The paper posits that kin sociality and eusociality are derived from the handicap-care principles based on the need-based care to the handicappers from the caregivers for the self-interest of the caregivers. In this p...The paper posits that kin sociality and eusociality are derived from the handicap-care principles based on the need-based care to the handicappers from the caregivers for the self-interest of the caregivers. In this paper, handicap is defined as the difficulty to survive and reproduce independently. Kin sociality is derived from the childhood handicap-care principle where the children are the handicapped children who receive the care from the kin caregivers in the inclusive kin group to survive. The caregiver gives care for its self-interest to reproduce its gene. The individual’s gene of kin sociality contains the handicapped childhood and the caregiving adulthood. Eusociality is derived from the adulthood handicap-care principle where responsible adults are the handicapped adults who give care and receive care at the same time in the interdependent eusocial group to survive and reproduce its gene. Queen bees reproduce, but must receive care from worker bees that work but must rely on queen bees to reproduce. A caregiver gives care for its self-interest to survive and reproduce its gene. The individual’s gene of eusociality contains the handicapped childhood-adulthood and the caregiving adulthood. The chronological sequence of the sociality evolution is individual sociality without handicap, kin sociality with handicapped childhood, and eusociality with handicapped adulthood. Eusociality in humans is derived from bipedalism and the mixed habitat. The chronological sequence of the eusocial human evolution is 1) the eusocial early hominins with bipedalism and the mixed habitat, 2) the eusocial early Homo species with bipedalism, the larger brain, and the open habitat, 3) the eusocial late Homo species with bipedalism, the largest brain, and the unstable habitat, and 4) extended eusocial Homo sapiens with bipedalism, the shrinking brain, omnipresent imagination, and the harsh habitat. The omnipresence of imagination in human culture converts eusociality into extended eusociality with both perception and omnipresent imagination.展开更多
Mammals generally form social groups to gain better protection from or to capture prey more efficiently.Open habitats are especially conducive to group formation because in those habitats social defenses are most effe...Mammals generally form social groups to gain better protection from or to capture prey more efficiently.Open habitats are especially conducive to group formation because in those habitats social defenses are most effective at deterring predators .Closed habitats are more conducive to solitary living because then concealment.is ofen the best defense against predation. Group size is typically limited by competition for resources, and hense it is usually larger when food is clumped or relatively abundant. Social organization often revolves around status relationship, which are frequently based on agonistic interactions.展开更多
Revenge permeates most of Shakespeare’s plays,and scholars understand the themes in historical context.Some believe that the plays involved revenge are evidence that the public was morally opposed to it.For others,ho...Revenge permeates most of Shakespeare’s plays,and scholars understand the themes in historical context.Some believe that the plays involved revenge are evidence that the public was morally opposed to it.For others,however,these plays expressed the frustrations and desires for justice because revenge could be used to eliminate social diseases and ensure social order.The justice crisis of revenge play reflects the judicial crisis in the early modern English legal system.Pufendorf believes that sociality is decisive factor of social order.The law in a hierarchical society has its natural bias,which will inevitably lead to social inequality and do harm to the cultivation of sociality.Shakespeare’s stage reproduces the crisis and threat faced by the whole judiciary due to a large number of social injustice in the early stage of modern British legal transformation,and reveals the great impact of sociality on the community.展开更多
Social dysfunction is a risk factor for several neuropsychiatric illnesses.Previous studies have shown that the lateral septum(LS)-related pathway plays a critical role in mediating social behaviors.Howeve r,the role ...Social dysfunction is a risk factor for several neuropsychiatric illnesses.Previous studies have shown that the lateral septum(LS)-related pathway plays a critical role in mediating social behaviors.Howeve r,the role of the connections between the LS and its downstream brain regions in social behavio rs remains unclea r.In this study,we conducted a three-chamber test using electrophysiological and chemogenetic approaches in mice to determine how LS projections to ventral CA1(vCA1)influence sociability.Our res ults showed that gamma-aminobutyric acid(GABA)-e rgic neuro ns were activated following social experience,and that social behavio rs were enhanced by chemogenetic modulation of these neurons.Moreover,LS GABAergic neurons extended their functional neural connections via vCA1 glutamatergic pyramidal neurons,and regulating LSGABA→vCA1Gluneural projections affected social behaviors,which were impeded by suppressing LSprojecting vCA1 neuronal activity or inhibiting GABAAreceptors in vCA1.These findings support the hypothesis that LS inputs to the vCA1 can control social prefe rences and social novelty behaviors.These findings provide new insights rega rding the neural circuits that regulate sociability.展开更多
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...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.展开更多
Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response.Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice ...Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response.Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region–specific,particularly involving the corticolimbic system,including the ventral tegmental area,nucleus accumbens,prefrontal cortex,amygdala,and hippocampus.Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology.In this review,we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression.We focused on associated molecular pathways and neural circuits,with special attention to the brain-derived neurotrophic factor–tropomyosin receptor kinase B signaling pathway and the ventral tegmental area–nucleus accumbens dopamine circuit.We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature,severity,and duration of stress,especially in the above-mentioned brain regions of the corticolimbic system.Therefore,BDNF might be a biological indicator regulating stress-related processes in various brain regions.展开更多
Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social ...Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.展开更多
Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph ...Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.展开更多
The dynamics of animal social structures are heavily influenced by environmental patterns of competition and cooperation.In folivorous colobine primates,prevailing theories suggest that larger group sizes should be fa...The dynamics of animal social structures are heavily influenced by environmental patterns of competition and cooperation.In folivorous colobine primates,prevailing theories suggest that larger group sizes should be favored in rainforests with a year-round abundance of food,thereby reducing feeding competition.Yet,paradoxically,larger groups are frequently found in high-altitude or high-latitude montane ecosystems characterized by a seasonal scarcity of leaves.This contradiction is posited to arise from cooperative benefits in heterogeneous environments.To investigate this hypothesis,we carried out a six-year field study on two neighboring groups of golden snub-nosed monkey(Rhinopithecus roxellana),a species representing the northernmost distribution of colobine primates.Results showed that the groups adjusted their movement and habitat selection in response to fluctuating climates and spatiotemporal variability of resources,indicative of a dynamic foraging strategy.Notably,during the cold,resource-scarce conditions in winter,the large group occupied food-rich habitats but did not exhibit significantly longer daily travel distances than the smaller neighboring group.Subsequently,we compiled an eco-behavioral dataset of 52 colobine species to explore their evolutionary trajectories.Analysis of this dataset suggested that the increase in group size may have evolved via home range expansion in response to the cold and heterogeneous climates found at higher altitudes or latitudes.Hence,we developed a multi-benefits framework to interpret the formation of larger groups by integrating environmental heterogeneity.In cold and diverse environments,even smaller groups require larger home ranges to meet their dynamic survival needs.The spatiotemporal distribution of high-quality resources within these expanded home ranges facilitates more frequent interactions between groups,thereby encouraging social aggregation into larger groups.This process enhances the benefits of collaborative actions and reproductive opportunities,while simultaneously optimizing travel costs through a dynamic foraging strategy.展开更多
We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwi...We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.展开更多
The insect mitogenome is typically a compact circular molecule with highly conserved gene contents.Nonetheless,mitogenome structural variations have been reported in specific taxa,and gene rearrangements,usually the t...The insect mitogenome is typically a compact circular molecule with highly conserved gene contents.Nonetheless,mitogenome structural variations have been reported in specific taxa,and gene rearrangements,usually the tRNAs,occur in different lineages.Because synapomorphies of mitogenome organizations can provide information for phylogenetic inferences,comparative analyses of mitogenomes have been given increasing attention.However,most studies use a very few species to represent the whole genus,tribe,family,or even order,overlooking potential variations at lower taxonomic levels,which might lead to some incorrect inferences.To provide new insights into mitogenome organizations and their implications for phylogenetic inference,this study conducted comparative analyses for mitogenomes of three social bee tribes(Meliponini,Bombini,and Apini)based on the phylogenetic framework with denser taxonomic sampling at the species and population levels.Comparative analyses revealed that mitogenomes of Apini and Bombini are the typical type,while those of Meliponini show diverse variations in mitogenome sizes and organizations.Large inverted repeats(IRs)cause significant gene rearrangements of protein coding genes(PCGs)and rRNAs in Indo-Malay/Australian stingless bee species.Molecular evolution analyses showed that the lineage with IRs have lower dN/dS ratios for PCGs than lineages without IRs,indicating potential effects of IRs on the evolution of mitochondrial genes.The finding of IRs and different patterns of gene rearrangements suggested that Meliponini is a hotspot in mitogenome evolution.Unlike conserved PCGs and rRNAs whose rearrangements were found only in the mentioned lineages within Meliponini,tRNA rearrangements are common across all three tribes of social bees,and are significant even at the species level,indicating that comprehensive sampling is needed to fully understand the patterns of tRNA rearrangements,and their implications for phylogenetic inference.展开更多
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
An accurate assessment of the evacuation efficiency in case of disasters is of vital importance to the safety design of buildings and street blocks.Hazard sources not only physically but psychologically affect the ped...An accurate assessment of the evacuation efficiency in case of disasters is of vital importance to the safety design of buildings and street blocks.Hazard sources not only physically but psychologically affect the pedestrians,which may further alter their behavioral patterns.This effect is especially significant in narrow spaces,such as corridors and alleys.This study aims to integrate a non-spreading hazard source into the social force model following the results from a previous experiment and simulation,and to simulate unidirectional pedestrian flows over various crowd densities and clarity–intensity properties of the hazard source.The integration include a virtual repulsion force from the hazard source and a decay on the social force term.The simulations reveal(i)that the hazard source creates virtual bottlenecks that suppress the flow,(ii)that the inter-pedestrian push forms a stabilisation phase on the flow-density curve within medium-to-high densities,and(iii)that the pedestrians are prone to a less orderly and stable pattern of movement in low clarity–intensity scenarios,possibly with lateral collisions passing the hazard source.展开更多
It has been reported both in clinic and rodent models that beyond spinal cord injury directly induced symptoms, such as paralysis, neuropathic pain, bladder/bowel dysfunction, and loss of sexual function, there are a ...It has been reported both in clinic and rodent models that beyond spinal cord injury directly induced symptoms, such as paralysis, neuropathic pain, bladder/bowel dysfunction, and loss of sexual function, there are a variety of secondary complications, including memory loss, cognitive decline, depression, and Alzheimer's disease. The largescale longitudinal population-based studies indicate that post-trauma depression is highly prevalent in spinal cord injury patients. Yet, few basic studies have been conducted to address the potential molecular mechanisms. One of possible factors underlying the depression is the reduction of adult hippocampal neurogenesis which may come from less physical activity, social isolation, chronic pain, and elevated neuroinflammation after spinal cord injury. However, there is no clear consensus yet. In this review, we will first summarize the alteration of hippocampal neurogenesis post-spinal cord injury. Then, we will discuss possible mechanisms underlie this important spinal cord injury consequence. Finally, we will outline the potential therapeutic options aimed at enhancing hippocampal neurogenesis to ameliorate depression.展开更多
A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social netw...A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.展开更多
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for ind...The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.展开更多
The advent of immersive technologies such as the metaverse,extended reality,artificial intelligence,and blockchain offers novel possibilities to transform healthcare services.These innovations coincide with clinicians...The advent of immersive technologies such as the metaverse,extended reality,artificial intelligence,and blockchain offers novel possibilities to transform healthcare services.These innovations coincide with clinicians'aspirations to deliver more comprehensive,patient-centered care tailored to individuals singular needs and preferences.Integration of these emerging tools may confer opportunities for providers to engage patients through new modalities and expand their role.However,responsible implementation necessitates deliberation of ethical implications and steadfast adherence to foundational principles of compassion and interpersonal connection underpinning the profession.While the metaverse introduces new channels for social prescribing,this perspective advocates that its ultimate purpose should be strengthening,not supplanting,human relationships.We propose an ethical framework centered on respect for patients'dignity to guide integration of metaverse platforms into medical practice.This framework serves both to harness their potential benefits and mitigate risks of dehumanization or uncompassionate care.Our analysis maps the developing topology of metaverse-enabled care while upholding moral imperatives for medicine to promote healing relationships and human flourishing.展开更多
In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure in...In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.展开更多
With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to mult...With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.展开更多
文摘The paper posits that kin sociality and eusociality are derived from the handicap-care principles based on the need-based care to the handicappers from the caregivers for the self-interest of the caregivers. In this paper, handicap is defined as the difficulty to survive and reproduce independently. Kin sociality is derived from the childhood handicap-care principle where the children are the handicapped children who receive the care from the kin caregivers in the inclusive kin group to survive. The caregiver gives care for its self-interest to reproduce its gene. The individual’s gene of kin sociality contains the handicapped childhood and the caregiving adulthood. Eusociality is derived from the adulthood handicap-care principle where responsible adults are the handicapped adults who give care and receive care at the same time in the interdependent eusocial group to survive and reproduce its gene. Queen bees reproduce, but must receive care from worker bees that work but must rely on queen bees to reproduce. A caregiver gives care for its self-interest to survive and reproduce its gene. The individual’s gene of eusociality contains the handicapped childhood-adulthood and the caregiving adulthood. The chronological sequence of the sociality evolution is individual sociality without handicap, kin sociality with handicapped childhood, and eusociality with handicapped adulthood. Eusociality in humans is derived from bipedalism and the mixed habitat. The chronological sequence of the eusocial human evolution is 1) the eusocial early hominins with bipedalism and the mixed habitat, 2) the eusocial early Homo species with bipedalism, the larger brain, and the open habitat, 3) the eusocial late Homo species with bipedalism, the largest brain, and the unstable habitat, and 4) extended eusocial Homo sapiens with bipedalism, the shrinking brain, omnipresent imagination, and the harsh habitat. The omnipresence of imagination in human culture converts eusociality into extended eusociality with both perception and omnipresent imagination.
文摘Mammals generally form social groups to gain better protection from or to capture prey more efficiently.Open habitats are especially conducive to group formation because in those habitats social defenses are most effective at deterring predators .Closed habitats are more conducive to solitary living because then concealment.is ofen the best defense against predation. Group size is typically limited by competition for resources, and hense it is usually larger when food is clumped or relatively abundant. Social organization often revolves around status relationship, which are frequently based on agonistic interactions.
文摘Revenge permeates most of Shakespeare’s plays,and scholars understand the themes in historical context.Some believe that the plays involved revenge are evidence that the public was morally opposed to it.For others,however,these plays expressed the frustrations and desires for justice because revenge could be used to eliminate social diseases and ensure social order.The justice crisis of revenge play reflects the judicial crisis in the early modern English legal system.Pufendorf believes that sociality is decisive factor of social order.The law in a hierarchical society has its natural bias,which will inevitably lead to social inequality and do harm to the cultivation of sociality.Shakespeare’s stage reproduces the crisis and threat faced by the whole judiciary due to a large number of social injustice in the early stage of modern British legal transformation,and reveals the great impact of sociality on the community.
基金supported by the National Natural Science Foundation of China,No.82171521(to CL)the Special Funds ofTaishan Scholars Project of Shandong Province,No.tsqn202211368(to CL)+2 种基金the Natural Science Foundation of Shandong Province,Nos.ZR2022YQ65(to CL),ZR2021MH073(to CL),ZR2019PH109(to WW)the Projects of Medical and Health Technology Development Program in Shandong Province,China,Nos.202003090720(to DZ),202003070728(to JL),2019 WS329(to DW)the Scientific Research Foundation of Binzhou Medical University,No.BY2018KJ21(to DW)。
文摘Social dysfunction is a risk factor for several neuropsychiatric illnesses.Previous studies have shown that the lateral septum(LS)-related pathway plays a critical role in mediating social behaviors.Howeve r,the role of the connections between the LS and its downstream brain regions in social behavio rs remains unclea r.In this study,we conducted a three-chamber test using electrophysiological and chemogenetic approaches in mice to determine how LS projections to ventral CA1(vCA1)influence sociability.Our res ults showed that gamma-aminobutyric acid(GABA)-e rgic neuro ns were activated following social experience,and that social behavio rs were enhanced by chemogenetic modulation of these neurons.Moreover,LS GABAergic neurons extended their functional neural connections via vCA1 glutamatergic pyramidal neurons,and regulating LSGABA→vCA1Gluneural projections affected social behaviors,which were impeded by suppressing LSprojecting vCA1 neuronal activity or inhibiting GABAAreceptors in vCA1.These findings support the hypothesis that LS inputs to the vCA1 can control social prefe rences and social novelty behaviors.These findings provide new insights rega rding the neural circuits that regulate sociability.
基金supported by the National Natural Science Foundation of China (32030011,31630071)National Key Research and Development Program of China (2022YFF1301600)Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘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.
基金supported financially by the National Natural Science Foundation of China,No.82071272(to YZ).
文摘Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response.Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region–specific,particularly involving the corticolimbic system,including the ventral tegmental area,nucleus accumbens,prefrontal cortex,amygdala,and hippocampus.Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology.In this review,we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression.We focused on associated molecular pathways and neural circuits,with special attention to the brain-derived neurotrophic factor–tropomyosin receptor kinase B signaling pathway and the ventral tegmental area–nucleus accumbens dopamine circuit.We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature,severity,and duration of stress,especially in the above-mentioned brain regions of the corticolimbic system.Therefore,BDNF might be a biological indicator regulating stress-related processes in various brain regions.
基金supported by the NationalNatural Science Foundation of China(61972136)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T201410,T2020017)+1 种基金the Natural Science Foundation of Xiaogan City(XGKJ2022010095,XGKJ2022010094)the Science and Technology Research Project of Education Department of Hubei Province(No.Q20222704).
文摘Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 62273272,62303375 and 61873277in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-243+2 种基金in part by the Natural Science Foundation of Shaanxi Province under Grants 2022JQ-606 and 2020-JQ758in part by the Research Plan of Department of Education of Shaanxi Province under Grant 21JK0752in part by the Youth Innovation Team of Shaanxi Universities.
文摘Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.
基金supported by the Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment,China(2019HJ2096001006)National Natural Science Foundation of China(32001099,32170512,32370524)China Postdoctoral Science Foundation(2020M683539)。
文摘The dynamics of animal social structures are heavily influenced by environmental patterns of competition and cooperation.In folivorous colobine primates,prevailing theories suggest that larger group sizes should be favored in rainforests with a year-round abundance of food,thereby reducing feeding competition.Yet,paradoxically,larger groups are frequently found in high-altitude or high-latitude montane ecosystems characterized by a seasonal scarcity of leaves.This contradiction is posited to arise from cooperative benefits in heterogeneous environments.To investigate this hypothesis,we carried out a six-year field study on two neighboring groups of golden snub-nosed monkey(Rhinopithecus roxellana),a species representing the northernmost distribution of colobine primates.Results showed that the groups adjusted their movement and habitat selection in response to fluctuating climates and spatiotemporal variability of resources,indicative of a dynamic foraging strategy.Notably,during the cold,resource-scarce conditions in winter,the large group occupied food-rich habitats but did not exhibit significantly longer daily travel distances than the smaller neighboring group.Subsequently,we compiled an eco-behavioral dataset of 52 colobine species to explore their evolutionary trajectories.Analysis of this dataset suggested that the increase in group size may have evolved via home range expansion in response to the cold and heterogeneous climates found at higher altitudes or latitudes.Hence,we developed a multi-benefits framework to interpret the formation of larger groups by integrating environmental heterogeneity.In cold and diverse environments,even smaller groups require larger home ranges to meet their dynamic survival needs.The spatiotemporal distribution of high-quality resources within these expanded home ranges facilitates more frequent interactions between groups,thereby encouraging social aggregation into larger groups.This process enhances the benefits of collaborative actions and reproductive opportunities,while simultaneously optimizing travel costs through a dynamic foraging strategy.
基金This work was supported by the National Natural Science Foundation of China(Grant No.12072340)the China Postdoctoral Science Foundation(Grant No.2022M720727)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2022ZB130).
文摘We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB31000000)Science and Technology Basic Resources Investigation Program of China(2021FY100200)+1 种基金Yunnan Revitalization Talent Support Program“Young Talent”and"Innovation Team"Projectsthe 14th Five-Year Plan of Xishuangbanna Tropical Botanical Garden,Chinese Academy of Science(XTBG-1450101)。
文摘The insect mitogenome is typically a compact circular molecule with highly conserved gene contents.Nonetheless,mitogenome structural variations have been reported in specific taxa,and gene rearrangements,usually the tRNAs,occur in different lineages.Because synapomorphies of mitogenome organizations can provide information for phylogenetic inferences,comparative analyses of mitogenomes have been given increasing attention.However,most studies use a very few species to represent the whole genus,tribe,family,or even order,overlooking potential variations at lower taxonomic levels,which might lead to some incorrect inferences.To provide new insights into mitogenome organizations and their implications for phylogenetic inference,this study conducted comparative analyses for mitogenomes of three social bee tribes(Meliponini,Bombini,and Apini)based on the phylogenetic framework with denser taxonomic sampling at the species and population levels.Comparative analyses revealed that mitogenomes of Apini and Bombini are the typical type,while those of Meliponini show diverse variations in mitogenome sizes and organizations.Large inverted repeats(IRs)cause significant gene rearrangements of protein coding genes(PCGs)and rRNAs in Indo-Malay/Australian stingless bee species.Molecular evolution analyses showed that the lineage with IRs have lower dN/dS ratios for PCGs than lineages without IRs,indicating potential effects of IRs on the evolution of mitochondrial genes.The finding of IRs and different patterns of gene rearrangements suggested that Meliponini is a hotspot in mitogenome evolution.Unlike conserved PCGs and rRNAs whose rearrangements were found only in the mentioned lineages within Meliponini,tRNA rearrangements are common across all three tribes of social bees,and are significant even at the species level,indicating that comprehensive sampling is needed to fully understand the patterns of tRNA rearrangements,and their implications for phylogenetic inference.
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
基金Project supported by National Key Research and Development Program of China(Grant Nos.2022YFC3320800 and 2021YFC1523500)the National Natural Science Foundation of China(Grant Nos.71971126,71673163,72304165,72204136,and 72104123).
文摘An accurate assessment of the evacuation efficiency in case of disasters is of vital importance to the safety design of buildings and street blocks.Hazard sources not only physically but psychologically affect the pedestrians,which may further alter their behavioral patterns.This effect is especially significant in narrow spaces,such as corridors and alleys.This study aims to integrate a non-spreading hazard source into the social force model following the results from a previous experiment and simulation,and to simulate unidirectional pedestrian flows over various crowd densities and clarity–intensity properties of the hazard source.The integration include a virtual repulsion force from the hazard source and a decay on the social force term.The simulations reveal(i)that the hazard source creates virtual bottlenecks that suppress the flow,(ii)that the inter-pedestrian push forms a stabilisation phase on the flow-density curve within medium-to-high densities,and(iii)that the pedestrians are prone to a less orderly and stable pattern of movement in low clarity–intensity scenarios,possibly with lateral collisions passing the hazard source.
基金supported by the Showalter Research Trust Fund (to XG)Indiana Spinal Cord&Brain Injury Research Fund (ISCBIRF) from the Indiana State Departm ent of Health (to XG)。
文摘It has been reported both in clinic and rodent models that beyond spinal cord injury directly induced symptoms, such as paralysis, neuropathic pain, bladder/bowel dysfunction, and loss of sexual function, there are a variety of secondary complications, including memory loss, cognitive decline, depression, and Alzheimer's disease. The largescale longitudinal population-based studies indicate that post-trauma depression is highly prevalent in spinal cord injury patients. Yet, few basic studies have been conducted to address the potential molecular mechanisms. One of possible factors underlying the depression is the reduction of adult hippocampal neurogenesis which may come from less physical activity, social isolation, chronic pain, and elevated neuroinflammation after spinal cord injury. However, there is no clear consensus yet. In this review, we will first summarize the alteration of hippocampal neurogenesis post-spinal cord injury. Then, we will discuss possible mechanisms underlie this important spinal cord injury consequence. Finally, we will outline the potential therapeutic options aimed at enhancing hippocampal neurogenesis to ameliorate depression.
基金supported by the National Natural Science Foundation of China Project(No.62302540)The Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)+2 种基金Natural Science Foundation of Henan Province Project(No.232300420422)The Natural Science Foundation of Zhongyuan University of Technology(No.K2023QN018)Key Research and Promotion Project of Henan Province in 2021(No.212102310480).
文摘A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.
基金Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
文摘The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.
文摘The advent of immersive technologies such as the metaverse,extended reality,artificial intelligence,and blockchain offers novel possibilities to transform healthcare services.These innovations coincide with clinicians'aspirations to deliver more comprehensive,patient-centered care tailored to individuals singular needs and preferences.Integration of these emerging tools may confer opportunities for providers to engage patients through new modalities and expand their role.However,responsible implementation necessitates deliberation of ethical implications and steadfast adherence to foundational principles of compassion and interpersonal connection underpinning the profession.While the metaverse introduces new channels for social prescribing,this perspective advocates that its ultimate purpose should be strengthening,not supplanting,human relationships.We propose an ethical framework centered on respect for patients'dignity to guide integration of metaverse platforms into medical practice.This framework serves both to harness their potential benefits and mitigate risks of dehumanization or uncompassionate care.Our analysis maps the developing topology of metaverse-enabled care while upholding moral imperatives for medicine to promote healing relationships and human flourishing.
基金This research was funded by the General Project of Philosophy and Social Science of Heilongjiang Province,Grant Number:20SHB080.
文摘In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.
文摘With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.
