GLOBAL L^∞ SOLUTIONS TO SYSTEM OF ISENTROPIC GAS DYNAMICS IN A DIVERGENT NOZZLE WITH FRICTION
《仪器分析》双语课程网络平台构建及其应用

J OURNAL OF DAL I UNI VE RS I T Y
第1 2 卷 第4 期 2 0 1 3 年4 月
Vo l 1 2 No. 4 Apr . 2 01 3
[ DOI ]1 0 . 3 9 6 9 / j . i s s n . 1 6 7 2 — 2 3 4 5 . 2 0 1 3 . 0 4 . 0 2 0
( 1 . S c h o o l o f C h e mi s t r y a n d Bi o t e c h n o l o g y , Yu n n a n Un i v e r s i t y o f Na t i o n a l i t i e s , Ku n mi n g 6 5 0 5 0 0 , C h i n a ; 2 . E d i t o r i a l De p a r t me n t o f J o u r n a 1 .
[ Ke y w o r d s ] i n s t um r e n t a n a l y s i s ; b i l i n g u a l c o u r s e s ; n e t w o r k p l a t f o r m
ቤተ መጻሕፍቲ ባይዱ
双语 教 学 是 指非 外 语 课 程 利 用外 语 辅 助 母 语 授课 , 把外 语 学 习 与学 科 知识 学 习 同步 进行 的一 种 教学方法 , 其 目的不 仅 是 增 加 一 种 教 学 语 言 , 促 进 学 生尽 早 接 触专 业 外语 及 其 国外 前 沿 知识 , 也 是 学
Y u n n a n U n i v e r s i t y o f N a t i o n a l i t i e s , K u n mi n g 6 5 0 0 3 1 , C h i n a ; 3 . D e p a r t me n t o f T e c h n o l o g y , K u n mi n g A i d i n e s , K u n mi n g 6 5 0 5 0 0 , C h i n a )
国际科学技术信息网(STN)的应用:关于纳米科技的信息

国际科学技术信息网(STN)的应用:关于纳米科技的信息纳米技术是21世纪关键技术之一,有些人甚至认为是关键技术。
其集合了多种学科:例如:生物,化学,物理和材料科学,纳米技术的应用已经对日常生活带来了巨大的影响,并将在未来引起更大变革。
在研究过程中,纳米技术帮助人们探索单细胞的微观世界,或通过涂层反射镜观察宏观世界。
依靠纳米技术带来新发展的工业有:² 电子² 材料科学² 汽车工业² 医药工业² 能源提供和更多无论您工作于纳米科技的哪一部分,STN数据库是不可缺少的最新可靠的数据来源,其覆盖了研究、应用、专利、经济、新闻等各个领域。
有关STN的相关数据库信息可登陆www.stn-international.de/stndatabases/c_datab.html。
包含纳米技术信息的数据库列表如下:数据库主题ABI-INFORM 经济和管理AEROSPACE 航空研究与发展APOLLIT 聚合物和塑料化学技术BABS 有机化学BEILSTEIN 有机化学BIOSIS 生物医药和生物科学BIOTECHNO 生物技术CAplus 化学,生化,化工CASREACT® 单步或多步化学反应CEABA-VTB 化工和生物技术COMPENDEX 机械工程DGENE 氨基酸和核酸序列DRUGU/DDFU 药物EMA 聚合物,陶瓷和化合物EMBASE 生物医药和药物ENERGY 能源ESBIOBASE 最近资料通告EUROPATFULL 欧洲专利GENBANK® 核酸序列IFIPAT 美国专利INPADOC 全球专利INSPEC 计算机,电子,工程,物理JAPIO 日本专利JICST-EPLUS 日本科技文献MEDLINE 医学METADEX 金属材料NLDB 经济和工业新闻PASCAL 多学科,包括应用科学,化学,地球科学,信息科学,生命科学,物理PATDPAFULL 德国专利PCTFULL PCT专利PROMT 科技经济信息RAPRA 橡胶和塑料工业REGISTRY 有机和无机物质SCISEARCH® 科技TEMA 技术和管理TOXCENTERSM 毒理学USPATFULL/USPAT2 美国专利WPIDS/WPINDEX/WPIX 全球专利WPIFV 全球专利最近资料通告纳米技术最原始的发明是扫描隧道显微镜(STM),1981年由GerdBinning 和Geinrich Rohrer共同研究,至今,该发明仍是一个非常重要的工具。
nscacscs第四版第十四章内容

nscacscs第四版第十四章内容第四版第十四章内容:人工智能与未来社会人工智能(Artificial Intelligence,简称AI)是当今科技领域最炙手可热的话题之一。
在第四版的第十四章中,我们将深入探讨人工智能与未来社会的关系,以及其对我们生活的影响。
首先,我们需要明确人工智能的定义。
人工智能是一种模拟人类智能的技术,通过计算机系统实现对复杂问题的分析、判断和决策。
它可以模拟人类的思维过程,具备学习、推理、识别和理解等能力。
随着科技的不断进步,人工智能已经在各个领域得到广泛应用,包括医疗、金融、交通、教育等。
人工智能的发展对未来社会产生了深远的影响。
首先,人工智能的出现将改变我们的工作方式。
许多重复性、繁琐的工作将被机器人或自动化系统取代,从而提高工作效率和生产力。
然而,这也意味着一些传统的工作岗位可能会消失,需要我们不断学习和适应新的技能。
其次,人工智能的应用将改变我们的生活方式。
例如,智能家居系统可以通过语音识别和自动化控制,实现对家庭设备的智能管理。
智能助手可以帮助我们处理日常事务,提供个性化的服务。
虚拟现实技术可以让我们身临其境地体验各种场景。
这些技术的出现将极大地提升我们的生活质量和便利性。
然而,人工智能的发展也带来了一些挑战和问题。
首先,人工智能的普及可能导致一些道德和伦理问题的出现。
例如,自动驾驶汽车在遇到危险情况时如何做出决策,成为了一个备受争议的话题。
其次,人工智能的发展可能会导致一些就业岗位的消失,增加社会的不平等。
此外,人工智能的算法可能存在偏见和歧视,需要我们加强监管和规范。
为了应对这些挑战和问题,我们需要制定相应的政策和法规。
首先,我们需要加强对人工智能技术的监管,确保其安全和可靠性。
其次,我们需要加强对人工智能的研究和发展,培养更多的专业人才。
同时,我们也需要加强对人工智能的教育和普及,提高公众对人工智能的认知和理解。
总之,人工智能是未来社会发展的重要驱动力之一。
Scalability Solutions in Operating Systems

Scalability Solutions in OperatingSystemsScalability in operating systems refers to the ability of an operating system to handle an increasing workload or its ability to accommodate growth in terms of users, requests, or transactions without compromising performance or reliability. As technology continues to advance and more and more data is being generated, scalability has become a crucial aspect of operating systems. In this article, we will explore some of the scalability solutions that are commonly implemented in operating systems.One of the key scalability solutions in operating systems is parallel processing. Parallel processing involves breaking down a task into smaller sub-tasks that can be processed simultaneously by multiple processors. This allows for faster execution of tasks and better utilization of system resources. Operating systems that support parallel processing can handle a higher workload and provide better performance as compared to systems that rely on a single processor.Another scalability solution is load balancing. Load balancing involves distributing incoming requests evenly across multiple servers or nodes in a network. This ensures that no single server is overloaded with requests, thereby improving the overall performance and reliability of the system. Operating systems that implement load balancing algorithms can efficiently manage resources and handle a larger number of users or requests without any degradation in performance.Virtualization is another important scalability solution in operating systems. Virtualization allows multiple virtual machines to run on a single physical machine, enabling efficient utilization of resources and easier management of hardware. By virtualizing resources, operating systems can dynamically allocate resources based on workload demands and scale up or down as needed. Virtualization also enhances system reliability and fault tolerance by isolating applications from the underlying hardware.Containerization is also a popular scalability solution in modern operating systems. Containers encapsulate applications and their dependencies in a lightweight and portable package, allowing them to run consistently across different environments. Containers are highly scalable and can be easily deployed and managed, making them ideal for modern cloud-based applications. Operating systems that support containerization enable developers to quickly scale their applications and services without the need for complex configurations.Furthermore, microservices architecture is another scalability solution that is gaining popularity in operating systems. Microservices break down applications into smaller, independently deployable services that can be scaled horizontally to meet growing demand. This modular approach allows for better resource utilization, fault isolation, and improved system performance. Operating systems that support microservices architecture enable developers to build scalable and resilient applications that can adapt to changing workloads.In conclusion, scalability solutions play a crucial role in ensuring the performance, reliability, and efficiency of operating systems in today's rapidly evolving technological landscape. By implementing parallel processing, load balancing, virtualization, containerization, and microservices architecture, operating systems can effectively handle increasing workloads, accommodate growth, and provide seamless user experiences. As the demand for scalable and high-performing systems continues to grow, it is essential for operating systems to embrace these scalability solutions to stay competitive and meet the needs of modern computing environments.。
Interpreting the Interpretive Structural Model

ORIGINAL ARTICLEInterpreting the Interpretive Structural ModelSushilReceived:15December 2011/Accepted:30April 2012/Published online:18September 2012ÓGlobal Institute of Flexible Systems Management 2012Abstract Interpretive structural modeling (ISM)is a process that transforms unclear and poorly articulated mental models of systems into visible,well-defined models useful for many purposes.The interpretation of links is comparatively weak in ISM;the interpretation of the directed link in terms of how it operates is lacking.This paper is an attempt to interpret the links in the interpretive structural models using the tool of Interpretive Matrix and leads to evolve the framework and methodology of total interpretive structural modeling (TISM).First,an overview of ISM is provided.This is taken-up further by highlighting the need of interpretation of interpretive structural models.In order to evolve the framework of TISM,the tool of Interpretive Matrix is briefly introduced,which is inte-grated into the methodology of TISM.The basic process of TISM is presented in a step-by-step manner with indicative directions for scaling-up this process.Some tests for vali-dating total interpretive structural models are also pro-posed.Finally,the basic process of TISM is illustrated with the help of an example in the context of organizational change.This process can be used for conceptualization and theory building in organizational research.Keywords Interpretive Matrix ÁInterpretive structural modeling ÁOrganizational change ÁTheory building ÁTotal interpretive structural modelingIntroductionIn any organizational research,conceptualization or theory building phase is crucial as the essence of research depends on it.In the process of developing a conceptual framework or theory,that is to be tested later,some basic questions need to be answered.The six fundamental questions are what ,how ,why ,who ,where ,and when as outlined by Whetten (1989),in the path breaking paper on ‘‘What constitutes a theoretical contribution?’’.Out of these,the most fundamental ones are what ,how and why those form the basis of this paper.Any theory is supposed to define the basic constructs,dimensions or elements constituting the framework (what ).For example,in organizational research,it is imperative to identify the performance dimensions and the antecedents influencing the performance.The next question to be delineated in the conceptualization phase is the hypothesized relationships among the research vari-ables (how ).Further,the causal thinking (why )need to be deliberated in order to interpret the linkages that are envisaged as hypotheses.The above questions in theory building may either be derived from existing literature (using past theories or models already validated by other researchers),or may be explored using grounded theory (Corbin and Strauss 1990)(in areas where adequate literature in research domain in a given context is not available).The grounded theory seeks the inputs from field in terms of qualitative views from working professionals/experts based on their experience in the problem domain under investigation,or by using case experiences in an inductive manner.Usually,organizational researchers find it convenient to answer ‘what’either from literature or field,or both so as to identify key research variables as the starting point in any research query.They may use past theories to backSushil (&)Department of Management Studies,Indian Institute of Technology,Delhi,Delhi,India e-mail:profsushil@Global Journal of Flexible Systems Management (June 2012)13(2):87–106DOI 10.1007/s40171-012-0008-3‘how’and‘why’,e.g.Maslow’s need hierarchy theory links ‘motivation’with fulfillment of needs at different levels of hierarchy.However,in the context of grounded theory applica-tions,though explicit procedure of content analysis meth-odology is provided to identify elements/variables,the methodological framework is comparatively weak to answer‘how’and‘why’in terms of relationships.Such inter-linkages of research elements are usually portrayed by organizational researchers using some possible logic as they seem to be working on a case to case basis.For example,while developing a theory of organizational change,field experiences may point out important change forces as globalization,changing customer needs,new technology,changes in government policy,and so on.In order to answer how these change forces are related and why they are expected to be related in that manner,ade-quate step by step methodology is lacking in the grounded theory in order to generate a transparent and well organized logic.Systems theory and systems engineering based methods may provide a helping hand to organizational researchers on this front.Identification of structure within a system is of great value in dealing effectively with the system and better decision-making.Structural models may include interaction matrices and graphs(Warfield1973a,b, 1974b);Delta charts;signalflow graphs,etc.,which lack an interpretation of the embedded object or representation system.This paper is an attempt to enhance interpretive logic of systems engineering tools in delineating not only a hierarchical structure of the intended organizational theory, but also to interpret the links in order to explain the cau-sality of the conceptual model by using strengths of paired comparison methodology.It takes interpretive structural modeling(ISM)(Warfield1973a)as the starting point for conceptualization of organizational research and further develops it into total interpretive structural modeling (TISM).An interpretive structural model(ISM)deals with the interpretation of the embedded object or representation system by systematic iterative application of graph theory resulting in a directed graph for the complex system for a given contextual relationship amongst a set of elements. Interpretive structural modeling,can,therefore,be defined as a process that transforms unclear and poorly articulated mental models of systems into visible,well-defined models useful for many purposes.In any interpretive structural model,the interpretation of the diagraph can be done at two levels,i.e.nodes and links. An ISM interprets the nodes in terms of the definition of elements.But the interpretation of links is comparatively weak in ISM;this is limited to interpreting the contextual relationship between the elements and the direction of relationship in a paired comparison.But the interpretation of the directed link in terms of how it operates is lacking. For example,two objectives A and B are related by the contextual relationship‘will help achieve’.If there is a directed relationship in an ISM from objective A to objective B,thereby implying that objective A will help achieve objective B,it does not interpret that in what way objective A will help achieve objective B.This paper,thus,is an attempt to interpret the links in the interpretive structural models using the tool of Inter-pretive Matrix(Sushil2005a)and leads to evolve the framework and methodology of total interpretive structural modeling(TISM)for conceptualizing poorly articulated mental models and theory building.First,an overview of ISM is provided in terms of its methodology and various matrices with its possible limitations in organizational research.This is taken-up further by highlighting the need of interpretation of interpretive structural models.In order to evolve the framework of TISM,the tool of Interpretive Matrix is briefly introduced,which is integrated into the methodology of TISM.Finally,the basic process of TISM is illustrated with the help of an example in the context of organizational change.In the concluding section,its pos-sible applications in organizational research and future directions are outlined.Interpretive Structural Modeling—An OverviewThe mathematical foundations of the methodology of ISM can be found in various reference works(Harary et al. 1965;Waller1980;Warfield2003).The applications of philosophical basis for the development of the ISM approach(Warfield1973c)and the conceptual and ana-lytical details of the ISM process(Warfield1974a,1976, 1994,1999)are dealt with by many authors.Malone (1975)discussed the application of ISM in structuring personal values and focusing on barriers to investment in a central city.Hawthorne and Sage(1975)used ISM for higher education program planning.Jedlicka and Meyer (1980)used ISM for cross-cultural studies.Saxena et al. (2006)have applied it in conjunction with other modeling methodologies in the context of energy conservation policy.There are multiple other applications of ISM in many areas;some representative ones are:decision sup-port systems(Hansen et al.1979),waste management (Sharma and Sushil1995),vendor selection(Mandal and Deshmukh1994),product design(Lin et al.2006),supply chain management(Agarwal et al.2007),decision making (Lee2008),value chain management(Mohammed et al. 2008),world-class manufacturing(Haleem et al.2012), and so on.ISM is a computer assisted interactive learning process whereby structural models are produced and studied. Structural models so produced portray the structure of a complex issue,a system or afield of study in carefully designed patterns employing graphics and words.It is a means by which a modeling group can impose order on the complexity of relationships among elements.The method is interpretive in that the group’s judgment decides whether and how elements are related,structural in that an overall structure is extracted from the complex set of elements on the basis of relationships,and modeling in that the specific relationships and overall structure are portrayed in a digraph(directed graph)model.ISM is primarily intended as a group learning process but it can also be used by individuals working alone.In a nutshell,ISM is a process that is based on relational mathematics,which clarifies and transforms ill-structured mental models about the system under study into a clear interrelated structured set of system elements(Warfield and Ca´rdenas1994).At the outset,an element set,that is composed of ele-ments relevant to the problem or issue,is generated.Any suitable group process can be used for this,e.g.brain-storming,nominal group technique(NGT)etc.Next a contextually relevant subordinate relation is chosen.It should be subordinate in the sense that a direction should be attached to it.It should be so phrased as to lead to paired comparisons, e.g.‘is objective A more important than objective B?’.Having decided on an element set and the contextual relation,the modeling group carries out all paired com-parisons.Two elements are picked and pair-wise compar-ison of the same is made.Group judgment on the paired comparison is determined by consensus majority vote after the discussion.On the basis of the replies of the pair-wise comparisons,it infers certain replies due to transitivity of the contextual relation and calls for replies to certain other paired comparisons.Transitivity of the contextual relation is a basic assumption in ISM,which states that if element A is related to B and B is related to C,then A is necessarily related to C.When the necessary input information is available,a structural model is generated in the form of a digraph in which the collective features of the group’s thinking are assumed to be reflected.Depending upon the needs of the modeling group and requirements of the problem at hand,many kinds of structural models can be generated in ISM.Development of an Interpretive Structural ModelThe elements are to be definedfirst;the contextual relations of the elements are then determined.Based on the con-textual relationship under consideration,the structural self-interaction matrix(SSIM),reachability matrix,the lower triangular format of reachability matrix,digraph for inter-pretive structural model,and the interpretive structural model are developed(Saxena et al.2006).The develop-ment of ISM along with TISM is illustrated with various matrix and graphical tools in the modeling of organiza-tional change forces in the last section.Structural Self-Interaction MatrixKeeping in view the contextual relationships in each ele-ment,the existence of a relation between any two elements (i and j)and the associated direction of relation R is questioned by a group of experts concerned with the pro-gram.Four symbols are used for the type of relation that exists between the two elements under consideration.The symbols are:i.V for the relation from element i to element j and notin both directions;ii.A for the relation from element j to element i but not in both directions;iii.X for both the direction relations from element i to j and j to i;iv.0(zero),if the relation between the elements does not appear valid.The structural self-interaction matrix(SSIM)for the element under consideration is then prepared byfilling in the responses of the group on each pair-wise interaction between the elements.Reachability MatrixThe SSIM format is transformed into the reachability matrix format by transforming the information in each entry of the SSIM into1’s and0’s in the reachability matrix.The four situations are:i.If the(i,j)entry in the SSIM is a V,the(i,j)entry inthe reachability matrix becomes1and the(j,i)entry becomes0.ii.If the(i,j)entry in the SSIM is an A,the(i,j)entry in the reachability matrix becomes0and the(j,i)entry becomes1.iii.If the(i,j)entry in the SSIM is an X,both the (i,j)entry and the(j,i)entry of the reachability matrix become1.iv.If the(i,j)entry of the SSIM is a0,then both the (i,j)and(j,i)entries of the reachability matrix become0.Following these rules,the reachability matrix for the elements is prepared.The reachability matrix as obtainedfrom the SSIM is checked for the transitivity rule,i.e.for any elements A,B,and C and set S,given that A R B and B R C,it necessarily follows that A R C.If the transitivity rule is found not to be satisfied,the SSIM is reviewed and modified by giving specific feedback about transitive relationship to the experts in the group.From the revised SSIM,the reachability matrix is again worked out and tested for the transitivity rule.This process is repeated till the reachability matrix meets the requirements of the transitivity rule.Partitions on the Reachability MatrixOnce the reachability matrix has been prepared,it must be processed to extract the digraph and associate structural models.Warfield(1974c)suggests the use of a series of partitions which are induced by the reachability matrix on the sets and sub-sets of the elements,i.e.relation partition and level partition.Lower-Triangular Format Reachability MatrixThe reachability matrix is further transformed into a lower triangular format by identifying the highest level elements and inserting them as thefirst elements in the new reach-ability matrix.Interactively,the next highest level elements are identified and transformed until the elements are arranged into a lower triangular format.The rows having the maximum number of0’s are the rows relating to the highest level elements and the rows having the maximum number of1’s relate to the lowest level elements.Lower triangular reachability matrix can be easily pre-pared with the help of computers(Warfield1973d).How-ever,when the number of elements is small and the intention is to develop ISM through manual operations,the preparation of the lower triangular reachability matrix is optional;the digraph can be developed directly from the reachability matrix.Digraph for Interpretive Structural ModelHaving identified the levels of the elements,the relation-ship between the elements is drawn indicating the serial number of the elements and the direction of relation with the help of an arrow.The digraphs thus drawn are quite complex and are examined interactively to eliminate tran-sitive relationships.After eliminating the transitive rela-tionships,the digraph isfinalized for the interpretive structural model.The digraphs give information about hierarchy among the elements.The digraph thus developed may have cycles at a par-ticular level and feedbacks across the levels between ele-ments.In normal circumstances,the feedbacks and cycles should be eliminated to arrive at a digraph with minimum edges;but the same should be retained in the matrix if the intention is to further study the influence of indirect rela-tionships between the elements.Interpretive Structural ModelThe structural model is derived from the connective information contained in the digraph.The details of ele-ments are indicated in the respective boxes with indicated relations as worked out in the digraph,thus obtaining the interpretive structural model for the element.The inter-pretive structural model depicts the elements and their reachability to the higher level elements and provides a clear picture with an understanding of the inter-relation-ships among the elements.The strongly connected ele-ments at the same level may be represented in the same box as separate bullets for a simplified presentation.Strengths and Limitations of ISM for Organizational ResearchStrengthsi.Helps in presenting a complex system in a simplifiedway.ii.Provides interpretation of the embedded object.iii.Transforms unclear and poorly articulated mental models of systems into visible,well-defined models thereby help in answering what and how in theory building.iv.Facilitates the identification of the structure within a system.Limitationsi.Technique of ISM can be used only by persons whoare knowledgeable about it and are trained to interpret the data.e of computers is necessary and the technique may be difficult to apply if computer facilities are not available.iii.The interpretation of links is partial thereby exposing the model to multiple interpretations by the user. iv.It remains silent on the causality of links and thus pose limitations in answering why in theory building. Interpretation in ISMInterpretive structural modeling was a major milestone in incorporating interpretation in the structural modeling.A structural model in the form of a diagraph has nodes as well as links.Interpretive structural modeling interprets the links in terms of the contextual relationship and the direction of the relationship for each pair of elements.This becomes the starting point of articulating the structure of any ill-defined system.At thefinal stage,it interprets the nodes of the diagraph in terms of the definition of respective elements so that a clear picture can be portrayed in terms of the relationships among the elements.However, interpretation of the links is limited to the contextual relationship and the direction of the relationship.There is a need to interpret it further in terms of clarifying the way in which the directed relationship is materialized.This can be achieved by making use of Interpretive Matrix as a tool to convert binary relationships in thefinal diagraph in the form of interpretive relationships.An overview of the Interpretive Matrix is provided in the next section.It has been applied in pair-wise relationships in the context of SAP-LAP linkages(Sushil2009a)and decision-making as interpretive ranking process(Sushil2009b).The interpretive ranking process(IRP)takes advantage of the analytical logic of the rational choice process and couples it with the strengths of the intuitive process at the elemental level.It is rooted into the strengths of the paired comparison approach to minimize the cognitive overload (Warfield1974c;Saaty1977).At the same time,it over-comes the weakness of the paired comparison approach the way it is applied in rational choice models,such as Ana-lytic Hierarchy Process(AHP)(Saaty1977).In AHP,an expert gives the judgment about the importance of one element over the other in the pair along with its intensity, but the interpretation of the same is left in a tacit manner with the expert,thereby making the interpretive logic of the decision opaque to the implementer.In IRP,the expert is supposed to spell out the interpretive logic for dominance of one element over the other for each paired comparison. In a similar manner,the paired relationships in ISM can be interpreted using Interpretive Matrix and structural model with total interpretation of nodes as well as links can be developed.Interpretive Matrix to Interpret LinksThe NeedThe structural modeling is used to portray the system structure in terms of relationships of elements.These relationships may or may not have a direction.The undi-rected relationships are used,for example,in Program Planning Linkages(Hill and Warfield1972)in terms of ‘self-interaction matrices’and‘cross-interaction matrices’which are binary in character.It uses paired comparison methodology to compare two elements for a relationship. If there is a relationship,an entry of‘1’is made in the relevant cell of the matrix,or else a‘0’entry is made.The manipulation of matrices is done using binary arithmetic. The directed relationships are used in directed graph theory (Harary et al.1965)and ISM(Warfield1994).In this case, the entries in the cells incorporate direction of relationships as well using some symbols and then converted into binary matrices.Thus,though the structural models depict relationships between elements/variables,these relationships need to be properly interpreted.The‘interpretive matrix’(Sushil 2005a)is a step in this direction to aid the interpretation of relationships in structural models in a given context.The PrincipleThe Interpretive Matrix represents interpretation of rela-tionships on pairs of elements in cells which are binary, fuzzy or statistically significant.The main question answered in interpreting an undirected relationship is ‘why’the relationship exists in between the two elements. In case of directed relationships the interpretation is done in terms of‘how’the relationship works,in a given con-text.The interpretation may change for different contextual relationships,which may be for example,influences, enhances,precedes,will help achieve,more important than,and so on.The ToolThe Interpretive Matrix represents a set of relationships in a matrix form,giving interpretation for each paired rela-tionship in the relevant cell.There could be three basic types of interpretive matrices,viz.triangular,square,and rectangular.The square Interpretive Matrix is used to interpret the relation in thefinal diagraph in TISM.A square Interpretive Matrix shows directed relation-ships among a set of elements/variables.For relationship between a pair of elements i and j,there are two entries in the matrix;one depicting the directed relation from i to j; and the other one from j to i.Total Interpretive Structural ModelingThe Interpretive Matrix can be directly applied in case of structural modeling to interpret directed and undirected binary or fuzzy relations.In case of a graphical model,the interpretation of the relation can be shown by the side of the link connecting the pair of elements having the relation. By interpreting both the nodes and links in the structuralmodel,an interpretive structural model can be upgraded as a total interpretive structural model(TISM),which may have higher applicability in real life situations.The explanation of some of the structures that can be made using TISM in terms of elements,relationships and possi-ble interpretation are summarized in Table1.The basic process of TISM is presented in a step-by-step manner with indicative directions for scaling-up this pro-cess.Some tests for validating the total interpretive struc-tural models are also proposed.Steps of the Basic ProcessThe steps of the basic process of TISM are diagrammati-cally portrayed in Fig.1and are briefly outlined as follows. These steps along with the matrices and other tools are illustrated in the example of TISM of organizational change.The central tool of ISM,i.e.reachability matrix and its partitions is adopted as it is in the process of TISM.Step I:Identify and Define ElementsThefirst step in any structural modeling would obviously be to identify and define the elements whose relation-ships are to be modeled.This can be done by using any idea generation method as a small group exercise or using grounded theory.The identified elements may also be related with past studies if such information is available.Step II:Define Contextual RelationshipIn order to develop the model of the structure relating the elements,it is crucial to state the contextual relationship between the elements.The contextual relationship is dependent on the type of structure we are dealing with such as intent,priority,attribute enhancement,process or mathematical dependence,as illustrated in Table1.For example,in case of intent structure,which is widely used in management,the contextual relationship between different objectives as elements could be:‘A should help achieve B’or‘A will help achieve B’.Step III:Interpretation of RelationshipThis is thefirst step forward over the traditional ISM. Though the contextual relationship interprets the nature of relationship as per the type of structure,it remains almost silent to interpret how that relationship really works.In order to interpret the ISM further to make it TISM,it is advisable to clarify the interpretation of the relationship, as illustrated in Table1for different types of structures. For example,in case of intent structures,we should bring out the deeper understanding by interpreting the rela-tionship as:‘In what way A should/will help achieve B?’. The interpretation of the relationship would be specific for each pair of objectives by answering the above interpre-tive query so as to make the deep rooted knowledge explicit.Step IV:Interpretive Logic of Pair-wise ComparisonIn ISM,the elements are compared to develop SSIM,as described previously.The only interpretation that is made here relate about the direction of the relationship.In order to upgrade it to TISM,it is proposed to make use of the concept of Interpretive Matrix so as to fully interpret each paired comparison in terms of how that directional rela-tionship operates in the system under consideration by answering the interpretive query as mentioned in step III. For paired comparison,the ith element is compared indi-vidually to all the elements from(i?1)th to the nth ele-ment.If there are n elements,there will be in all n(n-1)/2 paired comparisons.Since each pair of elements(i,j)may have two possible directional links i–j or j–i,there will be in all n(n-1)rows in the Knowledge Base.For each i–j link the entry could be‘Yes(Y)’or‘No(N)’and if it is ‘Yes’,then it is to be further interpreted.This will unearth the interpretive logic of the paired relationships in the form of‘Interpretive Logic—Knowledge Base’.This is illus-trated as Exhibit1in Appendix for the example of orga-nizational change forces.Table1Structures made in TISMType ExplanationIntent Elements:objectivesRelation:A should/will help achieve BInterpretation:in what way A should/will helpachieve B?Priority Element:projects,goals,etc.Relation:A is of equal or higher priority than B(The relationship must allow for the possibilityof cycles)Interpretation:on what basis priority is decided?Attribute enhancement Elements:problems,opportunities,causes Relation:A would cause/enhance B Interpretation:how would A cause/enhance B?Structure of process Elements:activities,events,etc. Relation:A should precede B Interpretation:why should A precede B?Mathematical dependence Elements:quantifiable parameters or factors Relation:A is a function of B Interpretation:what is the nature of function。
ZCL Phoenix System 凤凰内壁系统

GLOBAL SOLUTIONS 全球解决方案PDF 文件使用 "pdfFactory Pro" 试用版本创建 thePHOENIXSystem凤凰系统PDF 文件使用 "pdfFactory Pro" 试用版本创建 Underground tanks need replacing? 是否需要更换地下储罐?PDF 文件使用 "pdfFactory Pro" 试用版本创建 Do you have steel tanks? 您是否还有钢铁罐?PDF 文件使用 "pdfFactory Pro" 试用版本创建 Do you have single wall FRP tanks? 您是否还有单壁玻璃钢罐PDF 文件使用 "pdfFactory Pro" 试用版本创建 Removal & replacement can be costly and affect your business……. 移动和替换费用高昂,影响您的业务PDF 文件使用 "pdfFactory Pro" 试用版本创建 Now you have a choice -现在您有一个选择-凤凰系统PHOENIX SystemPDF 文件使用 "pdfFactory Pro" 试用版本创建 Upgrade your existing tanks with : PHOENIX System使用凤凰系统更新您现有的罐 - the corrosion resistant secondarycontainment system- 防腐二级防漏系统PDF 文件使用 "pdfFactory Pro" 试用版本创建 PHOENIX System 凤凰系统TMmanufactured on site 现场制作Ø is a unique manufactured on site’ internal fiberglass system that allows you to upgrade inservice steel or fiberglass single wall tanks to a double walled secondary contained system. Ø是独一无二的,现场制作的内部 玻璃钢系统,允许更新使用中的钢 铁单壁罐或玻璃钢单壁罐,使其成 为双壁罐的二级防漏系统。
LONMARK国际协会与EnOcean联盟建立战略合作关系共同发展低功耗智能控制系统

度的 工程 师和科 学 家们 ,可 以将 N P l 1 8 制器 的标 准 80 l X一 控 旋 转磁 场式磁 盘驱动器升级成3 GB X 2 的P l 固态硬盘驱 动器。配 有3 2GB 固态硬 盘驱 动 器 的 P l 8 以 提 供 0 E 5 O X一 O 可 81  ̄ 5 C的 扩 J 展工作 温度范 围 , 加读写文件和流数据 增 的速度和 可靠性 ,同时由于没有移动的部 件 ,在发 生撞 击和 振动 时提 高 持久 性 。 而且 ,所有最新型的N X和P l x r s I l X Epes P T due i Mo l软件 配合使用 ,未实现灵活 Wi ows me nd 以及实 时嵌 入 式控 制器都 可以 而持久的平台 ,用于确定性实时测量和控 升级 成 使用 新 型 的3 GB 2 固态 硬盘 驱 动 制。 器。 “ 利用 L b lw 中的多核 技术和 最 a VE 为 了最 大 化 P 系 统 的效 率 和 总 寿 XI 新 的NJ 多核 P l 入 式控 制 器 ,我 们 可 命 ,N公 司现在提供 了P l 2 0 X嵌 J X一 5 系统监视 8 以每 周 额 外增 加 1 工作 日的测 试 吞 吐 模块 。工程 师和 科学家们可以利用这个模 个 量 ,”S mla—CI an n S 公司的 制造测试 工 块进行编程 , 监测 系统的吸入和排出的 来 程 ̄Alj d o T r s ean r or 提到 , “ 重要的 温 度 、风扇 速 度 、处理 器和 存储 器使 用 e 最 是 ,我 们只需要 简单地将 原有 的P l X单核 情 况、电源电压等。P l 2 0 X一 5 前面板上的 8 嵌入式处理器 升级 到最新的NI X多核嵌 L D l P E 显示 了参数 是否处在合适 的工作范围 入式控制器 ,对代码做很小修改 ,就可以 内 ,前 面板上的继电器可以连接到外部设 实现吞吐量的提升。” 备 ,如状 态指示 灯或警报等 。 对 于对 系统要 求具备最强性能和可靠 ( 国 国 家仪 器有 限公 司提 供 ) 美
System Identification and Control

System Identification and Control System identification and control are essential components in the field of engineering and technology. They involve the process of building mathematical models of dynamic systems and using these models to design controllers that can manipulate the system to achieve desired outcomes. This process is crucial in various industries, including aerospace, automotive, robotics, and more. One of the key aspects of system identification is collecting data from the system to understand its behavior. This data can be obtained through experiments, simulations, or real-time monitoring. By analyzing this data, engineers can develop mathematical models that represent the system's dynamics accurately. These models can then be used to design controllers that can regulate the system's behavior and achieve specific objectives. Control systems play a crucial role in ensuring that a system behaves in a desired manner. These systems use feedback mechanisms to monitor the system's output and adjust the input to maintainstability and performance. By implementing control strategies, engineers can optimize the system's performance, improve efficiency, and enhance safety. In the field of autonomous vehicles, system identification and control are vital for ensuring safe and efficient operation. By accurately modeling the vehicle's dynamics and designing robust control systems, engineers can develop autonomous vehicles that can navigate complex environments, avoid obstacles, and respond to changing conditions in real-time. These technologies have the potential to revolutionize transportation and improve road safety. In industrial automation, system identification and control are used to optimize manufacturing processes, increase productivity, and reduce costs. By accurately modeling production systems and implementing advanced control strategies, engineers can improve product quality, minimize waste, and enhance overall efficiency. These technologies are essential for modern manufacturing facilities to stay competitive in today's global market. Overall, system identification and control are fundamental concepts in engineering that enable us to understand and manipulate complex systems effectively. By developing accurate mathematical models and implementing robust control strategies, engineers can design systems that meet specific performance requirements and achieve desired outcomes. These technologies have awide range of applications in various industries and play a crucial role in advancing technological innovation and improving quality of life.。
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Acta Mathematica Scientia, 2019, 39B(5): 1213-1218https:///10.1007/sl0473-019-0501-2©Wuhan Institute Physics and Mathematics,Chinese Academy of Sciences, 2019数学物理学报http: 〃a ct a GLOBAL L 00 SOLUTIONS TO SYSTEM OF ISENTROPIC GAS DYNAMICS IN A DIVERGENTNOZZLE WITH FRICTION ** Received August 31, 201& revised March 5, 2019. The first author was supported by the Zhejiang Natural Science Foundation of China (LQ13A010022). The second author was supported by the Qianjiang professorship of Zhejiang Province of China and the National Natural Science Foundation of China (11271105).t Corresponding author: Yunguang LU.Qingyou SUN (孙庆有) Yunguang LU (陆云光)tK. K. Chen Institute for Advanced Studies, Hangzhou Normal University, Hangzhou 311121, ChinaE-mail : qysun@hznu. edu. cn; ylu2005@ustc. edu. cnChristian KLINGENBERGDeptartment of Mathematics, Wuerzburg University, Wuerzburg 97070, GermanyE-mail : klingen@mathematik. uni-wuerzburg. deAbstract In this article, we study the global L°° entropy solutions for the Cauchy problem of system of isentropic gas dynamics in a divergent nozzle with a friction. Especially when the adiabatic exponent 7 = 3, we apply for the maximum principle to obtain the L°° estimates w(p s,e ,u s '£) < B(t) and z(p s,e ,u s,e ) < B(t) for the viscosity solutions (p s 'e ,u s,e ), where B(t) is a nonnegative bounded function for any finite time t. This work, in the special case 7 > 3, extends the previous works, which provided the global entropy solutions for the same Cauchy problem with the restriction w(p s,e ,u s,£) < 0 or z(p 6,e y u 6,e ) < 0.Key words global L°° solution; isentropic gas dynamics; source terms; flux approximation;compensated compactness2010 MR Subject Classification 35L45; 35L60; 46T991 IntroductionThe following system of isentropic gas dynamics in a divergent nozzle with a friction, whose physical phenomena called “choking or choked flow ”,occurs in the nozzle (see [1, 2] for the det a ils),Pt 十的"晋叫a f (x) 2{pu)t + (pu 2 + P(p))z = - apu\u\,(1-1)where p is the density of gas, u is the velocity, P = P(p) is the pressure, a(x) is a slowly variable cross section area at x in the nozzle, and a denotes a friction constant. For the polytropic gas, P takes the special form P(p) = where 7 > 1 is the adiabatic exponent.鱼Springer1214ACTA MATHEMATICA SCIENTIA Vol.39 Ser.BThe golobal entropy solutions for the Cauchy problem (1.1) with bounded initial data(p(ar, 0), u(x, 0)) = (po(x), uq (x )), po(x) > 0,(1-2)was first studied in [2] for the usual gases 1 < 7 < |, and later, by the author in [3] for any adiabatic exponent 7 > 1, provided that the initial data are bounded and satisfy the strong restriction condition on the Riemann invariant zo(po(e), "()(©)) S 0, where the Riemann invariants of (1.1) arez (P,u) = f w(p,tz) = /s (1-3)and c is a constant.It is well-known t h at, after we have a met h od to obtain the global exis t ence of solutions for the Cauchy problem of the following homogeneous systemPt + = 0,(pu)t + (pu 2 + P(p))x = 0(1.4)with the bounded initial data (1.2), the unique difficulty to treat the inhomogeneous system (1.1) is to obtain the a-priori L°° estimate of the approximation solutions of (1.1), for instance, the a-priori L°° estimate of the viscosity solutions for the Cauchy problem of the parabolic system a z (x)Pt + ^x = --^pu + ep xx ,a f (x) 2{pu)t + (pu 2 + P(p)L =----pu 2 一 apu\u\ + e{pu)xx (1.5)a(x)with the initial data (1.2).When a\x) = 0, (1.1) is the river flow equations, a shallow-water model describing the vertical depth p and mean velocity u, where apu\u\ corresponds physically to a friction term, and a is a nonnegative constant. This kind of inhomogeneous systems is simple because the source terms have, in some senses, the symmetric behavior. We may use the Riemann invariants (w, z) of system (1.4) to rewrite (1.1) as the following symmetric, coupled systemWt + 入2% = ew xx H ----Px^x — _7_t ==:(2F ,+ pP")危—石Q (s — z)|u|,P 2z t + 入1死=ez xx + —pxZ x — (2P Z + pP")危—尹(z 一 讪"|・(1.6)We can apply for the maximum principle directly to (1.6) to obtain the necessary a-priori L°° estimates on the Riemann invariants w(p £,< M and z(p £,u £) < N 、for two suitable constants M 、N (see [4] for the det a ils).When q = 0, that is, the nozzle flow without friction, system (1.1) was well studied in ([2, 5-8]). Roughly speaking, the technique, introduced in these articles, is to control the super-linear source terms pu and pw 2 by the flux functions pu and pu 2 + P(p) in (1.1), and to deduce a upper bound of w or z by a bounded nonegative function B(x), which depends on the function a(x).When 丰 0 and q 黑 0, both the above techniques do not work because the flux functions can not be used to control the super-linear friction source terms apu\u\, and the g SpringerNo.5Q.Y.Sun et al:GLOBAL L°°SOLUTIONS TO IGD SYSTEM IN DIVERGENT NOZZLE1215functions哥?pu destroyed the symmetry of the Riemann invariants(w,z).In fact,we may copy the method given in[2,3]to obtain the following process.First,to avoid the singularity of the flux function pu2near the vacuum p=0,we still use the technique of the5-flux-approximation given in[9],and introduce the sequence of systemsPt+(-26u+pu)x=A(x)(p一26)“,(17)(ptz)t+(pu2—6u2+Pi(p,5))x=A(x){p—25)162—apu\u\to approximate system(1.1),where A(x)=5>0denotes a regular perturbation con-stant and the perturbation pressurerp+_q c/(1.8)J26tSecond,we add the viscosity terms to the right-hand side of(1.7)to obtain the following parabolic systemPt+((p一26)u)x=A(x)(p一26)"+ep xx,(pu)t+(pu2—Su2+RSe))©=4(e)(p-2(5)u2一apu\u\+£(pu)xxwith initial data(p15,6^,0),^(2:,0))=(p0(x)+2d,“o(0)),(1-9) (1-10)where(po(^),^0(^))are given in(1.2).Now,we multiply(1.9)by(w p,w^)and(z p,Zm),respectively,where(w,2are given in (1.3),to obtainWt+=£w X x H-----Px^x---------(2P+pP")危+A(x)^p—26)乜必(")—P2p2y/P\p)P(1.11) andz t+入(先=ez xx H-----Px^x——z一(2P+pP")危+4(^)(“—+au\u\,P2p2y/P\p)P(1•⑵where俟沪宁存药乂专+今丙(1.13)are two eigenvalues of the approximation system(1.7).It is obvious that the terms A(x')(p-26)u^^-in(1.11)and(1.12)are not symmetric with respect to the Riemann invariants w,z.However,with the strong restriction(/?()(©),”o(e))S 0on the initial data,in[2,3],we may obtain the uniformly upper bounds of z and w by using the maximum principle.In some senses,it is similar to obtain the estimate u<0for the following scalar equationu t+/(u)x+S(u,x,i)u=eu xx(1.14) for a suitable bounded function S(iz,x,t)when the initial data uq^x)<0.In this article,we will remove the condition z()(po(龙),"()(©))S0,and prove the uniformly upper bound of2and w by a bounded nonnegative function B(t)of t,and obtain the global existence theorem of the entropy solutions for the Cauchy problem(1.1)and(1.2)as follows:g Springer1216ACTA MATHEMATICA SCIENTIA Vol.39 Ser.BTheorem 1.1 (I) Let P(p) = 7 > 3, the function 4(r ) < 0 (or A[x) > 0 ) be bounded. Then, there exists a bounded, nonegative function B(t), such that, u s,€) <B(t), < B(t), where E(t) depends only on the bound of the initial data.(II) Under the conditions in (I), there exists a subsequence of (p s,e (Xj t), u 6,e (x, t)), which converges pointwisely to a pair of bounded functions (p(x, t), u(x, t)) as 6, £ tend to zero, and the limit is a weak entropy solution of the Cauchy problem (1.1)-(1.2).2 Proof of Theorem 1.1In this section, we shall prove Theorem 1.1.When A(x) < 0, we rewrite systems (1.11) and (1.12) as follows:eWt + + — (—A(x)(p — 26)-----^―■ + q |u |)(w — z)2 P=£w xx + ~Px^x ~ T~Z~~ (2P + pP")危P 2p 2y /P\p)and「述 di)c SZt + 入f z ’ + —q |u |(2: — w) — A(x)(p — 25)— (=£z xx + —p x z x 一 -——y==^P ,+ pP")化.P 2p 2y/P\p)Letting — w — B(t),V2 = z — B(t), we have from (2.1) and (2.2) that⑵2)St + B f (t) + 入釦“ + -(—A(x)(p — 26)-----— + a|u|)(vi — s)2 p =+ —p x vix - 7==(2^ + pP'\化P 2p 2 yP^p)(2.1)(2.3)andV2t + B f (t) + xiv 2x + ^a|u|(v 2 一 5)+ A.(x){p 一 26)"" W2 P一%)(p 一 26)芒@ I p J,e""P ⑶ ds + B(t)A{x\p — 26)"回s P(2.4)=^2xx + —PxV2x 一 2 /577-T (2-P ,+ 淀. P 2pV p (p)Choose B(t) such that > 0,0 < B(t) < /3, where /3 be a positive constant. Then, whenV : 2)ds 2 0 or p is large as 7 > 3, we have旳)一4仗)(卩一 26)^^P P 住⑶ds + - 26) JP® > 0.s P (2.5)When V : &)ds v 0 or p is small, we can always choose a B(t), where B z (t) is suitable large, such that (2.5) is also true. Therefore, we have the following inequalities from (2.3) and (2.4)^it + 入如is + 石(一4@)(P — 26)------------F a|u|)(vi — V2)< svixx H -----Px^ix 2 P P (2.6)g SpringerNo.5Q.Y.Sun et al:GLOBAL L°°SOLUTIONS TO IGD SYSTEM IN DIVERGENT NOZZLE1217 and^2t+入力加+:Px^2x>(2.7)If the initial data satisfy zo(po(R),"()(©))S-B(O)and Wo(po(^)?"o(©)<B(0),then,%o(“o(忑),"o(z))<0,i=1,2,we can apply for the maximum principle given in[10]to(2.6)and(2.7)to obtain仇t),泌气①,t))<0, i=1,2,where the coefficient A(x)^p—2d)必号亘before g is only necessary to be local bounded.In fact,we rewrite inequalities(2.6)and(2.7)as follows:(2.8)Vit+入鈿+Ci(©,t)Wi—v2)<ev lxx H-----PxVlxpand(2.9)V2t+入恂2s+C2(R,t)(Q2—5)+C3(rr,t)v2<ev2xx H-----px^2x-Pwhere c\(x y t)=寺(-4(^)(/9—26)";的+a|u|)>0, C2(x,t)=|a|u|>0,and C3(x,t)= %)(p-2d)今回.Make a transformation如=(仇+"(*护心)評,。