behaviors, problem-solving strategies, thinking patterns, etc. Furthermore, there are also many design issues that should be taken into consideration that are beyond the user interfaces but are actually closely related to the user's interaction with machines. Röse,[9],listed some of the most prominent ones:machine functionality,appropriate technology,service model,technical documentation, and general machine design.To conclude, this paper was a quick survey of existing work to provide an understanding of the impact of cultural diversity in collaboration.Even with differing opinions on culture and its dimension, proposed differences between groups and teams, and concerns about generalizability of studies,it is still clear that cultural heterogeneity does influence team processes and team performance in some fashion.Thus clearly this field of study cannot be ignored.3. REFERENCES[1]Bourges-Waldegg,P.Globalization:A threat to culturaldiversity?Designing for Global Markets2,Second International Workshop on Internationalization of Products and Systems (pp. 115-124) IWIPS 2000, Baltimore, MA USA.Blackhouse Press.[2]Choong, Y. Y. Design of computer interfaces for the Chinesepopulation. Doctoral dissertation. Purdue University. 1996. [3]Cox, T. H., Lobel, S. A., and McLeod, P. L. Effects of ethnicgroup cultural differences on cooperative and competitive behavior in a group task.Academy of Management Journal, 34(4), pp. 827-847. 1991.[4]del Galdo, E. and Nielson J. International user interfaces. NewYork: Wiley. 1996.[5]Dong, J. and Salvendy, G. Designing menus for the Chinesepopulation: Horizontal or vertical? Behaviour and Information Technology, 18(6), pp. 467-471. 1999.[6]Fernandes, T. Global interface design: A guide to designinginternational user interfaces. Ap Professional, Boston. 1995.[7]Fernandez, J. E., Malzahn, D. E., Eyada, O. K., and Kim, C.H.Anthropmetry of Korean female industrial workers.Ergonomics, 32(5), pp. 491-495. 1989.[8]Gersick, C. J. G. Marking time: Predictable transition in taskgroups.Academy of Management Journal, 32, pp. 274-309.1989.[9]Röse, K. The Development of culture-oriented human machinesystems:Specification,analysis and integration of relevant intercultural variables.Cultural Ergonomics,Advances in Human Performance and Cognitive Engineering Research, Vol 4, 61-103. 2004[10]Smith, S. L. Exploring compatibility with words and pictures.Human Factors, 23(3), 305-315. 1981.[11]Woodworth, R. Experimental psychology, New York: HenryHolt. 1938.。
Focus Groups
Focus Groups: Introduction
• The participants are pre-recruited by administering a screening questionnaire (Screener) specifying criteria relevant to the research problem • Normal focus groups last around 1.5 to 2 hours, but some extended ones my last up to three hours • A discussion guide outlining the topics to be covered has to be prepared prior to the group discussion • Participants are paid a incentive for their timing and contribution
Focus Groups: Introduction
• Focus Group is the most popular form of qualitative research • A focus group normally consists of 6-8 participants, who are expected to have indepth conversation about a particular topic led by a moderator trained as such • The key to a successful focus group is group dynamics / interaction—the process by which one response may p消费者座谈会的方式收集信息:
超声引导下股神经、坐骨神经阻滞在跟骨骨折手术麻醉中的应用效果
超声引导下股神经、坐骨神经阻滞在跟骨骨折手术麻醉中的应用效果刘胜① 宋玉娟② 张争辉① 王守福① 【摘要】 目的:探究超声引导下股神经、坐骨神经阻滞在跟骨骨折手术麻醉中的应用效果。
方法:选取2020年2月—2023年2月菏泽医学专科学校附属医院收治的跟骨骨折患者80例,以随机数字表法将其均分为对照组(椎管内麻醉)及观察组(超声引导下股神经、坐骨神经阻滞)各40例,对比两组麻醉效果;对比两组麻醉前(T0)、麻醉后5 min(T1)、麻醉后10 min(T2)、麻醉后15 min(T3)、麻醉后30min(T4)时刻的心率(HR)、平均动脉压(MAP)、血氧饱和度(SpO2);对比两组应激反应指标[肾上腺素(E)、皮质醇(Cor)]、凝血功能[凝血酶时间(TT)、凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)]、不良反应发生率。
结果:观察组麻醉效果Ⅰ级率高于对照组,差异有统计学意义(P<0.05)。
两组T0、T1时刻HR、MAP、SpO2水平比较差异均无统计学意义(P>0.05);对照组T2、T3、T4时刻HR、MAP均高于T0时刻,SpO2均低于T0时刻,差异均有统计学意义 (P<0.05);观察组T2、T3、T4时刻HR、MAP、SpO2较T0时刻差异均无统计学意义(P>0.05);观察组T2、T3、T4时刻HR、MAP均低于对照组,SpO2均高于对照组,差异均有统计学意义(P<0.05)。
术前,两组E、Cor水平相较差异均无统计学意义(P>0.05);术后1 h两组E、Cor水平均升高,但观察组均低于对照组,差异均有统计学意义(P<0.05)。
术前,两组TT、PT、APTT水平相较差异均无统计学意义(P>0.05);术后1 h,两组TT、PT、APTT水平均升高,且观察组均高于对照组,差异均有统计学意义(P<0.05)。
观察组不良反应发生率低于对照组,差异有统计学意义(P<0.05)。
关于有趣作业的英语作文
Interesting homework assignments can make learning more enjoyable and engaging for students.Here are some ideas for fun and creative English assignments that can help students improve their language skills while also having a good time.1.Storytelling:Encourage students to write a short story with a beginning,middle,and end.They can use their imagination to create characters,settings,and plot twists.2.Poetry Writing:Introduce students to different types of poetry,such as haikus,sonnets, or free verse,and have them try writing their own poems.3.Playwriting:Have students write a short play or skit that includes dialogue,stage directions,and character development.4.Journaling:Assign a weekly journal entry where students can reflect on their experiences,thoughts,or feelings in English.5.Book Reviews:After reading a book,students can write a review discussing the plot, characters,themes,and their personal opinions.nguage Games:Create crossword puzzles,word searches,or word scrambles using vocabulary words from the curriculum.7.RolePlaying:Assign roles to students and have them act out a scene from a book or create their own dialogues based on a given situation.8.Descriptive Writing:Ask students to describe a person,place,or thing in detail,using sensory language and adjectives.9.Letter Writing:Have students write a formal or informal letter to a friend,a pen pal,ora fictional character.10.Interview Project:Students can conduct an imaginary interview with a historical figure,a celebrity,or a character from a book,and write a transcript of the conversation.11.Creative Writing Prompts:Provide students with a prompt,such as Write a story that starts with someone finding a mysterious letter or Describe a day in the life of an object.12.Group Projects:Divide students into groups and have them collaborate on a project, such as creating a newspaper,a magazine,or a blog.nguage Exploration:Have students research and present on different dialects or accents within the English language.14.Film Analysis:After watching a film in English,students can write an analysis of the plot,characters,and themes.15.Creative Presentations:Students can create a PowerPoint or Prezi presentation on a topic of their choice,using images,videos,and interactive elements.nguage Debates:Organize debates on various topics,allowing students to practice their argumentative writing and speaking skills.17.Character Analysis:Students can choose a character from a book or a movie and write an indepth analysis of their personality,motivations,and development.18.English Idioms and Phrases:Teach students common English idioms and have them create sentences using these expressions.19.Cultural Exchange:Students can research and present on the culture of an Englishspeaking country,focusing on aspects like food,music,and traditions.20.Blogging:Encourage students to start a blog where they can write about their interests,experiences,or observations in English.These assignments not only help students practice their English skills but also stimulate their creativity and critical thinking abilities.By making homework enjoyable,students are more likely to be motivated and engaged in their learning process.。
戴炜栋 新编简明语言学教程文档版
Linguistics is a scientific study of language .语言学是对语言进行的科学研究。
General linguistics is the study of language as a whole.普通语言学是对语言从整体上进行的研究the major branches of linguistics:语言学内部主要分支Phonetics:the study of the sounds used in linguistic communication..(语音学)对语言交流中语音的研究Phonology the study of how sounds are put together and used to convey meaning in communication. (音位学)如何组合在一起并在交流中形传达意义.Morphology:the study of the way in which morphemes are arranged to form words (词法学、形态学)如何排列以及组合起来构成词语Syntax:the study of those rules that govern the combination of words to form permissible sentences (句法学)如何在组成语法上可接受的句子Semantics(语义学) the study of meaning in abstraction语言是用来传达意义的。
Pragmatics(语用学) the study of meaning in context of use用来研究上下文的意义跨学科分支Sociolinguistics is the study of the relationship between language and society.社会语言学是语言和社会之间关系的研究Psycholinguistics is the study of the relationship between language and the mind.心理语言学是语言与心灵的关系的研究Applied linguistics is the study of the teaching of foreign and second languages.应用语言学是外国和第二语言教学的研究Some important distinctions in linguistic s语言学中一些基本区分1. Descriptive or PrescriptiveA linguistic study is descriptive if it describes and analyses facts observed; it is prescriptive if it tries to lay down rules for "correct" behavior.描述性是在描述和分析人们对语言的实际运用,规定性是在为语言“正确和规范的”使用确立规则。
低分子肝素钙对皮瓣术后动静脉循环障碍治疗效果评价
国际医药卫生导报 2018年 第24卷 第16期 IMHGN,August 2018,Vol. 24 No. 16低分子肝素钙对皮瓣术后动静脉循环障碍治疗效果评价周礼志 吴耀强 萧瑞宜 黄陈海 林婉媚 滕金美523142 东莞市麻涌医院药剂科通信作者:周礼志,E-mail:80926319@DOI:10.3760/cma.j.issn.1007-1245.2018.16.013 【摘要】 目的 探讨低分子肝素钙对皮瓣术后动静脉循环障碍治疗效果和安全性。
方法 选取2016年7月至2017年12月本院收治皮瓣术后血运障碍患者108例作为研究对象,根据患者血运障碍类型分为动脉供血障碍(A组,46例)和静脉回流障碍(B组,62例),采用随机表法将两组患者分为两个亚组,观察A组(23例)、对照组A组(23例)和观察B组(31例)和对照B组(31例);观察组采用脐周真皮下注射低分子肝素钙+静脉滴注治疗,对照组采用皮瓣真皮下注射低分子肝素钙治疗;比较各组皮温升高、肿胀、皮肤颜色异常、毛细血管回流征阳性及凝血功能和并发症发生情况。
结果 使用肝素前、使用肝素后6 h,观察组和对照组APTT、PT、Fib、D-D水平比较,差异均无统计学意义(均P>0.05);使用肝素后2 h,观察组患者APTT、PT水平高于对照组[(45.7±4.1)s 比(37.2±3.8)s,(19.7±2.3)s比(12.4±2.6)s],Fib、D-D水平低于对照组[(2.7±0.4)g/L 比(3.1±0.5)g/L,(103.8±13.5)μg/L 比(125.9±15.7)μg/L],均P<0.05。
观察A组患者皮温升高、肿胀、皮肤颜色异常、毛细血管回流征阳性发生率均低于对照A组[21.74% 比60.87%,17.39%比47.83%,8.70%比34.78%,13.04%比56.52%,均P<0.05];观察B组患者肿胀、毛细血管回流征阳性发生率均低于对照B组[29.03%比64.52%,6.45%比25.81%,均P<0.05]。
collaboration in study groups
collaboration in study groups, this collaborative learning process can be enhanced by synchronous or asynchronous technologies (Lui & Fang, 2007). Asynchronous tools such as chat boards, wikis and discussion boards, may facilitate meaningful communication, sharing of resources and sustained collaboration (Sang & Ellis, 2007). These tools also enable participants to communicate from distant locations and are particularly effective for virtual teams (De Wever, Schellens, Valcke & Van Keer, 2006).Synchronous tools such as videoconferencing, instant messaging and webinars, facilitate collaboration across distant locations and allow for more immediate discussion (De Wever et al, 2006). Similarly, the exchange of media files and audio recordings can enhance the collaborative process (Raman & Fischer, 2006). By providing a platform to view multimedia material, collaboration can be improved and group discussion can take place in real-time (Lui & Fang, 2007). Similarly, the use of shared whiteboards or ‘digital notepads’can help to facilitate the exchange of ideas (Lui & Fang, 2007).Overall, the combination of synchronous and asynchronous tools in collaborative learning can be a valuable resource and facilitate rapid development of ideas. Consequently, the integration of technology andcollaborative learning processes can lead to enhanced performance in student groups and improved academic outcomes.。
合作模式及分配英语
合作模式及分配英语
1.分配(Allocation)
2.合作模式(Collaboration mode)
1. Our team leader will decide the allocation of tasks
for this project.
我们的团队领导将决定这个项目任务的分配。
2. Different collaboration modes can be adopted depending on the nature of the project.
根据项目的性质,可以采用不同的合作模式。
3. The allocation of resources is crucial for the success of any project.
资源的分配对于项目的成功至关重要。
4. Effective collaboration modes can increase efficiency and productivity in a team.
有效的合作模式可以提高团队的效率和生产力。
5. The allocation of funds for research is a complex process that requires careful consideration.
研究资金的分配是一个复杂的过程,需要仔细考虑。
6. Collaboration with other companies is often necessary for businesses to achieve their goals.
与其他公司的合作常常是企业实现目标所必需的。
脑卒中合并意识障碍应用醒脑静注射液治疗的观察
第31卷第11期航空航天医学杂志2020年11月1303脑卒中合并意识障碍应用醒脑静注射液治疗的观察孙步时[摘要]目的分析脑卒中合并意识障碍应用醒脑静注射液治疗的效果。
方法选择脑卒中合并意识障碍患者共50例,数字表随机分两组每组25例,对照组的患者给予常规治疗联合纳洛酮治疗,观察组在常规治疗基础 上增加醒脑静注射液。
比较两组意识恢复时间、住院时间、治疗前后患者Glasgow评分、总有效率、不良反应。
结 果观察组意识恢复时间、住院时间短于对照组,P<0.05。
治疗前二组患者Glasgow评分比较,P>0.05,治疗后 1周,两组Glasgow评分均改善,且两组之间无显著的差异,P>0. 05,而治疗后2周,观察组Glasgow评分显著高 于对照组,P<0.05。
观察组总有效率高于对照组,P<0.05。
两组治疗过程不良反应未见明显的差异,P>0.05。
其中,对照组治疗过程,出现肝酶轻度升高的有2例,观察组有3例出现肝酶轻度上升,但均不影响两组的治疗,无需用药自动好转。
结论常规药物联合醒脑静注射液对于脑卒中合并意识障碍的治疗效果确切,可有效改善 患者的意识状态,缩短治疗时间,且安全性高,无严重副作用。
[关键词]脑卒中合并意识障碍;醒脑静注射液;Glasgow评分[中图分类号]R743.3 [文献标识码] A [文章编号]2095 -1434.2020. 11.007Observation of Xingnaojing Injection in the Treatment of Cerebral Apoplexy with Disturbance of Consciousness/SUN Bushi//(Liaoning Dahua Group Co., Ltd)[Abstract]Objective To analyze the e f f e c t of Xingnaojing Injection on cerebral apoplexy complicated with disturbance of consciousness.Methods A t o t a l of 50 stroke patients with disturbance of consciousness in our hospital were randomly divided i n t o two groups with 25 cases i n each group.The patients i n the control group were given conventional treatment combined with naloxone, while the observation group was added Xingnaojing Injection on the basis of conventional treatment.Consciousness recovery time,hospitalization time,Glasgow score,t o t a l effective rate and adverse reactions were compared between the two groups.Results The consciousness recovery time and hospitalization time of the observation group were shorter than those of the control group, P<0. 05. Before treatment, Glasgow score of the two groups was compared, P>0. 05. After 1 week of treatment, the Glasgow score of the two groups was improved, and there was no significant difference between the two groups,P> 0.05. However,a f t e r 2 weeks of treatment,the Glasgow score of the observation group was significantly higher than t h a t of the control group,P< 0.05. The t o t a le f f e ctive r ate of the observation group was higher than t h a t of the control group,P < 0.05. There was no significantdifference i n adverse reactions between the two groups (P> 0.05 ).Among them, there were 2 cases of s l i g h t increase i n l i v e r enzymes i n the control group and 3 cases i n the observation group during the treatment process, but they did nota f f e c t the treatment of the two groups,and they did not need medication t o automatically improve.ConclusionsConventional medicine combined with Xingnaojing injection has definite therapeutic e f f e c t on cerebral apoplexy complicated with disturbance of consciousness,which can e f f e ctively improve the s t a t e of consciousness of patients, shorten the treatment time, and has high s a fety and no serious side effects, which i s worthy of promotion and application.[Key words]stroke with disturbance of consciousness; Xingnaojing injection; Glasgow score脑卒中是世界上主要的致死致残疾病之一。
腔镜经腹腹膜前疝修补术在中老年腹股沟疝治疗中的应用观察
腔镜经腹腹膜前疝修补术在中老年腹股沟疝治疗中的应用观察张洪伟 顾 明 戴建军徐州医科大学附属第三医院普外科,江苏徐州 221003[摘要]目的 探讨腔镜经腹腹膜前疝修补(TAPP)在中老年腹股沟疝治疗中的临床应用价值。
方法 回顾性选取2020年1月至2023年6月徐州医科大学附属第三医院收治的中老年单侧腹股沟疝患者52例为研究对象,按照手术方式不同分为对照组和观察组,每组各26例。
对照组行开放式无张力疝修补术(OTFH),观察组行TAPP,比较两组手术时间、术中出血量、术后24 h炎症因子水平,并统计并发症及复发率。
结果 观察组术中出血量少于对照组,手术时间短于对照组,术后24 h超敏C反应蛋白、血清淀粉样蛋白A水平低于对照组,差异有统计学意义(P < 0.05)。
观察组并发症总发生率低于对照组,差异有统计学意义(P < 0.05)。
随访3个月后,两组总复发率比较,差异无统计学意义(P > 0.05)。
结论 与OTFH比较,TAPP治疗中老年腹股沟疝,手术时间短,能减少术中出血量,减轻机体炎症应激反应,且降低并发症发生率。
[关键词]腔镜经腹腹膜前;疝修补;中老年;腹股沟疝[中图分类号] R656.21 [文献标识码] A [文章编号] 2095-0616(2024)06-0139-04DOI:10.20116/j.issn2095-0616.2024.06.32Observation on the application of transabdominal preperitoneal prosthesis in the treatment of inguinal hernia in middle-aged and elderly peopleZHANG Hongwei GU Ming DAI JianjunDepartment of General Surgery, the Third Affiliated Hospital of Xuzhou Medical University, Jiangsu, Xuzhou 221003, China[Abstract] Objective To explore the clinical value of transabdominal preperitoneal prosthesis (TAPP) in the treatment of inguinal hernia in middle-aged and elderly people. Methods A total of 52 middle-aged and elderly patients with unilateral inguinal hernia admitted to the Third Affiliated Hospital of Xuzhou Medical University from January 2020 to June 2023 were analyzed retrospectively. They were divided into the control group and observation group according different surgical methods, with 26 cases in each group.The control group received open tension free hernioplasty (OTFH), while the observation group received TAPP. The operation time, intraoperative blood loss, inflammatory factor level in 24 hours after operation were compared and postoperative complications and recurrence rate were counted. Results The blood loss in the observation group was less than that in the control group, the operation time was shorter than that in the control group, and the levels of hypersensitive C-reactive protein and serum amyloid A at 24 hours after operation were lower than those in the control group, with statistically significant differences (P < 0.05). The total incidence of complications in observation group was lower than that in control group, with a statistically significant difference (P < 0.05). After 3-month follow-up, there was no significant difference in total recurrence rate between the two groups (P > 0.05). Conclusion Compared with OTFH, TAPP in the treatment of inguinal hernia in middle-aged and elderly people can shorten the operation time, reduce the intraoperative blood loss, alleviate the inflammatory and stress reaction of the body, and reduce the complications.[Key words] Transabdominal preperitoneal; Prosthesis; Middle-aged and elderly; Inguinal hernia中老年腹股沟疝是指发生在中老年人腹股沟区域的疝。
椎体成形术治疗老年骨质疏松性脊柱骨折的效果及对脊椎功能、影像学指标及并发症的影响
椎体成形术治疗老年骨质疏松性脊柱骨折的效果及对脊椎功能、影像学指标及并发症的影响*刘彬① 王伟群① 【摘要】 目的:探讨椎体成形术治疗老年骨质疏松性脊柱骨折的效果及对脊椎功能、影像学指标和并发症的影响。
方法:选取中山市中医院在2021年12月—2023年1月收治的122例老年骨质疏松性脊柱骨折患者,按照随机数字表法分为观察组(n=61)与对照组(n=61)。
对照组实施传统保守治疗,观察组实施椎体成形术。
对两组患者的临床疗效、脊柱功能、影像学指标、并发症发生情况和生活质量进行观察和比较。
结果:观察组临床总有效率(95.08%)高于对照组(83.61%),差异有统计学意义(P<0.05);治疗后,观察组脊柱功能评分均优于对照组,观察组伤椎后凸角Cobb显著低于对照组,观察组生活质量评分高于对照组,差异均有统计学意义(P<0.05);观察组并发症发生率(6.56%)低于对照组(21.31%),差异有统计学意义(P<0.05)。
结论:在老年骨质疏松性椎体骨折患者中实施椎体成形术,可更大程度改善脊柱功能,降低并发症发生率,提升生活质量,提高临床疗效。
【关键词】 老年骨质疏松 脊柱骨折 椎体成形术 脊椎功能 影像学指标 Effects of Vertebroplasty in the Treatment of Elderly Osteoporotic Spinal Fractures and Its Impact onSpinal Function, Imaging Indicators and Complications/LIU Bin, WANG Weiqun. //Medical Innovation ofChina, 2023, 20(33): 021-024 [Abstract] Objective: To explore the therapeutic effect of vertebroplasty on elderly osteoporotic spinalfractures and its impact on spinal function, imaging indicators and complications. Method: A total of 122 elderlypatients with osteoporotic spinal fractures admitted to Zhongshan Traditional Chinese Medicine Hospital fromDecember 2021 to January 2023 were randomly divided into an observation group (n=61) and a control group (n=61)using a random number table method. The control group received traditional conservative treatment, while theobservation group received vertebroplasty. The clinical efficacy, spinal function, imaging indicators, incidence ofcomplications and quality of life of two groups of patients were observed and compared. Result: The total clinicaleffective rate of the observation group (95.08%) was higher than that of the control group (83.61%), with a statisticallysignificant difference (P<0.05); after treatment, the spinal function scores of the observation group were significantlybetter than those of the control group, the Cobb of the injured vertebral protrusion angle of the observation group wassignificantly lower than that of the control group, and the quality of life score of the observation group was higher thanthat of the control group, the differences were statistically significant (P<0.05); the incidence of complications in theobservation group (6.56%) was lower than that in the control group (21.31%), with a statistically significant difference(P<0.05). Conclusion: Implementing vertebroplasty in elderly patients with osteoporotic vertebral fractures cangreatly improve spinal function, reduce complications, improve quality of life, and improve clinical efficacy. [Key words] Elderly osteoporosis Spinal fractures Vertebroplasty Spinal function Imaging indicators First-author's address: Zhongshan Traditional Chinese Medicine Hospital, Guangdong Province,Zhongshan 528403, China doi:10.3969/j.issn.1674-4985.2023.33.005*基金项目:广东省医学科学技术研究基金项目(B2021397)①广东省中山市中医院(广州中医药大学附属中山中医院) 广东 中山 528403通信作者:王伟群- 21 - 老年人随年龄增大机体功能下降,骨组织正常钙化流失后,骨量降低,骨微环境遭到破坏,极易出现骨质疏松,自身骨头脆性增加,极易出现骨折症状,以骨质疏松性脊柱骨折较为常见[1-2]。
The L-series of certain rigid Calabi-Yau threefolds
Variety bre, Zt Local equation Reference monodromy group parameter, t periods, f
Table 1. Examples
K3 surface elliptic curve
1 1 x + x + y + y = t2
ZA1
A1
THE L-SERIES OF CERTAIN RIGID CALABI-YAU THREEFOLDS
3
variety. To calculate the dimensions of the cohomology groups, it will be easier to work with singular cohomology. Then we use the fact that H i (ZA3 ; Q ` ) and H i (ZA3 ; R) have the same dimension. We will use the same notation ZR to refer to the variety, whether we are looking at it over C or some other eld, and what is meant should be clear from the context. Note also we will not calculate the Euler factors at the primes of bad reduction, so the L-series listed in the table are the L-series of the varieties up to possible Euler factors at 2 and 3. The vn in the table is the coe cient of the t expansion of the period f . The vn are related to the L-series as follows: If g is the Mellin transform of the L-series, P P and g = n q n , and f = vn tn then for ZA2 and ZA2 , p vp?1 mod p, and 1 for ZA3 and ZA3 , p vp mod p ; this holds for all primes p, except possibly those 1 of bad reduction. This can be veri ed for the examples in the table from the given data, and proofs of the congruences can be found in V3]. This relationship between the L-series and the solution of the Picard-Fuchs equation is expected because of the result in S2]. More examples of this kind of congruence can be found in V3]. The monodromy group ? is given using the notation in CN], where ?0 (a) + b is written to mean the group generated by ?0 (a) together with certain Fricke involutions, and ?0 (a) + b means we have quotiented by I: For example, ?0 (12) + 12 := 12a b 2 SL (R)ja; b; c; d 2 Z : 2 12 12c 12d Note that monodromy groups are only de ned up to conjugacy, and the groups listed are sometimes slightly di erent from the ones in the papers referred to, e.g., it is shown in SB] x 14, that the K3 surface A0 is an elliptic modular surface associated with the group ?0 (16) \ ?1 (4). But when we quotient this group by I , (since we are considering the action on the upper half plane H), this is the same as ?0 (16). Note that the parameter t given in the case of ZA3 is a uniformizing 1 2 parameter for ?0 (12) + 12 conjugated by 1 1 , rather than for ?0 (12) + 12, 0 since the conjugated group is a subgroup of ?0 (6) + 6, the group found in V1] to be the monodromy group for XA3 . Since ZA3 is constructed from XA3 it is most natural to use the parameter given in the table. In each case, t is a uniformizing parameter for the given monodromy group, and is a (non holomorphic) modular function for ?, and generates the space of modular functions for ?. The function f is a modular form of weight 2 for ?. The row for the periods gives a modular form f ( ) such that when the family is given as a bration over H=?, so the parameter t is given as a uniformizing parameter t( ), then the period lattice of the bre over 2 H=? is given by f ( )(Z Z 2 Z); for ZA1 A1 A1 and ZA3 , and f ( )(Z Z) for ZA1 A1 and ZA2 .
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GroupMorph: A Group Collaboration Mode Approach to Shared 3D Virtual Environments for Product DesignJohn M. LinebargerLehigh UniversityDepartment of Computer Science and Engineering19 Memorial Drive WestBethlehem, PA 18015 USA1 610 974-1774jmlg@G. Drew KesslerLehigh UniversityDepartment of Computer Science and Engineering19 Memorial Drive WestBethlehem, PA 18015 USA1 610 758-4818dkessler@ABSTRACTVirtual reality technology is increasingly being applied to globally distributed teams engaged in collaborative product design. Observations of product design teams have suggested at least four distinct modes of collaboration—complementary, competitive, peer-to-peer, and leader-follower. Another insight from observation is that collaboration consists of fluid transitions between these modes in the completion of the design task, driven by a flexible process of subgrouping and regrouping which reflects the structure and progress of the task. Yet most collaborative virtual environment systems support only one mode of collaboration—peer-to-peer—and those that do explicitly support multiple modes (or even individual roles) do not allow fluid transitions between them in the context of the same task. In addition, no explicit support is provided to allow subgroups to be formed and dissolved. To address this problem, a collaborative virtual environment (CVE) for product design that supports multiple collaboration modes and fluid transitions is proposed; two metaphors for collaborative product design (collaboration tree and infinitely recursive conference room) are introduced; the use of a collaboration tree interface widget is detailed; and the Simple Shared Virtual Environment (SSVE) toolkit for collaborative virtual environments is described.KeywordsCollaborative virtual environments, distributed virtual reality, computer supported cooperative work, collaborative product design, models of collaboration, group collaboration modes.1.INTRODUCTIONBecause the flexibility and plasticity of software models provides an economic advantage over physical models, especially when multiple iterations are required, product design has increasingly adopted a virtual prototyping process. Collaborative virtual environments have played a role in this process, generally in the design review stage. But as design teams become more globally distributed, the use of virtual environments earlier in the design process is being explored because of the shared mental models that such environments provide.2.GROUP COLLABORATION MODES2.1Case Study ObservedA recent product design competition was held at Lehigh University. The charter was to create (via assembly from prefabricated parts) a toy car according to several cost and performance criteria. Here’s the experience of one design team: •After a period of inactivity, one woman took the initiative and assembled a complete car while otherteam members looked on and made comments.•The car was performance-tested, and suggestions for improvement fell into upper and lower body categories.The car was separated into chassis and top and given totwo separate subgroups of the team for redesign.•After reassembling the car, more performance tests were conducted. This time a disagreement arose abouthow to remove a fundamental design flaw. The car wasduplicated and given to two competing subgroups.•The product of each subgroup was tested and one was selected to represent the final result of the entire team.To describe the process outlined above, the concept of group collaboration modes for product design is introduced in Table 1:Table 1. Group Collaboration ModesName Type Description Peer-to-Peer BehaviorAll team members contributeequallyLeader-Follower BehaviorOne team member functions asleader Complementary FormationTask is divided and assigned tocomplementary subgroups Competitive FormationTask is duplicated and assignedto competing subgroupsPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.Copyright is held by the author/owner(s).CVE’02,September 30–October 2, 2002, Bonn, Germany.ACM 1-58113-489-4/02/0009.These modes illustrate the collaborative functioning of the group, not individuals in the group, and can be divided into two types. Formation modes describe the reason why a subgroup was formed, and behavior modes depict how a subgroup is behaving. For group collaboration modes in another domain, see [1].2.2 Case Study AnalyzedThe case study of the four-person design team above is analyzed pictorially (using and/or trees) in Figure 1 in terms of group collaboration modes. The following observations can be made: Collaboration consists of fluid transitions from one mode to another in pursuit of a common goal; subgroup formation defines an interface and is recursive in nature; and in some sense, the subgroup formation process reflects the structure of the task (inthis case, the car being created).Leader-FollowerPeer-to-Peer3. CVE SUPPORT FOR GROUPSMost collaborative virtual environment systems support only one mode of group collaboration—peer-to-peer. Those that do explicitly support multiple modes (or even individual roles) do not allow fluid transitions between them in the context of the same project or task (e.g., the CALVIN system [2]). In addition, no explicit support is provided which allows subgroups to be formed and dissolved to reflect the structure and progress of the collaborative task. The concept of formations [3] comes closest; it treats groups as first class objects and presumably uses the third party object mechanism of the MASSIVE system [4]. However, formations coalesce around objects, not tasks, and to date have not been used in a fluidly recursive fashion for design.4. GROUPMORPH CVEThe GroupMorph collaborative virtual environment is currently being developed at Lehigh University to support group collaboration in product design tasks. In the spirit of the worlds-in-miniature (WIM) widget [5], it uses task-in-miniature widgets to represent and manipulate two different metaphors for product design. The first is the collaboration tree, depicted in Figure 2 for a hypothetical example in a six-person design team. As a widget it would be used for awareness, navigation, subgrouping and regrouping, and object transfer. The second in some ways is its inverse, the infinitely recursive conference room, portrayed in Figure 3. Breakout rooms are themselves conference rooms with a table in the center and breakout rooms on the side.5. SIMPLE SHARED VE (SSVE)GroupMorph is implemented using the Shared Simple Virtual Environment (SSVE), a multi-user extension to the Shared Virtual Environment (SVE) library [6]. Threaded peer-to-peer CORBA servers in each session are used for both TCP and UDP multicast communication, and objects are replicated locally.Figure 2: Collaboration Tree MetaphorFigure 1: Stages of Collaboration in the Case StudyFigure 3: Infinitely Recursive Conference Room Metaphor Because of its novel group support widgets, we believe that GroupMorph will better facilitate design team collaboration in a shared 3D virtual environment, and plan to demonstrate this through experimental trials. Relevant metrics include time, cost, and quality of the design, as well as subjective perception.6. REFERENCES[1] Maher, Mary Lou, Anna Cicognani, and Simeon Simoff.“An Experimental Study of Computer MediatedCollaborative Design.” International Journal of Design Computing 1 (1997-1998).[2] Leigh, J., A. Johnson, and T. DeFanti. “CALVIN: anImmersimedia Design Environment Utilizing Heterogeneous Perspectives.” IEEE Computer Graphics and Applications 16, No. 4 (July 1996): 47-51.[3] Lloyd, Dave, Steve Benford, and Chris Greenhalgh.“Formations: Explicit Group Support in Collaborative Virtual Environments.” Proceedings of VRST’99, 162-163.[4] Benford, S.D., and C.M. Greenhalgh. “Introducing ThirdParty Objects into the Spatial Model of Interaction.” Proceedings of ECSCW’97, 189-204.[5] Stoakley, Richard, Matthew J. Conway, and Randy Pausch.“Virtual Reality on a WIM: Interactive Worlds in Miniature.” Proceedings of CHI’95, 265-272.[6] Kessler, G. Drew, Doug A. Bowman, and Larry F. Hodges.“The Simple Virtual Environment Library: An Extensible Framework for Building VE Applications.” Presence 9, No. 2 (April 2000): 187-208.。