Search Results (4,987)

Detecting and identifying faults in industrial chemical plants is essential for safe and stable operation, and modern monitoring systems increasingly rely on deep learning to classify faults from multivariate sensor data. A practical obstacle to adoption is trust: most deep-learning diagnosers reach their decisions through a classification layer that operators cannot inspect, making it hard to see how the model maps process signals to a particular fault. This study targets fault diagnosis on the Tennessee Eastman (TE) process, a standard benchmark of simulated chemical-plant sensor data, and asks whether this final decision stage can be made directly inspectable without sacrificing accuracy. We propose CNN–Transformer–KAN (CTKAN), a hybrid model that learns local temporal patterns with a one-dimensional convolutional encoder, captures global inter-time-step dependencies with a Transformer encoder, and classifies faults with a Kolmogorov–Arnold Network (KAN) head whose learnable B-spline activations can be plotted and examined individually, in place of a conventional multi-layer perceptron (MLP). On the TE benchmark, CTKAN attains a Macro-F1 of 91.38 ± 0.26% over ten independent runs, comparable to a CNN + Transformer + MLP ablation (91.21 ± 0.32%) and a capacity-matched MLP-head variant (91.43 ± 0.37%) within seed-to-seed variability. The main finding is therefore not a higher score: at matched capacity the KAN and MLP heads are statistically indistinguishable in accuracy, so the KAN head’s value is to add a directly inspectable view of the classification stage at no measurable accuracy cost, helping process engineers sanity-check how the diagnoser separates faults in safety-critical settings. Full article

Background: Rosacea is a chronic inflammatory dermatosis characterized by vascular dysregulation, immune dysfunction, neurovascular alterations, and microbial involvement. Recent advances in understanding its pathophysiology have led to the development of targeted therapeutic strategies addressing multiple disease mechanisms. This systematic review aimed to evaluate contemporary evidence regarding emerging and established treatment approaches for rosacea. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Scopus, and Web of Science were searched for studies published between 2016 and 2025. Original human studies evaluating therapeutic interventions for rosacea were included. Study selection, data extraction, and risk-of-bias assessment were performed independently by two reviewers. Methodological quality was assessed using Joanna Briggs Institute (JBI) critical appraisal tools appropriate for each study design. Results: Fifteen studies involving 537 patients with rosacea and 77 controls (614 participants in total) met the eligibility criteria. Evaluated interventions included vascular-targeted therapies, topical anti-inflammatory agents, systemic and immunomodulatory treatments, and microbiome-oriented approaches. Oxymetazoline, pulsed-dye laser, platelet-rich plasma, ivermectin, azelaic acid, dapsone, sulfur preparations, and metronidazole demonstrated clinical benefits in reducing erythema, inflammatory lesions, or overall disease severity. Emerging therapies, including tofacitinib and oral ivermectin, showed promising results in refractory disease. Microbiome-related interventions, particularly Demodex-targeted therapies and Helicobacter pylori eradication, were also associated with clinical improvement. Risk-of-bias assessment identified two studies with low risk of bias, twelve with moderate risk of bias, and one study with high risk of bias. Conclusions: Current evidence supports a multimodal and mechanism-based approach to rosacea management, integrating vascular, inflammatory, immunological, and microbiological targets. However, the available evidence remains limited by small sample sizes, heterogeneous methodologies, short follow-up periods, and a predominance of non-randomized study designs. Large, well-designed randomized controlled trials are needed to establish optimal evidence-based treatment strategies and define the long-term efficacy and safety of emerging therapies. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Background: Medication errors remain a major threat to patient safety in acute care settings worldwide and are associated with preventable morbidity, mortality, and increased healthcare costs. Nurses play a critical role in identifying, intercepting, and preventing medication-related harm. However, limited qualitative evidence has explored nurses’ perspectives on medication safety within the Saudi Arabian healthcare context. This study explored nurses’ experiences of medication safety, perceived systemic challenges, and strategies for error prevention in Saudi hospitals. Methods: A qualitative descriptive design was employed. Fourteen (n = 14) nurses from two major referral hospitals in Saudi Arabia participated in semi-structured face-to-face interviews. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase thematic analysis framework. Results: Five overarching themes were identified: (1) Communication gaps; (2) Medication processes; (3) Technology and safety; (4) Workload and staffing; and (5) Staff competence. Participants described how communication failures, staffing pressures, workflow interruptions, and documentation ambiguities compromised medication safety. While barcode systems and EHRs were perceived as valuable safeguards, participants emphasized that their effectiveness depended on staff vigilance, adequate training, and supportive workplace cultures. Conclusions: Medication safety is a dynamic socio-technical process shaped by communication, competence, staffing capacity, and human interaction with technology. Improving safety requires integrated organizational strategies that combine workforce investment, structured communication practices, continuous professional education, and non-punitive incident reporting cultures. These findings provide practical insights for healthcare leaders seeking to strengthen medication safety systems in Saudi Arabia and comparable settings. Full article

Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article

Across the world’s electrical substations, water-treatment plants, and manufacturing lines, a single engineer with valid credentials and a laptop can today push new control logic to a programmable logic controller (PLC) and change the physical behaviors of safety-critical equipment within minutes. Firmware and ladder-logic updates on SCADA and industrial IoT systems are privileged operations: an attacker installing a malicious update controls the physical process. Existing protections concentrate install authority in a single party with no externally verifiable record; compromise of the vendor key, the engineering workstation, or any operator credential suffices to deliver an attacker-chosen payload to a PLC. CertiFlash binds every update to four independent approvals: a vendor signature, a FROST-Ed25519 threshold signature from an operator quorum, a per-session nonce from the PLC, and a monotonic counter. Every decision is recorded in an append-only Merkle transparency log. The PLC verifies the aggregate with a standard RFC 8032 Ed25519 verifier, requiring no FROST-specific device code. Four security properties (authenticity, authorization, rollback resistance, auditability) are machine-checked in Tamarin under a Dolev–Yao adversary with up to t − 1 compromised operators and corroborated through ten attack scenarios. The implementation runs with concurrent Modbus TCP and Siemens S7 traffic, blocks all attacks, signs in 27–192 ms (k = 3–10), keeps ML-DSA-65 within 6% of Ed25519 from 1 KiB to 10 MiB, and sustains 30.1 updates/s on 100 PLCs. The operator-quorum signature remains FROST-Ed25519: the design is partially post-quantum in the evaluated version. The framework maps to IEC 62443-3-3 SR 3.4 and NIS2 Article 21(2)(d–e). Full article

Metal oxide nanoparticles serve as crucial drivers in modern biomedical, catalytic, environmental, and energy technologies due to their high surface-to-volume ratios and quantum confinement properties. Traditional chemical and physical synthesis methods remain limited by significant energy footprints, high costs, and the use of hazardous reagents. To address these challenges, bioinspired (“green”) synthesis has emerged as a sustainable paradigm that employs biological systems as nature nanofactories. This critical review provides a provides a comprehensive and systematic analysis of the green synthesis of major metal oxide systems (ZnO, TiO₂, Fe₃O₄/Fe₂O₃, CuO, Co₃O₄, CeO_2, and MnO₂) using diverse biological templates, including plant extracts, bacteria, fungi, algae, and biopolymers. Moving beyond simple descriptive summaries, we critically evaluate the foundational electron-transfer and nucleation mechanism, systematically correlate processing parameters with physical outcomes, and offer a rigorous comparative analysis across different biological kingdoms. Finally, we directly address the underlying challenges facing the field: reproducibility bottlenecks, scalability limits, environmental safety variations, and regulatory hurdles necessary for industrial translation. Full article

Background: Selecting the most efficient and specific adeno-associated virus (AAV) capsids for gene delivery to the nervous system via minimally invasive routes is critical to gene therapy advancement. While AAV9, rAAV2-retro, AAV-PHP.eB, and AAV-MacpnS1 have demonstrated significant central nervous system (CNS) transduction ability after systemic delivery, their tropism, efficiency, and safety profiles in a developmentally relevant model have yet to be systematically compared. This study comparatively evaluated four capsids after intravenous administration in neonatal C57BL/6 mice. Methods: Transgene expression was quantitatively assessed across multiple CNS regions, as well as in the heart and liver. Associated biochemical indicators of hepatic stress were also evaluated. Results: The resulting transduction profiles were distinct and capsid-specific. Both AAV9 and AAV-MacpnS1 induced widespread CNS transduction and robust peripheral organ expression. However, AAV-MacpnS1-neuronal tropism in the thalamus was superior, and it was also associated with the most prominent biochemical indicators of hepatic stress. In contrast, rAAV2-retro was remarkably specific to the medulla and spinal motor neurons, demonstrating a valuable safety profile. AAV-PHP.eB achieved broad cellular transduction in the spinal cord, but it was the least specific towards cholinergic motor neurons. Furthermore, transduction in DRG neurons using AAV9 and AAV-MacpnS1 was efficient, but that using rAAV2-retro or AAV-PHP.eB was not. Conclusions: These findings provide an “atlas-like” comparative framework that clearly outlines the strengths and limitations of each vector. They also offer valuable guidance on selecting the most suitable AAV capsid for fundamental neuroscience applications and for developing targeted gene therapies, particularly for neurodevelopmental and motor neuron disorders, where intravenous administration in the early stages of life is a promising strategy. Full article

This review synthesizes findings from more than 100 journal articles, reports, and design standards on the design, simulation, and testing of steel beam-to-column connections, with emphasis on semi-rigid bolted extended end-plate (EEP) joints. The core objective of this study is to highlight the critical importance of accurately capturing this semi-rigid behavior, given the significant implications of improper modeling for the global response, safety, and design reliability of steel frames. While connections are often idealized as fully rigid or pinned, EEP connections typically exhibit a semi-rigid response governed by nonlinear moment–rotation (M–θ) behavior. The reviewed literature is organized around: (i) mechanical response and key failure mechanisms (end-plate yielding, bolt fracture, and prying action); (ii) analytical and numerical prediction methods, including component-based models and finite-element approaches capable of representing contact, bolt pretension, and cyclic degradation; and (iii) system-level implications for steel frames. Approaches used in major standards (AISC and Eurocode 3) for classifying connection stiffness and strength are compared, and experimental programs are summarized to identify the dominant parameters controlling resistance, ductility, and failure mode. Translating these component-level findings to the structural-system level, the review highlights how appropriately detailed semi-rigid EEP connections can enable moment redistribution, reduce member demands, and support stable inelastic deformation under seismic actions. Key research gaps include three-dimensional and multiaxial loading, impact and other high-rate actions, and the performance of alternative materials such as stainless steel. Full article

Oncolytic viruses represent a promising class of anticancer therapeutics, and rapid, accurate quantification of viral titers is critical for ensuring both efficacy and safety during clinical development. Conventional viral titering methods, such as 50% cell culture infectious dose (CCID₅₀), are time-consuming and limited in sensitivity, thereby restricting their application in real-time clinical monitoring. This study aimed to develop and validate a rapid titer assay for oHSV2-PD-L1/CD3-BsAb, an oncolytic herpes simplex virus expressing a PD-L1/CD3 bispecific antibody, to support preclinical and clinical monitoring. A dual-reporter cell system was established using Vero-PD-L1-GFP (Vero cells expressing PD-L1 and GFP) cells as target cells and Jurkat-NFAT-Fluc (Jurkat cells expressing NFAT and Fluc) cells as effector cells. Viral infection activates the NFAT signaling pathway, driving Fluc expression, thereby enabling rapid quantification of infectious virus. The assay was evaluated for specificity, limit of detection (LOD), and lower limit of quantification (LLOQ), and compared with the conventional CCID₅₀ method. Its applicability was further assessed using clinical simulation samples, including PBMCs and swabs. The rapid titer assay accurately quantified virus at 10³ CCID₅₀/mL after 8 h of incubation, consistent with CCID₅₀ results, while extending the incubation to 18 h improved the LLOQ to 10^2.5 CCID₅₀/mL, demonstrating enhanced sensitivity. The assay exhibited high reproducibility and stability in both PBMC and swab samples, enabling reliable quantification of low-titer virus in complex biological matrices. Compared with CCID₅₀, the method substantially reduced assay time (from 3–5 days to 8–18 h) while improving sensitivity and specificity. The developed rapid titer assay for oHSV2-PD-L1/CD3-BsAb provides a sensitive and specific platform for viral quantification. It offers a valuable tool for oncolytic virus development, production quality control, and clinical monitoring, facilitating efficient safety evaluation and risk management in ongoing and future clinical applications. Full article

Accurate multi-channel gas concentration prediction is very important for coal mine safety. However, the dynamic reconstruction of the sensor network often interferes with the input sequence. Existing models face a critical trade-off: channel-independent models are robust to sequence changes but ignore spatial coupling, while channel-dependent models overfit fixed sequences, leading to performance collapse during rearrangements. This paper presents a gas concentration prediction framework based on channel permutation-invariant interaction (CPiRi) to reconcile these limitations. CPiRi employs a spatio-temporal decoupling architecture where a frozen univariate pre-trained encoder independently extracts temporal features to ensure sequence robustness. Subsequently, a permutation-equivariant spatial module utilizes self-attention to model inter-channel gas emission relationships based on data content rather than positional indices. To achieve true permutation invariance, we introduce channel-shuffling regularization during training, forcing the model to learn content-driven relational reasoning. Evaluations on 15 real-world Chinese coal mine datasets demonstrate that CPiRi achieves highly competitive accuracy and consistently outperforms mainstream baselines in both prediction precision and structural adaptability. This study offers a robust technical pathway for gas monitoring in dynamic environments, substantially improving the reliability of intelligent mine safety systems. Full article

The frictional behavior and stability of skid shoe systems are critical to the safety and controllability of walking-type incremental launching for long-span steel truss bridges. Therefore, this study investigates friction control mechanisms and multilayer pad stability through two tests: (1) skid shoe tests to evaluate low-friction performance, sliding stiffness, and the stability of stacked pad assemblies, and (2) interface friction tests to examine the frictional behavior of different material combinations intended to provide high-friction restraint. The results show that Modified Graphene-Enhanced (MGE) plates, when combined with grease and stainless steel, reduce the friction coefficient to 0.017–0.074. High-stack pad assemblies (6–16 layers) exhibited a progressive interlayer slip, with cumulative displacements exceeding the allowable limit, leading to instability; anti-slip measures such as shear keys and segmented restraints were recommended. A load-dependent sliding stiffness relationship, y = 57.46 + 0.00886x, was established to characterize the variation in nominal sliding stiffness with vertical load. The findings provide experimental data and engineering recommendations for the design and operation of skid shoe systems in heavy-load incremental launching applications. The proposed criteria and regression model are applicable to the tested pad geometry, interface configuration, and loading conditions investigated in this study. Full article

Marine algae are emerging as important biological resources for the discovery and development of bioactive compounds with applications across food, pharmaceutical, cosmetic, agricultural, aquaculture, environmental, and biotechnological systems. This review critically synthesizes current knowledge on macroalgae and microalgae as sources of sulfated polysaccharides, carotenoids, phenolic compounds, proteins, peptides, vitamins, mycosporine-like amino acids, and polyunsaturated fatty acids. Emphasis is placed on the relationship between algal source, cultivation conditions, compound structure, extraction strategy, formulation, and biological activity. Key mechanisms of action are discussed, including antioxidant defense, modulation of inflammatory signaling, inhibition of metabolic enzymes, antimicrobial and antiviral activity, interactions with the gut microbiota, and regulation of cell-cycle-related pathways. Recent progress in biotechnological production, green extraction, purification, analytical characterization, bioaccessibility, bioavailability, and delivery systems is evaluated in the context of real product development. The review further highlights the use of algal bioactives in functional foods, nutraceuticals, pharmaceuticals, cosmeceuticals, aquafeeds, crop biostimulants, and environmental remediation. Current limitations, including biomass variability, compound instability, limited human validation, regulatory complexity, safety concerns, and scale-up costs, are also addressed. Overall, marine algae provide a sustainable and multifunctional platform for developing bioactive products when discovery, processing, validation, and commercialization are integrated. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article