bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrative Transcriptomic and Metabolomic Analysis Reveals Quinoa Leaf Response Mechanisms to Different Phosphorus Concentrations During Filling Stage
Authors: Wang Hongxin , Li Hanxue , Li Xiaorong , Wang Qianchao , Liu Junna , Zhang Ping , Xie Heng , Li Li , Qin Peng
Published: November 2024

Quinoa is an annual self-pollinating plant rich in polyphenols, flavonoids, saponins, and amino acids; its protein balance closely aligns with the ideal recommendation set by the Food and Agriculture Organization. Therefore, quinoa is considered the most suitable “all-nutrient food”. Phosphorus fertilization plays an important role in restricting the growth and development of quinoa; however, the effects of phosphorus fertilizer on quinoa growth remain unclear. Therefore, we conducted metabolome and transcriptome analyses on quinoa leaves during the filling stage, subjecting plants to different doses of phosphorus fertilizer. Overall, phosphorus treatment exerted a significant impact on the phenotypic characteristics of quinoa. Specifically, through a combined analysis of ultra-performance liquid chromatography–tandem mass spectrometry and transcriptome analysis, we identified the alteration and regulation of specific metabolites and genes within flavonoid biosynthesis pathways; this comprehensive evaluation helped elucidate the response mechanism of quinoa leaves during the grouting stage under various phosphorus conditions. Ultimately, the results of this study provide a reference for the selection of quinoa cultivars that exhibit tolerance to low- or high-phosphorus stress; additionally, we offer a theoretical basis for the rational application of phosphorus fertilizer and the enhancement of phosphorus utilization efficiency.
Atractylodes macrocephala Root Rot Affects Microbial Communities in Various Root-Associated Niches
Authors: Fan Huiyan , Han Jiayi , Li Xiujuan , Zhou Jingzhi , Zhao Limei , Ying Yiling , Kai Guoyin
Published: November 2024

Atractylodes macrocephala, a perennial herb widely used in traditional Chinese medicine, is highly prone to root rot, which significantly reduces its yield and quality. This study compared the physicochemical properties of soil from healthy and diseased A. macrocephala plants and analyzed the microbial diversity in the endophytic, rhizosphere, and root zone soils. The results showed that the diseased plants had higher levels of available potassium and electrical conductivity in the rhizosphere, both positively correlated with the severity of root rot, while soil pH was negatively correlated. The diversity and richness of endophytic bacterial and fungal communities were significantly reduced in diseased plants. Additionally, root rot led to major changes in the rhizosphere microbial community, with an increased abundance of Proteobacteria and Ascomycota, and a decrease in Firmicutes, Bacteroidetes, Actinobacteria, and Basidiomycota. Fusarium oxysporum, Fusarium solani, and Fusarium fujikuroi were identified as key pathogens associated with root rot. This study enhances our understanding of the microbial interactions in soils affected by root rot, offering a foundation for developing soil improvement and biological control strategies to mitigate this disease in A. macrocephala cultivation.
Adoption and Impact of ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
Authors: López-Fernández Daniel , Vergaz Ricardo
Published: November 2024

The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences are needed to better understand these tools and know how to use them to their fullest potential. This contribution presents an exploratory and correlational study conducted with 37 computer science students who used ChatGPT as a support tool to learn database administration. The article addresses three questions: The first one explores the degree of use of ChatGPT among computer science students to learn database administration, the second one explores the profile of students who get the most out of tools like ChatGPT to deal with database administration activities, and the third one explores how the utilization of ChatGPT can impact in academic performance. To empirically shed light on these questions the student’s grades and a comprehensive questionnaire were employed as research instruments. The obtained results indicate that traditional learning resources, such as teacher’s explanations and student’s reports, were widely used and correlated positively with student’s grades. The usage and perceived utility of ChatGPT were moderate, but positive correlations between students’ grades and ChatGPT usage were found. Indeed, a significantly higher use of this tool was identified among the group of outstanding students. This indicate that high-performing students are the ones who are using ChatGPT the most. So, a new digital trench could be rising between these students and those with a lower degree of fundamentals and worse prompting skills, who may not take advantage of all the ChatGPT possibilities.
Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs
Authors: Robitzsch Alexander
Published: November 2024

The Bayesian information criterion (BIC) is a widely used statistical tool originally derived for fully observed data. The BIC formula includes the sample size and the number of estimated parameters in the penalty term. However, not all variables are available for every subject in planned missingness designs. This article demonstrates that a modified BIC, tailored for planned missingness designs, outperforms the original BIC. The modification adjusts the penalty term by using the average number of estimable parameters per subject rather than the total number of model parameters. This new criterion was successfully applied to item response theory models in two simulation studies. We recommend that future studies utilizing planned missingness designs adopt the modified BIC formula proposed here.
Genome-Wide Selection Signals Reveal Candidate Genes Associated with Plateau Adaptation in Tibetan Sheep
Authors: Song Yufang , Yuan Chao , An Xuejiao , Guo Tingting , Zhang Wentao , Lu Zengkui , Liu Jianbin
Published: November 2024

Tibetan sheep have developed unique adaptations for survival in the Qinghai–Tibet Plateau environment. However, the functional genes and molecular mechanisms that regulate hypoxia adaptation have not been fully characterized. In this study, based on the whole-genome resequencing data for Tibetan sheep at different altitudes, the population differentiation index (FST) and nucleotide diversity ratio (θπ ratio) were evaluated in populations of 20 Oula sheep (3501 m altitude, OL), 20 Zashijia sheep (4369 m altitude, ZSJ), and 20 Awang sheep (4643 m altitude, AW) to reveal candidate loci related to high-altitude hypoxia. We screened 728 and 524 candidate genes in the AW vs. OL and ZSJ vs. OL groups, respectively, of which 134 genes were jointly screened. Candidate genes were mainly enriched in the Ras, melanoma, melanogenesis, VEGF, and PPAR signaling pathways. HIF1AN, PDGFA, PDGFD, ANXA2, SOCS2, NOXA1, WNT7B, MMP14, GNG2, ATF6, PGAM2, PPP3R1, GSTCD, and PPARA may play important roles in the high-altitude adaptation of Tibetan sheep. In conclusion, this study provides valuable insights into the genes and molecular mechanisms that underlie high-altitude hypoxia adaptation in Tibetan sheep.
Assessing Post-Vaccination Seroprevalence and Enhancing Strategies for Lumpy Skin Disease Vaccination in Korean Cattle
Authors: Kim Geun-Ho , Yoo Dae-Sung , Chu Keum-Suk , Cho Eun-Hyo , Wi Seung-Il , Song Kyung-Ok , Ra Do Kyung , Kim Woo H. , Park Choi-Kyu , Tark Dongseob , Oh Yeonsu , Cho Ho-Seong
Published: November 2024

Lumpy skin disease (LSD), caused by the LSD virus (LSDV), a dsDNA virus of the genus Capripoxvirus, represents a significant cross-border infectious threat, particularly impacting cattle and water buffaloes through transmission by blood-feeding insects. Traditionally endemic to Southern Africa, LSD has rapidly spread over the past decade through the Middle East to Eastern Europe and China, reaching Korea in October 2023. This outbreak prompted a nationwide vaccination campaign, addressing both the disease’s severe economic impact and its status as a notifiable disease under the World Organisation for Animal Health. This study assesses the seropositivity of the LSD vaccine in cattle across four Korean provinces 2–3 months post-vaccination, aiming to inform improvements in biosecurity and vaccination strategies. Overall, 30.59% of the cattle tested (1196 out of 3910) exhibited positive antibody responses, comparable to international post-vaccination findings. Analysis further revealed differences in the antibody positivity between farm types and management practices. Specifically, farms where vaccines were administered by veterinarians showed no significant difference in antibody positivity between Korean native cattle and dairy cattle, regardless of the presence of restraint facilities. However, on farms where vaccinations were conducted by the owners, dairy cattle demonstrated a higher seropositivity (43.30 ± 33.39%) compared to Korean native cattle (21.97 ± 20.79%) in the absence of restraint facilities. Further comparisons underscored the impact of restraint facilities on vaccination efficacy, with dairy farms generally achieving higher antibody positivity (29.43 ± 30.61%) than farms with Korean native cattle (23.02 ± 23.33%) (p < 0.05), suggesting that consistent vaccine delivery methods enhance immunogenic responses. Contrarily, no significant difference was noted in antibody positivity between large- and small-scale farms, indicating that farm size did not notably impact the effectiveness of the vaccinator. These findings emphasize that while current vaccines are sufficiently inducing immunity, enhancing vaccination strategies, particularly through trained personnel and improved restraint facilities, is crucial. This study’s insights into the impact of vaccination and farm management practices provide valuable guidance for refining LSD control measures in Korea and potentially other affected regions.
The Effect of Ketoconazole and Quinestrol Combination on Reproductive Physiology in Male Mice
Authors: Ji Yu , Wang Yujie , Liu Yuhang , Liu Yutong , Qin Jiao , Yuan Daohuan , Liu Quansheng
Published: November 2024

This study investigates whether ketoconazole, a CYP3A4 inhibitor, can enhance the suppressive effects of quinestrol on reproductive capacity, potentially allowing for a reduced quinestrol dosage while maintaining its efficacy. A total of 104 healthy adult male mice were divided into two groups, assessed at 10 and 30 days. Within each group, six treatment categories were tested: the control (CK), quinestrol alone (Q1, Q5), and quinestrol combined with varying doses of ketoconazole (Q1 + K0.4, Q1 + K2, Q5 + K0.4). The key parameters measured included internal and reproductive organ weights, sperm density, sperm motility, sperm abnormalities, and CYP3A4 enzyme content in intestinal and liver tissues. After 10 days, the combination of a low dose of quinestrol with ketoconazole (Q1 + K0.4) showed the most significant pronounced effects in reducing reproductive potential, with notable reductions in epididymal weight, sperm density, sperm abnormality rate and vitality, serum hormone levels, and CYP3A4 content in the small intestine and liver. Although some reproductive parameters returned to near-baseline levels after 30 days, the Q1 + K0.4 regimen continued to exhibit reduced seminal vesicle weight and testosterone levels. Importantly, the combination did not significantly increase CYP3A4 enzyme content, indicating effective metabolic inhibition. The combination of quinestrol and ketoconazole, especially the Q1 + K0.4 regimen, demonstrated the most noticeable impact on reducing reproductive capacity. This regimen significantly reduced key reproductive parameters and showed strong metabolic inhibition, suggesting that ketoconazole substantially enhances the efficacy of quinestrol in fertility control.
Genome-Wide Scan for Selective Sweeps Reveals Novel Loci Associated with Prolificacy in Iranian Sheep Breeds in Comparison with Highly Prolific Exotic Breed
Authors: Mohammadi Hossein , Khaltabadi Farahani Amir Hossein , Moradi Mohammad Hossein , Moradi-Shahrbabak Hossein , Gholizadeh Mohsen , Najafi Abouzar , Tolone Marco , D’Alessandro Enrico
Published: November 2024

Domestication and selection significantly changed phenotypic traits in modern domestic animals. To identify the genomic regions associated with prolificacy in this study, 837 ewes from three Iranian indigenous sheep breeds, consisting of Baluchi, Lori-Bakhtiari, and Zandi uniparous breeds, and one Greek highly prolific dairy sheep, namely Chios, were genotyped using OvineSNP50K arrays. Statistical tests were then performed using different and complementary methods based on either site frequency (FST) and haplotype (hapFLK) between populations, followed by a pathway analysis of the genes contained in the selected regions. The results revealed that for the top 0.01 percentile of the obtained FST values, 16 genomic regions on chromosomes 2, 3, 4, 7, 8, 9, 13, 14, 16, 18, 19, and 20, and for hapFLK values, 3 regions located on chromosomes 3, 7, and 13, were under selection. A bioinformatic analysis of these genomic regions showed that these loci overlapped with potential candidate genes associated with prolificacy in sheep including GNAQ, COL5A2, COL3A1, HECW1, FBN1, COMMD3, RYR1, CCL28, SERPINA14, and HSPA2. These regions also overlapped with some quantitative trait loci (QTLs) linked to prolificacy traits, milk yield, and body weight. These findings suggest that future research could further link these genomic regions to prolificacy traits in sheep.
Safety and Efficacy of Dalbavancin in Real Life: Retrospective Analysis of a Large Monocentric Case Series of Patients Treated for Skin/Soft Tissue and Other Difficult-to-Treat Infections
Authors: Parruti Giustino , Polilli Ennio , Coladonato Simona , Rapacchiale Giorgia , Trave Francesca , Mazzotta Elena , Bondanese Martina , Di Masi Francesco , Recinelli Davide , Corridoni Serena , Costantini Alberto , Ianniruberto Stefano , Cacciatore Pierluigi , Carinci Fabrizio
Published: November 2024

Background: Dalbavancin is a long-acting lipoglycopeptide, approved for treatment of skin and skin structure infections. Its PK/PD profile and safety allow for short hospital stays even in the case of difficult-to-treat infections requiring long courses of therapy, e.g., osteomyelitis, cardiovascular, and prosthetic infections. Objectives: We aimed to evaluate the safety and efficacy of dalbavancin in real life settings for both in-label and off-label indications. Methods: retrospective evaluation of all consecutive patients treated with dalbavancin at our site between May 2017 and September 2021. Results: A total of 100 patients treated with dalbavancin and followed up for 6 months after treatment (58% male; median age 63.5 years, median Charlson Comorbidity Index CCI = 2.7, 28% inpatients) were included with the following indications: acute bacterial skin and skin structure infections (22%), bone and prosthetic infections (57%), and cardiovascular infections (19%). Infections were caused by MSSA (30%), MRSA (5%), MR-CoNS (20%), and Streptococcus spp. (8%). In 32 cases, no isolate was obtained. The average number of infusions was 5 (s.d. = 3). Neither ensuing alteration of renal function nor neutropenia or thrombocytopenia were observed during treatment and follow-up. Two self-limiting skin rashes occurred. The overall clinical success rate was 84%—91% for registered and 82% for unregistered indications. The prescription of higher loading doses was the only predictor independently associated with better outcomes in multivariate models (OR: 5.2, 95%CI: 1.5–17.9, p < 0.01). Conclusions: Dalbavancin proved to be effective for skin and skin structure infections, as well as for difficult-to-treat infections in highly comorbid patients. Regarding tolerability, our results support the use of dalbavancin for long-lasting treatments of deep-seated infections.
Comprehensive Approaches to Combatting Acinetobacter baumannii Biofilms: From Biofilm Structure to Phage-Based Therapies
Authors: Grygiel Ilona , Bajrak Olaf , Wójcicki Michał , Krusiec Klaudia , Jończyk-Matysiak Ewa , Górski Andrzej , Majewska Joanna , Letkiewicz Sławomir
Published: November 2024

Acinetobacter baumannii—a multidrug-resistant (MDR) pathogen that causes, for example, skin and soft tissue wounds; urinary tract infections; pneumonia; bacteremia; and endocarditis, particularly due to its ability to form robust biofilms—poses a significant challenge in clinical settings. This structure protects the bacteria from immune responses and antibiotic treatments, making infections difficult to eradicate. Given the rise in antibiotic resistance, alternative therapeutic approaches are urgently needed. Bacteriophage-based strategies have emerged as a promising solution for combating A. baumannii biofilms. Phages, which are viruses that specifically infect bacteria, offer a targeted and effective means of disrupting biofilm and lysing bacterial cells. This review explores the current advancements in bacteriophage therapy, focusing on its potential for treating A. baumannii biofilm-related infections. We described the mechanisms by which phages interact with biofilms, the challenges in phage therapy implementation, and the strategies being developed to enhance its efficacy (phage cocktails, engineered phages, combination therapies with antibiotics). Understanding the role of bacteriophages in both biofilm disruption and in inhibition of its forming could pave the way for innovative treatments in combating MDR A. baumannii infections as well as the prevention of their development.
Modelling the Repair of Carbon-Centered Protein Radicals by Phenolic Antioxidants
Authors: Walton-Raaby Max , Floen Tyler , Mora-Diez Nelaine
Published: November 2024

Oxidative stress is a biological process that has been linked to many diseases, hence understanding how to prevent and repair it is essential to medicine. The thermodynamics and kinetics of the repair reactions of radically damaged leucine (a lateral chain in a simplified protein environment) by twenty phenolic antioxidants are studied at the M06-2X(SMD)/6-31++G(d,p) level of theory in water and pentyl ethanoate. The two repair mechanisms modelled are formal-hydrogen atom transfer (f-HAT) and single electron transfer (SET). Although all f-HAT reactions are thermodynamically favourable, only one of the phenols produced rate constants in the diffusion limit, exhibiting biological relevance. SET is not suspected to be an important repair pathway for the phenols studied. We show that the Bell–Evans–Polanyi principle, which relates thermodynamics and kinetics properties for a reaction, breaks down when comparing between the solvents, protein repair sites, and the phenolic antioxidants. While thermodynamic data can be used as valuable screening tools, the kinetic calculation of rate constants in solution is crucial for enhancing the biological relevance of theoretical studies.
Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data
Authors: Dingeto Hiskias , Kim Juntae
Published: November 2024

Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data augmentation methods that tackle issues like data scarcity and task variability in HAR. In this work, we investigate and expand the use of mixup and cutout data augmentation methods to sensor-based and skeleton-based HAR datasets. These methods were first widely used in Computer Vision and Natural Language Processing. We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. Specifically, we customize mixup data augmentation for sensor-based datasets and cutout data augmentation for skeleton-based datasets with the goal of improving model accuracy without adding more data. Our results show that using mixup and cutout techniques improves the accuracy and generalization of activity recognition models on both sensor-based and skeleton-based human activity datasets. This work showcases the potential of data augmentation techniques on transformers and Graph Neural Networks by offering a novel method for enhancing time series and skeletal HAR tasks.
Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
Authors: Albalooshi Fatema A.
Published: November 2024

Vegetation segmentation plays a crucial role in accurately monitoring and analyzing vegetation cover, growth patterns, and changes over time, which in turn contributes to environmental studies, land management, and assessing the impact of climate change. This study explores the potential of a multi-scale convolutional neural network (MSCNN) design for object classification, specifically focusing on vegetation detection. The MSCNN is designed to integrate multi-scale feature extraction and attention mechanisms, enabling the model to capture both fine and coarse vegetation patterns effectively. Moreover, the MSCNN architecture integrates multiple convolutional layers with varying kernel sizes (3 × 3, 5 × 5, and 7 × 7), enabling the model to extract features at different scales, which is vital for identifying diverse vegetation patterns across various landscapes. Vegetation detection is demonstrated using three diverse datasets: the CamVid dataset, the FloodNet dataset, and the multispectral RIT-18 dataset. These datasets present a range of challenges, including variations in illumination, the presence of shadows, occlusion, scale differences, and cluttered backgrounds, which are common in real-world scenarios. The MSCNN architecture allows for the integration of information from multiple scales, facilitating the detection of diverse vegetation types under varying conditions. The performance of the proposed MSCNN method is rigorously evaluated and compared against state-of-the-art techniques in the field. Comprehensive experiments showcase the effectiveness of the approach, highlighting its robustness in accurately segmenting and classifying vegetation even in complex environments. The results indicate that the MSCNN design significantly outperforms traditional methods, achieving a remarkable global accuracy and boundary F1 score (BF score) of up to 98%. This superior performance underscores the MSCNN’s capability to enhance vegetation detection in imagery, making it a promising tool for applications in environmental monitoring and land use management.
Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
Authors: Choi Jae-Young , Song Min-Ji , Shin You-Jin
Published: November 2024

Skin cancer is one of the most easily developed cancers and is continuously seeing an increased incidence rate. In this study, we propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which is commonly used in dermatology to evaluate lesion features such as asymmetry, border, color, and diameter. Our model consists of five distinct blocks, two of which focus on learning general image characteristics, while the remaining three focus on specialized features related to the ABCD rule. The final classification results are achieved through a weighted soft voting approach, where the generalization blocks are assigned higher weights to optimize performance. Through 15 experiments using various model configurations, we show that the weighted ABC ensemble model outperforms the baseline models, achieving the best performance with an accuracy of 0.9326 and an F1-score of 0.9302. Additionally, Grad-CAM analysis is employed to assess how each block in the ensemble focuses on distinct lesion features, further enhancing the interpretability and reliability of the model. Our findings demonstrate that integrating general image features with specific lesion characteristics improves classification performance, and that adjusting the soft voting weights yields optimal results. This novel model offers a reliable tool for early skin lesion diagnosis.
Sustainable Cutting Environment Evaluation for Drilling of Aluminum A380 Foam Produced by Semisolid Recycling
Authors: Ćulum Igor , Jozić Sonja , Bajić Dražen , Kalajžić Marijana
Published: November 2024

The development of sustainable cooling technologies and increased concern for recycled materials will affect the reduction of greenhouse gas emissions, which primarily originate from the production industry. In this research paper, a twofold contribution to sustainability is made through the efficient application of a workpiece, obtained by recycling waste in the form of metal chips, and the machining of the obtained workpiece by using alternative cooling techniques comparing them to cutting fluids. Minimum quantity lubrication and cold compressed air cooling were selected as two sustainable, alternative cutting environments. Using Taguchi’s L9 orthogonal array, the influence of cutting speed, feed rate and cutting environment on drilling thrust force, built-up edge formation and hole deviation was observed. Using the analysis of variance method, feed rate was identified to have the highest influence on the output parameters (31%), followed by cooling and lubrication techniques (18%) and lastly by cutting speed (5%). Based on the grey relation analysis, optimal controllable factors were identified. This analysis indicated that low cutting speeds and feed rates, coupled with the MQL cutting environment, produced the lowest thrust force, deviation of hole and built-up edge formation.
The Ketone-Based Varnish Removal from an Oil Painting by Wassily Kandinsky: Comparison and Assessment of Cleaning Methods Through Preliminary Test on Mock-Ups and Multi-Analytical Investigation
Authors: Ponza Valeria , Ricci Chiara , Scalarone Dominique , Cestelli Guidi Mariangela , Pronti Lucilla , Romani Martina , Previtali Angela Fabrizia , Bassi Alessandra , Avataneo Luca , Abram Sara , Cardinali Michela , Piccirillo Anna
Published: November 2024

This paper presents the removal of a non-original varnish from the surface of a painting by Wassily Kandinsky based on prior experimentation carried out on mock-ups, which were made according to the original artistic technique and painting stratigraphy. Due to a generally serious state of conservation, the artwork underwent several treatments over the years that have changed its original appearance. This work focused on the study and characterization of the ketone-based varnish present on the surface to outline how this non-original film layer interacted with paint layers and increased deterioration phenomena. Aiming to identify the most suitable approach for the artwork, some preliminary cleaning tests were made on artificially aged mock-ups. A multi-analytical investigation was carried out through non-invasive and invasive techniques to support all steps of the conservation treatment. By comparing cleaning results on mock-ups, a suitable protocol was developed and applied to the original painting. The best results were obtained by using the Nanorestore Gel® Dry MWR (Medium Water Retention) with ethanol. The project has shown that the dissolving power of the solvent can effectively be controlled and limited at the surface by confining it to the nanostructure of the gel, thus minimizing the risk of interaction with the original paint layers.
Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
Authors: Zhao Yingjie , Qi Yuming , Xie Bing
Published: November 2024

Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezier curves is proposed. Firstly, the robot’s motion law is analyzed to obtain trajectory data for a single work cycle. Then, Bezier curves are utilized for trajectory design to ensure a smooth transition during the spanning motion phase. Thirdly, with the average value of the minimum distance between the Bezier curve and the point set data of the spanning motion phase as the optimization objective function, the nonlinear planning based on the SQP algorithm was adopted for the optimization of the upper and lower trajectories. Finally, the results of the case calculations indicate that the standard deviation of the optimized upper and lower trajectories was reduced by 35.63% and 40.57%, respectively. Additionally, the ADAMS simulation validation demonstrates that the trajectory errors of the four wheels decreased by a maximum of 8.79 mm, 23.78 mm, 10.11 mm, and 14.97 mm, respectively, thereby confirming the effectiveness of the trajectory optimization.
Beyond xG: A Dual Prediction Model for Analyzing Player Performance Through Expected and Actual Goals in European Soccer Leagues
Authors: Malikov Davronbek , Kim Jaeho
Published: November 2024

Soccer is evolving into a science rather than just a sport, driven by intense competition between professional teams. This transformation requires efforts beyond physical training, including strategic planning, data analysis, and advanced metrics. Coaches and teams increasingly use sophisticated methods and data-driven insights to enhance decision-making. Analyzing team performance is crucial to prepare players and coaches, enabling targeted training and strategic adjustments. Expected goals (xG) analysis plays a key role in assessing team and individual player performance, providing nuanced insights into on-field actions and opportunities. This approach allows coaches to optimize tactics and lineup choices beyond traditional scorelines. However, relying solely on xG might not provide a full picture of player performance, as a higher xG does not always translate into more goals due to the intricacies and variabilities of in-game situations. This paper seeks to refine performance assessments by incorporating predictions for both expected goals (xG) and actual goals (aG). Using this new model, we consider a wider variety of factors to provide a more comprehensive evaluation of players and teams. Another major focus of our study is to present a method for selecting and categorizing players based on their predicted xG and aG performance. Additionally, this paper discusses expected goals and actual goals for each individual game; consequently, we use expected goals per game (xGg) and actual goals per game (aGg) to reflect them. Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting xGg and aGg, outperforming other models in our comparative assessment. Ridge regression’s ability to handle overlapping and correlated variables makes it an ideal choice for our analysis. This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model’s performance by as much as 12%. These features offer a more detailed understanding of player performance in specific leagues and roles, improving the model’s accuracy from 83% to nearly 95%, as indicated by the R-squared metric. Furthermore, our research introduces a player selection methodology based on their predicted xG and aG, as determined by our proposed model. According to our model’s classification, we categorize top players into two groups: efficient scorers and consistent performers. These precise forecasts can guide strategic decisions, player selection, and training approaches, ultimately enhancing team performance and success.
Development of a Novel Gluten-Free Cookie Premix Enriched with Natural Flours Using an Extreme Vertices Design: Physical, Sensory, Rheological, and Antioxidant Characteristics
Authors: Ferradji Sabrina , Bourekoua Hayat , Djeghim Fairouz , Ayad Radia , Krajewska Marta , Różyło Renata
Published: November 2024

This work aimed to develop novel alternative gluten-free premixes for use in the production of cookies by optimizing the formulation of three different starches (corn, potato, and tapioca starch) in a rice/corn formula and then enriching the optimized formula with various natural flours (acorn, soy, chestnut, chickpea, millet, and quinoa). An extreme vertices mixture design was used to optimize the mixing sub-part consisting of 30% (w/w) of starches. The enriched gluten-free cookies were characterized by physical, rheological, antioxidant, color, and sensory properties. An optimum was obtained by mixing 18.5084 g of corn starch, 6 g of potato starch, and 5.4916 g of tapioca starch. The enrichment results indicated that soy-enriched gluten-free cookies have a high specific volume (2.428 cm3/g) with the highest spread ratio (12.25) compared to other cookies. The rheological properties of enriched gluten-free doughs indicated a higher value of the consistency coefficient (K) of soy-enriched gluten-free cookies. The antioxidant characteristics were enhanced by replacing corn with several types of flours, with higher TPC for acorn (2.83 mg GAE/g dw) and soy-enriched cookies (2.49 mg GAE/g dw) with better antioxidant activities (DPPH, ABTS, and RED). The tasters gave all the cookies favorable ratings for overall acceptability. With an average rating of 7.09, cookies enhanced with chickpea flour have a high acceptability compared to the other cookies. Considering all the tested parameters, PCA analysis clustered millet, corn, quinoa, and chestnut gluten-free enriched cookies into the same group. However, acorn and soy cookies were placed in a separate group, and chickpea-enriched cookies were classified separately.
Effect of Super Retarder on Recycled Water and Concrete Properties of Waste Slurry in Mixing Plant
Authors: Wang Jinyue , Zuo Yanfeng , Gao Lixiong , Liu Siqin , Yang Guang
Published: November 2024

A large amount of waste slurry water will be generated in the production process of concrete mixing plant, due to the complex composition of waste slurry water, if it is not handled in time when stored, serious coagulation will occur, which will accelerate the loss of equipment and reduce the utilization rate. In this paper, a super retarder suitable for waste slurry recycled water from concrete mixing plant was prepared using composite technology. The waste slurry recycled water mixed with super retarding agent was characterized by using microscopic testing means XRD, TG and DTG. The waste slurry recycled water mixed with super retarding agent was used to replace tap water in the production of concrete, and its effect on the workability and mechanical properties of concrete was investigated. It was found that the compounding of Butane 2-phospho-1,2,4-tricarboxylic acid (PBTCA) with Reclaimed water treatment agent (ACS) resulted in a setting time of 64 h for 10% concentration of recycled water, with optimal retarding effect. When PBTCA:ACS was 1:20, mixed at 1.5% of the mass of recycled water, the 1 h slump of concrete had no loss, the loss of extension was 15 mm, the 7 days compressive strength was increased by 3.5 MPa, and the 28 days compressive strength was increased by 3.0 MPa. The microscopic results showed that the use of ACS and PBTCA does not affect the type of cement hydration products, but only affects the the rate of hydration product generation.
Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry
Authors: Cășeriu Bianca-Mihaela , Gabor Manuela-Rozalia , Blaga Petruța , Veres Cristina
Published: November 2024

This study proposes a qualitative analysis for identifying the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data, collected through acoustic measurements performed by the authors on materials from six different classes and employs a multidisciplinary approach, including Mann–Whitney U tests, Kruskal–Wallis analysis with Dunn’s post hoc multiple comparisons and multilinear regression. This research presents an analysis and evaluation of how the physical properties of various materials influence acoustic comfort, acoustic absorption class and absorption class performance and proposes quantitative models for material selection to address sustainable strategies in the automotive industry. The results highlight significant differences between material categories in terms of acoustic absorption properties and demonstrate the importance of rigorous material selection in vehicle design to enhance acoustic comfort. Additionally, the research contributes to the development of predictive models that estimate acoustic performance based on the physical properties of materials, providing a basis for optimizing material selection in the design phase.
Modeling Hydraulic Fracture Entering Stress Barrier: Theory and Practical Recommendations
Authors: Linkov Aleksandr , Rybarska-Rusinek Liliana , Rejwer-Kosińska Ewa
Published: November 2024

Numerical modeling of hydraulic fracturing is complicated when a fracture reaches a stress barrier. For high barriers, it may require changing the computational scheme. In view of the strong influence of stress barriers on the final footprint and opening of a hydraulic fracture, for decades, their modeling has been the subject of special investigations. Actually, classical models of propagation within a pay layer with impenetrable boundaries referred to the case of extremely high-stress contrast in neighbor layers. Further improvements tended to account for the fracture growth in these layers by including stress contrasts as input parameters of a model. This tendency resulted in the suggestion and successive enhancements of pseudo-three-dimensional models. All of them have used stress intensity factors (SIFs) to characterize the combined resistance caused by stress contrasts, material strength, and fluid viscosity. Specifically, the SIFs served to formulate the conditions that control the front penetration into a neighbor layer. This key concept presents the background of our research. Despite examples of modeling propagation through barriers, there is no general theory clarifying when and why conventional schemes may become inefficient and how to overcome computational difficulties. This paper presents the theory and practical recommendations following it. We start with the definition of the barrier intensity, which exposes that the barrier strength may change from zero for contrast-free propagation to infinity for channelized propagation. The analysis reveals two types of computational difficulties caused by spatial discretization as follows: (i) general, arising for fine grids and aggravated by a barrier, and (ii) specific, caused entirely by a strong barrier. The asymptotic approach, which avoids spatial discretization, is suggested. It is illustrated by solving benchmark problems for barriers of arbitrary intensity. The analysis distinguishes three typical stages of the fracture penetration into a barrier and provides theoretical values of the Nolte–Smith slope parameter and arrest time as functions of the barrier intensity. Special analysis establishes the accuracy and bounds of the asymptotic approach. It appears that the approach provides physically significant and accurate results for fracture penetration into high, intermediate, and even weak stress barriers. On this basis, simple practical recommendations are given for modeling hydraulic fractures in rocks with stress barriers. The recommendations may be promptly implemented in any program using spatial discretization to model fracture propagation.
Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
Authors: Liang Hao , Zhang Junhong , Yang Song
Published: November 2024

At present, researchers in the field of pipeline inspection focus on pipe wall defects while neglecting pipeline defects in special situations such as welds. This poses a threat to the safe operation of projects. In this paper, a multi-node fusion and modal projection algorithm of steel pipes based on guided wave technology is proposed. Through an ANSYS numerical simulation, research is conducted to achieve the identification, localization, and quantification of axial cracks on the surface of straight pipelines and internal cracks in circumferential welds. The propagation characteristics and vibration law of ultrasonic guided waves are theoretically solved by the semi-analytical finite element method in the pipeline. The model section is discretized in one-dimensional polar coordinates to obtain the dispersion curve of the steel pipe. The T(0,1) mode, which is modulated by the Hanning window, is selected to simulate the axial crack of the pipeline and the L(0,2) mode to simulate the crack in the weld, and the correctness of the dispersion curve is verified. The results show that the T(0,1) and L(0,2) modes are successfully excited, and they are sensitive to axial and circumferential cracks. The time–frequency diagram of wavelet transform and the time domain diagram of the crack signal of Hilbert transform are used to identify the echo signal. The first wave packet peak point and group velocity are used to locate the crack. The pure signal of the crack is extracted from the simulation data, and the variation law between the reflection coefficient and the circumferential and radial dimensions of the defect is calculated to evaluate the size of the defect. This provides a new and feasible method for steel pipe defect detection.
Multi-Objective Optimization of Steel Pipe Pile Cofferdam Construction Based on Improved Sparrow Search Algorithm
Authors: Jiang Zaolong , Yang Chengfang , Yue Hongbo
Published: November 2024

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more; the duration is shortened by 18 days, a reduction of 21%; and the total cost is reduced by CNY 220,000, saving 3% of the cost.
Cone Beam Computed Tomography Evaluation of Socket Healing After Third Molar Germ Extraction: A Case Series Study Including Adolescents with Osteogenesis Imperfecta Type I Treated with Bisphosphonates and Healthy Age-Matched Subjects
Authors: D’Angeli Giacomo , Zara Francesca , Arienzo Francesca , Messineo Daniela , Celli Mauro , Sfasciotti Gian Luca , Vozza Iole , Riminucci Mara , Polimeni Antonella , Corsi Alessandro
Published: November 2024

Bisphophonates (BPs) are widely used in Osteogenesis imperfecta (OI). Cone Beam Computed Tomography (CBCT) shows clinical usefulness in evaluating impacted teeth and adjacent structure relationships, extraction socket healing, bone mineral density (BMD) and BP-related jaw osteonecrosis (BRONJ). The aim of the study was to compare alveolar sockets and the adjacent bone area before and after third molar extraction in OI type I (OI-I) adolescents treated with BPs and age-matched healthy subjects (HSs) by CBCT. Methods: Five adolescents with genetically proven OI-I treated with BPs (three males and two females, mean age: 15.2 ± 1.78 years) and four age-matched healthy subjects (two males and two females, mean age: 15.5 ± 1.29 years) were included in this study. Eight Regions of Interest (ROIs) were evaluated: between 3.7 and 3.8 (ROI-1) and 4.7 and 4.8 (ROI-2); after 3.8 (ROI-3) and 4.8 (ROI-4); alveolar sockets 3.8 (ROI-5) and 4.8 (ROI-6); left (ROI-7) and right (ROI-8) cortical bone. Results: ROIs were evaluated on both sides of the mandible for all the subjects except one OI patient in which CBCTs were performed pre- and-post third molar extraction only on the right side. CBCT was performed 12.8 ± 4.60 and 11.5 ± 2.51 days before and 8.0 ± 1.41 and 7.7 ± 0.5 months after extraction in OI-I and HSs, respectively. BPs were discontinued 62.0 ± 36.5 months before extraction. None of the OI-I adolescents developed BRONJ. Statistically significant greater values were observed in OI-I for ROI-1 and -2 (p = 0.0464), ROI-3 and -4 (p = 0.0037) and ROI-7 and -8 (p = 0.0079) after extraction. Conclusions: This descriptive study confirms that, in OI-I adolescents treated with BPs, third molar extraction is safe, and socket healing occurs properly. In addition, it demonstrates that, if the same device and imaging conditions are used and comparisons to predetermined standard values are avoided, CBCT can be used to monitor BMD changes. The significant greater BMD observed for different ROIs in OI-I could reflect the increased secondary mineralization related to the BP-dependent reduction in bone turnover.
A Nation-by-Nation Assessment of the Contribution of Southeast Asian Open Biomass Burning to PM2.5 in Thailand Using the Community Multiscale Air Quality-Integrated Source Apportionment Method Model
Authors: Chantaraprachoom Nanthapong , Shimadera Hikari , Uranishi Katsushige , Mui Luong Viet , Matsuo Tomohito , Kondo Akira
Published: November 2024

This study utilized the Community Multiscale Air Quality (CMAQ) model to assess the impact of open biomass burning (OBB) in Thailand and neighboring countries—Myanmar, Laos, Cambodia, and Vietnam—on the PM2.5 concentrations in the Bangkok Metropolitan Region (BMR) and Upper Northern Region of Thailand. The Upper Northern Region was further divided into the west, central, and east sub-regions (WUN, CUN, and EUN) based on geographical borders. The CMAQ model was used to simulate the spatiotemporal variations in PM2.5 over a wide domain in Asia in 2019. The Integrated Source Apportionment Method (ISAM) was utilized to quantify the contributions from OBB from each country. The results showed that OBB had a minor impact on PM2.5 in the BMR, but transboundary transport from Myanmar contributed to an increase in PM2.5 levels during the peak burning period from March to April. In contrast, OBB substantially impacted PM2.5 in the Upper Northern Region, with Myanmar being the major contributor in WUN and CUN and domestic burning being the major contributor to EUN during the peak months. Despite Laos having the highest OBB emissions, meteorological conditions caused the spread of PM2.5 eastward rather than into Thailand. These findings highlight the critical impact of regional transboundary transport and emphasize the necessity for collaborative strategies for mitigating PM2.5 pollution across Southeast Asia.
Fixed-Point Results of F-Contractions in Bipolar p-Metric Spaces
Authors: Konwar Nabanita , Debnath Pradip
Published: November 2024

In this paper, we present new findings on F-contraction in bipolar p-metric spaces. We establish a covariant Banach-type fixed-point theorem and a contravariant Reich-type fixed-point theorem based on F-contraction in these spaces. Additionally, we include an example that demonstrates the applicability of our results. Our results non-trivially extend this covariant Banach-type fixed-point theorem and contravariant Reich type theorem via the concept of F-contraction.