Search Results (549)

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target—RAL Design System Plus (RAL+). Four spectrophotometers with a listed price between USD 100–1200 (Nix Spectro 2, Spectro 1 Pro, ColorReader, and Pico) and a smartphone RGB camera were tested on a representative subset of 183 RAL+ colors. Key performance metrics included the devices’ ability to match and measure RAL+ colors in the CIELAB color space using the color difference CIEDE2000 ΔE. The results showed that Nix Spectro 2 had the best performance, matching 99% of RAL+ colors with an estimated ΔE of 0.5–1.05. Spectro 1 Pro and ColorReader matched approximately 85% of colors with ΔE values between 1.07 and 1.39, while Pico and the Asus 8 smartphone matched 54–77% of colors, with ΔE of around 1.85. Our findings showed that low-cost, portable spectrophotometers offered excellent colorimetric measurements. They mostly outperformed existing RGB camera-based colorimetric systems, making them valuable tools in science and industry. Full article

This study examines the impact of issue-based teaching (IBT) on programming self-efficacy among non-Computer Science students. Grounded in social cognitive theory, the research investigates how IBT influences learning satisfaction and project success compared to traditional metrics. This study employed a mixed-methods approach, combining the quantitative analysis of student performance and self-efficacy measures with qualitative feedback from learning portfolios and project reports. The findings indicate that programming self-efficacy is a stronger predictor of learning satisfaction and project success than traditional performance metrics like grades. For novice programmers, IBT effectively enhances self-efficacy, positively influencing goal identification and performance. This cascade effect highlights the importance of fostering self-efficacy in programming education for non-technical students. Qualitative analysis reveals that IBT contributes to students’ sense of achievement, motivation, and learning satisfaction, encouraging them to view programming as a practical problem-solving tool. This study concludes that IBT offers an effective approach to enhancing interdisciplinary and STEAM education, recommending that educators focus on building self-efficacy through issue-based, learner-centered approaches. Full article

The Hirsch index (‘h-index’) is a widely recognized metric for assessing researchers’ impact, considering both the quantity and quality of their research work. Despite its global acceptance, the h-index has created some uncertainty about appropriate benchmark values across different disciplines. One such area of concern is architecture, which is often at a disadvantage compared to the fields of science and engineering. To examine this disparity, this study compared the citation count and h-index in architecture with those of other engineering disciplines. Data were collected extensively from Scopus database, focusing on the top 50 universities. The analysis revealed that architecture consistently recorded lower citation counts and h-index values than the selected engineering fields. Specifically, the average h-index for faculty members at the associate and full professor ranks was found to be 7.0 in architecture, compared to 22.8 in civil engineering and 25.6 in mechanical engineering. The findings highlight that a universal h-index benchmark is impractical, as research areas significantly vary in terms of research opportunities, challenges, and performance expectations. Thus, this study proposes the adoption of an additional relative h-index metric, ‘h_r-index’, which accounts for the deviation of individual researchers from the average h-index value within their fields of knowledge. This metric can serve as a complement to the standard h-index, providing a more equitable and accurate assessment of researchers’ performance and impact within their areas of expertise. Full article

Contemporary environmental challenges are increasingly significant. The primary cause is the drastic changes in climates. The prediction of solar radiation is a crucial aspect of solar energy applications and meteorological forecasting. The amount of solar radiation reaching Earth’s surface (Global Horizontal Irradiance, GHI) varies with atmospheric conditions, geographical location, and temporal factors. This paper presents a novel methodology for estimating surface sun exposure using advanced deep learning techniques. The proposed method is tested and validated using the data obtained from NASA’s Goddard Earth Sciences Data and Information Services Centre (GES DISC) named the SORCE (Solar Radiation and Climate Experiment) dataset. For analyzing and predicting accurate data, features are extracted using a deep learning method, Deep-FS. The method extracted and provided the selected features that are most appropriate for predicting the surface exposure. Time series analysis was conducted using Convolutional Neural Networks (CNNs), with results demonstrating superior performance compared to traditional methodologies across standard performance metrics. The proposed Deep-FS model is validated and compared with the traditional approaches and models through the standard performance metrics. The experimental results concluded that the proposed model outperforms the traditional models. Full article

Background: Traditional imaging techniques have limited efficacy in detecting occult cervical lymph node (LN) metastases in head and neck squamous cell carcinoma (HNSCC). Positron emission tomography/computed tomography (PET-CT) has demonstrated potential for assessing HNSCC, but the literature on its efficacy for detecting cervical LN metastases is scarce and exhibits varied outcomes, hindering comparisons. Aim: To compare the efficacy of CT, MRI, PET-CT, and US for detecting LN metastasis in HNSCC with clinically negative neck lymph nodes. Methods: A systematic search was performed using Web of Science, PubMed, Scopus, Embase, and Cochrane databases. Studies comparing CT, MRI, PET-CT, or US to detect cervical metastases in HNSCC were identified. The quality of the studies was assessed using the QUADAS-2 instrument. The positive likelihood ratios (+LR) and negative likelihood ratios (−LR), sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR), with 95% confidence intervals (C.I.), were calculated. Analysis was stratified according to lymph node and patient basis. Results: Fifty-seven studies yielded 3791 patients. At the patient level, PET-CT exhibited the highest diagnostic performance, with a SEN of 74.5% (95% C.I.: 65.4–81.8%) and SPE of 83.6% (95% C.I.: 77.2–88.5%). PET-CT also demonstrated the highest +LR of 4.303 (95% C.I.: 3.082–6.008) and the lowest −LR of 0.249 (95% C.I.: 0.168–0.370), resulting in the highest DOR of 15.487 (95% C.I.: 8.973–26.730). In the evaluation of diagnostic parameters for various imaging modalities on node-based analysis results, MRI exhibited the highest SEN at 77.4%, and PET demonstrated the highest SPE at 96.6% (95% C.I.: 94.4–98%). PET-CT achieved the highest DOR at 24.353 (95% C.I.: 10.949–54.166). Conclusions: PET-CT outperformed other imaging modalities across the majority of studied metrics concerning LN metastasis detection in HNSCC. Full article

The high salinity, humidity, and oxygen-rich environments of coastal marine areas pose serious corrosion risks to metal structures, particularly in equipment such as ships, offshore platforms, and port facilities. With the development of artificial intelligence technologies, image recognition-based intelligent detection methods have provided effective support for corrosion monitoring in marine engineering structures. This study aims to explore the performance improvements of different modified YOLOv5 models in small-object corrosion detection tasks, focusing on five IoU-based improved loss functions and their optimization effects on the YOLOv5 model. First, the study utilizes corrosion testing data from the Zhoushan seawater station of the China National Materials Corrosion and Protection Science Data Center to construct a corrosion image dataset containing 1266 labeled images. Then, based on the improved IoU loss functions, five YOLOv5 models were constructed: YOLOv5-NWD, YOLOv5-Shape-IoU, YOLOv5-WIoU, YOLOv5-Focal-EIoU, and YOLOv5-SIoU. These models, along with the traditional YOLOv5 model, were trained using the dataset, and their performance was evaluated using metrics such as precision, recall, F1 score, and FPS. The results showed that YOLOv5-NWD performed the best across all metrics, with a 7.2% increase in precision and a 2.2% increase in F1 score. The YOLOv5-Shape-IoU model followed, with improvements of 4.5% in precision and 2.6% in F1 score. In contrast, the performance improvements of YOLOv5-Focal-EIoU, YOLOv5-SIoU, and YOLOv5-WIoU were more limited. Further analysis revealed that different IoU ratios significantly affected the performance of the YOLOv5-NWD model. Experiments showed that the 4:6 ratio yielded the highest precision, while the 6:4 ratio performed the best in terms of recall, F1 score, and confusion matrix results. In addition, this study conducted an assessment using four datasets of different sizes: 300, 600, 900, and 1266 images. The results indicate that increasing the size of the training dataset enables the model to find a better balance between precision and recall, that is, a higher F1 score, while also effectively improving the model’s processing speed. Therefore, the choice of an appropriate IoU ratio should be based on specific application needs to optimize model performance. This study provides theoretical support for small-object corrosion detection tasks, advances the development of loss function design, and enhances the detection accuracy and reliability of YOLOv5 in practical applications. Full article

This study conducts a comprehensive bibliometric analysis of the concepts ‘physical edu- cation’ and ‘anxiety’ (PHYEDU_ANX) in the Web of Science (WoS) database. Background/Objectives: No previous biblio- metric studies were found that addressed this intersection, so this research is a pioneering exploration of this knowledge gap. The aim of the study is to examine the presence of both concepts in the scientific literature, identifying their trends, approaches, and future prospects. Methods: For this purpose, the methodology of co-word analysis was used. Results: The results of the study show that research on PHYEDU and ANX has traditionally focused on three main areas: motivation, exercise, and depression. In this first period, the focus was on the problem (ANX, depression…), Conclusions: whereas nowadays, research focuses on the subjects who suffer from it, mainly adolescents and students. The study suggests that future research in this field will focus on the areas of satisfaction, intervention, and association. This research also answers questions relevant to the field, such as which institutions or countries are the most prolific publishers of PHYEDU_ANX, as well as the most cited authors in this area of study. 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

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

Membrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to provide guidance for future studies. The results show that since the first article was published in 1964, the domain has gained popularity, especially since 2009. Thin-film composite (TFC) polymeric material has been the primary focus of RO membrane experts, with 550 articles published on this topic. The use of nanomaterials and polymers in membrane engineering is also high, with 821 articles. Common problems such as fouling, biofouling, and scaling have been the center of work dedication, with 324 articles published on these issues. Wang J. is the leader in the number of published articles (73), while Gao C. is the leader in other metrics. Journal of Membrane Science is the most preferred source for the publication of RO membrane engineering and related technologies. Author social networks analysis shows that there are five core clusters, and the dominant cluster have 4 researchers. The analysis of sentiment, subjectivity, and emotion indicates that abstracts are positively perceived, objectively written, and emotionally neutral. Full article

As natural predators of bacteria, tailed bacteriophages can be used in biocontrol applications, including antimicrobial therapy. Also, phage lysis is a detrimental factor in technological processes based on bacterial growth and metabolism. The spectrum of bacteria bacteriophages interact with is known as the host range. Phage science produced a vast amount of host range data. However, there has been no attempt to analyse these data from the viewpoint of modern phage and bacterial taxonomy. Here, we performed a meta-analysis of spotting and plaquing host range data obtained on strains of production host species. The main metric of our study was the host range value calculated as a ratio of lysed strains to the number of tested bacterial strains. We found no boundary between narrow and broad host ranges in tailed phages taken as a whole. Family-level groups of strictly lytic bacteriophages had significantly different median plaquing host range values in the range from 0.18 (Drexlerviridae) to 0.70 (Herelleviridae). In Escherichia coli phages, broad host ranges were associated with decreased efficiency of plating. Bacteriophage morphology, genome size, and the number of tRNA-coding genes in phage genomes did not correlate with host range values. From the perspective of bacterial species, median plaquing host ranges varied from 0.04 in bacteriophages infecting Acinetobacter baumannii to 0.73 in Staphylococcus aureus phages. Taken together, our results imply that taxonomy of bacteriophages and their bacterial hosts can be predictive of intraspecies host ranges. Full article

Stingless bees (Hymenoptera; Apidae; Meliponini), with a biodiversity of 605 species, harvest and transport corbicula pollen to the nest, like Apis mellifera, but process and store the pollen in cerumen pots instead of beeswax combs. Therefore, the meliponine pollen processed in the nest was named pot-pollen instead of bee bread. Pot-pollen has nutraceutical properties for bees and humans; it is a natural medicinal food supplement with applications in health, food science, and technology, and pharmaceutical developments are promising. Demonstrated synergism between Tetragonisca angustula pot-pollen ethanolic extracts, and antibiotics against extensively drug-resistant (XDR) bacteria revealed potential to combat antimicrobial resistance (AMR). Reviewed pot-pollen VOC richness was compared between Australian Austroplebeia australis (27), Tetragonula carbonaria (31), and Tetragonula hogkingsi (28), as well as the Venezuelan Tetragonisca angustula (95). Bioactivity and olfactory attributes of the most abundant VOCs were revisited. Bibliometric analyses with the Scopus database were planned for two unrelated topics in the literature for potential scientific advances. The top ten most prolific authors, institutions, countries, funding sponsors, and sources engaged to disseminate original research and reviews on pot-pollen (2014–2023) and direct injection food flavor (1976–2023) were ranked. Selected metrics and plots were visualized using the Bibliometrix-R package. A scholarly approach gained scientific insight into the interaction between an ancient fermented medicinal pot-pollen and a powerful bioanalytical technique for fermented products, which should attract interest from research teams for joint projects on direct injection in pot-pollen flavor, and proposals on stingless bee nest materials. Novel anti-antimicrobial-resistant agents and synergism with conventional antibiotics can fill the gap in the emerging potential to overcome antimicrobial resistance. Full article

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making. Full article

Sports performance initiation is of significant interest in sports sciences, particularly in beach volleyball (BV), where players usually combine indoor and BV disciplines in the formative stages. This research aimed to apply an electronic performance tracking system to quantify the physical-conditional performance of young male BV players during competition, considering age group (U15 or U19), sport specialisation (indoor or beach) and the set outcome (winner or loser). Thirty-two young male players, categorised by age and sport specialisation, were analysed during 40 matches using electronic performance tracking systems (Wimu PRO^TM). Data collected were the set duration, total and relative distances covered, and number and maximum values in acceleration and deceleration actions. U19 players and BV specialists, compared to their younger and indoor counterparts, covered more distance (719.25 m/set vs. 597.85 m/set; 719.25 m/set vs. 613.15 m/set) and exhibited higher intensity in terms of maximum values in acceleration (4.09 m/s² vs. 3.45 m/s²; 3.99 m/s² vs. 3.65 m/s²) and deceleration (−5.05 m/s² vs. −4.41 m/s²). More accelerations (557.50 n/set vs. 584.50 n/set) and decelerations (561.50 n/set vs. 589.00 n/set) were found in indoor players. Additionally, no significant differences were found in variables regarding the set outcome. These findings suggest that both age and specialisation play crucial roles in determining a great physical-conditional performance in young players, displaying a higher volume and intensity in external load metrics, whereas indoor players seem to need more accelerations and decelerations in a BV adaptation context. These insights highlight the age development and sport specialisation in young volleyball and BV athletes. Full article

While a flurry of studies and Integrated Assessment Models (IAMs) have independently investigated the impacts of switching mitigation policies in response to different climate scenarios, little is understood about the feedback effect of how human risk perceptions of climate change could contribute to switching climate mitigation policies. This study presents a novel machine learning approach, utilizing a probabilistic structural equation model (PSEM), for understanding complex interactions among climate risk perceptions, beliefs about climate science, political ideology, demographic factors, and their combined effects on support for mitigation policies. We use machine learning-based PSEM to identify the latent variables and quantify their complex interaction effects on support for climate policy. As opposed to a priori clustering of manifest variables into latent variables that is implemented in traditional SEMs, the novel PSEM presented in this study uses unsupervised algorithms to identify data-driven clustering of manifest variables into latent variables. Further, information theoretic metrics are used to estimate both the structural relationships among latent variables and the optimal number of classes within each latent variable. The PSEM yields an R² of 92.2% derived from the “Climate Change in the American Mind” dataset (2008–2018 [N = 22,416]), which is a substantial improvement over a traditional regression analysis-based study applied to the CCAM dataset that identified five manifest variables to account for 51% of the variance in policy support. The PSEM uncovers a previously unidentified class of “lukewarm supporters” (~59% of the US population), different from strong supporters (27%) and opposers (13%). These lukewarm supporters represent a wide swath of the US population, but their support may be capricious and sensitive to the details of the policy and how it is implemented. Individual survey items clustered into latent variables reveal that the public does not respond to “climate risk perceptions” as a single construct in their minds. Instead, PSEM path analysis supports dual processing theory: analytical and affective (emotional) risk perceptions are identified as separate, unique factors, which, along with climate beliefs, political ideology, and race, explain much of the variability in the American public’s support for climate policy. The machine learning approach demonstrates that complex interaction effects of belief states combined with analytical and affective risk perceptions; as well as political ideology, party, and race, will need to be considered for informing the design of feedback loops in IAMs that endogenously feedback the impacts of global climate change on the evolution of climate mitigation policies. Full article