Implementation of Generic AHP Evaluation System nd Its Application to Impact Factors of On-Line
高级架构师考试题库及答案
高级架构师考试题库及答案一、单选题1. 在软件架构中，以下哪一项不是微服务架构的特点？A. 服务独立性B. 服务自治性C. 服务集中管理D. 服务轻量级通信答案：C2. 以下关于分布式系统CAP理论的描述，哪一项是错误的？A. 一致性（Consistency）B. 可用性（Availability）C. 分区容错性（Partition tolerance）D. 所有分布式系统都可以同时满足CAP理论中的所有特性答案：D3. 在云原生架构中，以下哪个不是容器化技术的典型特点？A. 环境一致性B. 资源隔离C. 无需操作系统D. 快速启动答案：C二、多选题1. 以下哪些是微服务架构设计时需要考虑的关键因素？A. 服务拆分B. 服务发现C. 服务编排D. 数据一致性答案：A, B, C, D2. 在构建高可用性系统时，以下哪些措施是有效的？A. 负载均衡B. 冗余设计C. 单点故障D. 定期备份答案：A, B, D三、判断题1. 在分布式系统中，CAP理论告诉我们，一致性、可用性和分区容错性可以同时实现。
（对/错）答案：错2. 微服务架构中，服务之间通过同步调用可以提高系统的响应速度。
（对/错）答案：错四、简答题1. 描述一下在云原生架构中，服务网格（Service Mesh）的主要作用是什么？答案：服务网格的主要作用是管理微服务之间的通信，提供服务发现、负载均衡、故障恢复、度量和监控等功能，同时它还有助于实现服务间的安全通信，如加密和授权。
2. 解释一下在分布式系统中，为什么需要进行服务拆分？答案：服务拆分是为了提高系统的可维护性、可扩展性和容错性。
通过将一个大的单体应用拆分成多个小的、独立的服务，每个服务可以独立部署、升级和扩展，从而减少系统间的耦合，提高系统的灵活性和可维护性。
五、案例分析题1. 假设你是一个高级架构师，你的团队正在设计一个面向全球的在线购物平台。
请描述你会如何设计这个平台的架构，以确保它具有高可用性、可扩展性和良好的用户体验。
Dell EMC S4048-ON开放网络交换机说明说明书
The Dell EMC Networking S4048-ON switch empowers organizations to deploy workloads and applications designed for the open networking era.Businesses who have made the transition away from monolithicproprietary mainframe systems to industry standard server platforms can now enjoy even greater benefits from Dell open networking platforms. Using industry-leading hardware and a choice of leading network operating systems to simplify data center fabric orchestration and automation, organizations can accelerate innovation by tailoring their network to their unique requirements.These new offerings provide the needed flexibility to transform data centers. High-capacity network fabrics that are cost-effective and easy to deploy provide a clear path to a software-defined data center of the future, as well as freedom from vendor lock-in.The Dell EMC S4048-ON supports the open source Open Network Install Environment (ONIE) for zero-touch installation of alternate network operating systems including feature-rich Dell Networking OS.Ultra-low-latency, data center optimizedThe Dell EMC Networking S-Series S4048-ON is an ultra-low-latency 10/40GbE top-of-rack (ToR) switch built for applications in high-performance data center and computing environments. Leveraging a non-blocking switching architecture, the S4048-ON delivers line-rate L2 and L3 forwarding capacity with ultra-low-latency to maximize network performance. The compact S4048-ON design provides industry-leading density of 48 dual-speed 1/10GbE (SFP+) ports as well as six 40GbEQSFP+ uplinks to conserve valuable rack space and simplify the migration to 40Gbps in the data center core (each 40GbE QSFP+ uplink can also support four 10GbE ports with a breakout cable). In addition, the S4048-ON incorporates multiple architectural features that optimize data center network flexibility, efficiency and availability, including I/O panel to PSU airflow or PSU to I/O panel airflow for hot/cold aisle environments, and redundant, hot-swappable power supplies and fans.S4048-ON supports feature-rich Dell Networking OS, VLT, networkvirtualization features such as VRF-lite, VXLAN Gateway and support for Dell Embedded Open Automation Framework.• The S4048-ON is the only switch in the industry that provides customers an unbiased approach to Network Virtualization by supporting both network-centric virtualization method (VRF-lite) and Hypervisor centric virtualization method (VXLAN).• The S4048-ON also supports Dell Networking’s EmbeddedOpen Automation Framework, which provides enhanced network automation and virtualization capabilities for virtual data center environments.• The Open Automation Framework comprises a suite of interrelated network management tools that can be used together orindependently to provide a network that is flexible, available and manageable while helping to reduce operational expenses.Key applicationsDynamic data centers ready to make the transition to software-defined environments• Ultra-low-latency 10GbE switching in HPC, high-speed trading or other business-sensitive deployments that require the highest bandwidth and lowest latency • High-density 10GbE ToR server access in high-performance data center environments When running the Dell Networking OS9, Active Fabric™ implementation for large deployments in conjunction with the Dell EMC Z-Series, creating a flat, two-tier, nonblocking 10/40GbE data center network design:• High-performance SDN/OpenFlow 1.3 enabled with ability to inter-operate with industry standard OpenFlow controllers • As a high speed VXLAN Layer 2 Gateway that connects thehypervisor based ovelray networks with nonvirtualized infrastructure • Small-scale Active Fabric implementation via the S4048-ON switch in leaf and spine along with S-Series 1/10GbE ToR switches enabling cost-effective aggregation of 10/40GbE uplinks • iSCSI storage deployment including DCB converged lossless transactions Key features - general• 48 dual-speed 1/10GbE (SFP+) ports and six 40GbE (QSFP+) uplinks (totaling 72 10GbE ports with breakout cables) with OS support • 1.44Tbps (full-duplex) non-blocking switching fabric delivers line-rate performance under full load with sub 650ns latency • I/O panel to PSU airflow or PSU to I/O panel airflow • Supports the open source ONIE for zero-touch • Installation of alternate network operating systems • Redundant, hot-swappable power supplies and fans • Low power consumption• Support for multi-tenancy lilke VXLAN and NVGRE in hardwareDELL EMC NETWORKING S4048-ON SWITCH10/40GbE top-of-rack open networking switchKey features with Dell EMC Networking OS9Scalable L2 and L3 Ethernet switching with QoS and a full complement of standards-based IPv4 and IPv6 features, including OSPF, BGP and PBR (Policy Based Routing) support• VRF-lite enables sharing of networking infrastructure and provides L3traffic isolation across tenants• Increase VM Mobility region by stretching L2 VLAN within or across two DCs with unique VLT capabilities like Routed VL T, VLT Proxy Gateway • VXLAN gateway functionality support for bridging the nonvirtualizedand the virtualized overlay networks with line rate performance.• Embedded Open Automation Framework adding automatedconfiguration and provisioning capabilities to simplify the management of network environments. Supports Puppet agent for DevOps• Modular Dell Networking OS software delivers inherent stability as well as enhanced monitoring and serviceability functions.• Enhanced mirroring capabilities including 1:4 local mirroring, Remote Port Mirroring (RPM), and Encapsulated Remote Port Mirroring (ERPM). Rate shaping combined with flow based mirroring enables the user to analyze fine grained flows• Jumbo frame support for large data transfers• 128 link aggregation groups with up to 16 members per group, using enhanced hashing• Converged network support for DCB, with priority flow control (802.1Qbb), ETS (802.1Qaz), DCBx and iSCSI TLV support Fastboot feature enables min-loss software upgrade on a standalone S4048-ON without VL T/stacking• S4048-ON supports Routable RoCE to enable convergence of compute and storage on Active Fabric• User port stacking support for up to six units and a total stack bandwidth of up to 320Gbps bandwidth48 10 Gigabit Ethernet SFP+ ports6 40 Gigabit Ethernet QSFP+ ports1 RJ45 console/management port with RS232signaling1 USB 2.0 type A to support mass storage device1 Micro-USB 2.0 type B Serial Console PortSize: 1RU, 1.71 x 17.09 x 17.13” (4.35 x 43.4 x 43.5cm (H x W x D) Weight: 18.52 lbs (8.4kg)ISO 7779 A-weighted sound pressure level: 59.6 dBA at73.4°F (23°C)Power supply: 100–240V AC 50/60HzDC Power supply: -40.5V ~ -60VMax. thermal output: 799.64 BTU/hMax. current draw per system:2.344A/1953A at 100/120V AC,1.145A/0.954A at 200/240V ACMax. DC current : -40.5V/23.8A , -48V/19A ,-60V/15.6A.Max. power consumption: 234.35 Watts (AC), 800 Watts (DC)T ypical power consumption: 153 WattsMax. operating specifications:Operating temperature: 32°F to 113°F (0°C to 45°C)Operating humidity: 10 to 85% (RH), non-condensingMax. non-operating specifications:Storage temperature: –40°F to 158°F (–40°C to 70°C)Storage humidity: 5 to 95% (RH), non-condensingRedundancyHot swappable redundant powerHot swappable redundant fansPerformance generalSwitch fabric capacity:1.44Tbps (full-duplex)720Gbps (half-duplex)Forwarding Capacity: 1080 MppsLatency: Sub 650nsPacket buffer memory: 12MBCPU memory: 4GBOS9 Performance:MAC addresses: 160KARP table 128KIPv4 routes: 128KIPv6 hosts: 64KIPv6 routes: 64KMulticast hosts: 8KLink aggregation: 16 links per group, 128 groups Layer 2 VLANs: 4KMSTP: 64 instancesVRF-Lite: 511 instancesLAG load balancing: Based on layer 2, IPv4 or IPv6headersQOS data queues: 8QOS control queues: 12QOS: Default 768 entries scalable to 2.5K Egress ACL: 1KIEEE compliance with Dell Networking OS9802.1AB LLDP802.1D Bridging, STP802.1p L2 Prioritization802.1Q VLAN T agging, Double VLAN T agging, GVRP802.1Qbb PFC802.1Qaz ETS802.1s MSTP802.1w RSTP802.1X Network Access Control802.3ab Gigabit Ethernet (1000BASE-T) with QSA orbreakout802.3ac Frame Extensions for VLAN T agging802.3ad Link Aggregation with LACP802.3ae 10 Gigabit Ethernet (10GBase-X) with QSA802.3ba 40 Gigabit Ethernet (40GBase-SR4,40GBase-CR4,40GBase-LR4) on optical ports802.3u Fast Ethernet (100Base-TX)802.3x Flow Control802.3z Gigabit Ethernet (1000Base-X) with QSAANSI/TIA-1057 LLDP-MEDForce10 PVST+MTU 12,000 bytesRFC and I-D compliance with Dell NetworkingOS9General Internet protocols768 UDP793 TCP854 T elnet959 FTPGeneral IPv4 protocols791 IPv4792 ICMP826 ARP1027 Proxy ARP1035 DNS (client)1042 Ethernet Transmission1305 NTPv31519 CIDR1542 BOOTP (relay)1812 Requirements for IPv4 Routers1918 Address Allocation for Private Internets2474 Diffserv Field in IPv4 and Ipv6 Headers2596 Assured Forwarding PHB Group3164 BSD Syslog3195 Reliable Delivery for Syslog3246 Expedited Assured Forwarding4364 VRF-lite (IPv4 VRF with OSPF, BGP, IS-IS and V4multicast)5798 VRRPGeneral IPv6 protocols1981 Path MTU Discovery Features2460 Internet Protocol, Version 6 (IPv6)Specification2464 T ransmission of IPv6 Packets over Ethernet2711 IPv6 Router Alert Option4007 IPv6 Scoped Address Architecture4213 Basic T ransition Mechanisms for IPv6 Hosts and Routers4291 IPv6 Addressing Architecture4443 ICMP for IPv64861 Neighbor Discovery for IPv64862 IPv6 Stateless Address Autoconfiguration5095 Deprecation of T ype 0 Routing Headers in IPv6IPv6 Management support (telnet, FTP, TACACS,RADIUS, SSH, NTP)VRF-Lite (IPv6 VRF with OSPFv3, BGPv6, IS-IS)RIP1058 RIPv1 2453 RIPv2OSPF (v2/v3)1587 NSSA 4552 Authentication/2154 OSPF Digital Signatures Confidentiality for2328 OSPFv2 OSPFv32370 Opaque LSA 5340 OSPF for IPv6IS-IS5301 Dynamic hostname exchange mechanism forIS-IS5302 Domain-wide prefix distribution with two-level IS-IS5303 Three way handshake for IS-IS point-to-pointadjacencies5308 IS-IS for IPv6BGP1997 Communities2385 MD52545 BGP-4 Multiprotocol Extensions for IPv6 Inter-DomainRouting2439 Route Flap Damping2796 Route Reflection2842 Capabilities2858 Multiprotocol Extensions2918 Route Refresh3065 Confederations4360 Extended Communities4893 4-byte ASN5396 4-byte ASN representationsdraft-ietf-idr-bgp4-20 BGPv4draft-michaelson-4byte-as-representation-054-byte ASN Representation (partial)draft-ietf-idr-add-paths-04.txt ADD PATHMulticast1112 IGMPv12236 IGMPv23376 IGMPv3MSDPSecurity2404 The Use of HMACSHA- 1-96 within ESP andAH2865 RADIUS3162 Radius and IPv63579 Radius support for EAP3580 802.1X with RADIUS3768 EAP3826 AES Cipher Algorithm in the SNMP User BaseSecurity Model4250, 4251, 4252, 4253, 4254 SSHv24301 Security Architecture for IPSec 4302 IPSec Authentication Header 4303 ESP Protocol4807 IPsecv Security Policy DB MIB draft-ietf-pim-sm-v2-new-05 PIM-SMw Data center bridging802.1Qbb Priority-Based Flow Control802.1Qaz Enhanced Transmission Selection (ETS) Data Center Bridging eXchange (DCBx) DCBx Application TLV (iSCSI, FCoE)Network management 1155 SMIv1 1157 SNMPv11212 Concise MIB Definitions 1215 SNMP Traps 1493 Bridges MIB 1850 OSPFv2 MIB1901 Community-Based SNMPv22011 IP MIB2096 IP Forwarding T able MIB 2578 SMIv22579 T extual Conventions for SMIv22580 Conformance Statements for SMIv22618 RADIUS Authentication MIB 2665 Ethernet-Like Interfaces MIB 2674 Extended Bridge MIB 2787 VRRP MIB2819 RMON MIB (groups 1, 2, 3, 9)2863 Interfaces MIB3273 RMON High Capacity MIB 3410 SNMPv33411 SNMPv3 Management Framework3412 Message Processing and Dispatching for the Simple Network Management Protocol (SNMP)3413 SNMP Applications3414 User-based Security Model (USM) for SNMPv33415 VACM for SNMP 3416 SNMPv23417 Transport mappings for SNMP 3418 SNMP MIB3434 RMON High Capacity Alarm MIB3584 Coexistance between SNMP v1, v2 and v34022 IP MIB4087 IP Tunnel MIB 4113 UDP MIB 4133 Entity MIB 4292 MIB for IP4293 MIB for IPv6 T extual Conventions 4502 RMONv2 (groups 1,2,3,9)5060 PIM MIBANSI/TIA-1057 LLDP-MED MIB Dell_ITA.Rev_1_1 MIBdraft-grant-tacacs-02 TACACS+draft-ietf-idr-bgp4-mib-06 BGP MIBv1IEEE 802.1AB LLDP MIBIEEE 802.1AB LLDP DOT1 MIB IEEE 802.1AB LLDP DOT3 MIB sFlowv5 sFlowv5 MIB (version 1.3)FORCE10-BGP4-V2-MIB Force10 BGP MIB (draft-ietf-idr-bgp4-mibv2-05)FORCE10-IF-EXTENSION-MIB FORCE10-LINKAGG-MIBFORCE10-COPY-CONFIG-MIB FORCE10-PRODUCTS-MIB FORCE10-SS-CHASSIS-MIB FORCE10-SMI FORCE10-TC-MIBFORCE10-TRAP-ALARM-MIBFORCE10-FORWARDINGPLANE-STATS-MIB Regulatory compliance SafetyUL/CSA 60950-1, Second Edition EN 60950-1, Second EditionIEC 60950-1, Second Edition Including All National Deviations and Group DifferencesEN 60825-1 Safety of Laser Products Part 1:Equipment Classification Requirements and User’s GuideEN 60825-2 Safety of Laser Products Part 2: Safety of Optical Fibre Communication Systems FDA Regulation 21 CFR 1040.10 and 1040.11EmissionsAustralia/New Zealand: AS/NZS CISPR 22: 2009, Class ACanada: ICES-003, Issue-4, Class AEurope: EN 55022: 2006+A1:2007 (CISPR 22: 2006), Class AJapan: VCCI V3/2009 Class AUSA: FCC CFR 47 Part 15, Subpart B:2009, Class A ImmunityEN 300 386 V1.4.1:2008 EMC for Network EquipmentEN 55024: 1998 + A1: 2001 + A2: 2003EN 61000-3-2: Harmonic Current Emissions EN 61000-3-3: Voltage Fluctuations and Flicker EN 61000-4-2: ESDEN 61000-4-3: Radiated Immunity EN 61000-4-4: EFT EN 61000-4-5: SurgeEN 61000-4-6: Low Frequency Conducted Immunity RoHSAll S-Series components are EU RoHS compliant.CertificationsJapan: VCCI V3/2009 Class AUSA: FCC CFR 47 Part 15, Subpart B:2009, Class A T ested to meet or exceed Hi Pot and Ground Continuity testing per UL 60950-1Warranty1 Year Return to DepotIT Lifecycle Services for NetworkingExperts, insights and easeOur highly trained experts, withinnovative tools and proven processes, help you transform your IT investments into strategic advantages.Plan & Design Let us analyze yourmultivendor environment and deliver a comprehensive report and action plan to build upon the existing network and improve performance.Deploy & IntegrateGet new wired or wireless network technology installed and configured with ProDeploy. Reduce costs, save time, and get up and running cateEnsure your staff builds the right skills for long-termsuccess. Get certified on Dell EMC Networking technology and learn how to increase performance and optimize infrastructure.Manage & SupportGain access to technical experts and quickly resolve multivendor networking challenges with ProSupport. Spend less time resolving network issues and more time innovating.OptimizeMaximize performance for dynamic IT environments with Dell EMC Optimize. Benefit from in-depth predictive analysis, remote monitoring and a dedicated systems analyst for your network.RetireWe can help you resell or retire excess hardware while meeting local regulatory guidelines and acting in an environmentally responsible way.Learn more at/LifecycleservicesLearn more at /Networking。
软考中级系统集成项目管理工程师考试历年真题2023下半年第1批综合知识选择题真题
软考中级系统集成项目管理工程师考试历年真题2023下半年第1批综合知识选择题真题1.关于整体变更控制的描述，不正确的是o 。
A 所有变更请求，经客户批准后即可确认B. 项目经理对实施整体变更控制过程负责任C. 项目的任何干系人都可以提出变更请求D. 实施整体变更控制过程贯穿项目始终，并且应用于项目的各个阶段2. 项目管理的三个基本目标是: 质量、成本和o 。
A.进度B. 资源C. 风险D. 流程3. 0 定义了一种松散的、粗粒度的分布技术模式且使用HTTP 协议传送内容。
A. J ava EE 架松B.Web 服务+D. 软件引擎技术4.Nat u ra l Language Processi n g (NLP)i s an i mpo r t ant direc t io n in the fie lds of 0 。
A.AIB.VRC.A RD.LOT5. 2023 年，中共中央、国务院印发了《数字中国建设整体布局规划)) ，提出数字中国建设按照月的整体框架进行布局，其中明确提出努实数字基础设施和( ) ((两大基础"。
(t2522A 数字安全屏障B. 生态文明建设C. 数据资源体系D.数字技术创新体系6. 软件文档分为三类，开发文档、产品文档、管理文档。
o不属于开发文挡。
A . 进度变更的记录B. 需求规格说明 C. 安全和测试信息D. 开发计划7. 关于信息系统的有效性和可靠性，不正确的是o 。
A.有效性和可靠性都是信息系统的性能指标B. 有效性就是在系统中传递尽可能多的信息C. 增加冗余代码可以提高有效性和可靠性D. 信宿与信源间的信息差异越大可靠性越低8. 关于应用系统中访问控制的描述，不正确的是o 。
A . 如安全设计和控制合适，可以不用考虑组织的安全策略B. 避免未授权用户的信息访问C. 建立正式的授权程序，分配对应用系统和服务的访问权力D. 建立访问控制策略，并根据业务和安全要求进行评审9. 通常，0 不属于项目管理团队至少需要掌握的6 方面的专门领域知识。
SADISA包（版本1.2）：物种丰度分布与独立物种假设说明书
Package‘SADISA’October12,2022Type PackageTitle Species Abundance Distributions with Independent-SpeciesAssumptionVersion1.2Author Rampal S.Etienne&Bart HaegemanMaintainer Rampal S.Etienne<******************>Description Computes the probability of a set of species abundances of a single or multiple sam-ples of individuals with one or more guilds under a mainland-island model.One must spec-ify the mainland(metacommunity)model and the island(local)community model.It as-sumes that speciesfluctuate independently.The package also contains functions to simulate un-der this model.See Haegeman,B.&R.S.Etienne(2017).A general sampling formula for com-munity structure data.Methods in Ecology&Evolution8:1506-1519<doi:10.1111/2041-210X.12807>.License GPL-3LazyData FALSERoxygenNote6.1.1Encoding UTF-8Depends R(>=3.5)Imports pracma,DDD(>=4.1)Suggests testthat,knitr,rmarkdown,VignetteBuilder knitrNeedsCompilation noRepository CRANDate/Publication2019-10-2312:10:02UTCR topics documented:convert_fa2sf (2)datasets (2)fitresults (3)12datasets integral_peak (4)SADISA_loglik (5)SADISA_ML (6)SADISA_sim (8)SADISA_test (9)Index11 convert_fa2sf Converts different formats to represent multiple sample dataDescriptionConverts the full abundance matrix into species frequencies If S is the number of species and M is the number of samples,then fa is the full abundance matrix of dimension S by M.The for example fa=[010;321;010]leads to sf=[0102;3211];Usageconvert_fa2sf(fa)Argumentsfa the full abundance matrix with species in rows and samples in columnsValuethe sample frequency matrixReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution.In press.datasets Data sets of various tropical forest communitiesDescriptionVarious tree commnunity abundance data sets to test and illustrate the Independent Species ap-proach.•dset1.abunvec contains a list of6samples of tree abundances from6tropical forest plots(BCI, Korup,Pasoh,Sinharaja,Yasuni,Lambir).•dset2.abunvec contains a list of11lists with one of11samples from BCI combined with samples from Cocoli and Sherman.fitresults3•dset3.abunvec contains a list of6lists with2samples,each from one dispersal guild,for6tropical forest communities(BCI,Korup,Pasoh,Sinharaja,Yasuni,Lambir).•dset4a.abunvec contains a list of6samples from6censuses of BCI(1982,1985,1990,1995,200,2005)with dbh>1cm.•dset4b.abunvec contains a list of6samples from6censuses of BCI(1982,1985,1990,1995,200,2005)with dbh>10cm.Usagedata(datasets)FormatA list of5data sets.See description for information on each of these data sets.Author(s)Rampal S.Etienne&Bart HaegemanSourceCondit et al.(2002).Beta-diversity in tropical forest trees.Science295:666-669.See also11.Janzen,T.,B.Haegeman&R.S.Etienne(2015).A sampling formula for ecological communitieswith multiple dispersal syndromes.Journal of Theoretical Biology387,258-261.fitresults Maximum likelihood estimates and corresponding likelihood valuesfor variousfits to various tropical forest communitiesDescriptionMaximum likelihood estimates and corresponding likelihood values for variousfits to various trop-ical forest communities,to test and illustrate the Independent Species approach.•fit1a.llikopt contains maximum likelihood values offit of pm-dl model to dset1.abunvec•fit1a.parsopt contains maximum likelihood parameter estimates offit of pm-dl model to dset1.abunvec •fit1b.llikopt contains maximum likelihood values offit of pmc-dl model to dset1.abunvec•fit1b.parsopt contains maximum likelihood parameter estimates offit of pmc-dl model todset1.abunvec•fit2.llikopt contains maximum likelihood values offit of rf-dl model to dset1.abunvec•fit2.parsopt contains maximum likelihood parameter estimates offit of rf-dl model to dset1.abunvec •fit3.llikopt contains maximum likelihood values offit of dd-dl model to dset1.abunvec•fit3.parsopt contains maximum likelihood parameter estimates offit of dd-dl model to dset1.abunvec •fit4.llikopt contains maximum likelihood values offit of pm-dl model to dset2.abunvec(mul-tiple samples)4integral_peak •fit4.parsopt contains maximum likelihood parameter estimates offit of pm-dl model to dset1.abunvec(multiple samples)•fit5.llikopt contains maximum likelihood values offit of pm-dl model to dset3.abunvec(mul-tiple guilds)•fit5.parsopt contains maximum likelihood parameter estimates offit of pm-dl model to dset3.abunvec (multiple guilds)•fit6.llikopt contains maximum likelihood values offit of pr-dl model to dset1.abunvec•fit6.parsopt contains maximum likelihood parameter estimates offit of pr-dl model to dset1.abunvec •fit7.llikopt contains maximum likelihood values offit of pm-dd model to dset1.abunvec•fit7.parsopt contains maximum likelihood parameter estimates offit of pm-dd model to dset1.abunvec •fit8a.llikopt contains maximum likelihood values offit of pm-dd model to dset4a.abunvec•fit8a.parsopt contains maximum likelihood parameter estimates offit of pm-dd model todset4a.abunvec•fit8b.llikopt contains maximum likelihood values offit of pm-dd model to dset4b.abunvec•fit8b.parsopt contains maximum likelihood parameter estimates offit of pm-dd model todset4b.abunvecUsagedata(fitresults)FormatA list of20lists,each containing either likelihood values or the corresponding parameter estimates.See description.Author(s)Rampal S.Etienne&Bart HaegemanSourceCondit et al.(2002).Beta-diversity in tropical forest trees.Science295:666-669.integral_peak Computes integral of a very peaked functionDescription#computes the logarithm of the integral of exp(logfun)from0to Inf under the following assump-tions:Usageintegral_peak(logfun,xx=seq(-100,10,2),xcutoff=2,ycutoff=40,ymaxthreshold=1e-12)SADISA_loglik5Argumentslogfun the logarithm of the function to integratexx the initial set of points on which to evaluate the functionxcutoff when the maximum has been found among the xx,this parameter sets the width of the interval tofind the maximum inycutoff set the threshold below which(on a log scale)the function is deemed negligible,i.e.that it does not contribute to the integral)ymaxthreshold sets the deviation allowed infinding the maximum among the xxValuethe result of the integrationReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution.In press.SADISA_loglik Computes loglikelihood for requested modelDescriptionComputes loglikelihood for requested model using independent-species approachUsageSADISA_loglik(abund,pars,model,mult="single")Argumentsabund abundance vector or a list of abundance vectors.When a list is provided and mult=’mg’(the default),it is assumed that the different vectors apply to dif-ferent guilds.When mult=’ms’then the different vectors apply to multiplesamples from the same metacommunity.In this case the vectors should haveequal lengths and may contain zeros because there may be species that occur inmultiple samples and species that do not occur in some of the samples.Whenmult=’both’,abund should be a list of lists,each list representing multiple guildswithin a samplepars a vector of model parameters or a list of vectors of model parameters.Whena list is provided and mult=’mg’(the default),it is assumed that the differentvectors apply to different guilds.Otherwise,it is assumed that they apply tomultiple samples.model the chosen combination of metacommunity model and local community model as a vector,e.g.c(’pm’,’dl’)for a model with point mutation in the metacom-munity and dispersal limitation.The choices for the metacommunity modelare:’pm’(point mutation),’rf’(randomfission),’pr’(protracted speciation),’dd’(density-dependence).The choices for the local community model are:’dl’(dispersal limitation),’dd’(density-dependence).mult When set to’single’(the default),the loglikelihood for a single sample is com-puted When set to’mg’the loglikelihood for multiple guilds is computed.Whenset to’ms’the loglikelihood for multiple samples from the same metacommu-nity is computed.When set to’both’the loglikelihood for multiple guilds withinmultiple samples is computed.DetailsNot all combinations of metacommunity model and local community model have been implemented yet.because this requires checking for numerical stability of the integration.The currently avail-able model combinations are,for a single sample,c(’pm’,’dl’),c(’pm’,’rf’),c(’dd’,’dl’),c(’pr’,’dl’), c(’pm’,’dd’),and for multiple samples,c(’pm’,’dl’).ValueloglikelihoodReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution8:1506-1519.doi:10.1111/2041-210X.12807Examplesdata(datasets);abund_bci<-datasets$dset1.abunvec[[1]];data(fitresults);data.paropt<-fitresults$fit1a.parsopt[[1]];result<-SADISA_loglik(abund=abund_bci,pars=data.paropt,model=c( pm , dl ));cat( The difference between result and the value in fitresults.RData is: ,result-fitresults$fit1a.llikopt[[1]]);SADISA_ML Performs maximum likelihood parameter estimation for requestedmodelDescriptionComputes maximum loglikelihood and corresponding parameters for the requested model using the independent-species approach.For optimization it uses various auxiliary functions in the DDD package.UsageSADISA_ML(abund,initpars,idpars,labelpars,model=c("pm","dl"),mult="single",tol=c(1e-06,1e-06,1e-06),maxiter=min(1000*round((1.25)^sum(idpars)),1e+05),optimmethod="subplex",num_cycles=1)Argumentsabund abundance vector or a list of abundance vectors.When a list is provided and mult=’mg’(the default),it is assumed that the different vectors apply to dif-ferent guilds.When mult=’ms’then the different vectors apply to multiplesamples.from the same metacommunity.In this case the vectors should haveequal lengths and may contain zeros because there may be species that occur inmultiple samples and species that do not occur in some of the samples.initpars a vector of initial values of the parameters to be optimized andfixed.See labelpars for more explanation.idpars a vector stating whether the parameters in initpars should be optimized(1)or remainfixed(0).labelpars a vector,a list of vectors or a list of lists of vectors indicating the labels integers (starting at1)of the parameters to be optimized andfixed.These integers cor-respond to the position in initpars and idpars.The order of the labels in thevector/list isfirst the metacommunity parameters(theta,and phi(for protractedspeciation)or alpha(for density-dependence or abundance-dependent specia-tion)),then the dispersal parameters(I).See the example and the vignette formore explanation.model the chosen combination of metacommunity model and local community model as a vector,e.g.c(’pm’,’dl’)for a model with point mutation in the metacom-munity and dispersal limitation.The choices for the metacommunity modelare:’pm’(point mutation),’rf’(randomfission),’pr’(protracted speciation),’dd’(density-dependence).The choices for the local community model are:’dl’(dispersal limitation),’dd’(density-dependence).mult When set to’single’(the default),the loglikelihood for a single sample and single guild is computed.When set to’mg’,the loglikelihood for multiple guildsis computed.When set to’ms’the loglikelihood for multiple samples from thesame metacommunity is computed.tol a vector containing three numbers for the relative tolerance in the parameters,the relative tolerance in the function,and the absolute tolerance in the parameters.maxiter sets the maximum number of iterationsoptimmethod sets the optimization method to be used,either subplex(default)or an alternative implementation of simplex.num_cycles the number of cycles of opimization.If set at Inf,it will do as many cycles as needed to meet the tolerance set for the target function.8SADISA_simDetailsNot all combinations of metacommunity model and local community model have been implemented yet.because this requires checking for numerical stability of the integration.The currently avail-able model combinations are,for a single sample,c(’pm’,’dl’),c(’pm’,’rf’),c(’dd’,’dl’),c(’pr’,’dl’), c(’pm’,’dd’),and for multiple samples,c(’pm’,’dl’).ReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution8:1506-1519.doi:10.1111/2041-210X.12807Examplesutils::data(datasets);utils::data(fitresults);result<-SADISA_ML(abund=datasets$dset1.abunvec[[1]],initpars=fitresults$fit1a.parsopt[[1]],idpars=c(1,1),labelpars=c(1,2),model=c( pm , dl ),tol=c(1E-1,1E-1,1E-1));#Note that tolerances should be set much lower than1E-1to get the best results. SADISA_sim Simulates species abundance dataDescriptionSimulates species abundance data using the independent-species approachUsageSADISA_sim(parsmc,ii,jj,model=c("pm","dl"),mult="single",nsim=1)Argumentsparsmc The model parameters.For the point mutation(pm)model this is theta and I.For the protracted model(pr)this is theta,phi and I.For the density-dependentmodel(dd)-which can also be interpreted as the per-species speciation model,this is theta and alpha.ii The I parameter.When I is a vector,it is assumed that each value describes a sample or a guild depending on whether mult==’ms’or mult==’mg’.Whenmult=’both’,a list of lists must be specified,with each list element relates to asample and contains a list of values across guilds.jj the sample sizes for each sample and each guild.Must have the same structure as iimodel the chosen combination of metacommunity model and local community model as a vector,e.g.c(’pm’,’dl’)for a model with point mutation in the metacom-munity and dispersal limitation.The choices for the metacommunity modelare:’pm’(point mutation),’rf’(randomfission),’pr’(protracted speciation),’dd’(density-dependence).The choices for the local community model are:’dl’(dispersal limitation),’dd’(density-dependence).mult When set to’single’,the loglikelihood of a single abundance vector will be com-puted When set to’mg’the loglikelihood for multiple guilds is computed.Whenset to’ms’the loglikelihood for multiple samples from the same metacommu-nity is computed.When set to’both’the loglikelihood for multiple guilds withinmultiple samples is computed.nsim Number of simulations to performDetailsNot all combinations of metacommunity model and local community model have been implemented yet.because this requires checking for numerical stability of the integration.The currently available model combinations are c(’pm’,’dl’).Valueabund abundance vector,a list of abundance vectors,or a list of lists of abundance vectors,or a list of lists of lists of abundance vectors Thefirst layer of the lists corresponds to different simulations When mult=’mg’,each list contains a list of abundance vectors for different guilds.When mult =’ms’,each list contains a list of abundance vectors for different samples from the same meta-community.In this case the vectors should have equal lengths and may contain zeros because there may be species that occur in multiple samples and species that do not occur in some of the samples.When mult=’both’,each list will be a list of lists of multiple guilds within a sampleReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution8:1506-1519.doi:10.1111/2041-210X.12807SADISA_test Tests SADISA for data sets included in the paper by Haegeman&Eti-enneDescriptionTests SADISA for data sets included in the paper by Haegeman&EtienneUsageSADISA_test(tol=0.001)Argumentstol tolerance of the testReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution.In press.Index∗datasetsdatasets,2fitresults,3∗modelSADISA_loglik,5SADISA_ML,6SADISA_sim,8SADISA_test,9∗species-abundance-distributionSADISA_loglik,5SADISA_ML,6SADISA_sim,8SADISA_test,9convert_fa2sf,2datasets,2fitresults,3integral_peak,4SADISA_loglik,5SADISA_ML,6SADISA_sim,8SADISA_test,911。
系统架构师师题库
选择题在系统架构设计过程中，哪个阶段主要关注于确定系统的非功能性需求，如性能、可用性、安全性等？A. 需求分析B. 架构设计C. 系统测试D. 部署与维护下列哪项不是微服务架构的主要优势？A. 提高系统的可扩展性B. 简化部署流程C. 减少系统间的耦合度D. 便于集中式管理在分布式系统中，CAP理论指的是什么？A. 一致性（Consistency）、可用性（Availability）、分区容忍性（Partition tolerance）B. 并发性（Concurrency）、原子性（Atomicity）、持久性（Persistence）C. 缓存（Caching）、认证（Authentication）、持久层（Persistence Layer）D. 集群（Clustering）、异步（Asynchrony）、并行（Parallelism）在设计RESTful API时，用于表示资源删除操作的HTTP方法是？A. GETB. POSTC. PUTD. DELETE以下哪种设计模式常用于解决对象之间的复杂依赖关系，降低耦合度？A. 工厂模式B. 代理模式C. 依赖注入模式D. 观察者模式简答题简述系统架构设计中的高可用性（HA）策略有哪些？解释什么是微服务架构，并说明其与传统单体架构的主要区别。
在进行数据库设计时，如何平衡数据一致性与系统性能之间的关系？描述CAP理论中的三个属性，并解释在分布式系统中为什么无法同时满足这三个属性。
简述系统架构设计过程中，如何进行风险评估和应对策略的制定。
填空题系统架构设计需要综合考虑系统的功能性需求、______需求以及约束条件。
在微服务架构中，每个微服务通常负责一个______的业务功能。
RESTful API的设计原则之一是资源的______表示，即使用URL来标识资源。
分布式系统中，为了解决单点故障问题，常采用______部署策略。
在进行系统架构设计时，______模式是一种常用的设计模式，用于在对象之间建立灵活的依赖关系。
2024年电信5G基站建设理论考试题库(附答案)
2024年电信5G基站建设理论考试题库（附答案）一、单选题1.在赛事保障值守过程中，出现网络突发故障，需要启用红黄蓝应急预案进行应急保障，确保快速处理和恢复。
红黄蓝应急预案的应急逻辑顺序为（)A、网络安全->用户感知->网络性能B、网络性能->用户感知->网络安全C、用户感知->网络安全->网络性能D、用户感知->网络性能->网络安全参考答案：D2.2.1G规划，通过制定三步走共享实施方案，降配置，省TCO不包含哪项工作?A、低业务小区并网B、低业务小区关小区C、低业务小区拆小区D、高业务小区覆盖增强参考答案：D3.Type2-PDCCHmonsearchspaceset是用于（）。
A、A)OthersysteminformationB、B)PagingC、C)RARD、D)RMSI参考答案：B4.SRIOV与OVS谁的转发性能高A、OVSB、SRIOVC、一样D、分场景，不一定参考答案：B5.用NR覆盖高层楼宇时，NR广播波束场景化建议配置成以下哪项？A、SCENARTO_1B、SCENARIO_0C、SCENARIO_13D、SCENARIO_6参考答案：C6.NR的频域资源分配使用哪种方式？A、仅在低层配置(非RRC)B、使用k0、k1和k2参数以实现分配灵活性C、使用SLIV控制符号级别的分配D、使用与LTE非常相似的RIV或bitmap分配参考答案：D7.SDN控制器可以使用下列哪种协议来发现SDN交换机之间的链路？A、HTTPB、BGPC、OSPFD、LLDP参考答案：D8.NR协议规定,采用Min-slot调度时，支持符号长度不包括哪种A、2B、4C、7D、9参考答案：D9.5G控制信道采用预定义的权值会生成以下那种波束？A、动态波束B、静态波束C、半静态波束D、宽波束参考答案：B10.TS38.211ONNR是下面哪个协议（）A、PhysicalchannelsandmodulationB、NRandNG-RANOverallDescriptionC、RadioResourceControl(RRC)ProtocolD、BaseStation(BS)radiotransmissionandreception参考答案：A11.在NFV架构中，哪个组件完成网络服务（NS）的生命周期管理？A、NFV-OB、VNF-MC、VIMD、PIM参考答案：A12.5G需要满足1000倍的传输容量,则需要在多个维度进行提升，不包括下面哪个（）A、更高的频谱效率B、更多的站点C、更多的频谱资源D、更低的传输时延参考答案：D13.GW-C和GW-U之间采用Sx接口，采用下列哪种协议A、GTP-CB、HTTPC、DiameterD、PFCP参考答案：D14.NR的频域资源分配使用哪种方式？A、仅在低层配置(非RRC)B、使用k0、k1和k2参数以实现分配灵活性C、使用SLIV控制符号级别的分配D、使用与LTE非常相似的RIV或bitmap分配参考答案：D15.下列哪个开源项目旨在将电信中心机房改造为下一代数据中心？A、OPNFVB、ONFC、CORDD、OpenDaylight参考答案：C16.NR中LongTruncated/LongBSR的MACCE包含几个bit（）A、4B、8C、2D、6参考答案：B17.对于SCS120kHz，一个子帧内包含几个SlotA、1B、2C、4D、8参考答案：D18.SA组网中，UE做小区搜索的第一步是以下哪项？A、获取小区其他信息B、获取小区信号质量C、帧同步，获取PCI组编号D、半帧同步，获取PCI组内ID参考答案：D19.SA组网时，5G终端接入时需要选择融合网关，融合网关在DNS域名的'app-protocol'name添加什么后缀？A、+nc-nrB、+nr-ncC、+nr-nrD、+nc-nc参考答案：A20.NSAOption3x组网时，语音业务适合承载以下哪个承载上A、MCGBearB、SCGBearC、MCGSplitBearD、SCGSplitBear参考答案：A21.5G需要满足1000倍的传输容量,则需要在多个维度进行提升，不包括下面哪个（）A、更高的频谱效率B、更多的站点C、更多的频谱资源D、更低的传输时延参考答案：D22.以SCS30KHz，子帧配比7:3为例，1s内调度次数多少次，其中下行多少次。
java authorization 认证类型
一、概述Java是一种面向对象的编程语言，广泛应用于企业级应用开发中。
在企业级应用开发中，安全性是至关重要的，而认证是实现安全性的重要环节之一。
Java提供了多种认证类型，开发人员可以根据实际需求选择合适的认证类型来保护应用程序的安全性。
二、基本认证类型1. 基于用户名和密码的认证：这是最常见的认证类型，用户需要提供用户名和密码才能访问受保护的资源。
2. 基于角色的认证：通过角色来限制用户对资源的访问权限，可以在代码中进行硬编码，也可以与数据库中的角色进行动态关联。
三、OAuth2.0认证类型1. 密码授权模式：用户提供用户名和密码来获取访问令牌，访问令牌用于访问受保护的资源。
2. 客户端凭证授权模式：客户端使用自己的凭证来获取访问令牌，访问令牌用于访问受保护的资源。
3. 授权码模式：用户通过浏览器授权，客户端使用授权码来获取访问令牌，访问令牌用于访问受保护的资源。
4. 简化模式：用于移动应用程序或web应用程序中，通过重定向获取访问令牌，访问令牌用于访问受保护的资源。
四、JWT认证类型JWT（JSON Web Token）是一种基于JSON的开放标准（RFC 7519），用于声明跨域全球信息站和移动应用的用户身份。
JWT通过数字签名或加密保证信息的安全性，常用于单点登入系统。
五、SAML认证类型SAML（Security Assertion Markup Language）是基于XML的开放标准，用于在安全领域中交换身份验证和授权数据。
在Java应用程序中，可以使用SAML实现单点登入和跨域身份验证。
六、总结Java提供了多种认证类型，开发人员可以根据实际情况和需求选择合适的认证类型来提高应用程序的安全性。
无论是基本认证类型、OAuth2.0认证类型、JWT认证类型还是SAML认证类型，都可以在Java应用程序中轻松实现，保护应用程序的安全性。
掌握这些认证类型，对于Java开发人员来说至关重要，也是提高自身技术水平的必修课程之一。
智能运维安全监控引擎实践-终版
每一一个企业都很关心心的问题
企业是否遭受这个漏洞的影响？ 如何定位我们在互联网上的业务是否使用了这项技术？ 我们使用该技术的业务是否遭受该漏洞的影响？ 这会造成什么样的风险？如何修复？ 未来如何应对可能再次出现的安全风险？
企业业务系统是否使用用了这项技术？
全⺴网网业务系统梳理
应用用系统指纹识别
使用用该技术的业务系统是否遭受影响？
爬虫虫抓取
域名
IP
安全是一一个整体 来自自社区贡献的指纹识别策略
应用用
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深度⻛风险检测
自自研应用用程序⻛风险检测「SQL注入入/XSS/命令执行行等」 第三方方应用用程序漏洞「WP/OA/Discuz/Struts2等」 ⺴网网站内容安全⻛风险「挂⻢马/博彩/恶意内容/黑黑链等」 基础服务漏洞「服务配置错误/通用用漏洞等」 员工工安全意识⻛风险「Github代码泄露/弱口口令等」
深度⻛风险检测
指纹&插件 平台引擎
命中分红
社区两万白白帽子子 二二十十万漏洞积累
监测报表
企业
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业务不懂安全漏洞，一一脸懵逼
快速问题修复
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详细的修复方方案
100%准确率
专家⻛风险预警
持续⻛风险管理
周期性安全监测
风险趋势分析
谢谢
基于指纹识别结果的⻛风险检测
这会造成什么样的⻛风险？如何修复？
进行行⻛风险演示示
详细的修复方方案
未来如何应对再次出现的安全⻛风险？
全面资产识别
深度风险检测
快速风险修复
持续风险管理
解决问题的秘诀=优秀的方案+高效的实现
基于社区的持续风险管理平台实践
govaluate规则引擎原理解析
Govaluate规则引擎是一种基于规则的表达式求值引擎，它能够将规则表达式和键值对条件对象作为输入，然后根据规则表达式计算出结果。
在Govaluate中，规则表达式被构建成一个抽象语法树（AST），AST是一种以树形结构表示源代码语法结构的数据结构。
Govaluate通过解析输入的规则表达式，生成对应的AST，然后通过遍历AST并执行相应的操作来计算出结果。
Govaluate的规则表达式语法包括数字、布尔值、字符串、正则表达式等类型的操作符和函数，以及变量和常量。
它支持的操作符包括算术运算符、比较运算符、逻辑运算符、位运算符等。
在Govaluate中，规则表达式可以嵌套在其他表达式中，这使得它能够处理复杂的逻辑运算和条件判断。
Govaluate的输入是一个键值对条件对象，这个对象包含了用于求值的变量和常量。
在执行规则表达式之前，Govaluate会将键值对条件对象中的值绑定到AST中的变量上，这样在执行表达式时就可以直接使用这些值。
Govaluate的AST构建过程包括词法分析和语法分析两个阶段。
在词法分析阶段，Govaluate将输入的规则表达式分解成一个个的令牌（token），这些令牌构成了AST的节点。
在语法分析阶段，Govaluate使用一个解析器（parser）将令牌转换成AST。
这个过程中，解析器会检查令牌之间的语法关系，并根据这些关系构建出相应的AST。
构建好AST之后，Govaluate会通过一个计划器（planner）来优化AST的结构，并为AST中的每个节点分配优先级。
这个过程中，Govaluate会考虑不同运算符的优先级以及变量和常量的类型等因素。
优化后的AST会被转换成一个平衡树（avl tree），这个平衡树能够在计算结果时保持高效的性能。
最后，Govaluate会遍历平衡树并计算出结果。
在遍历过程中，Govaluate会按照优先级和运算顺序执行相应的操作，并将结果传递给下一个节点。
implementation在计算机程序中的意思
implementation在计算机程序中的意思
在计算机程序中，"implementation" 的意思是实现。
它指的是将一个程序或系统的设计理念转化为具体的代码和逻辑的过程。
在软件开发中，implementation 是指将软件架构或设计转化为实际的代码。
这包括编写代码、测试、调试和优化等步骤。
实施阶段的目标是确保软件的功能和性能符合设计要求，并且能够满足用户的需求。
在计算机科学中，implementation 还可以指将一种理论或算法转化为计算机程序的过程。
这涉及到选择适当的编程语言、编写代码、实现算法、进行测试和调试等步骤。
总之，"implementation" 在计算机程序中意味着将设计或理论转化为实际的代码和逻辑，以便实现程序的功能和性能要求。
基于改进AHP的模糊评价在路面质量控制中的应用
Ｊ ｕｎ ｌ ｆ ｈ ｎ ｏ ｇＡ ｒ ｕｔｒｌ ｎｖ ｒ ｔ （ ａ ｒｌ ｃｅ ｃ ） ｏ ｒａ ｏ ａ ｄ ｎ ｇｉ ｌ ａ Ｕ ｉｅｓ ｙ Ｎ ｔ ａ Ｓ ｉｎ ｅ Ｓ ｃ ｕ ｉ ｕ
Ａ Ｐ Ｉ ＡＩｏＮ Ｉ ＨＥ Ｐ ＶＥ Ｐ ＬＣ ’ Ｎ Ｔ Ａ ＭＥ Ｉ ＮＴ ＱＵＡ Ｉ ‘ Ｃ Ｌ ’Ｙ ０Ｎ’ ０Ｌ Ｂ Ｅ Ｉ Ｉ ’ Ｒ ＡＳ Ｄ
ｏＮ ＴＨＥ Ｍ ＰＲｏＶＥＤ Ｉ ＡＨＰ ｏＦ ＦＵＺＺＹ ＥＶＡＬＵＡＴＩ ｏＮ
Ｌ ｉ ｂｉ Ｓ Ｉ Ｈａ — ｎ． ＨＡ — ｍ ｉ Ａｉ ｎ
ｔｅ ｏ ｔ ａ ｒ ｎ ｆ ｒｍ ａｒｘ ｈ ｅ ｔｇ ｔ ｈ ｉ ｐ ｏ ｅ ＡＨＰ ａ ｄ ａ ｏ ｄ ｈ ｏ ｅ ｅ ｔｔｓｉ ｇ ａ ｈ ｅ ｉｎｉ ｈ ｐ ｉ ｌｔａ ｓｅ ｔｉ ｔ ｅ ｔｘ ｅｓｔ ｅ ｍ ｒ ｖ ｄ ｍ ｎ ｖ ｉ ｓｔ ｅｃ ｈ ｒ ｎ ｅ ｔｎ ｔｔ ｅ ｂ ｇｎ ｎｇ，ａ ｄ ｎ ｔｅ ｈ ｅ ｅ ｏ ｕ ｅ ｓ ｓ ｑ ｅ ｃｎｇ ｂａ ｅ ｎ ｔ ｒｎ ｉ ｌ ｆ ｉ ｒ ｖ ｄ Ａｎ ｌｔ Ｈｉｒ ｒ ｈ ｒ ｃ ｓ ｎ ｕ ｚ ｈ ｎ ａ ｃ ｉｖ ｓ ｃ ｍｐ ｔｒ ｅ ｕ ｎ ｉ ｓ ｄ ｏ ｈｅ ｐ ｉ ｃ ｐ ｅ ｏ ｍｐ ｏ ｅ ａｙ ｉ ｃ ｅａｃ ｙ Ｐ ｏ ｅｓ ａ ｄ ｆｚｙ
（ ｅ ａｏａｏｙｆ ｐｃａ Ａ ｅ ｇｗ ｙＥ ｇｎｅｉｇｏ ｎｓ ｙｏ ｄ ｃｔ ｎ Ｃ ａｇ ｎＵ ｉ ｒｔ，Ｘ ｈ７ ０６ Ｋ ｙＬｂ ｒｔ ｒＳｅ ｉ ｒａＨｉ ａ ｎｉｅｒｎ ｆ ｉｒ ｆ ｕａｉ ， ｈｎ ｈ ｎｖ ｓｙ ｉｎ １０ ４，Ｃ ｉ ） ｒ ｏ ｌ ｈ Ｍｉ ｔ ． Ｅ ｏ ｅｉ ｈｎ ａ
aot generic method
aot generic method"AOT Generic Method: Enhancing Efficiency in Various Domains"Introduction:In today's fast-paced world, efficiency is the need of the hour. Whether it is in business operations, problem-solving, or decision-making, having a method that can streamline processes and maximize outcomes is invaluable. This article explores the concept of the AOT Generic Method and discusses the step-by-step approach to implementing it in different domains. So, let's delve into the details and discover how this method can revolutionize efficiency.Defining the AOT Generic Method:The AOT Generic Method is a systematic approach that focuses on three key stages: Assess, Optimize, and Transform. This method can be applied to various domains, including business management, project execution, and personal development, among others. Each stage plays a crucial role in enhancing performance and driving positive changes.Step 1: Assessment - Identify the Gaps:The first step in implementing the AOT Generic Method is to assess the current state of affairs. This involves identifying the existing gaps, inefficiencies, and bottlenecks. In business management, this could mean analyzing processes, systems, and resources to determine where improvements can be made. For personal development, it may involve a self-assessment to identify strengths and weaknesses.Step 2: Optimization - Streamline Processes:Once the areas requiring improvement have been identified, the focus shifts to optimizing processes. This stage involves analyzing existing workflows, identifying redundancies, and eliminating wasteful activities. Through careful planning and analysis, efficiency can be enhanced significantly. In business, this could mean restructuring departments or implementing automation technologies. For personal development, it may involve setting clear goals, establishing priority tasks, and developing effective time management strategies.Step 3: Transformation - Implement Systemic Changes:In order to achieve sustainable results, the final stage of the AOT Generic Method is transformation. This involves implementing systemic changes to improve overall performance. In business management, this may include retraining staff or adopting new technologies to enhance productivity and output. For personal development, it could mean establishing new habits or acquiring new skills to maximize potential.Case Study: Applying AOT Generic Method in Project Execution:To illustrate the practical application of the AOT Generic Method, let's consider a case study in project execution. The assessment stage involves analyzing the project plan, identifying potential risks and limitations. The optimization stage involves streamlining communication channels, improving resource allocation, and reevaluating project timelines. Finally, the transformation stage involves implementing new project management tools, enhancing collaboration, and establishing accountability measures. Through these steps, project efficiency is significantly enhanced.Conclusion:The AOT Generic Method offers a structured approach to improving efficiency in various domains. By assessing, optimizing, and transforming processes, organizations and individuals can tap into their true potential. This approach provides a roadmap to identify weaknesses and streamline operations, resulting in improved productivity and outcomes. Whether it is in business management, project execution, or personal development, the AOT Generic Method is a powerful tool for achieving success and driving positive change. Embrace this method, and unlock the doorway to enhanced efficiency and growth.。
Value Chain(价值链)
•Value chain analysis is an analytical tool which can help provide
clarity to consultants and clients
Step one is determining the appropriate activities to map.
1. What are the activities?
Value Chain Methodology (2 of 2)
?
•Determine key steps in designing, producing, marketing,
VMR/Industry Collaboration
Competitive Positioning
valuech 7 ainValue Chain
Value Chain Analysis
•The concept •Value Chain methodology •Example
Agenda
valuech 8 ainValue Chain
Value Chain
Value Chaiห้องสมุดไป่ตู้ Analysis
•The concept •Value Chain methodology •Example
Agenda
2
Value Chain Analysis
The Concept
•Value chain analysis is a systematic method for disaggregating a
firmae 原理
firmae 原理Firmae原理Firmae是一种基于区块链技术的数字签名协议，它的原理是通过使用椭圆曲线密码学和零知识证明，实现对文档的数字签名验证和存证。
Firmae的设计目标是确保签名的不可伪造性、不可篡改性和匿名性。
Firmae的原理可以简单概括为以下几个步骤：1. 密钥生成：用户在使用Firmae前需要生成一对公私密钥。
私钥用于签名，公钥用于验证签名。
2. 文档哈希：用户需要对待签名文档进行哈希计算，生成文档的哈希值。
哈希函数是一种将任意长度的数据映射为固定长度的输出的函数。
3. 签名生成：用户使用私钥对文档的哈希值进行签名生成数字签名。
签名过程使用椭圆曲线密码学中的数字签名算法，确保签名的不可伪造性和不可篡改性。
4. 签名验证：签名验证是Firmae的核心功能。
接收者使用签名者的公钥对签名进行验证，确保签名的有效性和完整性。
验证过程使用椭圆曲线密码学中的公钥密码算法，通过验证签名者的公钥和签名者的签名，来验证签名的正确性。
5. 存证功能：Firmae还提供了文档的存证功能。
用户可以将文档的哈希值和签名存储在区块链上，确保文档的可追溯性和不可篡改性。
存证功能通过零知识证明实现，确保用户的隐私和匿名性。
Firmae的优势在于其安全性和可验证性。
通过使用椭圆曲线密码学和零知识证明，Firmae可以确保签名的不可伪造性和不可篡改性。
同时，Firmae的签名验证过程简单高效，可以在短时间内完成验证。
除此之外，Firmae还具有可扩展性和灵活性。
用户可以根据自己的需求选择不同的椭圆曲线和哈希函数，以满足不同安全级别的要求。
同时，Firmae的存证功能可以与其他区块链应用进行集成，为用户提供更多的应用场景。
总结起来，Firmae是一种基于区块链技术的数字签名协议，通过使用椭圆曲线密码学和零知识证明，实现对文档的数字签名验证和存证。
它的原理包括密钥生成、文档哈希、签名生成、签名验证和存证功能。
Firmae具有安全性、可验证性、可扩展性和灵活性等优势，在数字签名领域具有广泛应用前景。
基于改进AHP结合模糊评判的碳酸盐岩储层定量评价
（ ． ｏｌ ｅｏ ｅ ｕｃｓ ｎ ｎｉ ｎ ｅｔ Ｓ ｕ ｗ ｓ Ｐ ｔ ｌ ｍ Ｕ ｉ ｒ ｔ， ｈｎ ｄ １５ ０， ｈｎ ； １Ｃ ｌｇ ｅ ｆＲ ｓ ｒ ｄＥ ｖ ｏ ｍ ｎ ， ｏ ｔ ｅ ｅ ｏ ｕ ｎ ｅ ｉ Ｃ ｅｇ ｕ６ ０ ０ Ｃ ｉａ ｏ ｅａ ｒ ｈ ｔ ｒｅ ｖ ｓｙ ２ Ｃ ｕ ｎｈ ｎ ｉ ａ ｄＧ ｓ ｅ ａｃ ｎｔｕ ， ｏ ｔｗ ｓ Ｏｌ Ｇ ｓＣ ｍ ａｙ ｕｎ ｇ６ ９ ０ ，Ｃ ｉ ） ． ｈ ａ ｚｏｇ Ｏｌ ｎ ａ ｓ ｒ Ｉｓｔｔ Ｓ ｕ ｅ ｉ＆ ａ ｏ ｐ ｎ ，Ｓ ｉｉ ２ ０ ０ ｈ ａ Ｒｅ ｈ ｉｅ ｈ ｔ ｎ ｎ
ｏ ｅ ｅ ｖ ｉ ｕ ｌ ｙ，ｔ ｏ ｒ ｌｔｏ ｔ ｅ ｖ ｌ ａ ｉｎ ｐ ｒ ｍｅｅｓ ａ ｈｅ ｕ ｃ ｒａｎ ｙ ａ ｅ ｒ ｓ ｌｅ ．Ｅｖ ｌ ｔｏ ｅ ｆｒ ｓ ｒ ｏｒｑ ａ ｉ ｔ ｈｅ ｃ ｒｅａｉｎ ｂｅｗｅ ｎ ｅ ａｕ ｔ ａ ａ ｔｒ ｎｄ ｔ ｎ ｅ ｔｉ ｔ ｒ ｅ ｏｖ ｄ ｏ ａｕａｉｎ ｒ — ｓ ｔｈ ｓａ ｇ ｏ ｏ ｓｓｅ ｃ ｔ ｈ ｃｕａ ｏ ｃｉｎ ｏ ｉ ａ ｄ ｇ ｓ ｎ ｉａｉ ｇ ｔ ａ ｈ ｍｐ ｏ ｅ ｕｌ ａ ｏ ｄ ｃ ｎ ｉｔ ｎ ｙ ｗｉ ｔ ｅ ａ ｔ ｌｐｒｄｕ ｔｏ ｆ ｏｌ ｎ ａ ，ｉ ｄ ｃ ｔ ｈ ｔｔ ｅ ｉ ｒ ｖ ｄ ＡＨＰ ｍｅｈｏ ｈ ｎ ｔ ｄ ｃ ｍｂｎｅ ｔ ｕｚｙ ｃ ｍｐ ｅ ｅ ｉ ｅｅ ａｕ ｔｏ ｓｅｆ ｃｉｅ ｏ ｕ ｎ ｉ ｔ ｅ ｅ ａｕ ｔ ｎ ｏ ｅ ｅ ｖ ｉ ，ｗｈ ｃ ａ ｉｎｆ— ｏ ｉ ｄ ｗｉｈ ｆ ｚ ｏ ｒ ｈ ｎｓｖ ｖ ｌ ａｉｎ ｉ ｆ ｔｖ ｎ ｑ ａ ｔｔ ｉ ｖ ｌ ａ ｉ ｆｒ ｓ ｒｏ ｒ ｅ ａｖ ｏ ｉｈ ｈ ｓｓｇ ｉ ｉ
Java适配器模式
Java适配器模式 在阎宏博⼠的《JAVA与模式》⼀书中开头是这样描述适配器（Adapter）模式的： 适配器模式把⼀个类的接⼝变换成客户端所期待的另⼀种接⼝，从⽽使原本因接⼝不匹配⽽⽆法在⼀起⼯作的两个类能够在⼀起⼯作。
作⽤ 类似于三相插头到两相插头转换器（适配器）所起的作⽤。
结构 适配器模式有类的适配器模式和对象的适配器模式这两种不同的形式。
类适配器模式 把适配类的API转换成⽬标类的API，见下图： Adaptee类没有sampleOperation2⽅法，⽽客户端需要使⽤这个⽅法。
Adapter类继承了Adaptee类，把Adaptee类的API与Target类的API衔接起来。
涉及到的⾓⾊： ⽬标(Target)⾓⾊：类适配器模式中⽬标不可以是类，只能是接⼝。
源(Adapee)⾓⾊：具体类。
适配器(Adaper)⾓⾊：具体类。
1public interface Target {2/**3 * 这是源类Adaptee也有的⽅法4*/5public void sampleOperation1();6/**7 * 这是源类Adapteee没有的⽅法8*/9public void sampleOperation2();10 }1public class Adaptee {23public void sampleOperation1(){}45 }1public class Adapter extends Adaptee implements Target {2/**3 * 由于源类Adaptee没有⽅法sampleOperation2()4 * 因此适配器补充上这个⽅法5*/6 @Override7public void sampleOperation2() {8//写相关的代码9 }1011 } 对象适配器模式 对象的适配器模式把被适配的类的API转换成为⽬标类的API，使⽤委派关系连接到Adaptee类，见下图： Adapter与Adaptee是委派关系。
六、GAIA
六、GAIA1. GAIACSR GAIA (Generic Application Interface Architecture)提供了⼀个端到端的，与主机⽆关的⽣态系统来实现主机应⽤程序对设备的功能集访问。
1.1 接⼝实现GAIA是处于较上层的应⽤层协议，其依赖的传输协议有多个，⽬前ADK⽀持的传输协议有:RFCOMM,SPP,GATT，不同的传输协议，实现相同的功能，但是在实现这些功能时⼀些机制和细节⼜存在着⼀些差异。
因此ADK对传输层定义了多个抽象的接⼝，这些接⼝根据当前连接所采取的传输协议⽽⾃动映射到该协议对应的接⼝，具体映射关系如下图所⽰：当采⽤不同传输协议时，部分通⽤接⼝可能⽆法找到对应的接⼝，⽐如当采⽤GATT传输协议时，gaiaTransportGetSink()通⽤接⼝没有对应的实现接⼝，因为GATT没有SINK和SOURCE这些实体。
下⾯来看看GAIA库和Sink_GAIA的主要结构体和函数接⼝。
Sink_gaia.c主要提供了GAIA可注册事件发⽣时，向GAIA_Client推送事件的接⼝，如gaiaReportEvent()等。
另外⼀个主要功能是管理GAIA事务，这主要通过handleGaiaMessage()对外提供的外部钩⼦函数实现。
Gaia.c提供了GAIA库的⼤部分功能实现，主要包括以下⼏个部分：1.初始化GAIA库，开启GAIA服务器。
2.处理连接，断开连接请求和响应。
3.构建GAIA数据包，应答包，接收并解析GAIA数据包。
4.GAIA模块参数获取和修改接⼝。
1.2 重要流程先来看看GAIA如何建⽴GAIA连接的。
连接通常⽤GAIA_Client通过调⽤GaiaBtConnectRequest()发起。
断开连接的请求可以有个client或者server任何⼀端发起。
再来看看GAIA连接建⽴之后，如何进⾏GAIA交互的。
在上图中，上半部分展⽰了⼀次典型的GAIA交互——GAIA_Client构造⼀个GAIA后，通过GAIA传输层发送给对端，GAIA_Server在接收到GAIA命令后，进⾏解析，如果参数合法，处理该请求后应答该次请求的结果，如果参数有错，则应答错误。
Android恶意应用HTTP行为特征生成与提取方法
Android恶意应用HTTP行为特征生成与提取方法
罗亚玲;黎文伟;苏欣
【期刊名称】《电信科学》
【年(卷),期】2016(032)008
【摘要】Android恶意应用数量的不断增加不仅严重危害Android市场安全,同时也为Android恶意应用检测工作带来挑战.设计了一种基于HTTP流量的Android恶意应用行为生成与特征自动提取方法.该方法首先使用自动方式执行恶意应用,采集所生成的网络流量.然后从所生成的网络流量中提取基于HTTP的行为特征.最后将得到的网络行为特征用于恶意应用检测.实验结果表明,所设计的方法可以有效地提取Android恶意应用行为特征,并可以准确地识别Android恶意应用.【总页数】10页(P136-145)
【作者】罗亚玲;黎文伟;苏欣
【作者单位】广东松山职业技术学院计算机系,广东韶关512126;湖南大学信息科学与工程学院,湖南长沙410082;湖南大学信息科学与工程学院,湖南长沙410082;湖南警察学院网络侦查技术湖南省重点实验室,湖南长沙410138
【正文语种】中文
【中图分类】TP393.08
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5.基于特征生成方法的Android恶意软件检测方法 [J], 冯垚;王金双;张雪涛因版权原因，仅展示原文概要，查看原文内容请购买。
多服务器环境下基于扩展混沌映射的认证密钥协商协议
多服务器环境下基于扩展混沌映射的认证密钥协商协议舒剑【期刊名称】《计算机应用研究》【年(卷),期】2016(33)1【摘要】传统的单服务器环境下基于智能卡认证方案，单个服务器对所有的注册远程用户提供服务。
如果用户想要从不同的服务器获得网络服务，必须分别在不同的服务器注册。
为解决以上问题，研究者提出了多服务器认证方案，然而，文献中的大部分方案都不能实现强安全特性。
受到切比雪夫映射的半群特性和基于扩展混沌映射的密钥协商协议启发，提出一种多服务器环境中的认证方案。
新方案不需要使用验证表并且允许用户访问不同的服务器而不需要分别注册；新方案不仅可以抵抗各类攻击，还实现了用户的强匿名性。
与以前的相关协议相比，新协议具有高效性和安全性，因而适合在实际环境中应用。
%In a traditional single server smart card authentication scheme,one server is responsible for providing services to all the registered remote users.If a user wishes to access network services from different servers,he or she has to register with these servers separately.To handle this issue,multi-server authentication scheme has been proposed.However,most of these schemes available in the literature couldn’t achieve strong security.Inspired by the semi-group property of Chebshev maps and key agreement protocols based on extended chaotic maps,this paper proposed an authentication scheme for multiserver envi-ronment that not only resisted various attacks but also achieved strong anonymity for hiding login user’s real identity from otherservers.It eliminated the use of verification table and permited the registered users to access multiple servers without separate pared with other previous related schemes,the proposed scheme keeps the efficiency and security,thus it is more suitable for the practical applications.【总页数】4页(P232-235)【作者】舒剑【作者单位】江西财经大学电子商务系，南昌 330013; 电子科技大学计算机科学与工程学院，成都 611731【正文语种】中文【中图分类】TN918.4【相关文献】1.基于混沌映射的用户匿名三方口令认证密钥协商协议 [J], 王彩芬;陈丽;刘超;乔慧;王欢2.多服务器架构下基于混沌映射的认证密钥协商协议 [J], 潘恒;郑秋生3.基于扩展混沌映射的三方认证密钥协商协议 [J], 闫丽丽;昌燕;张仕斌4.基于智能卡的扩展混沌映射异步认证密钥协商协议 [J], 王松伟;陈建华5.基于扩展混沌映射的动态身份认证密钥协商协议 [J], 曹阳因版权原因，仅展示原文概要，查看原文内容请购买。