Search Results (5,330)

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

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Decarbonising the built environment has increased the importance of prefabricated construction, yet its cost and resource efficiency are still constrained by fragmented supply chain collaboration. This study examines how lifecycle supply chain collaboration affects cost control performance in prefabricated construction. Based on supply chain management theory and expert consultation, a conceptual model was developed and tested through structural equation modelling using 517 valid responses from stakeholders in China’s prefabricated construction supply chain. The results show that management factors across all four project phases (decision and design, component production, transportation, and construction and installation) significantly improve cost control performance, with design standardisation, production scheduling, transport logistics, quality assurance, and workforce proficiency as key drivers. Process coordination exerts a significant mediating effect, while environmental factors significantly moderate the relationships. In practical terms, the findings indicate that stakeholders should prioritise design standardisation at the early stage, strengthen coordination across production, transport, and installation activities, and enhance quality control and workforce training to reduce avoidable cost overruns and resource waste. Beyond their theoretical contribution to research on supply chain collaboration in prefabricated construction, these results offer concrete direction for practitioners seeking to improve cost efficiency and make better use of resources within industrialised building systems. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article

Higher education policy on generative artificial intelligence has developed rapidly, yet much of this development remains stronger on governance, permission, disclosure, and assurance than on pedagogy. Universities increasingly move beyond blanket prohibition by distinguishing between restricted and permitted contexts, requiring acknowledgement of tool use, and introducing verification mechanisms to protect authorship and understanding. However, publicly visible institutional approaches appear less developed in providing structured, student-facing workflows that guide responsible AI engagement during assessment completion. This article, informed by a bounded qualitative document analysis, uses the term pedagogical middle layer to describe the process guidance needed between institutional permission settings and academic-integrity or misconduct procedures. Drawing on recent literature and a purposive scan of selected publicly available university policy and guidance documents, the paper argues that current public-facing models are often effective at defining boundaries but less explicit in guiding disciplined, transparent, and defensible forms of human–AI collaboration. In response, the paper presents the Structured AI-Guided Education Assessment Policy (SAGE-AP) as a theoretically grounded policy proposal for AI-assisted assessment, rather than as an empirically validated policy intervention. SAGE-AP frames assessment as a staged process in which students begin from their own understanding, engage with AI critically, document evaluative decisions, refine outputs responsibly, and defend the reasoning represented in the final submission. The paper contributes to institutional policy development by clarifying how permission settings may be complemented by pedagogical process guidance in the generative AI era. Full article

Large-amplitude and long-term vibration deformation under external environmental loads often occurs on tensile membrane structures. Proper sensor placement plays a vital role in effectively achieving the objectives of a structural health monitoring system. In order to optimize the sensor placement to identify the modal vibration parameters for tensile membrane structures, this paper proposes an optimal sensor placement method based on the Fisher information matrix (FIM) and improved random strategy discrete particle swarm optimization algorithm (IRSDPSO). Firstly, the structural modal order is selected by using the two-norm difference and trace change rate of FIM, and the number of sensors is determined based on the QR decomposition and MAC criterion. Secondly, an improved particle swarm optimization algorithm named IRSDPSO, which has the discrete characteristic, is proposed to arrange the placement of sensors. Finally, the convergence, stability and sensitivity are used to evaluate the effectiveness of optimal sensor placement results using a numerical modal test example of the plane bidirectional tensile membrane structure. The results show that the first nineteen modal frequencies can be accurately identified. This indicates that the proposed optimal sensor placement method can determine the number of sensors and arrange the placement of the sensors. The work is reasonable and feasible in the optimal sensor placement for the tensile membrane structure. Full article

The growing complexity of software-defined automotive systems generates massive heterogeneous sensor and ECU data during real and virtual validation, and conventional rule-based analysis of such multivariate time series struggles under dynamic operating conditions, noise, and diverse fault scenarios. Deep learning-based anomaly detection has shown promising performance, yet existing approaches remain limited by static thresholds, insufficient robustness, and reduced interpretability. This study proposes an adaptive framework for intelligent fault detection in test recordings of automotive software systems (ASSs), integrating deep denoising autoencoders (DAEs), adaptive Gaussian thresholding, and explainable artificial intelligence (XAI) techniques. Four DAE architectures (ANN-, RNN-, GRU-, and LSTM-DAE) are systematically evaluated under different noise levels, system versions, and fault conditions, with detection thresholds that adapt dynamically to the statistical behavior of the reconstructed signals, thereby reducing false alarms under varying operating conditions. The framework was evaluated using real-world test recordings from IAV and Hardware-in-the-Loop (HIL)-based digital test drives, where ANN-DAE achieved the most robust detection performance, with F1-scores of 93.91% and 96.39% on the real and virtual test-drive data, respectively. Furthermore, the integration of XAI improved the transparency of anomaly interpretation at the signal level. Overall, the proposed framework shows strong potential for intelligent anomaly detection and quality assurance in safety-critical automotive systems. Full article

This study examines how assurance level and assurer type jointly influence non-professional investors’ reactions to sustainability disclosures in an emerging market context. It employs a controlled 2 × 2 mixed-design experiment that manipulates assurance level (limited vs. reasonable) and assurer type (audit firm vs. non-audit firm). Data were collected from MBA and DBA students in Egypt as proxies for non-professional investors. Investor reaction is captured through multiple measures, including perceived sustainability performance, reliance on sustainability information, investment intention, stock valuation, and decision confidence. Non-parametric statistical techniques are used to test hypotheses, complemented by exploratory machine learning using SHAP values. The results provide strong and consistent evidence that the assurance level is the dominant factor shaping investor reactions. Reasonable assurance significantly enhances investor judgments across all key measures, whereas the type of assurer does not have a statistically significant independent effect. Additional analyses reveal that reasonable assurance from a non-audit firm elicits more favorable reactions than limited assurance from an audit firm, underscoring the primacy of assurance strength over provider identity. Exploratory findings further indicate that assurance influences investment decisions primarily through perceived sustainability performance and reliance on information. This study contributes to the literature by clarifying the relative roles of assurance level and assurer type and providing novel evidence from an emerging market setting (i.e., Egypt). The findings offer important implications for firms, assurance providers, and regulators seeking to enhance the credibility and decision usefulness of sustainability reporting. Full article

Prompt specifications for multi-agent large language model (LLM) systems carry data contracts and integration logic across interdependent files but are rarely subjected to structured-inspection rigor. We report a single-system case study of iterative, agent-driven auditing applied to AEGIS (Autonomous Engineering Governance and Intelligence System), a seven-lane production pipeline whose 7152-line specification surface was audited across nine rounds, surfacing 51 consistency defects (per-round counts of 15, 8, 12, 2, 8, 1, 4, 1, 0). We present a seven-category post hoc taxonomy with explicit coding rules, non-monotonic convergence consistent with cascading edits and audit-scope expansion, and a locked audit protocol. We further report two partial replications on a public synthetic mini-specification: a cross-LLM panel of four frontier vendors (OpenAI, Anthropic, Google, xAI; 12 traces; multi-vendor union detects all five seeded defects) and an inter-rater reliability check on a stratified subsample (Cohen’s $\kappa$ = 0.80 on category, 0.46 on severity). The full reproducibility bundle accompanies the submission. Full article

Quality assurance in higher education increasingly depends on the capacity of institutions to transform stakeholder engagement into usable evidence for decision-making and continuous improvement. Among external stakeholders, alumni represent a potentially strategic but underutilized source of information on the relevance of training processes and their alignment with professional trajectories. However, the existence of alumni engagement does not guarantee its integration into formal quality assurance systems. This study analyzes how an institutional alumni management model is designed to articulate graduate engagement with internal quality assurance processes. Adopting a qualitative case study approach based on documentary analysis, the research examines the organizational architecture of a Chilean university, focusing on the mechanisms through which alumni participation is expected to be translated into evidence for academic decision-making. The findings show that the model combines strong relational infrastructures with emerging mechanisms for data capture and circulation. However, the institutionalization of processes for interpreting and using evidence remains less developed, revealing an asymmetry between participation, data production, and decision-making. Based on these results, the study conceptualizes alumni integration into quality assurance as a multi-stage process involving participation, data capture, circulation, and use, highlighting the organizational conditions required for each stage. The study contributes by proposing a process model of institutional translation that identifies the organizational breakdowns through which alumni engagement may remain disconnected from formal quality assurance processes. In doing so, it shows that the effectiveness of quality assurance systems depends not on the availability of data alone, but on the governance arrangements that enable evidence to be interpreted, circulated, and used. Full article

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it. Full article

Universities increasingly rely on digital self-service channels to manage high volumes of time-sensitive admissions enquiries. AI enabled chatbots represent a prominent solution because they can provide round-the-clock responses, standardize guidance, and potentially reduce uncertainty for applicants. Yet evidence on whether such chatbots meaningfully assist students and improve their satisfaction with admissions support remains limited in many developing higher education contexts. This quantitative study evaluates the perceived effectiveness of AI chatbots used for university admissions in Pakistan, with a focus on student assistance and satisfaction as key outcomes. Using a cross-sectional survey design, data were collected from students who had recently engaged with university admissions information services (e.g., website chat widgets, messaging-based virtual assistants, and admissions enquiry portals) across private universities in Pakistan. Admissions chatbot effectiveness was measured through established information systems and service quality constructs system quality (ease of use, responsiveness, accessibility), information quality (accuracy, clarity, completeness), and service quality and trust cues (assurance, privacy confidence, and appropriateness of conversational support). Student assistance captured the extent to which chatbot interactions helped participants complete admissions related tasks and navigate application procedures. Student satisfaction reflected overall evaluation of the admissions support experience. The results indicate a positive association between perceived chatbot quality and perceived student assistance, and a further positive association between student assistance and student satisfaction with admissions support. The overall pattern suggests that student assistance functions as a key mechanism through which chatbot effectiveness translates into satisfaction. At the same time, respondents highlighted limitations in resolving complex or exception based queries, emphasizing the importance of transparent escalation to human admissions staff. The study contributes context specific evidence from Pakistan and offers an empirically grounded framework that university administrators can use to evaluate and improve admissions chatbots. Practical implications emphasize maintaining accurate knowledge bases, designing clear handoff pathways, and implementing governance practices that strengthen students’ confidence in information reliability and data privacy. Full article

Saudi Arabia’s audit market faces three governance challenges that existing frameworks may not fully address. These challenges concern a potential regulatory gap around autonomous AI accountability, a trust dimension that standard technology-adoption models may not fully capture, and limited mechanisms for independently verified ESG assurance under Vision 2030. This study adopts a conceptual design approach within the design science research tradition and proposes the CMA Agentic AI Platform as a practical response to these challenges. The platform comprises two segments. Segment 1 deploys autonomous drone swarms to verify corporate assets across four audit tasks—asset valuation, ESG compliance, anomaly detection and construction progress—using deep learning, thermal imaging and social-media cross-referencing. Segment 2 continuously monitors discretionary accruals and uses objective earnings-management data to inform auditor assignment and rotation decisions. This approach replaces subjective reputational assessments with transparent, quantifiable governance criteria. The platform is governed through the Triadic Agentic Framework, which extends classical agency theory by distributing authority across the Principal, the Human Agent and the AI Agent. The framework also operationalises Trust Expectancy as the primary adoption condition. The evidence base draws on two complementary streams: a PRISMA-guided systematic review and bibliometric analysis of thirty-nine peer-reviewed studies, and a documentary analysis of four national agentic-AI regulatory frameworks (SDAIA, MDDI/IMDA, NIST and ICO). The study contributes the concept of Algorithmic Accountability as a distinct governance domain, the Triadic Agentic Framework as an operational architecture for autonomous regulatory monitoring, and a reframing of the UTAUT trust construct for agentic-AI adoption in mature professional contexts. The platform converts theoretical governance into a regulatory architecture with direct implications for concentrated capital market regulators. Full article

Introduction: The invasive European catfish (Silurus glanis) is actively spreading across Iberian freshwaters, with no effective management measures in place to control its growing abundance or prevent its establishment in new localities. It poses a severe threat to endemic and already endangered species, and is simultaneously a preferred target by few anglers who continuously promote its spread. The LIFE-PREDATOR project aims to stop the spread of European catfish in lentic systems in Portugal and Italy, particularly in protected areas. Objectives: This talk will present the mid-term results of the LIFE-PREDATOR in Portugal, and discuss the difficulties and future challenges to reduce the size of local populations of European catfish. Methodology: The LIFE-PREDATOR team developed several tasks in Portugal: (1) established the reference situation of fish communities in six reservoirs in the Tagus Basin, using scientific fishing, fish telemetry and eDNA-based tools; (2) determined the optimal protocols for sampling catfish; (3) implemented an early detection programme based on warning teams, data-mining and eDNA tools; (4) developed population control actions in four reservoirs; and (5) organised dissemination events for the general public, anglers, and students from kindergarten to university levels. Results: Overall, there is a grim view about recipient communities in the studied lentic systems, which tend to be dominated by invasive fish species, including common carp (Cyprinus carpio), gibel carp (Carassius gibelio), European catfish, pikeperch (Sander lucioperca), European perch (Perca fluviatilis) and largemouth bass (Micropterus nigricans). At least three new localities harbouring catfish were identified from online data-mining and warning teams. A total of 8 tons of catfish were removed by mid-June of 2025, mostly from the Natural Park of International Tagus. Outreach activities were conducted in nearly 60 schools, reaching more than 5000 students. Moreover, 67 general public events have reached more than 4500 people since the project started (September 2023). Conclusions: Despite its positive outcomes, the LIFE-PREDATOR team has encountered challenges in engaging key stakeholders such as anglers, involving local municipalities, and implementing catfish removal actions in remote areas. Difficulties and challenges in catfish management must therefore be debated in order to assure the after-LIFE implementation across Portuguese protected areas. Full article

Obtaining market approval and reimbursement are necessary but not sufficient conditions for the implementation of new vaccines in high-income countries to maximize their long-term preventative value. Comprehensive pre-launch and launch-phase economic evaluations of the disease and the vaccine are necessary to support long-term public health improvement by the vaccination program. This review highlights the construction of these evaluations conceived as a plan, methods, and a tool. They can be generated by different stakeholders (e.g., payers, producers, target groups) interested in the value success of vaccination. A Vaccine Launching Value Project (VLVP) has been developed based on the experience gained from helping to launch 10 new vaccines worldwide over 15 years (2005–2020). It comprises information on the following: (1) identification of new vaccines that should require a VLVP approach; (2) country-specific characteristics of healthcare; (3) methods to assess economic values for specific stakeholders; (4) identification of the money flow in managing the disease and infection spread; and (5) optimal implementation strategies at the initiation of new vaccination programs. The benefits of applying the VLVP are illustrated using rotavirus vaccination as an example. The VLVP program starts with the development of a Broad Country Linked Inventory (Brocoli) Plan that interconnects eight baskets of information specifying a framework of activities. This is followed by the Cauliflower and Artichoke Methods to assess the vaccine value for additional key stakeholders (e.g., employers, hospital managers, working mothers, the Ministry of Finance) and the money flow amongst the payers (who pays what to whom, when, for what, and how). The evaluation process finishes with the Total Management Tool (Tomato) to identify the optimal implementation conditions at the start of a new vaccination program necessary to obtain the best long-term value for the stakeholders selected. The critical interconnections between these information blocks are discussed. This improves the positioning of a new vaccine by articulating its total economic value within a societal and public health environment over time, outside the conventional Health Technology Assessment box. The Tomato Tool emerges as the most pivotal component of the VLVP. It provides the best assurance of long-term economic value with strong sustainability support. Full article