THE EFFECT OF TRUST ON INFORMATION DIFFUSION IN ONLINE SOCIAL NETWORKS

online social networks have a explosive growth in recent years and they provide a perfect platform for information diffusion. Many models have been given to explore the information diffusion procedure and its dynamics. But the trust relationship and memory effect are ignored. Based on the complex network theory, The information diffusion model is proposed and the network users, considered as agents, are classified into susceptible, infected and recovered individuals. The users’ behaviour rule and diffusion process are designed. The proposed agent-based model is tested by simulation experiments in four different complex networks: regular network, small world network, random network and scale-free network. Moreover, the effect of four immunization strategies are explored. The research results show that the influence of users’ trust relationship on different networks is varied, and the vertex weight priority immunization strategy is the best one in all four networks

Moderating effects of perceived social support on self-efficacy and psychological well-being of Chinese nurses: a cross-sectional study

IntroductionNurses experience significant physical and psychological stress that negatively influences their psychological well-being. The objective of this study was to explore the association between self-efficacy and psychological well-being among Chinese nurses and to assess the moderating effects of perceived social support (PSS).MethodsIn 2020, a hospital-based cross-sectional study using a multistage random sampling approach was performed in five regions of Liaoning, China. Of the 1,200 surveyed nurses, 1,010 completed questionnaires that evaluated the demographic information, 14-item Hospital Anxiety and Depression Scale, General Self-Efficacy Scale, and Multidimensional Scale of Perceived Social Support. To examine the factors associated with mental health parameters, hierarchical multiple regression analysis was performed. The interactions were visualized using a simple slope analysis.ResultsThe mean depression and anxiety scores for Chinese nurses were 8.74 ± 3.50 and 6.18 ± 3.26, respectively. The association between self-efficacy and depression differed between the low perceived social support (PSS) group (1 SD below the mean, β = −0.169, p < 0.01) and high PSS group (1 SD above the mean, β = −0.077, p < 0.01). Similarly, the association between self-efficacy and anxiety differed between the low PSS group (1 SD below the mean, β = −0.155, p < 0.01) and high PSS group (1 SD above the mean, β = −0.044, p < 0.01).ConclusionWe found that Chinese nurses experienced high levels of anxiety and depression. Furthermore, PSS moderates the relationship between self-efficacy and psychological well-being. Therefore, interventions targeting self-efficacy and PSS should be implemented to improve the psychological well-being of nurses

Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

Abstract A modified particle swarm optimization (PSO) approach is presented for the multi‐mode resource‐constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple‐AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi‐mode resource‐constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm

Can Large Language Models Exhibit Cognitive and Affective Empathy as Humans?

Empathy, a critical component of human-social interaction, has become a core concern in human-computer interaction. This study examines whether current large language models (LLMs) can exhibit empathy in both cognitive and affective dimensions, akin to humans. We proposed a novel paradigm for LLMs' evaluation based on the standardized questionnaires. Four main experiments were reported on LLMs' empathy abilities. Specifically, GPT-4 and Llama3 were tested as indexed by the Interpersonal Reactivity Index (IRI) and the Basic Empathy Scale (BES). Two levels of evaluations were conducted to investigate whether the structural validity of the questionnaire in LLMs is aligned with humans and to compare the LLMs’ empathy abilities with humans directly. GPT-4 showed identical empathy dimensions to humans while exhibiting significantly lower empathy abilities in both cognitive and affective dimensions. The completely different empathy ability was more evident in Llama3 by showing its failure to exhibit the same empathy dimensions as we humans. All these findings indicate that LLMs cannot convey empathy ability as we humans currently. This highlights the need for further development and fine-tuning of LLMs to enhance their empathy abilities. In addition, the way to prompt LLMs to simulate diverse LLMs-based participants was discussed as well as the sampling strategy

Early warning of burst passenger flow in public transportation system

Burst passenger flow in the public transportation system is serious to public safety. Existing works mainly focused on prediction and monitoring of regular passenger flow, which are not suitable for burst passenger flow. In this article, we first formulate the problem as early warning of burst passenger flow. Next, we design a novel framework to solve this problem by our observation that a burst passenger in-flow usually comes after an abnormal passenger out-flow for a subway station, especially when there is a large-scale social crowd event. Our framework consists of two models: (1) Abnormal out-flow detection (AOFD) which detects abnormal out-flows and warns the city administration of the burst in-flow fairly ahead of time. (2) Burst in-flow peak estimation (BIFPE) which estimates burst in-flow peak time and volume. We evaluate our framework with real-world smartcard data of the largest city in China and use large-scale social crowd event data to further explain our model. The result shows that: (1) AOFD can detect abnormal out-flows that would later result in bursts in-flows with better performance and can send warning signal ahead of the time of burst passenger in-flow. (2) BIFPE can effectively estimate the peak time of burst in-flow and can reduce peak volume estimation error compared with the traditional passenger flow prediction models

Simulation and experimental research on high-power microwave coupling with computer box

The electromagnetic coupling characteristics of a computer box under the high-power microwave (HPM) which includes the narrow-band and ultra-wideband HPM are studied. The simulations of radiation field in the computer box show that the optical drive and floppy disk drive are main channels through which the high-power microwave penetrates. The cut-off frequency of this corresponding dielectric waveguide and resonant frequency of computer box are the frequencies on which the coupling field is enhanced. The induced current on the cable show that the peak current value decreases with the increasing frequency. At the same time, the damage effect experiments of the computer under high-power microwave are carried out and the thresholds of three effect levels are summarized. The experimental results and the simulation are verified to some extent

Metal-Organic Framework-Derived Sea-Cucumber-like FeS2@C Nanorods with Outstanding Pseudocapacitive Na-Ion Storage Properties

Sodium-ion batteries (SIBs) are supposed to be attractive energy strorage and supply devices due to the abundant reserves of sodium. Their limited specific capacity and rate capacity, however, are standing in the way of the extensive application of SIBs. It is reported herein that porous sea-cucumber-like FeS2@C nanorods can act as efficient cathode materials to satisfy the rigorous requirements of the proposed applications. The fabrication of the sea-cucumber-like FeS2@C nanorods involves the hydrothermal growth of F-MIL (where F = Fe, MIL = materials from the Lavoisier Institute) nanorods, and subsequent sulfidation. The electrochemical results demonstrate that the FeS2@C nanorods are an outstanding cathode material for SIBs with high specific capacity (385 mAh/g), ultralong lifetime (160 mAh/g after 10 000 cycles at 20 A/g), and exceptional rate capability. The metal−organic framework (MOF) template method provides a useful route toward the development of high-performance electrode materials with robust power and cyclability

Co9S8@carbon nanospheres as high-performance anodes for sodium ion battery

Co 9 S 8 @C nanospheres with a 5 nm carbon shell and 50 nm Co 9 S 8 core have been synthesized by a facile hydrothermal method followed by an annealing process. The electrochemical results indicate that the Co 9 S 8 @C nanospheres are an excellent anode material for sodium ion batteries. They deliver a specific capacity of 305 mA h g −1 after 1000 cycles at current density of 5 A g −1 and a high specific capacity of 405 mA h g −1 at current density of 500 mA g −1 . The good cycling performance and rate capability are attributed to the synergistic effects of the carbon shell and the ultra-small size of Co 9 S 8 , which can enhance the electrical conductivity of the composite, as well as resisting the strain caused by repeated charge/discharge processes

Nanoconfined SnS in 3D interconnected macroporous carbon as durable anodes for lithium/sodium ion batteries

Nanoconfined SnS in 3D interconnected macroporous carbon (3D SnS/C) has been produced using silica opals as template following a carbonization and sulfuration route. The electrochemical properties of the 3D SnS/C were examined comprehensively as anode materials for lithium/sodium ion batteries (LIBs/SIBs). It delivers a high specific capacity of 869 mA h g −1 at 1 A g −1 after 1000 cycles in LIBs and 400 mA h g −1 at 100 mA g −1 after 100 cycles in SIBs. The rate performance is also excellent (550 mA h g −1 at 3 A g −1 in LIBs and 220.9 mA h g −1 at 5 A g −1 in SIBs). The outstanding electrochemical performance of the 3D SnS/C is ascribed to its 3D porous carbon interconnected structure and nanoconfined SnS nanoparticles distributing broadly in carbon matrix, which not only improve the conductivity, but also keep the structure integrity, and as a result of enhancing the cycling stability of the material. In addition, this facile and novelty strategy can be potentially utilized for preparing other 3D metal sulfides interconnected macroporous carbon composite for energy storage

3D spongy CoS2nanoparticles/carbon composite as high-performance anode material for lithium/sodium ion batteries

A spongy CoS 2 /carbon composite assembled from CoS 2 nanoparticles (∼20 nm) homogeneously anchored on a spongy carbon matrix was synthesized through a facile freeze-drying method and a hydrothermal process. As anode material for lithium/sodium ion batteries (LIBs/SIBs), this composite shows significantly enhanced lithium/sodium storage performance with the synergetic effects due to the electrical conductivity of the carbon matrix and the porous structure, which provide buffer spaces for volume expansion during charge/discharge processes and feasible transfer pathways for electrons/ions. The electrochemical results demonstrate that the spongy CoS 2 /carbon composite is an outstanding anode material for LIBs and SIBs. It delivers a high specific capacity of 610 mAh g −1 at 500 mA g −1 after 120 cycles in LIBs and 330 mAh g −1 at 500 mA g −1 after 60 cycles in SIBs, respectively. Moreover, the freeze-drying/hydrothermal process developed in this work could be useful for the construction of many other high-capacity metal sulfide composites as electrode materials for sodium ion batteries