Weaving Space into the Web of Trust: An Asymmetric Spatial Trust Model for Social Networks
Mohamed Bishr
Institute for Geoinformatics,
University of Muenster,
Robert-Koch-Str. 26-28,
48149 Muenster, Germany
m.bishr@uni-muenster.de
Abstract. The proliferation of Geo-Information (GI) production in web-based
collaboration environments such as mapping mashups built on top of mapping
APIs such as GoogleMaps API poses new challenges to GI Science. In this
environment, millions of users are not only consumers of GI but they are also
producers. A major challenge is how to manage this huge flow of information and
identify high value contributions while discarding others. The social nature of the
collaborative approaches to GI provides the inspiration for innovative solutions. In
this paper, we propose a novel spatial trust model for social networks. This model
is part of our research to formalize the spatio-temporal regularities of trust in social
networks. The presented model provides a metric for trust as a proxy for GI quality
to assess the value of collaborative contributions. We also introduce the underlying
network for the model, which is a hybrid network structure for collaborative GI
applications based on affiliation networks and one-mood continuous trust
networks. Trust calculation in the affiliation network takes into account the
geographic distance between the actors and their information contributions –also
known as events–, while the one-mood network does not. This leads to an
asymmetric model with respect to the representation of space.
Keywords: Trust, space, spatial, quality, proxy, social networks, trust model.
1 Introduction
The notion of trust in computer science has many definitions depending on the application domain. It is a descriptor of security and encryption [KA98], a name for authentication methods or digital signatures [An01], a factor in game theory [MRS03], and a motivation for online interaction and recommender systems [AH98]. In this paper we are concerned with the web of trust and particularly consider the social aspect of trust in web based social networks (WBSN) and define it as a representative of personal values, beliefs and preferences. This type of social trust has been studied in previous work such as [Go05, MSC04, RAD03, ZL04]. As further explained in this paper, the majority of this research has been on the usage of trust as a measure of the quality and relevance of information in social networks of online communities.
In [BK07] we discussed two different examples of collaborative GI environments, https://www.doczj.com/doc/6515156380.html, and https://www.doczj.com/doc/6515156380.html,. Each has its own unique aspects as a collaborative GI application. https://www.doczj.com/doc/6515156380.html, in particular demonstrates mapping
mashups built over mapping data exposed through new Mapping APIs such as Google and Yahoo Mapping APIs. The wealth of information layers added to these mapping data are characterized by locality, as users contribute local knowledge and by breadth of scope with a variety of information such as pictures, restaurant reviews, jogging tracks and much more. Such layers of knowledge are otherwise hard to acquire on such a large scale and they make the underlying datasets ever richer and more valuable. We identified some shortcomings associated with these collaborative GI environments due to the transformation of users from GI content consumers to GI content producers. This transformation certainly raises new challenges with respect to the management of GI and its semantics. Also in [BK07] we presented a scenario where we pointed out the increasing demands for up-to-date geographic information in the context of road navigation for truck drivers. To overcome the above challenges, it is important to understand which users provide valuable contributions and which do not, both in data and its metadata. A major obstacle to this is the lack of quality measures of collaboratively generated GI since traditional quality measures (lineage, accuracy, consistency and completeness) are almost impossible to determine in such application environments.
In this paper, we present a novel approach to use trust in social networks as a proxy of GI quality. The hypothesis of this approach is that trust as a proxy for geospatial information quality has a spatio-temporal dimension that needs to be made explicit for the development of effective trust based GI quality metrics. The spatial dimension acts as a measure of the confidence in the trust rated events. Our goal is a spatio-temporal model of trust in social networks. This spatio-temporal trust model should identify trusted information contributions by users as well as identify and reaffirm trust in those users who contributed and continue to contribute information. In our approach, space and time are orthogonal dimensions and therefore the proposed model separates between them. In this paper, we only focus on the spatial dimension of trust in social networks.
The asymmetric spatial model of trust in social networks introduced in this paper is based on a hybrid social network model consisting of two interlinked networks: ? a two-mood non-dyadic affiliation network represented by a bipartite graph.
In the affiliation network, we refer to the information contributions of the
actors as events. The links between actors and events in the affiliation
network are weighted by the distance between the location of the actors
(presumably home base address) and the location of the events.
? A one-mode network of actors as one-mood continuous trust-rating network [Go05].
The proposed model is said to be asymmetric because space is represented only in the affiliation network in terms of geographic distance between actors and events, while the confidence in one-mood network trust ratings remains insensitive to space.
2 Trust In Social Networks
Trust from a sociological point of view is a prerequisite for the existence of a community, as functioning societies rely heavily on trust among their members [Co01, Fu96, Us02]. “The existence of trust is an essential component of all enduring social relationships” [Se97 p. 13]. Online communities much like real life communities rely on trust that is either implicit between the community members or explicit where users rate each others with a numerical measure of trust as studied in [Go05, ZPM05].
The inclusion of a computable notion of trust into social networks requires a simple definition of the term that preserves relevant properties of trust in our social life [Go05].
A simple yet inclusive definition of trust, which we adopt for our work is “Trust is a bet about the future contingent actions of others” [Sz99]. There are two components to this definition, belief and commitment [Go05]. The person (trustor) believes that another person (trustee) will act in a certain way. Then, the trustor commits to an action based on that belief. Four properties of trust on WBSN are also identified [Go05], namely: ?Transitivity
?Composability
?Personalization
?Asymmetry
These basic properties of trust are well grounded in our adopted definition of trust and will be considered in the formal model discussed in this paper. For further details on these trust properties and their relation to the adopted definition we refer the reader to [Go05, RAD03, ZL04]. An additional property of trust that we endorse for our work is presented in [Sz99], trust is essentially about people, and behind complex constructs, that we may vest trust in, always lays individuals whom ultimately we endow with trust. In Sztompka’s [Sz99] view to trust Lufthansa for a flight to Tokyo means you trust the pilots, cabin crew, and ground crew and so on. It easily follows that if you trust certain information, you trust the person or persons behind this information. This allows for a possible transition from trust between individuals to trust the information provided by those trusted individuals.
Trust in WBSNs is a measure of how information produced by some network users is relatively valuable to others [Go05, ZPM05, ZL04]. Trusted users tend to provide more useful and relevant information compared to less trusted or un-trusted users. With the lack of traditional information-quality attributes (lineage, accuracy, consistency and completeness) in collaborative environments, we propose to use trust as a proxy for geospatial information quality. Quality is a subjective measure here (and always, to some extent); if some trust-rated geospatial information is useful and relevant to a larger group of users, it can then be assumed to have a satisfactory quality in a more objective sense.
3 Spatial Aspects of Trust in Social Networks
The evolution of social networks with respect to the spatial dynamics, directly relating network evolution to constraints defined by the geographic space was studied in [MM05]. In their study of trust [BK95, BK96] provide evidence about the effects of social network structures on trust. Networks of high density compel actors to confirm each other’s opinions rather than argue about differences. Therefore, actors in dense networks tend to have more extreme opinions about the trustworthiness of others compared to actors in less dense networks. These opinions can veer towards trust or distrust. The dynamics of the emergence of these opinions are not explained. However, the network dynamics approaches discussed in [NBW06,Wa04,WS98] provide interesting insights on the role network dynamics can play in enhancing our understanding the trusting behavior of actors in social networks.
“Homophily is the principle that a contact between similar people occurs at a higher rate than among dissimilar people. The pervasive fact of homophily means that cultural, behavioral, genetic, or material information that flows through networks will tend to be localized”[MSC01 p.416]. In their study of homophily, [MSC01] asserts that space is a
very basic source of homophily. People are more likely to have contact with those others who are geographically closer than with others further away. A justification for this natural tendency is provided in [Zi49] as a matter of effort: it takes more effort (time, attention, etc.) to connect to those who are faraway than those who are closer. Many studies have illustrated this correlation between geography and establishment of connections between people [Ca90,Ga68]. Furthermore, factors that seem trivial such as arrangement of streets do have a direct effect on the formation of relatively weak ties and the potential for strong friendship formation [HW00, Su88].
With the proliferation of communications, the web and its pervasiveness one can expect that these technologies have had an effect on the extent to which geography affects social networks. These new technologies have apparently loosened the effects of geography by lowering the effort involved in creating and maintaining contacts [KC93]. Despite this loosening effect of new technologies [We96a] shows that social networks maintained through technological means still show geographic patterns. Also [Ve83] shows that geographic proximity is still the single best determinant of how often friends socialize together. In [We96b] it also becomes clear that new communication technologies have allowed people greater affordances in making homophiles relations through other dimensions (e.g., those with pet hobbies can form relations over the internet with others anywhere in the world more often than in the past). “these technologies seem to have introduced something of a curvilinear relationship between physical space and network association, with very close proximity no longer being so privileged over intermediate distances but both being considerably more likely than distant relations”[MSC01 p.430]. Also [MSC01] continues to assert that geography seems more important in determining the “thickness” of a relationship, which pertains to its multiplexity and frequency of contact.
Although the effects of the spatial dimension on trust particularly in social networks are not fully understood, this “thickness” of the relationships described by McPherson [MSC01] is a strong determinant of trust [Se97, Sz99]. Other factors such as the outcome of each encounter between actors (positive/negative experiences), the nature of those encounters and their importance to the parties involved also strongly affect trust [Bu02, Se97, Sz99]. One notices the multidimensional nature of social relations [Wa04], where space is a factor that helps shape the current nature and the future of those relations and consequently the levels of trust inherent in those relations.
Temporal and spatial aspects of trust in organizations are discussed in [R?04] by grounding his discussions in the Greek notions of chronos(clock time)/kiros(right moments) for time and their spatial counter parts chora(abstract space)/topos(concrete place). He tries to lay the philosophical foundations of how those notions affect trust particularly in time management and virtual organization settings. However, no conclusions are made about a direct effect of geographical space on trust. In the context of studying networks of organizations [NE92] argues that partners in close proximity to each other are almost often preferred partners, which means that network embeddedness (the over all network structures and mutual relations of actors) is ultimately affected by geographical space. In addition, [Bu02] suggests a direct link between geographical space and trust in social networks. He uses geographic distance as an indicator of network density among firms. His assumption is that there is a higher probability that firms located geographically closer together will have ties to common third parties and also that these third parties have more contacts among each others making the social
network dense in social ties as a result of geographic space which eventually fosters trust in the social network.
4 An Asymmetric Spatial Trust Model for Social Networks
In our view trust is inferred about people not about inanimate objects [Sz99]. We are adopting here a social definition of trust as we previously discussed. To trust information contributed by the actors is to implicitly trust actors who contributed this information. In that sense there is a certain unity or tie between actors and information they contribute, our proposition is that geographic distance affects the confidence we have in a user’s trust rating of a certain information entity. We suggest that this tie can be viewed as a physical affiliation in graph theoretic terms. Initially we propose to view the actors contributing information as a bipartite graph (Fig. 1). We will refer from now on to information contributions where a user is reporting a roadblock for example as “events”. In a bipartite graph, there are two subsets of nodes and all lines are between nodes belonging to the subsets. Formally, in a bipartite graph, nodes in a given subset are adjacent to nodes from the other subset, but no node is adjacent to any node in its own subset [WF94]. Our hybrid network consists of both an affiliation network and a one-mood [Wf94] actors network. Before discussing the model, we address two basic assumptions that are derived from our previously discussed study of the prior work on the spatial aspects of social networks and trust:
? assumption 1: trust in events increases as the number of users
tagging the event increases.
? assumption 2: we have higher confidence in trust ratings of actors
who are geospatially closer to events. In that sense distance in our
model affects the confidence in a trust rating of an information entity.
Model Description
In social network terms, the resulting bipartite graph of actors and contributions is a two mood non-dyadic affiliation network. Actors are possibly affiliated to many contributions and contributions are possibly contributed by many actors. Hence, our affiliation network consists of a set of actors and a set of events. We observe two sets of nodes representing the two moods of the network (Fig. 1):
N : is the set of all actors who are contributing information (events) to the network 12{,,......,}g N n n n =. Those actors can be viewed in terms of the subset of events to which he contributed.
M : Is the set of all events that are contributed by actors n, 12{,,.....}h M m m m =similar to the members of the set N, events can also be viewed in terms of the subset of actors who contributed to these events.
The affiliation network shown in (Fig. 1) is said to be non-dyadic. In non-dyadic networks the affiliations formed around the events in this network consist of subsets of actors such that event 1256{,,}M n n n =. Therefore, relations are not anymore between pairs of nodes as opposed to one-mood networks. Of course in such a network actors can also be viewed as subsets of events such that actor 213{,,}N m m m 5=. However, at this stage of the model we are interested only in the events as subsets h M . From this initial
setting, our first measure of the quality of the events can be conveyed with the graph theoretic nodal degree measure.
Fig. 1. The affiliation network of actors and the events (information they contributed). The links representing the affiliation is weighted by geographic distance to incorporate distance effects in the
model For a graph with g nodes, the degree of a node denoted is simply the number of lines that are incident with it. The nodal degree can range from 0 if no lines are incident with a given node to ()h d m 1g ? if a node is adjacent to all other nodes in a graph. In our bipartite graph of actors and events one can measure the nodal degrees of the events, which essentially would represent the number of members in the subset h M . One should note that in the proposed model the nodal degree will never be 0 and will have a minimum of 1 since each event only exists in response to an initial user contribution. The nodal degree here is a simple albeit a very telling measure of the importance of a particular node/event [WF94]. In our case it tells us how many actors/users have actually bookmarked a certain location as a roadblock for example. Considering such a measure, the higher the nodal degree of the event the more we can reaffirm trust in the information provided since this means that more people are reaffirming it. Given the property of trust we adopted earlier from [Sz99] this is a highly acceptable measure.
However, when calculating the nodal degree in the standard graph theoretic method it is assumed that all the links from actors to events are of equal importance. This is contrary to reality since some users will be inclined to provide more relevant information. As previously discussed, the hypothesis underlying this model is that the spatial dimension is a strong governing factor to this inclination to trust users in providing more accurate information. This view is supported by evidence from our discussion of the spatial aspects of trust in social networks. Hence, we propose a representation of space, particularly distance as a weight to the links when calculating the nodal degrees. Events whose users are closer to the event location (e.g. higher relative proximity) receive a better rating of trust than these events whose users are further away. This spatial nodal degree measure will be denoted . We assume the location of the actor to dynamically his location when reporting an event (i.e. collected from a mobile device, personal reporting) and the distance from the event location to be (`h d m )
the direct geographic distance in a straight line. In the nodal degree calculation every link between an actor and an event is counted as one. For example the nodal degree of the event is 3 and is 4 (Fig. 1). For our distance adjusted nodal degree for event nodes, we take a na?ve representation of distance by dividing each nodal degree by the corresponding distance , thus:
1m 4m 1e =c 1()`k h i i
e d m c ==∑
(1) In our case is always 1, thus: e 11()`k h i i d m c ==∑
(2) The spatial nodal degree makes the trust inference about the events spatially sensitive, hence adding effects of geography to the social space. However, we don’t intend to use this measure alone as a measure of trust in information quality provided by the user. Rather the intention of our model is to make the effects of social trust on the model more explicit. Therefore, we proceed to discuss the second element of the model that integrates continuous trust networks into the model [Go05].
As a common analysis method of affiliation networks, the bipartite graph of the affiliation network can be folded into two one-mood networks one which presents the connections between actors based on affiliation with events, and another which represents the connections between events based on affiliation with actors. At this stage we are not trying to define the meaning of both networks. The distinction here is that the one-mood network of actors that will be discussed next is not resulting from the folded bi-partite graphs. Rather it is a continuous trust network of actors where ties represent trust ratings on a scale of 1-10 [Go05]. Our model requires the underlying system to provide facility for these direct user-to-user ratings forming a standard one-mood
network of actors (Fig. 2)
Fig. 2. : The one-mood trust network. This trust network is by definition directed and represents
the asymmetry property of trust earlier discussed (rating from A ?B ≠B ?A)
The resulting network is presented in (Fig. 3). This form of network is not strictly an affiliation network. In an affiliation network the actors are not connected, but rather
connect through affiliation with events. In our model, actors have two types of connections:
? Connection with events (the affiliation network)
? Connections among themselves (one-mood continuous trust network).
Fig. 3. The depicted network structure represents the fusion of both the one-mood network (white ??white) and the affiliation network (white ?black). The model depends on the interplay
between those two networks, as distinct networks within the same structure.
The continuous trust network is used to provide a more explicit account of trust which is bound to a formally well defined notion of trust [Go05, ZL04]. This is best explained by an example. In the network in (Fig.1) Alice () has contributed to the event (let us assume it is a roadblock). When calculating the nodal degrees we weight the links by distance. This is assuming that all actors in the set 2n 3m 3256{,,}M n n n = contributing to the event are similar in every respect. This assumption does not hold true when actors are part of a continuous trust network themselves. In that case, trust ratings of the individual actors can be used as weights to reaffirm the ties between the actors and the events through differentiation between actors by their reputation inherited from their trust levels within the social network. Alice in that example is a member of a social network and she has been rated by her peers on the trust scale. We then need to identify what is the over all trust rating of Alice inferred from the social network? This trust rating will then be used as the weight for Alice when making the overall trust rating for an event to which Alice contributed.
3m The problem of trust inference in the one-mood network is different from [Go05], where the problem of trust is focused on determining how much does A (source) trust G (sink) (Fig. 4(a)) who are not directly connected. In our problem Alice is the sink G (Fig. 4(b)) for which we would like to find a trust rating from the adjacent neighbors and there is no source to infer trust from along the paths. The solution to our inference problem can follow a different methodology albeit with a slightly similar averaging technique as in [Go05].
Fig. 4. In [Go05] (a) the problem is computing trust along the paths from source A to sink G. In
our case (b), the problem is computing trust to the sink at one degree of separation
Trust rating for a certain sink node (g n ) will be taken as the average of trust ratings directed inwards from the nodes immediately adjacent to it at one degree of separation. That is to say, the geodesic (social) distance is always equal to 1 between the neighbors and the sink (G). The sink node (,)d i g g n would have a trust rating of g n t defined
by Equation 3 letting be the number of adjacent nodes. Equation 3 respects the properties of trust discussed earlier, particularly composability, asymmetry and personalization. However, transitivity is not relevant at this stage, since we compute trust at one degree of separation from the sink.
Ν
(),1i g i g g n n n adj n i n t t Ν∈==Ν
∑ (3) The integration of the spatial nodal degree as a trust measure (Equation 2) and the trust measure from the one-mood actors network (Equation 3) as a weight for the actors will then provide our global trust level in a certain event. For an event the final trust rating is defined by Equation 4.
h m h m t 1,1g h k n m i g i t t c ===∑ (4)
Equation 4 establishes a trust metric for information contributed by actors. This metric has an explicit account of geographic distance inherited through the affiliation network as well as an explicit account of trust inherited through the one-mood network. An important decision we made is what we refer to as the asymmetry of the model. This asymmetry of the model is revisited in the last section.
5 Conclusions and Future Work
In this paper we proposed an approach to formalize how distance affects the confidence we might have in trust of certain information entity reported by moving social network agents. A major challenge we have is in establishing a distance threshold after which the confidence effect would have a stable maximum value. Generally, defining such a threshold should involve real world analysis of actual data to fine tune the current model. Also the representation of distance as introduced in the model should be normalized. However the distance measure as represented is remains relevant, as suggested by some recent research [MWB07].
An important point is our choice not to make the continuous one-mood trust network spatially sensitive (asymmetry of the model). This is contrary to evidence suggesting that the continuous one-mood trust network is sensitive to the spatial dimension [Bu02, NE92]. It is clear then that our model will have to be symmetric. That is to account for space in the one-mood continuous trust network when doing trust calculations to the sink. However, accounting for space in both networks at this stage will make the results of any analysis very hard to interpret since it will be difficult to disentangle the effects of space in the one-mood network from those of space in the affiliation network. Hence, the best way forward was to study both as flip sides of a coin before integrating the spatial dimension into both networks in a unified model if so proves more accurate.
Currently our model depends on structural social network analysis methods such as the nodal degree and averaging of trust values of adjacent nodes. However, a modern and emerging view in the science of networks is network dynamics [BC03,Wa04]. In this view, analysis for networks including social networks without a corresponding theory of dynamics is essentially un-interpretable [Wa04]. Actors are not static nodes in a network structure that is the an embodiment of their relations. Actors are human beings moving in space and time, doing activates and responding to a changing environment. In studying trust, our model assumes a network of a given structure as a still image and tries to propagate trust across this network. From a network dynamics perspective we are interested in how trust evolves and develops over the network in both space and time and how trust propagation ultimately affects the structure of the networks. In other words, our presented spatial trust model will have to be accompanied by a corresponding theory of dynamics of trust on social networks.
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图2图图 期末检测题 【本检测题满分：120分，时间：120分钟】 一、选择题（每小题3分，共30分） 1.(2013?湖南张家界中考)-2 013的绝对值是（ ） A.-2 013 B.2 013 C. D.12013 12013 -2.已知两数在数轴上的位置如图所示，则化简代数式,a b 的结果是（ ） 12a b a b +--++A.1 B. C. D.-1 23b +23a -3.某商店把一件商品按标价的九折出售（即优惠10％），仍可获利20％，若该商品的标价为每件28元，则该商品的进价为（ ） A.21元 B.19.8元 C.22.4元 D.25.2元 4.(2013?湖南株洲中考)一元一次方程的解是（ ） 24x =A. B. C. D.1x =2x =3x =4 x =5.如图，，则与之比为（ ）11,,34 AC AB BD AB AE CD ===CE AB A.1∶6 B.1:8 C.1:12 D.1:16 6.如果∠1与∠2互补，∠2与∠3互余，则∠1与∠3的关系是（ ） A.∠1=∠3 B.∠1=180°-∠3 C.∠1=90°+∠3 D.以上都不对 7.如图是某班学生参加课外兴趣小组的人数占总人数比 例的统计图，则参加人数最多的课外兴趣小组是（ ） A.棋类组 B.演唱组 C.书法组 D.美术组 8.某中学开展“阳光体育活动”，九年级一班全体同学分别参加了巴 山舞、乒乓球、篮球三个项目的活动，陈老师统计了该班参加这三项活动的人数，并绘制了如图所示的条形统计图和扇形统计图．根据这两个统计图，可以知道该班参加乒乓A B C D E 第5题图 习题到位。在管设备进行调整使度内来确保机组
最新版人教版七年级数学下册知识点 第五章相交线与平行线 一、知识网络结构 相交线 相交线垂线 同位角、内错角、同旁内角 平行线：在同一平面内，不相交的两条直线叫平行线 定义 : __________ __________ ________ 平行线及其判定判定 1：同位角相等，两直线平行 判定 2 平行线的判定：内错角相等，两直线平行 相交线与平行线判定 3：同旁内角互补，两直线平行 判定 4：平行于同一条直线的两直线平行 平行线的性质性质 1：两直线平行，同位角 性质 2：两直线平行，内错角 性质 3：两直线平行，同旁内 性质 4：平行于同一条直线 相等 相等 角互补 的两直线平行命题、定理 平移 二、知识要点 1、在同一平面内，两条直线的位置关系有两种：相交和平行，垂直是相交的一种特殊情况。 2、在同一平面内，不相交的两条直线叫平行线。如果两条直线只有一个公共点，称这两条直线相交；如果两条直线没有公共点，称这两条直线平行。 3、两条直线相交所构成的四个角中，有公共顶点且有一条公共边的两个角是邻补角。邻补角的性质：邻补角互补。如图 1 所示，与互为邻补角，与互为邻补角。+=180°；+ =180° ； + =180°；+ =180°。321 4 4、两条直线相交所构成的四个角中，一个角的两边分别是另一个角的两边的图 1反 向延长线，这样的两个角互为对顶角。对顶角的性质：对顶角相等。如图1
所示，与互为对顶角。=； =。 5、两条直线相交所成的角中，如果有一个是直角或90° 时，称这两条直线互相垂直， 其中一条叫做另一条的垂线。如图 2 所示，当= 90°时，⊥b。 a 垂线的性质：2 1 3 4 性质 1：过一点有且只有一条直线与已知直线垂直。 图 2 性质 2：连接直线外一点与直线上各点的所有线段中，垂线段最短。 性质 3：如图 2 所示，当 a⊥ b 时，==== 90°。点到直线的距离：直线外一点到这条直线的垂线段的长度叫点到直线的c距离。 6、同位角、内错角、同旁内角基本特征：21 3 46 a 75 8 ①在两条直线 ( 被截线 ) 的同一方，都在第三条直线 ( 截线 ) 的同一侧，这样 b 同位角。图 3 中，共有图 3 的两个角叫对同位角：与是同位角； 与是同位角；与是同位角；与是同位角。 ②在两条直线 ( 被截线 )之间，并且在第三条直线 ( 截线 ) 的两侧，这样的两个角叫内错角。图 3中，共有对内错角：与是内错角；与是内错角。 ③在两条直线 ( 被截线 ) 的之间，都在第三条直线 ( 截线 ) 的同一旁，这样的两个角叫同旁内角。图 3中，共有对同旁内角：与是同旁内角；与是同旁内角。 7、平行公理：经过直线外一点有且只有一条直线与已知直线平行。 平行公理的推论：如果两条直线都与第三条直线平行，那么这两条直线也互相 平行。c 2 3 1 4 6 5 平行线的性质：a78性质 1：两直线平行，同位角相等。如图 4 所示，如果 a∥ b，图4 b 则 =； =； =； =。
第五章知识结构如下图所示： 第六章知识结构 第七章知识结构框图如下：
（二）开展好课题学习 可以如下展开课题学习： （1）背景了解多边形覆盖平面问题来自实际． （2）实验发现有些多边形能覆盖平面，有些则不能． （3）分析讨论多边形能覆盖平面的基本条件，发现问题与多边形的内角大小有密切关系，运用多边形内角和公式对实验结果进行分析． （4）运用进行简单的镶嵌设计． 首先引入用地砖铺地，用瓷砖贴墙等问题情境，并把这些实际问题转化为数学问题：用一些不重叠摆放的多边形把平面的一部分完全覆盖．然后让学生通过实验探究一些多边形能否镶嵌成平面图案，并记下实验结果：
（1）用正三角形、正方形或正六边形可以镶嵌成一个平面图案（图1）．用正五边形不能镶嵌成一个平面图案． （2）用正三角形与正方形可以镶嵌成一个平面图案．用正三角形与正六边形也可以镶嵌成一个平面图案． （3）用任意三角形可以镶嵌成一个平面图案, 用任意四边形可以镶嵌成一个平面图案(图2)．
观察上述实验结果，得出多边形能镶嵌成一个平面图案需要满足的两个条件: （1）拼接在同一个点（例如图2中的点O）的各个角的和恰好等于360°（周角）； （2）相邻的多边形有公共边（例如图2中的OA两侧的多边形有公共边OA）． 运用上述结论解释实验结果，例如，三角形的内角和等于180°，在图2中，∠1+∠2+∠3=180°．因此,把6个全等的三角形适当地拼接在同一个点（如图2）, 一定能使以这点为顶点的6个角的和恰好等于360°,并且使边长相等的两条边贴在一起．于是, 用三角形能镶嵌成一个平面图案．又如，由多边形内角和公式，可以得到五边形的内角和等于 (5－2)×180°=540°． 因此,正五边形的每个内角等于 540°÷5=108°, 360°不是108°的整数倍,也就是说用一些108°的角拼不成360°的角．因此，用正五边形不能镶嵌成一个平面图案． 最后，让学生进行简单的镶嵌设计，使所学内容得到巩固与运用．１．利用二（三）元一次方程组解决问题的基本过程 ２．本章知识安排的前后顺序
Web of Knowledge平台服务功能使用简介 ISI Web of Knowledge为读者提供了个性化服务和定题服务功能。个性化定制指用户可以在Web of Konwledge主页上注册并设置自己密码，然后每次登录后即可浏览自己订制主页，包括：保存检索策略、建立并编辑自己经常阅读期刊列表；浏览保存检索策略、及时了解一个定题服务是否有效及过期时间。 电子邮件定题服务可让用户方便地跟踪最新研究信息。新定题服务功能允许用户通过web of science中任一种检索途径（普通检索、引文检索、化学结构检索）创建定题服务服务策略，将检索策略保存并转化为用电子邮件通知定题服务。 如图，在Web of Knowledge主页右侧提供了个性化定制服务和定题服务管理功能，下面就这几项功能一一说明如下： 图一：web of knowledge主页 一、注册 点击“Register”超链接，进入注册页面。如图二：分别按要求填入您电子邮件地址，您选择密码以及您姓名。您可以选择自动登录或者普通方式进入您个性化服务管理功能。自动登录可以免除您每次登录Web of Knowledge平台时输入电子邮件地址和密码。该功能使用是cookie技术。如果使用公共计算机，最好选择普通登录方式。 在完成以上操作之后，点击“Submit Registration”完成整个注册过程。
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第一章：整式的运算 单项式 式 多项式 同底数幂的乘法 幂的乘方 积的乘方 同底数幂的除法 零指数幂 负指数幂 整式的加减 单项式与单项式相乘 单项式与多项式相乘 整式的乘法 多项式与多项式相乘 整式运算 平方差公式 完全平方公式 单项式除以单项式 整式的除法 多项式除以单项式 一、单项式 1、都是数字与字母的乘积的代数式叫做单项式。 2、单项式的数字因数叫做单项式的系数。 3、单项式中所有字母的指数和叫做单项式的次数。 4、单独一个数或一个字母也是单项式。 5、只含有字母因式的单项式的系数是1或―1。 6、单独的一个数字是单项式，它的系数是它本身。 7、单独的一个非零常数的次数是0。 8、单项式中只能含有乘法或乘方运算，而不能含有加、减等其他运算。 9、单项式的系数包括它前面的符号。 10、单项式的系数是带分数时，应化成假分数。 11、单项式的系数是1或―1时，通常省略数字“1”。 12、单项式的次数仅与字母有关，与单项式的系数无关。 二、多项式 1、几个单项式的和叫做多项式。 2、多项式中的每一个单项式叫做多项式的项。 3、多项式中不含字母的项叫做常数项。 4、一个多项式有几项，就叫做几项式。 5、多项式的每一项都包括项前面的符号。 6、多项式没有系数的概念，但有次数的概念。 7、多项式中次数最高的项的次数，叫做这个多项式的次数。 三、整式 1、单项式和多项式统称为整式。 2、单项式或多项式都是整式。 3、整式不一定是单项式。 4、整式不一定是多项式。
5、分母中含有字母的代数式不是整式；而是今后将要学习的分式。 四、整式的加减 1、整式加减的理论根据是：去括号法则，合并同类项法则，以及乘法分配率。 2、几个整式相加减，关键是正确地运用去括号法则，然后准确合并同类项。 3、几个整式相加减的一般步骤： （1）列出代数式：用括号把每个整式括起来，再用加减号连接。 （2）按去括号法则去括号。 （3）合并同类项。 4、代数式求值的一般步骤： （1）代数式化简。 （2）代入计算 （3）对于某些特殊的代数式，可采用“整体代入”进行计算。 五、同底数幂的乘法 1、n个相同因式（或因数）a相乘，记作a n，读作a的n次方（幂），其中a为底数，n为指数，a n的结果叫做幂。 2、底数相同的幂叫做同底数幂。 3、同底数幂乘法的运算法则：同底数幂相乘，底数不变，指数相加。即：a m﹒a n=a m+n。 4、此法则也可以逆用，即：a m+n = a m﹒a n。 5、开始底数不相同的幂的乘法，如果可以化成底数相同的幂的乘法，先化成同底数幂再运用法则。 六、幂的乘方 1、幂的乘方是指几个相同的幂相乘。（a m）n表示n个a m相乘。 2、幂的乘方运算法则：幂的乘方，底数不变，指数相乘。（a m）n =a mn。 3、此法则也可以逆用，即：a mn =（a m）n=（a n）m。 七、积的乘方 1、积的乘方是指底数是乘积形式的乘方。 2、积的乘方运算法则：积的乘方，等于把积中的每个因式分别乘方，然后把所得的幂相乘。即（ab）n=a n b n。 3、此法则也可以逆用，即：a n b n =（ab）n。 八、三种“幂的运算法则”异同点 1、共同点： （1）法则中的底数不变，只对指数做运算。 （2）法则中的底数（不为零）和指数具有普遍性，即可以是数，也可以是式（单项式或多项式）。 （3）对于含有3个或3个以上的运算，法则仍然成立。 2、不同点： （1）同底数幂相乘是指数相加。 （2）幂的乘方是指数相乘。 （3）积的乘方是每个因式分别乘方，再将结果相乘。 九、同底数幂的除法
来源：2011-2012学年广东省汕头市潮南区中考模拟考试数学卷（解析版） 考点：三角形 如图，已知，等腰Rt△OAB中，∠AOB=90o，等腰Rt△EOF中，∠EOF=90o，连结AE、BF． 求证：（1）AE=BF；（2）AE⊥BF． 【答案】 见解析 【解析】解：（1）证明：在△AEO与△BFO中， ∵Rt△OAB与Rt△EOF等腰直角三角形， ∴AO=OB，OE=OF，∠AOE=90ｏ-∠BOE=∠BOF， ∴△AEO≌△BFO， ∴AE=BF； （ 2）延长AE交BF于D，交OB于C，则∠BCD=∠ACO， 由（1）知：∠OAC=∠OBF， ∴∠BDA=∠AOB=90ｏ， ∴AE⊥BF． （1）可以把要证明相等的线段AE，CF放到△AEO，△BFO中考虑全等的条件，由两个等腰直角三角形得AO=BO，OE=OF，再找夹角相等，这两个夹角都是直角
减去∠BOE的结果，所以相等，由此可以证明△AEO≌△BFO； （2）由（1）知：∠OAC=∠OBF，∴∠BDA=∠AOB=90°，由此可以证明AE⊥BF 来源：2012-2013学年吉林省八年级上期中考试数学试卷（解析版） 考点：四边形 如图，在正方形ABCD中，E是AD的中点，F是BA延长线上的一点，AF=AB,已知△ABE≌△ADF. （1）在图中，可以通过平移、翻折、旋转中哪一种方法，使△ABE变到△ADF 的位置； （2）线段BE与DF有什么关系？证明你的结论. 【答案】 （1）绕点A旋转90°；（2）BE=DF，BE⊥DF． 【解析】本题考查的是旋转的性质，全等三角形的判断和性质 （1）根据旋转的概念得出； （2）根据旋转的性质得出△ABE≌△ADF，从而得出BE=DF，再根据正方形的性质得出BE⊥DF． （1）图中是通过绕点A旋转90°，使△ABE变到△ADF的位置． （2）BE=DF，BE⊥DF； 延长BE交DF于G；
七年级下册初中数学知识点总结 第一章 整式的运算 一. 整式 ※1. 单项式 ①由数与字母的积组成的代数式叫做单项式。单独一个数或 字母也是单项式。 ②单项式的系数是这个单项式的数字因数，作为单项式的系数，必须连同数字前面的性质符号,如果一个单项式只是字母的积,并非没有系数. ③一个单项式中,所有字母的指数和叫做这个单项式的次数. ※2.多项式 ①几个单项式的和叫做多项式.在多项式中,每个单项式叫做多 项式的项.其中,不含字母的项叫做常数项.一个多项式中,次 数最高项的次数,叫做这个多项式的次数. ②单项式和多项式都有次数,含有字母的单项式有系数,多项式 没有系数.多项式的每一项都是单项式,一个多项式的项数就 是这个多项式作为加数的单项式的个数.多项式中每一项都有 它们各自的次数,但是它们的次数不可能都作是为这个多项式 的次数,一个多项式的次数只有一个,它是所含各项的次数中 最高的那一项次数. ※3.整式单项式和多项式统称为整式. ????????其他代数式多项式单项式整式代数式 二. 整式的加减 ¤1. 整式的加减实质上就是去括号后,合并同类项,运算结果是一个多项式或是单项式. ¤2. 括号前面是“－”号,去括号时,括号内各项要变号,一个 数与多项式相乘时,这个数与括号内各项都要相乘. 三. 同底数幂的乘法 ※同底数幂的乘法法则: n m n m a a a +=?(都是正数)是幂的运算中最 基本的法则,在应用法则运算时,要注意以下几点: ①法则使用的前提条件是：幂的底数相同而且是相乘时，底数a 可以是一个具体的数字式字母，也可以是一个单项或多项式； ②指数是1时，不要误以为没有指数； ③不要将同底数幂的乘法与整式的加法相混淆，对乘法，只要底
空间基础信息发布和城市管理应用摘要：在分析了当前空间信息发布典型应用模式案例的基础之上，基于城市范围内对政府各部门、相关企业等单位进行空间基础信息需求调研的数据，分析其特征。在上述基础之上，提出适应于政府部门、企业和公众的多层次应用模式，对每种模式给出详细的说明，并描述了多层次服务的基本内容。 关键词：空间信息服务共享模式 abstract: this paper analyzes the current typical application cases about spatial information sharing model and also analyzes the characteristics of the data that researched the using of the geo-spatial information from the government departments and related enterprises in city area. on the basis of above, we make the multi-level adapted model for the government departments, enterprises and public applications, for each model the details are explained and the basic contents of a multi-level service are described. key words:space information service sharing model 中图分类号：f291.1文献标识码：a 文章编号： 1 引言 当今社会已步入信息时代，信息资源的整合及分发机制在城市公共管理与服务中占据重要地位，是影响管理和服务水平、决策效
2014-2015学年度配套中学教材全解七年级数学（下）（北京课改版） 期末检测题附答案详解 （本检测题满分：120分，时间：120分钟） 一、选择题（每小题3分，共36分） 1.若不等式组 12 x x m ì ?? ， 有解，则m的取值范围是（） A.m＜2 B.m≥2 C.m＜1 D.1≤m＜2 2.（2014?南充中考）不等式组 () 1 12, 2 331 x x x ì? ?+? ?í ?? -<+ ?? 的解集在数轴上表示正确的是（） A．B．C．D． 3.若方程组 2313, 3530.9 a b a b ì- ?? í? + ?? ＝ ＝ 的解是 8.3, 1.2, a b ì?? í? ?? ＝ ＝ 则方程组 ()() ()() 223113, 325130.9 x y x y ì+-- ?? í? ++- ?? ＝ ＝ 的解是（） A. 6.3, 2.2 x y ì?? í? ?? ＝ ＝ B. .3, .2 x y ì?? í? ?? ＝8 ＝1 C. 10.3, 2.2 x y ì?? í? ?? ＝ ＝ D. 10.3, 0.2 x y ì?? í? ?? ＝ ＝ 4.下列语句：①一条直线有且只有一条垂线；②不相等的两个角一定不是对顶角；③两条不相交的直线叫做平行线；④若两个角的一对边在同一直线上，另一对边互相平行，则这两个角相等；⑤不在同一直线上的四个点可画6条直线；⑥如果两个角是邻补角，那么这两个角的平分线组成的图形是直角．其中错误的有（） A.2个 B.3个 C.4个 D.5个 5.某中学课外科技小组的同学们设计制作了一个电动智能玩具，玩具中的四个动物小鱼、小羊、燕子和熊猫分别在1、2、3、4号位置上（如图），玩具的程序是：让四个动物按图中所示的规律变换位置，第一次上、下两排交换位置；第二次是在第一次换位后，再左、右两列交换位置；第三次再上、下两排交换位置；第四次再左、右两列交换位置；按这种规律，一直交换下去，那么第2 008次交换位置后，熊猫所在位置的号码是（） A.1号 B.2号 C.3号 D.4号 6.如图，直线a和直线b被直线c所截，给出下列条件： 第5题图
政务地理空间信息资源共享服务平台建设方案 2. 总体框架 2.1总体框架与体系结构 2.1.1总体框架 北京市政务地理空间信息资源共享服务平台（以下简称“平台”）总体由数据资源层、数据引擎层、运维支撑层、服务接口层、应用层五个层面构成。 图1 北京市政务地理空间信息资源共享服务平台技术总体框架 数据资源层 北京市重点规划、建设了政务基础共享地理空间信息资源数据库，并积累了大量的政务地理空间信息资源，包括在基础框架层面上的遥感影像数据库和数字线划图数据库、政务支撑层面的政务电子地图数据库和地址数据库以及政务应用层面的政务信息图层数据库（如图2所示）。
图2政务地理空间信息资源数据库 数据引擎层 数据引擎层指对各类数据进行高效管理、入库更新、网络发布的各类数据引擎。 海量矢量数据引擎：高速、稳定的提供海量矢量数据发布服务，严格遵循OGC规范，实现WMS/WFS服务接口并且扩展。支持矢量图层自动更新入库发布、支持超过3000个以上图层的集中发布、支持集群服务器矢量图层发布、支持多种空间数据格式、支持主流平台数据格式的相互转换、支持并发访问大数据量图层时快速查询、出图速度快且稳定。 地址匹配引擎：为地址匹配提供准确、高效、可靠的地址匹配引擎。 遥感影像数据引擎：采用影像金字塔技术为用户提供在线浏览访问服务时，能够根据用户浏览影像的范围、比例尺与事先生成的影像金字塔进行快速匹配，系统仅需检索和拼接包含与用户需求尺度最接近、范围最小的影像集合即可，能够大幅减少对系统的I/O访问操作，从而提升服务响应和反馈的效率。 地图图片引擎：支持海量地图数据的发布与快速浏览、多用户并发操作、响应速度、地图美观等方面的功能。地图图片引擎是下一代WEBGIS的主流，它采用地图拼接机制、地图缓存机制、VML绘制技术，并且采用Javascript技术封装出来地图服务访问API，有效满足了用户对性能和易于二次开发的需求。 运维支撑层 由数据管理子系统、运维支撑子系统、服务巡检子系统构成。数据管理指后台对数据的入库、更新、删除、维护等操作；运维支撑主要是指平台的用户权限管理、安全管理、日志及服务监控等。 服务接口层 该层是平台的核心功能，对外提供多个层次的多个功能的服务接口。以满足全市政府部门对政务地理空间信息资源共享应用的强烈需求。 业务应用层 指调用服务接口层的各类服务接口搭建的各业务部门的电子政务业务系统。 2.1.2平台子系统构成 北京市政务地理空间信息资源共享服务平台自2005年7月开始启动，截止目前已进行了三期建设。平台通过对政务基础共享地理空间信息资源进行集中化管理，主要采用网络系统在线共享，二次接口开发服务等方式为北京市政府、各
第十四章实数 (2) 第一节平方根 (2) 第二节立方根 (6) 第三节实数 (8) 中考链接 (11) 单元检测 (12) 第十五章不等式与不等式组 (15) 第一节不等式 (15) 第二节实际问题与一元一次不等式 (17) 第三节一元一次不等式组 (19) 中考链接 (21) 单元检测 (23) 第十六章数据的分析 (26) 第一节数据的代表 (26) 第二节数据的波动 (29) 中考链接 (31) 单元检测 (33) 第十七章三角形 (37) 第一节与三角形有关的线段 (37) 第二节与三角形有关的角 (40) 第三节多边形及其内角和 (44) 中考链接 (47) 单元检测 (49) 第十八章全等三角形 (52) 第一节全等三角形 (52) 第二节三角形全等的条件 (55) 第三节角的平分线的性质 (59) 中考链接 (65) 单元检测 (68) 期中试卷 (72) 期末试卷 (75) 参考答案 (78)
第十四章实数 单元目标 1. 理解平方根、立方根的概念和性质； 2. 掌握算术平方根，算术平方根的非负性的应用． 3. 理解无理数和实数的概念以及有理数和无理数的区别； 4. 掌握实数和数轴上的点的关系，平面直角坐标系中的点和有序实数对之间的关系. 第一节平方根 要点精讲 1、算术平方根 一般地，如果一个正数x的平方等于a，即x2=a，那么这个正数x叫做a的算术平方根. a的算术平方根记为，读作“根号a”，a叫被开方数. 0的算术平方根是0. 2、用计算器求一个数的算术平方根 有的计算器上有“”键，就可以使用这个键直接求出一个数的算术平方根. 3、平方根 一般地，如果一个数的平方等于a，那么这个数叫做a的平方根（或二次方根），这就是说：如果x2=a，那么x叫做a的平方根. 4、开平方 求一个数a的平方根的运算，叫做开平方. 5、平方根的性质 正数有两个平方根，它们互为相反数； 0的平方根是0； 负数没有平方根. 6、平方根的表示 正数a的算术平方根用表示； 正数a的负的平方根用表示； 正数a的平方根用符号表示. 7、平方根重要性质： （1）a≥0时，；
摘要：本文主要使用了百度、谷歌等搜索引擎和Web of science数据库对包信和院士的研究内容及其研究成果进行了分析，通过百度、谷歌、个人主页对包信和院士的基本信息进行了解；通过Web of science数据库对包信和院士的研究方向、引文数据、合作者、基金资助机构、出版物进行了了解。并对其2014年5月的一篇文章进行了深入的分析。 一、基本信息 包信和，理学博士，研究员，博士生导师、中科院院士、物理化学家，中国科学院大连化学物理研究所研究员，现任中科院沈阳分院院长，复旦大学常务副校长，兼任中国科学技术大学化学物理系主任。 他的个人工作经历为： 1989年至1995年获洪堡基金资助，在德国马普学会Fritz-Haber研究所任访问学者，1995年应聘回国。 1995年至2000年在中科院大连化学物理研究所工作。 2000年8月至2007年3月任大连化学物理研究所所长。 2003年3月起任中国科技大学化学物理系系主任。 2009年3月起任沈阳分院院长。 2009年当选为中国科学院院士。 2015年9月经教育部研究决定，任命包信和为复旦大学常务副校长 其次在大连化学物理研究所的个人介绍和包信和院士的课题组主页里搜集了对其研究方向的简介： 包信和研究员主要从事表面化学与催化基础和应用研究。发现次表层氧对金属银催化选择氧化的增强效应，揭示了次表层结构对表面催化的调变规律，制备出具有独特低温活性和选择性的纳米催化剂，解决了重整氢气中微量CO造成燃料电池电极中毒失活的难题。发现了纳米催化体系的协同限域效应，研制成碳管限域的纳米金属铁催化剂和纳米Rh-Mn催化剂，使催
化合成气转化的效率成倍提高。在甲烷活化方面，以分子氧为氧化剂，实现了甲烷在80℃条件下直接高效氧化为甲醇的反应；创制了Mo/MCM-22催化剂，使甲烷直接芳构化制苯的单程收率大幅度提高。 二、研究成果分析 利用Web of Science搜索包老师的文章，总共搜索到497篇文章，对检索报告创建引文报告，如图2.1所示。文章被引总频次达到12804次，平均每篇文章被引25.76次，h-index值为56，表示在包老师所发的文章中，每篇被引用了至少56次的论文总共有56篇左图为每年出版的文献数图标，2000年以来，每年出版的文献数量基本稳定，在30篇左右，研究状态保持稳定。其中2015年发表文章篇数最高，2015年是个高产年。 根据每年的引文数图标可以看出，每年的引文数不断上升，表明其发表的文章是有生命力、有价值的。也表明每年发文的质量不断在上涨。 图2.1创建引文报告 对检索结果进行分析。图2.2是对作者进行分析，得到如下图所示的结果，可以看到合作者的信息，其中与293名作者有过合作。其中合作最多的为韩秀文老师（大连化物所）、马丁老师(北京大学)。
应用技巧一：如何了解您的论文被SCI收录的情况？ 1．访问Web of Science数据库检索论文 请访问：https://www.doczj.com/doc/6515156380.html,，进入ISI Web of Knowledge平台；选择Web of Science数据库，(以下图示为WOK4.0版新界面)。 示例：如果我们希望检索“中国科技大学” “侯建国”院士在Science Citation Index (SCI)中收录文章的情况。 2．检索结果及输出 检索结果告诉我们找到了152篇“侯建国”院士的文章。（如果有重名的现象，请参考我们随后提供的有关作者甄别工具的应用技巧。）
我们可以选择先做Mark标记所有相关文章，再选择打印输出的方式，见下图： 下图是可打印的检索到的152篇侯建国院士所发表文章被SCI收录的记录。 结论：通过在Web of Science中用作者、及机构名称或地址的限制，检索到某一作者的文章，并做Mark标记后，选择打印输出，就可以了解您的论文被SCI收录的情况了。 应用技巧二：如何了解国际上都有哪些科学家在关注您的课题？ 通过Web of Science的引文跟踪服务（Citation Alerts），您可以及时地跟踪您的一篇论文被新发表论文引用的情况，从而了解国际上都有哪些人在关注您的研究工作，他们为什么要引用您的论文，是否在您的课题基础上做了新的改进，是否对您的假说或理论提出了事实证据，是否指出了您研究工作的不足，他论文中的工作展望是否对您的下一步工作有借鉴意义，引文跟踪服务会直接将跟踪结果发到您的邮箱中。 1、注册个人帐号 为了让Web of Science知道您的邮箱地址，在做引文跟踪之前，首先要用您已有的e-mail邮箱在ISI Web of Knowledge中进行注册，注册方式如下：
七年级数学《知识点》总结2019.12.27 相交线与平行线 一、知识网络结构 垂线的性质： 性质1：过一点有且只有一条直线与已知直线垂直。 性质2：连接直线外一点与直线上各点的所有线段中，垂线段最短。 性质3：如图2所示，当 a ⊥ b 时， = = = = 90°。 点到直线的距离：直线外一点到这条直线的垂线段的长度叫点到直线的距离。 同位角、内错角、同旁内角基本特征： 平行公理：经过直线外一点有且只有一条直线与已知直线平行。 平行公理的推论：如果两条直线都与第三条直线平行，那么这两条直线也互相平行。 ???? ? ?????? ??????????? ? ??? ?????? ??????????? ??????????????????平移 命题、定理 的两直线平行：平行于同一条直线性质角互补 ：两直线平行，同旁内性质相等：两直线平行，内错角性质相等：两直线平行，同位角性质平行线的性质的两直线平行 ：平行于同一条直线判定直线平行 ：同旁内角互补，两判定线平行 ：内错角相等，两直判定线平行 ：同位角相等，两直判定定义平行线的判定平行线，不相交的两条直线叫平行线：在同一平面内平行线及其判定内角同位角、内错角、同旁垂线 相交线相交线相交线与平行线 4321 4321____________________________:图2 1 3 4 2 a b a 5 7 8 6 1 3 4 2 b c
平行线的性质： 10、平移：在平面内，将一个图形沿某个方向移动一定的距离，图形的这种移动叫做平移变换，简称平移。 平移后，新图形与原图形的形状和大小完全相同。平移后得到的新图形中每一点，都是由原图形中的某一点移动后得到的，这样的两个点叫做对应点。平移性质：平移前后两个图形中①对应点的连线平行且相等；②对应线段相等； ③对应角相等。 实数 实数的分类 1、按定义分类： 2.按性质符号分类： 注：0既不是正数也不是负数. 【知识点二】实数的相关概念 1.相反数 (1)代数意义：只有符号不同的两个数，我们说其中一个是另一个的相反数．0的相反数是0. (2)几何意义：在数轴上原点的两侧，与原点距离相等的两个点表示的两个数互为相反数，或数轴上，互为相反数的两个数所对应的点关于原点对称. (3)互为相反数的两个数之和等于0.a、b互为相反数 a+b=0. 2.绝对值 |a|≥0． 3.倒数（1）0没有倒数 (2)乘积是1的两个数互为倒数．a、b互为倒数 . 4.平方根 (1)如果一个数的平方等于a，这个数就叫做a的平方根．一个正数有两个平方根，它们互为相反数；0有一个平方根，它是0本身；负数没有平方根．a(a≥0)的平方根记作． (2)一个正数a的正的平方根，叫做a的算术平方根．a(a≥0)的算术平方根记作． 5.立方根 如果x3=a，那么x叫做a的立方根．一个正数有一个正的立方根；一个负数有一个负的立方根；零的立方根是零． 【知识点三】实数与数轴 数轴定义：规定了原点，正方向和单位长度的直线叫做数轴，数轴的三要素缺一不可． 【知识点四】实数大小的比较 1.对于数轴上的任意两个点，靠右边的点所表示的数较大.
引言 ISI web of Knowledge是根据www的超连接特性，建立了一个以知识为基础的学术信息资源整合平台。它是一个采用“一站式”信息服务的设计思路构建而成的数字化研究环境。该平台以三大引文索引数据库作为其核心，利用信息资源之间的内在联系，把各种相关资源提供给研究人员。兼具知识的检索、提取、管理、分析与评价等多项功能。在ISI web of Knowledge平台上，还可以跨库检索ISI proceedings、Derwent 、Innovations Index、BIOSIS Previews、CAB Abstracts、INSPEC以及外部信息资源。ISI web of Knowledge还建立了与其他出版公司的数据库、原始文献、图书馆OPAC以及日益增多的网页等信息资源之间的相互连接。实现了信息内容、分析工具和文献信息资源管理软件的无缝连接。 一个综合性、多功能的研究平台，涵盖了自然科学、社会科学、艺术和人文科学等方方面面的高品质、多样化的学术信息，配以强大的检索和分析工具，使各种宝贵资源触手可及。无论所要找的资料在国际性期刊、开放资源、书籍、专利、汇编资料或网站上，您都可以轻松搞定，绝不会混在铺天盖地的垃圾信息里让您无所适从。 本次上机实验将对以下几个方面进行学习和实践： ◆查找出高影响力综述类文章。 ◆获取本技术领域的主要研究国家、核心期刊、高产出研究人员和机构等信息。 ◆定制Web of Science的跟踪服务，了解技术领域每月的最新进展。 ◆查询自己的论文(或某一重要论文)引用情况，并定制该论文的引文跟踪服务。 ◆获取本领域的Top10期刊信息。 一．获取高影响力综述 登陆并进入ISI Web of Knowledge主页：
第10章 一次方程组检测题 （时间：90分钟，满分：100分） 一、选择题（每小题3分，共30分） 1. 下列方程中，是二元一次方程的是（ ） A ． B ． C ． 1x D ． 2 4 y - 中，是二元一次方程组的有（ ） A.2个 B.3个 C.4个 D.5个 3. 二元一次方程（ ） A ．有且只有一解 B ．有无数个解 C ．无解 D ．有且只有两个解 4. 方程组 的解与的值相等，则等于（ ） A.0 B.1 C.2 D.3 5. 某商店有两种进价不同的耳机都卖64元，其中一个盈利60%，另一个亏本20%，在这次买卖中，这家商店（ ） A.赔8元 B.赚32元 C.不赔不赚 D.赚8元 6. （2019?山东泰安中考）小亮的妈妈用28元钱买了甲、乙两种水果，甲种水果每千克 4元，乙种水果每千克6元，且乙种水果比甲种水果少买了2千克，求小亮妈妈两种水果各买了多少千克?设小亮妈妈买了甲种水果x 千克，乙种水果y 千克，则可列方程组 为（ ） A. B. C. D. 7. (2019·浙江宁波中考)如图，小明家的住房平面图是长方形， 被分割成3个正方形和2个长方形后仍是中心对称图形．若只知 道原住房平面图长方形的周长，则分割后不用测量就能知道周长 的图形的标号为( ) A.①② B.②③ C.①③ D.①②③ 8. 已知是方程组的解，则间的关系是（ ） A. B. C. D. 9．如图，点O 在直线AB 上，OC 为射线，1∠比2∠的3倍少?10，设1∠，2∠的度数分别 为x ,y ,那么下列求出这两个角的度数的方程组正确的是（ ） A.180,10x y x y +=?? =-? B.180, 310 x y x y +=??= -? C.180,10x y x y +=?? =+? D.3180, 310 y x y =??=-? 第7题图
Web of Science 数据库的检索与利用 解放军医学图书馆杜永莉? 一、引文检索概述 （一）基本概念 1. 引文（Citation）：文献中被引用、参考的文献(Cited Work),也称施引文献，其作者称为被引着者（Cited Author）。 2. 来源文献（Source）：提供引文的文献本身称为来源文献，其作者称为引用着者（Citing Author）。 3. 引文索引（Citation Index）：通过搜集大量来源文献及其引文，并揭示文献之间引用与被引用关系的检索工具。 4. 引文检索：是以被引用文献为检索起点来查找引用文献的过程。 （二）引文的历史回顾 引文的创始人Garfield博士是美国科学信息研究所（ISI）的创始人，现在仍然是科学信息研究所的名义董事长，还是美国信息科学协会的前任主席、The Scientist董事会的主席、Research America董事会的成员。另外他还是文献计量学的创始人。 “Citation Indexes for Science: 于1955年在Science上发表了具有化时代意义的学术论文： A New Dimension in Documentation through Association of Ideas.”他在这篇文章中描述科研人员可以利用引文加速研究过程、评估工作影响、跟踪科学趋势；阐明引文是学术研究中学术信息获取的重要工具。1957 他创建了美国科学信息研究所(Institute for Scientific Information，ISI)。
1961 年，ISI 推出了Science Citation Index ，SCI 。一种5卷印刷型刊物，包括613种期刊140万条引文的索引。1966年，ISI发布磁带形式的数据，1989年推出CD-ROM光盘版，1992年ISI为汤姆森科技信息集团接管（Thomson Scientific），1997年推出系列引文数据库（Web of Science)，2001年建立具有跨库检索功能的（ISI Web of Knowledge）。 20世纪30年代中期，另外一个着名计量学家布拉德福（在对大量的期刊分布进行研究之后，得出了布拉德福定律(二八定律），揭示出各学科核心期刊的存在，这些核心期刊组成了所有学科的文献基础,重要论文会发表在相对较少的核心期刊上；因此从文献学的角度,没有必要将已经出版的所有期刊全部收录，从数据库的质量上说，则需要有一套科学的流程筛选高质量期刊，为读者提供高质量的学术信息。 Garfield 博士从建立引文数据库开始，经过几十年的时间，建立了一整套期刊筛选的工作流程，每年从全球出版的学术期刊中，筛选出各学科中质量高、信息量大、使用率高的核心期刊。由于这套流程对期刊一些客观指数的长期跟踪，衍生出了另外两个数据库：期刊引证报告（Journal Citation Reports，JCR)和基本科学计量指标（Essential Science Indicators）。 （三）引文的作用 了解某一课题发生、发展、变化过程；查找某一重要理论或概念的由来；跟踪当前研究热点；了解自已以及同行研究工作的进展；查询某一理论是否仍然有效，而且已经得到证明或已被修正；考证基础理论研究如何转化到应用领域；评估和鉴别某一研究工作在世界学术界产生的影响力；发现科学研究新突破点；了解你的成果被引用情况；引文检索为科研人员开辟了一条新颖、实用的检索途径；同时为文献学、科学学、文献计量学等分析研究提供参考数据，如衡量期刊质量、测定文献老化程度、观察学科之间的渗透交叉关系、评价科研人员的学术水平，引文数据库是不可缺少重要工具。 二、Web of Science的检索途径 （一）科学引文索引简介