Search Results (2,290)

This study examines the socio-spatial dynamics of rural overtourism and landscape commodification in a peripheral municipality in Northwestern Italy. Employing a qualitative, longitudinal ethnographic design, data were collected between May 2020 and October 2021 in San Giovanni (Piedmont). The methodology combined extensive participant observation in public, commercial, and agricultural spaces with ethnographic interviews with six stakeholder groups: local promoters, municipal administrators, professional farmers, long-term residents, shopkeepers, and visiting tourists. Qualitative data were processed using inductive thematic analysis. The results indicate that visitor surges were initiated endogenously by local promoters through the installation of an open-access, gamified “Big Bench” attraction designed to counter long-term demographic and economic contraction. The influx of day-trippers generated localized spatial intrusion, traffic congestion, and physical disruptions to agricultural operations during peak harvest periods, as well as to residents’ daily activities. Rather than driving generalized economic revitalization, the intervention produced host–visitor friction and intra-community polarization between local promoters seeking external visibility and residents experiencing spatial disruptions and loss of privacy. The study demonstrates that overtourism can develop endogenously through promoter-led self-commodification and provides a three-tiered governance framework encompassing policy, destination management, and community-level strategies for fragile rural territories. Full article

Multi-server private information retrieval (PIR) based on function secret sharing (FSS) has emerged as a prominent paradigm for achieving sublinear communication. However, standard FSS constructions require full server participation, making them highly vulnerable to single-node fail-stop faults. Existing fault-tolerant schemes mitigate this but inevitably inflate the response overhead to scale with the database size N (e.g., $O\left(\sqrt{N}\right)$). To overcome this limitation, we propose a fault-tolerant PIR (FT-PIR) protocol based on a newly designed $(t,p)$-threshold distributed point function (FT-DPF). By introducing a hierarchical recursive patching mechanism, our scheme transforms rigid all-party evaluations into flexible t-out-of-p reconstructions. This architecture completely decouples the response communication from N and ensures efficient client-side reconstruction via lightweight XOR aggregations. Formal analysis proves that our stateless protocol guarantees $(t-1)$-computational privacy under the semi-honest model. Theoretical analysis demonstrates that the proposed FT-PIR achieves a response complexity bounded by $\mathcal{O}\left({\mathcal{F}}_{\mathrm{maxlevel}}(t,p)\right)$. Comprehensive experimental evaluations confirm that our implementation significantly reduces practical communication and computation overheads, outperforming the state-of-the-art scheme. Full article

AI-powered conversational agents are becoming part of the everyday Internet information ecosystem, reshaping how users seek, interpret, and act on health-related information outside clinical encounters. As large language model (LLM)-based chatbots are increasingly used as on-demand digital health information tools, understanding how users perceive their credibility, usefulness, and limitations is essential for the responsible design of future Internet-based health services. This mixed-method survey study examined how general adults evaluated healthcare-related question–answer pairs provided by physicians and generated by AI chatbots. A sample of U.S.-based adults recruited through Prolific (N = 62) rated each answer on clarity, usefulness, appropriateness of detail, trustworthiness, and perceived evidence, and provided open-ended explanations of their judgments. Primary mixed-effects analyses showed that both ChatGPT- and Claude-generated responses received higher overall participant ratings than physician-provided responses, although the estimated difference was substantially larger for Claude (ChatGPT–physician estimate = 0.250, 95% CI [0.135, 0.364]; Claude–physician estimate = 0.825, 95% CI [0.710, 0.939]). ChatGPT received higher ratings on four of the five dimensions but not on clarity, whereas Claude received higher ratings across all five dimensions. However, physician, ChatGPT, and Claude responses were always presented first, second, and third, respectively. Response source was therefore confounded with presentation position, and the observed differences cannot be attributed exclusively to source. The responses were also not matched for length or format. Qualitative findings showed that participants valued detailed, specific, and evidence-like explanations. Participants also expressed concerns about hallucination, privacy, over-reliance, and the need for clinician verification. These findings suggest that LLM-based chatbots may be perceived as useful supplemental information tools within future Internet health ecosystems, but their deployment should include safeguards that support transparency, verification, and appropriate reliance. Full article

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency. Full article

Background: Diabetes mellitus is a major public health concern in Saudi Arabia, affecting approximately 18.3% of adults. Although over 95% of people in the Qassim region own smartphones, limited information exists regarding how and why patients with diabetes utilize digital health technologies. Objective: This study employed an extended Technology Acceptance Model (TAM) incorporating trust, privacy concerns, and self-efficacy to investigate the factors influencing digital health technology adoption among patients with diabetes in Qassim Unaizah, Saudi Arabia. Methods: A cross-sectional study was conducted from November 2025 to January 2026 following institutional review board approval. The quantitative phase utilized a structured survey (n = 203) measuring TAM constructs via validated 5-point Likert scales. Descriptive statistics, Pearson correlations, and one-way ANOVA were performed. Qualitative themes were derived from open ended responses to contextualize quantitative findings. Results: Most participants were male (62.6%), with a mean age of 47.2 years (SD ± 13.1). Smartphone ownership was nearly universal (99.5%), and 82.8% used blood glucose tracking applications. All TAM constructs exhibited significant positive correlations with behavioural intention (p < 0.001): attitude (r = 0.450), perceived usefulness (r = 0.438), self-efficacy (r = 0.394), trust (r = 0.379), ease of use (r = 0.340), and privacy concern (r = 0.309). Mean scores indicated strong acceptance: perceived usefulness (4.31, SD ± 0.39), behavioural intention (4.21, SD ± 0.44), and attitude (4.17, SD ± 0.42). Daily usage was reported by 63.1% of participants, 81.8% expressed satisfaction, and 63.1% reported that digital tools greatly improved their diabetes management. Privacy concerns were notably low (mean 1.70, SD ± 0.89, reverse-coded; Cronbach’s α = 0.921). Supplementary qualitative content analysis of optional open-ended comments (n = 40 respondents) identified six recurring topics: clinical utility, digital literacy, trust, data security awareness, patient empowerment, and social support. Conclusions: Patients with diabetes in Qassim Unaizah demonstrate substantial digital health adoption, predominantly driven by perceived usefulness, positive attitudes, and self-efficacy. The extended TAM effectively explains adoption within this Saudi Arabian context. Findings support interventions emphasizing clinical benefits, user friendly design, and trust building to optimize digital health utilization in diabetes care. Full article

Recent progress in intelligent, adaptive, and security-aware Software-Defined Wireless Sensor Networks (SDWSNs) is driven by the integration of Machine Learning (ML) with Software-Defined Networking (SDN) and Wireless Sensor Networks (WSNs). This systematic survey analyzes 46 peer-reviewed articles published between 2024 and 2025, providing a problem-oriented synthesis of ML-SDWSN research. Emphasizing security, routing, and performance optimization, with a particular focus on deployment architectures, the survey identifies three major trends: increased adoption of ensemble and Reinforcement Learning (RL) methods for security and adaptive control; broader implementation of edge-based ML to minimize inference latency; and greater emphasis on privacy-preserving techniques, especially Federated Learning (FL). The survey presents a structured taxonomy encompassing seven thematic areas: Distributed Denial-of-Service (DDoS) mitigation, Intrusion Detection Systems (IDSs), routing optimization, Quality of Service (QoS) management, privacy preservation, data integrity, and network-efficiency optimization. Findings are synthesized from over 120 experimental configurations reported in the literature. Due to substantial differences among the reviewed studies in terms of datasets, network topologies, hardware platforms, measurement definitions, and validation methodologies, the reported values are presented as descriptive cross-study aggregates rather than direct comparative benchmarks or formal effect-size estimates. Within these constraints, the survey identifies recurring trade-offs among accuracy, latency, scalability, and privacy. It provides evidence-based design considerations for researchers and practitioners. The survey also highlights eight critical research gaps, including limited multi-dataset validation, a lack of real-world deployments, insufficient scalability analysis, and the need for rigorous evaluation of RL-based SDWSN control. Full article

For national statistics offices (NSOs), data sharing is essential for the production of official statistics, yet it must comply with stringent confidentiality and governance mandates. Blockchain technology has emerged as a promising means to enable trusted and auditable data exchange under these constraints, motivating a growing body of research across public-sector domains. This paper presents an up-to-date survey of blockchain-based data-sharing solutions in government and NSO contexts, reviewing studies published between 2018 and 2026. Given the limited research specific to official statistics, this survey evaluates public-sector solutions with particular attention to their alignment with NSOs’ strict privacy and data governance requirements. Departing from prior reviews that primarily organise the literature by technical architectures or application domains, the survey adopts the Five Safes framework as a unifying analytical lens. Through this governance-focused synthesis, the survey identifies recurring architectural patterns and design principles that support secure and accountable data sharing, including permissioned or consortium blockchains, hybrid on/off-chain storage, selective integration of privacy-enhancing technologies, and smart contracts to automate governance functions such as access control and workflow management. Building on these insights, the paper outlines two distinct reference architectures that distil best practices from existing studies for blockchain-enabled data sharing under public-sector constraints, and highlights open challenges—most notably scalability and integration with legacy systems—pointing to future research directions towards solutions that combine strong security assurances with practical governance compliance. Full article

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

Vernacular heritage conservation often separates material fabric, environmental adaptation, and social meaning. This study addresses that separation for one settlement. Socio-environmental heritage is defined here as heritage whose significance resides in the documented interdependence of environmental conditions, material practice, social organisation, and customary governance, rather than in fabric or imagery alone. The single purpose of the article is to develop and demonstrate an evidence-based method for interpreting and conserving Thee Ain Heritage Village in Al-Baha, Saudi Arabia, as such a system. A qualitative architectural case-study design combines a structured literature search, regional comparison, the author’s 2014 field observations and photographs, and published digital-heritage, energy-retrofit, and conservation studies; no human-participant data are analysed. Evidence is organised through three analytical layers—climatic material, socio-spatial, and customary governance—and each evidence–interpretation proposition is classified as directly observed, supported architectural inference, or hypothesis requiring measurement; conservation translation is treated as the output of this sequence rather than as a parallel analytical layer. Coded claim units are documented individually so that every interpretation and implication can be traced to its source, strength, and limitation. The analysis links rocky siting, stone and timber assemblies, thick load-bearing madameek walls, limited openings, vertical domestic hierarchy, controlled thresholds, and the agricultural setting to conservation priorities at landscape, construction, spatial, and adaptation scales. These priorities include compatible repair, retention of wall depth and opening logic, protection of privacy gradients and threshold sequences, and service integration without reducing the village to stone-clad imagery. Unlike previous work centred on digital documentation, energy modelling, or policy-level preservation, the contribution is an evidence-structured method linking architectural observation to bounded interpretation, conservation decisions, and explicit future testing requirements. Full article

Cross-organization cyber defense must reconcile collaborative learning with privacy and adversarial robustness, yet standard federated learning ships full gradient tensors, leaking sensitive posture and inviting Byzantine manipulation. We present FedMARL-LTI, a federated multi-agent reinforcement learning framework whose architecture answers both pressures with a single decision: each organization’s threat intelligence is shared only as a differentially private 768-dimensional semantic embedding, never as raw data. In the evaluated system, a Weight-DP-protected model-weight delta is also exchanged through the federated aggregator (the semantic abstraction embedding is a parallel channel); the privacy guarantee below is stated for the semantic abstraction channel, and an embeddings-only architecture—which the guarantee enables—is the design this points toward. The contribution is fourfold. (1) Semantic Abstraction (SA) channel: per organization, each round, the local gradient is summarized by an LLM, projected to a 768-dim embedding, L2-clipped, and Gaussian-noised before any numeric quantity leaves the host. The bottleneck reduces the aggregate noise magnitude—the expected L2 norm of the DP noise vector—from $O\left(\sqrt{d_{\mathrm{model}}}\right)$ to $O\left(\sqrt{m}\right)$ with $m=768\ll d_{\mathrm{model}}\approx 3\times {10}^5$. (2) Formal privacy analysis: the SA + DP cascade satisfies $(\epsilon ,\delta )$-DP and bounds per-round mutual information leakage by $\min \{T_{\mathrm{tok}}{\log }_{2}V,m/2{\log }_2(1+C^2/\left(m{\sigma }^2\right)\left)\right\}$, with Rényi composition over $T$ federation rounds. Scope of the guarantee: this bound certifies (i) the semantic-abstraction channel. It does not by itself cover (ii) the weight-aggregation channel, whose Weight-DP protection is analyzed separately, nor (iii) the whole deployed system, which is the composition of the two. We therefore state the ≈1.4-bit/MI bound as a per-round guarantee on information leaving the organization through the SA channel not over every byte the system emits; an embeddings-only configuration—which this bound enables—closes the gap to a whole-system guarantee. (3) Byzantine-resilient ClippedClustering aggregator combining L2 clipping with cosine-similarity clustering. (4) Hierarchical MARL policy with threat-profile-aware LLM-IRR reward shaping, wired end-to-end and disclosed honestly (the evaluated system uses a deterministic Johnson–Lindenstrauss projection in place of the LLM call for reproducibility; the architecture is thus LLM-compatible rather than dependent on a specific model, and a full LLM deployment is the planned extension). We evaluate on CybORG CAGE-4 with $n=5$ organizations, 30 federation rounds × 5 episodes × 100 steps per round. Releasing the SA channel in parallel shows no statistically detectable reward cost at N = 5 vs. the no-privacy baseline; this is measured at reward-shaping coefficient β = 0, so it establishes that the private semantic release does not disturb weight-channel training rather than that semantic sharing improves defense: SA-only $\Delta$reward = $+4.58$ ($t=+1.37$, NS), dual SA + Weight-DP $\Delta$reward = $+4.31$ ($t=+1.30$, NS), all $N=5$ seeds, all $\left|t\right|<1.4$. A controlled signal/noise probe confirms a $19.58\times$ improvement of SA over Weight-DP at a fixed DP budget—matching the predicted $\sqrt{d/m}\approx 19.8$. Under Byzantine sign_flip at 30% ($N=15$), ClippedClustering is directionally strongest ($F_1=0.025$ vs. FedAvg $0.020$, Krum $0.016$) but the edge is not statistically significant (CC vs. Krum $t=+1.59$, $p=0.15$, $d=+0.58$; the earlier $N=5$ “$3.4\times$” gap was small-sample optimism); its Byzantine behavior is on the harsher random_noise attack. Under a corrected implementation, the undefended baselines do not diverge or collapse; the earlier reading (Krum $0.002$, ClippedClustering $0.020$) was a noise-injection artifact and is withdrawn; ClippedClustering is now directionally best on F1 but not significantly, and trails Krum on reward (superseded Cohen’s $d=+3.77$). The cooperative-PPO family (MAPPO, IPPO) outperforms value/actor-critic (QMIX, MADDPG) by $\approx 20$ reward units, $p<0.001$. All host-level $F_1$ values stay below $0.05$ at the 15K-step training horizon used here; the relative claims of the paper (no detectable privacy reward cost, ClippedClustering’s competitive (not decisive) Byzantine behavior on the harsher attacks, cooperative-PPO dominance) are unaffected by this scope. A 200K-step long-horizon replication lifts $F_1$ above the 15K plateau (to $\approx 0.044$, $N=5$)—confirming that horizon, not the privacy/Byzantine machinery, gates absolute accuracy—but a finer 60-checkpoint run shows the climb is volatile and non-monotonic and does not reach deployment-grade, an honest stability-not-compute limitation. FedMARL-LTI is therefore presented as a proof-of-concept for the relative privacy and robustness trade-offs it isolates, not as an operationally deployable cyber defense system. We release all 141 raw run JSON outputs (Phases 1–3, the L4 backend comparison, and the algorithm/aggregator baselines), the figures, and analysis scripts for replication. Full article

Retrieval-Augmented Generation (RAG) has become a practical approach for question answering over external corpora, particularly when answers should be grounded in source documents rather than generated only from model parameters. While recent large language models can process increasingly long contexts, they do not remove the need for selecting, organizing, and auditing evidence, especially when systems rely on smaller local models for privacy, cost, or deployment constraints. In this paper, we frame retrieval granularity as an evidence-design variable for answer grounding in small-model RAG question answering. After a brief exploratory NewsQA phase that motivates the error categories, the main study uses the HotpotQA distractor validation split with 7405 hard multi-hop questions and sentence-level supporting-fact annotations. With Qwen3-8B as the fixed generator, we compare closed-book, fixed-budget whole-context, retrieved-context, gold-document, and gold-supporting-fact conditions while varying retrieval granularity, retriever type, and context budget. Retrieved context substantially outperforms closed-book answering and the 1024-token fixed-budget whole-context condition but remains below gold-document and gold-supporting-fact upper bounds, indicating that retrieval, generation, and evaluation limitations should be analyzed separately. Sentence-level retrieval under-recovers multi-hop evidence, especially for questions with three or more supporting facts, while paragraph-level and moderate token-level chunks recover substantially more complete evidence. In the full condition matrix, hybrid retrieval with 256-token chunks and no overlap achieves an F1 of 0.6816 with a supporting-fact recall of 0.9609, compared with an F1 of 0.6166 and supporting-fact recall of 0.7801 for BM25 sentence retrieval. Additional ablations show that fixed-budget whole-context performance is strongly affected by truncation, that overlap has little practical effect under the tested 1024-token budget, and that a stronger BGE dense retriever improves the best retrieved-context F1 to 0.7027. These results align with a diagnostic perspective on chunking: using evidence at a task-appropriate level of granularity can improve grounding, auditability, and answer quality, but the observed patterns should be interpreted within the HotpotQA distractor setting, fixed generator, and tested context budgets. Full article

The exponential growth of the Internet of Things (IoT) has generated massive data streams traditionally processed by centralized cloud architectures, which increasingly face latency, bandwidth, and privacy limitations. Shifting artificial intelligence to resource-constrained edge nodes, known as TinyML, offers a robust decentralized alternative, though it introduces severe memory, compute, and energy bottlenecks. To map this transition, a systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases. The analysis identifies primary architectural paradigms and evaluates the efficacy of state-of-the-art model compression techniques, such as quantization, pruning, and knowledge distillation. Furthermore, the findings reveal that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks, while also highlighting persistent security and privacy challenges in on-device learning. Ultimately, while deploying complex models on microcontrollers is increasingly viable, achieving optimal performance demands holistic optimization strategies. This review synthesizes current research gaps and provides a strategic roadmap to guide future interdisciplinary efforts toward resilient, energy-efficient, and secure next-generation intelligent edge systems. Full article

This article describes a new hardware/software alert system designed to assist elderly people and prevent falls due to bed-exit events at night. To assist the elderly during the night, it is important to implement automatic lighting activation in the bedroom. This helps reduce the risk of falls when they need to, for example, go to the bathroom. The caregiver can be alerted immediately via Wi-Fi, during the night, providing immediate assistance to the elderly person. The developed HW/SW system combines a passive infrared motion sensing device, a light sensor, and dedicated hardware based on the ESP32-C6 RISC-V microcontroller that communicates via Wi-Fi with a developed Android dedicated App, which the caregiver can access using a tablet or smartphone. Nighttime falls remain one of the most serious health problems for older people. The main innovative contribution of this work is the development of a low-cost preventive battery-free assistive system that does not require an internet access contract, preserves the elderly person’s privacy, and promptly alerts the caregiver whenever the elderly person gets out of bed during the night. The system is directly integrated with automated lighting, preventing the elderly person from walking in the dark and without appropriate aid. The system also supports the caregiver by generating alerts through an open-source mobile App. Full article