Search Results (93)

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

Quantitative microbial risk assessment (QMRA) has become a central framework for evaluating foodborne microbial hazards by integrating microbiological data, exposure assessment, dose–response modelling, and probabilistic simulation. Over the past three decades, its rapid expansion has created challenges in obtaining a coherent overview of the field’s structure, dominant themes, and research trajectories. This study presents a bibliometric and systematic review of QMRA research in food safety. Bibliographic data were retrieved from the Scopus database (search conducted in January 2026), including peer-reviewed articles published in English between 1995 and 2024, and analysed using performance analysis and science mapping techniques to assess publication trends, influential contributors, collaboration patterns, and thematic evolution. Risk of bias assessment was not applicable due to the bibliometric nature of the study. The results indicate steady long-term growth of QMRA research, based on a final dataset of 186 articles across multiple journals and countries, with a concentrated influence structure dominated by a limited number of specialised journals, institutions, and research groups. International collaboration is particularly strong within European networks. Thematic analysis identifies probabilistic exposure assessment, Monte Carlo simulation, predictive microbiology, and dose–response modelling as the methodological core, with a primary focus on major foodborne pathogens such as Campylobacter, Salmonella, Listeria monocytogenes, and Escherichia coli. Persistent emphasis on uncertainty, cross-contamination, and dose–response relationships highlights key methodological challenges. Limitations include reliance on a single database and potential exclusion of studies using alternative terminology. These findings provide a structured overview of the QMRA landscape and identify priorities for methodological refinement and future application in food safety risk assessment. This study received no external funding and was not prospectively registered. Full article

The rapid increase in companion animal ownership has intensified the demand for innovative tools that support animal health and overall welfare. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising approach in veterinary medicine. However, its application beyond clinical diagnostics, especially in behaviour and personality assessment, remains fragmented and insufficiently integrated into routine practice. This systematic review aims to synthesise current knowledge on AI-based applications in companion animal care, with a focus on behavioural monitoring, personality prediction, and welfare-related challenges. Following PRISMA guidelines, a structured literature search was conducted in the Scopus and PubMed databases from 2020 to 2025. In addition, grey literature sources were searched to capture relevant non-peer-reviewed data. A total of 115 studies met the inclusion criteria and were included in the analysis. Eligibility criteria included studies applying AI methods (machine learning or deep learning) to companion animals (dogs, cats, and exotic pets), while studies on humans, farm animals, or without AI methods were excluded. Due to the heterogeneity of included studies, no formal risk of bias assessment was performed, and results were synthesised narratively. The findings indicate that AI applications are most advanced in diagnostic imaging and clinical decision support, where data availability and methodological maturity are highest. In contrast, AI-based approaches for behaviour and personality prediction remain limited, particularly in cats and exotic companion animals, largely due to small, heterogeneous datasets, potential bias, and a lack of external validation. Emerging technologies such as wearable sensors, computer vision, and multimodal data integration demonstrate substantial potential for continuous behavioural monitoring and early detection of welfare-related issues in real household environments. Nevertheless, significant challenges persist, including data heterogeneity, limited model explainability, ethical considerations, and the absence of regulatory frameworks specifically addressing AI-based veterinary applications. Overall, this review highlights a substantial gap between the technical potential of AI and its current readiness for widespread application in companion animal behaviour and welfare assessment. Future research should prioritise large-scale and standardised data collection, cross-species validation, and interdisciplinary collaboration to ensure that AI-driven tools effectively support veterinary decision-making, animal welfare, and the well-being of owners. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Semi-arid territories in Latin America face chronic water stress; limited observability and fragmented institutions constrain effective water resources management (WRM). This narrative review synthesizes peer-reviewed evidence (2020–2026) on smart technologies that strengthen basin- and utility-level WRM, using Peru (Piura-like coastal semi-arid contexts) as an anchor and Latin America as a comparative lens. We used a structured, traceable database-based workflow and synthesized studies reporting measurable outcomes across five application categories: drought/flood early warning, hydrometeorological forecasting, water quality surveillance, non-revenue water (NRW)/leakage, and allocation and compliance. Findings were organized into an application-oriented taxonomy spanning remote sensing (RS) and GIS, Internet of Things (IoT)/telemetry, analytics/AI-enabled decision support, and hybrid approaches. Evidence most consistently reports operational gains (coverage, timeliness, predictive performance), while governance outcomes are less frequently measured and appear contingent on interoperability, digital capacity, and sustainable operations and maintenance (O&M) conditions. We conclude with a territorial adoption agenda specifying minimum enabling conditions and a phased pathway from pilots to scalable, eco-efficient smart WRM in Peru and comparable semi-arid settings across Latin America. Full article

Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research. Full article

Background/Objectives: Breast cancer awareness is essential for early detection and timely help-seeking among women and represents a key component of multidisciplinary breast cancer prevention. The Breast Cancer Awareness Measure (Breast-CAM) is widely used to assess awareness of breast cancer symptoms, risk factors, and screening behaviors. Its measurement quality across populations has not yet been comprehensively evaluated. As Breast-CAM is a population-reported measurement instrument, evaluation using a standardized framework for measurement properties is required. This systematic review and meta-analysis aimed to assess the psychometric properties of the Breast-CAM across diverse populations and cultural adaptations, in accordance with COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) methodological standards. Methods: Major bibliographic databases and trial registries were systematically searched for peer-reviewed English-language studies published between 2010 and 2025 that evaluated at least one psychometric property of the Breast-CAM in adult women. Methodological quality was assessed using the COSMIN Risk of Bias checklist. Measurement properties were evaluated according to COSMIN criteria, and the certainty of evidence was graded using a modified GRADE approach. Meta-analysis was performed when data were sufficiently comparable. Results: Seventeen studies met the inclusion criteria for narrative synthesis, of which eleven were included in a meta-analysis, representing fourteen cultural adaptations of the instrument. A descriptive random-effects meta-analysis of reported Cronbach’s α yielded a pooled estimate of 0.89 (95% confidence interval 0.85–0.92). This value should be interpreted cautiously, as structural validity was frequently insufficient across cultural adaptations, limiting interpretation of internal consistency according to COSMIN guidance. Other measurement properties, including reliability and measurement error, were frequently inadequately assessed or unreported. The certainty of evidence ranged from very low to moderate. Conclusions: Content validity was generally rated as sufficient, although certainty of evidence was low. Despite the high pooled α estimate, the reliability of Breast-CAM cannot be firmly established because structural validity was frequently insufficient across cultural adaptations. In accordance with the COSMIN ceiling rule, internal consistency was not considered sufficient in the absence of adequate structural validity. Key measurement properties, including test–retest reliability, measurement error, and responsiveness, were rarely evaluated. Further high-quality psychometric studies, particularly in culturally diverse populations, are needed to address these gaps and support appropriate use of the instrument in research and public health practice. Full article

The Fourth Industrial Revolution has accelerated the adoption of advanced digital technologies in construction, with Digital Twin (DT) emerging as a data-driven framework for enhancing project performance, efficiency, and sustainability. Despite these advantages, DT adoption in construction remains limited due to high implementation costs, data integration challenges, and a lack of standardized practices, especially in real-time data utilization and lifecycle management. This study presents a PRISMA-guided systematic literature review of DT applications across the construction lifecycle. The study addresses three main objectives: (1) to analyze DT’s adoption across construction lifecycle phases, (2) to identify barriers and benefits to DT adoption, and (3) to explore research gaps and potential advancements. Peer-reviewed journal articles published between 2003 and 2024 were retrieved from the Scopus and Web of Science databases using structured keyword combinations related to Digital Twin and the built environment. From an initial pool of 3109 records, 53 studies met predefined inclusion criteria. They were analyzed using a lifecycle-oriented thematic coding framework examining application domains, enabling technologies, reported benefits, and implementation constraints. Unlike prior reviews that focus on specific technologies or lifecycle segments, this study provides a lifecycle-wide synthesis of DT maturity across design, construction, operation, and demolition phases. The findings indicate that DT applications are most developed in the design and operation phases, particularly through integration with Building Information Modeling (BIM) and Internet of Things (IoT) systems for simulation, monitoring, and predictive maintenance. In contrast, construction-phase adoption is constrained by challenges in real-time data integration, while demolition and end-of-life applications remain largely conceptual. Overall, current DT implementations are predominantly phase-specific rather than lifecycle-integrated, therefore emphasizing the need for standardized data frameworks, scalable architectures, and cross-phase governance strategies to enable end-to-end lifecycle digitalization in construction. Full article

The construction industry is under mounting pressure to enhance its sustainability performance. Increasing project complexity and risk require real-time data collection, monitoring, and assistance in decision making via the Internet of Things (IoT). IoT has emerged as a critical enabling technology to overcome these hurdles. This study provides a bibliometric and thematic overview of IoT applications in sustainable construction project management to identify research trends, key themes, and practical implications for project managers. We used a structured screening process to analyze peer-reviewed journal papers, conference articles, and book chapters listed in the Scopus database. We identified 77 publications published between 2019 and 2025. Using VOSviewer_1.6.20_exe, we analyzed publication trends, source influences, geographical dispersion, and keyword co-occurrence patterns. Since 2023, research output and citation impact have increased dramatically, with sustainability, project management, and IoT serving as the main conceptual foundations recorded. Real-time monitoring, wireless sensor networks, safety improvement, BIM and digital twin integration, and resource and energy optimization are the five main application domains recognized using thematic synthesis. This shows a marked transition from standalone sensing applications to integrated, intelligent, and predictive systems that enable data-driven decision making throughout the construction lifecycle. This review highlights the ongoing difficulties associated with data quality, sensor dependability, system interoperability, and energy limitations. IoT is progressing from a support technology to a core operational and managerial infrastructure for sustainable construction, with major consequences for project management and future research. Full article

Sediment management is defined as the strategic monitoring and control of erosion, transport, and deposition processes to maintain environmental and infrastructural stability. Terrestrial laser scanning (TLS) has emerged as a critical high-precision technology for monitoring sediment dynamics, erosion processes, and geomorphic change detection across diverse environments, including riverine, coastal, watershed, and infrastructure-related landscapes. While the field of TLS technology has seen significant advancements in recent years, including improvements in data accuracy, enhanced operational performance, artificial intelligence (AI), machine learning-based processing, and integration with other remote sensing tools such as unmanned aerial vehicles (UAVs) and satellite light detection and ranging (LiDAR), the study has focused on these developments. These advancements have further extended the application prospects of TLS technology. Despite these advancements, there remains a crucial need to systematically identify global research trends to identify the effectiveness, limitations, and knowledge gaps of TLS in sediment management. The methodological advantages and challenges of TLS applications provide insights into its gradual development role in enhancing sediment monitoring and environmental resilience. The objective of this study is to synthesize the current state of sediment management by conducting a systematic review of 108 peer-reviewed research papers retrieved from academic databases, including Google Scholar, ResearchGate, ScienceDirect, Scopus, and Web of Science, from 28 countries, published between 2000 and 2025. The study will evaluate the effectiveness of TLS methodologies in comparison to conventional techniques and management procedures, following the PRISMA 2020 guidelines. It will examine their capacity to enhance measurement accuracy, reduce error margins, and improve structural guidelines, particularly by advancing TLS technology through the integration of AI and machine learning (ML) algorithms. The findings of the study indicate that TLS and Iterative Closest Point (ICP) techniques can enhance the analysis of 3D models of dam deformation, ensuring improved structural monitoring and safety. The findings offer insights into the evolving role of TLS in sediment monitoring, emphasizing its potential for enhancing environmental management and climate resilience strategies. Furthermore, this review identifies future research directions to optimize TLS applications in sediment management through interdisciplinary approaches. Full article

Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as large language models, presents new opportunities and risks for CT development. This study conducts a systematic literature review to synthesize empirical evidence on the pedagogical implications and cognitive impact of GenAI on students’ CT. Following PRISMA guidelines, and search terms around GenAI Tools, Critical Thinking And Higher Education, on five major education research databases—Web of Science; Scopus; EBSCOhost (Education Source, ERIC, and APA PsycInfo); and Compendex and Inspec (Elsevier)—63 empirical studies published between January 2023 and April 2025 were analyzed across higher education contexts, disciplines, and intervention designs. Results indicate that GenAI offers notable cognitive affordances, including scaffolding reflective reasoning, promoting self-regulation, and facilitating iterative dialogue and argument evaluation. Pedagogical strategies clustered into four primary integration typologies: AI-based feedback prompts, dialogue simulation and reflection, AI-supported peer review, and critical engagement with AI-generated content. Nearly half of the studies reported statistically significant CT improvements, particularly when GenAI use was guided by structured prompts, reflective activities, and performance-based assessment. However, multiple risks persist, including cognitive offloading, uncritical acceptance of AI outputs, and diminished intellectual autonomy, especially in unguided or surface-level usage. This review highlights the need for intentional pedagogical design, validated CT assessment tools, and longitudinal studies to ensure GenAI acts as a catalyst rather than a substitute for human reasoning. By identifying effective integration strategies and outlining potential pitfalls, this study provides evidence-informed guidance for educators and institutions aiming to responsibly leverage GenAI to strengthen students’ CT skills. Full article

Unintentional doping, often caused by contaminated supplements or misinterpreted therapeutic prescriptions, poses significant health, ethical, and regulatory challenges in competitive sports. Understanding the cardiovascular risks associated with performance-enhancing substances (PESs) and the preventive role of nutrition requires integrated analysis. A systematic review was conducted in accordance with PRISMA guidelines. Searches of comprehensive bibliographic databases yielded studies published between 2015 and November 2025. Inclusion criteria encompassed peer-reviewed research on doping prevalence, cardiovascular outcomes, nutritional strategies, and supplement regulation. Data extraction focused on prevalence estimates, odds ratios (ORs), hazard ratios (HRs), and effect sizes for nutritional interventions. Quality assessment employed GRADE and risk-of-bias tools. From 1320 records screened, 60 studies were included in the qualitative synthesis and 31 in the meta-analysis. Surveys using indirect questioning estimated that 30–45% of elite athletes may engage in doping, while official anti-doping reports indicated that approximately 20–25% of confirmed rule violations are classified as unintentional. Supplement contamination accounted for 10–15% of unintentional cases. PES use significantly increased cardiovascular risk (HR for arrhythmias and myocardial infarction up to 3.5). Nutritional strategies—such as carbohydrate loading, optimized protein intake, omega-3 supplementation, and hydration—improved endurance by 8–12%, reduced resting heart rate by ~3 bpm, and lowered LDL cholesterol. Unintentional doping remains a major contributor to ADRVs, primarily driven by supplement contamination. Evidence-based nutrition offers safe alternatives to PESs (evidence-based nutritional strategies and structured hydration protocols), enhancing performance and cardiovascular health. Forensic toxicology and pharmacogenomic screening are essential for accurate detection and interpretation. Regulatory reforms, mandatory third-party supplement certification, and athlete education are critical to mitigate unintentional doping and ensure fair competition. Full article

To address the small ATC-63 record set for collapse-oriented motion selection and the limited interpretability of data-driven approaches, this study proposes a framework for identifying structural-collapse-critical ground motions. Using 5074 records from the PEER NGA-West2 database, we applied STA/LTA event detection and extracted multi-source features. A Gaussian mixture model (GMM) was then used to perform unsupervised clustering and identify four physically interpretable groups. LightGBM, XGBoost, and Random Forest were employed to test the separability of the cluster labels, with all three models achieving F1 scores above 0.89 and LightGBM reaching an accuracy of about 93%. SHAP-based feature-importance analysis was used at the model level to clarify feature contributions and improve interpretability. Cluster 2 exhibits markedly higher relative seismic energy, stronger time-domain variability, and more dominant frequencies, forming a typical strong-motion hazard signature. For external engineering verification, 22 ATC-63 far-field records were mapped onto the full dataset to examine cluster-level enrichment and coverage. Cluster 2 shows significant enrichment in engineering markers and high coverage and is therefore identified as the collapse-sensitive phenotype cluster (COP). Overall, the framework provides a technical basis for ground-motion selection in collapse assessment, fragility analysis, and design evaluation. Full article

Background: Thrombotic thrombocytopenic purpura (TTP) is a rare but life-threatening thrombotic microangiopathy (TMA) caused by severe deficiency of the von Willebrand factor–cleaving protease ADAMTS13. Pregnancy is a recognized trigger for both immune-mediated and congenital TTP and is associated with increased maternal and fetal morbidity. Clinical overlap with other pregnancy-associated TMAs, including preeclampsia and Hemolysis, Elevated Liver enzymes, and Low Platelet count (HELLP) syndrome, often delays diagnosis. This review synthesizes current evidence on pathophysiology, diagnostic uncertainty, and gestation-specific management of pregnancy-associated TTP, highlighting differences between immune-mediated and congenital disease. Methods: This is a narrative review. We performed a targeted literature search of PubMed/MEDLINE (from inception to December 2025) to identify English-language publications. The study types included were case reports/series, observational studies, large database studies, randomized trials, reviews, and relevant guidelines addressing TMA in pregnancy, with emphasis on immune-mediated and congenital TTP. Search terms included “pregnancy”, “thrombotic thrombocytopenic purpura”, “hereditary TTP”, “acquired TTP”, “ADAMTS13,” “thrombotic microangiopathy,” “HELLP,” “postpartum”, and “complement-mediated TMA” alone or in combination. The search was supplemented by manual screening of reference lists and key guidelines. Articles were selected based on relevance to diagnosis and management of pregnancy-associated TTP. Conference abstracts and non-peer-reviewed sources were not routinely included and were considered only when peer-reviewed evidence was limited. Results: Pregnancy-associated TTP remains a major diagnostic challenge due to overlapping clinical and laboratory features with other obstetric thrombotic microangiopathies. Distinguishing immune-mediated from congenital TTP is essential, as management and prognosis differ substantially. Prompt recognition and early initiation of therapeutic plasma exchange, immunosuppression, or prophylactic plasma therapy markedly improve maternal outcomes. Rapid ADAMTS13 testing, structured risk stratification, and multidisciplinary care are central to optimal management. Fetal outcomes are closely linked to gestational age at onset and timeliness of therapy. Conclusions: Early differentiation of TTP from other pregnancy-associated TMAs is critical for maternal and fetal survival. Advances in rapid ADAMTS13 diagnostics and emerging targeted therapies, including caplacizumab and recombinant ADAMTS13, offer opportunities to improve precision management and outcomes in future pregnancies. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article