Search Results (5,485)

The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories. Full article

The growing popularity of biophilic design in architecture is often linked to the physical and mental well-being of building occupants. At the same time, increasing climate change requires greater emphasis on passive material strategies that support environmental regulation in buildings. In the literature, biophilic elements encompass a wide range of components, from natural materials, through plant systems, to environmental factors such as light and water. However, their classification often relies on perceptual criteria. The aim of this article was to critically analyse and evaluate the issues of materials and biophilic systems in the context of architectural adaptation to climate change. A classification based on impact mechanisms, rather than on the origin of materials or their aesthetic character, was proposed, which allows for associating material properties with measurable indoor environmental quality parameters such as temperature and humidity. The proposed framework suggests that the impact of biophilic materials and systems on users may be indirect, linked in part to the shaping of the interior microclimate and indoor environmental quality (IEQ) parameters. This perspective highlights the potential importance of environmental mechanisms as a complementary dimension of interpreting biophilia, an aspect that, in the authors’ view, has not yet been sufficiently explored in the literature. A mechanism-based approach could serve as a foundation for further assessment of the potential of biophilic materials and systems in the context of architectural adaptation to climate change. Full article

Multi-Key Searchable Encryption (MKSE) enables data owners (DOs) to outsource their data to a cloud server (CS) while supporting fine-grained data sharing with other authorized users. Most existing MKSE schemes can protect data users’ (DUs’) search query privacy against collusion attacks between malicious DOs and the CS. However, the CS is not fully trusted and may maliciously return forged or incomplete search results. To address this issue, Verifiable MKSE (VMKSE) is proposed by leveraging Garbled Bloom Filter (GBF), which can support verifiability even when the search results are empty. Unfortunately, due to the massive native storage redundancy of GBF, the storage and computational overhead of verification evidence generated in the sharing phase increases as the number of shared documents grows. Therefore, in this paper, we present a novel VMKSE scheme (VMKSE-BFF) by adopting BFF, which can simultaneously support verifiability of and secure data sharing in a multi-user setting. We provide a comparison with the existing VMKSE schemes. Experimental results on a real-world dataset show a significant performance improvement of VMKSE-BFF. Full article

Background/Objectives: Telerehabilitation is increasingly used globally to deliver physiotherapy services remotely, yet its adoption in Jordan remains limited. This quantitative cross-sectional study aimed to explore the awareness, perceptions, and willingness of Jordanian physiotherapists regarding telerehabilitation, and to identify barriers and facilitators to its adoption. Methods: An online self-administered questionnaire was completed by a convenience sample of 200 physiotherapists (111 males, 89 females; age range 23–62 years). Descriptive statistics and chi-square tests were used for analysis. Results: Results revealed that 116 participants (58.0% of the total sample) were aware of telerehabilitation. Among those aware, 79 (68.1%) had used it, with online video (27.0% of total sample; 46.6% of users) being the most common modality. A significant association was found between higher education level (Master’s/Ph.D.) and greater awareness (p < 0.001). Key barriers included lack of reimbursement (33.0% of total sample; 56.9% of aware respondents), technical difficulties (47.0%), infrastructure issues (39.0%), and lack of training (34.0%). Among aware participants who responded to attitude items, the majority agreed that telerehabilitation enhances patient access (85/116, 73.3% of aware respondents) and is a valuable addition to practice (102/116, 87.9% of aware respondents), while 43.0% of the total sample (86/200) believed it can never fully replace in-person consultation. Conclusions: Awareness of telerehabilitation among Jordanian physiotherapists is moderate, and actual clinical use remains low. Higher education level was significantly associated with greater awareness, highlighting a key demographic correlate. However, significant educational, financial, and infrastructural barriers hinder the transition from awareness to adoption. Policy reforms and structured training programs are urgently needed to support the integration of telerehabilitation into routine physiotherapy practice in Jordan. Full article

Traditional handicraft knowledge is not only embodied in final products and craft procedures but also embedded in the context formed by production spaces, tools, materials, practitioners, and their interrelationships. However, existing digital preservation practices often focus on individual objects or specific data types, making the spatial and semantic relationships among heterogeneous resources insufficiently represented and limiting public understanding of the broader context of craft practices. To address this issue, this paper proposes a digital resource organization and multi-terminal presentation framework. Using 3D point clouds as a unified spatial reference for the site, the framework links images, videos, interviews, craft records, and object-related materials to spatial locations through structured annotation, and visualizes relationships among practitioners, tools, materials, processes, and spaces through node-link representations. The Web-based viewer and CAVE immersive system access the same content dataset, enabling “input once, reuse across terminals”. User feedback suggests that the framework supports the integrated representation of spatial context, craft resources, and associated information within traditional handicraft sites, with relational visualization contributing to a more holistic understanding of craft practices. The framework provides a reusable workflow for organizing, linking, and presenting heterogeneous heritage resources in traditional handicraft transmission sites, offering digital support for a contextual understanding of craft practices. Full article

With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronization of background knowledge between the transmitter and receiver, making the semantic knowledge base (SKB) a critical cornerstone. However, effectively selecting appropriate content from massive cloud-based knowledge repositories for edge deployment remains a significant challenge. This paper conducts systematic research to address the key issues in the flow deployment of SKBs at the edge, including insufficient adaptation to personalized preferences, inadequate timeliness management, and the complexity of multi-objective optimization. First, a comprehensive system model is constructed, integrating user preferences, knowledge relevance, transceiver matching degree, and the Age of Information (AOI). Second, the Generative Adversarial Network (GAN)-assisted Preference-based Reinforcement Learning (GaPbRL) algorithm is proposed. The experimental results demonstrate that this method outperforms traditional schemes in terms of knowledge-base hit rate, transceiver matching degree, and algorithm convergence speed, while significantly reducing the overhead of manual fine-tuning. This study provides a robust framework for the personalized and efficient cloud–edge collaborative deployment of SKBs. Full article

The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management. Full article

Machine learning (ML) models have achieved remarkable success in areas ranging from healthcare to autonomous systems. Yet, their inherent complexity frequently obscures the reasoning behind their decisions, undermining transparency, accountability, and user trust. Compounding this issue is the absence of a universal methodology for comparing and ranking interpretability across diverse ML models and interpretability techniques. This paper addresses this gap by introducing a configurable framework of quantitative metrics to evaluate and rank interpretability. Our approach offers a structured, heuristic basis for assessing model clarity, decision logic, and accessibility, enabling practitioners to systematically compare interpretability across a wide range of algorithms and techniques. The resulting scores are intended as a practical, domain-configurable heuristic guide for comparison rather than a universal notion of interpretability. Full article

In regulated cross-institution electronic health record (EHR) sharing, access control may need to respect patient consent while also providing issuance accountability and revocation correctness. Existing multi-authority ciphertext-policy attribute-based encryption (MA-CP-ABE) schemes provide decentralized attribute management but generally do not bind key issuance and ciphertext use to explicit patient-authorized sharing episodes, nor do they provide proactive verification that accepted key packages are consistent with authenticated user requests and the current revocation state. In this paper, we propose a verifiable, consent-bound MA-CP-ABE scheme for cloud-assisted EHR sharing. The scheme binds ciphertexts and attribute key packages issued to a patient-signed consent identifier, supports request-consistent and verifiable key issuance, integrates revocation freshness and stale-key detection, and provides a two-layer accountability framework consisting of proactive issuance verification together with reactive tracing and audit. In addition, each issued package is bound to the enrolled user public key and to a user-originated commitment, supporting restricted designated-user delivery. Theoretical and experimental evaluations indicate that, under the stated trust and non-collusion assumptions, the proposed design strengthens consent-scoped access control and accountability while maintaining competitive computational efficiency for cloud-based EHR systems. Full article

This study develops an interpretable machine learning framework to predict the compressive strength of concrete incorporating blast-furnace slag (BFS). To address the critical issue of data leakage prevalent in conventional random splitting, a rigorous grouped validation strategy, specifically the GroupKFold algorithm, was implemented based on unique mixture proportions. Seven machine learning algorithms, including linear baselines and tree-based ensembles, were comprehensively evaluated. Results indicate that the XGBoost model achieved the highest predictive accuracy and stability, yielding a mean Root Mean Squared Error (RMSE) of 5.39 MPa and a minimal standard deviation of 0.54 MPa. A multi-criteria decision-making (MCDM) approach mathematically confirmed XGBoost as the optimal model. Furthermore, SHapley Additive exPlanations (SHAP) combined with data-density rug plots were utilized to uncover the non-linear interactions between BFS and other components. Rather than asserting direct causality, the SHAP analysis provides robust model-based associations that align with macroscopic physical expectations while strictly preventing over-interpretation in sparse data regions. Finally, a conceptual graphical user interface (GUI) is proposed to bridge the gap between theoretical models and future batch-plant deployment. This research balances rigorous high-precision prediction with transparent interpretability for BFS concrete design. Full article

Large language models are increasingly embedded in e-commerce assistants that recommend links to shopping destinations. When the candidate set contains adversarially crafted phishing URLs, a fluent model can recommend a malicious link with the same confidence it recommends a legitimate one, turning a helpful assistant into a delivery channel for fraud. Rather than treating phishing defense as per-link URL classification, this work frames the problem as recommendation-level exposure minimization: the output set itself must be secured, and a recommendation is counted as useful and safe only when it surfaces a benign destination and exposes no phishing URL. We first organize phishing URL constructions into four families spanning brand padding, typosquatting, homograph substitution, and subdomain impersonation, and use them to build candidate pools that mix benign links with plausible distractors. Evaluating four widely used open models under prompt-only defenses reveals a persistent gap: the strongest prompt baseline reaches only a 47.4 percent four-model average useful-safe rate, and weaker models fall below 20 percent even after careful prompting. We then present SAFER, a Security-Adaptive Filtering framework for Exposure-Minimized URL Recommendation. SAFER separates contextual selection from safety admission by coupling a deterministic lexical and structural pre-filter, an evidence-augmented single reasoning pass, and a deny-by-default post-verification stage with a deterministic fallback. The same deterministic URL evidence is injected before generation to condition the model’s reasoning and reused after generation to constrain which model-selected URLs may reach the user. SAFER issues exactly one model call per query, matching the prompt baselines, so its gains come from structure rather than additional inference. Across the four models SAFER raises the average useful-safe rate to 87.0 percent, a 39.6-point improvement over the best prompt baseline, and the deny-by-default stage yields a positive net gain for every model, largest where the model is weakest. Shrinking the deterministic layer’s brand coverage in a held-out analysis degrades the pipeline gracefully rather than collapsing it, indicating that within the range we tested its robustness does not rest solely on memorizing a fixed brand list. Additional robustness experiments confirm that SAFER transfers to real phishing URLs from the OpenPhish feed, generalizes to unseen attack families, maintains zero exposure on hard benign negatives, outperforms supervised URL classifiers as an admission gate, and maintains exposure minimization under realistic pool structures within the evaluated threat model. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Consumer electroencephalography (EEG)-triggered fragrance delivery raises a narrow control problem: a noisy state estimate must not bypass user authority, and each actuator decision should remain reconstructible. We present a domain-specific control architecture that maps versioned Sleep Readiness Index (SRI) records to bounded ranking among three user-authorised cartridge identifiers. Input quality, permission, exposure limits, and an independent stop monitor constrain every proposed pulse. For 105 synthetic records with complete strict-v1 metadata, the validator derived $Q=1$ for every record, committed a 29-record baseline, completed two simulated acknowledged pulses with one completed cooldown, entered Stop at the fixture cap, issued one DISARM, and verified a 114-event hash chain. A paired legacy OSC fixture used legacy profile and provenance fields and lacked source continuity telemetry; it accepted no quality-eligible record, remained in Calibrate, and emitted no proposal. The replay validates controller mechanics and software consistency only: its acknowledgements, SRI changes, and rewards are synthetic, and no cartridge–SRI relationship, meaningful preference learning, device operation, physiological benefit, or sleep improvement is demonstrated. Full article

Unwanted telemarketing calls are a persistent source of consumer frustration and a legally regulated issue in Germany. At first glance, the idea of addressing this problem with AI-based voice technology appears contradictory: why should an automated caller help restore trust in a communication channel that has been damaged by aggressive outbound practices? This design-oriented case and prototype study argues that the paradox can be resolved through a different design logic. Rather than using AI to intensify persuasion, we present a role-based voice-agent architecture that constrains conversational behavior through narrow task boundaries, explicit escalation rules, and auditable data handling. The paper reports a transfer project involving FH Aachen students, Capgemini, and Fairdient GmbH. Methodologically, the work is positioned as a design-oriented case study with a prototype artifact. The contribution is threefold: first, we describe a three-agent architecture for outbound screening, consent-aware explanation, and inbound service; second, we derive governance principles for legally and ethically sensitive telephony, including transparency, bounded knowledge, privacy-preserving deployment, and human fallback; and third, we propose an evaluation framework covering conversion, compliance, hallucination control, user trust, and cost per validated outcome. The prototype does not yet claim large-scale field effectiveness. Instead, it offers a structured and empirically testable design for trustworthy voice automation in a domain where misuse, opacity, and user distrust are especially pronounced. Full article

Directed evolution has revolutionized protein engineering by applying the principles of natural selection to the laboratory. However, traditional in vitro methods are quite labor-intensive, while common in vivo methods suffer from low mutation rates and high rates of off-target mutations. To address these issues, researchers have developed targeted mutagenesis tools for rapid in vivo evolution of biomolecules. In this review, we discuss recent in vivo hypermutation tools that enable rapid sampling of the vast evolutionary landscape, all while supporting simultaneous selection of the best proteins within living organisms. We focus on three main mechanisms of hypermutation: (i) orthogonal replication, which uses error-prone replication machinery to replicate the target gene with low fidelity; (ii) CRISPR-Cas-guided mutators, where mutagenic proteins are localized to virtually any user-defined loci; and (iii) transcription-coupled mutagenesis, a simple, yet elegant tool that exploits the innate processivity of orthogonal ribonucleic acid (RNA) polymerases to guide mutagenic proteins along the target gene during transcription. We highlight key advantages of these systems, as well as some clinically- and biotechnology-relevant applications. We discuss important limitations and how they could be addressed in the future to make hypermutation tools with broad mutational spectra and windows that span entire genes with minimal off-target effects. Full article