Spatiotemporal dynamics of the social structure of Indo-Pacific humpback dolphins(Sousa chinensis)in Xiamen waters from 2007 to 2019

As highly social animals,Indo-Pacific humpback dolphins(Sousa chinensis)exhibit community differentiation.Nevertheless,our understanding of the external and internal factors influencing these dynamics,as well as their spatiotemporal variations,is still limited.In the present study,variations in the social structure of an endangered Indo-Pacific humpback dolphin population in Xiamen Bay,China,were monitored over two distinct periods(2007–2010 and 2017–2019)to analyze the effects of habitat utilization and the composition of individuals within the population.In both periods,the population demonstrated a strikingly similar pattern of social differentiation,characterized by the division of individuals into two main clusters and one small cluster.Spatially,the two primary clusters occupied the eastern and western waters,respectively,although the core distribution area of the eastern cluster shifted further eastward between the two periods.Despite this distribution shift,the temporal stability of the social structure and inter-associations within the eastern cluster remained unaffected.A subset of 16individuals observed in both periods,comprising 51.6%and 43.2%of the population in each respective period,emerged as a foundational element of the social structure and may be responsible for sustaining social structure stability,especially during the 2007–2010 period.These observations suggest that the composition of dominant individuals,an internal factor,had a more substantial influence on the formation of the social network than changes in habitat use,an external factor.Consequently,the study proposes distinct conservation measures tailored to each of the two main clusters.

Dynamic model and driving strategy of corporate social responsibility

Corporate social responsibility is the inevitable requirement for the survival and development of enterprises in modern society,as well as the basic guarantee for the sustainable development of economy and society.In order to analyze the driving factors of corporate social responsibility from both internal and external aspects,the dynamic model of corporate social responsibility was constructed,and the driving strategies of corporate social responsibility were also suggested.The driving factors of corporate social responsibility include not only the external constraints of policies,regulations and stakeholders,but also the internal requirements of the sustainable development of enterprises.Only when the external driving force is transformed into the spontaneous and conscious responsibility behavior of the enterprise,can the enterprise achieve its due effect.Moreover,in order to better fulfill the social responsibility,enterprise should build a management system of social responsibility,enhance the awareness of social responsibility,and promote the effective implementation of social responsibility projects.The relevant department should strengthen the all-round supervision of corporate social responsibility,and further improve the policies,laws and regulations related to social responsibility.Enterprises should strengthen communication with stakeholders,actively disclose social responsibility information,and constantly improve social responsibility behavior.By implementing social responsibility,enterprise can improve its business environment,enhance corporate brand image and core competitiveness,and promote the sustainable development of economy and society.

Role of permafrost in resilience of social-ecological system and its spatio-temporal dynamics in the source regions of Yangtze and Yellow Rivers

Permafrost is one of the key components of terrestrial ecosystem in cold regions. In the context of climate change, few studies have investigated resilience of social ecological system(SER) from the perspective of permafrost that restricts the hydrothermal condition of alpine grassland ecosystem. In this paper, based on the structural dynamics, we developed the numerical model for the SER in the permafrost regions of the source of Yangtze and Yellow Rivers, analyzed the spatial-temporal characteristics and sensitivity of the SER, and estimated the effect of permafrost change on the SER. The results indicate that: 1) the SER has an increasing trend, especially after 1997, which is the joint effect of precipitation, temperature, NPP and ecological conservation projects; 2) the SER shows the spatial feature of high in southeast and low in northwest,which is consistent with the variation trends of high southeast and low northwest for the precipitation, temperature and NPP, and low southeast and high northwest for the altitude; 3) the high sensitive regions of SER to the permafrost change have gradually transited from the island distribution to zonal and planar distribution since 1980, moreover, the sensitive degree has gradually reduced; relatively, the sensitivity has high value in the north and south, and low value in the south and east; 4) the thickness of permafrost active layer shows a highly negative correlation with the SER. The contribution rate of permafrost change to the SER is-4.3%, that is, once the thickness of permafrost active layer increases 1 unit, the SER would decrease 0.04 units.

A Dynamic Social Network Data Publishing Algorithm Based on Differential Privacy

Social network contains the interaction between social members, which constitutes the structure and attribute of social network. The interactive relationship of social network contains a lot of personal privacy information. The direct release of social network data will cause the disclosure of privacy information. Aiming at the dynamic characteristics of social network data release, a new dynamic social network data publishing method based on differential privacy was proposed. This method was consistent with differential privacy. It is named DDPA (Dynamic Differential Privacy Algorithm). DDPA algorithm is an improvement of privacy protection algorithm in static social network data publishing. DDPA adds noise which follows Laplace to network edge weights. DDPA identifies the edge weight information that changes as the number of iterations increases, adding the privacy protection budget. Through experiments on real data sets, the results show that the DDPA algorithm satisfies the user’s privacy requirement in social network. DDPA reduces the execution time brought by iterations and reduces the information loss rate of graph structure.

Dynamic Social Network Analysis with Heterogeneous Sensors in Ambient Environment

This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications. Moreover, we present our design and implementation of a prototype system for quasi-realtime social network construction. We finally present preliminary experimental results of dynamic social network analysis for six-person social gatherings in a real environment, and discuss the feasibility of dynamic social network analysis and its effectiveness.

Community Detection in Dynamic Social Networks

There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.

Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network

Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.

OPINION DYNAMICS ON SOCIAL NETWORKS

Opinion dynamics has recently attracted much attention,and there have been a lot of achievements in this area.This paper first gives an overview of the development of opinion dynamics on social networks.We introduce some classical models of opinion dynamics in detail,including the DeGroot model,the Krause model,O-1 models,sign networks and models related to Gossip algorithms.Inspired by some real life cases,we choose the unit circle as the range of the individuals'opinion values.We prove that the individuals'opinions of the randomized gossip algorithm in which the individuals'opinion values are on the unit circle reaches consensus almost surely.

The Social Dynamics of Illegal Wood Harvesting on Indigenous Woody Vegetation: A Case Study of Chimanimani National Park, Eastern Zimbabwe

Illegal wood harvesting, a factor that threats conservation efforts, was assessed in Chimanimani National Park (CNP), Zimbabwe, in April 2016. The study identified preferred indigenous woody species, determined the uses, and collected local people’s perceptions on forest resource management by-laws and forest resource conservation in quantity. A three-stage sampling design was adopted;that is, 1) purposive sampling, where three wards (Chikukwa ward 11, Chikwakwa ward 17 and Ngorima ward 5b) were selected as study areas from a total of seventeen wards in Chimanimani District, 2) random sampling, where three villages [Batanai (75 households), Chikukwa (110 households) and Tamuka (46 households)], totalling 231 households, were selected from a total of 15 villages spread over the three wards. The third stage was the random of households where 46 questionnaires were administered and collected in the 3 randomly selected villages representing 20% of the targeted population. The study results highlighted that illegally harvested indigenous woody plants were principally for household uses. Preferred woody species included those of Brachystegia, Terminalia, Acacia, Bauhinia, Pericopsis, Combretum and Pericopsis genera. Quantities collected ranged from 0.2 to 0.4 tons of firewood monthly to 3 to 6 tons mostly for tobacco curing. Most respondents reported that by-laws were not effective in combating illegal wood harvesting. It was concluded that CNP woodlands are under stress due to illegal wood harvesting. It is recommended that a robust community-based strategy to conserve woody species and natural resource utilisation be developed.

Incremental Influence Maximization for Dynamic Social Networks

Influence maximization is one fundamental and important problem to identify a set of most influential individuals to develop effective viral marketing strategies in social network. Most existing studies mainly focus on designing efficient algorithms or heuristics to find Top-K influential individuals for static network. However, when the network is evolving over time, the static algorithms have to be re-executed which will incur tremendous execution time. In this paper, an incremental algorithm DIM is proposed which can efficiently identify the Top-K influential individuals in dynamic social network based on the previous information instead of calculating from scratch. DIM is designed for Linear Threshold Model and it consists of two phases: initial seeding and seeds updating. In order to further reduce the running time, two pruning strategies are designed for the seeds updating phase. We carried out extensive experiments on real dynamic social network and the experimental results demonstrate that our algorithms could achieve good performance in terms of influence spread and significantly outperform those traditional static algorithms with respect to running time.

Social Dynamics

THE sprawling expanse of Dadaab constitutes the largest refugee camp in the world by population, according to the United Nations High Commissioner for Refugees （UNHCR）. Situated in Kenya＇s Garissa County near the border with Somalia, this camp has been a city of exiles since 1993 and currently hosts almost 257,000 refugees who mainly come from Somalia. To put it into perspective, if Dabaab were a normal urban settlement, it would be Kenya＇s fourth largest city by population with an economy worth about $100 million, a result of donor funds delivered here every year.

On Dynamic Social Factors for Development of China’s Human Rights Theory

China＇s mainstream human rights theory has undergone tremendous changes since the reform-and-opening scheme was kicked off in the early 1980s. ＂Human rights,＂ which used to be seen as a concept of capitalist ideology, is now an important part of the Chinese system of socialist values.

Human Interaction Dynamics for Its Use in Mobile Robotics:Impedance Control for Leader-follower Formation

A complete characterization of the behavior in human-robot interactions(HRI) includes both: the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields.As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities: least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure.Finally, with the best fictitious force and its identified impedance,an impedance control is designed for a mobile robot Pioneer 3AT,which is programmed to follow a human in a structured scenario.According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.

A new collision avoidance model for pedestrian dynamics

The pedestrians can only avoid collisions passively under the action of forces during simulations using the social force model, which may lead to unnatural behaviors. This paper proposes an optimization-based model for the avoidance of collisions, where the social repulsive force is removed in favor of a search for the quickest path to destination in the pedestrian＇s vision field. In this way, the behaviors of pedestrians are governed by changing their desired walking direction and desired speed. By combining the critical factors of pedestrian movement, such as positions of the exit and obstacles and velocities of the neighbors, the choice of desired velocity has been rendered to a discrete optimization problem. Therefore,it is the self-driven force that leads pedestrians to a free path rather than the repulsive force, which means the pedestrians can actively avoid collisions. The new model is verified by comparing with the fundamental diagram and actual data. The simulation results of individual avoidance trajectories and crowd avoidance behaviors demonstrate the reasonability of the proposed model.

Dynamic programming methodology for multi-criteria group decision-making under ordinal preferences

A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.

CONSENSUS FORMATION OF TWO-LEVEL OPINION DYNAMICS

Opinion dynamics have received significant attention in recent years. This paper proposes a bounded confidence opinion model for a group of agents with two different confidence levels. Each agent in the population is endowed with a confidence interval around her opinion wiih radius αd or （1 - α）d, where α∈ （0, 1/2] represents the differentiation of confidence levels. We analytically derived the critical confidence bound dc = 1/（4α） for the two-level opinion dynamics on Z. A single opinion cluster is formed with probability 1 above this critical value regardless of the ratio p of agents with high/low confidence. Extensive numerical simulations are performed to illustrate our theoretical results. Noticed is a clear impact of p on the collective behavior： more agents with high confidence lead to harder agreement. It is also experimentally revealed that the sharpness of the threshold dc increases with a but does not depend on p.

Forecasting investment and consumption behavior of economic agents through dynamic computable general equilibrium model

Much research has been devoted to examination of the financial easing policy of the European Central Bank(ECB).However,this study is one of the first to use a dynamic micro-founded model to investigate empirically the impact of the ECB’s Quantitative Easing(QE)policy on consumption and investment by economic agents in Italy(households,government,firms,and the rest of the world).For this purpose,we constructed a Financial Social Accounting Matrix(FSAM)for the Italian economy for the year 2009 to calibrate a dynamic computable general equilibrium model(DCGE).This model allowed us to evaluate the direct and indirect impact of money flow on the behavior of consumption and investment.The findings of the study confirmed the positive impact of the ECB’s monetary policy on the level of investment and consumption.

一种基于改进DynamicTriad模型的动态链路预测方法

针对动态社交网络的链路预测,文章提出了一种改进的DynamicTriad模型,该模型以动态三元闭环结构为载体,三个顶点组成一个基本网络单元,结合网络同质性和节点相似性指标,对动态网络进行建模,跨时间片对每个节点进行向量表示,从而实现社交网络个体行为的动态预测,并通过t+1时段的嵌入向量验证t时段的预测效果。实验表明,该模型在动态表示节点关系的同时,链路预测效果优于传统算法,支持对动态社交网络的建模和分析。

A Game-Theoretic Modeling Approach to Comprehend the Advantage of Dynamic Health Interventions in Limiting the Transmission of Multi-Strain Epidemics

In the situation of inadequate vaccines and rapid mutation of virulent strains, alternative health interventions play a crucial role in the containment of emerging epidemics. This study elucidates the critical aspects of health interventions to control epidemic resurgence. Besides, human behavioral response to epidemics plays an instrumental role in bringing the success of control efforts. The appearance of multi-strain epidemics has become a global health concern that requires special attention. Here, we introduce a novel mean-field epidemic game approach to predict the evolutionary dynamics of flu-like epidemics having multiple disease strains. Our model illustrates the importance of multiple provisions alongside their timely execution for better disease attenuation. In addition to vaccination, we introduce self-protection as a potential alternative that yields safeguard against either strain. Both these imperfect provisions render better efficacy against primary (resident) strain than secondary (mutant) to contain epidemic transmission. The simulation-backed model analysis further sheds some light on the crucial impacts of control interventions to limit the invasion of virulent strains from qualitative and quantitative viewpoints. It explicates how vaccination and self-protection complement each other as per situation demands. Our full-fledged theoretical approach further illustrates the dynamic trade-off between the cost and efficacy of a certain intervention. We confirm that the disease dies out when the basic reproduction number of individual strains is less than one and becomes endemic if it is greater than one. Finally, the model addresses the evolutionary consequences when mutation takes place from primary to secondary strain. Some impressive facts while employing dual provisions have been reinforced using a game-theoretic framework.