Search Results (386)

Uncertainty exists in reality decision-making problems. Therefore, fuzzy theories, fuzzy intervals, intuitionistic fuzzy, and Z-numbers have been proposed and successfully applied to multi-criteria decision-making (MCDM). However, since the information presented in the Z-numbers method is the subjective opinion of the decision-maker, the problem of overestimating or underestimating the reliability of the information may occur in both individual and group decision-making. The Extended Z-numbers (Z^E-numbers) method was proposed in 2021 to solve this problem; hence, the decision-making process no longer relies on subjective opinions only but seeks external experts related to the problem to further vote on the evaluation value given by the internal decision-maker, in order to modify the information’s reliability, and thus to obtain a more realistic result. This paper combines the Z^E-numbers method with the improved Elimination et Choix Traduisant la Realite II (ELECTRE II) proposed in 2022 and proposes a new MCDM method based on Z^E-numbers, named Z^E-ELECTRE II. The green supplier selection problem was used as an illustrative example. Meanwhile, the close analysis in this paper examines two primary dimensions of variability: (1) simulation of external expert voting situations to analyze the variations in information reliability and decision-making results and to cross-compare them with other MCDM methods; (2) investigation of the impact of internal preferences, as reflected through systematic adjustments to the weights of the evaluation criteria. The results show that uncertainty of information, reliability, and the perspectives of different decision-makers and expert groups can be considered through Z^E-numbers. The proposed Z^E-ELECTRE II is applicable to group decision-making, validates the robustness of the process, and is suitable for dynamic decision-making under varying decision-maker preferences. Furthermore, using the Z^E-numbers along with the MCDM method can obtain more flexibility and more reliable results. Full article

This paper applies the concept of symmetry to the design of a research methodology for public opinion evolution, emphasizing that both the construction and analysis processes of the method embody symmetrical principles. In today’s information age, dominated by social media, online platforms have become crucial venues for information dissemination. While the free flow of information promotes public participation, it also introduces certain challenges. Therefore, analyzing the evolution of public opinion and extracting public sentiment holds significant practical value for managing online public sentiment. This study takes the Zibo barbecue incident as a case study, utilizing the two-dimensional theory of emotion and Top2Vec for thematic analysis of public opinion comments. By combining sentiment dictionary methods with the RoBERTa model, we conduct a sentiment polarity analysis of public opinion comments. The results show that the RoBERTa model achieved an accuracy of 98.46% on the test set. The proposed method effectively uncovers public sentiment biases and the influencing factors on public emotions during the evolution of public opinion events, providing a more comprehensive understanding of the emotional dynamics throughout the development of public sentiment. This deeper insight aids in addressing issues related to public opinion more effectively. Full article

In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals’ persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments. Full article

Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) about the correctness of autonomy decisions, which is distorted by human anchoring and omission bias. Specifically, (i) anchoring bias (tendency to confirm prior opinion) causes the imperception of key information and miscomprehending correctness of autonomy decisions; (ii) omission bias (inaction tendency) causes the overestimation of predicted loss caused by takeover. This paper proposes a novel HAIP safety assessment method considering effects of the above biases. First, an SA-based takeover decision model (SAB-TDM) is proposed. In SAB-TDM, SA perception and comprehension affected by anchoring bias are quantified with the Adaptive Control of Thought-Rational (ACT-R) theory and Anchoring Adjustment Model (AAM); behavioral utility prediction affected by omission bias is quantified with Prospect Theory. Second, guided by SAB-TDM, a dynamic Bayesian network is used to assess HAIP safety. A case study on autonomous ship collision avoidance verifies effectiveness of the method. Results show that the above biases mutually contribute to seriously threaten HAIP safety. Full article

Residents of coal mining communities include both coal mine workers and local residents, making the guidance of public opinion essential for maintaining social stability in such communities. Therefore, this paper utilizes an improved word2Vec model to extract factors influencing public opinion from a large number of accident cases. It then develops a coal mining community public opinion dissemination model based on game theory, focusing on two groups: official media and opinion leaders. By integrating cellular automata (CA) and agent-based modeling, this study examines the dissemination patterns of public opinion in coal mining communities. The simulation results and practical applications indicate that public opinion in coal mining communities spreads rapidly and can be effectively shaped. Positive guidance from official media plays a crucial role in directing public opinion. However, as interactions evolve, public opinion dynamics may become less favorable. Strengthening the intensity of positive guidance from official media further enhances its ability to shape and influence public opinion in coal mining communities. This research provides a novel perspective and methodology for studying community safety management, offering significant theoretical and practical implications. Full article

In the wave of digitalization, organizations are increasingly focused on whether to prioritize digital culture or organizational strategy for the use of artificial intelligence (AI); there are mixed opinions, particularly when AI-augmented HRM draws attention as a tool for achieving sustainable organizational performance (SOP) in developing countries. This study aims to explore the influence of digital culture and organizational strategy on AI-augmented HRM and SOP, focusing on the mediating role of AI-augmented HRM in these relationships. To investigate the hypothesized relationships, 219 sample data were gathered from employees associated with HRM-oriented activities in Bangladesh, and SPSS 23 and AMOS software were used to test the SEM model. The results proved that digital culture has an insignificant effect and organizational strategy has a significant effect on AI-augmented HRM, and AI-augmented HRM has a substantial effect on SOP and partially mediates the relationship between organizational strategy and SOP. Based on the results, we infer that the successful implementation of AI-augmented HRM can lead to organizational sustainability in developing countries, where organizational strategy plays a pivotal role rather than digital culture. This research incorporates the resource-based view (RBV) and dynamic capabilities theories, which are crucial for the groundbreaking development of the research model. The results suggest that managers and responsible authorities should prioritize organizational strategy over digital culture when implementing AI-augmented HRM systems to ensure sustainability in developing countries. However, in the long run, organizations also need to concentrate on generating digitally favorable environments. Full article

To investigate public perceptions regarding tunnel fire disasters and optimize the tunnel fire disaster prevention framework, this study takes the emerging social media platform Douyin as a case study, conducting an in-depth analysis of 2133 short videos related to tunnel fires on the platform. A computational communication method was used for analysis, Latent Dirichlet Allocation was used to cluster the discussion topics of these tunnel fire short videos, and a spatiotemporal evolution analysis of the number of videos posted, user comments, and emotional inclinations across different topics was performed. The findings reveal that there is a noticeable divergence in public opinion regarding emergency decision making in tunnel fires, related to the complexity of tunnel fire incidents, ethical dilemmas in tunnel fire escape scenarios, and insufficient knowledge popularization of fire safety practices. The study elucidates the public’s actual needs during tunnel fire incidents, and a dynamic disaster prevention framework for tunnel fires based on social media and artificial intelligence is proposed on this basis to enhance emergency response capabilities. Utilizing short videos on social media, the study constructs a critical target dataset under real tunnel fire scenarios. It proposes a computer vision-based model for identifying critical targets in tunnel fires. This model can accurately and in real-time identify key targets such as fires, smoke, vehicles, emergency exits, and people in real tunnel fire environments, achieving an average detection precision of 77.3%. This research bridges the cognitive differences between the general public and professionally knowledgeable tunnel engineers regarding tunnel fire evacuation, guiding tunnel fire emergency responses and personnel evacuation. Full article

The advent of social commerce platforms fueled by the growing commercialization of social media and networking sites represents a significant evolution in e-commerce dynamics. This study investigates the pivotal role of key opinion leaders (KOLs), particularly YouTubers, in shaping consumer purchasing behavior. Recognizing the powerful influence exerted by KOLs, we examined their ability to promote product diffusion through credibility, specialized knowledge, and strategic word-of-mouth campaigns. This study employs a robust theoretical framework that foregrounds the influence of KOLs while integrating critical constructs, such as perceived value and risk, into a comprehensive model. Our empirical analysis, based on data from 411 valid responses, yields the following insights: the expertise and renown of KOLs exert a profound effect on consumer purchase intentions; consumer perceptions of value positively correlate with trust, whereas perceived risk negatively affects it; and trust mediates the relationship between KOL characteristics (popularity and professionalism) and consumers’ relationship strength with purchase intentions. The findings advocate leveraging KOLs’ renown and expertise while mitigating perceived risks to amplify consumer purchase intentions, thus providing actionable strategies for marketers in the burgeoning social commerce landscape. Full article

Building maintenance and operation costs represent a significant portion of the life cycle costs (LCC) of construction projects. The accurate estimation of these costs is essential for ensuring the long-term sustainability and financial efficiency of buildings. This study aims to develop a novel framework for predicting maintenance and operation costs in construction projects by integrating an emotional artificial neural network (EANN). Unlike traditional models that rely on linear regression or static machine learning, the EANN dynamically adapts its learning through synthetic emotional feedback mechanisms and advanced optimization techniques. The research collected input data from 313 experts in the field of building management and construction in Ha’il, Saudi Arabia, through a comprehensive questionnaire. The integration of expert opinions with advanced machine learning techniques contributes to the innovative approach, providing more reliable and adaptive cost predictions. The proposed EANN model was then compared with a classic artificial neural network (ANN) model to evaluate its performance. The results indicate that the EANN model achieved an R² value of 0.85 in training and 0.81 in testing for buildings aged 0 to 10 years, significantly outperforming the ANN model, which achieved R² values of 0.78 and 0.72, respectively. Additionally, the Root Mean Squared Error (RMSE) for the EANN model was 1.57 in training and 1.60 in testing, lower than the ANN’s RMSE values of 1.82 and 1.90. These findings show that the superior capability of the EANN model in estimating maintenance and operation costs.. This led to more accurate long-term maintenance cost projections, reduced budgeting uncertainty, and enhanced decision-making reliability for building managers. Full article

The purpose of this study was to explore the experiential knowledge of tennis coaches as it related to the development of grip positions in tennis athletes. Accredited tennis coaches (n = 11) completed semi-structured interviews consisting of open-ended questions about their coaching background, the importance of grip positions compared with other areas of foundational development, and their opinions on using physically-constraining tools (PCTs). Two major themes, “Grip positions are an adaptive skill” and “Why and how do I modify an athlete’s grip?”, were identified. Coaches expressed the opinion that grip positions were dynamic and a modifiable component of tennis stroke technique. Irrespective of shot type, grip positions were viewed as a non-negotiable aspect of talent development and intrinsically linked to other components of the stroke. Coaches questioned the necessity of technique refinement for grip positions given the complex and time-costly nature of bringing about effective motor-behaviour change. Some coaches expressed reservations about skill transfer into live match-play, intuitively expressing the concepts of the constraints-led approach to manipulate key variables within the athlete’s environment to foster learning. Future research should aim to assess the short- and long-term effects of PCT use in tennis and establish the extent to which PCTs can impact learning and skill transfer. Full article

Image contrast enhancement is an essential process that improves the visibility of many features that may remain hidden due to low-contrast conditions arising from environmental causes, limitations of the device, or the wrong setting of the camera. This paper introduces a new technique of image contrast enhancement that combines insights from social sciences and image processing. In this model, the intensity of each pixel represents the opinion of an individual, and all the neighboring pixels interact by influencing each other. The algorithm operates to first increase the similarity of those pixels in the regions where pixels maintain similar intensities and, second, to amplify the differences in regions where differences exist. This process increases the contrast in regions with significant differences and reduces variation in uniform regions, hence enhancing clarity in the visual information and details of the image. The effectiveness and high performance of the proposed method are evaluated by a variety of experiments conducted on different image datasets using different quality indexes. The results obtained after experimentation highlight the superiority of the approach with respect to the state-of-the-art techniques of contrast enhancement. Full article

Starting from a symmetrical multiple-choice individual, I build a sociophysics model of decision-making. Reducing the choices to two and interactions to pairs recovers the Ising model from physics at zero temperature. The associated equilibrium state results from a spontaneous symmetry breaking, with the whole group sharing a unique choice, which is selected at random. However, my focus departs from physics, which aims at identifying the true equilibrium state, discarding any possible impact of the initial conditions, the size of the sample, and the update algorithm used. Memory of past history is erased. In contrast, I claim that dealing with a social system, the history of the system must be taken into account in identifying the relevant social equilibrium state, which is always biased by its history. Accordingly, using Monte Carlo simulations, I explore the spectrum of non-universal equilibrium states of the Ising model at zero temperature. In particular, I show that different initial conditions with the same value of the order parameter lead to different equilibrium states. The same applies for different sizes and different update algorithms. The results indicate that in the presence of a social network composed of agents sharing different initial opinions, it is their interactions that lead them to share a unique choice and not their mere membership in the network. This finding sheds a new light on the emergence of echo chambers, which appear to be the end of a dynamical process of opinion update and not its beginning with a preferential attachment. Furthermore, polarization is obtained as a side effect of the random selection of the respective unanimous choices of the various echo chambers within a social community. The study points to social media exchange algorithms, which are purely technical levers independent of the issue and opinions at stake, to tackle polarization by either hindering or accelerating the completion of symmetry breaking between agents. Full article

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state ($S$) into active ($S_a$) and inactive ($S_i$) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks. Full article

Sustainability issues constitute a distinct subdiscipline of librarianship and information science, with its own areas of study, methods, and areas of application. Despite being nearly 30 years old, there are still divergent opinions on its current phase of development and its links to other scientific disciplines. The authors aim to clarify and summarize the ongoing discussion through citation analysis, shedding light on the lifecycle of research papers in sustainability-oriented library and information science, the current research subjects of focus, the influence of subdomains within the discipline on other scientific areas, and the overall quantitative and qualitative landscape of the discipline. A detailed elucidation of the inquiry’s results is intended to outline the discipline’s cognitive structure and its impact on sustainability science. The lifecycle of disciplinary papers indicates the dynamic development of the field. Sustainability-oriented library and information science is well-established, and its research focus has already been consolidated. The optimal citation window for measuring the impact strength in this discipline is a period of 3 to 4 years. “Culture” and “Education” have been identified as the most forward-looking subdisciplines, whereas “Buildings” and “Collections” exhibit less dynamic growth. The social sustainability pillar is the dominant one, while the environmental pillar is slightly less prominent. The economic pillar is the least represented. Although the majority of information exchange occurs within the discipline, it maintains strong and numerous links with several other fields, including both technical and social sciences, as well as the humanities. Full article

Anatomy education has evolved significantly with the introduction of diverse instructional techniques. This review evaluates these methods, including traditional cadaver dissection, three-dimensional (3D) model printing, virtual dissection using tools like the Anatomage table, problem-based learning (PBL), and the use of wax and plastinated models. Each approach presents unique benefits and challenges. Cadaver dissection remains invaluable for providing hands-on experience and a deep understanding of anatomical structures, although it faces ethical, logistical, and financial constraints. Wax and plastinated models offer durable, precise representations of anatomical structures without the ethical concerns associated with cadavers. Additionally, 3D printing and virtual dissection have emerged as effective supplementary tools, enhancing spatial understanding and allowing repeated practice. PBL integrates anatomical knowledge with clinical reasoning, promoting critical thinking and problem-solving skills. The main aim of this study was to gather and analyze students’ opinions on various anatomy teaching methods, while a secondary objective was to review the literature on novel and traditional approaches in anatomy education. This review emphasizes the importance of incorporating a variety of teaching methods to create a dynamic and engaging anatomy curriculum, preparing students for clinical practice. Full article