Search Results (139)

Cooperative local navigation of multiple mecanum robots requires efficient coordination around robot–robot conflicts, pedestrians, and static obstacles while preserving direct waypoint following on clear path segments. This paper presents Conflict-Aware Graph-Attention Multi-Agent Proximal Policy Optimization (CA-GAT-MAPPO), a learning-based residual control and coordination framework. Predicted closest-approach events construct a conflict-conditioned robot-interaction graph, so actor message passing is restricted to the local robot and its predicted conflict neighbors. A residual graph encoder preserves waypoint-conditioned state, while a bounded right-of-way coordinator and an interaction gate regulate longitudinal, lateral, and angular residual authority. State-dependent adaptive scalarization combines multiple reward components into a single training objective. The framework is evaluated within a common A*-based waypoint guide, optimal reciprocal collision avoidance (ORCA)-style prior, command-limiting, and safety-envelope interface shared by the compared controllers. Across three four-robot Robot Operating System 2 (ROS 2)/Gazebo scenarios, eight independently trained checkpoints per method–scenario pair were each evaluated in ten randomized episodes. Across the four learned controllers, all 960 main-comparison episodes were completed without a geometric collision or a recorded robot–robot or robot–pedestrian near-miss at the 0.1 s sampled poses under the shared execution boundary. CA-GAT-MAPPO obtained the lowest reported mean completion time, makespan, and waiting time in all three scenarios. The results support a descriptive efficiency advantage for the integrated intelligent-control stack under the evaluated conditions, without establishing universal superiority or a formal safety guarantee. Full article

Wearable glucose monitoring demands low-power local processing, but conventional neural networks rely on energy-intensive multiply–accumulate (MAC) operations that limit battery life. This study shows that a Spiking Neural Network (SNN), built on a regression-adapted Leaky Integrate-and-Fire (LIF) neuron, can estimate blood glucose from multi-frequency bioimpedance and auxiliary biosignals with clinically auditable accuracy at low computational and memory cost. Using data from 98 patients (717 measurements, eGluco3 device, Azambuja Hospital, Brusque, Brazil) evaluated by 5-fold walk-forward cross-validation under ISO 15197:2013, three main findings emerge. First, a new calibration method—the Patient Fingerprint, built from each patient’s first K sensor readings—outperforms conventional one-hot patient encoding (14.2 ± 2.6 mg/dL vs. 15.4 ± 3.3 mg/dL mean absolute error) and, unlike one-hot, requires only these K readings rather than the patient’s presence in the training set; a leave-patients-out analysis confirms that the fingerprint captures individual physiology and that unseen-patient accuracy improves with calibration depth but remains clinically insufficient (MAE $94.8\to 65.9$ mg/dL from $K=3$ to $K=5$), positioning clinical-grade cross-patient generalization on a larger cohort as the primary scaling axis. Second, the direct-injection fingerprint model reaches 100% of the samples within Consensus Error Grid Zones A+B across all validation folds (the rate-coding variant reaches 98.8%, just below the 99% Criterion B threshold), without requiring any demographic or clinical metadata; sensor history alone renders such records redundant; and Criterion A, however, stays below the 95% normative threshold, so the results support clinical safety rather than formal certification. Third, replacing the analog input encoding with a multiplication-free rate-coding scheme removes all first-layer MAC operations at a cost of 2.7 mg/dL additional error; because the additional microticks raise the total operation count, this defines a design lever whose energy payoff is specific to neuromorphic hardware rather than a net saving on conventional microcontrollers. Together, these results demonstrate that SNNs offer a clinically auditable, self-calibrating, and memory-efficient path to continuous glucose estimation on embedded wearable devices. Full article

The formulation of Quantum Field Theory (QFT) in phase space offers a unique alternative to operator and path-integral paradigms, providing distinct conceptual advantages for semiclassical expansions. In this work, we present a systematic and self-consistent functional framework that maps stationary Schrodinger functional equations directly onto phase-space star-eigenvalue equations across different spin statistics. Operating within a non-manifestly covariant equal-time formalism, we derive explicit vacuum Wigner functionals for scalar, gauge, and fermionic fields, establishing the rigorous theoretical consistency of the formalism from first principles prior to phenomenological applications. We analyze how ordering prescriptions and continuous symmetries manifest under the functional star-product, including an explicit phase-space formulation of Noether’s theorem and field regularization. Finally, the framework is extended to interacting systems via a functional Rayleigh–Schrodinger perturbative scheme, illustrated explicitly through the non-trivial first-order Wigner functional correction $W^{\left(1\right)}$ and the vacuum energy correction for a ${\varphi }^4$ self-interacting theory, establishing a solid foundation for evaluating real-time quantum field dynamics. Full article

The availability of environmental indicators at the NUTS-2 level remains limited in European statistical sources, often resulting in regional sustainability analyses that reflect short-term policy dynamics rather than long-term conditions. To address this gap, this paper proposes a parsimonious framework based on a spatial stock indicator measuring the share of artificial land within each region (ENV_ARTIFICIAL_LAND), derived from CORINE Land Cover data and aggregated at the NUTS-2 level. Rather than serving as a short-term metric of policy performance, the indicator describes an inherited territorial stock reflecting historical land-use trajectories, consistent with path-dependent development processes. Using the Italian NUTS-2 regions as a case study, the indicator is analysed alongside key socio-economic variables covering economic capacity, social vulnerability, and human capital formation through a non-aggregative, quadrant-based trade-off framework. The results suggest pronounced regional asymmetries and structural mismatches, demonstrating that territorial rigidities and socio-economic outcomes follow differentiated, non-linear alignments. From a policy perspective, the analysis highlights the limits of uniform benchmarking and underscores the necessity of place-based strategies tailored to inherited spatial constraints. Future developments will include integration into territorialised lifecycle frameworks, to account for the cumulative effects of land occupation and environmental debt. While this framework offers a transparent screening tool for regional spatial rigidity, its convergent validity against high-resolution spatial datasets (such as HRL Imperviousness) and disaggregated land-use subclasses remains to be formally tested in future empirical research. Full article

The COVID-19 pandemic caused major disruptions to agricultural value chains in the Global South, revealing vulnerabilities in food production, processing and distribution. Global estimates reveal that the COVID-19 pandemic did, in fact, worsen food insecurity in the Global South. This paper, through a systematic literature review, clarifies the relationship between agricultural value chains and the four dimensions of food security and proposes policy paths to enhance the resilience of agricultural value chains in the Global South. The literature reveals that resilience of agricultural value chains is not an intrinsic trait, but a dynamic result of ecosystem governance. It is essential, therefore, that policy interventions move beyond simple input subsidies and toward the integration of digital financial tools and formalized relational governance to link farmers to formal markets. Full article

Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures. Full article

Autonomous AI agents now hold funds, delegate authority to other agents, and transact at machine speed, while the governance apparatus meant to constrain them (policies, audits, compliance) remains documentation-based and limited by human latency. Better monitoring or filtering cannot close this mismatch: compliance must become a runtime, compositional, proof-carrying property of computation itself. We call the resulting discipline computational jurisprudence. This article is an integrative review of the four literatures the discipline must synthesize, namely, object-capability security; verifiable, proof-carrying, and zero-knowledge computation; policy-as-code and computational law; and agentic AI with its emerging payment protocols. Each supplies a mature mechanism the others lack, and none supplies a complete normative substrate. The synthesis is organized into three pillars: (i) a delegation calculus, under which authority can only attenuate as it propagates between agents, for which we prove monotone attenuation in the conjunctive caveat fragment and exhibit a counterexample outside it; (ii) runtime compliance proofs, a three-tier evidence regime (attested, optimistic, and zero-knowledge); and (iii) sealed delegation chains with graduated attribution, reconciling capability-based privacy with the accountability adjudication requires. A case study on agentic payments grounds the architecture and evaluates three components on two platforms, with five independent executions each: local capability verification against a centralized policy decision point, enforcement on the x402 payment path, and accumulator-based revocation. What the article offers is therefore a survey, a conceptual architecture with a formal core, and a partial evaluation of three components, not a fully implemented system; a status table marks that boundary component by component. Eight open problems define the research agenda. Full article

Background/Objectives: The aim of the study is to determine factors that affect informal caregivers’ ability to care for older migrants with dementia, with a broad aim of providing a framework for improving regional formal support. We used patient journey mapping as a method to identify experiences of informal caregivers. Methods: The study took place in three phases. In the first phase, the content of the patient journey was outlined, and semi-structured interviews were conducted with 14 informal caregivers of people with dementia and a migrant background. In the second phase, results of the interviews were discussed in workshops with professionals. In the third phase, professionals worked together on a plan for improving care and support. Results: Besides positive experiences with care and support, informal caregivers expressed the need for knowledge and training, timely information as well as practical culturally sensitive care and support. Professionals expressed a need for more knowledge of culturally sensitive issues and conversation techniques. They also indicated the need for more integrated care and the ability to offer practical solutions for informal caregivers. As a final result, a multidisciplinary programme on improving knowledge, information for clients, informal caregivers and professionals, use of key figures, culturally sensitive support, a multidisciplinary care path and education for future professionals was launched for the region. Conclusions: A patient journey is a valuable method to pinpoint problems and needs of informal caregivers as well as professionals. It provides a foundation for multidisciplinary communication and collaboration to improve care and support. Full article

The widespread penetration of generative artificial intelligence is reshaping adolescents’ social ecosystems and emotional experiences, while challenging the interpretive boundaries of traditional connectedness theories. Following the logical path of “connotation reconstruction–extension transformation–concept construction”, this study integrates the ethics of care and neo-ecological theory to systematically construct a theoretical framework of artificial intelligence connectedness. First, tracing theories across philosophy, sociology, and psychology, this research reconstructs the core connotation of connectedness rooted in the ethics of care, proposes the continuum hypothesis of caring relationships, and clarifies AI’s unique position on this continuum. Second, from the neo-ecological perspective, this paper sorts out the extended structure of connectedness and demonstrates the ecological shifts brought by the rise of virtual microsystems and the entry of AI actors. On this basis, the study formally defines artificial intelligence connectedness and establishes its three-dimensional structure: demand identification, two-way behavioral engagement, and responsive confirmation. Through systematic comparison with adjacent concepts, this paper identifies its uniqueness and positions it as a specific subtype of connectedness for the digital era. It defines it as a perceived bond that is both psychologically real and ethically asymmetric, carrying asymmetric risks under particular usage conditions and design logics. Finally, this paper builds a dual interpretive framework integrating traditional connectedness and artificial intelligence connectedness, verifies its incremental validity and unique predictive power, and puts forward falsifiable research propositions. This study expands the boundary of connectedness theory and provides an integrated analytical framework for parsing the complex mental health mechanisms of adolescents in the digital age. However, it should be noted that the artificial intelligence connectedness proposed in this study is currently a theoretical construct; its scientific validity and applicability remain to be verified through the development of standardized measurement tools and systematic empirical research. Full article

Angle-resolved photoemission spectroscopy (ARPES) clearly resolves surface-localized (SL) states, yet conventional density functional theory (DFT) band structures from slab calculations do not readily distinguish weakly confined SL states on surfaces such as Al(001), as traditional layer-threshold criteria fail for surfaces with long surface-state decay lengths. We establish a dual-criterion scheme using the surface ratio R and the localization integral L weighted by the inelastic mean free path (IMFP) to quantitatively distinguish SL states: R quantifies the surface-projected charge fraction, while L incorporates the photoelectron IMFP to mimic ARPES surface sensitivity, both evaluated within a fully converged 81-layer Al(001) slab that eliminates artificial inter-surface coupling. Band structures color-coded by R and L intuitively highlight SL states as bright yellow-white bands against red bulk backgrounds. Our calculations show that continuum SL features arise from multi-band hybridization (sharp surface resonances). Notably, R and L alone cannot separate absolute surface states from resonances. All DFT calculations were performed using the PBEsol exchange-correlation functional within the GGA framework, the PAW formalism, and an 81-layer Al(001) slab model. This work reveals the electronic nature of surface features on Al(001) and provides a quantitative SL-state identification tool that is conceptually transferable to other crystalline surfaces. Full article

Financial development can influence carbon emissions through capital allocation, technological support, and policy transmission. To investigate the inherent association between grassroots inclusive financial institutions and territorial green transformation, this study establishes a city-level panel dataset of the Yangtze River Delta urban agglomeration covering the period from 2010 to 2022. Within the analytical framework of the Spatial Durbin Model, this research decomposes the baseline effect, functional transmission pathways, and cross-sectional heterogeneity of the impact of the development of small loan company (SLC) providers on urban carbon intensity. The results show that SLC expansion significantly increases local carbon emission intensity and produces spatial spillover effects across neighboring cities. Mechanism analysis indicates that SLCs increase emissions mainly by supporting the expansion of small- and micro-sized enterprises in energy-intensive manufacturing sectors, while their role in promoting green technological innovation remains limited. Further analysis shows that local government willingness to pursue green transition weakens the carbon-increasing effect of SLCs, whereas digital inclusive finance strengthens it. The effect also varies by location and regulatory environment, with stronger effects in medium-distance cities and under lower regulatory intensity. These findings reveal how grassroots inclusive financial institutions affect regional carbon outcomes and offer policy implications for aligning inclusive finance with green transition goals. This paper innovatively transcends the conventional low-carbon research paradigm focusing on macro-finance and large formal financial institutions, and instead takes SLCs, a typical micro-level inclusive finance entity, to explore their unique paths affecting regional carbon emissions, and clarifies their action boundaries from multiple dimensions including government governance and digital finance empowerment, which enriches interdisciplinary research literature integrating inclusive finance and low-carbon economy. But this study has limitations: its sample is limited to the Yangtze River Delta urban agglomeration, so the universality of the conclusion needs further verification. This research provides theoretical support and policy reference for regulating the sustainable development of the small loan industry, promoting the integration of inclusive finance and green low-carbon transformation, and advancing high-quality regional low-carbon development. Full article

Ship path planning is a central challenge in autonomous navigation for unmanned surface vehicles and maritime autonomous surface ships. It is not simply a shortest-path problem, but a constrained sequential decision process that must reconcile collision risk, route efficiency, COLREGs compliance, vessel dynamics, and environmental uncertainty. Here we review the field through a unified framework based on planning scope, decision basis, and deployment requirements. We examine search- and sampling-based, geometric and rule-based, optimization-based, learning-driven, and hybrid methods, with particular emphasis on deep reinforcement learning for discrete decisions, continuous maneuvering, multi-vessel interaction, and safety-oriented control. Representative studies are compared across objective and reward design, state representation, exploration and policy optimization, rule integration, disturbance modeling, simulation platforms, and operational validation. The synthesis identifies persistent barriers, including ambiguous rule formalization, partial observability, strategic coupling among vessels, inconsistent benchmarks, limited cross-scenario generalization, and insufficient full-scale validation. We further discuss priority directions in explicit safety constraints, digital twins, transfer and meta-learning, world models, scalable multi-agent coordination, and large-model-assisted mission reasoning. We argue that progress will depend less on further algorithmic proliferation than on integrated, verifiable architectures that combine data-driven adaptation with model-based structure, standardized evaluation, and staged real-world assurance. Full article

Main memory carries data outside the processor’s trust boundary, so commodity systems-on-chip (SoCs) increasingly encrypt it; yet, in-line memory encryption engine itself becomes a differential power analysis (DPA) target whose key, if recovered, unlocks all of dynamic random-access memory (DRAM). We present PACE, a page-adaptive, cache-anchored memory encryption engine for RISC-V that makes nth-order DPA resistance practical and keeps cryptographic latency off the cache eviction critical path. PACE inserts a TileLink adapter between the last-level cache and the memory port and applies, per physical page, one of four policies (plaintext/confidentiality/confidentiality+integrity/+masking-order-d) selected from RISC-V page table bits through a memory-mapped control plane. Confidentiality uses counter mode whose per-line keystream is precomputed during cache residency; integrity is tree-free at the embedded operating point via on-chip counters and tags, with a live split counter block-MAC Bonsai Merkle tree for scale-out. DPA resistance is layered: ISAP-style fresh re-keying caps the data complexity per key at $q\approx 1$, and domain-oriented masking (DOM, d + 1 shares) protects the sole key processing block to order d. We implement PACE in Chisel on a Rocket SoC (Chipyard) and evaluate it with open-source tooling. A deterministic TileLink-level harness proves ciphertext-in-memory and detects tamper/replay/splice, and the live Tier-B engine (DRAM counters and per-line message authentication codes (MACs) plus an on-chip-rooted block-MAC tree) is validated from end to end on full Rocket and BOOM SoCs and on the FPGA; the masked Ascon-p S-box is proven order-d secure (d = 1, 2) under a glitch- and transition-aware model by three independent formal tools (COCO, PROLEAD, and SILVER, the last also deciding the full composability lattice and confirming exact glitch-robust order-2 probing security), with COCO extending the exact verdict to the highest synthesized order d = 3 (secure at probing orders 1–3); a simulated trace correlation power analysis (CPA) recovers the full key from an unprotected core and is defeated by masking, with a mutual information analysis confirming the $N\propto {\sigma }^{2(d+1)}$ trace amplification law. We further realize PACE on field-programmable gate array (FPGA) silicon: the engine plus an on-chip ring oscillator power sensor is placed, routed, timing-closed at 100 MHz, and programmed on a Xilinx XC7Z020, and we drive a fixed-vs-random Test Vector Leakage Assessment (TVLA) campaign read back entirely over a JTAG (Joint Test Action Group). A multi-core configuration and a Linux control-plane driver are likewise validated. Across synthetic access patterns and named application kernels (AES, SHA-256, matrix multiplication, pointer chasing) on both in-order Rocket and out-of-order BOOM, application-level overhead is within measurement noise of plaintext for cache resident workloads (masking, in particular, is cycle-identical to plain confidentiality), and we characterize the cost of each policy, masking order, and re-keying interval, demonstrating side-channel-hardened memory encryption on open RISC-V hardware. Full article

The transportation sector faces growing supply chain disruptions driven by workforce shortages, digital skills gaps, and weak cultural readiness for risk. Human Resource Management (HRM) offers a credible path toward stronger supply chain resilience (SCR), yet existing decision support tools lack the formal reasoning and auditability that systematic HR risk assessment requires. This paper proposes a Task-Typed Reasoning Module-Enhanced Retrieval-Augmented Generation (RAG) Expert System framework for HR-driven risk assessment in transportation supply chains. The framework employs a six-layer architecture integrating four core components: a Task-Aware RAG layer for knowledge extraction from heterogeneous HR documents, a Task-Routed Evidence Extractor for risk factor identification and source reliability scoring, a Task-Based Reasoning Core applying weighted Mamdani fuzzy inference, and a Task-Guided Synthesis Module using Dempster–Shafer evidential reasoning for risk profiling. Task-Typed Reasoning Modules (TTRMs) organise reasoning into reusable, adaptable units covering Likelihood, Impact, Vulnerability, Mitigation, and Prioritisation, refined through an expert-gated feedback loop. Applied to an illustrative UK transportation logistics case study, the framework ranked HR-driven workforce risks, quantified uncertainty through belief-plausibility intervals, and generated traceable recommendations linked to four HRM-SCR dimensions. As a proof-of-concept demonstration, the framework provides a conceptual foundation for HR risk management in transportation, extending expert system reasoning through adaptive AI to offer mathematically grounded, traceable outputs for practitioners and researchers; empirical validation in live organisational settings remains a necessary next step. By formalising the human dimension of resilience, the framework contributes toward socially, economically, and environmentally sustainable transportation supply chains, aligning HR risk management with the sustainable development objectives examined in this study. Full article

Background/Objectives: Appearance ideals are increasingly transmitted through digital media, reshaping the sociocultural context in which disordered eating develops. Central and Eastern European populations remain underrepresented in this literature. This study examined the prevalence of disordered eating attitudes in a Hungarian adult community sample and tested an integrated model of body image disturbance and media-transmitted sociocultural pressures as statistical predictors, including formal tests of mediation. Methods: Hungarian adults (N = 675; 70.8% female; aged 16–81 years, M = 44.09) completed the Eating Attitudes Test–26 (EAT-26), the Body Attitude Test (BAT), and the Sociocultural Attitudes Towards Appearance Questionnaire–3 (SATAQ-3) in an online survey; body mass index (BMI) was calculated from self-reported height and weight. All instruments demonstrated good to excellent internal consistency in the present sample (Cronbach’s α = 0.84–0.93). Results: One in five participants (20.7%) scored at or above the EAT-26 clinical cut-off, with elevated rates at both extremes of the BMI spectrum (underweight: 30.0%; obese: 28.4%). Women scored higher than men on the EAT-26 (d = −0.59), BAT (d = −0.80), and three SATAQ-3 subscales; no gender difference emerged for athletic-ideal internalisation (d = −0.02), consistent with a cross-gender athletic ideal. The predictor set accounted for 40.5% of the variance in EAT-26 scores; BAT was the dominant predictor (β = 0.61), while SATAQ-3 Athletic retained a direct path (β = 0.08), and the remaining media-influence subscales lost significance once body image was controlled. A binary logistic regression using these predictors correctly classified 83.6% of participants (Nagelkerke R² = 0.35), with BAT and athletic-ideal internalisation again the only significant predictors of clinical-range classification. Bootstrapped mediation analyses confirmed that the associations between sociocultural influences and disordered eating attitudes were largely mediated by body image disturbance; only athletic-ideal internalisation retained a significant direct association (partial mediation). Conclusions: Body image disturbance constitutes the proximal core of disordered eating attitudes, whereas internalisation of the athletic ideal shows a small independent association, with implications for screening and prevention across the adult lifespan. Full article