bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulation of Pear Fruit Quality: A Review Based on Chinese Pear Varieties
Authors: Zhang Ying , Cheng Yudou , Ma Yuru , Guan Junfeng , Zhang Hao
Published: December 2024

Fruit quality is one of the most important economic traits of fruit crops, directly influencing market prices and orchard revenues. Enhancing fruit quality has therefore become a critical objective in both fruit production and scientific research. External quality traits of fruits typically include size, coloration, shape, uniformity, and consistency. Internal quality traits refer to the nutritional value of fruits, which largely determine their flavor and palatability. Over the past decade, significant progress has been made in understanding the formation and regulatory mechanisms of fruit quality, with numerous key functional genes associated with quality traits being identified. While substantial advancements have been achieved in studying specific aspects of fruit quality, there remains a lack of comprehensive and systematic reviews addressing the overall physiology of fruit quality, the interplay among various quality traits, and the diversity of regulatory mechanisms. Using Chinese pears as an example, this review summarizes the research progress in fruit quality regulation over the past five years. Key aspects include metabolic regulation of fruit traits such as sweetness, color, texture, and physiological disorders; factors influencing stone cell formation; sugar content regulation; roles of plant hormones including ethylene, gibberellins, and abscisic acid; translational regulation and post-translational modifications such as ubiquitination, methylation, and acetylation; as well as the application of genomic sequencing technologies. Furthermore, the review offers practical suggestions for improving pear fruit quality and provides insights for researchers in related fields. Finally, future trends in fruit quality research are discussed, offering a forward-looking perspective for advancing the field.
Integrating Genomic Selection and a Genome-Wide Association Study to Improve Days Open in Thai Dairy Holstein Cattle: A Comprehensive Genetic Analysis
Authors: Fathoni Akhmad , Boonkum Wuttigrai , Chankitisakul Vibuntita , Buaban Sayan , Duangjinda Monchai
Published: December 2024

Days open (DO) is a critical economic and reproductive trait that is commonly employed in genetic selection. Making improvements using conventional genetic techniques is exceedingly challenging. Therefore, new techniques are required to improve the accuracy of genetic selection using genomic data. This study examined the genetic approaches of traditional AIREML and single-step genomic AIREML (ssGAIREML) to assess genetic parameters and the accuracy of estimated breeding values while also investigating SNP regions associated with DO and identifying candidate genes through a genome-wide association study (GWAS). The dataset included 59415 DO records from 36368 Thai–Holstein crossbred cows and 882 genotyped animals. The cows were classified according to their Holstein genetic proportion (breed group, BG) as follows: BG1 (>93.7% Holstein genetics), BG2 (87.5% to 93.6% Holstein genetics), and BG3 (<87.5% Holstein genetics). AIREML was utilized to estimate genetic parameters and variance components. The results of this study reveal that the average DO values for BG1, BG2, and BG3 were 97.64, 97.25, and 96.23 days, respectively. The heritability values were estimated to be 0.02 and 0.03 for the traditional AIREML and ssGAIREML approaches, respectively. Depending on the dataset, the ssGAIREML method produced more accurate estimated breeding values than the traditional AIREML method, ranging from 40.5 to 45.6%. The highest values were found in the top 20% of the dam dataset. For the GWAS, we found 12 potential candidate genes (DYRK1A, CALCR, MIR489, MIR653, SLC36A1, GNA14, GNAQ, TRNAC-GCA, XYLB, ACVR2B, SLC22A14, and EXOC2) that are believed to have a significant influence on days open. In summary, the ssGAIREML method has the potential to enhance the accuracy and heritability of reproductive values compared to those obtained using conventional AIREML. Consequently, it is a viable alternative for transitioning from conventional methodologies to the ssGAIREML method in the breeding program for dairy cattle in Thailand. Moreover, the 12 identified potential candidate genes can be utilized in future studies to select markers for days open in regard to dairy cattle.
Formation of Adaptive Trophic Niches of Euryphagous Fish Species in Response to Off-Seasonal Water Level Regulation in Hongze Lake
Authors: Luo Si , Wang Zexin , Zhang Shengyu , Mu Huan , Jiao Yubin , Qu Xiao , Wang Qishuo , Yang Ruiqi , Zuo Yanxia , Jin Shiyu
Published: December 2024

Off-seasonal water level regulations disrupt the biological traits and phenological rhythms of native fish species, yet their impacts on interspecific trophic interactions remain understudied. This study employed stable isotope analysis to assess the trophic dynamics of three fish species (Parabramis pekinensis, Carassius auratus, and Toxabramis swinhonis) across different water periods in Hongze Lake. The findings revealed that all three species occupied similar mid-level trophic positions, with no significant difference among water periods (p > 0.05). During high-water periods, P. pekinensis and T. swinhonis exploited broader niches, while C. auratus relied on a narrower diet. In contrast, during low-water periods, C. auratus expanded its niche, while P. pekinensis and T. swinhonis reduced their isotopic niche widths. Niche overlap analysis showed minimal trophic overlap among the three species during high-water periods, with increased overlap during low-water periods, except for the highest overlap between C. auratus and T. swinhonis during mid-water periods. This variation in niche overlap aligns with shifts in dietary reliance, as POM was the predominant dietary component for all three species, but its contribution varied significantly across different water periods. These findings indicated that adaptive trophic niche facilitated the coexistence of these fish species, while off-seasonal water level regulation may intensify interspecific competition. These insights are essential for refining water management policies and developing sustainable fishery management strategies of Hongze Lake and other water-level-regulated systems.
Near-Complete Avipoxvirus Genome Assembled from Skin Lesions of Dead Eurasian Crane (Grus grus)
Authors: Kaszab Eszter , Sós Endre , Bali Krisztina , Sós-Koroknai Viktória , Perge Edina , Ursu Krisztina , Marton Szilvia , Hoitsy Márton , Kemenesi Gábor , Bányai Krisztián
Published: December 2024

Avian pox is a globally spread viral disease affecting a wide spectrum of wild and domesticated bird species. The disease is caused by a diverse group of large DNA viruses, namely, avipoxviruses (genus Avipoxvirus, family Poxviridae). In this study, gross pathological examination and histopathological examination of skin lesions and several organs suggested acute poxvirus infection of a Eurasian crane (Grus grus, Linnaeus, 1758). Avipoxvirus infection was confirmed by testing wart-like lesions via gene-specific PCR assay and sequencing the obtained amplicon. Phylogenetic analysis of the gene encoding the DNA polymerase revealed that the crane poxvirus clustered in clade A, subclade A3. A large fragment of the poxvirus genome (306,477 bp in length) was assembled from the DNA of a skin specimen. Our study reaffirms previous findings that even complex virus genomes can be determined from a metagenomic assemblage generated directly from avian tissue samples without prior virus isolation, a promising approach for the epidemiologic surveillance of avipoxvirus infections in wild birds and domestic poultry.
Towards New Scaffolds for Antimicrobial Activity—In Silico/In Vitro Workflow Introducing New Lead Compounds
Authors: Mangana Maria , Lambrinidis George , Kostakis Ioannis K. , Kalpaktsi Ioanna , Sagnou Marina , Nicolaou Chrysoula , Mikros Emmanuel , Chatzipanagiotou Stylianos , Ioannidis Anastasios
Published: December 2024

Background/Objectives: The rapid evolution of bacterial resistance and the high cost of drug development have attributed greatly to the dearth in drug design. Computational approaches and natural product exploitation offer potential solutions to accelerate drug discovery. Methods: In this research article, we aimed to identify novel antibacterial hits. For the in silico studies, molecular scaffolds from the in-house chemical library of the Department of Pharmacy of Athens (Pharmalab) and the National Cancer Institute (NCI) were screened and selected for further experimental procedures. Compounds from both libraries that were not previously screened for their antimicrobial properties were tested in vitro against Gram-positive and Gram-negative bacterial strains. The microdilution method was used to determine the minimum inhibitory concentrations (MICs). Results: In silico screening identified twenty promising molecules from the NCI and seven from the Pharmalab databases. The unexplored compounds for their antibacterial activity can be characterized as weak strain-specific antimicrobials. The NSC 610491 and NSC 610493 were active against Staphylococcus aureus (MIC: 25 and 12.5 µg/mL, respectively) and methicillin-resistant S. aureus (MRSA) (MIC: 50 and 12.5 µg/mL, respectively). Six out of seven hydroxytyrosol (HTy) compounds were moderately active (MIC: 25–50 µg/mL) against S. aureus, MRSA and Enterococcus faecalis. For the Gram-negative bacteria, no activity was detected (≥100 µg/mL). Conclusions: The tested scaffolds could be considered as promising candidates for novel antimicrobials with improvements. Further experimentation is required to assess mechanisms of action and evaluate the efficacy and safety.
Content of Selected Macroelements and Zinc in Relation to Stage of Lactation of Montbéliarde and Polish Holstein-Friesian Cows
Authors: Sobczuk-Szul Monika , Pogorzelska-Przybyłek Paulina , Momot Martyna , Nogalski Zenon
Published: December 2024

The aim of this study was to determine the effect of a cow’s breed on the content of minerals in milk depending on the stage of lactation. The study material was the milk of cows of two breeds, Montbéliarde (mb; n = 15) and black-and-white Polish Holstein-Friesian (phf; n = 15), kept at the same farm. The phf cows were matched with the mb cows by analogs considering the stage and number of successive lactations. Milk for analysis was collected six times during lactation when the official test milking was performed, and a total of 180 milk samples were collected. The fresh milk was analyzed for its proximate chemical composition and mineral content (Ca, Mg, K, Na and Zn), which were identified in the mineralization by flame atomic absorption spectrophotometry. Milk of the Montbéliarde cows contained significantly more sodium, calcium, magnesium and zinc compared to milk of the Polish Holstein-Friesian cows. This enables the conclusion that the contents of these elements are determined by breed. This study demonstrates the variability of the contents of the analyzed minerals with time after calving. During lactation, the contents of potassium, calcium, magnesium and sodium were the highest at the late stage of lactation, especially in the tenth month of lactation.
Use of Attention Maps to Enrich Discriminability in Deep Learning Prediction Models Using Longitudinal Data from Electronic Health Records
Authors: Carrasco-Ribelles Lucía A. , Cabrera-Bean Margarita , Llanes-Jurado Jose , Violán Concepción
Published: December 2024

Background: In predictive modelling, particularly in fields such as healthcare, the importance of understanding the model’s behaviour rivals, if not surpasses, that of discriminability. To this end, attention mechanisms have been included in deep learning models for years. However, when comparing different models, the one with the best discriminability is usually chosen without considering the clinical plausibility of their predictions. Objective: In this work several attention-based deep learning architectures with increasing degrees of complexity were designed and compared aiming to study the balance between discriminability and plausibility with architecture complexity when working with longitudinal data from Electronic Health Records (EHRs). Methods: We developed four deep learning-based architectures with attention mechanisms that were progressively more complex to handle longitudinal data from EHRs. We evaluated their discriminability and resulting attention maps and compared them amongst architectures and different input processing approaches. We trained them on 10 years of data from EHRs from Catalonia (Spain) and evaluated them using a 5-fold cross-validation to predict 1-year all-cause mortality in a subsample of 500,000 people over 65 years of age. Results: Generally, the simplest architectures led to the best overall discriminability, slightly decreasing with complexity by up to 8.7%. However, the attention maps resulting from the simpler architectures were less informative and less clinically plausible compared to those from more complex architectures. Moreover, the latter could give attention weights both in the time and feature domains. Conclusions: Our results suggest that discriminability and more informative and clinically plausible attention maps do not always go together. Given the preferences within the healthcare field for enhanced explainability, establishing a balance with discriminability is imperative.
The Impact of Downtime on the Stability of the Production Schedule
Authors: Kaźmierczak Piotr , Żywicki Krzysztof , Rewers Paulina
Published: December 2024

Production scheduling is a fundamental element for the efficient control of material flow in production processes. Research work concerns the approaches, methods and tools used to optimize the loading of production resources with production orders, taking into account various evaluation criteria. From a manufacturers’ point of view, one important objective is to ensure the timely fulfilment of customer orders. The emergence of disruptive factors causing downtime (e.g., machine breakdowns and adjustments) in the realization of production processes negatively affects the realization of this objective. In this case, it is necessary to rebuild the production schedule to make the production capacity more realistic. This paper presents a study on the impact of downtime on production schedules developed according to different principles and rules. The aim was to determine which of these affects the stability of the production schedule, i.e., causing less need for changes as a result of the occurrence of downtime in the execution of production orders. The research experiments were conducted for various production flow models and structures of products and production orders. The subject of this research is important and relevant to the impact and inclusion of maintenance tasks in the operation of production systems.
Structural Designing of Supersonic Swirling Devices Based on Computational Fluid Dynamics Theory
Authors: Huang Qian , Huang Huirong , Long Xueyuan , Tian Yuan , Meng Jiang
Published: December 2024

The supersonic swirling device is a new apparatus that can be used for natural-gas liquefaction. The structure of the supersonic swirling device has an important impact on the liquefaction efficiency. Therefore, this study presents a structural design method for supersonic cyclones based on CFD theory. Using the production parameters of a liquefied natural gas (LNG) peak-shaving station as the study case, a detailed design and design comparison of each part of the supersonic swirling separator are carried out. An optimum LNG supersonic swirling separator design was obtained. To ensure that the designed supersonic swirling separator achieved better liquefaction effectiveness, it was ascertained that no large shockwaves were generated in the de Laval nozzle, the pressure loss on the swirler was small, and the swirler was able to produce a large centripetal acceleration. The opening angle of the diffuser and the length of the straight tube were designed considering the location at which normal shockwaves were generated. The location at which shockwaves are generated and the friction effect are important parameters that determine the gap size. With this design guidance, the optimal structural dimensions of the supersonic swirling device for a given processing capacity were determined as follows: a swirler with six vanes and an 8 mm wide channel; a 10D-long straight tube, an opening angle of 20° between the straight tube and the divergent section, and a gap size of 2 mm. Compared with “Twister II”, the new device has better liquefaction efficiency.
Friction Behavior of Ceramic Materials for the Development of Bridge-Bearing Friction Materials
Authors: Park Ji-Hun , Lee Jung-Woo
Published: December 2024

This study evaluates the potential of ceramic materials as friction components in bridge bearings, focusing on durability and frictional behavior under high-load conditions. Bridge bearings traditionally use materials such as PTFE and UHMWPE, which suffer from wear, oxidation, and deformation over time, leading to costly maintenance and frequent replacements. To address these limitations, zirconia-based ceramics were selected for their high hardness, wear resistance, and low friction coefficient. Frictional tests on ceramic samples, including surface roughness adjustments and stress conditions, indicated a stable frictional performance with minimal wear over extended cycles. The results suggest that ceramic materials can maintain consistent frictional properties without lubricant use, potentially reducing bearing maintenance costs and extending their service life. These findings suggest that ceramics could serve as a promising alternative to conventional friction materials in bridge bearings by offering enhanced durability, reduced maintenance requirements, and improved operational reliability.
Stability Analysis of Slopes Under Seismic Action with Asynchronous Discounting of Strength Parameters
Authors: Wang Ruipeng , Yan Hongye , Yao Junkai , Li Zhuqing
Published: December 2024

A non-proportional reduction in strength parameters is widely used in slope stability assessment, but the current asynchronous reduction in strength parameters only considers the cohesion c and internal friction angle φ, which is suitable for slope stability assessment under static loads. Under seismic loads, however, tension at the rear edge of the slope often accompanies the appearance of ground cracks. In order to consider the relationship between tensile strength, cohesion, and the internal friction angle reduction coefficient, starting with the linear softening attenuation law of soil material strength parameters, a functional relationship between cohesion and internal friction angle is obtained. Then, considering that the failure of microelements in the tensile and shear zones conforms to the tension and shear of joint failure, the relationship between tensile strength, cohesion, and the internal friction angle reduction coefficient is derived. By establishing a homogeneous slope model and comparing and analyzing the progressive instability failure modes of slopes under static and seismic conditions, the stability and potential slip surface differences of slopes under two different working conditions are explored. The research results indicate that slope instability is a gradual, cumulative failure process under both static and dynamic conditions. The instability mode of the slope under static conditions is shear failure. In contrast, under dynamic loads, the instability failure of the slope is manifested as shear failure upward at the foot of the slope and tensile failure downward at the top of the slope. The stability coefficient of slopes under earthquake conditions is reduced by 17.3% compared to that under static conditions. Under earthquake conditions, the potential sliding surface under an asynchronous reduction in strength parameters is shallower than that under static conditions and deeper than that without an asynchronous reduction in strength parameters. Overall, the research results provide a reference for slope stability analysis and support design optimization under earthquake loads.
Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
Authors: Qin Shengwei , Jin Yao , Hu Hailong
Published: December 2024

The upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learning technology, many neural network-based methods have been proposed for point cloud upsampling. However, there are common challenges among these methods such as blurring sharper points (e.g., corner or edge points) and producing points gathered together. These problems are caused by similar feature replication or insufficient supervised information. To address these concerns, we present SSPU-FENet, a self-supervised network consisting of two modules specifically designed for geometric detail-preserved point cloud upsampling. The first module, called the feature enhancement module (FEM), aims to prevent feature blurring. This module retains important features such as edges and corners by using non-artificial encoding methods and learning mechanisms to avoid the creation of blurred points. The second module, called the 3D noise perturbation module (NPM), focuses on high-dimensional feature processing and addresses the challenges of feature similarity. This module adjusts the spacing of reconstructed points, ensuring that they are neither too close nor too far apart, thus maintaining point uniformity. In addition, SSPU-FENet proposes self-supervised loss functions that emphasize global shape consistency and local geometric structure consistency. These loss functions enable efficient network training, leading to superior upsampling results. Experimental results on various datasets show that the upsampling results of the SSPU-FENet are comparable to those of supervised learning methods and close to the ground truth (GT) point clouds. Furthermore, our evaluation metrics, such as the chamfer distance (CD, 0.0991), outperform the best methods (CD, 0.0998) in the case of 16× upsampling with 2048-point input.
Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
Authors: Macko Dávid , Hrmo Ľubomír , Hrbček Jozef , Nagy Peter
Published: December 2024

Modern railway signalling systems are critical for ensuring the safe and efficient operation of railway networks. Educational institutions play a vital role in training students to design and implement these systems, replicating practical scenarios to bridge the gap between theory and application. Advanced electronic interlocking systems, such as SIMIS W, are widely used to manage complex signalling requirements in real-world railway environments. Existing model railway systems lack the capability to simulate advanced interlocking systems with real-time performance. This paper presents a model railway signalling system based on the SIMIS W interlocking system. The setup integrates ILTIS-N dispatcher workstations with a SIMATIC S7-1200 PLC, controlling signals, switches, and track circuits. A REST API ensures seamless data exchange between ILTIS-N and the PLC, achieving efficient system responsiveness. The system effectively replicates real-world signalling conditions with compatibility with existing protocols. Performance testing demonstrated a response time of approximately 1.2 s, achieving the design objective of a maximum 1.5 s. This ensures rapid and accurate signalling under real-time constraints. The developed system provides a practical educational platform for students to gain experience with advanced interlocking systems and serves as a basis for further research into scalable railway signalling solutions.
Using Virtual Reality to Complement Traditional Wheelchair Skills Training Methods: A Literature Review
Authors: Zorzi Chantal , Covaci Alexandra , Marcelli Gianluca
Published: December 2024

Training wheelchair skills are vital for enhancing independence and safety. Traditional training methods require significant time and resources, limiting accessibility. Virtual reality (VR) offers an innovative solution by simulating real-life environments for training, reducing risks and costs. However, the effectiveness of VR in complementing real-life training remains underexplored. This review investigates how VR can complement traditional wheelchair training by assessing the strengths and limitations of existing VR systems. A literature review of 28 studies on VR applications for wheelchair training from 2017 to 2024 was conducted, focusing on studies that detailed VR environments and training programs. It was found that most VR systems were designed for powered wheelchair users with joystick navigation. VR environments included tasks from basic navigation to complex real-world scenarios. While VR showed potential in improving skills and engagement, challenges included the lack of standard methods for evaluating effectiveness and cybersickness. Overall, VR can be a valuable complementary tool for wheelchair training, especially for powered users. Future research should standardise protocols, and address side effects.
Optimizing Breeding Strategies for Pekin Ducks Using Genomic Selection: Genetic Parameter Evaluation and Selection Progress Analysis in Reproductive Traits
Authors: Zhou Jun , Yu Jiang-Zhou , Zhu Mei-Yi , Yang Fang-Xi , Hao Jin-Ping , He Yong , Zhu Xiao-Liang , Hou Zhuo-Cheng , Zhu Feng
Published: December 2024

Reproductive performance is an important trait in poultry production. Traditional methods of improving reproductive traits can only use recorded information from females, making it difficult to effectively assess the reproductive potential of males. Although genomic selection is thought to remedy this shortcoming, most studies now use simulated data or one or two generations of data to assess its effects. Also, the effectiveness of genomic selection for use in the improvement of reproductive traits in ducks has hardly been reported. In this study, data from four consecutive generations of Pekin duck populations were used to assess the effect of genomic selection on reproductive trait improvement. Whole-genome resequencing was performed for genotyping, and pedigree and SNP genetic parameters were evaluated. Using the BLUP (Best Linear Unbiased Prediction), GBLUP (Genomic Best Linear Unbiased Prediction), and ssGBLUP (Single-step Genomic Best Linear Unbiased Prediction) models, we assessed selection progress for body weight at 6 weeks, age at first egg, and egg number from 25 to 44 weeks over multiple generations. Ten-fold cross-validation was used to evaluate the genomic prediction performance. The results indicated that the heritability of growth traits decreased after routine selection, while reproductive and egg quality traits maintained moderate heritability (0.2–0.4). Selection progress showed a one-day advancement in age at first egg and an increase of one egg per generation from the 13th to 15th generations. The GBLUP model performance significantly outperformed BLUP, but ssGBLUP showed minimal improvement due to comprehensive genotyping. In conclusion, this study provides crucial insights for optimizing breeding strategies and improving economic efficiency in Pekin duck breeding.
Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
Authors: Wei Song , Li Yongye , Song Xiaoteng , Tao Siyuan
Published: December 2024

In order to explore the influence of change in the structural parameters ofguide vane cyclones on the cyclone spinning effect, this paper mainly used numerical simulations and physical experiments to analyze the energy of the hydrodynamic flow of acyclone with different guide vane heights by taking the structuralparameters of the guide vane height as the research object. The results show that the rotational kinetic energy of the water flow inside the cyclone was almost zero in the upstream and straight sections of the guide vane section, and it only existed in the leading edge section of the guide vane. In the twisted section of the guide vane, the rotational kinetic energy increased along the flow path, while it decreased in the downstream section of the guide vane. An increase in the height of the guide vanes led to an increase in local mechanical energy loss at the leading and trailing edges of the guide vanes of the cyclone. In the guide vane section, the mechanical energy loss of the water flow remained almost constant along the path, but the mechanical energy loss was faster for cyclones with greater heights. During the deflection of the guide vane, pressure energy was converted into kinetic energy, and the higher the height of the guide vane, the greater the kinetic energy growth and mechanical energy consumption. The proportion of additional mechanical energy loss in the total loss increased with the increase in guide vane height, and the influence of guide vane height was greater than that of the Reynolds number. The mechanical efficiency ηdecreased with the increase in guide vane height, whereas the mechanical efficiency increased slightly with the increase in Reynolds number. The research results in this paper provide a theoretical basis for further optimizing the structural parameters of cyclones.
Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model
Authors: Li Enhui , Wang Zixi , Liu Jin , Huang Jiandong
Published: December 2024

The traditional graduate admission method is to evaluate students’ performance and interview results, but this method relies heavily on the subjective feelings of the evaluators, and these methods may not be able to comprehensively and objectively evaluate the qualifications and potential of the applicants. At present, artificial intelligence has played a key role in the reform of the education system, and the data processing function of artificial intelligence has greatly reduced the workload of screening work. Therefore, this study aims to optimize the graduate enrollment evaluation process by applying a new composite model, the random forest–improved sparrow search algorithm (RF–ISSA). The research used seven data sets including research, cumulative grade point average (CGPA), letter of recommendation (LOR), statement of purpose (SOP), university rating, TOEFL score, and graduate record examination (GRE) score, and carried out the necessary data pre-processing before the model construction. The experimental results show that the RMSE and R values of the composite model are 0.0543 and 0.9281, respectively. The predicted results of the model are very close to the actual data. In addition, the study found that the importance score of CGPA was significantly higher than other characteristics, and that this value has the most significant impact on the outcome of the graduate admissions assessment. Overall, this study shows that combining the integrated strategy sparrow search algorithm (ISSA) with hyperparameter optimization and focusing on the most influential features can significantly improve the predictive performance and applicability of graduate admissions models, providing a more scientific decision support tool for school admissions professionals.
Effect of Heat Treatment Methods on Color, Bioactive Compound Content, and Antioxidant Capacity of Carrot Root
Authors: Narwojsz Agnieszka , Sawicki Tomasz , Piłat Beata , Tańska Małgorzata
Published: December 2024

Carrot (Daucus carota L.) is a globally consumed root vegetable, enjoyed both raw and after thermal processing. This study aimed to evaluate the effect of different heat treatment methods (traditional boiling in water, steaming in a steel pot, steaming in a combi oven, baking in a combi oven, and the sous-vide technique) on carrot color, total phenolic and flavonoid content, phenolic and carotenoid compound profiles, and antioxidant capacity. It was found that heat treatments resulted in significant (p ≤ 0.05) changes in carrot color, with reductions in lightness (L* decreased by 19–24%), redness (a* decreased by 52–67%), and yellowness (b* decreased by 15–25%). Interestingly, processed carrots showed an increase in total phenolics (by 25–133%), total flavonoids (by 60–126%), and total carotenoids levels (by 16–48%) compared to raw carrots. However, specific phenolic and carotenoid compounds showed notable reductions (p ≤ 0.05) after heat treatment, including vanillic acid and rutin (100% reduction after all treatments), α-carotene (up to 33.3% reduction after baking), zeaxanthin (up to 33.3% reduction after baking), and 13-cis-β-carotene (up to 40.7% reduction after steaming in a combi oven). In addition, heat treatment significantly (p ≤ 0.05) increased the antioxidant capacity of carrots, as determined by DPPH and ABTS assays, with increases up to 2.2-fold and 1.6-fold, respectively. The antioxidant properties of processed carrots were strongly correlated (p ≤ 0.05) with total phenolic content and the levels of chlorogenic acid, p-coumaric acid, and β-carotene (r = 0.86–0.96).
Vegetarianism Discourse in Russian Social Media: A Case Study
Authors: Gorduna Nikita , Vanetik Natalia
Published: December 2024

Dietary choices, especially vegetarianism, have attracted much attention lately due to their potential effects on the environment, human health, and morality. Despite this, public discourse on vegetarianism in Russian-language contexts remains underexplored. This paper introduces VegRuCorpus, a novel, manually annotated dataset of Russian-language social media texts expressing opinions on vegetarianism. Through extensive experimentation, we demonstrate that contrastive learning significantly outperforms traditional machine learning and fine-tuned transformer models, achieving the best classification performance for distinguishing pro- and anti-vegetarian opinions. While traditional models perform competitively using syntactic and semantic representations and fine-tuned transformers show promise, our findings highlight the need for task-specific data to unlock their full potential. By providing a new dataset and insights into model performance, this work advances opinion mining and contributes to understanding nutritional health discourse in Russia.
The Effect of the Use of the Beneficial Acetic Acid Bacteria Starter Cultures on the Microbiological and Physicochemical Quality of Raw Ripening Sausages
Authors: Okoń Anna , Łepecka Anna , Szymański Piotr , Neffe-Skocińska Katarzyna
Published: December 2024

The aim of the study was to assess the possibility of using novel Gluconobacter oxydans strains in the technology of raw ripening sausages and to assess their impact on the microbiological and physico-chemical quality after production and after 6 months of storage process. Four variants of sausages were prepared: two control sausages (with salt and with curing salts addition), and two study variants with different acetic acid bacteria starters addition. Microbiological and physicochemical analyses were carried out. All variants of study sausages showed good microbiological quality concerning the total number of microorganisms, lactic acid bacteria, and the absence of pathogenic microorganisms. The synergistic effect of lactic acid bacteria and acetic acid bacteria was observed and a positive effect on the survivability of the native lactic microbiota of study sausages was shown. It was shown that sausage with the addition of acetic acid bacteria (AAB) strains were characterized by higher antioxidant properties compare to control samples. These results were confirmed by the analysis of oxidation–reduction potential and lipid oxidation products, which showed low oxidation–reduction potential (ORP value) and amounts of malondialdehyde (MDA) in the study products. Based on the obtained results of microbiological and physicochemical analysis, the technological usefulness of the study AAB strains, as starter cultures for the production of raw ripening meat products, was demonstrated.
Transformation of Hydroacoustic Energy into Seismoacoustic Energy at 22 Hz in Medium Depth- and Deep-Sea Conditions
Authors: Dolgikh Grigory , Bolsunovskii Mikhail , Budrin Sergey , Dolgikh Stanislav , Ivanov Mikhail , Ovcharenko Vladimir , Pivovarov Aleksandr , Samchenko Aleksandr , Chupin Vladimir , Yaroshchuk Igor
Published: December 2024

This work is devoted to an experiment studying the regularities of the propagation of hydroacoustic low-frequency signals in the conditions of the sea at intermediate depth and deep in terms of their transformation into vibrations in the upper layer of the Earth’s crust. This experiment belongs to the field of acoustic tomography and is aimed at solving the problems of non-contact methods for studying the geological structure of the shelf areas of the World Ocean. The novelty and uniqueness of the work lies in the use of a harmonic low-frequency hydroacoustic signal with a frequency of 22 Hz of high power, capable of creating Rayleigh surface waves at the “water–bottom” interface. The surface waves propagating at the bottom are registered by a coastal laser-interference measuring system capable of recording deformations in the upper crustal layer with an accuracy of 0.01 nm. The experimental results showed that the radiated hydroacoustic energy is not localized in the liquid half-space and propagates predominantly according to the law close to spherical divergence, even when the shelf depth is comparable to the wavelength of the radiated signal.
Accusations and Law Articles Prediction in the Field of Environmental Protection
Authors: Leng Sihan , Kang Xiaojun , Liang Qingzhong , Li Xinchuan , Fan Yuanyuan
Published: December 2024

Legal judgment prediction is a common basic task in the field of Legal AI, aimed at using deep domain models to predict the outcomes of judicial cases, such as charges, legal provisions, and other related tasks. This task has practical applications in environmental law, including legal decision assistance and legal advice, offering a promising and broad prospect. However, most previous studies focus on using high-quality labeled data for strong supervised training in criminal justice, often neglecting the rich external knowledge contained in various charges and laws. This approach fails to accurately simulate the decision-making steps of judges in real scenarios, overlooking the semantic information in case descriptions that significantly impacts judgment results, leading to biased outcomes. In judicial environmental protection, the high overlap and similarity between different charges can cause confusion, and there is a lack of relevant judicial decision labeling datasets. To address this, we propose the External Knowledge-Infused Cross Attention Network (EKICAN), which leverages the robust semantic understanding capabilities of large models. By extracting information such as fact descriptions and court opinions from documents of criminal, civil, and administrative cases related to judicial environmental protection, we construct the Judicial Environmental Law Judgment Dataset (JELJD). We address data imbalance in this dataset using the text generation capabilities of judicial large models. Finally, EKICAN fuses semantic information from different parts with external knowledge to output prediction results. Experimental results show that EKICAN achieves state-of-the-art performance on the JELJD compared to advanced models.
Methodology Proposal, Virtual Simulations and 3D Printed Prototype of a Car Steering Wheel
Authors: Fandáková Miriam , Kudela Pavol , Jakubec Maroš , Lieskovská Eva
Published: December 2024

The design and production of car steering wheel prototypes have evolved significantly with advancements in virtual and additive manufacturing technologies. This study presents a comprehensive methodology for the development of a car steering wheel prototype, integrating theoretical analysis, virtual simulations, and 3D printing. Initially, material characteristics are defined to ensure structural integrity and suitability for automotive use. Stress-strain simulations in a 3D virtual environment validate the mechanical feasibility and functional properties of the prototype. The methodology further includes criteria for selecting 3D printers based on material compatibility, build volume, and aesthetic requirements, such as color options. Finally, the study incorporates experimental testing of standardized samples, including compression and hardness tests, to verify the mechanical properties and deformation behavior under load. This integrated approach aims to streamline the prototype development process while ensuring precision, reliability, and customization.
Seismic Reliability Evaluation Strategy for Substation Systems Based on the Quasi-Monte Carlo Simulation Method and Triangle Algorithm
Authors: Liu Xiaohang , Zheng Shansuo , Qin Qing , Hou Piji
Published: December 2024

Power systems are the backbone of modern society. As critical facilities for the transmission and conversion of electric power, substations are vital to the stable operation of power systems. Quantifying the residual seismic capacity of the substation system is an urgent issue to be addressed. Therefore, this study proposes a reliability assessment process based on network matrix algorithms and efficient simulation methods to evaluate substation systems. A typical 220 kV substation in China with 6 incoming lines and 10 outgoing lines was studied with this assessment process. The results of the case study showed that the redundancy of transformer units, due to their excessive fragility, does not effectively guarantee the complete function of the system after an earthquake. In this work, 220 kV circuit breakers, voltage transformers, and 110 kV disconnect switches were evaluated as the most vulnerable functional equipment in the substation system; improving their seismic performance can significantly improve the seismic reliability of the entire system. Furthermore, the evaluation process demonstrated that substations are more susceptible to earthquakes than individual pieces of equipment, indicating that the redundancy of existing substations is insufficient.
News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
Authors: Jiang Shuhao , Song Haoran , Lu Yizi , Zhang Zhixin
Published: December 2024

Personalized news recommendations focus on providing users with news that fits their interests and alleviates their information overload. User preference modeling is crucial for achieving personalized news recommendations, and user preferences are usually expressed as long-term and short-term user preferences. Existing news recommendation methods have difficulty accurately matching user preferences with candidate news, which is the main challenge plaguing the news recommendation field. For this purpose, we propose a novel news recommendation method based on candidate-aware long- and short-term preference modeling, named NRCLS. The method contains: (1) a Candidate-Enhanced graph attention network, which learns high-order structural information in graphs, mines the potential relationship between long-term user preferences and candidate news; and (2) a candidate-aware attention network that incorporates news subtopics. In this network, the dynamic impact of candidate news on short-term user preferences is considered. Results on real-world datasets demonstrate the effectiveness of our proposed method in improving the performance of news recommendations. We have also conducted several ablation studies that demonstrate the effectiveness of the core module in NRCLS.
Nesting Process Automation in the Footwear Industry: A Hybrid Approach to Minimize Material Waste
Authors: Aguilar-Tortosa Eliseo , Duta-Costache Eduard-Andrei , Vera-Brazal Elías , Sánchez-Romero José-Luis , Gómez-Hernández José Francisco , Jimeno-Morenilla Antonio , Maciá-Lillo Antonio
Published: December 2024

In any industry, maximizing the use of raw materials is essential to reduce waste and costs, which also positively impacts the environment. In footwear production, components are typically derived from cutting processes, requiring optimized systems to maximize the use of different materials, minimize waste, and accelerate production. In this context, nesting is a technique that arranges shapes within a confined space to maximize area utilization and reduce unused space. As this problem is classified as NP-Hard, only algorithmic approximations can be employed. This paper focuses on optimizing the cutting of leather parts for shoe manufacturing. Footwear parts are cut from cattle hides, which are not only irregular in shape but also vary in resistance and quality across different areas of the same piece of leather. This study proposes automated nesting methods that aim to compete with current manual approaches, which are conducted exclusively by experts with deep knowledge of the characteristics of both the pieces and the leather, making the manual process time-intensive. This research reviews current methods and introduces hybrid ones, achieving up to 38.4× acceleration and up to 10.18% increase in nested pieces over manual methods.
Quantitative 31P NMR Spectroscopy: Principles, Methodologies, and Applications in Phosphorus-Containing Compound Analysis
Authors: Liu Yaqin , Gao Lina , Yu Zeling
Published: December 2024

Nuclear magnetic resonance (NMR) spectroscopy is gaining prominence as a vital quantitative method for sample analysis, with significant progress being made in the investigation of heteronuclei like phosphorus, a key element in numerous physiological functions. This paper provides a comprehensive review of the principles, methodologies, and applications of quantitative 31P nuclear magnetic resonance (qNMR) spectroscopy. It begins with an introduction to the fundamental principles of NMR spectroscopy, highlighting the specific advantages of qNMR and the unique properties of the 31P nucleus, including its high natural abundance and broad chemical shift range. While 1H qNMR is widely used, signal overlap in complex mixtures can limit its accuracy. Additionally, this work explores diverse applications of 31P qNMR across fields such as food analysis, pharmaceuticals, and biology, emphasizing its contributions to real-time drug quantification, metabolomics, and environmental analysis. A key advantage of 31P NMR is its ability to provide exclusive detection and direct quantification of phosphorus in phosphorus-containing compounds. The internal standard method is favored for its simplicity, as it avoids the need for calibration curves, while the external standard method is better suited for natural products with established reference materials. This review aims to consolidate the applied prospects of 31P qNMR, emphasizing its potential to expand the horizons of quantitative detection technologies and facilitate advancements in future research and practical applications.