bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Pulse Electrodeposition Mode on Microstructures and Properties of Ni-TiN Composite Coatings
Authors: Ma Chunyang , He Hongxin , Xia Fafeng , Cao Mengyu
Published: October 2024

Mild steel is a kind of material commonly used in the chemical industry and to manufacture machinery, farm tools, and other parts in order to improve the surface performance of the parts and prolong their service life. The Ni-TiN composite coating was fabricated through ultrasonic electroplating using a Ni-based sulfamic acid bath with added nano-TiN particles. The effects of three distinct current modes, direct current (DC), positive pulse current (PC), and positive–negative pulse current (PNPC), on various aspects of the coating, including surface morphology, TiN content and distribution, interface bonding strength, microhardness, friction and wear properties, as well as corrosion resistance, were investigated. The findings demonstrated that, in comparison to DC electroplating, PC electrodeposited Ni-TiN composite coatings yielded finer grains, smoother surfaces, reduced surface cracks, increased interface bonding strength, and enhanced corrosion resistance. Furthermore, PNPC electrodeposited Ni-TiN composite coatings showed higher interface bonding strength than those of PC electrodeposited Ni-TiN coatings and had the densest structure, leading to the best corrosion resistance. Pulse current electroplating enhanced the incorporation of nano-TiN particles, with a higher deposition rate observed in positive–negative pulse current electroplating compared to positive pulse current electroplating. Furthermore, the PNPC electrodeposited coating displayed improved microhardness and demonstrated the best friction and wear properties, while the DC electroplated coating displayed the least favorable performance in these aspects.
Effects of the Piston Skirt’s Surface Structure on Coating Quality and Friction Functions
Authors: Zhang Jian , Guo Shenggang , Xiong Peiyou , Li Yanjun , Sun Weitao , Deng Lijun
Published: October 2024

It is necessary to take effective ways to reduce friction and wear grading of a friction pair for the purpose of improving the thermal efficiency and operating reliance of the internal combustion engine. As an effective way, coordinated multi-scaling structure optimization has gained more and more attention, however, its effect on coating adhesion strength remains unclear, and there is less systematic research on its interactive role in friction properties. The paper takes advantage of the stretching test and dynamic simulation calculation to study the influence of piston skirt waviness on coating adhesion as well as profile, waviness, and roughness on friction and wear performance. The research results show that coating adhesion strength will increase first and then decrease in the conditions of enlarging waviness depth, width, and roughness; in addition, surface roughness could generate a bigger effect on coating adhesion than waviness shape. Increasing the waviness width also reduces friction losses and wear in the piston skirt. When the waviness width increases from 0.25 mm to 0.40 mm, the friction losses of the piston skirt decrease by 27%, and the cumulative wear load on the skirt is reduced by 26%. However, under conditions of limited lubrication, smaller waviness widths are more effective in reducing wear. Additionally, increased roughness has a negative impact on the friction and wear characteristics of the piston skirt. This study provides valuable guidance for optimizing designs aimed at reducing friction and wear in internal combustion engine pistons and other mechanical components subject to friction and wear.
Kondo Versus Fano in Superconducting Artificial High-Tc Heterostructures
Authors: Campi Gaetano , Logvenov Gennady , Caprara Sergio , Valletta Antonio , Bianconi Antonio
Published: October 2024

Recently, the quest for high-Tc superconductors has evolved from the trial-and-error methodology to the growth of nanostructured artificial high-Tc superlattices (AHTSs) with tailor-made superconducting functional properties by quantum design. Here, we report the growth by molecular beam epitaxy (MBE) of a superlattice of Mott insulator metal interfaces (MIMIs) made of nanoscale superconducting layers of quantum confined-space charge in the Mott insulator La2CuO4 (LCO), with thickness L intercalated by normal metal La1.55Sr0.45CuO4 (LSCO) with period d. The critical temperature shows the superconducting dome with Tc as a function of the geometrical parameter L/d showing the maximum at the magic ratio L/d = 2/3 where the Fano–Feshbach resonance enhances the superconducting critical temperature. The normal state transport data of the samples at the top of the superconducting dome exhibit Planckian T-linear resistivity. For L/d > 2/3 and L/d < 2/3, the heterostructures show a resistance following Kondo universal scaling predicted by the numerical renormalization group theory for MIMI nanoscale heterostructures. We show that the Kondo temperature, TK, and the Kondo scattering amplitude, R0K, vanish at L/d = 2/3, while TK and R0K increase at both sides of the superconducting dome, indicating that the T-linear resistance regime competes with the Kondo proximity effect in the normal phase of MIMIs.
Detection of a Water-Soluble Hypericin Formulation in Glioblastoma Tissue with Fluorescence Lifetime and Intensity Using a Dual-Tap CMOS Camera System
Authors: Mischkulnig Mario , Reichert David , Wightman Lionel , Roth Vanessa , Hölz Marijke , Körner Lisa I. , Kiesel Barbara , Vejzovic Djenana , Giardina Gabriel A. , Erkkilae Mikael T. , Unterhuber Angelika , Andreana Marco , Rinner Beate , Kubin Andreas , Leitgeb Rainer , Widhalm Georg
Published: October 2024

Background: High hypericin-loaded polyvinylpyrrolidone (HHL-PVP) constitutes a novel approach to utilize the promising characteristics of hypericin for photodynamic diagnosis (PDD) and therapy (PDT) of brain tumors in an orally bioavailable formulation. The aim of this study was to investigate the ability of a Complementary Metal-Oxide-Semiconductor (CMOS) camera-based fluorescence imaging system to selectively visualize HHL-PVP in glioblastoma tissue even in the presence of 5-Aminolvevulinic acid (5-ALA) induced fluorescence, which is widely utilized in brain tumor surgery. Methods: We applied a previously established system with a non-hypericin specific filter for 5-ALA fluorescence visualization and a newly introduced hypericin-specific filter at 575–615 nm that transmits the spectrum of hypericin, but not 5-ALA fluorescence. Glioblastoma specimens obtained from 12 patients (11 with preoperative 5-ALA intake) were ex vivo incubated with HHL-PVP. Subsequently, fluorescence intensity and lifetime changes using both the non-hypericin specific filter and hypericin-specific filter were measured before and after HHL-PVP incubation and after subsequent rinsing. Results: While no significant differences in fluorescence signal were observed using the non-hypericin specific filter, statistically significant increases in fluorescence intensity (p = 0.001) and lifetime (p = 0.028) after HHL-PVP incubation were demonstrated using the hypericin-specific filter. In consequence, specimens treated with HHL-PVP could be identified according to the fluorescence signal with high diagnostic sensitivity (87.5%) and specificity (100%). Conclusions: Our CMOS camera-based system with a hypericin-specific filter is capable of selectively visualizing hypericin fluorescence in glioblastoma tissue after ex vivo HHL-PVP incubation. In the future, this technique could facilitate clinical investigations of HHL-PVP for PDD and PDT while maintaining the current standard of care with 5-ALA guidance.
A Retrospective Cohort Study of a Newly Proposed Criteria for Sporadic Creutzfeldt–Jakob Disease
Authors: Nonaka Toshiaki , Ae Ryusuke , Kosami Koki , Tange Hiroya , Kaneko Miho , Nakagaki Takehiro , Hamaguchi Tsuyoshi , Sanjo Nobuo , Nakamura Yoshikazu , Kitamoto Tetsuyuki , Kuroiwa Yoshiyuki , Kasuga Kensaku , Doyu Manabu , Tanaka Fumiaki , Abe Koji , Murayama Shigeo , Yabe Ichiro , Mochizuki Hideki , Matsushita Takuya , Murai Hiroyuki , Aoki Masashi , Fujita Koji , Harada Masafumi , Takao Masaki , Tsukamoto Tadashi , Iwasaki Yasushi , Yamada Masahito , Mizusawa Hidehiro , Satoh Katsuya , Nishida Noriyuki
Published: October 2024

Background/Objectives: Sporadic Creutzfeldt–Jakob disease (sCJD) is a fatal neurodegenerative disorder traditionally diagnosed based on the World Health Organization (WHO) criteria in 1998. Recently, Hermann et al. proposed updated diagnostic criteria incorporating advanced biomarkers to enhance early detection of sCJD. This study aimed to evaluate the sensitivity and specificity of Hermann’s criteria compared with those of the WHO criteria in a large cohort of patients suspected of prion disease in Japan. Methods: In this retrospective cohort study, we examined the new criteria using data of 2004 patients with suspected prion disease registered with the Japanese Prion Disease Surveillance (JPDS) between January 2009 and May 2023. Patients with genetic or acquired prion diseases or incomplete data necessary for the diagnostic criteria were excluded, resulting in 786 eligible cases. The sensitivity and specificity of the WHO and Hermann’s criteria were calculated by comparing diagnoses with those made by the JPDS Committee. Results: Of the 786 included cases, Hermann’s criteria helped identify 572 probable cases compared with 448 by the WHO criteria. The sensitivity and specificity of the WHO criteria were 96.4% and 96.6%, respectively. Hermann’s criteria demonstrated a sensitivity of 99.3% and a specificity of 95.2%, indicating higher sensitivity but slightly lower specificity. Fifty-five cases were classified as “definite” by both criteria. Conclusions: The findings suggest that Hermann’s criteria could offer improved sensitivity for detecting sCJD, potentially reducing diagnostic oversight. However, caution is advised in clinical practice to avoid misdiagnosis, particularly in treatable neurological diseases, by ensuring thorough exclusion of other potential conditions.
Elastographic Assessment of Atherosclerotic Plaques and Determination of Vascular Risk in Patients with Rheumatoid Arthritis
Authors: Popova Velichka , Popova-Belova Stanislava , Geneva-Popova Mariela , Karalilova Rositsa , Batalov Zguro , Batalov Konstantin , Doykov Mladen , Mitkova-Hristova Vesela
Published: October 2024

Objectives: The present study aimed to examine the role of two-dimensional shear wave elastography (SWE) in the assessment of the vascular wall of the carotid arteries and atherosclerotic plaques in patients with rheumatoid arthritis with moderate and low disease activity versus healthy controls. Methods: An observational case–control study was carried out at the University Medical Hospital “Kaspela” in Plovdiv, Bulgaria, from June 2023 to August 2024. This study included 24 patients with rheumatoid arthritis (RA) and 25 healthy controls. We employed two-dimensional SWE (2D-SWE) to examine the vessels around the plaques. The potential links with the degree of stenosis, plaque type, and cardiovascular risk were analyzed. Results: In the RA group, the 2D-SWE values showed significant positive correlations with the severity of the atherosclerotic plaques (rs = 0.461; 95% CI: 0.049 to 0.739; p = 0.023) and the degree of stenosis (rs = 0.920; 95% CI: 0.793 to 0.970; p < 0.001). Based on 2D-SWE, a ROC curve analysis distinguished higher severity plaques from lower severity plaques with an AUC = 0.818, 95% CI: 0.683 to 0.913. The optimal cut-off value of 2D-SWE > 32.40 kPa was associated with a sensitivity of 96%, a specificity of 56%, a positive predictive value (PPV) of 66.70%, and a negative predictive value (NPV) of 92.90%. Conclusion: Elastography can be an effective technique for assessing and stratifying atherosclerotic plaques in patients with RA, as well as for aiding in the early detection and subsequent prevention of future complications.
Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes
Authors: Kladov Danil E. , Berikov Vladimir B. , Semenova Julia F. , Klimontov Vadim V.
Published: October 2024

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.
Validity of the Strengths and Difficulties Questionnaire for Screening and Diagnosis in Western Australian Adolescents
Authors: Maxwell Carolyn , Chapman Elaine , Houghton Stephen
Published: October 2024

Background/Objectives: The Strengths and Difficulties Questionnaire (SDQ) is a widely used 25-item screening and diagnostic tool for behavioral and emotional problems in young people. Despite its popularity, evaluations of the SDQ’s factor structure in adolescent populations have produced disparate results, and its relationships with theoretically related variables are rarely evaluated. In the present study, these two elements of validity were evaluated based on a large sample of Western Australian adolescents. Methods: Participants were 1489 adolescents, n = 623 males with a mean age of 13.79 years (SD = 1.61) and n = 866 females, with a mean age of 14.29 years (SD = 1.51). Participants completed the SDQ alongside measures of loneliness, sense of belonging, depression, bullying, and diagnostic status to evaluate its internal structure and correlations with theoretically related variables. Results: Confirmatory factor analyses supported the internal structure of the SDQ both for males and for females. Relationships between the SDQ subscale scores and those from theoretically related variables were also aligned with the instrument’s underpinning framework. Conclusions: Despite the somewhat disparate results of previous studies, overall, this study supported the validity of the SDQ for use in the Western Australian context.
Sentinel Lymph Node Biopsy in Breast Cancer Using Different Types of Tracers According to Molecular Subtypes and Breast Density—A Randomized Clinical Study
Authors: Faur Ionut Flaviu , Dobrescu Amadeus , Clim Ioana Adelina , Pasca Paul , Prodan-Barbulescu Catalin , Tarta Cristi , Neamtu Carmen , Isaic Alexandru , Brebu Dan , Braicu Vlad , Feier Catalin Vladut Ionut , Duta Ciprian , Totolici Bogdan
Published: October 2024

Background: Sentinel lymph node biopsy (SLNB) has become a method more and more frequently used in loco-regional breast cancer in the initial stages. Starting from the first report on the technical feasibility of the sentinel node method in breast cancer, published by Krag (1993) and Giuliano (1994), the method underwent numerous improvements and was also largely used worldwide. Methods: This article is a prospective study that took place at the “SJUPBT Surgery Clinic Timisoara” over a period of 1 year between July 2023 and July 2024, during which 137 underwent sentinel lymph node biopsy (SLNB) based on the current guidelines. For the identification of sentinel lymph nodes, we used various methods, including single traces and also a dual tracer and triple tracer. Results: Breast density represents a predictive biomarker for the identification rate of a sentinel node, being directly correlated with BMI (above 30 kg/m2) and with an age of above 50 years. The classification of the patients according to breast density represents an important criterion given that an adipose breast density (Tabar-Gram I-II) represents a lower IR of SLN compared with a density of the fibro-nodular type (Tabar-Gram III-V). We did not obtain any statistically significant data for the linear correlations between IR and the molecular profile, whether referring to the luminal subtypes (Luminal A and Luminal B) or to the non-luminal ones (HER2+ and TNBC), with p > 0.05, 0.201 [0.88, 0.167]; z = 1.82.
Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review
Authors: Alharbi Shuaa S. , Alhasson Haifa F.
Published: October 2024

Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. Conclusions: By providing a detailed overview of AI’s role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.
Assessing Pain Levels Using Bioelectrical Impedance in Low Back Pain Patients: Clinical Performance Evaluation
Authors: Jang Seungwan , Ho Jong , Jo A-Ram , Yoo Seung , Lee Hoonyoung , Lee Hyunyoung , Kim Young , Min Se
Published: October 2024

Background/Objectives: Musculoskeletal pain is one of the leading causes of years lived with disability worldwide and has a negative impact on daily life and quality of life. Methods: The purpose of this study was to analyze the electrical characteristics of back pain by measuring and calculating bioelectrical impedance variables (R, Z, PA) in 85 subjects (45 in the Healthy group and 40 in the LBP group). Additionally, impedance measurements were conducted on 20 subjects (10 in the Young group and 10 in the Older group) to assess the impact of aging. Results: Bioelectrical impedance parameter values were higher in cases of back pain, and correlation analysis showed that there was a statistically significant difference between the Healthy and LBP groups (p < 0.05). A positive correlation was found between impedance parameters and pain related indices (ODI, RMDQ, VAS) (mean R, Z, PA: 0.68, 0.54, 0.75), with BMI positively correlating only with PA (0.493). Diagnostic accuracy for detecting back pain exceeded 95% (R, Z, PA: 0.984, 0.984, 0.963). Conclusions: Results indicated that aging did not significantly affect impedance values. The bioelectrical impedance measurement device used in this study, with its simultaneous diagnostic and therapeutic capabilities, proved useful for real-time pain diagnosis and treatment monitoring, highlighting its potential clinical utility.
Integrative Cross-Talk in Asthma: Unraveling the Complex Interactions Between Eosinophils, Immune, and Structural Cells in the Airway Microenvironment
Authors: Januskevicius Andrius , Vasyle Egle , Rimkunas Airidas , Malakauskas Kestutis
Published: October 2024

Asthma is a chronic inflammatory process that leads to airway narrowing, causing breath loss followed by spasms, wheezing, and shortness of breath. Within the asthmatic lungs, interaction among various immune cells and structural cells plays a significant role in orchestrating an inflammatory response in which eosinophils hold central importance. In these settings, allergens or other environmental exposures commonly drive the immune response to recruit eosinophils to the airways. The appearance of eosinophils in the airways indicates a dynamic interplay of various cell types within lung tissue and does not represent a passive effect of inflammation. The cellular cross-talk causes the persistence of eosinophilic inflammation, and if left untreated, it results in long-term damage to the airway structure and function. Further exacerbation of the condition occurs because of this. We discuss how this complex interplay of eosinophils, immune, and structural cells within the airway microenvironment leads to the distinct pathophysiological features in asthma, the variability in disease severity, and the response to biological treatments.
Interactive Technologies in Online Teacher Education in Africa: A Systematic Review 2014–2024
Authors: Oubibi Mohamed , Fute Antony , Kangwa Daniel , Barakabitze Alcardo Alex , Adarkwah Michael Agyemang
Published: October 2024

This systematic review investigates the integration of interactive technologies in online teacher education across Africa from 2014 to 2024. The study aims to identify the types of interactive technologies employed, the leading countries in regard to their adoption, and the outcomes of their usage on teacher training programs. Despite significant advancements, particularly in countries like Kenya and South Africa, the effectiveness of these technologies varies widely due to infrastructural, socio-economic, and policy-related challenges. The findings highlight the critical role of reliable information communication technology (ICT) infrastructure, culturally relevant content, and comprehensive training for educators in optimizing the use of interactive technologies. Additionally, the study emphasizes the need for greater policy coordination and collaboration among African nations to fully leverage these technologies. The review concludes that, while interactive technologies hold substantial potential in terms of transforming teacher education in Africa, sustained efforts are required to overcome the existing barriers and ensure equitable access to quality education across the continent.
Longitudinal Changes in Adolescents’ Sedentary, Light, Moderate and Vigorous Physical Activity Levels
Authors: Mikalsen Hilde Kristin , Martins Joao , Marques Adilson , Lagestad Pål Arild
Published: October 2024

Background and purpose: In recent years, sedentary behaviour (SB) has become a significant risk factor for health, alongside the decline of physical activity (PA) in the population. This study explored adolescents’ PA and SB using a longitudinal design, from the age of 13 to 15, to elucidate any changes and when they potentially occur. Method: The data material from the accelerometer measurements was analysed using the program SPSS, version 28. Repeated measures ANOVA (and follow-up test with Bonferroni corrections) were used to assess changes in the four activity levels during the three years. Independent t-tests were used to examine gender differences in each year and at each activity level and to determine differences in activity level between participants with valid data for all three years and those with valid data only at 13 and/or 14 years of age. Results: Statistical analyses of the accelerometer measurements revealed a significant increase in time in the lowest activity level and a decrease in light, moderate and vigorous intensity PA levels during the three years. These changes were larger from 13 to 14, than from 14 to 15, years of age. However, no significant interaction was found between time and gender. Analyses also showed a significantly higher time spent in SB and lower time spent in moderate activity among girls compared to boys at 13, but not at the age of 14 and 15. Conclusions: The findings, which indicate a reduction in PA and increased sedentary time with increasing age, are discussed, which we argue are important to increase and solidify involvement in PA among adolescents. Emphasized here is the importance of communicating the diverse possibilities of movement and challenging understandings of what PA can be for youth.
Integrating Youth Perspectives into the Design of AI-Supported Collaborative Learning Environments
Authors: Humburg Megan , Dragnić-Cindrić Dalila , Hmelo-Silver Cindy E. , Glazewski Krista , Lester James C. , Danish Joshua A.
Published: October 2024

This study highlights how middle schoolers discuss the benefits and drawbacks of AI-driven conversational agents in learning. Using thematic analysis of focus groups, we identified five themes in students’ views of AI applications in education. Students recognized the benefits of AI in making learning more engaging and providing personalized, adaptable scaffolding. They emphasized that AI use in education needs to be safe and equitable. Students identified the potential of AI in supporting teachers and noted that AI educational agents fall short when compared to emotionally and intellectually complex humans. Overall, we argue that even without technical expertise, middle schoolers can articulate deep, multifaceted understandings of the possibilities and pitfalls of AI in education. Centering student voices in AI design can also provide learners with much-desired agency over their future learning experiences.
A Comparative Analysis of Collaborative Problem-Solving Skills Between German and Chinese High School Students in Chemistry
Authors: Ying Yike , Tiemann Rüdiger
Published: October 2024

Collaborative problem-solving (CPS) skills are recognized as an essential aspect of 21st-century skills and STEM education. This study investigates the CPS skills of high school students from Germany and China within the context of chemistry. A total of 594 students, aged 15, were selected, comprising 249 females and 345 males. The German participants were drawn from five public high schools in Berlin, while the Chinese participants were selected from two high schools in Zhejiang. The assessment tool, developed based on the PISA frameworks and refined through pilot testing, was used to assess students’ CPS skills. The study found that Chinese students significantly outperformed their German counterparts in CPS tasks, exhibiting higher average scores and more consistent performance levels. Gender differences were also noted, with female students in both countries outperforming males. The analysis highlighted cultural influences on education, with the collectivist culture in China fostering stronger CPS skills compared to the individualistic tendencies in Germany. These findings suggest that cultural and gender differences need to be taken into account when developing and improving CPS skills. Future research will explore additional factors influencing CPS skills, such as student interest and motivation, to further understand and improve educational practices globally.
Physical Education Teacher’s Continuing Professional Development Affects the Physiological and Cognitive Well-Being of School-Age Children
Authors: Latino Francesca , Romano Generoso , Tafuri Francesco
Published: October 2024

A burgeoning corpus of scholarly inquiry indicates that engagement in physical activity among children yields a plethora of advantageous outcomes, including enhanced cardiorespiratory endurance, improved academic performance, augmented cognitive functioning, as well as advancements in social and psychological well-being. Given that students participate in schooling for up to 200 days per annum, physical education (PE) possesses the potential to substantially influence the physiological and cognitive maturation of school-aged children through purposeful pedagogical practices. The notion of quality physical education (QPE), whose paramount objective is the cultivation of physical literacy, represents a critical element in the facilitation of both physiological and cognitive growth in children. Consequently, the objective of this investigation was to examine the ramifications of a continuing professional development program on educators’ self-efficacy and, in turn, on their students’ physical fitness and educational outcomes. The inquiry was conducted over a 32-week span during which teachers and students participated in a continuing professional development training (CPD) intervention and a physical literacy (PL) program, respectively. At both the initiation and conclusion of the intervention programs, a comprehensive series of standardized assessments were administered, including the Motorfit battery, Spirometry, Physical Education Teaching Efficacy Scale (PETES), and Amos 8–15. As a consequence, a significant Time × Group interaction effect for the Motorfit battery, Spirometry, PETES, and Amos 8–15 was identified. This finding suggests a meaningful improvement in the treatment groups (p < 0.001). Conversely, no notable alterations were recorded within the comparison groups. The outcomes of this research reinforce the assertion that exemplary instruction in physical education exerts a profound influence on the physiological well-being and academic achievements of students.
Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image
Authors: Ren Yong , Zhou Mingquan , Zhou Pengbo , Wang Shibo , Liu Yangyang , Geng Guohua , Li Kang , Cao Xin
Published: October 2024

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details of the non-visible side remains difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles on the model’s surface. However, we have identified challenges with existing normal prediction networks when dealing with complex scenes, such as a lack of focus on local features and insufficient modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced Multi-scale Attention-Driven Single-Image 3D Human Reconstruction. This approach incorporates a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention mechanisms by adaptively adjusting the weights between features, enabling the network to more effectively leverage information across various scales. Furthermore, we have improved the feature fusion method to better integrate representations from different scales. This enhanced feature fusion allows the network to more comprehensively understand both fine details and global structures within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention mechanism and feature fusion method, optimizing the network’s training process and enhancing the quality of reconstruction results. Our network demonstrates significant improvements in performance for 3D human model reconstruction. Experimental results show that our method exhibits greater robustness to challenging poses compared to traditional single-scale approaches.
Research on Stator Sections Switching Process of High-Thrust Linear Motors
Authors: Liu Xing , Li Jie , Wang Lianchun , Qu Minghe , Zhou Danfeng , Chen Qiang
Published: October 2024

The high-thrust linear motor used for the electromagnetic launch is switched by switches composed of anti-shunt thyristors. During the switching process, different current path states will appear, which will lead to changes in motor parameters and current fluctuation. Focusing on the air-core synchronous linear motor with parallel and series power supply, this paper analyzes the change in current path caused by the change in the trigger signal of anti-parallel thyristors in the process of sectionalized stator switching. The motor circuit model of the switching process is derived. A new sectionalized stators switching method is proposed to realize the smooth switching of the sectionalized stators linear motor. Finally, the correctness of the analysis and modeling is verified by simulation, and the effectiveness of the sectionalized stators switching method is tested by using the test prototype.
A Temporal–Geospatial Deep Learning Framework for Crop Yield Prediction
Authors: Wang Lei , Chen Zhengkui , Liu Weichun , Huang Hai
Published: October 2024

With the rapid development of information technology, the demand for digital agriculture is increasing. As an important agricultural production topic, crop yield has always attracted much attention. Currently, artificial intelligence, particularly machine learning, has become the leading approach for crop yield prediction. As a result, developing a machine learning method that accurately predicts crop yield has become one of the central challenges in digital agriculture. Unlike traditional regression prediction problems, crop yield prediction has a significant time correlation. For example, weather data for each county show strong temporal correlations. Moreover, geographic information from different regions also impacts crop yield to a certain extent. For example, if a county’s neighboring counties have a good harvest, then this county is likely to have high yields as well. This paper introduces a novel hybrid deep learning framework that combines convolutional neural network (CNN), graph attention network (GAT) and long short-term memory (LSTM) modules to enhance prediction accuracy. Specifically, CNN is employed to extract the features from the input data for each county in each year. GAT is introduced to model the geographical relationships between neighboring counties, allowing the model to capture spatial dependencies more effectively. LSTM is used to extract the temporal information within many years. The proposed hybrid deep learning framework CNN-GAT-LSTM captures both the temporal and spatial relationships, thereby improving the accuracy of yield prediction. We conduct experiments on a nationwide dataset that includes data from 1115 soybean-producing counties in 13 states in the United States covering the years from 1980 to 2018. We evaluate the performance of our proposed CNN-GAT-LSTM model based on three metrics, namely root of the mean squared error (RMSE), R-squared (R2) and correlation coefficient (Corr). The experimental results demonstrate that the proposed model achieves significant performance improvements over the existing state-of-the-art model, with RMSE reduced by 5%, R2 improved by 6% and Corr enhanced by 4%.
A Linear Quadratic Regulation Controller Based on Radial Basis Function Network Approximation
Authors: Liu Chao , Qiu Xiaoxia , Xu Teng , Wei Wei , Sun Hemin , Hou Yonghui
Published: October 2024

This paper proposes a linear quadratic regulation (LQR) tracking control method based on a radial basis function (RBF) that successfully compensates for the shortcomings of the LQR method. The LQR method depends on the linearity of a model. Specifically, an RBF neural network is used to approximate and compensate for the nonlinear part of a controlled object in the PID type-I, type-II and type-III control loops to improve the performance of the system. Through the simulation of different industrial systems, such as underdamped, overdamped and critically damped systems, the method significantly improves the dynamic response performance indices, such as the rise time and settling time, of the system.
The Algorithm for Determining the TCP Point of a 2D Scanner Using a Conical Element
Authors: Burghardt Andrzej , Kurc Krzysztof , Szybicki Dariusz , Muszyńska Magdalena , Tutak Jacek
Published: October 2024

In the engineering practice of performing robotic measurements with a 2D scanner, a major difficulty is to accurately determine the tool center point (TCP) quickly and easily since it does not exist as a physical object. The article proposes an algorithm for determining the coordinates of the TCP point. It relies on the possibility of using a conical element, such as a milling cutter of unknown geometry. So far, the algorithms found in the literature were based on a sphere of known size. The presented solution was simulated in RobotStudio 2019 software, while calculations were performed in Maple 14 software. In addition, the correctness of the presented solutions was verified on a real object, an IRB 2400 robot equipped with a Keyence 2D scanner.
ACMamba: A State Space Model-Based Approach for Multi-Weather Degraded Image Restoration
Authors: Wang Wei , Zhao Pei , Lei Weimin , Ju Yingjie
Published: October 2024

In computer vision, eliminating the effects of adverse weather conditions such as rain, snow, and fog on images is a key research challenge. Existing studies primarily focus on image restoration for single weather types, while methods addressing image restoration under multiple combined weather conditions remain relatively scarce. Furthermore, current mainstream restoration networks, mostly based on Transformer and CNN architectures, struggle to achieve an effective balance between global receptive field and computational efficiency, limiting their performance in practical applications. This study proposes ACMamba, an end-to-end lightweight network based on selective state space models, aimed at achieving image restoration under multiple weather conditions using a unified set of parameters. Specifically, we design a novel Visual State Space Module (VSSM) and a Spatially Aware Feed-Forward Network (SAFN), which organically combine the local feature extraction capabilities of convolutions with the long-range dependency modeling capabilities of selective state space models (SSMs). This combination significantly improves computational efficiency while maintaining a global receptive field, enabling effective application of the Mamba architecture to multi-weather image restoration tasks. Comprehensive experiments demonstrate that our proposed approach significantly outperforms existing methods for both specific and multi-weather tasks across multiple benchmark datasets, showcasing its efficient long-range modeling potential in multi-weather image restoration tasks.
On-Chip Data Reduction and Object Detection for a Feature-Extractable CMOS Image Sensor
Authors: Morikaku Yudai , Ujiie Ryuichi , Morikawa Daisuke , Shima Hideki , Yoshida Kota , Okura Shunsuke
Published: October 2024

In order to improve image recognition technologies in an IoT environment, we propose a data reduction scheme for a feature-extractable CMOS image sensor and present simulation results for object recognition using feature data. We evaluated the accuracy of the simulated feature data in object recognition based on YOLOX trained with a feature dataset. According to our simulation results, the obtained object recognition accuracy was 56.6% with the large-scale COCO dataset, even though the amount of data was reduced by 97.7% compared to conventional RGB color images. When the dataset was replaced with the RAISE RAW image dataset for more accurate simulation, the object recognition accuracy improved to 76.3%. Furthermore, the feature-extractable CMOS image sensor can switch its operation mode between RGB color image mode and feature data mode. When the trigger for switching from feature data mode to RGB color image mode was set to the detection of a large-sized person, the feature data achieved an accuracy of 93.5% with the COCO dataset.
A Day-Ahead Economic Dispatch Method for Renewable Energy Systems Considering Flexibility Supply and Demand Balancing Capabilities
Authors: Yang Zheng , Xiong Wei , Wang Pengyu , Shen Nuoqing , Liao Siyang
Published: October 2024

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system’s balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance.
Digitalization for Resilient and Sustainable Energy Transitions
Authors: Zakeri Behnam
Published: October 2024

The energy sector was challenged by the COVID-19 pandemic in different ways [...]
Investigation of Chinese-Style Greenhouse Usage Across Europe
Authors: Erdem Serkan , Onan Cenk
Published: October 2024

Chinese-style greenhouses (CSGs), characterized by a distinct geometric shape compared to traditional greenhouses, are extensively utilized in China. In this study, this type of greenhouse was modeled using TRNSYS software version 18 and experimentally validated. The model can transiently determine the indoor conditions of the greenhouse and the requirement for additional heating. It calculates the heat loss due to plant evapotranspiration as well as all the heat gains and losses from the surfaces. The application of this greenhouse has been investigated from the southernmost to the northernmost regions of Europe. For this purpose, cities located at different latitudes (between 32.63° N and 69.65° N) were entered into the model, and the results were obtained and compared. The analysis conducted over the entire year demonstrated that the CSG indoor temperature is more dependent on solar energy during the day and on outdoor temperature at night. The two southernmost cities in our survey, Funchal, Portugal (32.63° N) and Luqa, Malta (35.83° N), had no winter heating requirement. The thermal covering was sufficient to minimize night heat loss and maintain a suitable indoor temperature. In northern cities, the heating requirement was relatively high due to the lower outdoor temperature and solar radiation. Consequently, the duration of the heating season increases towards the north. In the northernmost city, Tromso, Norway (69.65° N), the heating season was determined to last 12 months. In the absence of solar energy, the transparent surface of the greenhouse is covered with thermal insulation to prevent heat loss. It has been shown that with the appropriate selection of this thermal covering, which is controlled based on the presence of instantaneous solar energy, up to 80% savings can be achieved from additional heating in southern cities. In the north, this rate can be increased up to a maximum of 70% by increasing the thermal covering thickness.
Comparative Study of an Energy Information System and Energy Management System According to ISO 50001
Authors: Kouaho Ebagninin Séraphin , N’Guessan Yao , Marvillet Christophe
Published: October 2024

Energy management, especially in communities, is attracting growing interest but is often marred by confusion between the terms “energy information system” (EIS) and “energy management system” (EMS). This ambiguity hinders understanding of their uses and roles in improving energy performance. Our objective is to clarify these terms and their contributions to energy management. We used a mixed qualitative and quantitative approach to evaluate and compare the criteria of definition, objective, functionality, and role, as well as correlated elements, such as organizational structure, responsibilities, planning, and policy, according to the requirements of ISO 50001, in addition to criteria specific to the two systems. The study revealed differences in the roles of the EIS and the EMS, with a complementarity of 40% between the two systems depending on the positioning of the EIS in relation to the elements of the EMS process. The EIS plays a crucial role in facilitating the implementation of the EMS according to ISO 50001. This clarification contributes to a better understanding of the respective roles of these systems in energy management, thus helping communities to improve their energy performance in an effective manner.
Impact of Access to Electricity and Socio-Economic Environment on Poverty Reduction: An Empirical Study on Myanmar
Authors: Xiong Qian , Shen Wenxin , Liu Chunna , Zhang Xuteng , Tang Wenzhe , Duffield Colin F. , Hui Felix Kin Peng , Zhang Lihai
Published: October 2024

Previous studies have identified the importance of access to electricity and the socio-economic environment for poverty reduction, but the comprehensive interplay and synergistic relationships between them remain unclear. Using data collected from Myanmar, this study establishes and tests conceptual models to explore the paths from access to electricity and socio-economic environment to capability and income poverty reduction. The results of structural equation modeling show that capability poverty reduction acts as a complete mediator between access to electricity and income poverty reduction, and plays a partial mediating role between socio-economic environment and income poverty reduction. Analysis of fuzzy-set qualitative comparative analysis demonstrates that lack of electricity is a key barrier impeding poverty alleviation efforts. Moreover, five effective configurations of poverty reduction factors are identified for Myanmar by considering the heterogeneity of different regions. This indicates that individual factors could not ensure a highly effective poverty reduction and different factors need to be appropriately configured for achieving the synergistic effects. These findings contribute to practical insights into poverty reduction policy making and sustainable development for developing countries.