Search Results (9,421)

The circular economy (CE) can help businesses use resources more efficiently, but empirical evidence on CE adoption among non-European SMEs remains limited. This study examines CE practices, benefits, challenges, and future intentions in 220 Saudi Arabian SMIs. A structured survey collected data on four CE practice domains (resource efficiency, waste management, eco-design, and reverse logistics), four benefit dimensions (economic, environmental, operational, and reputational), four challenge dimensions (financial, organizational, technical, and regulatory), and six future intention items. CE adoption was moderate (M = 3.29 on a five-point scale) and balanced across all four practice domains, with resource efficiency scoring highest (M = 3.32). Benefit scores averaged 3.46, far outpacing challenges (M = 2.78). This benefit surplus of 0.68 points (on a five-point scale) indicates that Saudi SMIs perceive CE as worthwhile and view its barriers as manageable rather than prohibitive. Together, perceived benefits and perceived challenges explained 54.3% of the variance in CE adoption (R² = 0.543) in multiple regression analysis. Reducing perceived challenges may be a more effective lever for promoting CE adoption than amplifying perceived benefits, as challenges exerted a larger absolute standardised effect (β = −0.50) than perceived benefits (β = 0.39). Once perceptions were controlled, perceived benefits and challenges significantly predicted future CE intentions, but current CE practices did not. According to the Theory of Planned Behavior’s attitudinal pathway, firms without CE experience can develop strong forward-looking intentions if the business case is convincing and barriers are perceived as manageable. Technical and organizational barriers outweighed financial ones, indicating the need for capacity-building interventions over supplementary financing, unlike European findings. About 79% of respondents were neutral or positive about government-supported CE expansion. CE adoption did not differ significantly by firm size, geographic location, or ownership structure, suggesting that Vision 2030’s sustainability messaging has established a broad baseline of CE awareness across Saudi SMIs. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

Extended Reality (XR) and Artificial Intelligence (AI) are increasingly converging within cyber–physical infrastructures, including digital twins, the Spatial Web, and smart-city systems. These environments require new frameworks for understanding how human performance emerges through sustained interaction with immersive interfaces and adaptive computational agents. This paper introduces the TAXI–XI-CAP framework, a two-layer model that links psychobiological mechanisms of XR–AI interaction to higher-level, experimentally testable capability constructs. The TAXI layer defines 42 mechanisms spanning perception, cognition, physiology, sensorimotor control, and social coordination, while XI-CAP organizes these into capability patterns such as remote dexterity, distributed cognition, and adaptive workload regulation. Derived through a theory-guided synthesis across XR, neuroscience, and human–automation interaction, the framework models performance as emerging from interacting mechanisms under real-world constraints. A validation-oriented research agenda is proposed, emphasizing mechanism-level measurement, capability-level evaluation, and longitudinal testing. The TAXI–XI-CAP framework provides a structured basis for hypothesis generation, comparative analysis, and empirical validation of XR–AI systems, supporting the development of reliable, scalable, and human-centered Extended Intelligence infrastructures. Full article

This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the ${\mathfrak{K}}_1<1$ condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications. Full article

Generative artificial intelligence (AI) has demonstrated significant potential in education, yet empirical research on its application in “hardware-driven” interdisciplinary design courses remains scarce. This study employed a randomized crossover experimental design in an IoT Hardware and Design Innovation course at Hunan University. Twelve industrial design undergraduates with no prior IoT background alternated between AI-assisted (ChatGPT-4o) and traditional learning resource conditions across six short-cycle tasks. The crossover design enabled each participant to serve as both experimental and control subjects, yielding 72 observation-level data points. Grounded in Cognitive Load Theory, the study examined three dimensions: process efficacy, difficulty structure, and switching adaptation costs. Results indicated that AI significantly improved perceived task completion efficiency, self-reported goal attainment, and learning experience, yet self-assessed knowledge transfer did not differ significantly between conditions. AI reduced the total number of reported difficulties but altered the difficulty-type distribution: resource-retrieval difficulties decreased while information-verification difficulties increased—a phenomenon we term “difficulty restructuring”. Furthermore, switching from AI back to traditional resources incurred significantly higher adaptation costs than the reverse transition, revealing emerging dependency risks. These findings suggest that generative AI may function more as a “difficulty restructurer” than a “difficulty eliminator” in hardware-driven design education, providing exploratory empirical evidence for incorporating verification literacy into future course design and calling for calibrated scaffold fading that may help mitigate emerging dependency risks. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in agricultural unmanned aerial vehicle (UAV) electromechanical systems for their high efficiency and power density. While sliding mode control (SMC) offers robustness for PMSM drives, conventional designs face challenges like slow convergence, singularity, and chattering. This paper proposes a model-free improved non-singular fast terminal SMC scheme with an improved adaptive super-twisting algorithm and a disturbance observer (MFINFTSMC-IADSTA-IFTSMO) for agricultural UAV applications. The designed sliding surface ensures fixed-time convergence without singularity, the adaptive reaching law reduces chattering, and the observer enables feedforward compensation of disturbances. Closed-loop stability is proven via Lyapunov theory. DSP-based experiments demonstrate that the proposed method outperforms existing SMC variants in dynamic response, steady-state accuracy, chattering suppression, and disturbance rejection. Specifically, the proposed method achieves a start-up convergence time of only 0.35 s, which is 56.25% shorter than that of the classic SMC-STA method, fully verifying its superior fast convergence performance. Full article

Arsenic contamination in polymetallic mining areas is closely linked to surrounding iron-rich manganese minerals. However, conclusive evidence remains limited regarding the retention and migration process of As(V) in naturally manganese-rich manganese ores (especially those with different manganese/iron mass ratios) under dynamic flow conditions. This study investigated As(V) adsorption and transport by four natural manganese minerals (FM1–FM4) through batch/column experiments, characterization, and numerical modeling. Their Mn/Fe mass ratios were 22.7 for FM1, 4.2 for FM2, 3.7 for FM3, and 16.4 for FM4. Batch experiments showed that As(V) adsorption on FM1–FM3 was better described by the Freundlich model, indicating heterogeneous adsorption behavior. Under the tested experimental conditions, the apparent Langmuir qₘ values of these minerals decreased from 0.066 to 0.015 mmol·g⁻¹ with decreasing Mn/Fe ratio. However, As(V) adsorption on FM4, which had the lowest Mn and Fe contents, followed the Langmuir model (qₘ = 0.012 mmol·g⁻¹), suggesting monolayer adsorption. Column experiments demonstrated rapid As(V) retention for all minerals. In the time domain, increasing the flow rate from 0.5 to 2.0 mL·min⁻¹ generally advanced breakthrough and shortened the desorption tail, although the breakthrough behavior expressed in pore-volume coordinates was not strictly monotonic for all minerals. The Two-Site Kinetic Attachment Model (TSKAM) successfully simulated these dynamics (R² > 0.90, RMSE < 0.05), revealing adsorption controlled by fast and slow kinetic sites, with slow-site contributions diminishing at higher flow rates. Characterization results indicated that adsorbed arsenic on FM1 remained mainly as As(V) and was immobilized primarily through surface complexation involving surface hydroxyl and Fe/Mn–O groups. XRD and SEM-EDS suggested the participation of Fe/Mn-bearing phases, while XPS on FM1 showed pronounced changes in Mn surface species during adsorption. Therefore, As(V) removal by these natural manganese minerals is a coupled physicochemical process influenced by both mineral properties, including Mn/Fe ratio, specific surface area, pore structure, pH_PZC, and Mn surface-state changes, and hydrodynamic conditions in the polymetallic mining areas. Full article

Natural gas hydrate is a clean energy resource critical for global energy security and low-carbon transition. Understanding its geological accumulation mechanisms is essential for exploration and development. However, the current research on NGH geological accumulation lacks a systematic and quantitative analysis of its global research evolution, hotspots, and frontiers. To fill this gap, this study conducts a bibliometric analysis of 5891 articles (1999–2025) from the Web of Science Core Collection using CiteSpace and VOSviewer to map research trends, contributions, and frontiers. The results show that annual publications followed a three-stage trajectory: slow initiation, rapid growth, and stable development, with key boosts from production tests in Japan (2013) and China (2017). Marine and Petroleum Geology emerged as the most cited journal. China, the United States, and Germany lead research output, with the Chinese Academy of Sciences serving as the central hub (centrality: 0.46). Core researchers such as Jinqiang Liang have established foundational knowledge through highly cited studies on accumulation theory and resource–environment interactions. Research focus has shifted from early resource assessment to controlling factors, and recently toward production technologies and parameter optimization, highlighting a transition from basic to applied research with strong interdisciplinary integration. While bibliometrics reveals structural evolution and hotspots, limitations in data sources and analytical scope remain. Future efforts should integrate multi-source data and deepen content analysis to address unresolved challenges in NGH geological accumulation. Full article

Flexible circular shafts are critical components for power transmission in engineering systems. However, they are susceptible to complex three-dimensional coupled vibrations under multidirectional excitations, which can compromise operational stability and lead to structural fatigue. To address this issue, this paper presents an active control method for the three-dimensional vibration of a piezoelectrically driven flexible circular shaft via a fuzzy adaptive PID algorithm. The study begins by establishing a dynamic model of the system based on the Euler–Bernoulli beam theory and Lagrange equation. This model forms the foundation for the design of a fuzzy adaptive PID controller. The accuracy of the developed model is then validated through simulations and experiments. Subsequently, active vibration control (AVC) experiments are carried out to evaluate the vibration attenuation effectiveness of various control strategies (including a conventional PID controller as the benchmark for comparison) under different types of excitations applied at the shaft root. The results demonstrate that the proposed active control method has superior control performance, and exhibits excellent vibration suppression performance, especially under bidirectional excitation at the natural frequency, where the vibration suppression ratios in the two orthogonal directions reach 93.03% and 92.09%, respectively. Full article

Sol–gel processing provides an unusually controllable route to nanoporous solids, making silica aerogels the leading reference systems for extremely low thermal conductivity due to their high porosity, nanoscale pore sizes, and tunable solid frameworks. Under near-ambient conditions, thermal transport is multi-scale and multiphase, arising primarily from coupled solid conduction through the skeletal network and gas conduction within the pore space. Accordingly, aerogel design has emphasized suppressing solid-phase transport by reducing network connectivity, increasing tortuosity, and enhancing boundary scattering, while also limiting gaseous conduction through the control of pore size and gas pressure. This critical review provides an integrated overview of these mechanisms and the theory-to-experiment toolbox used to quantify the separate and combined contributions of the solid and gas phases to the effective thermal conductivity. We link key structural and environmental parameters (porosity, pore size distribution, density, backbone morphology, and pressure) to dominant transport regimes and the assumptions embedded in common models. Classical approaches, including effective-medium and percolation-based models, are assessed alongside phonon-scaling descriptions that incorporate characteristic length scales. Particular attention is given to the Knudsen effect and pressure-sensitive gas-conduction models, which are central to interpreting performance at atmospheric conditions and under vacuum or low-pressure operation. This review highlights inconsistencies across datasets and modeling practices, identifies persistent knowledge gaps, and outlines practical directions toward processable structure–property guidelines for manufacturing aerogels with targeted thermal performance, with regard to conduction-dominated heat transport mechanisms. Full article

This paper examines the factors associated with valuation patterns in FinTech and RegTech mergers and acquisitions (M&A) using a global sample of 3739 completed transactions sourced from S&P Global Market Intelligence from 2008 to 2025. We develop and empirically validate an integrated theoretical framework combining digital platform theory, open innovation theory, and control-based theories of the firm. We test our five hypotheses using semi-log regression models with heteroskedasticity-robust standard errors. We document five main findings. First, full acquisitions are associated with valuation premiums nearly three times larger than traditional M&A control premiums in baseline specifications, which remain economically large (~188%) after correcting for sample selection. Second, cross-border transactions are associated with significantly higher valuations. Third, infrastructure-oriented FinTech and RegTech segments are valued more highly than consumer-facing segments. Fourth, transaction values increase systematically over time, consistent with declining uncertainty as the sector matures. Fifth, deal structure explains more variation in transaction values than temporal or geographic factors, reversing conventional valuation patterns observed in financial-sector M&A. We further document that tighter financing conditions significantly depress valuations, though the underlying structural drivers of the FinTech premium remain robust to these macroeconomic shifts. Our findings contribute to the banking and finance literature by demonstrating that M&A in FinTech and RegTech exhibit a distinct valuation regime shaped by digital platforms and innovation-driven control mechanisms. Full article

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

Achieving high selectivity in the hydrogenation of CO₂ to light olefins remains challenging because of the complex reaction network and the difficulty of regulating key intermediates. Motivated by green-hydrogen-enabled power-to-chemicals pathways, we combine density functional theory (DFT) with first-principles microkinetic simulation (FPMS) to construct a quantitatively predictive reaction-energy landscape and elucidate structure–selectivity relationships. A comprehensive reaction network is established through energy-surface fitting, and steady-state rate constants are solved to capture the microkinetic competition between elementary steps. By introducing electronic density-of-states (DOS) modulation as a design variable, we directly correlate surface structural parameters with rate-controlling steps, thereby enabling targeted regulation of C–C coupling and hydrogen transfer processes. The calculated barrier for CO₂ adsorption to COOH* is 1.35 eV, while the transition state barrier for C–C coupling is 1.50 eV, corresponding to a reaction rate of 9.7 × 10³ s⁻¹; the olefin desorption rate reaches 1.7 × 10⁷ s⁻¹. Crucially, shifting the d-band center from −2.35 eV to −1.60 eV increases the C₂–C₄ olefin selectivity from 42.6% to 68.3%, establishing an actionable electronic structure lever for catalyst optimization. These results reveal the intrinsic mechanism by which surface electronic and geometric regulation governs intermediate stabilization and rate control, providing a verifiable, mechanism-based design principle for efficient CO₂-to-olefin catalysts aligned with green hydrogen deployment. Full article

Objective: This study examined psychological resilience (PR) as a potential moderator and mediator of the association between borderline personality symptoms (BPS) and suicidal ideation (SI) among university students. Method: A cross-sectional design was used with (N = 257) university students. Moderation and mediation were tested in separate, theory-guided models using the PROCESS macro for SPSS, version 28. The moderation model (Model 1) and the mediation model (Model 4) were estimated with heteroskedasticity-consistent standard errors (HC3). In the adjusted analyses, sex, age, previous psychological consultation, previous psychotropic medication use, and family history of mental illness were entered as covariates. The indirect effect was evaluated using percentile bootstrap confidence intervals based on (5000) resamples. Results: BPS was positively correlated with SI, whereas PR was negatively correlated with both BPS and SI. In the adjusted moderation model, BPS was positively associated with SI (b = 0.118, p < 0.001) and PR was negatively associated with SI (b = −0.204, p = 0.048), but the interaction term was not significant (b = −0.001, p = 0.820) with negligible explained variance (ΔR² = 0.0003). In the adjusted mediation model, BPS was significantly associated with lower PR (a: b = −0.135, p < 0.001), and PR was associated with lower SI while controlling for BPS and the covariates (b: b = −0.216, p = 0.028). The total effect of BPS on SI was significant (c: b = 0.146, p < 0.001), and the direct effect remained significant after including PR (c′: b = 0.117, p < 0.001). The indirect effect was significant (ab = 0.029; 95% bootstrap CI [0.005, 0.061]). Conclusions: Psychological resilience did not moderate the association between BPS and suicidal ideation, but it showed a statistically significant indirect association consistent with the proposed mediation model. Higher BPS were associated with lower resilience, which in turn was associated with higher suicidal ideation. These findings suggest that resilience-related targets may complement interventions addressing core BPS-related risk processes, while the cross-sectional design precludes causal conclusions. Full article

This paper presents a unified theoretical and numerical investigation of Hilfer-type fuzzy fractional control systems with infinite continuous delay. By employing contraction mapping principles and compact semigroup theory, we establish rigorous solvability conditions together with Ulam–Hyers–Rassias stability results expressed in terms of Mittag–Leffler functions. To complement the analytical framework, we design and implement numerical schemes based on Euler and IMEX approaches, which confirm the theoretical predictions through simulations. The computational experiments demonstrate the robustness of the proposed framework under delayed feedback and fractional memory effects, highlighting its relevance to practical domains such as biological regulation, porous media transport, and intelligent traffic systems. The contribution of this study lies in the bridge between mathematical rigor and computational implementation, thus advancing the theory of fractional differential inclusions and providing a versatile tool for the stability analysis and control of complex systems with uncertainty and hereditary dynamics. Full article