bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Relationship Between Ejection Fraction, Arterial Stiffness, NT-proBNP, and Hospitalization Risk in Heart Failure Patients
Authors: Osser Gyongyi , Osser Brigitte , Toth Csongor , Miuța Caius Calin , Marconi Gabriel Roberto , Bondar Laura Ioana
Published: December 2024

Background/Objectives: Heart failure (HF) remains a leading cause of hospitalization and morbidity. Arterial stiffness, measured by pulse wave velocity (PWV) and the augmentation index (AIx), has been linked to HF severity and prognosis. This study investigates the relationship between clinical parameters, biochemical indicators, and arterial stiffness in hospitalized patients with HF, aiming to identify predictors of hospitalization and improve patient management. Methods: This cross-sectional study included 98 patients admitted with HF: 53 with acutely decompensated HF (sudden worsening of symptoms) and 45 with chronic HF (stable symptoms of HF). Clinical and biochemical parameters, including ejection fraction (EF), N-terminal prohormone of brain natriuretic peptide (NT-proBNP) levels, and arterial stiffness indicators (PWV and AIx), were measured at admission. During follow-up, 59 patients required re-hospitalization due to acutely decompensated HF, while 39 remained outpatients without further hospitalization. The relationship between these parameters was analyzed using Pearson correlation coefficients, and multiple Cox regression analysis was conducted to identify independent predictors of re-hospitalization. Results: A significant negative correlation between EF and PWV was found (r = −0.853, 95% CI [−0.910, −0.764]), suggesting an association between improved heart function (higher EF) and reduced arterial stiffness (lower PWV). A moderate positive correlation between EF and AIx (r = 0.626, 95% CI [0.473, 0.805]) suggests that, while higher EF is associated with increased AIx, the relationship is weaker compared to EF and PWV. This may reflect differing contributions of vascular and myocardial factors to HF severity. Hospitalized patients exhibited significantly poorer clinical and biochemical profiles, including higher NT-proBNP levels (p < 0.001) and worse blood pressure (BP) measurements (systolic and diastolic, p < 0.01). Multiple Cox regression analysis identified PWV, Aix, and NT-proBNP as independent predictors of re-hospitalization in HF patients, with significant hazard ratios: PWV (HR = 1.15, p = 0.02), AIx (HR = 1.03, p = 0.02), and NT-proBNP (HR = 1.0001, p < 0.01). Conclusions: Arterial stiffness indices (PWV and AIx), EF, and NT-proBNP were identified as significant predictors of re-hospitalization in HF patients. These findings suggest that integrating arterial stiffness measurements into routine clinical assessments may enhance the risk stratification and inform targeted interventions to reduce hospitalizations and improve outcomes.
Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion
Authors: Aznar-Gimeno Rocío , Perez-Lasierra Jose Luis , Pérez-Lázaro Pablo , Bosque-López Irene , Azpíroz-Puente Marina , Salvo-Ibáñez Pilar , Morita-Hernandez Martin , Hernández-Ruiz Ana Caren , Gómez-Bernal Antonio , Rodrigalvarez-Chamarro María de la Vega , Alfaro-Santafé José-Víctor , del Hoyo-Alonso Rafael , Alfaro-Santafé Javier
Published: December 2024

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.
The Role of Metabolic Syndrome in Psoriasis Treatment Response: A One-Year Comparative Analysis of PASI Progression
Authors: Mustață Maria-Lorena , Ionescu Mihaela , Radu Lucrețiu , Neagoe Carmen-Daniela , Ahrițculesei Roxana-Viorela , Cîmpeanu Radu-Cristian , Matei Daniela , Amzolini Anca-Maria , Predoi Maria-Cristina , Ianoși Simona-Laura
Published: December 2024

Background/Objectives: Psoriasis is a chronic dermatological condition with systemic implications, especially with metabolic syndrome (MS). This study evaluated the vicious cycle where obesity and MS exacerbate systemic inflammation that complicates the efficacy of psoriasis therapies by examining the PASI score over a one-year period. Patients were classified into two subgroups: those with psoriasis alone (PSO) and those with both psoriasis and metabolic syndrome (PSO-MS). Methods: A total of 150 patients, half of whom also concomitantly presented with metabolic syndrome, received biologic therapies comprising anti-IL-17, anti-IL-23, and anti-TNF-a, or methotrexate, with PASI scores assessed at baseline and at 3, 6, and 12 months. Results: All treatments showed significant reductions in PASI; however, patients with PSO showed more marked reductions in PASI score than those in the PSO-MS group. Anti-IL-17 treatments produced the greatest sustained long-term improvements, whereas anti-IL-23 produced prompt early improvements. Increases in BMI and leptin concentrations were associated with a modest rate of reduction in PASI score, underlining the impact of obesity and metabolic dysfunction on treatment efficacy. Conclusions: This study highlights the importance of managing comorbidities such as MS in the treatment of psoriasis, as the interplay between systemic inflammation and metabolic health further complicates therapeutic outcomes.
Diagnostic and Therapeutic Aspects of Monoclonal Gammopathies of Renal Significance (MGRS): An Update
Authors: Netti Giuseppe Stefano , Troise Dario , Rossini Michele , Catalano Valeria , De Luca Federica , Khalid Javeria , Camporeale Valentina , Ritrovato Fabiana , Infante Barbara , Sanguedolce Francesca , Stallone Giovanni , Ranieri Elena
Published: December 2024

Monoclonal gammopathy of renal significance (MGRS) refers to a group of renal disorders caused by a monoclonal immunoglobulin (MIg), secreted by a non-malignant B-cell clone. Unlike overt multiple myeloma or B-cell proliferation, MGRS does not meet those diagnostic criteria. However, it is associated with significant morbidity, due to severe renal, and sometimes systemic, lesions induced by the MIg. Early recognition is crucial, as chemotherapy to suppress MIg secretion often improves outcomes. The spectrum of renal diseases in MGRS is broad, including both well-known conditions like AL amyloidosis and newly described lesions. Kidney biopsy is essential to determine the specific lesion associated with MGRS and assess its severity. Diagnosis involves integrating morphologic alterations using techniques such as light microscopy, immunofluorescence (IF), electron microscopy, and, in some cases, IF staining for Ig isotypes, immunoelectron microscopy, and proteomic analysis. Additionally, a complete hematologic evaluation, including serum and urine protein electrophoresis, immunofixation, and a serum-free light-chain assay, is necessary.
The Comparison of Accuracy of Post Space Digital Impressions Made by Three Different Intraoral Scanners: An In Vitro Study
Authors: Meshni Abdullah A. , Jain Saurabh , Osaysi Hanan Nasser Marie , Hezam Khadijah Nasser , Adlan Samar Samir Gomaan
Published: December 2024

Background and Objectives: The present study aims to assess and compare the accuracy of post-space impressions captured by three different intraoral scanners (IOS) using various canal diameters. Methods: Three extracted natural maxillary central incisors were selected and prepared for a 1 mm wide margin and a 3 mm ferrule. All steps required for the endodontic procedure were performed, and the post space was prepared using post drills. The post length was kept constant at 12 mm, whereas the width was varied (Group 1: 1.4 mm, Group 2: 1.6 mm, and Group 3: 1.8 mm). Three IOSs (Trios3, iTero2, and Medit i700) were used to acquire a digital impression of the prepared post space. Each tooth was scanned 10 times by each scanner. So, in the end, 90 digital images were recorded, and the STL files were stored. GC Pattern resin was used to fabricate resin post and core patterns, which were scanned using an extraoral scanner (EOS). The STL file obtained was used as the reference file. To evaluate the trueness of the tested IOSs, each three-dimensional scan from an IOS was superimposed on the reference scan with the help of the Medit Design software 2.1.4. The software generates color plots and gives numerical values as deviations in the Root mean square (RMS) for the variance between the two superimposed scans. The data collected was tabulated for statistical analysis. One Way ANOVA was used to test the significance difference between three different IOSs, followed by Bonferroni Post-hoc test pairwise test to identify the differences between every two different IOS. Statistical significance was set at p < 0.05. Results: The mean deviation for trueness in post space impression values recorded by the Medit i700 was highest among groups 1, 2, and 3 [0.825 (±0.071), 0.673 (±0.042) and 0.516 (±0.039), respectively], followed by iTero2 [0.738 (±0.081), 0.569 (±0.043) and 0.470 (±0.037), respectively] and Trios3 [0.714 (±0.062), 0.530 (±0.040) and 0.418 (±0.024), respectively]. Significant differences were found between the groups for all three IOSs (Trios3: p-value < 0.0001; iTero2: p-value < 0.0001; Medit i700: p-value < 0.0001). Conclusions: Within the limitations of this study, it can be concluded that Trios3 IOS has higher accuracy (as it exhibited minimal deviation for trueness) in recording post space, followed by iTero2 and Mediti700 IOS. As the diameter of the post space is increased, the accuracy of recording by IOS increases. For all the tested IOSs (except for Trios3 and iTero2, when used to record post space with 1.8 mm canal diameter), the deviations in trueness were higher than the clinically acceptable limits. Thus, IOSs should be used cautiously when recording impressions of post spaces.
Personalized Antenatal Corticosteroid Therapy and Central Nervous System Development: Reflections on the Gold Standard of Fetomaternal Therapy
Authors: Babović Ivana R. , Sparić Radmila , Plešinac Snežana D. , Belović Dušica M. Kocijančić , Plešinac Jovana D. , Akšam Slavica S. , Plešinac Vera D. , Pecorella Giovanni , Tinelli Andrea
Published: December 2024

Background: The term “fetal programming” refers to the effects of endogenous and exogenous corticosteroids, whether received from the mother or the fetus, on brain development and the hypothalamic–pituitary–adrenal axis reset. The authors of this narrative review examine the WHO’s guidelines for prenatal corticosteroids in pregnant women who are at high risk of premature delivery. These guidelines are regarded as the best available for preventing late-life problems resulting from preterm. Methods: In order to find full-text publications published in peer-reviewed journals between 1990 and 2023 that were written in English, the authors searched PubMed, Scopus, Cochrane Library, and Web of Science. Results: The authors highlight the possible adverse long-term effects of prenatal corticosteroid medication on human brain development and function. This pharmacological feature is therapeutically significant because there is less evidence in the scientific literature regarding the potential role that the timing, mode, and dosage of exogenous steroid treatment may have in neurological illnesses down the road. Conclusions: The authors expect that these studies will shed light on the relationship between specially designed prenatal corticosteroid therapy and the molecular mechanisms underlying the prenatal programming of neurodevelopment in childhood and adulthood.
Outcomes of Patients with Heart Failure Hospitalized for COVID-19—A Study in a Tertiary Italian Center
Authors: Cianci Rossella , Caldarelli Mario , Rio Pierluigi , Pignataro Giulia , Sacco Fernandez Marta , Ocarino Francesca , Della Polla Davide Antonio , Franceschi Francesco , Gasbarrini Antonio , Gambassi Giovanni , Candelli Marcello
Published: December 2024

Background: Coronavirus Disease 2019 (COVID-19), triggered by SARS-CoV-2, has represented a global pandemic associated with an elevated rate of mortality, mainly among older individuals. The extensive pulmonary involvement by the viral infection might have precipitated pre-existing chronic conditions in this vulnerable population, including heart failure (HF). Materials and Methods: The aim of this retrospective, observational study was to assess the impact of COVID-19 in patients with a prior diagnosis of HF referred to the Emergency Department of the Agostino Gemelli University Hospital between March 2020 and January 2023. A total of 886 HF patients (444 men and 442 women, mean age of 80 ± 10 years) were identified. Patients were matched in a 1:1 ratio by gender, age, number of comorbidities (excluding HF), and vaccination status, using a propensity score matching (PSM) procedure. We compared the outcomes of 189 patients with a concomitant diagnosis of HF with those of 189 matched controls without HF. Results: Among patients with HF, there was a significantly higher prevalence of valvular disease (p = 0.004), atrial fibrillation (p = 0.003), use of anticoagulants (p = 0.001), chronic obstructive pulmonary diseases (p = 0.03), and chronic kidney disease (p = 0.001). In contrast, hypertension was more prevalent among controls than HF patients (p = 0.04). In addition, controls exhibited higher lymphocytes counts and a higher PaO2/FiO2 ratio compared to HF patients. During hospitalization, patients with HF were more frequently treated with high-flow nasal cannulas (p = 0.01), required more frequent admission to an intensive care unit (ICU) (p = 0.04), and showed a significantly higher mortality rate (p 0.0001) than controls. Conclusions: HF is an independent risk factor for ICU admission and death in COVID-19 patients.
Arresting Early Childhood Caries with Silver Diamine Fluoride Gel Among Preschool Children: Protocol for a Randomised Clinical Trial
Authors: Cheng Anthony Yihong , Chen Jieyi , Zheng Faith Miaomiao , Duangthip Duangporn , Chu Chun Hung
Published: December 2024

Background: The World Health Organisation (WHO) included silver diamine fluoride (SDF) in the WHO Model List of Essential Medicines for the management of early childhood caries. SDF is typically available as a 38% aqueous solution, which is watery to apply. A 38% SDF gel has recently been developed, but its caries-arrest effectiveness remains unsubstantiated. The objective of this study is to determine whether the efficacy of a 38% SDF gel is non-inferior to a 38% SDF solution in arresting early childhood caries. Methods: This is a 30-month, randomised, active-controlled, parallel-group non-inferiority pragmatic clinical trial with two arms. The hypothesis is that the 38% SDF gel is not worse than the 38% SDF solution by a non-inferiority margin of 10% caries-arrest rate when applied semi-annually to preschool children. This trial will recruit 630 3-year-old kindergarten children through block randomisation to receive either an application of SDF gel or SDF solution on cavitated carious lesions in their primary teeth every 6 months. The primary outcome is the proportion of soft (active) carious tooth surfaces that turn hard (arrested) at the 30-month follow-up. The same calibrated dentist will conduct 6-monthly dental examinations in the kindergartens to assess the status of carious lesions over 30 months. The examiner, the children, and parents will be blinded to treatments. The parents will be surveyed on their child’s oral health-related behaviours and socioeconomic background to allow adjustment for effect modification. Results: If the anticipated results are obtained, clinicians can use the 38% SDF gel as an alternative of the 38% SDF solution in arresting early childhood caries. Conclusions: As SDF gel is cost-effective, non-invasive, and non-aerosol-generating, it can be widely recommended for caries control. Trial registration: ClinicalTrials.gov NCT06241261. Registered on 7 February 2024.
Laser Technology in Dentistry: From Clinical Applications to Future Innovations
Authors: Sachelarie Liliana , Cristea Roxana , Burlui Ecaterina , Hurjui Loredana Liliana
Published: December 2024

This narrative review comprehensively synthesizes laser technology’s clinical applications, advantages, and limitations in modern dentistry. The review of 67 articles published between 2018 and 2023 highlights the latest advancements, including photobiomodulation (PBM) for enhanced tissue healing and inflammation control, alongside innovative uses in implantology, endodontics, and teeth whitening. The findings underscore the transformative potential of lasers in improving dental treatment precision and patient outcomes while addressing the barriers to their widespread adoption, such as costs and training needs. This review emphasizes the integration of laser technology into routine clinical practice and identifies pathways for future innovations in dentistry.
A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs
Authors: Park Miso , Kim Heung-Min , Kim Youngmin , Bak Suho , Kim Tak-Young , Jang Seon Woong
Published: December 2024

This study proposes a novel framework for detecting and managing non-point-source (NPS) pollution in agricultural areas using unmanned aerial vehicles (UAVs) and geospatial artificial intelligence (GeoAI). High-resolution UAV imagery, combined with the YOLOv8 instance segmentation model, was employed to accurately detect and classify various NPS sources, such as livestock barns, compost heaps, greenhouses, and mulching films. The spatial information, including the area and volume of detected objects, was analyzed to track temporal changes and evaluate management strategies. The framework integrates remote sensing, deep learning, and geographic information system (GIS) analysis to enhance decision-making processes, providing detailed insight into NPS pollution dynamics over time. This approach not only improves the efficiency of NPS monitoring but also facilitates proactive management by offering precise location and environmental impact data. The results indicate that this framework can significantly improve resource allocation and environmental management practices, particularly in agriculture-dominated regions susceptible to NPS pollution, thereby contributing to the sustainable development of these areas.
Refining the Allostatic Self-Efficacy Theory of Fatigue and Depression Using Causal Inference
Authors: Hess Alexander J. , von Werder Dina , Harrison Olivia K. , Heinzle Jakob , Stephan Klaas Enno
Published: December 2024

Allostatic self-efficacy (ASE) represents a computational theory of fatigue and depression. In brief, it postulates that (i) fatigue is a feeling state triggered by a metacognitive diagnosis of loss of control over bodily states (persistently elevated interoceptive surprise); and that (ii) generalization of low self-efficacy beliefs beyond bodily control induces depression. Here, we converted ASE theory into a structural causal model (SCM). This allowed identification of empirically testable hypotheses regarding causal relationships between the variables of interest. Applying conditional independence tests to questionnaire data from healthy volunteers, we sought to identify contradictions to the proposed SCM. Moreover, we estimated two causal effects proposed by ASE theory using three different methods. Our analyses identified specific aspects of the proposed SCM that were inconsistent with the available data. This enabled formulation of an updated SCM that can be tested against future data. Second, we confirmed the predicted negative average causal effect from metacognition of allostatic control to fatigue across all three different methods of estimation. Our study represents an initial attempt to refine and formalize ASE theory using methods from causal inference. Our results confirm key predictions from ASE theory but also suggest revisions which require empirical verification in future studies.
Fundamental Limits of an Irreversible Heat Engine
Authors: Fu Rui
Published: December 2024

We investigated the optimal performance of an irreversible Stirling-like heat engine described by both overdamped and underdamped models within the framework of stochastic thermodynamics. By establishing a link between energy dissipation and Wasserstein distance, we derived the upper bound of maximal power that can be delivered over a complete engine cycle for both models. Additionally, we analytically developed an optimal control strategy to achieve this upper bound of maximal power and determined the efficiency at maximal power in the overdamped scenario.
Spatial Dependence in the Cyclical Sensitivity of Labour Supply: An Analysis at the Regional Level in Ecuador
Authors: Maridueña-Larrea Ángel , Martín-Román Ángel
Published: December 2024

The labour supply has historically been subject to influence from the economic cycle. On the other hand, there is a paucity of research in the Latin American region examining the impact of social factors on labour participation in diverse contexts. This study examines the spatial dependence of the cyclical sensitivity of labour supply in 23 provinces of Ecuador. A time series analysis was conducted to calculate the cyclical sensitivities of labour supply, and spatial econometric techniques were applied to assess spatial dependence using monthly data for the period 2021 to 2024. We found evidence of a positive and significant spatial dependence in the cyclical sensitivity of labour supply. Our findings suggest that labour decisions in one province are influenced by those in neighbouring provinces, thereby providing a framework for the design of public policies that take into account these interdependencies.
Coaches’ Subjective Perceptions and Physical Performance: Key Factors in Youth Football Talent Identification—An Exploratory Study
Authors: Abate Daga Federico , Allois Ruben , Abate Daga Massimiliano , Veglio Franco , Agostino Samuel
Published: December 2024

This study examines the subjective attributes that coaches consider most important for identifying and developing the talent of junior élite football players. It also explores whether players’ physical fitness efficiency moderates these attributes and influences playing time during the regular season. Forty-three junior élite football players and four Italian Serie A club coaches participated in the study, contributing their unique perspectives and experiences. Players’ physical fitness was assessed using the Yo-Yo Intermittent Recovery Level 1 test, while coaches rated players’ abilities through a structured questionnaire. A significant positive relationship was found between ’understanding of the game and position on the field’ and total playing time (t = 3.498, p < 0.01, β = 0.953). Physical efficiency further strengthened this relationship when players’ fitness levels were average (b = 0.624, p < 0.001) and one standard deviation above the mean (b = 0.891, p < 0.001). These findings highlight the importance of tactical awareness in earning playing time and suggest that physical fitness enhances the effect of cognitive abilities on performance. This study provides insights into how coaches assess talent and underscores the value of integrating physical and tactical development in youth football, providing a testament to the power of collaboration in advancing our understanding of talent identification in sports.
Implementation of Innovations in Skill Ecosystems: Promoting and Inhibiting Factors in the Indian Context
Authors: Regel Julia , Rajagopalan Anjana , Mukherji Arnab , Basu Sankarshan , Pilz Matthias
Published: December 2024

Being considered a driver of economic growth as well as social participation and inclusion, skill development and vocational education and training (VET) policies constitute possible solutions to present challenges and ongoing problems. Therefore, VET has been the distinct subject of attempts toward development and improvement over time. For more than two decades, VET reforms have been a specific focus of Indian educational governance. However, in the subcontinent, reform initiatives have only partially succeeded in terms of their successful and sustainable implementation. This paper investigates promoting and inhibiting factors for vocational education and training policy implementation in Indian skill ecosystems. Taking the example of bottom-up policy implementation in the form of the Mahatma Gandhi National Fellowship (MGNF) programme, factors that affect the VET policy initiative are identified, based on a combination of quantitative and qualitative methods. Through a comprehensive questionnaire administered to a sample of 63 graduates of the programme, their perceptions on key implementation challenges regarding the local policy context, stakeholder cooperation, and institutional as well as implementer capacity were identified. The findings point to areas of further focus for policymakers for the effective implementation of VET initiatives while at the same time adding theoretical contributions to understanding and evaluating VET initiatives.
Use of Interactive Technologies to Increase Motivation in University Online Courses
Authors: Guaña-Moya Javier , Arteaga-Alcívar Yamileth , Criollo-C Santiago , Cajamarca-Carrazco Diego
Published: December 2024

This study analyzes the impact of interactive technologies on the motivation and engagement of university students in online courses. Through a systematic literature review and a comparative analysis of existing technologies, 64 articles published between 2020 and 2024 were examined. The results reveal a significant increase in student motivation (23%) and knowledge retention (31%) with the use of these tools. Augmented reality stands out with a mean effect size of 0.67 on academic performance. The bibliometric analysis shows a concentration of research in countries such as Spain, Colombia and China, suggesting the need for more diverse studies. Challenges such as the digital divide and lack of teacher training were identified, with 42% of teachers reporting difficulties in implementation. The findings underline the transformative potential of interactive technologies in online higher education, but emphasize the importance of a holistic approach that considers technical, pedagogical and socioeconomic aspects. It is recommended to invest in teacher professional development, promote equitable access policies and encourage research in diverse educational contexts to maximize the benefits of these technologies.
Exploring Sustainable Approaches to Improving Student Intelligence Through Basketball-Specific Means in Higher Education
Authors: Porfireanu Cristiana Maria , Leonte Nicoleta , Popescu Ofelia , Moanta Alina Daniela , Saftel Mihaita Alin
Published: December 2024

Sustainable education involves educating young people about the importance of varied physical activities and developing long-term skills for an active lifestyle. We choose basketball as a method for sustainable education because it is a sport with diverse technical procedures and tactical actions anda game that plays a significant role in cognitive functional development. This study aims to modernize physical education and sports education to meet current trends and needs. A modern approach to lessons can contribute to the mental and motor development of young people, forming individuals who value movement for health and physical well-being. Thus, integrating VR into physical education lessons (using the means of basketball) represents a paradigm shift in how students prepare and improve their motor function. The study focuses on non-specialized higher education, combining the cognitive benefits of sports with sustainable practices, as we found his to be a gap in our literature review. The experimental program used Raven’s Progressive Matrices, a cognitive ability test, to measure general intelligence. The data were collected on two groups of children (control and experimental, at the beginning of the experiment and after 39 lessons of 100 min each). The t Test results applied in SPSS showed a significant increase in average scores for Raven’s Progressive Matrices after performing the motor program using basketball-specific means, especially for the experimental group. In conclusion, basketball can be a valuable tool for improving cognitive abilities, along with academic and social stimulation, being the key to complete intellectual development.
Coral Reef Surveillance with Machine Learning: A Review of Datasets, Techniques, and Challenges
Authors: Chowdhury Abdullahi , Jahan Musfera , Kaisar Shahriar , Khoda Mahbub E. , Rajin S M Ataul Karim , Naha Ranesh
Published: December 2024

Climate change poses a significant threat to our planet, particularly affecting intricate marine ecosystems like coral reefs. These ecosystems are crucial for biodiversity and serve as indicators of the overall health of our oceans. To better understand and predict these changes, this paper discusses a multidisciplinary technical approach incorporating machine learning, artificial intelligence (AI), geographic information systems (GIS), and remote sensing techniques. We focus primarily on the changes that occur in coral reefs over time, taking into account biological components, geographical considerations, and challenges stemming from climate change. We investigate the application of GIS technology in coral reef studies, analyze publicly available datasets from various organisations such as the National Oceanic and Atmospheric Administration (NOAA), the Monterey Bay Aquarium Research Institute, and the Hawaii Undersea Research Laboratory, and present the use of machine and deep learning models in coral reef surveillance. This article examines the application of GIS in coral reef studies across various contexts, identifying key research gaps, particularly the lack of a comprehensive catalogue of publicly available datasets. Additionally, it reviews the existing literature on machine and deep learning techniques for coral reef surveillance, critically evaluating their contributions and limitations. The insights provided in this work aim to guide future research, fostering advancements in coral reef monitoring and conservation.
A Triboelectric Nanogenerator Utilizing a Crank-Rocker Mechanism Combined with a Spring Cantilever Structure for Efficient Energy Harvesting and Self-Powered Sensing Applications
Authors: Wang Xinhua , Xu Xiangjie , Sun Tao , Yin Gefan
Published: December 2024

With the advancement of industrial automation, vibrational energy generated by machinery during operation is often underutilized. Developing efficient devices for vibration energy harvesting is thus essential. Triboelectric nanogenerators (TENGs) based on spring and cantilever beam structures show considerable potential for industrial vibration energy harvesting; however, traditional designs often fail to fully harness vibrational energy due to their structural limitations. This study proposes a triboelectric nanogenerator (TENG) based on a crank-rocker mechanism and a spring cantilever structure (CR-SC TENG), which combines a crank-rocker mechanism with a spring cantilever structure, designed for both energy harvesting and self-powered sensing. The CR-SC TENG incorporates a spring cantilever beam, a crank-rocker mechanism, and lever amplification principles, enabling it to respond sensitively to low-frequency, small-amplitude vibrations. Utilizing the crank-rocker and lever effects, this device significantly amplifies micro-amplitudes, enhancing energy capture efficiency and making it well suited for low-amplitude, complex industrial environments. Experimental results demonstrate that this design effectively amplifies micro-vibrations and markedly improves energy conversion efficiency within a frequency range of 1–35 Hz and an amplitude range of 1–3 mm. As a sensor, the CR-SC TENG’s dual-generation units produce output signals that precisely reflect vibration frequencies, making it suitable for the intelligent monitoring of industrial equipment. When placed on an air compressor operating at 25 Hz, the first-generation unit achieved an output voltage of 150 V and a current of 8 μA, while the second-generation unit produced an output voltage of 60 V and a current of 5 μA. These findings suggest that the CR-SC TENG, leveraging spring cantilever beams, crank-rocker mechanisms, and lever amplification, has significant potential for micro-amplitude energy harvesting and could play a key role in smart manufacturing, intelligent factories, and the Internet of Things.
Digital LDO Analysis and All-Stable High-PSR One-LSB Oscillator Design
Authors: Vasudevan Utsav , Rincón-Mora Gabriel A.
Published: December 2024

Digital low-dropout (LDO) regulators are popular in research today as compact power supply solutions. This paper provides a unique approach to analyze digital LDO feedback mechanics and stability, to reduce voltage ripple and extend operating speed over the state-of-the-art. A novel error-subtracting counter is proposed to exponentially improve the response time of any digital LDO, to keep the loop stable outside the typical operating limits, and to increase power-supply rejection (PSR). This leverages the fact that digital LDOs are fundamentally one-bit relaxation oscillators in steady-state. Theory and simulations show how the analog-to-digital (ADC) and digital-to-analog converters (DAC) in these systems affect stability. When compromised, a digital LDO produces uncontrolled sub-clock oscillations at the output that the proposed error-subtracting counter removes.
Hybrid Edge–Cloud Models for Bearing Failure Detection in a Fleet of Machines
Authors: Leroux Sam , Simoens Pieter
Published: December 2024

Real-time condition monitoring of machinery is increasingly being adopted to minimize costs and enhance operational efficiency. By leveraging large-scale data acquisition and intelligent algorithms, failures can be detected and predicted, thereby reducing machine downtime. In this paper, we present a novel hybrid edge–cloud system for detecting rotational bearing failures using accelerometer data. We evaluate both supervised and unsupervised neural network approaches, highlighting their respective strengths and limitations. Supervised models demonstrate high accuracy but require labeled datasets representative of the failures of interesting data that are challenging to acquire due to the rarity of anomalies. Conversely, unsupervised models rely on data from normal operational conditions, which is more readily available. However, these models classify all deviations from normalcy as anomalies, including those unrelated to failure, leading to costly false positives. To address these challenges, we propose a distributed system that integrates supervised and unsupervised learning. A compact unsupervised model is deployed on edge devices near the machines to compress sensor data, which are then transmitted to a centralized cloud-based system. Over time, these data are automatically labeled and used to train a supervised model, improving the accuracy of failure predictions. Our approach enables efficient, scalable failure detection across a fleet of machines while balancing the trade-offs between supervised and unsupervised learning.
Tuning of PID Controller in PLC-Based Automatic Voltage Regulator System Using Adaptive Artificial Bee Colony–Fuzzy Logic Algorithm
Authors: Altınkaya Hüseyin , Ekmekci Dursun
Published: December 2024

The voltage control of synchronous generators, particularly under varying load conditions, remains a significant and complex challenge in the field of engineering. Although various control methods have been implemented for automatic voltage regulator (AVR) systems to control the terminal voltage of synchronous generators, the PID-based control method continues to be one of the most basic and widely used approaches. Determining the optimal values for the Kp, Ki, and Kd values is essential to ensuring efficient and rapid performance in a PID controller. This study presents PLC-based PID controller tuning using an adaptive artificial bee colony–fuzzy logic (aABC-FL) approach for voltage control in a micro-hydro power plant installed as an experimental setup. The real-time control and monitoring of the system was conducted using an S7-1200 programmable logic controller (PLC) integrated with a totally integrated automation (TIA) portal interface and a SCADA screen. The aABC-Fuzzy design was developed using the MATLAB/Simulink platform, with PLC-MATLAB communication established through OPC UA and the KEPServerEX interface. The results obtained from experiments conducted under different load conditions showed that the proposed aABC-FL PID significantly minimized settling time and overshoot compared to the classical PLC-PID. Additionally, the proposed method not only provided a good dynamic response but also proved to be robust and reliable for real physical AVR systems.
Parking Trajectory Planning for Autonomous Vehicles Under Narrow Terminal Constraints
Authors: Cao Yongxing , Li Bijun , Deng Zejian , Guo Xiaomin
Published: December 2024

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach.
A Fuzzy Decision Support System for Real Estate Valuations
Authors: Gutiérrez-García Francisco-Javier , Alayón-Miranda Silvia , Pérez-Díaz Pedro
Published: December 2024

The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability.
Research on Waveform Adaptability Based on Lunar Channels
Authors: Jia Min , Li Jonghui , Wang Zijie , Zhao Chao , Yan Daifu , Wang Hui , Li Dongmei , Sun Weiran
Published: December 2024

In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it is necessary to consider the performance of communication systems and the extreme conditions of the lunar environment, such as signal attenuation and frequency selection, to ensure the reliability and stability of communication systems. Therefore, providing technical performance adapted to the lunar environment is crucial. In this article, we investigated the applicability of Orthogonal Frequency Division Multiple Access (OFDMA) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) waveforms in the lunar communication environment. Specifically, we used Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) as performance indicators. By studying the impact of different modulation schemes and cyclic prefix lengths on communication performance, we completed the research on waveform adaptability based on lunar channels. Simulation results indicate that the transmission structure we designed can meet the system-level performance requirements of lunar communications. This research provides valuable insights for the design and optimization of communication systems for future lunar missions, paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments.
YOLOv8-STE: Enhancing Object Detection Performance Under Adverse Weather Conditions with Deep Learning
Authors: Jing Zhiyong , Li Sen , Zhang Qiuwen
Published: December 2024

Object detection powered by deep learning is extensively utilized across diverse sectors, yielding substantial outcomes. However, adverse weather conditions such as rain, snow, and haze interfere with images, leading to a decline in quality and making it extremely challenging for existing methods to detect images captured in such environments. In response to the problem, our research put forth a detection approach grounded in the YOLOv8 model, which we named YOLOv8-STE. Specifically, we introduced a new detection module, ST, on the basis of YOLOv8, which integrates global information step-by-step through window movement while capturing local details. This is particularly important in adverse weather conditions and effectively enhances detection accuracy. Additionally, an EMA mechanism was incorporated into the neck network, which reduced computational burdens through streamlined operations and enriched the original features, making them more hierarchical, thus improving detection stability and generalization. Finally, soft-NMS was used to replace the traditional non-maximum suppression method. Experimental results indicate that our proposed YOLOv8-STE demonstrates excellent performance under adverse weather conditions. Compared to the baseline model YOLOv8, it exhibits superior results on the RTTS dataset, providing a more efficient method for object detection in adverse weather.
An Enhanced Verilog-A Model for Graphene Field-Effect Transistors Using Variable Fermi Velocity
Authors: Ji Shuwei , Mappes John , Koudelka Peter , Scardelletti Maximilian C. , Zorman Christian , Lavasani Hossein Miri
Published: December 2024

This paper presents a novel Verilog-A model for the Fermi velocity in Graphene Field-Effect Transistors (GFETs). The Fermi velocity is an important parameter associated with the energy spectrum of the delocalized bonds in graphene which impact the performance of a GFET. Unlike existing GFET models where the Fermi velocity is assumed to have a constant value, the proposed model considers carrier concentrations in the channel and gate dielectrics to create a closed-form solution for the Fermi velocity, a parameter previously demonstrated to vary based on these two factors. The proposed mathematical model is then adapted to Verilog-A for interfacing with computer-aided design (CAD) circuit simulators. To demonstrate the accuracy of the proposed model, the simulation results are compared to measured drain–source currents obtained from various GFET devices (including GFETs measured by authors). The measured results show good agreement with the values predicted using the proposed model (<±1%), demonstrating the superior accuracy of the model compared to other published Verilog-A-based models, especially around the Dirac point.
A Weakly Supervised Crowd Counting Method via Combining CNN and Transformer
Authors: Cai Yuhang , Zhang De
Published: December 2024

During the past five years, there has been an increasing trend of weakly supervised crowd counting methods being developed since such methods just rely on count-level annotations and avoid a laborious labeling process. But, the existing weakly supervised methods usually fail to achieve comparable counting performance to the fully supervised methods. To improve the accuracy of crowd counting tasks, we propose to combine the convolutional neural network (CNN) and Transformer frameworks. Since CNN focuses on capturing local detail information and Transformer can effectively extract global context information, we believe that the combination of CNN and Transformer could learn more efficient feature representations for crowd images. Our proposed framework is named CrowdCCT (Crowd Counting via CNN and Transformer), and it is composed of a CNN feature extraction part, a Transformer feature extraction part, and a counting regression part. In the CNN part, we utilize DenseNet121 to learn rich semantic features with its inherent dense connection structure. In the Transformer part, we introduce two attention modules, Multi-Scale Dilated Attention (MSDA) and Location-Enhanced Attention (LEA), working together to extract more expressive features. The output features are then fed into the regression part to generate the predicted counting results. Experiments on four crowd counting benchmark datasets demonstrate that our proposed CrowdCCT can achieve superior performance. Also, the experimental results validate the feasibility and effectiveness of combining CNN and Transformer for weakly supervised counting tasks. Our work could be expected to promote further combination research on CNN and Transformer.