Search Results (2,806)

This review examines the integration of advanced ultrasonic techniques and artificial intelligence (AI) for monitoring and analyzing concrete structures, focusing on detecting and classifying internal defects. Concrete structures are subject to damage over time due to environmental factors and dynamic loads, compromising their integrity. Non-destructive techniques, such as ultrasonics, allow for identifying discontinuities and microcracks without altering structural functionality. This review addresses key scientific challenges, such as the complexity of managing the large volumes of data generated by high-resolution inspections and the importance of non-linear models, such as the Hammerstein model, for interpreting ultrasonic signals. Integrating AI with advanced analytical models enhances early defect diagnosis and enables the creation of detailed maps of internal discontinuities. Results reported in the literature show significant improvements in diagnostic sensitivity (up to 30% compared to traditional linear techniques), accuracy in defect localization (improvements of 25%), and reductions in predictive maintenance costs by 20–40%, thanks to advanced systems based on convolutional neural networks and fuzzy logic. These innovative approaches contribute to the sustainability and safety of infrastructure, with significant implications for monitoring and maintaining the built environment. The scientific significance of this review lies in offering a systematic overview of emerging technologies and their application to concrete structures, providing tools to address challenges related to infrastructure degradation and contributing to advancements in composite sciences. Full article

Evolving from an ethical consideration to a strategic imperative, green innovation (GI) compels businesses to continually enhance their processes to achieve sustainable growth. Based on bibliometric analysis of 594 Web of Science (WOS)-sourced articles from 2000 to 2023 using VOSviewer-1.6.20 and Bibliometrix-4.3.0, this study sheds light on the existing trends of green innovation, its contributors, and potential future directions in today’s business landscape. Our findings unveil significant insights from GI literature; an upward growth trajectory in publications; limited collaboration among researchers and institutions (notable collaborative networks among countries include China, Spain, and the United Kingdom); and trending GI terms and themes include green intellectual capital, GI efficiency, green product innovation, green absorptive capacity, green knowledge acquisition, big data, etc. These insights serve as a comprehensive guide for practitioners and scholars navigating the study of GI within the business and management sphere. Full article

In recent years, the intensification of the urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores the current status of surface UHI research, emphasizing the role of land use and land cover changes (LULC) in urban environments. We conducted a systematic review of 8260 journal articles from the Web of Science database, employing bibliometric analysis and keyword co-occurrence analysis using CiteSpace to identify research hotspots and trends. Our investigation reveals that vegetation cover and land use types are the two most critical factors influencing UHI intensity. We analyze various computational intelligence techniques, including machine learning algorithms, cellular automata, and artificial neural networks, used for simulating urban expansion and predicting UHI effects. The study also examines numerical modeling methods, including the Weather Research and Forecasting (WRF) model, while examining the application of Computational Fluid Dynamics (CFD) in urban microclimate research. Furthermore, we evaluate potential mitigation strategies, considering urban planning approaches, green infrastructure solutions, and the use of high-albedo materials. This comprehensive survey not only highlights the critical relationship between land use dynamics and UHIs but also provides a direction for future research in computational intelligence-driven urban climate studies. Full article

Buildings are responsible for approximately 40% of global energy consumption, putting pressure on the construction industry to mitigate its environmental impact. Therefore, there is an urgent need for innovative solutions to reduce power consumption, particularly in lighting systems. This study’s primary objective was to investigate novel integrated lighting solutions that significantly reduce energy use, as well as to explore their enhancement through Building Information Modelling (BIM) and the Internet of Things (IoT) to improve energy efficiency further and reduce the carbon footprint of buildings. Hence, this literature review examined energy-saving actions, retrofitting practices and interventions across a range of multi-use buildings worldwide, focusing on research from 2019 to 2024. The review was conducted using Scopus and Web of Science databases, with inclusion criteria limited to original research. The objective was to diagnose the goals being undertaken and ultimately validate new actions and contributions to minimise energy consumption. After applying eligibility criteria, 48 studies were included in the review. First, daylight harvesting and retrofitting solutions were examined using the latest technologies and external shading. The review indicates a lack of proper coordination between daylight and electrical lighting, resulting in energy inefficiency. Secondly, it reviews how the integration of BIM facilitates the design process, providing a complete overview of all the building variables, thus improving indoor daylight performance and proper lighting with energy analysis. Lastly, the review addresses the role of the Internet of Things (IoT) in providing real-time data from sensor networks, allowing for continuous monitoring of building conditions. This systematic literature review explores the integration of these fields to address the urgent need for innovative strategies and sustainability in the built environment. Furthermore, it thoroughly analyses the current state of the art, identifying best practices, emerging trends and concrete insight for architects, engineers and researchers. The goal is to promote the widespread adoption of low-carbon systems and encourage collaboration among industry professionals and researchers to advance sustainable building design. Ultimately, a new parametric design framework is proposed, consisting of five iterative phases that cover all design stages. This framework is further enhanced by integrating BIM and IoT, which can be used together to plan, reconfigure, and optimise the building’s performance. Full article

Zinc (Zn) deficiency in crops is a global issue, particularly in plants grown in calcareous soils, where Zn is often adsorbed or precipitated by calcium carbonates. The aim of this study was to identify and quantify, through bibliometric analysis, the scientific production related to Zn deficiency in calcareous soils over the last 36 years (1989–2024). A total of 374 documents were retrieved through a search on the Web of Science (WOS) platform, specifically in the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) databases. Of these, only 198 articles were directly relevant to the topic and were used for the analysis. Unidimensional and multidimensional bibliometric indicators were evaluated using Excel and VOSviewer software. The results confirm that the number of articles has increased in recent years. The most influential authors, journals, articles, institutions, and countries in this research area were identified. In addition, collaboration networks between authors and countries, as well as the predominant research topics, were determined. This study provides a comprehensive overview of this field on a global scale and serves as a useful reference for scientists interested in conducting future research on related topics. Full article

(1) Background: Respiratory dysfunction is a debilitating consequence of cervical and thoracic spinal cord injury (SCI), resulting from the loss of cortico-spinal drive to respiratory motor networks. This impairment affects both central and peripheral nervous systems, disrupting motor control and muscle innervation, which is essential for effective breathing. These deficits significantly impact the health and quality of life of individuals with SCI. Noninvasive stimulation techniques targeting these networks have emerged as a promising strategy to restore respiratory function. This study systematically reviewed the evidence on noninvasive electrical stimulation modalities targeting respiratory motor networks, complemented by previously unpublished data from our research. (2) Methods: A systematic search of five databases (PubMed, Ovid, Embase, Science Direct, and Web of Science) identified studies published through 31 August 2024. A total of 19 studies involving 194 participants with SCI were included. Unpublished data from our research were also analyzed to provide supplementary insights. (3) Results: Among the stimulation modalities reviewed, spinal cord transcutaneous stimulation (scTS) emerged as a particularly promising therapeutic approach for respiratory rehabilitation in individuals with SCI. An exploratory clinical trial conducted by the authors confirmed the effectiveness of scTS in enhancing respiratory motor performance using a bipolar, 5 kHz-modulated, and 1 ms pulse width modality. However, the heterogeneity in SCI populations and stimulation protocols across studies underscores the need for further standardization and individualized optimization to enhance clinical outcomes. (4) Conclusions: Developing standardized and individualized neuromodulatory protocols, addressing both central and peripheral nervous system impairments, is critical to optimizing respiratory recovery and advancing clinical implementation. Full article

This systematic literature review evaluates predictive models in education, focusing on their role in forecasting student performance, identifying at-risk students, and personalising learning experiences. The review compares the effectiveness of machine learning (ML) algorithms such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Decision Trees with traditional statistical models, assessing their ability to manage complex educational data and improve decision-making. The search, conducted across databases including ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar, yielded 400 records. After screening and removing duplicates, 124 studies were included in the final review. The findings show that ML algorithms consistently outperform traditional models due to their capacity to handle large, non-linear datasets and continuously enhance predictive accuracy as new patterns emerge. These models effectively incorporate socio-economic, demographic, and academic data, making them valuable tools for improving student retention and performance. However, the review also identifies key challenges, including the risk of perpetuating biases present in historical data, issues of transparency, and the complexity of interpreting AI-driven decisions. In addition, reliance on varying data processing methods across studies reduces the generalisability of current models. Future research should focus on developing more transparent, interpretable, and equitable models while standardising data collection and incorporating non-traditional variables, such as cognitive and motivational factors. Ensuring transparency and ethical standards in handling student data is essential for fostering trust in AI-driven models. Full article

Cultural diversity often complicates the understanding of sustainability, sometimes making its concepts seem vague. This issue is particularly evident in food systems, which rely on both renewable and nonrenewable resources and drive significant environmental changes. The widespread impacts of climate change, aggravated by the overuse of natural resources, have highlighted the urgency of balancing food production with environmental preservation. Society faces a pivotal challenge: ensuring that food systems produce ample, accessible, and nutritious food while also reducing their carbon footprint and protecting ecosystems. Agriculture 5.0, an innovative approach, combines digital advancements with sustainability principles. This study reviews current knowledge on digital agriculture, analyzing scientific data through an undirected bipartite network that links journals and author keywords from articles retrieved from Clarivate Web of Science. The main goal is to outline a framework that integrates various sustainability concepts, emphasizing both well-studied (economic) and underexplored (socioenvironmental) aspects of Agriculture 5.0. This framework categorizes sustainability concepts into material (tangible) and immaterial (intangible) values based on their supporting or influencing roles within the agriculture domain, as documented in the scientific literature. Full article

The analysis of corrosion images is crucial in materials science, where acquiring clear images is fundamental for subsequent analysis. The goal of deblurring metal corrosion images is to reconstruct clear images from degraded ones. To the best of our knowledge, this study introduces the first paired blurry-sharp image dataset specifically designed for the metal corrosion domain, filling a critical gap in the existing research. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. We propose a novel metal corrosion images deblurring network (MCIDN) that employs a dual-domain attention mechanism, integrating both spatial and frequency domains to enhance image clarity. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. While self-attention is widely used in visual tasks, its quadratic complexity often leads to high computational costs. To address this issue, we introduce a new spatial channel attention module (SCAM) that employs dynamic group convolutions to achieve self-attention, effectively integrating information from local regions and enhancing representation learning capabilities. Recognizing the critical role of frequency components in image restoration, we develop a frequency channel attention module (FCAM) that selectively focuses on different frequency components of images, thereby enhancing deblurring performance. These two attention modules are seamlessly integrated into our network. Compared to existing methods, our approach demonstrates superior performance on datasets of blurry metal corrosion images, achieving a peak signal-to-noise ratio (PSNR) of 32.8645 dB and a structural similarity (SSIM) of 0.9768. These metrics indicate that our method provides clearer and more detailed reconstructions, significantly enhancing the image quality. Full article

Introduction: Irritable bowel syndrome (IBS) is a common condition that alters the quality of life of patients. A variety of dietary interventions have been introduced to address this debilitating condition. The low-FODMAP diet (LFD), gluten-free diet (GFD), and Mediterranean diet are examples showing efficacy. The aim of this network meta-analysis was to compare these interventions to find the best approach. Methods: We performed a systematic review of the available literature through 14 March 2024 in the following databases: Embase, PubMed, MEDLINE OVID, Web of Science, CINAHL Plus, and Cochrane Central. We only included randomized controlled trials (RCTs). We used a random effects model and conducted a direct meta-analysis based on the DerSimonian–Laird approach and a network meta-analysis based on the frequentist approach. Mean differences (MDs) with 95% confidence interval (CI) were calculated. The primary outcomes included IBS quality of life (IBS QOL) and IBS symptom severity scale (IBS-SSS). Results: We finalized 23 studies including 1689 IBS patients. In the direct meta-analysis, there was no statistically significant difference in any IBS score between GFD and either LFD or standard diet. Meanwhile, the LFD was statistically superior to the standard diet in the IBS-SSS (MD: −46.29, CI: −63.72–−28.86, p < 0.01) and IBS QOL (MD: 4.06, CI: 0.72–7.41, p = 0.02). On ranking, the Mediterranean diet showed the greatest improvement in IBS-SSS, IBS-QOL, distension, dissatisfaction, and general life interference, followed by the LFD alone or in combination with the GFD. Conclusions: We demonstrated the efficacy of dietary interventions such as the LFD and Mediterranean diet in improving IBS. There is a need for large RCTs with head-to-head comparisons, particularly for the Mediterranean diet. Full article

The Mar Menor (Murcia, Spain) has faced a eutrophication crisis in recent decades, significantly affecting local residents, including children. Considering the importance of involving children in scientific activities and the potential societal benefits of working with them, we conducted two environmental citizen science activities with students from the Los Nietos school. The study aimed to evaluate their knowledge about the Mar Menor crisis, understand their opinions and experiences, and assess the broader social impact of these activities. The children first created drawings related to the Mar Menor during a visit to Los Nietos beach, followed by a survey completed weeks later. Analysis of the drawings and survey responses revealed that while children may not fully grasp the causes of the ecological catastrophe, they are aware of its existence and maintain a hopeful perspective on the lagoon’s future. Additionally, a social network analysis of texts referencing children highlighted the societal reach of their actions and voices regarding the Mar Menor crisis. Our findings demonstrate that citizen science activities not only engage and educate children but also position them as influential communicators within their communities. This underscores the potential of such initiatives to amplify environmental awareness and drive social change by empowering younger generations as advocates for ecological sustainability. Full article

This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearings. Using data from Scopus and Web of Science, the review analyses key trends, influential publications, and significant contributions to the field from 2000 to 2024. The findings highlight the performance of ANNs in handling large datasets and modelling complex, non-linear relationships, as well as the high accuracy of SVMs in fault classification tasks, particularly with small-to-medium-sized datasets. However, the study also identifies several limitations, including the dependency on high-quality data, significant computational resource requirements, limited model adaptability, interpretability challenges, and practical implementation complexities. This review provides valuable insights for researchers and engineers, guiding the selection of appropriate diagnostic models and highlighting opportunities for future research. Addressing the identified limitations is crucial for the broader adoption and effectiveness of machine learning-based predictive maintenance strategies across various industrial contexts. Full article

Background: Actinic keratoses (AK) usually occur on sun-exposed areas in elderly patients with Fitzpatrick I–II skin types. Dermatoscopy and ultrasonography are two non-invasive tools helpful in examining clinically suspicious lesions. This study presents the usefulness of image-processing algorithms in AK staging based on dermatoscopic and ultrasonographic images. Methods: In 54 patients treated at the Department of Dermatology of Poznan University of Medical Sciences, clinical, dermatoscopic, and ultrasound examinations were performed. The clinico-dermoscopic AK classification was based on three-point Zalaudek scale. The ultrasound images were recorded with DermaScan C, Cortex Technology device, 20 MHz. The dataset consisted of 162 image pairs. The developed algorithm includes automated segmentation of ultrasound data utilizing a CFPNet-M model followed by handcrafted feature extraction. The dermatoscopic image analysis includes both handcrafted and convolutional neural network features, which, combined with ultrasound descriptors, are used in support vector machine-based classification. The network models were trained on public datasets. The influence of each modality on the final classification was evaluated. Results: The most promising results were obtained for the dermatoscopic analysis with the use of neural network model (accuracy 81%) and its combination with ultrasound scans (accuracy 79%). Conclusions: The application of machine learning-based algorithms in dermatoscopic and ultrasound image analysis machine learning in the staging of AKs may be beneficial in clinical practice in terms of predicting the risk of progression. Further experiments are warranted, as incorporating more images is likely to improve classification accuracy of the system. Full article

Background: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. Objectives: The study aimed to develop ECG-AI models predicting FCHD risk from ECGs. Methods (Retrospective): Data from 10 s 12-lead ECGs and demographic/clinical data from University of Tennessee Health Science Center (UTHSC) were used for model development. Of this dataset, 80% was used for training and 20% as holdout. Data from Atrium Health Wake Forest Baptist (AHWFB) were used for external validation. We developed two separate convolutional neural network models using 12-lead and Lead I ECGs as inputs, and time-dependent Cox proportional hazard models using demographic/clinical data with ECG-AI outputs. Correlation of the predictions from the 12- and 1-lead ECG-AI models was assessed. Results: The UTHSC cohort included data from 50,132 patients with a mean age (SD) of 62.50 (14.80) years, of whom 53.4% were males and 48.5% African American. The AHWFB cohort included data from 2305 patients with a mean age (SD) of 63.04 (16.89) years, of whom 51.0% were males and 18.8% African American. The 12-lead and Lead I ECG-AI models resulted in validation AUCs of 0.84 and 0.85, respectively. The best overall model was the Cox model using simple demographics with Lead I ECG-AI output (D1-ECG-AI-Cox), with the following results: AUC = 0.87 (0.85–0.89), accuracy = 83%, sensitivity = 69%, specificity = 89%, negative predicted value (NPV) = 92% and positive predicted value (PPV) = 55% on the AHWFB validation cohort. For this, the 2-year FCHD risk prediction accuracy was AUC = 0.91 (0.90–0.92). The 12-lead versus Lead I ECG FCHD risk prediction showed strong correlation (R = 0.74). Conclusions: The 2-year FCHD risk can be predicted with high accuracy from single-lead ECGs, further improving when combined with demographic information. Full article

In this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanced natural language processing model T5 (Text-to-Text Transfer Transformer) to enhance the accuracy and efficiency of screening and data extraction processes. We assess strategies for handling the concept drift in machine learning using high-impact publications from notable databases that were made accessible via the IEEE and Science Direct APIs. The chronological analysis covering the past two decades provides a historical perspective on methodological advancements, recognizing their strengths and weaknesses through citation metrics and rankings. This review aims to trace the growth and evolution of concept drift mitigation strategies and to provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. Key findings highlight the effectiveness of diverse methodologies such as drift detection methods, window-based methods, unsupervised statistical methods, and neural network techniques. However, challenges remain, particularly with imbalanced data, computational efficiency, and the application of concept drift detection to non-tabular data like images. This review aims to trace the growth and evolution of concept drift mitigation strategies and provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. Full article