Search Results (98)

Evaluating the human–machine interface (HMI) of service robots remains challenging due to the complex integration of perceptual aesthetics and functional rationality. To address this, we propose a hybrid multidimensional HMI evaluation method that quantifies three key dimensions—layout aesthetics, color aesthetics, and functional layout rationality—by integrating visual cognition theory and axiomatic design (AD). The framework operationalizes five layout principles (balance, proportion, unity, regularity, density) and a four-component color model (color difference, distribution, harmony, and personality), complemented by a biologically grounded metric—visual perceptual intensity (VPI)—derived from cone cell response theory. Subjective weights from expert judgments (via analytic hierarchy process, AHP) and objective weights from the entropy weighting method (EWM) are fused within an AD-based information axiom framework to enable balanced, data-driven assessment. Applied to five candidate HMIs for a medical service robot (N = 15 participants), the method identified the design scheme x₃ as optimal when the balancing coefficient α ≥ 0.5 (reflecting greater emphasis on subjective judgment), whereas design scheme x₂ was preferred when α < 0.5 (prioritizing objective data). Given the modest sample size, correlation analysis revealed moderate-to-large—though not reaching conventional significance—between evaluation indicator scores and eye-tracking behavior: unity correlated with total fixation duration (Pearson_r = 0.682), and color harmony with first fixation duration (Pearson_r = 0.788), suggesting alignment between design attributes and visual attention patterns. These preliminary findings suggest that key design attributes may influence visual attention patterns, supporting the framework’s potential to link aesthetic and visual choices to measurable perceptual outcomes. Full article

This study presents a tripartite analytical framework for the (1+1)-dimensional nonlinear Klein–Fock–Gordon equation, a key model for spinless particles in relativistic quantum mechanics. The investigation begins with a Painlevé analysis showing that the equation is completely integrable via the Painlevé test by using Maple. Subsequently, classical Lie symmetry analysis is employed to derive the infinitesimal generators of the equation. A Lagrangian formulation is constructed for these generators, from which similarity variables are systematically obtained. This framework enables a complete similarity reduction, transforming the complex nonlinear partial differential equation into a more tractable ordinary differential equation. To solve this reduced ordinary differential equation and to obtain a spectrum of soliton solutions, we implement the new generalized exponential differential rational function method. This advanced technique utilizes a rational trial function based on the $ith$ derivatives of exponentials, generating a diverse spectrum of closed-form soliton solutions through strategic choices of arbitrary constants. The novelty of this approach provides a unified framework for handling higher-order nonlinearities, yielding solutions such as multi-peakons and lump solitons, which are vividly characterized using Mathematica-generated 3D, 2D, and contour plots. These findings provide significant insights into nonlinear wave dynamics with potential applications in quantum field theory, nonlinear optics, plasma physics, etc. Full article

Although viral outbreaks are increasing, vaccination rates are decreasing. Our aim was to explain this baffling behavior that seems to contradict rational self-interest, and, thus, be beyond the purview of rational choice theories. We integrated fuzzy-trace theory and major theoretical alternatives and applied them to influenza, testing theoretical predictions in two samples: young adults (who are major viral vectors), N = 722, and community members, N = 185. Controlling for prior knowledge and other psychosocial factors that influence vaccination, explained variance jumped significantly when key predictors from fuzzy-trace theory were added, reaching 62% and 80% for vaccination intentions and 37% and 59% for behavior for each sample, respectively. Single items assessing global gist perceptions of risks and benefits achieved remarkable levels of diagnosticity. Key predictors were intuitive in that they were gisty, imprecise, and non-analytical. In contrast, rational system 2 measures—numeracy and cognitive reflection—were not predictive. These results provide new insights into why individuals vaccinate or not and new avenues for interventions to improve shared clinical decision-making. Full article

A strategic fulcrum for leading high-quality economic development and shaping the nation’s future. Core competitiveness lies in how governments can effectively stimulate consumer demand for green consumption and motivate enterprises to pursue green technology innovation through the development of precise and efficient innovative regulation models. In this paper, a tripartite evolutionary game model is constructed based on evolutionary game theory, encompassing the government, enterprises, and consumers. We analyze the strategic interactions and evolutionary path among these three entities under conditions of bounded rationality and information asymmetry. The research reveals the following: (1) the government can effectively guide enterprises towards genuine green innovation through enhanced rewards for substantive innovation and increased penalties for strategic innovation; (2) consumer purchasing decisions are significantly shaped by economic benefits, perceived social value, and government subsidies, with their market choices forming a critical external supervisory force; and (3) government regulatory strategies are dynamically adjusted in response to market integrity levels and social welfare, with a tendency to implement innovative regulation when “greenwashing” risk is elevated. In conclusion, simulation analysis is conducted using MATLAB 2018a, and governance recommendations are offered based on three dimensions: precise government regulation, enhanced corporate responsibility, and enhanced consumer capabilities. These recommendations offer both a theoretical basis and a practical path for establishing an integrated green innovation governance system based on incentive constraint empowerment. Full article

Metaverse safety training (MST) is popular in highway construction projects (HCPs). While researchers have statically examined the influence of MST, one of the essential gaps is that the interaction among stakeholders on how to improve MST effect is neglected. This paper adopts a game theory approach to illustrate the dynamics among stakeholders, namely, contractors, subcontractors, and construction crews, regarding MST within the framework of HCPs. A tripartite evolutionary game model is developed to analyze the interaction among contractors, subcontractors, and construction crews. The evolutionary stability of the stakeholders’ strategies and the equilibrium point were elucidated by solving the proposed model. A numerical simulation was conducted to validate the rationality of the results. The results show that the choice of behavioral strategies and their evolutionary paths for each stakeholder are closely related to the behavioral strategies of other stakeholders in the game, with significant differences in effects on each other’s initial strategies. The incentive mechanism must match the incentive measures provided to subcontractors and construction crews, ensuring a stable MST. The reward and penalty system implemented by contractors heightens the awareness of subcontractors and construction crews partly. This model provides practical recommendations to enhance training interactions, optimize strategies, increase security awareness, and streamline resource allocation. Full article

Decision-making theory has developed over many decades at the intersection of economics, mathematics, psychology, and engineering. Its classical foundations include Bernoulli’s expected utility theory, von Neumann and Morgenstern’s rational choice theory, and the criteria proposed by Savage, Wald, Hurwicz, and others. However, in real-world contexts, decisions are made under uncertainty, incompleteness, and unreliability of information, which classical approaches do not adequately address. To overcome these limitations, modern multi-criteria decision-making methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (Compromise solution approach) (VIKOR), and ELimination Et Choix Traduisant la REalité (Elimination and Choice Expressing Reality) (ELECTRE), as well as their fuzzy and Z-number extensions, are widely applied to the modeling and evaluation of complex systems. These Z-number extensions are based on the concept of Z-numbers introduced by Lotfi Zadeh in 2011 to formalize higher-order uncertainty. This study introduces the Z-Regret principle, which extends Savage’s regret criterion through the use of Z-numbers. Supported by Rafik Aliev’s mathematical justifications concerning arithmetic operations on Z-numbers, the model evaluates regret not only as a loss relative to the best alternative but also by incorporating the degree of confidence and reliability of this evaluation. Calculations for the selection of digital advertising platforms in terms of performance assessment under various scenarios demonstrate that the Z-Regret principle enables more stable and well-founded decision-making under uncertainty. Full article

Against the backdrop of inclusive development and modernization of employment governance, the limitations of traditional approaches to promoting employment for persons with disabilities—such as information asymmetries and inefficient resource allocation—have become increasingly salient. Building a systematic promotion framework for disability employment has therefore emerged as a critical agenda for advancing modern social governance. Drawing on bounded rationality and information asymmetry theories, this study develops a tripartite evolutionary game model encompassing government, employers, and persons with disabilities. By incorporating key elements such as initial intentions, skill matching, and policy signal transmission, the model analyzes the strategic choices and dynamic interactions among stakeholders. We conduct numerical simulations using delay differential equations (DDEs), perform stability and sensitivity analyses in MATLAB R2024b, and triangulate findings with a practice-based case from Shanghai. The results indicate that persons with disabilities exhibit the highest policy responsiveness within the employment ecosystem and act as the core driver of convergence toward desirable equilibria through four mechanisms: skill-matching effects, policy signal diffusion, perceived institutional fairness, and system-level synergy gains. Although employer subsidies and penalties directly target firms, they exert the strongest psychological incentive effects on persons with disabilities, revealing a “misaligned incentives” feature in policy signaling. Systemic synergy gains activate market network effects, facilitating a pivotal shift from “policy transfusion” to “market self-sustenance.” Based on these findings, we propose a diversified policy toolkit, enhanced policy signaling mechanisms, and innovations in concentrated employment models to support the modernization of disability employment governance. Full article

The concept of green cities promotes efficient utilisation of resources, with urban rooftop farms (URFs) being a key initiative involving a series of actions and decisions between stakeholders and the state. The new public governance discourse (NPGD) emphasises this interplay between the state, the market, and civil society to strengthen collaboration and network-driven social innovation and requires a comprehensive understanding of human/stakeholder behaviour. In this study, we explore the connection between organisational rational choice in URF policy development and social innovation. Through a review of the existing literature on URF policies and a case study of Dhaka, Bangladesh, we investigate the development of a comprehensive policy via participation and collaboration, considering the popularity of URFs and the absence of governing mechanisms in Dhaka. The results suggest that, despite the rising popularity of URFs in Dhaka, existing policies and strategies lack clarity. The review findings suggest that a participatory and co-productive approach is optimal for URF policy formulation. This would require active engagement from community members, local governments, and non-governmental organisations and gaining an enhanced understanding of stakeholder dynamics by testing stakeholder salience and co-production theories for successful URF governance. Full article

Green food serves as a bridge connecting healthy lifestyles with environmental values, particularly in the context of sustainable consumption transitions. However, existing research lacks a systematic understanding of how consumers negotiate cognitive evaluations and emotional responses when forming green food purchase intentions. This study addresses that gap by exploring the cognitive–affective negotiation process underlying consumers’ green food choices. Based on 26 semi-structured interviews with Chinese consumers across diverse socio-economic backgrounds, the grounded theory methodology was employed to inductively construct a conceptual model. The coding process achieved theoretical saturation, while sentiment analysis was integrated to trace the emotional valence of key behavioral drivers. Findings reveal that external factors—including price sensitivity, label ambiguity, access limitations, social influence, and health beliefs—shape behavioral intentions indirectly through three core affective mediators: green trust, perceived value, and lifestyle congruence. These internal constructs translate contextual stimuli into evaluative and motivational responses, highlighting the dynamic interplay between rational judgments and symbolic–emotional interpretations. Sentiment analysis confirmed that emotional trust and psychological reassurance are pivotal in facilitating consumption intention, while price concerns and skepticism act as affective inhibitors. The proposed model extends the Theory of Planned Behavior by embedding affective mediation pathways and structural constraint dynamics, offering a more context-sensitive framework for understanding sustainable consumption behaviors. Given China’s certification-centered trust environment, these findings underscore the cultural specificity of institutional trust mechanisms, with implications for adapting the model in different market contexts. Practically, this study offers actionable insights for policymakers and marketers to enhance eco-label transparency, reduce structural barriers, and design emotionally resonant brand narratives that align with consumers’ identity aspirations. Full article

Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios—including intelligent transportation coordination and UAV swarm scheduling—we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems. Full article

As cyber threats escalate in scale and sophistication, the imperative to secure public data through theoretically grounded and practically viable frameworks becomes increasingly urgent. This review investigates whether and how criminology theories have effectively informed the development and implementation of information security management frameworks (ISMFs) to prevent cybercrime and fortify the digital ecosystem’s resilience. Anchored in a comprehensive bibliometric analysis of 617 peer-reviewed records extracted from Scopus and Web of Science, the study employs Multiple Correspondence Analysis (MCA), conceptual co-word mapping, and citation coupling to systematically chart the intellectual landscape bridging criminology and cybersecurity. The review reveals those foundational criminology theories—particularly routine activity theory, rational choice theory, and deterrence theory—have been progressively adapted to cyber contexts, offering novel insights into offender behaviour, target vulnerability, and systemic guardianship. In parallel, the study critically engages with global cybersecurity standards such as National Institute of Standards and Technology (NIST) and ISO, to evaluate how criminological principles are embedded in practice. Using data from the Global Cybersecurity Index (GCI), the paper introduces an innovative visual mapping of the divergence between cybersecurity preparedness and digital development across 170+ countries, revealing strategic gaps and overperformers. This paper ultimately argues for an interdisciplinary convergence between criminology and cybersecurity governance, proposing that the integration of criminological logic into cybersecurity frameworks can enhance risk anticipation, attacker deterrence, and the overall security posture of digital public infrastructures. Full article

The underreporting of gender-based violence (GBV) in institutions of higher learning can be attributed to a range of causes and has an impact on students’ physical and mental health. Institutions of higher learning have made efforts to eradicate the problem, yet incidences are still on the rise, calling for urgent attention. This paper focuses on the causes of the underreporting of GBV in higher education institutions (HEIs) as a point of reference to understanding the root magnitude of the pandemic in order to devise problem-specific interventions to eradicate GBV in institutions of higher learning. The rational choice theory and cultural acceptance of violence theory guided the analysis of the findings discussed in this paper. The rational choice theory provides insights into why victims choose not to report their victimization. The cultural acceptance of violence theory highlights how cultural norms can normalize and perpetuate GBV, creating barriers for victims to come forward. The findings discussed in this paper emanate from a qualitative study that gathered data using 22 one-on-one interviews with students and one focus group comprising seven supporting staff members from the University of Umvoti. Data were thematically analyzed to address the research objectives. The findings indicate that intimidation and distrust in law enforcement agents and institutions are the main reasons why GBV is underreported. Other factors that may be at play include fear of the perpetrator taking revenge, fear of not being believed, stigma and shame, the patriarchy, reliance on money, and a lack of awareness about GBV. To address these issues, this paper recommends that higher education institutions should uphold the principles of justice, fairness, and transparency in handling GBV cases. Moreover, there should be ongoing facilitation of awareness campaigns on GBV covering issues of consent, gender equality, safety, and reporting and support. When victims of GBV feel supported, they are more likely to trust the institution and report any victimization. Full article

Developing novel gas-sensing materials is critical for overcoming the limitations of current metal oxide semiconductor technologies, which, despite their widely commercial use, require high operating temperatures to achieve optimal performance. In this context, integrating graphene with molecular organic layers provides a promising platform for next-generation gas-sensing materials. In this work, we systematically explore the gas-sensing properties of metal phthalocyanine/graphene (MPc/Gr) interfaces using density functional theory calculations. Specifically, we examine the role of different MPcs (FePc, CoPc, NiPc, and CuPc) and Gr doping levels (p-doped, neutral, and n-doped) in the detection of NH₃ and NO₂ molecules, used as representative electron-donor and -acceptor testing gases, respectively. Our results reveal that a p-doped Gr is necessary for NH₃ detection, while the choice of metal cation plays a crucial role in determining sensitivity, following the trend FePc/Gr > CoPc/Gr > NiPc/Gr, with CuPc/Gr exhibiting no response. Remarkably, FePc/Gr demonstrates sensitivity down to the limit of a single NH₃ molecule per FePc. Conversely, NO₂ detection is possible under both neutral and n-doped Gr, with the strongest response observed for n-doped FePc/Gr and CoPc/Gr. Crucially, we identify the d_z2 orbital of the MPc as a key factor in mediating charge transfer between the gas molecule and Gr, governing the electronic interactions that drive the sensing response. These insights provide valuable guidelines for the rational design of high-sensitivity graphene-based gas sensors. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

The standard expected utility model applied by economists and decision scientists assumes both that decision makers (DMs) are rational and that their information retrieval behavior and choices are determined by the observed and potential values of the multiple characteristics defining the alternatives. In this regard, if DMs can formalize the information acquisition structures determined by the main postulates of expected utility theory, they should also be able to perform standard operations regarding the potential combinatorial outcomes that may be obtained when evaluating the alternatives. We define an information retrieval scenario where DMs account for the different combinatorial possibilities arising among the realizations of the characteristics defining the alternatives before evaluating them. We demonstrate the indifference that arises among risk-neutral DMs endowed with standard expected utilities within sequential information acquisition environments such as those defined by online search engines. We also illustrate the reticence of DMs to acquire information on new alternatives when increasing their aversion to risk or modifying the relative importance assigned to the different characteristics defining the alternatives. The main strategic consequences that follow from the enhanced information retrieval scenario proposed are also analyzed. Full article