Search Results (257)

Agentic AI is increasingly framed as enabling consumers to delegate commerce decisions and actions to digital assistants, yet consumer-facing evidence still centers on assistive chatbots and recommender-like systems, with scarce evaluation of execution-level delegation. This study provides an evidence-mapping review of empirical work on agentic commerce and synthesizes determinants and outcomes of delegation across three questions: (RQ1) how systems are operationalized (autonomy, task scope, interaction mode, and transaction capability/evidence realism), (RQ2) what facilitates or inhibits delegation, and (RQ3) what downstream outcomes follow for marketing performance and consumer experience. We searched Scopus and Web of Science for English-language, peer-reviewed primary studies (2015–2026) and applied conservative coding rules that distinguish claimed capability from simulated or demonstrated execution. The mapped literature is concentrated in text-based, low-autonomy assistants focused on recommendation and post-purchase support; coverage drops sharply for workflow-level autonomy, cart building, checkout/payment execution, and negotiation. Across studies, findings cluster into two motifs: a utility/assurance pathway in which performance cues and interaction quality increase perceived usefulness, satisfaction, and trust, and a governance pathway in which autonomy cues and system-initiated control trigger reactance/powerlessness and reduce acceptance unless mitigated by safeguards; urgency can attenuate governance resistance. Because most outcomes are intention- or vignette-based, calibration, verification, and error-recovery behaviors remain under-measured. Overall, delegation appears to depend less on maximizing autonomy than on coupling capability with user governance (consent, oversight, recourse, accountability), and we outline measurement priorities for evaluating execution-capable agents. Full article

Rapid urbanization increasingly threatens urban cultural heritage. While buffer zones are crucial for mitigating external pressures, conventional delineation relies on value-based or geometric rules, overlooking parcel-scale heterogeneous externalities. This study addresses this gap by proposing a parcel-based, risk–value coupling framework that delineates heritage buffer zones and supports differentiated land-use regulations. In this study, “negative-impact risk” is operationalized as a composite proxy of cumulative urban development pressures that may increase the likelihood and potential severity of adverse externalities on heritage settings, rather than a full hazard–exposure–vulnerability risk model. And we construct a multi-source indicator system with 12 parcel-level indicators to characterize negative impact risk and heritage value, and adopt a hybrid weighting strategy integrating an AHP, entropy weighting, and game-theoretic combination to reconcile expert judgement and data-driven heterogeneity. To address uncertainty in multi-criteria evaluation, a cloud model maps indicator sets into discrete management levels. The framework is applied to the Pingjiang Historic District in Suzhou, China, using 121 land parcels as decision units. Results show that the approach identifies spatial risk–value patterns and delineates an operational buffer prioritizing parcels with elevated coupled scores. Compared with a fixed-distance buffer, it achieves greater coverage of high-risk parcels while maintaining a smaller regulatory scope. The parcel classification is then translated into tiered planning controls, including development intensity limits, land-use rules, and monitoring priorities. The framework integrates risk management and heritage conservation to support uncertainty-aware, proactive, and transferable zoning decisions. Full article

Latino families in the United States experience persistent maternal and child health (MCH) inequities driven by a fragmented immigration and public benefits policy environment rather than inherent health differences. Although most Latino children are U.S.-born citizens, many live in mixed-status families in which immigration status determines eligibility for health care, nutrition assistance, and other essential services. This narrative policy review examines U.S. immigration and public benefit policies from 1965 to 2025 to assess how eligibility rules, enforcement practices, and policy instability shape access to maternal and child health services among Latino populations. Drawing on public health, legal, and social science literature, the review documents substantial variation in access to Medicaid, CHIP, nutrition programs, and emergency care by immigration status and state policy. Findings indicate that restrictive eligibility criteria, expansions and contractions of the public charge rule, and immigration enforcement practices have produced chilling effects that deter eligible families from accessing care, reduce prenatal and postpartum service utilization, and contribute to adverse birth outcomes and intergenerational health inequities. The review concludes that immigration policy functions as a structural determinant of MCH and identifies two key policy priorities: 1. maintaining the 2022 Final Public Charge Rule that excludes public safety-net programs, and 2. waiving the five-year Medicaid waiting period for all pregnant immigrants regardless of documentation status to ensure equitable access to essential maternal and child health care. Full article

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of sections with non-uniform geometry, which in practice leads to a “flattening” of the transmitter constellation and a deterioration of the conditioning of the trilateration problem. As a result, even small changes in input parameters (e.g., related to infrastructure geometry, distance-measurement quality, or the adopted model) may cause a significant change in the position-estimation error, thereby reducing the reliability of roadheader localization across the entire working area. In this study, a local sensitivity analysis is employed to identify the parameters that dominate the positioning outcome. Sensitivity coefficients are defined in a normalized form and are determined numerically using a perturbation approach (changing a given input parameter by a prescribed percentage), which avoids analytical differentiation of the complex relationships arising from the trilateration equations. The analysis is performed for a roadway scenario supported by an ŁP10 steel arch yielding support, with transmitters installed under the support arch and the roadheader trajectory represented by a sequence of consecutive position vectors. The obtained results allow the solution’s susceptibility to errors and uncertainties in the parameters to be assessed and indicate which parameters require priority control in practical implementation. On this basis, recommendations are formulated for the design and maintenance of the localization infrastructure, including transmitter placement and reconfiguration rules (relocation or adding an additional transmitter), to maintain stable positioning quality under operational mining conditions. Full article

This study examined differences in finishing techniques and positional contexts across three temporal windows in the Ultimate Fighting Championship (2003–2004, 2013–2014, and 2023–2024), revealing differences consistent with a shift from greater diversity to a specialized and systematized model. Analysis of 906 finalized bouts demonstrated a marked concentration of submission finishes, with rear naked choke increasing from 15.8% to 46.8% (p ≤ 0.001), while back control was the dominant positional context (45.5%, p ≤ 0.001). In striking-based finishes, punches maintained prevalence, evolving from 77.4% (2003–2004) to 86.1% (2023–2024, p ≤ 0.001), whereas kicks declined from 20.5% to 11.3% (p ≤ 0.001). Submissions increased from 37.0% to 52.0% of all finalized bouts (p ≤ 0.001). These findings indicate a growing emphasis on specific finishing techniques, with modern mixed martial arts demonstrating increased distributional concentration in the methods used to finalize bouts. The increased frequency of certain techniques (e.g., rear naked choke and punches) among finalized fights may reflect strategic preferences, training priorities, or rule-driven changes in technique effectiveness, but cannot be interpreted as evidence of inherent technical superiority without additional data on success rates or efficiency metrics. Our data suggest that contemporary fighters have developed more direct and systematized approaches to finishing fights, reflecting the evolution of training methodologies and competitive strategies. Full article

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework that separates on-chain accountability from off-chain decision intelligence. Off-chain, fuzzy context inference parameterizes scenario priorities, Pareto-based multi-objective search generates candidate governance policies, an emergency-aware feasibility guard filters unsafe trade-offs, and a bargaining-based selector chooses a single deployable policy. On chain, the blockchain layer records consent commitments, access events, and hashes of the selected policy and decision trace, serving as an immutable audit and accountability substrate rather than an online decision or optimization engine, while raw health data remain off-chain. Using simulation studies of home remote monitoring, clinic telehealth, and emergency triage under stochastic network variation and adversarial device behavior, FiB-MOBA-EAFG improves robustness and yields more repeatable policy selection than rule-based control and scalarized baselines within the evaluated simulation scenarios, while maintaining latency within ranges compatible with modeled edge deployment constraints through explicit emergency-aware feasibility constraints. A budget-matched random-search ablation further indicates that structured Pareto exploration is needed to reliably obtain robust, low-risk governance policies. Full article

This paper offers a comprehensive analysis of the multifaceted implications of genomic medicine’s evolving regulatory frameworks on individual autonomy. As genomic technologies increasingly permeate healthcare and society, they fundamentally reshape the boundaries of health and disease, profoundly impacting personal identity and self-understanding. The expansion of genomic surveillance and risk classification introduces new forms of scrutiny and vigilance, as individuals are redefined according to probabilistic genetic markers rather than traditional clinical symptoms. Regulatory developments facilitate compulsory interventions and challenge established notions of informed consent, as genetic risk factors in otherwise healthy individuals prompt preemptive medicalization and intervention. These changes heighten the risk of genetic discrimination and reinforce social stratifications, as access to care, insurance, and employment may become contingent upon genomic profiles. Furthermore, the commodification of genetic information raises significant concerns about privacy, ownership, and the potential misuse of personal data by commercial and governmental entities. The increasingly blurred lines between medical necessity and social control highlight constitutional and ethical dilemmas, particularly regarding the balance of public health priorities and the preservation of individual freedoms. Drawing on theoretical frameworks such as Stehr’s knowledge society and governmentality, the paper critically examines how regulatory responses both reflect and shape broader societal values, often introducing persistent uncertainty and vulnerability into the core of personal and collective identity. Ultimately, the analysis underscores the urgent need for innovative governance models that can effectively balance the promise of scientific and technological advances with the protection of personal autonomy, democratic knowledge control, and social justice in the genomic era. Lay statement: This paper explores how new rules and regulations around genetic medicine can impact people’s personal freedoms and sense of identity. It highlights concerns about privacy, discrimination, and the ways in which our understanding of health and disease is changing, calling for better protections and fairer policies as genetic technologies become more common. Full article

Edge computing has garnered significant attention in recent years due to its potential in distributed systems. However, the dynamic and heterogeneous nature of edge environments introduces substantial challenges for task scheduling. Conventional rule-based scheduling algorithms often fail to adapt to rapid load fluctuations, resulting in cluster load imbalance and suboptimal resource utilization. To address this issue, we propose a container-based edge cluster scheduling framework designed to enhance load balancing. Within this framework, we introduce an Experience-Prioritized Reinforcement Scheduler (EPRS), which leverages a priority-driven sample selection mechanism to facilitate focused learning of high-value samples. The EPRS dynamically monitors node resource states via a real-time resource monitor and optimizes multi-dimensional resource allocation by jointly considering node-level metrics (e.g., computational resources, memory pressure, storage performance, and container density) and task-specific resource requirements. To validate our approach, we implemented a system prototype integrated with the proposed framework and EPRS in a Kubernetes-based edge cluster. Experimental results demonstrate that the proposed method significantly improves multi-dimensional load balancing performance, achieving an average gain of 28.25% over existing reinforcement learning-based scheduling approaches and a 29.78% improvement compared with the traditional scheduling algorithm. Full article

Effective clash management is critical to the success of complex construction projects, yet BIM coordinators face severe information overload when modern detection tools generate thousands or even millions of collision reports, making interdisciplinary coordination increasingly difficult. This article presents a conceptual framework for using AI for collision triage in a Building Information Modeling (BIM) environment. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of Artificial Intelligence (AI) methods for post-processing of results, which constitutes the main innovation of this work. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components, offering adaptive, data-driven decision support unlike static rule-based methods. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite ‘AI Triage Score’ indicator, and a module that assigns responsibility to the appropriate trades and process participants. The framework leverages supervised machine learning methods (gradient boosting algorithms, selected for their effectiveness with tabular data) for noise filtering, density-based clustering (HDBSCAN, chosen for its ability to detect clusters of varying densities without predefined cluster count) for clash aggregation, and multi-criteria scoring models for priority assessment. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources. As a conceptual study, the framework provides a foundation for future empirical validation and its limitations include dependency on historical training data availability and the need for calibration to project-specific contexts. Full article

Background: European population-based cancer registries (PBCRs) provide the foundation for monitoring skin cancer, yet registration practices and coverage vary, particularly for non-melanoma skin cancer (NMSC). Methods: We conducted a narrative review combining descriptive analyses of European Cancer Information System (ECIS) outputs with evidence from the European Network of Cancer Registries (ENCR) Working Group on NMSC and from national reports. A targeted PubMed search (2015–2025) assessed scientific usage of European registry data. Results: Nearly 200 PBCRs operate across about 40 European countries, with heterogeneous structures and timeliness. The ECIS estimated 101,500 incident cutaneous melanomas (CM) in the European Union in 2022. Long-term data from Nordic countries show a tenfold increase in CM incidence over the last six decades, with recent plateauing in younger cohorts. NMSC registration remains inconsistent: some countries record both cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), others record cSCC only, and several omit NMSC entirely. Consequently, Europe-wide NMSC figures are not available from the ECIS. Global estimates exclude BCC and understate the true burden, which is likely between 1 and 1.6 million incident cases annually in Europe. The PubMed search identified 538 European registry-based publications on skin cancer (2015–2025). Conclusions: Melanoma registration in Europe is robust, but NMSC remains under-registered. Priorities include harmonized definitions and counting rules, better integration of outpatient and pathology data, streamlined EU-level reporting, digital/AI-enabled case ascertainment, and sentinel regions to generate reliable NMSC estimates. Full article

Smart control technologies that can manage the complexity of urban traffic while also reducing response times for emergency vehicles are necessary. This article proposes AETM (Adaptive and Equitable Traffic Management), an adaptive and equitable traffic management system that integrates contextual methods for handling emergencies with traffic light control based on reinforcement learning. The system uses Q-learning to optimize traffic light phases under normal traffic conditions and integrates a dedicated emergency vehicle module, which includes detection, dynamic rerouting and contextual preemption functions. The system adaptively optimizes traffic light phases under normal traffic conditions and integrates a specialized module for emergency vehicles, which ensures their detection, dynamic rerouting and contextual preemption. The priority level is evaluated through an auxiliary fuzzy mechanism, based on interpretable rules, which takes into account local conditions without influencing the learning process. The performance of the framework is evaluated in a microscopic simulation environment by comparing classical control, adaptive control, and the full AETM configuration. The results highlight significant reductions in travel times and stops for emergency vehicles while maintaining overall traffic stability. Full article

In the event of a fault in a shipboard medium-voltage direct-current (MVDC) power system, a fault reconfiguration method issues control commands to the switchgear to execute switching actions, thereby redistributing power flow, isolating the faulted zone, and restoring power to the de-energized loads. Existing fault reconfiguration strategies mainly use classical optimization methods. These methods are usually centralized, and as the system scale increases, they suffer from the curse of dimensionality, which degrades real-time performance and reduces computational efficiency. This paper proposes a MADRL-based fault reconfiguration method for shipboard MVDC power systems. The proposed method considers load priority levels, maximizes total restored load, and improves load balancing. To this end, a QMIX-based method, Dependency-Corrected QMIX with Action Masking (Dep-QMIX-Mask), was developed, introducing a dependency correction mechanism to handle action dependencies during decentralized execution and applying action masking to rule out invalid switching actions under operational constraints. A shipboard MVDC power system model was established and used for validation. Across three representative fault cases, Dep-QMIX-Mask achieves served load rates of 0.88, 0.67, and 0.43, with SLR improvements of up to 19.6% over baseline methods. It consistently produces feasible switching sequences in all 20 independent runs per case, improving feasibility by 10 to 30 percentage points. In addition, Dep-QMIX-Mask improves zonal load balancing by reducing the PUR variance by 40.5% to 99.2% compared with baseline methods. These results indicate that Dep-QMIX-Mask can generate feasible sequential reconfiguration strategies while improving both load restoration and load balancing. Full article

Self-healing polymer nanocomposites are increasingly investigated as damage-tolerant materials for structural and functional applications; however, their engineering translation remains limited by the difficulty of achieving high mechanical reinforcement while retaining sufficient polymer mobility for effective repair. Previous reviews have largely summarized healing chemistries or nanofiller classes but have rarely established quantitative structure–property–healing relationships or resolved contradictory trends reported across studies. In this review, we develop an integrated framework that links polymer network architecture, nanofiller geometry/percolation behavior, and interfacial dynamics to healing kinetics, and we compile quantitative design windows for nanofiller loading, percolation thresholds, activation conditions, and durability metrics. The synthesis reveals that healing performance is maximized within intermediate filler contents near the percolation regime, whereas excessive nanofiller loading commonly suppresses healing by nanoscale confinement and interphase immobilization despite improving modulus and conductivity. Finally, we propose application-oriented design rules and benchmarking priorities, emphasizing standardized fracture/fatigue-based evaluation, multi-cycle healing retention, and scalable interphase engineering as the key pathways for translating self-healing nanocomposites from laboratory demonstrations to validated engineering systems. Full article

Deep reinforcement learning (DRL) has demonstrated considerable potential for autonomous collision avoidance in unmanned surface vessels (USVs). However, its application in complex multi-agent maritime environments is often limited by challenges such as convergence issues and high computational costs. To address these issues, this paper proposes an expert-guided DRL algorithm that integrates a Dual-Priority Experience Replay (DPER) mechanism with a Hybrid Reciprocal Velocity Obstacles (HRVO) expert module. Specifically, the DPER mechanism prioritizes high-value experiences by considering both temporal-difference (TD) error and collision avoidance quality. The TD error prioritization selects experiences with large TD errors, which typically correspond to critical state transitions with significant prediction discrepancies, thus accelerating value function updates and enhancing learning efficiency. At the same time, the collision avoidance quality prioritization reinforces successful evasive actions, preventing them from being overshadowed by a large volume of ordinary experiences. To further improve algorithm performance, this study integrates a COLREGs-compliant HRVO expert module, which guides early-stage policy exploration while ensuring compliance with regulatory constraints. The expert mechanism is incorporated into the Soft Actor-Critic (SAC) algorithm and validated in multi-vessel collision avoidance scenarios using maritime simulations. The experimental results demonstrate that, compared to traditional DRL baselines, the proposed algorithm reduces training time by 60.37% and, in comparison to rule-based algorithms, achieves shorter navigation times and lower rudder frequencies. Full article

Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. To address this, simulation has become a core component in validation by providing scalable, controllable, and repeatable testing environments. This study proposes a trajectory-based scenario classification framework that emphasizes both generality and interpretability. Specifically, we define a set of rule-based maneuver classification criteria using lateral acceleration patterns and apply them to simulated urban driving scenarios modeled with OpenSCENARIO. To address overlapping maneuver characteristics, a priority ordering of classification rules is introduced to resolve ambiguities. The proposed method was evaluated on a dataset comprising 7 types of maneuvers, including straight driving, lane changes, turns, roundabouts, and U-turns. Experimental results demonstrate the effectiveness of rule-driven classification based on vehicle trajectory dynamics and highlight the potential of this approach for structured scenario definition and validation in ADS simulation environments. Full article