Overlooked issues in the conceptualization of organizational counterproduct behavior
2022年辽宁省部分中学高二下学期期末英语试题
省部分中学2023届新高三摸底考试暨高二年级期末质量检测英语试题考生注意：考试时间120分钟，试卷满分150分第二部分阅读（共两节，满分50分）第一节（共15小题；每小题2.5分，满分37.5分）阅读下列短文，从每题所给的A、B、C、D四个选项中选出最佳选项。
ABelow is a list of the most worthwhile writing competitions available.TALF Flash Fiction CompetitionThe theme of this contest from Theme Arts and Literature Festival is "The Prime of Lile" in recognition of the l5th anniversary of the death of Muriel Spark. You can deal with this theme in any genre (体裁) and in any way you choose, although you are limited to 500 words.Prizes : £200, £100, £50.Entry Fee : £8.Wild Nature Poetry Award 2022Here we have a new contest from Indigo Dreams Publishing. It is for poems of up to 48 lines on the subject of cruel sports, wildlife in general, the natural world, or the environment.Prizes: £200, £100, £75.Entry Fee: £5.SPM Poetry Book CompetitionThis international contest from Sentinel Poetry Movement is for full-length poetry collections on any theme and in any style. To enter, you submit up to 20 pages initially. If shortlisted (入围), you have to submit the full collection before December 31 .Prizes: £500, £250, £100.Entry Fee: £25.Poetry Space Competition 2022Here's a new contest from Poetry Space, an online platform for modern poetry from around the world, which requires poems of up to 40 lines on any subject. You have to be over 16 to enter. The judge is Rosie Jackson, a poet and creative writing tutor.Prizes: £300, £200, £100.Entry Fee: £5.1. What is special about TALF Flash Fiction Competition?A. It requires no entry fee.B. It is about a certain theme.C. It has the longest history.D. It was started by a famous person.2. Which contest requests part of the entry first?A. SPM Poetry Book Competition.B. Poetry Space Competition 2022.C. Wild Nature Poetry Award 2022.D. TALF Flash Fiction Competition.3. What can we know about the contest from Poetry Space?A. It is a yearly contest.B. Anyone can take part.C. It has more than one limit.D. In offers the most prize money.【答案】1. B 2. A 3. C【解析】【详解】这是一篇应用文，主要介绍了几个值得参加的写作竞赛。
UnderstandingandManagingPublicorganizations,…
B ook ReviewUnderstanding and ManagingPublic organizations, fifth Editionby Hal g. raineyPaperback: 571 pagesIndependence, KYJossey-Bass (2014)paperback ISBN: 978–1–118–58371–5e-book ISBN: 978–1–118–58449–1REvIEw AUTHoRJennifer F. WoodMillersville University of PennsylvaniaIn today’s society, the slight mention of the term management may cause someone to sub d ivide the concept into the roles of management, such as information, marketing, operations, per s onnel, production, strategic, and financial management. At the same time, someone else may subdivide the term into types of organizations, such as small business, sports, or university management. Then at another level, it may subdivide into theoretical frameworks such as scientific management or management by objectives.No matter the subdivisions, management of any organization covers an array of viewpoints, approaches, assumptions, and frameworks—about numerous aspects, including strategic planning, organizational situations, leadership, change and innovation, and many others. In addition, literature about management often must engage in the conversation about the differences and similarities between private and public organizations. The question is, can one book capture all of this? The answer is yes.In Understanding and Managing Public Organi-zations, author Hal G. Rainey does a thorough job of providing insight, evidence, and analysis about several aspects of organizational life in the public sector. The book is divided into three parts. It may be effectively viewed as having many components of an extensive literature review, because it situates public organizations in various contexts. “The book’s chapters flesh out the conceptual framework by reviewing the theories, research, and practices associated with major topics in the field of organizations and their management” (p. xii). PArT 1Part 1, “The Dynamic Context of Public Org a n i z ations,” has five chapters designed to provide evidence of gaps in research that limit or ignore the public sector.Chapter 1, “The Challenge of Effective Public Organization and Management,” focuses on the integration of management and organ i zational literature within the public sector context. Rain e y argues that sustained attention to research about organizations and management that incor p orates the distinctive characteristics of public sector organizations may contribute to ad v ancJPAE 22 (1), 141–144Journal of Public Affairs Education 141ing knowledge and developing quality debates about the effectiveness of public manage m ent.Chapter 2, “Understanding the Study of Org a nizations: A Historical Review,” exposes the reader to several theoretical frameworks asso c i a t ed with the study of organizations. For exam p le, the history of organizational theories often begins with a machine metaphor before ad v anc i ng to a focus on human beings. At the same time, a historical review includes systems theory and other approaches. The goal in this chapter is not simply to provide a review but to situate the gap where the analysis of public organizations is excluded or brushed to the side in the study of organizations. According to Rainey, “this historical review shows that most of the prominent organization and management theor i sts have been concerned with developing the general theory of organizations and have not been particularly interested in public organizations as a category” (p. xvi).Chapter 3, “What Makes Public Organizations Distinctive,” expands the previous two chapters to demonstrate that a comparison of public versus private management is dangerous. B y oversimplifying the distinction, researchers have often overlooked the overlapping nature of the public, private, and nonprofit sectors. Rainey provides the evidence to begin the steps toward understanding literature and research in an effort to expose the viewpoint provided about public organizations.Chapter 4, “Analyzing the Environment of Pub l ic Organizations,” focuses on the literature of organizational environments—“particularly the political and institutional environments of public organizations” (p. xvi). The literature shows both research applicable to the public sector as well as gaps where once again the public sector lacks attention in the literature.In Chapter 5, “The Impact of Political Power and Public Policy,” the author is clear that sev e ral sources of authority and influence by government institutions and entities exert power over public organizations. The chapter effectively sets the stage for a discussion about power and authority relations.PArT 2Part 2, “Key Dimensions of Organizing and Man a g i ng,” has seven chapters. With an emphasis on major topics in organization theory and management, the chapters in this section “describe current research on these topics and discuss how it applies to public organizations” (p. xvii).Chapter 6, “Organizational Goals and Effec t iven ess,” focuses on performance and effec t ive n ess. This chapter also includes models for assessing organizational effectiveness. Speci fi c ally, the chapter focuses on the goal approach, the systemsresource approach, participantsatis f ac t ion models, and human resource and inter n al process models. Finally, the Government Performance Project is discussed as an example of an initiative in assessing effectiveness of governments and government agencies.Chapter 7, “Formulating and Achieving Purpose: Power, Decision Making, and Strategy,” begins with contextualizing a prominent decisionmaking trend in public organizations. The chapter may be a strong unit alongside a discussion about the Office of Management and Budget and the Government Performance and Results Act of 1993, which requires all federal agencies to create strategic plans. “This chapter describes concepts, theories, and re s earch that experts and scholars on organizations have developed about three of these topics—power, decision making, and strategy—and suggests applications and examples for public organi z ations and management” (p. 175).Chapter 8, “Organizational Structure, Design, T echnology, Information T echnology, and So cial Media,” examines the public versus pri v ate organization in terms of structure—that is, the ways in which an organization groups its resources to accomplish its mission. The chap t er opens with a focus on the “division of opin ion about whether public organizations have distinctive structural characteristics, such as more red tape than private organizations” (p. 211). It progresses with research on technology and design, then concludes with a focus on social media and public management.Review by J. F. Wood142 Journal of Public Affairs EducationReview, Understanding and Managing Public organizationsChapter 9, “Understanding People in Public Organizations: Motivation and Motivation The o ry,” continues with an examination of organizations while bringing people to the forefront. Rainey provides detail about the concept of work motivation and several issues sur r ounding it within the public organization. He also addresses many of the foundational theo r ies, such as Freud’s Theory X, McGregor’s Theory Y, Maslow’s Theory Z, and many others.In Chapter 10, “Understanding People in Pub lic Organizations: Values, Incentives, and WorkRelated Attitudes,” Rainey teases out these three factors that he views as “distinct from moti v a t ion and motivation theory” (p. 297). At the same time, he is intentional about exploring workrelated attitudes developed by organ i za t ional behavior researchers, such as job satisfac t ion. Chapter 11, “Leadership, Managerial Roles, and Organizational Culture,” examines leaders in the public sector and the research surrounding them. Organizational culture is often said to be manifested in the core values and principles of its leaders. Therefore, “this chapter takes the approach of first reviewing many of the theories and ideas about leadership and managerial roles that have developed the field of organizational behavior and organizational psychology, and then examining concepts and ideas about organizational culture” (p. 336).Chapter 12, “T eamwork: Understanding Communication and Conflict in Groups,” concludes Part 2 with a key focus on group dynamics such as group formation, contexts, and advantages and disadvantages, as well as groupthink. The focus is then shifted to con fl ict, including types and stages of conflict and the special con s id e rations public organi z ations must keep at the forefront. PArT 3Part 3, “Strategies for Managing and Improv i ng Public Organizations,” has two chapters that focus on managing organizational change and development and advancing effective man a gement in the public sector. Specifically, Chapter 13 explores the management of org a ni z ational change and development, discussing organi z ational life cycles, innovation, largescale planned change, organizational develop m ent inter v entions, and much more.In Chapter 14, Rainey acknowledges that pub lic organizations continually perform crucial func t ions and offers an effective discussion related to organizational excellence. In order to advance effective management, all parts of an organization must optimize the use and effectiveness of its resources. Thus, the author reviews profiles of wellperforming organi z a t ions in both the public and private sectors. Next, he reviews some recent trends in man a gement reform and the pursuit of high per f or m ance that have had important influences on public management. Finally, he explores “one of the most prominently discussed and fre q uently employed strategies for enhancing the per f ormance of government—privatization of governmental services, especially through con t racting out” (p. 449).Each chapter offers a box titled “Instructor’s Guide for Chapter,” which provides a list of what can be found on the text’s accompanying website—including key terms, discussion questions, topics for writing assignments or reports, and case discussions. The book also includes an extensive alphabetical reference section, additional reference materials organized based on the three parts of the book, a name index, and a subject index.Overall the author successfully uses diverse literature in the areas of organizational behavior, organizational development, and management to situate the public sector in ways that help a reader gain understanding about public organizations. At the same time, the content of the book is effective for any persons who (a) manage public organizations, (b) question the management of public organizations, or (c) study the practice and theory of public organizations.Journal of Public Affairs Education 143Review by J. F. WoodABOUT THe revieWerJennifer F. Wood is an associate professor in theDepartment of Communication and Theatre atMillersville University of Pennsylvania. Her areasof expertise include business and pro f es s ionalcommunication, organizational com m un i ca t ion,communication management, ped a g ogy, andpub l ic relations. She is the 2014–2015 recipientof Millersville University’s Educator of the YearAward. She received her PhD from B owlingGreen State University.errATUMJPAE regrets the inadvertent mistype of ‘multicontextuality’ in the Fall 2015 article entitled “Intersectionality, Stereotypes of African American Men, and Redressing Bias in the Public Affairs Classroom” by Richard Greggory Johnson III & Mario Antonio Rivera.144 Journal of Public Affairs Education。
马克斯·韦伯论“议会民主制”——理性“铁笼”的祛除与一战后德国政治生活的重建
2021年第1期 政治思想史总第45期Vol. 12 No. 1Journal of the History of Political Thought Sum No.45马克斯•韦伯论“议会民主制”®一理性“铁笼”的祛除与一战后德国政治生活的重建陈敬国(北京大学社会学系）摘要：韦伯毕生所关心的问题是理性化/官僚制与自由的关 系，他试图在现代社会理性化不断推进的过程中去寻求捍卫人类自 由和尊严的政治方案。
以往的研究一般集中在对韦伯“领袖民主制”的探讨上，并认为领袖民主制的宪政框架无法清除板权主义要素，因而也就无法实质地去应对理性化问题。
这些研究忽视了韦伯为消除德 国官僚制危机而提出的另一种可能——议会民主制。
韦伯设想的议 会民主制体现了其试图用卡里斯玛来疗救官僚制，同时又希望用议会 民主来约束卡里斯玛的良苦用心，理论层面上是比领袖民主制在应对 德国官僚制所带来的自由危机方面更为合宜的选择。
关键词：议会民主制；领袖民主制；官僚制；卡里斯玛―、引言：韦伯是自由主义者吗？在对韦伯的肖像刻画中，韦伯的自由主义一面很少被提及。
反 对将韦伯视为自由主义者的主流观点是从帝国主义和民族主义的视角理解他的。
卢卡奇认为，韦伯首先是一个具有帝国主义情结①本文曾在2019年社会学年会上宣读感谢杨勇在文献方面的帮助，感谢审稿人的宝贵意见。
文责自负。
133政治思想史2021年第1期的人，他对德意志帝国的期待是具有“世界政治（殖民主义）使命的”®。
韦伯虽然把民主程序引入到政治领袖的选择上来，但是他并不是要提倡民主背后的自由和人权，而是把它们限制为为帝国强盛服务的一种手段。
②蒙森也认为，韦伯的宪政理想一以贯之地从属于民族的权力和利益，他把德国的权力和利益看作高于任何的政体形式。
®马尔库塞、帕森斯、哈贝马斯等人则指出，韦伯的政治构想含有独裁的成分。
马尔库塞认为，韦伯的卡里斯玛领袖实际上是德国这样一种前资产阶级国家迈向资产阶级民主国家过程中的一个产物。
An Introduction to Description Logics
1An Introduction to Description LogicsDaniele NardiRonald J.BrachmanAbstractThis introduction presents the main motivations for the development of Description Logics(DL)as a formalism for representing knowledge,as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition,we provide the reader with an overview of the entire book and some guidelines for reading it.Wefirst address the relationship between Description Logics and earlier seman-tic network and frame systems,which represent the original heritage of thefield. We delve into some of the key problems encountered with the older efforts.Subse-quently,we introduce the basic features of Description Logic languages and related reasoning techniques.Description Logic languages are then viewed as the core of knowledge represen-tation systems,considering both the structure of a DL knowledge base and its associated reasoning services.The development of some implemented knowledge representation systems based on Description Logics and thefirst applications built with such systems are then reviewed.Finally,we address the relationship of Description Logics to otherfields of Com-puter Science.We also discuss some extensions of the basic representation language machinery;these include features proposed for incorporation in the formalism that originally arose in implemented systems,and features proposed to cope with the needs of certain application domains.1.1IntroductionResearch in thefield of knowledge representation and reasoning is usually focused on methods for providing high-level descriptions of the world that can be effectively used to build intelligent applications.In this context,“intelligent”refers to the abil-56 D.Nardi,R.J.Brachmanity of a system tofind implicit consequences of its explicitly represented knowledge. Such systems are therefore characterized as knowledge-based systems. Approaches to knowledge representation developed in the1970’s—when thefield enjoyed great popularity—are sometimes divided roughly into two categories:logic-based formalisms,which evolved out of the intuition that predicate calculus could be used unambiguously to capture facts about the world;and other,non-logic-based representations.The latter were often developed by building on more cognitive notions—for example,network structures and rule-based representations derived from experiments on recall from human memory and human execution of tasks like mathematical puzzle solving.Even though such approaches were often developed for specific representational chores,the resulting formalisms were usually expected to serve in general use.In other words,the non-logical systems created from very specific lines of thinking(e.g.,early Production Systems)evolved to be treated as general purpose tools,expected to be applicable in different domains and on different types of problems.On the other hand,sincefirst-order logic provides very powerful and general ma-chinery,logic-based approaches were more general-purpose from the very start.In a logic-based approach,the representation language is usually a variant offirst-order predicate calculus,and reasoning amounts to verifying logical consequence.In the non-logical approaches,often based on the use of graphical interfaces,knowledge is represented by means of some ad hoc data structures,and reasoning is accomplished by similarly ad hoc procedures that manipulate the structures.Among these spe-cialized representations wefind semantic networks and frames.Semantic Networks were developed after the work of Quillian[1967],with the goal of characterizing by means of network-shaped cognitive structures the knowledge and the reasoning of the system.Similar goals were shared by later frame systems[Minsky,1981],which rely upon the notion of a“frame”as a prototype and on the capability of expressing relationships between frames.Although there are significant differences between se-mantic networks and frames,both in their motivating cognitive intuitions and in their features,they have a strong common basis.In fact,they can both be regarded as network structures,where the structure of the network aims at representing sets of individuals and their relationships.Consequently,we use the term network-based structures to refer to the representation networks underlying semantic networks and frames(see[Lehmann,1992]for a collection of papers concerning various families of network-based structures).Owing to their more human-centered origins,the network-based systems were often considered more appealing and more effective from a practical viewpoint than the logical systems.Unfortunately they were not fully satisfactory because of their usual lack of precise semantic characterization.The end result of this was that every system behaved differently from the others,in many cases despite virtually identical-An Introduction to Description Logics7 looking components and even identical relationship names.The question then arose as to how to provide semantics to representation structures,in particular to semantic networks and frames,which carried the intuition that,by exploiting the notion of hierarchical structure,one could gain both in terms of ease of representation and in terms of the efficiency of reasoning.One important step in this direction was the recognition that frames(at least their core features)could be given a semantics by relying onfirst-order logic[Hayes, 1979].The basic elements of the representation are characterized as unary pred-icates,denoting sets of individuals,and binary predicates,denoting relationships between individuals.However,such a characterization does not capture the con-straints of semantic networks and frames with respect to logic.Indeed,although logic is the natural basis for specifying a meaning for these structures,it turns out that frames and semantic networks(for the most part)did not require all the ma-chinery offirst-order logic,but could be regarded as fragments of it[Brachman and Levesque,1985].In addition,different features of the representation language would lead to different fragments offirst-order logic.The most important consequence of this fact is the recognition that the typical forms of reasoning used in structure-based representations could be accomplished by specialized reasoning techniques, without necessarily requiringfirst-order logic theorem provers.Moreover,reason-ing in different fragments offirst-order logic leads to computational problems of differing complexity.Subsequent to this realization,research in the area of Description Logics began under the label terminological systems,to emphasize that the representation lan-guage was used to establish the basic terminology adopted in the modeled domain. Later,the emphasis was on the set of concept-forming constructs admitted in the language,giving rise to the name concept languages.In more recent years,after at-tention was further moved towards the properties of the underlying logical systems, the term Description Logics became popular.In this book we mainly use the term“Description Logics”(DL)for the represen-tation systems,but often use the word“concept”to refer to the expressions of a DL language,denoting sets of individuals;and the word“terminology”to denote a (hierarchical)structure built to provide an intensional representation of the domain of interest.Research on Description Logics has covered theoretical underpinnings as well as implementation of knowledge representation systems and the development of appli-cations in several areas.This kind of development has been quite successful.The key element has been the methodology of research,based on a very close interaction between theory and practice.On the one hand,there are various implemented sys-tems based on Description Logics,which offer a palette of description formalisms with differing expressive power,and which are employed in various application do-8 D.Nardi,R.J.Brachmanmains(such as natural language processing,configuration of technical products,or databases).On the other hand,the formal and computational properties of reason-ing(like decidability and complexity)of various description formalisms have been investigated in detail.The investigations are usually motivated by the use of cer-tain constructors in implemented systems or by the need for these constructors in specific applications—and the results have influenced the design of new systems. This book is meant to provide a thorough introduction to Description Logics, covering all the above-mentioned aspects of DL research—namely theory,imple-mentation,and applications.Consequently,the book is divided into three parts:•Part I introduces the theoretical foundations of Description Logics,addressing some of the most recent developments in theoretical research in the area;•Part II focuses on the implementation of knowledge representation systems based on Description Logics,describing the basic functionality of a DL system,survey-ing the most influential knowledge representation systems based on Description Logics,and addressing specialized implementation techniques;•Part III addresses the use of Description Logics and of DL-based systems in the design of several applications of practical interest.In the remainder of this introductory chapter,we review the main steps in the development of Description Logics,and introduce the main issues that are dealt with later in the book,providing pointers for its reading.In particular,in the next section we address the origins of Description Logics and then we review knowledge representation systems based on Description Logics,the main applications devel-oped with Description Logics,the main extensions to the basic DL framework and relationships with otherfields of Computer Science.1.2From networks to Description LogicsIn this section we begin by recalling approaches to representing knowledge that were developed before research on Description Logics began(i.e.,semantic networks and frames).We then provide a very brief introduction to the basic elements of these approaches,based on Tarski-style semantics.Finally,we discuss the importance of computational analyses of the reasoning methods developed for Description Logics, a major ingredient of research in thisfield.1.2.1Network-based representation structuresIn order to provide some intuition about the ideas behind representations of knowl-edge in network form,we here speak in terms of a generic network,avoiding ref-erences to any particular system.The elements of a network are nodes and links.An Introduction to Description Logics9Typically,nodes are used to characterize concepts,i.e.,sets or classes of individ-ual objects,and links are used to characterize relationships among them.In some cases,more complex relationships are themselves represented as nodes;these are carefully distinguished from nodes representing concepts.In addition,concepts can have simple properties,often called attributes,which are typically attached to the corresponding nodes.Finally,in many of the early networks both individual objects and concepts were represented by nodes.Here,however,we restrict our attention to knowledge about concepts and their relationships,deferring for now treatment of knowledge about specific individuals.Let us consider a simple example,whose pictorial representation is given in Fig-ure1.1,which represents knowledge concerning persons,parents,children,etc.The structure in thefigure is also referred to as a terminology,and it is indeed meant to represent the generality/specificity of the concepts involved.For example the link between Mother and Parent says that“mothers are parents”;this is sometimes called an“IS-A”relationship.The IS-A relationship defines a hierarchy over the concepts and provides the basis for the“inheritance of properties”:when a concept is more specific than some other concept,it inherits the properties of the more general one.For example,if a person has an age,then a mother has an age,too.This is the typical setting of the so-called (monotonic)inheritance networks(see[Brachman,1979]).A characteristic feature of Description Logics is their ability to represent other kinds of relationships that can hold between concepts,beyond IS-A relationships. For example,in Figure1.1,which follows the notation of[Brachman and Schmolze, 1985],the concept of Parent has a property that is usually called a“role,”expressed10 D.Nardi,R.J.Brachmanby a link from the concept to a node for the role labeled hasChild.The role has what is called a“value restriction,”denoted by the label v/r,which expresses a limitation on the range of types of objects that canfill that role.In addition,the node has a number restriction expressed as(1,NIL),where thefirst number is a lower bound on the number of children and the second element is the upper bound, and NIL denotes infinity.Overall,the representation of the concept of Parent here can be read as“A parent is a person having at least one child,and all of his/her children are persons.”Relationships of this kind are inherited from concepts to their subconcepts.For example,the concept Mother,i.e.,a female parent,is a more specific descendant of both the concepts Female and Parent,and as a result inherits from Parent the link to Person through the role hasChild;in other words,Mother inherits the restriction on its hasChild role from Parent.Observe that there may be implicit relationships between concepts.For example, if we define Woman as the concept of a female person,it is the case that every Mother is a Woman.It is the task of the knowledge representation system tofind implicit relationships such as these(many are more complex than this one).Typically,such inferences have been characterized in terms of properties of the network.In this case one might observe that both Mother and Woman are connected to both Female and Person,but the path from Mother to Person includes a node Parent,which is more specific then Person,thus enabling us to conclude that Mother is more specific than Person.However,the more complex the relationships established among concepts,the more difficult it becomes to give a precise characterization of what kind of relation-ships can be computed,and how this can be done without failing to recognize some of the relationships or without providing wrong answers.1.2.2A logical account of network-based representation structures Building on the above ideas,a number of systems were implemented and used in many kinds of applications.As a result,the need emerged for a precise character-ization of the meaning of the structures used in the representations and of the set of inferences that could be drawn from those structures.A precise characterization of the meaning of a network can be given by defining a language for the elements of the structure and by providing an interpretation for the strings of that language.While the syntax may have differentflavors in different settings,the semantics is typically given as a Tarski-style semantics.For the syntax we introduce a kind of abstract language,which resembles other logical formalisms.The basic step of the construction is provided by two disjoint alphabets of symbols that are used to denote atomic concepts,designated by unaryAn Introduction to Description Logics11 predicate symbols,and atomic roles,designated by binary predicate symbols;the latter are used to express relationships between concepts.Terms are then built from the basic symbols using several kinds of constructors. For example,intersection of concepts,which is denoted C D,is used to restrict the set of individuals under consideration to those that belong to both C and D.Notice that,in the syntax of Description Logics,concept expressions are variable-free.In fact,a concept expression denotes the set of all individuals satisfying the properties specified in the expression.Therefore,C D can be regarded as thefirst-order logic sentence,C(x)∧D(x),where the variable ranges over all individuals in the interpretation domain and C(x)is true for those individuals that belong to the concept C.In this book,we will present other syntactic notations that are more closely related to the concrete syntax adopted by implemented DL systems,and which are more suitable for the development of applications.One example of concrete syntax proposed in[Patel-Schneider and Swartout,1993]is based on a Lisp-like notation,where the concept of female persons,for example,is denoted by(and Person Female).The key characteristic features of Description Logics reside in the constructs for establishing relationships between concepts.The basic ones are value restrictions. For example,a value restriction,written∀R.C,requires that all the individuals that are in the relationship R with the concept being described belong to the concept C (technically,it is all individuals that are in the relationship R with an individual described by the concept in question that are themselves describable as C’s).As for the semantics,concepts are given a set-theoretic interpretation:a concept is interpreted as a set of individuals and roles are interpreted as sets of pairs of individuals.The domain of interpretation can be chosen arbitrarily,and it can be infinite.The non-finiteness of the domain and the open-world assumption are distinguishing features of Description Logics with respect to the modeling languages developed in the study of databases(see Chapters4,and16).Atomic concepts are thus interpreted as subsets of the intepretation domain, while the semantics of the other constructs is then specified by defining the set of individuals denoted by each construct.For example,the concept C D is the set of individuals obtained by intersecting the sets of individuals denoted by C and D, respectively.Similarly,the interpretation of∀R.C is the set of individuals that are in the relationship R with individuals belonging to the set denoted by the concept C. As an example,let us suppose that Female,Person,and Woman are atomic con-cepts and that hasChild and hasFemaleRelative are atomic ing the operators intersection,union and complement of concepts,interpreted as set operations,we can describe the concept of“persons that are not female”and the concept of“in-12 D.Nardi,R.J.Brachmandividuals that are female or male”by the expressionsPerson ¬Female and Female Male.It is worth mentioning that intersection,union,and complement of concepts havebeen also referred to as concept conjunction,concept disjunction and concept nega-tion,respectively,to emphasize the relationship to logic.Let us now turn our attention to role restrictions by lookingfirst at quantifiedrole restrictions and,subsequently,at what we call“number restrictions.”Mostlanguages provide(full)existential quantification and value restriction that allowone to describe,for example,the concept of“individuals having a female child”as∃hasChild.Female,and to describe the concept of“individuals all of whose children are female”by the concept expression∀hasChild.Female.In order to distinguish thefunction of each concept in the relationship,the individual object that correspondsto the second argument of the role viewed as a binary predicate is called a rolefiller.In the above expressions,which describe the properties of parents havingfemale children,individual objects belonging to the concept Female are thefillers ofthe role hasChild.Existential quantification and value restrictions are thus meant to characterizerelationships between concepts.In fact,the role link between Parent and Person inFigure1.1can be expressed by the concept expression∃hasChild.Person ∀hasChild.Person.Such an expression therefore characterizes the concept of Parent as the set of indi-viduals having at least onefiller of the role hasChild belonging to the concept Person;moreover,everyfiller of the role hasChild must be a person.Finally,notice that in quantified role restrictions the variable being quantifiedis not explicitly mentioned.The corresponding sentence infirst-order logic is∀y.R(x,y)⊃C(y),where x is again a free variable ranging over the interpreta-tion domain.Another important kind of role restriction is given by number restrictions,whichrestrict the cardinality of the sets offillers of roles.For instance,the concept( 3hasChild) ( 2hasFemaleRelative)represents the concept of“individuals having at least three children and at mosttwo female relatives.”Number restrictions are sometimes viewed as a distinguishingfeature of Description Logics,although one canfind some similar constructs in somedatabase modeling languages(notably Entity-Relationship models).Beyond the constructs to form concept expressions,Description Logics provideconstructs for roles,which can,for example,establish role hierarchies.However,An Introduction to Description Logics13 the use of role expressions is generally limited to expressing relationships between concepts.Intersection of roles is an example of a role-forming construct.Intuitively, hasChild hasFemaleRelative yields the role“has-daughter,”so that the concept expressionWoman 2(hasChild hasFemaleRelative)denotes the concept of“a woman having at most2daughters”.A more comprehensive view of the basic definitions of DL languages will be given in Chapter2.1.2.3ReasoningThe basic inference on concept expressions in Description Logics is subsumption, typically written as C D.Determining subsumption is the problem of checking whether the concept denoted by D(the subsumer)is considered more general than the one denoted by C(the subsumee).In other words,subsumption checks whether thefirst concept always denotes a subset of the set denoted by the second one. For example,one might be interested in knowing whether Woman Mother.In order to verify this kind of relationship one has in general to take into account the relationships defined in the terminology.As we explain in the next section, under appropriate restrictions,one can embody such knowledge directly in concept expressions,thus making subsumption over concept expressions the basic reason-ing task.Another typical inference on concept expressions is concept satisfiability, which is the problem of checking whether a concept expression does not necessarily denote the empty concept.In fact,concept satisfiability is a special case of sub-sumption,with the subsumer being the empty concept,meaning that a concept is not satisfiable.Although the meaning of concepts had already been specified with a logical se-mantics,the design of inference procedures in Description Logics was influenced for a long time by the tradition of semantic networks,where concepts were viewed as nodes and roles as links in a network.Subsumption between concept expressions was recognized as the key inference and the basic idea of the earliest subsumption al-gorithms was to transform two input concepts into labeled graphs and test whether one could be embedded into the other;the embedded graph would correspond to the more general concept(the subsumer)[Lipkis,1982].This method is called structural comparison,and the relation between concepts being computed is called structural subsumption.However,a careful analysis of the algorithms for structural subsumption shows that they are sound,but not always complete in terms of the logical semantics:whenever they return“yes”the answer is correct,but when they14 D.Nardi,R.J.Brachmanreport“no”the answer may be incorrect.In other words,structural subsumptionis in general weaker than logical subsumption.The need for complete subsumption algorithms is motivated by the fact that inthe usage of knowledge representation systems it is often necessary to have a guar-antee that the system has not failed in verifying subsumption.Consequently,newalgorithms for computing subsumption have been devised that are no longer basedon a network representation,and these can be proven to be complete.Such algo-rithms have been developed by specializing classical settings for deductive reasoningto the DL subsets offirst-order logics,as done for tableau calculi by Schmidt-Schaußand Smolka[1991],and also by more specialized methods.In the paper“The Tractability of Subsumption in Frame-Based Description Lan-guages,”Brachman and Levesque[1984]argued that there is a tradeoffbetweenthe expressiveness of a representation language and the difficulty of reasoning overthe representations built using that language.In other words,the more expressivethe language,the harder the reasoning.They also provided afirst example of thistradeoffby analyzing the language FL−(Frame Language),which included inter-section of concepts,value restrictions and a simple form of existential quantification.They showed that for such a language the subsumption problem could be solvedin polynomial time,while adding a construct called role restriction to the languagemakes subsumption a co np-hard problem(the extended language was called FL).The paper by Brachman and Levesque introduced at least two new ideas:(i)“efficiency of reasoning”over knowledge structures can be studied using thetools of computational complexity theory;(ii)different combinations of constructs can give rise to languages with differentcomputational properties.An immediate consequence of the above observations is that one can study for-mally and methodically the tradeoffbetween the computational complexity of rea-soning and the expressiveness of the language,which itself is defined in termsof the constructs that are admitted in the language.After the initial pa-per,a number of results on this tradeofffor concept languages were obtained(see Chapters2and3),and these results allow us to draw a fairly completepicture of the complexity of reasoning for a wide class of concept languages.Moreover,the problem offinding the optimal tradeoff,namely the most ex-pressive extensions of FL−with respect to a given set of constructs that still keep subsumption polynomial,has been studied extensively[Donini et al.,1991b; 1999].One of the assumptions underlying this line of research is to use worst-case com-plexity as a measure of the efficiency of reasoning in Description Logics(and moregenerally in knowledge representation formalisms).Such an assumption has some-An Introduction to Description Logics15 times been criticized(see for example[Doyle and Patil,1991])as not adequately characterizing system performance or accounting for more average-case behavior. While this observation suggests that computational complexity alone may not be sufficient for addressing performance issues,research on the computational com-plexity of reasoning in Description Logics has most definitely led to a much deeper understanding of the problems arising in implementing reasoning tools.Let us briefly address some of the contributions of this body of work.First of all,the study of the computational complexity of reasoning in Description Logics has led to a clear understanding of the properties of the language constructs and their interaction.This is not only valuable from a theoretical viewpoint,but gives insight to the designer of deduction procedures,with clear indications of the language constructs and their combinations that are difficult to deal with,as well as general methods to cope with them.Secondly,the complexity results have been obtained by exploiting a general tech-nique for satisfiability-checking in concept languages,which relies on a form of tableau calculus[Schmidt-Schaußand Smolka,1991].Such a technique has proved extremely useful for studying both the correctness and the complexity of the algo-rithms.More specifically,it provides an algorithmic framework that is parametric with respect to the language constructs.The algorithms for concept satisfiability and subsumption obtained in this way have also led directly to practical implemen-tations by application of clever control strategies and optimization techniques.The most recent knowledge representation systems based on Description Logics adopt tableau calculi[Horrocks,1998b].Thirdly,the analysis of pathological cases in this formal framework has led to the discovery of incompleteness in the algorithms developed for implemented systems. This has also consequently proven useful in the definition of suitable test sets for verifying implementations.For example,the comparison of implemented systems (see for example[Baader et al.,1992b;Heinsohn et al.,1992])has greatly benefitted from the results of the complexity analysis.The basic reasoning techniques for Description Logics are presented in Chapter2, while a detailed analysis of the complexity of reasoning problems in several languages is developed in Chapter3.After the tradeoffbetween expressiveness and tractability of reasoning was thor-oughly analyzed and the range of applicability of the corresponding inference tech-niques had been experimented with,there was a shift of focus in the theoretical research on reasoning in Description Logics.Interest grew in relating Description Logics to the modeling languages used in database management.In addition,the discovery of strict relationships with expressive modal logics stimulated the study of so-called very expressive Description Logics.These languages,besides admit-ting very general mechanisms for defining concepts(for example cyclic definitions,。
关于阿尔兹海默症研究方向被误导的英语作文
关于阿尔兹海默症研究方向被误导的英语作文全文共3篇示例，供读者参考篇1Over the past few decades, research on Alzheimer's disease has made significant progress in understanding the underlying mechanisms and potential treatments. However, in recent years, there has been growing concern that the focus of research may have been misguided, leading to limited advancements in the field. In this essay, we will discuss the possible reasons for this misdirection and propose alternative research directions that may hold promise for future breakthroughs in the treatment of Alzheimer's disease.One of the primary reasons for the misdirection of Alzheimer's disease research is the predominant focus on amyloid plaques as the primary cause of the disease. For many years, researchers have targeted these plaques in hopes of finding a cure for Alzheimer's disease. However, clinical trials targeting amyloid have largely been unsuccessful, raising questions about the validity of this approach. While amyloid plaques may play a role in the pathology of Alzheimer's disease,they may not be the sole cause of the cognitive decline seen in patients.Another reason for the misdirection of research is the lack of emphasis on other potential factors that may contribute to the development of Alzheimer's disease. For example, there is emerging evidence suggesting that neuroinflammation, mitochondrial dysfunction, and epigenetic changes may also play a role in the disease process. However, these factors have received limited attention in comparison to amyloid plaques, leading to a narrow focus in research efforts.In light of these limitations, it is crucial for researchers to explore alternative avenues of research that may shed light on the complex mechanisms underlying Alzheimer's disease. One promising research direction is the study of the gut-brain axis and its impact on neurodegenerative diseases. The gut microbiome has been shown to play a crucial role in brain health and cognitive function, and dysregulation of the gut-brain axis has been implicated in the pathogenesis of Alzheimer's disease. By investigating the interactions between the gut microbiome, inflammation, and neurodegeneration, researchers may uncover novel therapeutic targets for the treatment of Alzheimer's disease.In addition, research on the role of neuroinflammation in Alzheimer's disease holds promise for identifying new treatment strategies. Chronic inflammation in the brain has been linked to the progression of neurodegenerative diseases, including Alzheimer's disease. By targeting the inflammatory response in the brain, researchers may be able to slow or halt the cognitive decline associated with the disease. Furthermore, exploring the role of mitochondrial dysfunction and oxidative stress in Alzheimer's disease may provide insights into potential treatment options that target these pathways.Overall, it is essential for researchers to reconsider the current research direction in Alzheimer's disease and explore alternative avenues that may lead to new insights and breakthroughs in the field. By broadening the scope of research to include factors beyond amyloid plaques, such as the gut-brain axis, neuroinflammation, and mitochondrial dysfunction, researchers may uncover novel therapeutic targets for the treatment of Alzheimer's disease. Through collaborative efforts and innovative approaches, we can advance our understanding of this devastating disease and work towards finding effective treatments for individuals living with Alzheimer's disease.篇2Title: Misdirection in Alzheimer's Disease ResearchIntroduction:Alzheimer's disease is a progressive neurodegenerative disorder that primarily affects older individuals, causing memory loss, cognitive decline, and eventually leading to death. With no known cure, researchers around the world have been working tirelessly to unravel the mysteries of this devastating disease in hopes of finding a treatment or preventative measure. However, recent concerns have been raised about the direction of Alzheimer's disease research, with critics arguing that the focus has been misguided and potentially leading to dead-end research avenues.Misdirection in Alzheimer's Disease Research:One of the primary areas of concern in Alzheimer's disease research is the overemphasis on amyloid plaques as the primary cause of the disease. Amyloid plaques are abnormal clumps of protein fragments that accumulate between nerve cells in the brains of Alzheimer's patients. For decades, researchers have focused on developing drugs to target and eliminate amyloid plaques in the hopes of slowing or stopping the progression of the disease. However, despite numerous clinical trials and billions of dollars invested in anti-amyloid therapies, the resultshave been disappointing, with many drugs failing to show any significant benefit in patients.Critics argue that the focus on amyloid plaques has led to a neglect of other potential contributing factors to Alzheimer's disease, such as tau tangles, neuroinflammation, mitochondrial dysfunction, and vascular abnormalities. These factors may interact in complex ways to contribute to the development and progression of the disease, yet they have been largely overlooked in favor of the amyloid hypothesis. This narrow focus on amyloid plaques has not only resulted in failed drug trials but has also hindered progress in understanding the underlying mechanisms of the disease.Furthermore, the emphasis on amyloid plaques has overshadowed research into potential preventative measures, such as lifestyle interventions and environmental factors that may influence the risk of developing Alzheimer's disease. Studies have shown that modifiable lifestyle factors, such as diet, exercise, sleep, and social engagement, may play a significant role in maintaining brain health and reducing the risk of cognitive decline. Yet, these areas of research have received far less attention and funding compared to drug development targeting amyloid plaques.Moving Forward:In order to advance Alzheimer's disease research and make meaningful progress towards finding a cure or effective treatments, it is essential to broaden the focus beyond amyloid plaques and explore other potential mechanisms of the disease. Researchers should consider a more holistic approach that takes into account the complexity of Alzheimer's disease and the interactions between various biological, genetic, and environmental factors.Moreover, greater collaboration and data sharing among researchers is needed to accelerate progress and avoid duplication of efforts. By pooling resources and sharing information, researchers can effectively leverage the collective knowledge and expertise of the scientific community to address the multifaceted challenges of Alzheimer's disease.Conclusion:Alzheimer's disease is a complex and devastating condition that continues to elude effective treatment. The misdirection in research towards amyloid plaques has hindered progress and led to numerous failed drug trials. By broadening the focus and exploring alternative hypotheses, researchers can potentially uncover new insights into the disease and pave the way forinnovative treatments and preventative measures. It is imperative that the scientific community reevaluate its approach to Alzheimer's disease research and adopt a more comprehensive and collaborative strategy to accelerate progress in understanding and combating this debilitating condition. Only through a concerted effort and a reexamination of research priorities can we hope to make meaningful advancements in the fight against Alzheimer's disease.篇3Title: Misdirection in Alzheimer's Disease ResearchAlzheimer's disease is a devastating neurodegenerative disorder that affects millions of people worldwide. Despite decades of research and billions of dollars invested, a cure for Alzheimer's remains elusive. One of the reasons for this lack of progress may be that current research efforts are being misdirected, focusing on ineffective or inadequate strategies instead of exploring alternative avenues of investigation.One common approach in Alzheimer's research is to target the accumulation of amyloid-beta plaques in the brain, which is believed to be a hallmark of the disease. However, numerous clinical trials aimed at clearing these plaques have failed to showsignificant benefits in terms of cognitive function or disease progression. This has led some researchers to question whether amyloid-beta is truly the cause of Alzheimer's, or merely a byproduct of the disease process.Another area of research that has garnered significant attention is the role of tau protein in Alzheimer's. Abnormal accumulation of tau in the brain is associated with neuronal damage and cognitive decline, but efforts to develop treatments that target tau pathology have also met with limited success. It is possible that targeting tau may be more promising than targeting amyloid-beta, but more research is needed to fully understand its role in Alzheimer's.In addition to these mainstream research directions, there are other aspects of Alzheimer's disease that have received less attention but may hold the key to better treatments. For example, emerging evidence suggests that inflammation, vascular factors, and mitochondrial dysfunction may all contribute to the development and progression of Alzheimer's. By exploring these alternative mechanisms, researchers may uncover new therapeutic targets and strategies that could lead to more effective treatments for Alzheimer's.Furthermore, recent advances in genetics have revealed a complex interplay of genetic and environmental factors that contribute to the risk of developing Alzheimer's. By studying these factors in more detail, researchers may be able to identify novel pathways for intervention and personalized treatment approaches that take into account individual differences in disease progression.Overall, it is clear that the current research landscape in Alzheimer's disease is in need of a shift towards more innovative and diverse approaches. By exploring alternative mechanisms, thinking outside the box, and collaborating across disciplines, researchers may be able to make greater strides towards understanding the underlying causes of Alzheimer's and developing effective treatments. Only by challenging traditional assumptions and exploring new ideas can we hope to finally conquer this devastating disease.。
人工智能的利弊 英语作文
Artificial Intelligence AI has become an integral part of modern society,with its influence permeating various sectors including healthcare,transportation,and communication.As with any technological advancement,AI brings with it a mix of benefits and drawbacks.Here,we will explore both sides of the AI spectrum.Advantages of Artificial Intelligence:1.Efficiency and Productivity:AI systems can perform tasks with incredible speed and accuracy,often outperforming human capabilities.This leads to increased efficiency and productivity in various industries,such as manufacturing and data processing.2.Innovation and Creativity:AI can assist in the creative process by generating new ideas and solutions.For instance,AI algorithms can compose music,create art,and even write stories,pushing the boundaries of human creativity.3.Safety and Security:AI can enhance safety in various ways,such as through autonomous vehicles that can reduce the number of accidents caused by human error,or surveillance systems that can detect and alert authorities to potential threats.4.Accessibility:AI technologies,like voice assistants and translation services,make information and services more accessible to people with disabilities or those who speak different languages.5.Healthcare Improvements:AI can analyze vast amounts of medical data to assist in diagnosis,treatment planning,and patient monitoring,potentially leading to better health outcomes.Disadvantages of Artificial Intelligence:1.Job Displacement:The automation facilitated by AI can lead to job losses,particularly in industries where repetitive tasks are common.This can have significant social and economic impacts.2.Ethical Concerns:The use of AI raises ethical questions,such as privacy issues with data collection and surveillance,and the potential for AI to make biased decisions based on the data it has been trained on.3.Dependence on Technology:Overreliance on AI systems can lead to a loss of human skills and abilities,as well as a potential lack of human oversight in critical decisionmaking processes.4.Security Vulnerabilities:AI systems can be vulnerable to hacking and misuse,which could have serious implications,especially in areas like cybersecurity and autonomous weapons.5.Misuse and Unpredictability:There is a risk that AI could be misused for malicious purposes,or that it could develop in ways that are unpredictable and potentially harmful to society.In conclusion,while AI offers numerous benefits that can enhance our lives and solve complex problems,it also presents challenges that must be carefully managed.It is essential for society to engage in thoughtful discussions about the ethical use of AI,to ensure that its development is guided by principles that prioritize the wellbeing of all.。
人文科学过时的英语作文120词
人文科学过时的英语作文120词The Enduring Relevance of Humanities.In the fast-paced, technology-driven world we live in, it is easy to overlook the importance of humanities. However, the study of humanities, encompassing subjectslike history, philosophy, literature, and art, remains crucial in understanding our past, present, and future.Firstly, the humanities provide a unique perspective on human experience and behavior. By studying history, we gain insights into the decisions and actions of past civilizations, which can inform our own choices. Philosophy challenges us to think critically about the world and our place within it, fostering a culture of curiosity and inquiry. Literature and art allow us to explore human emotions and experiences in a way that is both personal and universal.Moreover, the humanities foster cultural understandingand empathy. As the world becomes increasingly interconnected, it is essential that we understand the diverse perspectives and histories of other cultures. The study of humanities helps us bridge divides and build bridges of understanding, essential for promoting harmony and cooperation in our globalized world.Finally, the humanities contribute to the development of critical thinking and problem-solving skills. In an era where complex issues like climate change and social inequality demand innovative solutions, the ability tothink critically and creatively is paramount. The humanities, by encouraging us to question assumptions and think outside the box, prepare us to address these challenges.In conclusion, while technology and science continue to shape our world, the study of humanities remains integral to our understanding of ourselves and others. It is through the humanities that we gain a deeper understanding of our past, a clearer vision of our future, and the tools to navigate the challenges of our time.。
liuhuiandmolly阅读理解
liuhuiandmolly阅读理解Liu Hui and Molly are discussing the issue of educational quality at a workshop.Liu Hui:Hi, Molly.Today's topic is educational quality. First, what does educational quality mean to you?Molly:As far as I'm concerned, quality education means good learning standards in educational institutions.So. educational quality ensures a desirable outcome for learners.Liu Hui:Sounds like after some serious thinking.However, many definitions of quality in education exist,testifying to the complexity and multifaceted nature of the concept. Molly:Definitely,establishing a contextualized understanding of quality means including relevant stakeholders.Key stakeholders often hold different views and meanings of educational quality.Liu Hu:There are many prestigious universities in the Us.They all provide high-quality education.But some universities aren't known for their quality. It's hard to imagine the gap.Molly:Yes. in the Us the quality in higher education is quite mixed. Universities like Harvard,Yale, MlT, etc, you know, are well-known all over the world.However, there are someinstitutions providing poor education,so called "diploma mills".Liu Hui:In China,we have similar issues in educational quality.Some universities pay more attention to profits instead of quality.Molly:How to improve educational quality is an international issue.But, solutions are grounded in values.cultures and traditions and may be specific to a given nation as well.1.Molly thinks that educational quality ensures a satisfactory outcome for learners.(T; F)2.Liu Hui disagrees with Molly on the meaning of education quality.(T; F)3.All universities in the U.S. offer high-quality education.(T; F)4.Diploma mills cannot provide high-quality education.(T: F)5.In China. there isn"t any diploma mill(T: F)正确答案：1.T.2.T.3.F.4.F.5.F.。