Search Results (779)

Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions. Full article

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article

Background: Peritonitis in peritoneal dialysis and cirrhosis remains common and leads to morbidity. Neutrophil gelatinase-associated lipocalin (NGAL) has been evaluated as a rapid adjunctive biomarker. Methods: Following PRISMA-DTA and PROSPERO registration (CRD420251105563), we searched MEDLINE, Embase, Cochrane Library, LILACS, Scopus, and Web of Science from inception to 31 December 2024, and ran an update on 30 June 2025 (no additional eligible studies). Diagnostic accuracy studies measuring NGAL in peritoneal/ascitic fluid against guideline reference standards were included. When 2 × 2 data were not reported, we reconstructed cell counts from published metrics using a prespecified, tolerance-bounded algorithm (two studies). Accuracy was synthesized with a bivariate random effects (Reitsma) model; 95% prediction intervals (PIs) were used to express heterogeneity; small-study effects were assessed by Deeks’ test. Results: Thirteen studies were included qualitatively and ten were entered into a meta-analysis (573 cases; 833 controls). The pooled sensitivity was 0.95 (95% CI, 0.90–0.97) and specificity was 0.86 (0.70–0.94); likelihood ratios were LR+ ≈7.0 and LR− 0.06. Between-study variability was concentrated on specificity: the PI for a new setting was 0.75–0.98 for sensitivity and 0.23–0.99 for specificity. Deeks’ test showed evidence of small-study effects in the primary analysis; assay/platform and thresholding contributed materially to heterogeneity. Conclusions: NGAL in peritoneal/ascitic fluid demonstrates high pooled sensitivity but variable specificity across settings. Given the wide prediction intervals and the signal for small-study effects, NGAL should be interpreted as an adjunct to guideline-based criteria—not as a stand-alone rule-out test. Standardization of pre-analytics and assay-specific, locally verified thresholds, together with prospective multicenter validations and impact/economic evaluations, are needed to define its clinical role. Full article

Reflective assessment is central to knowledge building, as it enables learners to evaluate and improve their collective ideas. In recent years, a wide range of analytic technologies have been developed to support this process, yet there is still a limited understanding of how these tools are designed, implemented, and connected to knowledge building principles. This study addresses this gap through a systematic review of the literature focused on analytic technologies that foster reflective assessment in knowledge building environments. Following a rigorous PRISMA methodology, a search in Web of Science was conducted. Studies that did not meet the inclusion criFteria were excluded, resulting in a final selection of 31 empirical studies. The analysis shows that most analytic tools (e.g., KBDeX, KCA, KBAT) are applied to data generated in Knowledge Forum, supporting students in visualizing, analyzing, and reflecting on the collective knowledge advancement process. The review highlights a growing diversity of tools designed to enhance processes such as idea improvement and epistemic agency. By mapping these contributions, the study provides a clearer understanding of how analytic technologies can be used to strengthen collaborative practices and reflective assessment in knowledge building contexts. Full article

Background/Objectives: Babywearing is a carrying system that ensures consistent contact and proper posture between the baby and carrying adult, in which there is no age or weight limit, and it is rarely inadvisable. Although babywearing has been growing in popularity and acclaim due to the comfort and emotional closeness between the carrier and baby, there are a number of physical and physiological consequences for the adult carrier when using an ergonomic babywearing device, such as muscular, postural, cardiorespiratory, and energy expenditure, and the perception of effort and pain. The objective is to explore the physical implications affecting the carrier, as well as the subjective perception of strain and pain. Methods: A systematic review was carried out including articles up to December 2023 in the Web of Science (WOS), Medline, and SportDiscus databases. Studies dealing with ergonomic babywearing and the physical implications of babywearing were included; systematic reviews or case studies were excluded. Results: After applying the inclusion and exclusion criteria, a total of 14 original articles were obtained for analysis. Methodological quality was rated using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies with scores between 3 and 8 points. All articles included valid and reliable information on exposure, outcome measures, and results. Conclusions: The studies reviewed cover different aspects, such as muscle activation and postural stability, as well as specific ergonomic design for particular groups. In general terms, it seems that the use of certain babywearing devices, especially back or front carry, seems to be the one that generates fewer physiological alterations in the carriers compared to carrying babies in arms or other positions. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

Frequent pattern mining is a fundamental method for Data Mining, applicable in market basket analysis, recommendation systems, and academic analytics. Widely adopted and foundational algorithms such as Apriori and FP-Growth, which represent the standard approaches in frequent pattern mining, face limitations related to candidate set generation and memory usage, especially when applied to extensive relational datasets. This work presents the Recursive Queried Frequent Patterns (RQFP) algorithm, an SQL-based approach that utilizes recursive queries on relational Mining Tables to detect frequent itemsets without the need for explicit candidate development. The algorithm was implemented using a Microsoft SQL Server and demonstrated through a custom-developed C# web application interface. RQFP facilitates easy integration with database systems and enhances result interpretability. Comparative analyses of Apriori and FP-Growth on an academic dataset reveal competitive efficacy, accompanied with diminished memory requirements and enhanced clarity in pattern extraction. The paper further contextualizes RQFP using benchmark datasets from the previous literature and delineates a roadmap for future evaluations in healthcare and retail data. The existing implementation is educational, although the technique demonstrates the potential for scalable, database-native pattern mining. Full article

Background and Objective: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by widespread inflammation and multisystem involvement, leading to substantial morbidity. Given the immunomodulatory role of vitamin D and its association with disease activity in SLE, supplementation has emerged as a potential therapeutic strategy. However, findings across individual studies remain inconsistent, underscoring the need for a systematic review and meta-analysis to synthesize the current evidence on vitamin D supplementation for this disease. Thus, this study aimed to conduct a systematic review and meta-analysis on the effects of vitamin D supplementation on disease activity among patients with SLE. Methods: Systematic searches were carried out in four electronic databases (PubMed, Scopus, Web of Science, and Science Direct) with only studies published after 2013 as a restriction for the search strategy. An assessment of the included studies was conducted according to the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions, using the risk of bias assessment tool in Review Manager (Revman) version 5.3. Included studies were randomized trials with vitamin D supplementation in patients with SLE and with pre–post intervention measures of disease activity. Meta-analyses were performed using random-effects models to estimate mean differences with 95% confidence intervals (CIs). Heterogeneity was evaluated using the I² test, and sensitivity analysis and publication bias assessment were also performed. Results: A total of 186 articles were retrieved, of which 21 studies met the inclusion criteria. These studies had a combined sample size of 3177 adult participants and were conducted across 16 different countries. Regarding the impact of vitamin D supplementation on SLE patients, twelve (n = 12) studies reported positive associations, including reduced disease activity and improvements in clinical and laboratory parameters such as inflammatory markers, fatigue, and bone mineral density. In contrast, nine (n = 9) studies found no significant effects. In terms of meta-analytical data, our results indicate that, at the end of the supplementation, participants with vitamin D supplementation had significantly higher serum vitamin D levels compared to participants that receive a placebo (MD: 13.11 ng/mL; 95% CI: 8 to 19; p < 0.00001) despite comparable values before the onset of the supplementation. In addition, participants with vitamin D supplementation had lower scores in the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) compared to participants who received a placebo (MD: −1; 95% CI: −2 to −0.43; p = 0.002) despite comparable values before the onset of the supplementation. Conclusions: Our systematic review and meta-analysis suggest that vitamin D supplementation leads to a statistically significant reduction in SLEDAI scores, reflecting a meaningful decrease in disease activity. Given its immunomodulatory effects and favorable safety profile, vitamin D supplementation represents a simple and accessible adjunctive strategy that could support SLE management and improve patient outcomes in clinical practice. Full article

Background/Objectives: Despite its growing application, life-cycle assessment (LCA) in the nutraceutical sector has not been systematically studied, leaving a gap in our understanding of the unique challenges of assessing its environmental footprint. The main objective of this study was to provide an overview of scientific publications related to nutraceuticals from the LCA perspective. Methods: This review combined bibliometric analysis, using VOSViewer as an analytic tool, with the search of the Web of Science database, aiming to identify the most relevant papers associated with nutraceuticals and life-cycle assessment. Results: The final selection of the most relevant publications was set at 65, analyzing 78 different nutraceuticals. Results reveal that the main sources of raw materials for extraction of nutraceuticals are marine-based, plant-based, and from agri-food waste. Polyphenols were analyzed 34 times and were predominantly sourced from plants, while carotenoids, analyzed 17 times, were mainly linked with marine-based and food waste-derived sources. The main environmental footprints were focused on climate change, covering most of the nutraceuticals analyzed (97.4%), followed by acidification (78.2%) and eutrophication (74.4%). SimaPro was the prevailing software used for 43.6% nutraceuticals, while the prevailing database was Ecoinvent, used in two thirds of the cases (66.7%). ReCiPe, as a life-cycle inventory assessment method, was used for calculating 34.6% of analyzed cases, followed by CML (33.3%). Conclusions: This systematic review highlights the main challenge in LCA studies, outlining great variability in study boundaries, functional units, and reported environmental footprints, and making it difficult to compare the environmental impacts of similar nutraceutical groups from a life-cycle perspective. This underscores the urgent need to improve input-data quality and develop standardized methodologies to validate sustainability claims using LCA. Full article

Background/Objectives: Brain-derived neurotrophic factor (BDNF) is a neurotrophin critical for neurogenesis and synaptic plasticity, and alterations in its peripheral levels have been associated with schizophrenia and other psychotic disorders. However, findings on peripheral BDNF levels in individuals at ultra-high risk (UHR) for psychosis have been inconsistent. This review synthesizes current evidence comparing peripheral BDNF levels in UHR populations with those in healthy controls (HCs), first-episode psychosis (FEP), and chronic schizophrenia (CS), focusing on BDNF’s potential relevance as a biomarker of psychosis risk and subsequent clinical course. Methods: A systematic search of PubMed, Scopus, and Web of Science identified studies reporting baseline peripheral BDNF levels in UHR individuals compared with HC, FEP, or CS. Of 755 records retrieved, 608 unique titles/abstracts were screened, 49 full texts reviewed, and 8 studies included. Two reviewers independently screened, extracted data, and assessed risk of bias. Given marked clinical and methodological variability, results were synthesized narratively. Results: Eight studies met eligibility criteria and were synthesized across three analytical categories: (1) UHR vs. HC; (2) UHR vs. FEP or CS; and (3) longitudinal outcomes. Findings were inconsistent; some studies reported lower BDNF in UHR relative to comparison groups, whereas others found no differences or higher levels, often influenced by clinical or methodological factors. Longitudinal analyses did not reveal consistent prognostic value, and heterogeneity precluded meta-analysis. Conclusions: Findings across studies were inconsistent and limited by small samples, as well as by methodological heterogeneity. While current evidence does not support its prognostic use, peripheral BDNF may still hold potential as part of a biomarker framework if evaluated in larger, standardized, and rigorously controlled studies. Full article

Drones have rapidly emerged as transformative tools in landscape research, enabling high-resolution spatial data acquisition, real-time environmental monitoring, and advanced modelling that surpass the limitations of traditional methodologies. This scoping review systematically explores and synthesises the technological applications of drones within the context of landscape studies, addressing a significant gap in the integration of Uncrewed Aerial Systems (UASs) into environmental and spatial planning disciplines. The study investigates the typologies of drone platforms—including fixed-wing, rotary-wing, and hybrid systems—alongside a detailed examination of sensor technologies such as RGB, LiDAR, multispectral, and hyperspectral imaging. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a comprehensive literature search was conducted across Scopus, Web of Science, and Google Scholar, utilising predefined inclusion and exclusion criteria. The findings reveal that drone technologies are predominantly applied in mapping and modelling, vegetation and biodiversity analysis, water resource management, urban planning, cultural heritage documentation, and sustainable tourism development. Notably, vegetation analysis and water management have shown a remarkable surge in application over the past five years, highlighting global shifts towards sustainability-focused landscape interventions. These applications are critically evaluated in terms of spatial efficiency, operational flexibility, and interdisciplinary relevance. This review concludes that integrating drones with Geographic Information Systems (GISs), artificial intelligence (AI), and remote sensing frameworks substantially enhances analytical capacity, supports climate-resilient landscape planning, and offers novel pathways for multi-scalar environmental research and practice. Full article

Cardiovascular disease remains the world’s leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care. Full article

The Internet of Things (IoT) has emerged as a transformative technology in managing Alzheimer’s Disease (AD), offering novel solutions for early diagnosis, continuous patient monitoring, and assistive care. This review presents a comprehensive analysis of IoT-enabled systems tailored to AD care, focusing on wearable biosensors, cognitive monitoring tools, smart home automation, and Artificial Intelligence (AI)-driven analytics. A systematic literature survey was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify, screen, and synthesize 236 relevant studies primarily published between 2020 and 2025 across IEEE Xplore, PubMed, Scopus and Web of Science. The inclusion criteria targeted peer-reviewed articles that proposed or evaluated IoT-based solutions for AD detection, progression monitoring, or patient assistance. Key findings highlight the effectiveness of the IoT in detecting behavioral and cognitive changes, enhancing safety through real-time alerts, and improving patient autonomy. The review also explores integration challenges such as data privacy, system interoperability, and clinical adoption. The study reveals critical gaps in real-world deployment, clinical validation, and ethical integration of IoT-based systems for Alzheimer’s care. This study aims to serve as a definitive reference for researchers, clinicians, and developers working at the intersection of the IoT and neurodegenerative healthcare. Full article

Soil–groundwater pollution is a complex environmental phenomenon formed by the coupling of multiple processes. Due to the concealment of pollution, the persistence of harm, and the complexity of the system, soil–groundwater pollution has become a major environmental issue of increasing concern. The simulation and prediction of different types of models, different pollutants, and different scales in soil and groundwater have always been the research hotspots for pollution prevention and control. Starting from the mathematical mechanism of pollutant transport in soil and groundwater, this study reviews the method models represented by empirical models, analytical models, statistical models, numerical models, and machine learning, and expounds the characteristics and applications of the various representative models. Our Web of Science analysis (2015–2025) identifies 3425 relevant studies on soil–groundwater pollution models. Statistical models dominated (n = 1155), followed by numerical models (n = 878) and machine learning (n = 703). Soil pollution studies (n = 1919) outnumber groundwater research (n = 1506), with statistical models being most prevalent for soil and equally common as numerical models for groundwater. Then this study summarizes the research status of soil–groundwater pollution simulation and prediction at the level of multi-scale numerical simulation and the pollutant transport mechanism. It also discusses the development trend of artificial intelligence innovation applications such as machine learning in soil–groundwater pollution, looks forward to the challenges and measures to cope with them, and proposes to systematically respond to core challenges in soil and groundwater pollution simulation and remediation through new technology development, multi-scale and multi-interface coupling, intelligent optimization algorithms, and pollution control collaborative optimization methods for pollution management, so as to provide references for the future simulation, prediction, and remediation of soil–groundwater pollution. Full article

Background: The ageing population demands effective health solutions for the elderly. Wearable devices offer real-time monitoring and early alerts, but a comprehensive review of research in this field is lacking. This study uses bibliometric methods to analyse trends and advances in wearable devices for the elderly. Methods: Literature from 2014 to 2024 was retrieved from the Web of Science Core Collection using keywords related to the elderly and wearable devices. A total of 1015 English-language papers were analysed using tools including CiteSpace, VOSviewer, and R-Bibliometrix. Results: The annual growth rate of publications was 7.65%, with research increasing from 4 in 2014 to 1015 in 2024. Major contributors were the United States and China, with key authors including Bijan Najafi and Lynn Rochester. Research shifted from fall detection and activity monitoring to heart rate variability, balance, and AI integration. Key themes included “digital health”, “wearable technology”, and “cardiac health monitoring”. Conclusions: Research on wearable devices for the elderly is growing rapidly. Future studies should focus on multimodal sensor fusion, AI-enhanced analytics and personalised health interventions, and long-term, real-world validation of wearable solutions to improve elderly health management. Full article