Search Results (9,963)

Engineering education continues to grapple with the shift from lecture-centered instruction to approaches that connect theory with practice and strengthen transferable competencies. This study examines an educational intervention in the Bachelor’s Degree in Mechanical Engineering at Miguel Hernández University of Elche. Our objective was to evaluate the impact of a challenge-based learning (CBL) strategy, supported by optional artificial intelligence (AI) tools and aligned with the Sustainable Development Goals (SDGs). The intervention took the form of a design challenge in which 48 students, working in teams, developed a mechanical artifact using laboratory resources, prepared a technical report, and justified design, material, and process decisions. Data were collected through student surveys to assess perceptions of skill development, AI use, and SDG awareness. Findings indicate improved understanding of manufacturing processes, more critical and selective use of AI, stronger sustainability awareness, and gains in transferable competencies such as creativity, decision-making, and technical communication. These results suggest that integrating CBL with emerging technologies can enhance learning outcomes and motivation in technical degree programs, while offering a practical model that other engineering courses can adapt. Full article

This review systematically analyzes 215 papers on the remote sensing monitoring of Spartina alterniflora (S. alterniflora) indexed in the Web of Science database to clarify research progress and future development directions in this field. We applied CiteSpace 6.3.R1 to conduct a bibliometric analysis of remote sensing literature on S. alterniflora, summarizing the technical methodologies across three major domains: distribution dynamics, parameter inversion, and ecosystem functions and services. We traced the technological evolution of multi-source remote sensing and artificial intelligence, and explored application prospects in addressing current challenges and supporting precision management. Our research indicates that the primary challenge lies in the complex and diverse spatiotemporal dynamics of S. alterniflora. To achieve timely monitoring of S. alterniflora changes and large-scale ecological impact assessments, it is essential to fully utilize the advantages of multi-source remote sensing big data. Harnessing artificial intelligence technologies to fully exploit the potential of remote sensing data, enhancing multi-source data fusion, and expanding sample libraries are essential to achieve comprehensive monitoring spanning spatial patterns, ecological processes, and ecosystem functions and services. These efforts will provide a scientific basis and decision-making support for the sustainable management of coastal wetlands. Full article

Greenhouse gas (GHG) emissions from rail infrastructure are increasingly examined in response to climate policy demands. Yet current assessment methods, such as ISO-based LCAs, FTIP, “Standardisierte Bewertung”, EN 15804 with c-PCR 023, and EIB’s Climate Proofing, differ substantially in assumptions and comparability. This study investigates the transferability of systematic criteria from semi-quantitative risk assessment as defined in the German pre-standard DIN V VDE V 0831-101 to GHG assessment methods. A two-step analysis was conducted. First, risk assessment criteria, including scope definition, granularity, conservatism, justification, system definition, sensitivity, monotonicity, transparency, calibration, variable interdependency, and result applicability, were reviewed for relevance to GHG assessment. Second, these criteria were applied to existing GHG methods to assess their coverage and identify shortcomings. The findings indicate that many systematic criteria are transferable and are largely fulfilled in LCA-based approaches, although LCAs are often very time and cost-intensive, especially regarding data collection and analysis. Current semi-quantitative frameworks, such as FTIP, lack granularity, justification, and calibration. The results suggest that a semi-quantitative GHG assessment method integrating systematic, legal, and topic-specific requirements could offer a harmonized, transparent, and practical tool for infrastructure planning. Such an approach promises balanced rigor and usability, facilitating more consistent decision-making and comparability across and within projects. Full article

Background/Objectives: Minimally invasive valve surgery (MIVS) is increasingly employed as an alternative to conventional median sternotomy (MS) in the treatment of valvular heart disease. However, its impact on postoperative quality of life (QoL) remains incompletely understood. This systematic review and meta-analysis aimed to compare QoL outcomes between MIVS and MS, focusing on physical, psychological, and social dimensions, both in the short- and long-term postoperative periods. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, and Wiley Online Library databases for studies published between January 2020 and September 2025. Eligible studies included adult patients undergoing MIVS or MS and assessed QoL using validated instruments (SF-36, EQ-5D, MLHFQ, KCCQ). Random-effects models were used for meta-analysis, and standardized mean differences (SMDs) were calculated to estimate pooled effects. Results: Fifty-six studies with a combined sample of over 10,000 patients were included. MIVS was associated with significantly better short-term QoL outcomes across physical (SMD = 0.88; 95% CI: 0.74–1.02) and psychological domains (SMD = 0.47; 95% CI: 0.35–0.59). Patients also experienced earlier social reintegration and improved body image perception. Although these benefits diminished beyond 12 months, MIVS maintained a modest but persistent advantage in long-term QoL (≥5 years). Structured psychological support and cardiac rehabilitation programmes further enhanced physical and emotional recovery. Conclusions: MIVS confers meaningful benefits in postoperative QoL, particularly during the early recovery phase. Sustained improvements depend on comprehensive postoperative care, including rehabilitation and psychosocial support. Further long-term, standardized research is required to strengthen evidence and guide patient-centred surgical decision-making. Full article

Background/Objectives: Viral diseases remain a major threat to global public health, particularly during outbreaks when limited therapeutic resources must be rapidly and fairly distributed to large populations. Although Convalescent Plasma (CP) transfusion has shown clinical promise, existing allocation frameworks treat patient prioritization, donor selection, and validation as separate processes. This study proposes a credible, converged smart framework integrating multicriteria decision-making (MCDM) and regression-based validation within a telemedicine environment to enable transparent, data-driven CP allocation. Methods: The proposed framework consists of three stages: (i) Analytic Hierarchy Process (AHP) for weighting five clinically relevant biomarkers, (ii) dual prioritization of patients and donors using Order Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR) with Group Decision-Making (GDM), and (iii) regression-based model selection to identify the most robust prioritization model. An external dataset of 80 patients and 80 donors was used for independent validation. Results: The external GDM AHP-VIKOR prediction model demonstrated strong predictive performance and internal consistency, with R² = 0.971, MSE = 0.0010, RMSE = 0.032, and MAE = 0.025. Correlation analysis confirmed biomarker behavior consistency and stability in ranking, thereby reinforcing the reliability of the prioritization outcomes. Conclusions: The proposed patient–donor matching framework is accurate, interpretable, and timely. This work presents an initial step toward realizing safe AI-enabled transfusion systems within telemedicine, supporting transparent and equitable CP allocation in future outbreak settings. Full article

The pharmaceutical industry’s pursuit of enhanced product quality, regulatory compliance, and operational efficiency has catalyzed the integration of Artificial Intelligence (AI) into Ongoing Process Verification (OPV) frameworks. This comprehensive review examines the synergistic application of Human-in-the-Loop (HITL) AI systems within OPV, contextualized by the evolving regulatory landscape, particularly the newly introduced Annex 22 of the European Union Good Manufacturing Practices (EU-GMP). The review delineates the sector’s strategic shift from traditional validation models toward dynamic, data-driven approaches that leverage AI for real-time monitoring, predictive analytics, and proactive process control. Central to this transformation is the HITL paradigm, which ensures that human expertise remains embedded in critical decision-making loops, thereby safeguarding patient safety, product quality, data integrity, and ethical responsibility. Annex 22 explicitly mandates deterministic behavior, traceability, and explainability for AI models used in GMP-critical applications, excluding adaptive and probabilistic systems from such contexts. The document also reinforces the necessity of multidisciplinary governance, rigorous validation protocols, and risk-based oversight throughout the AI lifecycle. This paper synthesizes current industry practices, regulatory expectations, and technological capabilities, offering a structured framework for compliant AI deployment in OPV. By aligning AI implementation with Annex 22 principles and existing GMP frameworks (e.g., Annex 11 and ICH Q9), the pharmaceutical sector can harness AI’s transformative potential while maintaining robust regulatory compliance. The review concludes with actionable recommendations for integrating HITL AI into OPV strategies, fostering a resilient, transparent, ethical, and future-ready manufacturing ecosystem. Full article

Machine learning (ML) excels over regression by automatically capturing complex, non-linear relationships and interactions, enabling more flexible and accurate predictions without strict assumptions. This study focuses on developing ML-based predictive models for key post-PCI outcomes: 30-day mortality, in-hospital major bleeding, and one-year mortality. Data from 104,665 consecutive PCI cases in the Victorian Cardiac Outcomes Registry (VCOR), collected between 2013 and 2022, will be analyzed. Candidate variables, informed by prior systematic reviews and dataset availability, will undergo multiple imputations for missing values. The Boruta method will be applied to identify influential predictors. Risk-adjusted models will be developed using sophisticated ML algorithms, with performance compared across standard metrics for validation. The dataset will be split, optimized via 10-fold cross-validation, and class imbalance addressed using Adaptive Synthetic resampling technique. SHapley Additive exPlanations will interpret the most influential predictors. The variables from the best model will be converted into simplified numeric scores. External validation will be performed using the Tasmanian dataset or equivalent datasets. This study is expected to identify the most influential variables associated with 30-day all-cause mortality, in-hospital major bleeding, and long-term mortality post-PCI. These variables will form the basis for developing robust risk-scoring models to support clinical decision-making and outcome prediction. Full article

Forest foods play a vital role in enhancing dietary diversity, human health, and the sustainable use of forest ecosystems. However, structured and machine-readable resources that systematically describe their taxonomic and nutritional attributes remain scarce. To fill this gap, we introduce ForestFoodKG, a comprehensive resource that integrates taxonomic hierarchy and nutritional composition of 1191 forest food items. The resource consists of two components—(i) the ForestFoodKG dataset, containing standardized taxonomic and nutritional records across seven biological levels, and (ii) the ForestFoodKG Knowledge Graph (ForestFoodKG-KG), which semantically links forest food entities using named entity recognition and relation extraction. The constructed graph comprises 4492 entities and 14,130 semantic relations, providing a structured foundation for intelligent querying, nutrition analytics, and ecological informatics. All data were manually verified and made publicly available in CSV format on GitHub. ForestFoodKG serves as the first structured knowledge base for forest foods, promoting data-driven research in nutrition science, sustainable forestry, and knowledge-based decision-making. Full article

We introduce an ultra-compact, single-neuron-per-class end-to-end readout for binary classification of noisy, image-encoded sensor time series. The approach compares a linear single-unit perceptron (E2E-MLP-1) with a resonate-and-fire (RAF) neuron (E2E-RAF-1), which merges feature selection and decision-making in a single block. Beyond empirical evaluation, we provide a mathematical analysis of the RAF readout: starting from its subthreshold ordinary differential equation, we derive the transfer function $H\left(\mathrm{j}\omega \right)$, characterize the frequency response, and relate the output signal-to-noise ratio (SNR) to ${\left|H\left(\mathrm{j}\omega \right)\right|}^2$ and the noise power spectral density $S_n\left(\omega \right)\propto {\omega }^{\alpha }$ (brown, pink, and blue noise). We present a stable discrete-time implementation compatible with surrogate gradient training and discuss the associated stability constraints. As a case study, we classify walk-in-place (WIP) in a virtual reality (VR) environment, a vision-based motion encoding (72 × 56 grayscale) derived from 3D trajectories, comprising 44,084 samples from 15 participants. On clean data, both single-neuron-per-class models approach ceiling accuracy. At the same time, under colored noise, the RAF readout yields consistent gains (typically +5–8% absolute accuracy at medium/high perturbations), indicative of intrinsic band-selective filtering induced by resonance. With ∼8 k parameters and sub-2 ms inference on commodity graphical processing units (GPUs), the RAF readout provides a mathematically grounded, robust, and efficient alternative for stochastic signal processing across domains, with virtual reality locomotion used here as an illustrative validation. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the critical need for scalable, timely, and unbiased methods to monitor disease transmission at the population level. Wastewater-based epidemiology (WBE) provides an effective method for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission by detecting viral RNA shed into the sewage system. Because it does not rely on individual testing, WBE can offer timely, cost-effective, and community-level insights into infection trends. In this study, we present a hierarchical Beta-Binomial model that integrates SARS-CoV-2 RNA concentration in wastewater with reported COVID-19 case counts to enhance the monitoring of community-level transmission dynamics. The model incorporates wastewater viral loads as a predictor and reported cases as the response, while adjusting for testing volume to account for biases introduced by fluctuations in testing practices. This approach enables reliable estimation of the effective reproduction number ($R_t$), even in the absence of consistent reporting of clinical data. Applied to twenty counties in California, our modeling framework demonstrates the potential of wastewater surveillance to inform public health decision making, particularly in locations with sparse clinical data. Full article

Purpose: The aim of this review is to evaluate the role of intraoperative flow cytometry (IFC) in tumor surgery. Methods: The Medline, Scopus, and Cochrane databases were searched up to 21 June 2025 to identify all available studies that met the inclusion criteria for final evaluation. To assess the risk of bias and applicability concerns, the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used. Results: A total of 22 studies involving 1511 patients with various tumor types were analyzed to assess the utility of IFC in the rapid diagnosis of tumors. The studies investigated IFC’s role in tumor grading, margin delineation, prognostic evaluation, and in differentiating neoplastic from benign lesions, as well as normal from cancerous tissues. In brain tumors, particularly gliomas and meningiomas, IFC demonstrated high diagnostic performance, with reported sensitivities ranging from 61% to 100% and specificities from 66% to 100%. Studies on non-brain tumors also showed high accuracy in distinguishing neoplastic from normal tissues, with sensitivities and specificities exceeding 85% in most cases. The most promising results were observed in brain tumor surgery, although its application in other tumor types continues to expand. Conclusions: IFC appears to be a valuable intraoperative tool in surgical oncology, providing rapid results within minutes and assisting in surgical and therapeutic decision-making. Nonetheless, studies remain limited, and further research is needed, particularly for non-brain tumors, to establish standardized cut-off values and enhance diagnostic reliability. Full article

Blood pressure (BP) monitoring is essential in managing critically ill patients in the intensive care unit (ICU), particularly for ensuring adequate end-organ perfusion in hypotensive states. Invasive arterial catheters and noninvasive oscillometric cuffs are often used together, but discrepancies between the two methods are common. These differences can arise from technical factors (e.g., transducer leveling, cuff size and placement, arterial waveform damping) as well as patient-related factors (e.g., vasoconstriction, arrhythmias, altered arterial compliance). This creates a clinical dilemma: which measurement best reflects the patient’s true perfusion pressure, and how should management be guided? This review offers a practical approach for addressing discrepancies between invasive and noninvasive BP measurements in adult hypotensive ICU patients, including those with shock requiring vasopressor support. Based on contemporary data, we propose that a difference greater than 10 mmHg in mean arterial pressure (MAP) between the two methods can serve as a pragmatic threshold to trigger structured evaluation, rather than a universal definition of clinical significance. MAP is prioritized as the key variable for assessing perfusion pressure. When a discrepancy is detected, clinicians are encouraged to integrate both measurements with clinical signs of hypoperfusion and to perform a systematic assessment of technical and physiologic contributors before deciding which value should guide treatment. We present a stepwise clinical decision-making algorithm that helps practitioners (1) recognize when a discrepancy is large enough to matter, (2) evaluate perfusion using bedside and laboratory markers, (3) identify technical or anatomic reasons for discordant readings, and (4) determine when more central arterial monitoring may be appropriate. By structuring the evaluation of discordant BP measurements, this approach aims to reduce the risk of unrecognized hypotension or overtreatment, support more consistent hemodynamic decision-making, and ultimately improve the management of critically ill, hypotensive patients. Full article

Synchronous interactions from different locations have become a globally accepted modus of interaction since the COVID-19 outbreak. For centuries, professional cadastral survey activities always required an interaction modus whereby surveyors, neighboring landowners, and local officers were present simultaneously. During the systematic adjudication and land registration project in Indonesia, multiple problems in the land information systems emerged, which, up to date, remain unsolved. These include the presence of plots of land without a related title, incorrect demarcations in the field, and the listing of titles without a connection to a land plot. We argue that these problems emerged due to ineffective survey workflows, which draw on inflexible process steps. This research assesses how and how much the use of augmented and virtual reality (AR/VR) technologies can make land registration services more effective and expand collaboration in a synchronous and at distant manner (the so-called same time, different place principle). The tested cadastral survey workflows include the procedure for a first land titling, the one for land subdivision, and the updating and maintenance of the cadastral database. These are common cases that could potentially benefit from integrated uses of augmented and virtual reality applications. Mixed reality technologies using VR glasses are also tested as tools, allowing individuals, surveyors, and government officers to work together synchronously from different places via a web mediation dashboard. The work aims at providing alternatives for safe interactions of field surveyors with decision-making groups in their endeavors to reach fast and effective collaborative decisions on boundaries. Full article

Rapid progress in sustainable and intelligent transportation has intensified interest in smart airport initiatives, driven by the need to support environmentally responsible and technology-enabled aviation development. As complex sociotechnical subsystems of smart aviation, smart airports integrate advanced digital, operational, and organizational technologies to enhance efficiency, resilience, and passenger experience. With increasing emphasis on such transformations, multiple strategic development plans have emerged, each with distinct priorities and implementation pathways, which necessitates a rigorous and transparent evaluation mechanism to support informed decision-making under uncertainty. This study proposes an integrated spherical fuzzy multi-criteria decision-making (MCDM) framework for assessing and ranking smart airport development plans. Subjective expert judgments are modeled using spherical fuzzy sets, allowing for the simultaneous consideration of positive, neutral, and negative membership degrees to better capture linguistic and ambiguous information. Expert importance is determined through a hybrid weighting scheme that combines a social trust network model with an entropy-based objective measure, thereby reflecting both relational credibility and informational contribution. Criterion weights are computed through an integrated approach that merges criteria importance through the inter-criteria correlation (CRITIC) method with the stepwise weight assessment ratio analysis (SWARA) method, balancing data-driven structure and expert strategic preferences. The weighted evaluations are aggregated using a spherical fuzzy extension of the combined compromise solution (CoCoSo) method to obtain the final rankings. A case study involving smart airport development planning in China is conducted to illustrate the applicability of the proposed approach. Sensitivity, ablation, and comparative analyses demonstrate that the framework yields stable, discriminative, and interpretable rankings. The results confirm that the proposed method provides a reliable and practical decision support tool for smart airport development and can be adapted to other smart transportation planning contexts. Full article

Distributed acoustic sensing (DAS) technology is gaining popularity for real-time monitoring during the hydraulic fracturing of unconventional reservoirs. By transforming a standard optical fiber into a dense array of acoustic sensors, DAS provides continuous spatiotemporal measurements along the entire wellbore. Although accurate DAS-based real-time diagnosis of multistage hydraulic fracturing is critical for optimizing the efficiency of stimulation operations and mitigating operational risks in horizontal tight oil wells, existing methods often fail to provide integrated qualitative and quantitative insights. To address this gap, we present an original diagnostic workflow that synergistically combines frequency band energy (FBE), low-frequency DAS (LF-DAS), and surface injection data for simultaneous fluid/proppant allocation and key downhole anomaly identification. Field application of the proposed framework in a 47-stage well demonstrates that FBE (50–200 Hz) enables robust cluster-level volume estimation, while LF-DAS (<0.5 Hz) reveals fiber strain signatures indicative of mechanical integrity threats. The workflow can successfully diagnose sand screenout, diversion, out-of-zone flow, and early fiber failure—events often missed by conventional monitoring. By linking distinct acoustic fingerprints to specific physical processes, our approach transforms raw DAS data into actionable operational intelligence. This study provides a reproducible, field-validated framework that enhances understanding in the context of fracture treatment, supports real-time decision making, and paves the way for automated DAS interpretation in complex completions. Full article