Search Results (1,687)

Background/Objectives: Medication-related harm is a leading global patient safety challenge, yet patients’ lived experiences of medication safety remain underexplored in Central and Eastern European healthcare systems, where structural constraints significantly shape everyday medication use. Methods: This study provides an in-depth qualitative analysis of Hungarian patients’ online narratives, building on a prior netnographic mixed-methods study. Using grounded theory-informed principles and a patient-centred medication safety framework, we inductively analysed 5174 publicly accessible Hungarian-language comments posted on health forums and social media platforms between August 2020 and August 2023. The COM-B model was applied as a secondary lens to map findings onto modifiable behavioural determinants. Results: Access to services and communication emerged as the dominant medication safety concerns. Patients reported long waiting times, limited rural emergency services, and brief consultations leading to delayed or inadequate treatment. Communication gaps included insufficient information on medication duration, side effects, and follow-up, as well as conflicting advice from multiple sources, all of which eroded trust and prompted treatment discontinuation or reliance on informal online communities. Community pharmacists were largely absent from patients’ mental models of care, representing a significant missed opportunity given their accessibility. Less frequently mentioned were medication shortages, healthcare professional workload, and systemic safety culture. Conclusions: Clear, respectful communication and timely access to care are central to medication safety from the patient perspective. Netnography combined with a grounded theory-informed methodology offers a valuable approach for capturing authentic patient perspectives in structurally burdened healthcare systems, with findings relevant beyond the Hungarian context. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

Mobile RFID inventory in libraries must be planned and evaluated under noisy observations, configuration-dependent read regimes, and incomplete supervision. This paper presents an uncertainty-aware analytics framework for robot-assisted RFID inventory using the public RFID Location dataset. The framework has three phases. Phase 1 converts irregular list-encoded logs into atomic RFID events and quantifies how operating configuration changes read density and signal variability. Phase 2 performs map-constrained Bayesian shelf inference by synchronizing RFID reads with robot trajectory and antenna geometry and by fusing RSSI and carrier phase over feasible shelf candidates. Phase 3 translates posterior spread and non-convergence into proxy review workload and cost, enabling configuration comparison and certainty–throughput trade-off analysis when strict EPC-to-item linkage is unavailable. Across 688,073 aligned RFID observations, the pipeline produces 18,190 posterior tag estimates from five inventory runs. The empirical results show strong run dependence: the best run achieves a mean posterior spread of 0.906 m with a convergence rate of 0.553, whereas a degraded run reaches only 0.004 convergence with a mean spread above 2.1 m. Because EPC-to-item linkage is unavailable, these values are posterior concentration and workload indicators rather than ground-truthed localization-accuracy metrics. A saved phase-weight ablation further shows that adding phase information substantially sharpens posterior concentration relative to an RSSI-only baseline. Under the proxy workload model, autonomous-S1-P30 provides the most favorable balance among posterior certainty, scan effort, and implied review burden. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

In flexible job shop scheduling, industrial robots undertake both workpiece transportation and loading/unloading operations. Equipping each machine with dedicated buffers tends to increase transportation workload and further intensify transport bottlenecks. Shared buffers are therefore introduced to temporarily store workpieces and relieve congestion in the production process. This paper establishes a transport-integrated flexible job shop scheduling model with shared buffer constraints, which minimizes makespan, total energy consumption, and machine load range simultaneously. Correspondingly, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is developed to achieve better solution performance. A time-window-based path-planning decoding scheme is constructed to address buffer constraints and transportation conflicts in the coordinated production and transportation process. In parallel, four initialization rules are designed to improve the quality and diversity of the initial population, and a variable neighborhood search algorithm (VNS) is embedded to enhance the local exploitation ability of the proposed algorithm. The performance of the presented method is evaluated through two groups of numerical experiments. The first group is carried out on extended benchmark instances. Comparisons with the conventional Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization algorithms (MOPSO) validate the efficacy of the proposed strategies and demonstrate the superiority of ENSGA-II in both solution quality and computational efficiency. Experimental results on real-world cases further illustrate that the proposed method can effectively solve the integrated scheduling problem in flexible manufacturing systems where industrial robots are employed as the main transport resources. Full article

Spatial data supply chains (SDSCs) transform authoritative observations into widely reused spatial products, but users typically lack machine-actionable provenance describing lineage, processing configurations, and custodianship. We introduce GeoPROV, a domain-specialised provenance model that profiles the W3C PROV framework and aligns with OGC GeoSPARQL to support standards-based representation and querying of spatial provenance. GeoPROV extends the Entity–Activity–Agent pattern with spatially meaningful entity types, explicit supply-chain roles, and first-class configuration artefacts. The model is formalised through a conceptual UML model and implementation-ready physical schema. We instantiate a provenance repository using the Geoscape Administrative Boundaries dataset and evaluate GeoPROV under three SDSC-relevant workloads: bounded upstream lineage traversal, downstream impact analysis, and GeoSPARQL-enabled spatial provenance queries. GeoPROV-based provenance infrastructures provide predictable, scalable performance when applying bounded traversal, index-aware spatial filtering, and validation-before-persistence. Overall, GeoPROV offers a reproducible, interoperable, and operationally viable foundation for spatial provenance, addressing key transparency, trust, and governance requirements in contemporary SDSCs. Full article

The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, memory hierarchy, packaging, timing, and design automation. Rather than converging on a single hardware solution, the field is expanding into a heterogeneous ecosystem that includes data-center graphics processing units (GPUs), edge neural processing units (NPUs), and application-specific integrated circuits (ASICs), field-programmable gate array (FPGA)-based and hybrid AI system-on-chip (SoC) platforms, chiplet-enabled systems, and emerging beyond-conventional-silicon approaches such as photonic, neuromorphic, and analog in-memory processors. This paper presents a comprehensive review of AI-on-chip systems from a cross-layer perspective. It examines AI chip architectures and hardware platforms, network-on-chip (NoC) designs for AI communication patterns, and algorithm–hardware co-design methods for model acceleration, including compression, quantization, and sparsity-aware optimization. It also reviews clocking, synchronization, and clock-domain-crossing (CDC) challenges in large heterogeneous systems and chiplets, as well as manufacturing, advanced packaging, and reliability issues, including two-and-a-half-dimensional (2.5D) and three-dimensional (3D) integration, thermal and mechanical constraints, assembly quality, and long-term yield considerations. In parallel, the paper surveys the growing role of AI in chip design itself, covering machine-learning-assisted analysis, Bayesian and reinforcement-learning-based optimization, and the emerging use of large language models (LLMs) and AI agents for register-transfer level (RTL) generation, design-space exploration, and autonomous electronic design automation (EDA) workflows. Finally, it discusses beyond-silicon AI chip directions and the broader economic and industry context shaping cloud, on-premises, and edge deployment. By integrating these topics into a unified framework, this review highlights the key technological drivers, system-level tradeoffs, and future research directions that will define next-generation scalable, reliable, and energy-efficient AI-on-chip systems. Full article

Lymphoid neoplasms such as multiple myeloma (MM), indolent non-Hodgkin lymphoma, and chronic lymphocytic leukemia are increasingly managed as chronic, relapsing conditions characterized by prolonged surveillance, repeated treatment transitions, and cumulative self-management demands. These trajectories expose patients and caregivers to persistent illness uncertainty, fluctuating fear of progression, symptom and comorbidity burden, communication challenges, and treatment-related workload. This theory-informed framework development paper uses an overview of selected psycho-oncological, hematological, nursing, theoretical, and patient-reported outcome literature to propose the LUMINA framework: Longitudinal illness trajectory, Uncertainty fields, Multidimensional symptom and comorbidity load, Information and interaction context, Navigation work and self-management load, and Adaptive outcomes and alignment. LUMINA is intended as a hypothesis-generating conceptual structure to organize clinically relevant domains, clarify potential relationships among uncertainty, symptom burden, communication, navigation work, and adaptive outcomes, and guide future assessment, validation, and intervention research in chronic lymphoid neoplasms. The framework builds on prior theories of illness uncertainty, treatment burden, workload–capacity balance, fear of recurrence/progression, and lymphoma-specific qualitative work on uncertainty management and psychosocial adaptation. Potential research applications include structured assessment, shared decision-making research, and domain-matched supportive-care concepts; however, these applications remain theoretical and require empirical testing. Future studies should evaluate feasibility, acceptability, construct validity, domain overlap, predictive validity beyond quality of life, and the clinical utility of LUMINA-informed research profiles. Until such validation is available, LUMINA should be interpreted as a conceptual model rather than a validated clinical tool or care pathway. Full article

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, and employee resistance. This study develops a human-centered decision framework for prioritizing algorithmic management governance packages in third-party logistics (3PL) warehousing. The main contribution is to translate employee-level governance concerns into a scenario-sensitive decision model that helps managers select appropriate governance packages under different operational pressures. The study uses survey data from 380 warehouse employees to examine key psychological and behavioral mechanisms, including procedural fairness, transparency, system/information quality, autonomy, privacy concern, workload, trust, acceptance, and resistance/disengagement. These survey-supported constructs are then converted into six governance criteria: procedural fairness, transparency and contestability clarity, system and information quality, autonomy support, privacy boundary governance, and workload protection. A seven-expert panel evaluates five governance packages under three scenarios: peak season surge, labor shortage/high turnover, and audit pressure/compliance scrutiny. Methodologically, the framework combines Dynamic Multi-Facet Fuzzy Sets to capture membership, non-membership, hesitancy, engagement, and resistance; Bayesian Network weighting to reflect dependencies among governance criteria; and PCA-based ranking optimization to generate scenario-specific and robust rankings. Comparative validation with SAW and TOPSIS is also used to assess ranking consistency. The findings show that effective algorithmic management governance is not a fixed compliance solution. Transparency, workload protection, autonomy support, privacy boundary governance, and procedural fairness become more or less important depending on the operational scenario. A2, which combines transparency, workload protection, and autonomy support, emerges as the strongest robust package. A1 performs best under labor shortage/high turnover, while A3 performs best under audit pressure/compliance scrutiny. These results suggest that 3PL warehouses should adopt adaptive governance routines that combine explainability, contestability, workload safeguards, privacy boundaries, and employee voice mechanisms. The study contributes to the literature on AI in socio-technical systems by showing how human, organizational, and ethical concerns can be embedded into an interpretable decision framework for responsible algorithmic management in logistics work environments. Full article

In large-scale rice farming systems, the design of efficient cropping schedules is essential for improving labor management and operational efficiency. However, climate change, including rising temperatures and increased frequency of extreme weather events, has altered crop growth dynamics, making it difficult to achieve optimal management using conventional experience-based scheduling. In addition, the need to distribute operations across numerous fields and optimize labor allocation has increased the complexity of schedule design. In this study, we propose a decision-support method for designing rice cropping schedules using a heading date prediction model and climatological temperature data. The method adjusts transplanting dates based on predicted heading and maturity dates and determines operation periods through both forward and backward scheduling. A case study conducted on a large-scale farming system in Chiba Prefecture demonstrated that the proposed method effectively dispersed the distribution of heading and maturity dates, leading to improved temporal distribution of operations. The standard deviation of heading dates decreased from 11.7 to 8.7 days, indicating a reduction in peak labor demand. The novelty of this study lies in extending a heading date prediction model from growth prediction to practical applications in cropping schedule design and visualization. This approach enables a transition from experience-based planning to data-driven decision-making and contributes to labor distribution in large-scale farming under climate change conditions. Full article

Multi-cloud is becoming more challenging to secure as traditional perimeter-based security models have a hard time protecting workloads running across multiple cloud platforms, identities, and services. To address this challenge, organizations are shifting to Zero-Trust Architecture (ZTA), which focuses on constant verification and stringent access control, coupled with anomaly detection methodologies to gain better visibility and threat detection in the distributed cloud environment. This paper presents a Systematic Literature Review (SLR) of anomaly detection approaches in multi-cloud environments and how these are applied in zero-trust security models. The review is conducted according to the guidelines of the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), and is based on studies published between 2020 and 2025 selected from the databases of the following journals: Institute of Electrical and Electronics (IEEE) Xplore, Science Direct, MDPI, Google Scholar, and the Saudi Digital Library. Studies found on benchmark datasets such as CICIDS-2017 and UNSW-NB15 are not evaluated, as none addressed real multi-cloud environments. Although zero trust is highlighted in general, very few studies have implemented basics of zero trust such as micro-segmentation, identity federation, and enforcement through policy. Overall, this review identifies gaps around cross-cloud validation, explainability, and compliance-aware security design, including lack of attention to regulations such as the GDPR and HIPAA. These findings provide helpful recommendations for future research and development on practical and security solutions for multi-cloud environments. Full article

Background: Breast cancer (BC) remains one of the leading causes of cancer-related deaths worldwide, with early detection being essential for improving survival rates, treatment outcomes, and preventive women’s healthcare strategies. Artificial Intelligence (AI), particularly deep learning (DL) and machine learning (ML) algorithms, has emerged as a promising tool for improving the accuracy and efficiency of BC diagnosis. This systematic review explores the role of AI in early BC detection and its implications for preventive and patient-centered women’s healthcare. Methods: A comprehensive search was conducted in PubMed and Scopus for studies published between January 2015 and December 2025, following PRISMA guidelines. The search strategy included combinations of MeSH terms and free-text keywords related to artificial intelligence, machine learning, deep learning, BC screening, mammography, magnetic resonance imaging (MRI), ultrasound, and BC detection. Eleven studies involving approximately 148,170 participants were included. Methodological quality was assessed according to study design. Results: AI-driven diagnostic systems demonstrated improved accuracy, sensitivity, specificity, and efficiency compared with conventional approaches. AI applications in mammography and ultrasound reduced radiologists’ workload and healthcare costs while enhancing cancer detection rates, particularly in women with high breast density. AI models also showed potential in identifying metastases and predicting clinical outcomes, supporting more efficient patient management and follow-up care. Conclusions: AI-based tools represent a promising advancement in BC detection and screening efficiency. Their integration into BC screening programs may strengthen preventive women’s healthcare services and improve patient outcomes. However, further large-scale clinical validation and real-world implementation studies are required before widespread clinical implementation. Full article

Adverse weather conditions, particularly thunderstorms, are the primary cause of flight delays and safety threats, accounting for approximately 58.7% of irregular flights in 2025. Traditional static rerouting methods often fail to adapt to the non-linear evolution of convective weather. This paper proposes a high-fidelity dynamic rerouting framework to enhance flight safety and efficiency. In the perception layer, a RainNet deep learning model is employed for short-term recursive nowcasting of radar reflectivity, which is subsequently transformed into Dynamic Avoidance Zones (DAZ) via clustering and convex hull algorithms. In the decision layer, a two-stage improved Genetic Algorithm (GA) is developed to solve the rerouting path. The first stage generates initial collaborative solutions under a receding-horizon framework, while the second stage applies a “path-straightening” module to reduce cumulative turning angles and curvature fluctuations. The comparative results in actual scenarios demonstrate a distinct dual-advantage over baseline methodologies. Compared to sampling-based strategies, the proposed model reduces the path length by 14.79%. Furthermore, when compared to heuristic algorithms, it actively trades a negligible 1% distance margin to achieve a massive 92.7% reduction in the cumulative turning angle. With a maximum single turn of only 32.51°, the trajectory completely eliminates sawtooth jitter and redundant detours. Ultimately, this research provides essential technical support for improving air traffic management efficiency and reducing controller workload during severe weather events. Full article