Search Results (1,424)

As artificial intelligence (AI) becomes increasingly integrated into organizational processes to enhance efficiency, decision-making, and innovation, aligning AI systems with human teams remains a major challenge to realizing their full potential. Although academic interest is growing, the conceptual landscape of human–AI relationships remains fragmented. This study employs a bibliometric co-word analysis of 4093 peer-reviewed documents indexed in Scopus to map the intellectual structure of the field. Using a strategic diagram, we assess the relevance and maturity of five major thematic clusters identified in the field. Results highlight the structural dominance of Human–AI Interactions (Centrality: 1595), Human–AI Collaboration (1150), and Teaming and Augmentation (1131) as foundational themes, while Conversational AI (655), and Ethics and Responsibility (431) emerge as specialized domains. Based on the analysis, we propose a conceptual framework that classifies human–AI relationships into four categories—symbiotic, augmented, assisted, and substituted intelligence—according to the level of AI autonomy and human involvement. Rather than providing prescriptive guidance for practitioners, this framework is intended primarily as a scholarly contribution that clarifies the conceptual landscape and supports future theoretical and empirical work. While potential implications for organizational contexts can be inferred, these are secondary to the study’s main goal of offering a research-based synthesis of the field. Ultimately, our work contributes to academic consolidation by offering conceptual clarity and highlighting opportunities for future research, while underscoring the critical need for ethical alignment and interdisciplinary dialogue to guide future AI adoption. Full article

Contemporary medical practitioners increasingly recognize the critical impact of healing-environment design on patients’ recovery, positioning it as a pivotal consideration in healthcare facility planning. While existing research has predominantly focused on enhancing the functionality and efficiency of healthcare environments, it has often overlooked the significance of individual patient needs and their distinct experiences. This paper aims to utilize the principles of epidemiology and empirical analysis to explore the application and research trends of the patient-centered care (PCC) concept in healthcare facility design, to promote interdisciplinary collaboration and achieve customized healthcare environments. Based on bibliometric analysis and key literature review methods, this paper systematically examines and interprets the research development trends of PCC in healing environment design, integrating both macro and micro perspectives, and reveals how design factors in therapeutic environments support the realization of PCC principles, thereby improving patients’ rehabilitation experiences and health outcomes. The results indicate that current research on PCC is trending towards increasingly diversified integration via high-frequency keywords such as recovery, healing environment, and evidence-based design, highlighting the shift from functional optimization to emotional care, technological integration, and nature-based interactions in design. Notably, patient-centered care has become a consensus and core integrating concept in this field. This paper not only reveals the key role of healing environments in constructing PCC practice pathways but also provides theoretical support and strategic reference for the planning of healthcare spaces and the collaborative design of nursing processes, and demonstrates that healing environments have evolved from passive spaces into active rehabilitation mediums through interdisciplinary collaboration, thereby facilitating the implementation of the patient-centered healthcare philosophy. Full article

Background/Objectives: Wound infections caused by multidrug-resistant Gram-negative bacteria, such as Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii, pose a major clinical challenge. This study evaluated the interactions between gamma-polyglutamic acid (γ-PGA), cefiderocol, and silver nanoparticles (AgNPs) within multilayer wound dressing configurations. The primary goal was to clarify the dual role of γ-PGA as a healing promoter and a potential protector of bacterial cells against antimicrobial agents. Methods: Multilayer dressing models were assembled in 96-well plates to simulate vertical stratification of antimicrobial layers4. Bacterial viability was assessed through relative OD₆₀₀ measurements following incubation with varying concentrations and spatial arrangements of cefiderocol, AgNPs, and γ-PGA. Data were analyzed using generalized linear modeling (GLM) with a gamma distribution and random forest regression to determine the relative importance of each factor in modulating bacterial survival. Results: γ-PGA concentration emerged as the dominant factor influencing bacterial viability, accounting for nearly 100% of variable importance in random forest analysis. Despite high antimicrobial pressure from cefiderocol and AgNPs, bacterial viability stabilized at approximately 40% in the presence of γ-PGA. The vertical positioning of γ-PGA significantly impacted survival; direct physical contact between the polymer and bacteria, particularly at high concentrations, enhanced bacterial persistence in P. aeruginosa and E. coli. Cefiderocol showed strain-specific potency, while AgNPs provided consistent growth inhibition. Conclusions: γ-PGA plays a paradoxical role in wound care by providing moisture retention while simultaneously acting as a cytoprotective agent that reduces antimicrobial efficacy, likely by facilitating biofilm formation. These findings underscore the necessity of optimizing the spatial layering and concentration of biopolymers in advanced dressings. Strategic design is crucial to balance regenerative benefits with maximal antimicrobial control to improve clinical outcomes in chronic wound management. Full article

Archaeological research on Santiago Island (Cape Verde) offers a strategic framework for investigating ceramic material culture shaped by Iberian and African interactions during the early modern period. This study presents first-stage results from a non-destructive archaeometric analysis of pottery fragments recovered from early colonial sites and curated at the Museu de Arqueologia in Praia. Using portable X-ray fluorescence spectroscopy (pXRF), low-fired, handmade vessels associated with African technological traditions were analysed to determine their elemental composition and potential provenance. The work also focused on sugar moulds, containers used in the refining of this product, one of the most important in Atlantic colonisation. The resulting geochemical data is compared with established reference groups from the Iberian Peninsula, Atlantic Africa, and Macaronesia. Elemental variability indicates the use of diverse clay sources and production techniques, reflecting hybrid technological practices shaped by cultural interaction and provisioning constraints. These results contribute to ongoing research within the CERIBAM (Iberian Atlantic Expansion in North Africa and Macaronesia) and Palarq-funded projects, which aim to reconstruct early colonial ceramic networks and sociotechnical dynamics. By integrating archaeometric data with archaeological and historical perspectives, this study aims to demonstrate the utility of non-invasive analytical protocols for understanding ceramic technology, intercultural exchange, and Atlantic material connectivity in early Creole formations while preserving the integrity of the collections. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

As transparency and sustainability gain strategic importance, the mass balance approach under chain of custody (MB-CoC) has become a central mechanism for assessing product carbon footprints (PCFs) in complex chemical value chains. The MB-CoC enables the attribution of renewable and recycled feedstock characteristics via certified bookkeeping when physical segregation or molecular tracing is infeasible—thus complementing PCF methodologies based on ISO 14067 and the LCA standards ISO 14040/44. However, the methodological integration of the MB-CoC into ISO-conformant PCFs remains insufficiently defined and empirically underexplored. This paper systematically reviews the interaction between the MB-CoC and PCF/LCA frameworks. It (i) synthesizes the allocation rules of ISO 14040/44/67 and the attribution principles of the MB-CoC according to ISO 22095 and key industry initiatives; (ii) analyzes academic publications, guidelines, and corporate applications; and (iii) identifies methodological tensions concerning system boundaries, allocation logic, residual mixes, treatment of biogenic and recycled carbon, and risks of double counting. Our review reveals five recurring insights across the literature: the need for certification and standardization; the importance of primary data and residual mixes; the requirement for ISO conformity; the necessity of transparent reporting of conventional versus alternative inputs; and the lack of independent empirical case studies. Addressing these gaps through harmonized rules, residual mix development, and comparative applications will be essential for establishing the MB-CoC as a robust instrument for circularity, decarbonization, and regulatory compliance, developed by interdisciplinary research and industry approaches. Full article

The growing need to foster inclusive education in university environments has driven the development of technological solutions aimed at improving the academic experiences of students with disabilities. These individuals often face barriers to autonomy and participation, especially on large and complex campuses. This article presents the performance evaluation of a LoRaWAN network specifically designed for monitoring people with disabilities on a university campus. The system aims to provide equitable access to campus resources and real-time support to students with disabilities. Leveraging the advantages of Low-Power Wide-Area Networks (LPWAN), particularly LoRaWAN, the proposed system enables real-time tracking with broad coverage and minimal power consumption, without requiring any active user interaction. Each student receives a wearable LoRa-enabled device that wirelessly communicates with a network of gateways strategically installed throughout the campus. To evaluate the system’s performance, this work conducts link-level experiments focusing on the communication between the LoRa end devices (nodes) and the central gateway. The analysis focuses on the network coverage, signal strength (RSSI), signal-to-noise ratio (SNR), and packet reception rate (PRR). The experimental results confirmed that the proposed system is technically robust and operationally effective under real campus conditions. Beyond its technical contributions, the proposed solution represents a concrete step toward building safer and more accessible academic environments that reinforce the autonomy and inclusion of students with disabilities. Full article

As a vital ecological security barrier and climate regulator in northwestern China, the spatial patterns and evolving formation mechanisms of ecosystem services within the Qinghai Lake basin hold significant strategic value for ecological conservation and national park development in the region. This study selected land use data during 2000–2020, integrating the equivalent factor method, spatial correlation analysis, and the geodetector approach to systematically investigate the spatial heterogeneity characteristics of ESV in the Qinghai Lake basin and its corresponding driving mechanisms. The results indicate the following: (1) During the period 2000–2020, grassland consistently constituted the primary land cover category within the Qinghai Lake Basin, accounting for over 60% of the total area; water bodies (16.67%) and unused land (16.56%) represented the secondary land use categories. Over this twenty-year period, the total ESV exhibited a slight increasing trend, rising from USD 30.30 × 10⁸ to USD 30.75 × 10⁸, representing a growth of 0.31%. Regulating services constituted the primary component of ESV. The highest contribution to ESV originated from water bodies, with grassland ranking second. (2) ESV displayed a spatial arrangement marked by “high values in the lake center and low values in the surrounding areas” and “higher values in the southeast and lower values in the northwest.” Its spatial correlation exhibits a pronounced positive relationship. The number of units classified as high-high clusters (primarily water bodies at low elevations) and low-low clusters (mainly grasslands and unused land at high elevations) both increased over the study period, indicating a continuous intensification of ESV spatial agglomeration. (3) Results from the geographical detector reveal that both natural and anthropogenic factors collectively drive the spatial variation in ESV, with natural factors exhibiting stronger explanatory capacity. Among these, elevation and temperature are identified as the dominant drivers of ESV spatiotemporal differentiation. The combined effect of two interacting factors surpasses the influence exerted by any single factor in isolation. This research clarifies that the spatial distribution of ESV in the Qinghai Lake Basin, which features “high values in the lake center and low values in the surrounding areas” as well as “higher values in the southeast and lower values in the northwest,” is jointly shaped by the combined control of vertical zonality governed by topographic and climatic factors and the spatial differentiation of human activities. In low-altitude lakeshore zones, ESV rose as a consequence of water body expansion and the enforcement of ecological conservation measures, leading to the emergence of high-value clusters. In contrast, ESV improvement in high-elevation regions remained limited, constrained by fragile natural conditions and minimal human intervention. The insights derived from this research offer a scientific foundation for refining the “one core, four zones, one ring, multiple points” functional zoning framework of the Qinghai Lake National Park, as well as for developing tailored management approaches suited to distinct elevation-based regions. Full article

The rise of “Internet + Recycling” platforms is transforming the domestic waste management landscape, creating dual-channel reverse supply chains where new platforms interact with traditional recyclers. However, these platforms face critical strategic decisions regarding their service portfolios (convenient but costly door-to-door vs. economical fixed-point drop-off) and their relationship with incumbents (cooperation vs. competition). This study aims to determine the optimal pricing, service level, and relationship strategies for an “Internet + Recycling” center to maximize profitability under the influence of consumer channel preferences and government subsidies. We developed four Stackelberg game-theoretic models representing different scenarios of service modes (fixed-point only vs. fixed-point with door-to-door) and relationship structures (cooperation vs. competition). We derived equilibrium solutions for recycling prices, service levels, and profits. Our results reveal that while cooperation generally leads to higher systemic profits, the addition of a door-to-door service significantly alters the strategic landscape. We find that a higher consumer preference for the platform channel allows the center to lower prices while increasing profits, and that government subsidies are the most effective at enhancing service levels in cooperative models. Crucially, intense competition incentivizes recycling centers to reduce rather than increase their service levels to cut costs. This research provides a decision-making framework for recycling enterprises to select optimal service and competitive strategies. It also offers insights for policymakers on how to design subsidies to effectively promote high-convenience recycling services and foster a more efficient circular economy. Full article

User Interface (UI) and User Experience (UX) design play a critical role in shaping human interaction with digital systems, particularly in professional environments where accuracy, safety, and efficiency are essential. Poor visual design increases cognitive load and the likelihood of human error, whereas ergonomically informed interfaces can substantially improve task performance. This systematic literature review analyzes 20 peer-reviewed studies published between 2020 and 2024 to examine how visual ergonomics embedded in UI/UX design contributes to error reduction across industrial and professional contexts. The reviewed studies report measurable improvements when ergonomic principles are applied, including reductions in operational errors ranging from approximately 30% to 70%, improvements in task completion time between 20% and 60%, and increased user accuracy and satisfaction in safety-critical and high-workload environments. The findings indicate that visual hierarchy, modular layouts, adaptive components, and real-time feedback are consistently associated with improved performance outcomes. Moreover, task complexity, user expertise, and working conditions were identified as moderating factors influencing ergonomic demands. Overall, the review demonstrates that visual ergonomics should be treated not merely as a usability enhancement but as a strategic design approach for minimizing human error and supporting reliable human–machine interaction in complex digital environments. Full article

With the acceleration of urbanization and the continuous rise in private car ownership, urban traffic congestion has become a critical issue constraining sustainable development. As an important extension of carbon reduction policies, the personal carbon trading mechanism provides a new approach to regulate travel behavior through economic incentives. This study constructs a game model incorporating stakeholders from both government and private car owners, explores their decision-making behaviors under the personal carbon trading mechanism, and conducts simulation analysis of evolutionary paths using MATLAB 2019a. The findings reveal that choosing public transportation results from interactive strategic interactions between government and private car owners. Proactive implementation of personal carbon trading policies by the government can accelerate private car owners’ adoption of public transportation strategies. Reducing government implementation costs of personal carbon trading (PCT), increasing carbon trading costs for private cars (through higher carbon prices or lower allowances), and improving public transit comfort are key factors in achieving equilibrium between government and private car owners’ strategies. Carbon trading costs exhibit differentiated impacts on the convergence speed of both parties’ states. This research aims to provide decision-making references for governments in formulating and implementing personal carbon trading systems, as well as motivating private car owners to adopt green and environmentally friendly travel behaviors. Full article

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a temporal exponential random graph model. The findings reveal three primary insights: First, the overall network exhibits “high connectivity and strong clustering” traits. Enhanced efficiency in intercity resource allocation fosters cross-regional factor flows, resulting in multi-tiered connectivity corridors. Industrial linkages and policy interventions drive the development of a polycentric and clustered configuration. Second, the individual city network exhibits a core–periphery dynamic structure. A diamond-shaped framework dominated by hub cities in the national strategic regions directs factor flows. Development of strategic corridors enables peripheral cities to evolve into secondary hubs by leveraging structural hole advantages, reflecting the continuous interplay between network structure and geo-economic factors. Third, driving factors involve nonlinear interactions within a multi-layered system. Path dependence in topology, gradient potential from nodal attributes, spatial counterbalance between geographic decay laws and multidimensional proximity, and adaptive self-organization are collectively associated with the transition of the urban network toward a multi-tiered synergistic pattern. By revealing the dynamic interplay between network topology and multidimensional driving factors, this study deepens and advances the theoretical connotations of the “Space of Flows” theory, providing an empirical foundation for optimizing regional governance strategies and promoting high-quality coordinated development of Chinese cities. Full article

As global energy demand rises, developing unconventional oil and gas resources has become a strategic priority, with horizontal well technology playing a key role. However, wellbore instability during drilling often leads to irregular geometries, such as enlargement or elliptical deformation, causing issues like increased friction and stuck-pipe incidents. Most studies rely on idealized, regular wellbore models, leaving a gap in understanding cuttings transport in irregular wellbore conditions. To address this limitation, this study employs a coupled CFD-DEM approach to investigate cuttings transport in enlarged wellbores by modeling the two-way interactions between drilling fluid and cuttings. The study analyzes the impact of various factors, including drilling-fluid flow rate, drill pipe rotational speed, rheological parameters, wellbore enlargement ratio, and ellipticity, on wellbore cleaning efficiency. The result indicates that increasing the flow rate in conventional wellbores reduces cuttings volume by 75%, while in wellbores with a 0.7 enlargement ratio, the same flow rate only reduces it by 37.8%, highlighting the limitations of geometric complexity. In conventional wellbores, increasing drill pipe rotation reduces cuttings volume by 42.6%, but in enlarged wellbores, only a 13% reduction is observed, indicating that rotation alone is insufficient in large wellbores. Optimizing drilling fluid rheology, such as by increasing the consistency coefficient from 0.3 to 1.2, reduces cuttings volume by 58.78%, while increasing the flow behavior index from 0.4 to 0.7 results in a 38.17% reduction. Although higher enlargement ratios worsen cuttings deposition, a moderate increase in ellipticity improves annular velocity and enhances transport efficiency. This study offers valuable insights for optimizing drilling parameters in irregular wellbores. Full article

Recurrence resonance, a phenomenon that enhances system computational capability by exploiting noise to amplify hidden attractors, holds significant potential for applications such as edge computing and neuromorphic computing. Although previous studies have extensively explored its characteristics, the underlying mechanism regarding its generation remains unclear. Here, we employed a Stochastic Recurrent Neural Network to simulate neural networks under various coupling conditions. By introducing appropriate inhibitory connections and examining the state transition matrices, we analyzed the characteristics and correlations of attractor landscapes in both global and local systems to elucidate the generative mechanism behind the “Edge of Chaos” dynamics observed under the quantum logic connectivity structure during recurrence resonance. The results show that the strategic introduction of inhibitory connections enriches the system’s attractor landscape without compromising the intensity of recurrence resonance. Furthermore, we find that when neurons are coupled via quantum logic and noise intensity meets specific conditions, the strong attractors of the global system decompose into those of distinct local subsystems, accompanied by the sharing of structurally similar weak attractors. These findings suggest that under quantum logic connectivity, the interaction between the strong attractors of different subsystems is mediated by a background of shared weak attractors, thereby enhancing both the system’s robustness against noise and the diversity of its state evolution. Full article

Behavioral deviations from subjective expected utility theory, most famously captured by the Allais paradox and the Ellsberg paradox, have inspired extensive theoretical and experimental research into risk and ambiguity preferences. While the existing analyze these paradoxes independently, little work explores how such heterogeneously biased agents interact in networked strategic environments. Our paper fills this gap by modeling a convergent Markov–network game between Allais-type and Ellsberg-type players, each endowed with fully enriched loss matrices that reflect their distinct probabilistic and ambiguity attitudes. We define convergent priors as those inducing a spectral radius of <1 in iterated enriched matrices, ensuring iterative convergence under a matrix-based update rule. Players minimize their losses under these priors in each iteration, converging to an equilibrium where no further updates are feasible. We analyze this convergence under three learning regimes—homophily, heterophily, and type-neutral randomness—each defined via distinct neighborhood learning dynamics. To validate the equilibrium, we construct a risk-neutral measure by transforming losses into payoffs and derive a riskless rate of return representing players’ subjective indifference to risk. This applies risk-neutral pricing logic to behavioral matrices, which is novel. This framework unifies paradox-type decision makers within a networked Markovian environment (stochastic adjacency matrix), extending models of dynamic learning and providing a novel equilibrium characterization for heterogeneous, ambiguity-averse agents in structured interactions. Full article