Interactions in social networks have become an integral part of people's daily lives. In various decision-making situations, individuals usually hold diverse prior beliefs and engage in communication with their social connections to make informed decisions. However, most existing research focuses on isolated steps of this process, overlooking the overall complexity of decision-making in social networks. To bridge this important research gap, our paper aims to explore the key steps involved in the process and develop a holistic framework for analyzing how individuals form, exchange and update beliefs, ultimately leading to opinion dynamics and group decision behaviors in asocial network. Specifically, relevant literature that focuses on different steps will be reviewed and drawn together to characterize the decision-making process in a comprehensive and systematic manner: individuals form initial beliefs following the principle of multiple criteria decision-making intuitively, information propagates in the social network and affects individuals' beliefs differently in a form of social influence, beliefs evolve through dynamic interactions with others, and eventually individuals make their decisions, leading to group decision behaviors in the social network. Applications will be briefly discussed to illustrate the practical implications of this research. Finally, conclusions and future research outlook will be discussed in detail. It is expected that the holistic framework developed on the basis of the comprehensive literature review can provide in-depth insights into decision analysis in social networks and shed light on future research and applications towards effective integration of decision science, operational research, and social network analysis.

The past decade has seen a rapid and vast adoption of social media globally and over sixty percent of people were connected online through various social media platforms as of the start of 2024. Despite many advantages social media offers, one of the most significant challenges is the rapid rise of fake news and AI-generated deepfakes across these social networks. The spread of fake news and deepfakes can lead to a series of negative impacts, such as social trust, economic consequences, public health and safety crises, as demonstrated during the COVID-19 pandemic. Hence, it is more important now than ever to develop solutions to identify such fake news and deepfakes, and curb their spread. This paper begins with a review of the literature on the definitions of fake news and deepfakes, their different types and major differences, and the ways they spread. Building on this literature research, this paper aims to analyse how fake news can be identified using machine learning models, and understand how data analytics can be leveraged to evaluate the impact of such fake news on public behaviour and trust. A fake news detection framework is developed, where TF-IDF vectorization and bag of n-grams methods are implemented to extract text features, and six typical machine learning models are used to detect fake news, with the XGBoost classifier achieving the highest accuracy using both feature extraction methods. Additionally, a convolutional neural network model is designed to detect deepfake images with two distinct architectures, namely, ResNet50 and DenseNet121. To analyse the emotional impact of fake news on public behaviour and trust, a trained natural language toolkit called VADER lexicon is used to assign sentiment polarity and emotion strength to articles. The rampant rise of deepfake technology poses huge risks to social trust and privacy issues, which impacts both individuals and society at large, and leveraging the effective use of data analytics, machine learning and AI techniques can help prevent irreparable damage and mitigate the negative impacts of deepfakes in social networks. Finally, the paper discusses some practical solutions to mitigate the negative impacts of fake news and deepfakes.

Social media users are playing an increasingly important role in disseminating information, but their ability to diffuse information may vary significantly. Therefore, evaluating the influential ability of users has become crucial to promote or curb the dissemination of specific information. Existing centrality measures have produced varying results in identifying the most influential users, but it remains a challenge to identify the most influential users in a multifaceted and consistent way in social networks, especially when only a limited number of users can be nominated. To fill this gap, this work developed an evidential reasoning-based influential users evaluation (ERIUE) model that considers multiple sources of structural information in networks. Our proposed model collates information about users’ influential ability from multiple forms of centrality measures and maps their scores to different grades in an informative belief distribution. To determine the weight of each centrality, three types of information are considered: conflict of belief distributions, similarity of probability sets, and overlap of evaluations. The information is aggregated using the recursive evidential reasoning approach based on a formulated criterion hierarchy, thereby determining the influential ability of users. The applicability of our proposed model is demonstrated by comparing it with existing measures in three real-world social networks. Our proposed model is also applicable to relevant problems beyond identifying influential users, including preventing epidemic spread, cascade failure, and misinformation dissemination in social networks.

This paper aims to examine the short-term impact of government interventions on 11 industrial sectors in the Indonesian Stock Exchange (IDX) during the COVID-19 pandemic. Whereas earlier studies have widely investigated the impact of government interventions on the financial markets during the pandemic, there is lack of research on analysing the financial impacts of various interventions in different industrial sectors, particularly in Indonesia. In this research, five key types of government interventions are selected amid the pandemic from March 2020 to July 2021, including economic stimulus packages, jobs creation law, Jakarta lockdowns, Ramadan travel restrictions, and free vaccination campaign. Based on an event study methodology, the research reveals that the first economic stimulus package was critical in reviving most sectors following the announcement of the first COVID-19 case in Indonesia. Jakarta lockdowns impacted stock returns negatively in most sectors, but the impacts were relatively insignificant in comparison to other countries in the region. The recurrence of lockdowns in Jakarta had a minor detrimental impact, showing that the market had acclimated to the new normal caused by the COVID-19 pandemic. Additionally, Ramadan travel restrictions caused minor negative impacts on the stock market. Furthermore, the second Ramadan travel restrictions generated a significant reaction from the technology sector. Finally, while free vaccination campaign and job creation law did not significantly boost the stock market, both are believed to result in a positive long-term effect on the country’s economy if appropriately executed. The findings are critical for investors, private companies, and governments to build on recovery action plans for major industrial sectors, allowing the stock market to bounce back quickly and efficiently. As this study limits its analysis to the short-term impact of individual interventions, future studies can examine long-term and combined effects of interventions which could also help policy makers to form effective portfolios of interventions in the event of a pandemic.

Energy development concerns not only the development of renewable energies but also the shift from centralised to clean, decentralised power generation. The development of decentralised energy (DE) is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future. This paper reviews the concepts, development status, trends, benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems. A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis, which considers the identification, definition and assessment grade of decision criteria. The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system. Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.

In order to attain the general goal of energy security and environmental protection in a balanced way, sustainable energy development should take into account not only energy saving, but also energy efficiency and flexible combination of different types of energy. The trend of future energy should therefore develop more renewable energy resources while transferring a centralized energy system to a clean and decentralized energy system. This paper described the concept, development status, development trends, benefits and challenges of DE systems, and proposed a performance evaluation model based on the multiple criteria decision analysis (MCDA) method which can be used to incorporate objectives in the decision making process of DE systems.

Multiple criteria decision analysis (MCDA) methods have become increasingly popular in the performance assessment and decision making of renewable energy systems due to the multi-dimensionality and complexity of its technical, environmental, economic and social impacts. The aim of this paper is to develop a preliminary performance assessment model for the feasibility analysis of constructing different multi-vector decentralized renewable energy systems in an industrial park. Four micro-grid energy systems are identified as alternative solutions in terms of the local environmental conditions and energy profile of the industrial park, and these alternatives are evaluated against a set of performance criteria. The evidential reasoning approach with the implementation of Intelligent Decision System (IDS) is then applied to aggregate assessment information in order to obtain the overall ranking of alternative solutions. sensitivity and trade-off analysis is conducted to validate the robustness of the decision making process.

The modelling for renewable energy system (RES) performance evaluation and impact analysis can be a challenging task, where there are multiple criteria with both quantitative and qualitative forms under uncertainty. Multi-criteria decision analysis (MCDA) methods have become increasingly popular in the decision-making for renewable energy systems because of the multi-dimensionality of the sustainability goal and the complexity of the technical, environmental, economic and social perspectives. This paper aims to review the applications of MCDA methods to the performance modelling and impact analysis of RESs, primarily in four relevant areas, including renewable energy planning and policy, renewable energy evaluation and assessment, renewable energy project selection and allocation and RES environmental impact assessment. Further research can be conducted to study the feasibility of different RESs and the selection of an appropriate MCDA methodology in alternative energy decision-making.

A recognition method based on Wavelet Packet Decomposition - Common Spatial Patterns (WPD-CSP) and Kernel Fisher Support Vector Machine (KF-SVM) is developed and used for EEG recognition in motor imagery brain–computer interfaces (BCIs). The WPD-CSP is used for feature extraction and KF-SVM is used for classification. The presented recognition method includes the following steps: (1) some important EEG channels are selected. The 'haar' wavelet basis is used to take wavelet packet decomposition. And some decomposed sub-bands related with motor imagery for each EEG channel are reconstructed to obtain the relevant frequency information. (2) A six-dimensional feature vector is obtained by the CSP feature extraction to the reconstructed signal. And then the within-class scatter is calculated based on the feature vector. (3) The scatter is added into the radical basis function to construct a new kernel function. The obtained new kernel is integrated into the SVM to act as its kernel function. To evaluate effectiveness of the proposed WPD-CSP + KF-SVM method, the data from the 2008 international BCI competition are processed. A preliminary result shows that the proposed classification algorithm can well recognize EEG data and improve the EEG recognition accuracy in motor imagery BCIs.

目的 设计一款满足消费者不同体验需求的双屏手机,可以方便消费者的操作,提高消费者在使用智能手机时的体验兴趣.方法 通过对用户使用手机的习惯和人体特征的研究,提出一种新型手机的设计理念——双屏手机.双屏手机集全触屏手机与键盘手机于一体,实现同系统异屏操作,在三维软件下完成了双屏手机的效果设计和基本功能设计,最后采用3D打印技术制作出了双屏手机模型.结论 该手机打破了消费者使用手机的局限生,并提高了消费者使用手机的交互体验兴趣.

Based on the design principles for open systems,a motion control card is developed for miniature CNC milling machines.First,the function of motion control card and hardware arrangement are designed.To meet the demand of system functions,several systems are developed, including a DSP system,a main shaft motor control system,a stepping motor control system in three directions,and a dual RAM communication port system.The PCB diagram of the control card is designed,and the circuit board debugged.The control card has successfully been applied in aplatform of miniature CNC milling machine.the method described in this paper is a good reference in further design of control system.

Due to the urgent requirements of the current domestic robots development and technical talents, this paper has proposed a methodology on service robot development platform, which is used to achieve the target of basic teaching and stimulate students' creativity. The development platform is composed of mechanical structure, hardware and software control systems. The advantages of the platform are modular, openly designed and easily restructured. So, the robot technology can be developed on the platform. The platform integrates machinery, electronics, communications, computer technology etc. Especially, the control software adopts 3D simulation technology besides network management and remote control functions. It is well-known that Single-arm robot is representative in the development platform. This robot is taken for examples of the mechanical structure, control systems and software design methods during the development platform. The results have been proved that the development platform is beneficial to improve students' practical abilities, help employment and highlight advanced studies.

To study brain-computer interface,a method of feature classification used for two kinds of imaginations is proposed.The method is based on support vector machine,and classification is achieved using an adaptive genetic algorithm with optimal support vector machine parameters.We study the experimental background and theoretical foundation using the data sets of BCI 2005,and compare the classification error with other methods,especially with the best result in a competition.It have been shown that the method is effect and has advantages for applying to practical systems.

In brain-computer interfaces (BCIs), a feature selection approach using a gene optimization algorithm (GO) is described in this paper. GO algorithm realizes self-organizing optimization from bottom to top from microcosmic to macroscopic based on genetic mutations; it can save much online data processing time and improve EEG classification accuracy. The performance of GO algorithm is compared with those of adaptive genetic algorithm (AGA) and filter selection method in selecting feature subset for BCIs. Simulation results show that the proposed GO algorithm provides superior performance in classification accuracy.

在脑机接口研究中,针对脑电信号的特征提取,提出一种基于EMD的Hilbert变换的方法.在变换过程中根据信号的局部特征自动选择基函数,求得信号在每个时间段的希尔波特谱;以时频窗口内的统计特性作为特征,利用Fisher距离选择最佳特征集输入分类器.最后利用BCI 2003竞赛数据,通过对特征矢量的可分性和识别精度两个指标的评估,表明了所提出方法的有效性.

Traveling salesman problem(TSP) has wide applications on optimization theory and engineering practice.With the definition of discrete state variables and local fitness,we analyze the microscopic characteristics of TSP solutions and present a novel self-organized optimization algorithm with extremal dynamics.In this algorithm,the local optimal solutions can be effectively found by the optimization dynamics combining greedy search with fluctuated explorations.Computational results on typical TSP benchmark problems in TSPLIB demonstrate that the proposed algorithm outperforms competing optimization techniques,such as simulated annealing(SA) and genetic algorithm(GA).Since this optimization method considers the micro-mechanisms of computational systems,it provides a systematic viewpoint on computational complexity and effectively helps the design of optimization dynamics on a wide spectrum of combinatorial optimization problems.

It introduces a novel auto-control mode of the ship loader.Presents a method of bulk ship loader’s remote-operation that controlled by the port’s central-monitor. Meanwhile,Design a load-inspect system for the auto-control loader by the technology of sensor inspection and image manipulation.

The main components of hydraulic excavators and the main characteristics of ALP75 multiple unit valve were introduced.And the hydraulic system was improved according to the applicative requirement of excavating-robot.The result shows that the type of multiple unit valve is selected reasonable,and the requirement of function is met by the improvement design of excavating-robot.

To ensure that the structured light vision sensor on welding robot can effectively work in a specified range,we design a structured light vision sensor with an internal cooling system,and analyze the inner temperature field distribution with ANSYS.The experimental results show that it can work for a long time at high temperature when welding.

Aiming at the topic of electroencephalogram (EEG) pattern recognition in brain computer interface (BCI), a classification method based on probabilistic neural network (PNN) with supervised learning was presented in this paper. It applied the recognition rate of training samples to the learning progress of network parameters, The learning vector quantization is employed to group training samples and the Genetic algorithm (GA) is used for training the network's smoothing parameters and hidden central vector for determining hidden neurons. Utilizing the standard dataset 1(a) of BCI Competition 2003 and comparing with other classification methods, the experiment results show that this way has the best performance of pattern recognition, and the classification accuracy can reach 93.8%, which improves over 5% compared with the best result (88.7%) of the competition. This technology provides an effective way to EEG classification in practical system of BCI.