bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantifying Stream Return Flow of Agricultural Water Using SWAT-MODFLOW-PADDY Model in Korean Paddy Fields
Authors: Han Jeongho , Lee Seoro , Kim Jonggun
Published: October 2024

In many countries, the irrigation return flow focuses only on surface and subsurface flows. In contrast, South Korea adopts a broader approach, defining the stream return flow as encompassing both quick and delayed return flows, which include subsurface flow and deep percolation. This study proposes redefining the stream return flow to include only the subsurface return flow, excluding deep percolation. We quantified the subsurface return flow and deep percolation using the SWAT-MODFLOW-PADDY (SMP) model, confirming that the current definition overestimated the stream return flow in Korea. The results show that the subsurface return flow accounted for 20% to 60% of the total infiltration, with the remaining 40% to 80% contributing to deep percolation and groundwater recharge. These findings reveal significant regional variations in the subsurface return flow rates, underscoring the limitations of applying a uniform stream return flow rate. We propose that allocated management water, subsurface, and quick return flows should be the primary components considered in stream return flow calculations, as the current practice of including delayed return flow leads to overestimated results. This study highlights the challenges in monitoring the subsurface return flow and the need for region-specific models that account for local conditions such as topography, soil characteristics, and climate. Our findings provide a more accurate approach to estimating the subsurface return flow, which is crucial for improving the efficiency and sustainability of agricultural water management in Korea.
Altitude Distribution Patterns and Driving Factors of Rhizosphere Soil Microbial Diversity in the Mountainous and Hilly Region of Southwest, China
Authors: Li Yanlin , Wang Yonggang , Liu Yunpeng , Chen Yangyang , Yang Shuangrong
Published: October 2024

The distribution characteristics of the microbial community in rhizosphere soils of different altitudinal gradients were explored to uncover ecological factors affecting microbial community composition. In this study, the community variations of bacteria and fungi in the rhizosphere soil of Chrysanthemum indicum L. were analyzed. Samples were distributed along an altitudinal gradient of 300–1500 m above sea level in the Fuling watershed of the Three Gorges Reservoir area, China. The analysis was conducted using Illumina MiSeq high-throughput sequencing and bioinformatics analyses. Through correlation analysis with ecological factors, the altitude distribution pattern and driving factors of soil microbial diversity in the mountainous and hilly region of Chongqing were explored. According to the results, the richness and diversity of rhizosphere soil bacteria increased with altitude, while fungi were the richest and most diverse at an altitude of 900 m. The composition of the microbial community differed among different altitudes. Actinobacteria, Proteobacteria, Acidobacteriota, Chloroflexi, Bacteroidota, Ascomycota, unclassified_k_Fungi, Basidiomycota, and Mortierellomycota dominated the microbial community in rhizosphere soil. Correlation analysis showed that the distribution of rhizosphere soil microbial communities correlated with soil ecological factors at different altitudes. Moisture, pH, total nitrogen, total potassium, available potassium, urease, and catalase were significantly positively correlated with rhizosphere soil bacterial α-diversity, while their correlations with fungi were not significant. Variation partition analysis showed that the combined effects of soil physical and chemical factors, enzyme activity, and microbial quantity regulated bacterial community structure and composition. Their combined contributions (19.21%) were lower than the individual effects of soil physical and chemical factors (48.49%), enzyme activity (53.24%), and microbial quantity (60.38%). The effects of ecological factors on fungal communities differed: While the soil physical and chemical factors (44.43%) alone had a clear effect on fungal community structures, their combined contributions had no apparent effect. The results of this study not only contribute to a deeper understanding of the impact mechanism of altitude gradient on the diversity of rhizosphere soil microbial communities, but also provide a scientific basis for the protection and management of mountainous and hilly ecosystems. It lays a foundation for the future exploration of the relationship between microbial communities and plant–soil interactions.
Agronomic Performance and Resistance to Maize Lethal Necrosis in Maize Hybrids Derived from Doubled Haploid Lines
Authors: Sadessa Kassahun , Beyene Yoseph , Ifie Beatrice E. , Gowda Manje , Suresh Lingadahalli M. , Olsen Michael S. , Tongoona Pangirayi , Offei Samuel K. , Danquah Eric , Prasanna Boddupalli M. , Wegary Dagne
Published: October 2024

Maize (Zea mays L.) is one of the most widely cultivated grain crops globally. In sub-Saharan Africa (SSA), it plays an important role in ensuring both food and income security for smallholder farmers. This study was conducted to (i) assess the performances of testcross hybrids constituted from maize lethal necrosis (MLN) tolerant doubled haploid (DH) lines under various management conditions; (ii) estimate the combining ability effects and determine the nature of gene action in the DH lines; and (iii) identify DH lines and testcross hybrids for resistance to MLN, high grain yield, and other important traits. Eleven DH lines were crossed with 11 single-cross testers using the line-by-tester mating design, and 115 successful testcross hybrids were generated. These hybrids, along with five commercial check hybrids, were evaluated across four optimum management conditions, two MLN artificial inoculations, and one managed drought environment in Kenya. Under each management condition, the effects of genotypes, environments, and genotype-by-environment interactions were significant for grain yield (GY) and most other traits. Hybrids T1/L3, T10/L3, and T11/L3 exhibited higher grain yields under at least two management conditions. A combining ability analysis revealed that additive gene effects were more important than non-additive effects for GY and most other traits, except for leaf senescence (SEN) and MLN disease severity score. DH line L3 exhibited a desirable general combining ability (GCA) effect for GY, while L5 was the best general combiner for anthesis date (AD) and plant height (PH) across all management conditions. DH lines L2, L6, and L7 showed negative GCA effects for MLN disease severity. Single-cross testers T11 and T10 were good general combiners for GY under all management conditions. Hybrids T2/L11, T9/L10, and T2/L10 demonstrated high specific combining ability (SCA) effects for GY under all conditions. This study identified DH lines and testers with favorable GCA effects for grain yield, MLN resistance, and other agronomic traits that can be used in breeding programs to develop high-yielding and MLN-resistant maize varieties. Better-performing testcross hybrids identified in the current study could be verified through on-farm testing and released for commercial production to replace MLN-susceptible, low-yield hybrids grown in the target ecologies.
Comparison of Single-Trait and Multi-Trait GBLUP Models for Genomic Prediction in Red Clover
Authors: Osterman Johanna , Gutiérrez Lucia , Öhlund Linda , Ortiz Rodomiro , Hammenhag Cecilia , Parsons David , Geleta Mulatu
Published: October 2024

Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more than one trait or cut. The target traits were dry matter yield, crude protein content, net energy for lactation, and neutral detergent fiber. The MT models either combined dry matter yield with one forage quality trait, all traits at one cut, or one trait across all cuts. The results show an increase with MT models where the traits had a genetic correlation of 0.5 or above. This study indicates that non-additive genetic effects have significant but varying effects on the predictive ability and reliability of the models. The key conclusion of this study was that these non-additive genetic effects could be better described by incorporating genetically correlated traits or cuts.
Maize Phenotypic Parameters Based on the Constrained Region Point Cloud Phenotyping Algorithm as a Developed Method
Authors: Zhu Qinzhe , Bai Miaoyuan , Yu Ming
Published: October 2024

As one of the world’s most crucial food crops, maize plays a pivotal role in ensuring food security and driving economic growth. The diversification of maize variety breeding is significantly enhancing the cumulative benefits in these areas. Precise measurement of phenotypic data is pivotal for the selection and breeding of maize varieties in cultivation and production. However, in outdoor environments, conventional phenotyping methods, including point cloud processing techniques based on region growing algorithms and clustering segmentation, encounter significant challenges due to the low density and frequent loss of point cloud data. These issues substantially compromise measurement accuracy and computational efficiency. Consequently, this paper introduces a Constrained Region Point Cloud Phenotyping (CRPCP) algorithm that proficiently detects the phenotypic traits of multiple maize plants in sparse outdoor point cloud data. The CRPCP algorithm consists primarily of three core components: (1) a constrained region growth algorithm for effective segmentation of maize stem point clouds in complex backgrounds; (2) a radial basis interpolation technique to bridge gaps in point cloud data caused by environmental factors; and (3) a multi-level parallel decomposition strategy leveraging scene blocking and plant instances to enable high-throughput real-time computation. The results demonstrate that the CRPCP algorithm achieves a segmentation accuracy of 96.2%. When assessing maize plant height, the algorithm demonstrated a strong correlation with manual measurements, evidenced by a coefficient of determination R2 of 0.9534, a root mean square error (RMSE) of 0.4835 cm, and a mean absolute error (MAE) of 0.383 cm. In evaluating the diameter at breast height (DBH) of the plants, the algorithm yielded an R2 of 0.9407, an RMSE of 0.0368 cm, and an MAE of 0.031 cm. Compared to the PointNet point cloud segmentation method, the CRPCP algorithm reduced segmentation time by more than 44.7%. The CRPCP algorithm proposed in this paper enables efficient segmentation and precise phenotypic measurement of low-density maize multi-plant point cloud data in outdoor environments. This algorithm offers an automated, high-precision, and highly efficient solution for large-scale field phenotypic analysis, with broad applicability in precision breeding, agronomic management, and yield prediction.
Innovative Design and Optimization of High-Quality Peanut Digging-Inverter
Authors: Shen Haiyang , Gu Man , Yang Hongguang , Ling Jie , Wu Feng , Gu Fengwei , Pan Liang , Yu Zhaoyang , Hu Zhichao
Published: October 2024

To rapidly dry peanut pods and effectively address mold issues resulting from rainy weather, this article provides an analysis of the current research status of global peanut digging, inverting, and harvesting technologies. Based on a two-stage peanut harvesting model, the operating principles of a high-quality peanut digging-inverter are elaborated upon, and the design of key machine components is discussed. The evaluation metrics for the operation of a high-quality peanut digging-inverter include the rate of vine inverting, the soil content rate, the rate of fallen pods, and the rate of buried pods. Utilizing theoretical analysis, the Box–Behnken experimental design is employed to investigate the operating parameters of these machines, with the tractor’s running speed, the linear speed of the inverting roller, and the linear speed of the conveyor chain serving as the experimental factors in a three-factor, three-level experimental study. A regression model is established to analyze the impact of each factor on operational quality and to comprehensively optimize the influencing factors. The experimental results indicate that all factors significantly affect the rate of vine inverting. The tractor’s running speed (X1) and the linear speed of the inverting roller (X3) significantly influence the rate of buried pods, while the linear speed of the conveyor chain (X2) does not have a significant effect on this rate. Similarly, the tractor’s running speed (X1) and the linear speed of the inverting roller (X3) significantly affect the rate of fallen pods, whereas the linear speed of the conveyor chain (X2) does not. Furthermore, the linear speed of the conveyor chain (X2) and the linear speed of the inverting roller (X3) significantly impact the soil content rate, while the tractor’s running speed (X1) does not. The optimal combination of operating parameters is a tractor running speed of 1 m/s, a conveyor chain linear speed of 1.3 m/s, and an inverting roller linear speed of 2.1 m/s. Under these conditions, the high-quality peanut digging-inverter achieves a vine inverting rate of 89.29%, a buried pod rate of 0.31%, a fallen pod rate of 0.74%, and a soil content rate of 8.11%. The experimental values for each evaluation index exhibit relative errors of 1.14%, 6.45%, 1.35%, and 0.13% compared to the optimized values provided by the Design-Expert software. The findings of this research will facilitate the development of hardware conditions for the future intelligent and information-based harvesting of peanuts, thereby establishing a solid foundation for the high-quality initial processing of oilseeds.
Isolation and Characterization of Canine Adipose-Derived Mesenchymal Stromal Cells: Considerations in Translation from Laboratory to Clinic
Authors: Rivera Orsini Michael A. , Ozmen Emine Berfu , Miles Alyssa , Newby Steven D. , Springer Nora , Millis Darryl , Dhar Madhu
Published: October 2024

In allogeneic MSC implantation, the cells are isolated from a donor different from the recipient. When tested, allogeneic MSCs have several advantages over autologous ones: faster cell growth, sufficient cell concentration, and readily available cells for clinics. To ensure the safe and efficient use of allogeneic MSCs in clinics, the MSCs need to be first tested in vitro. With this study, we paved the way by addressing the in vitro aspects of canine adipose-derived MSCs, considering the limited studies on the clinical use of canine cells. We isolated cAD-MSCs from canine falciform ligament fat and evaluated their viability and proliferation using an MTS assay. Then, we characterized the MSC-specific antigens using immunophenotyping and immunofluorescence and demonstrated their potential for in vitro differentiation. Moreover, we established shipping and cryobanking procedures to lead the study to become an off-the-shelf therapy. During expansion, the cells demonstrated a linear increase in cell numbers, confirming their proliferation quantitatively. The cells showed viability before and after cryopreservation, demonstrating that cell viability can be preserved. From a clinical perspective, the established shipping conditions demonstrated that the cells retain their viability for up to 48 h. This study lays the groundwork for the potential use of allogeneic cAD-MSCs in clinical applications.
CFD Simulation of Dynamic Temperature Variations Induced by Tunnel Ventilation in a Broiler House
Authors: Choi Lak-yeong , Daniel Kehinde Favour , Lee Se-yeon , Lee Chae-rin , Park Ji-yeon , Park Jinseon , Hong Se-woon
Published: October 2024

Maintaining the optimal microclimate in broiler houses is crucial for bird productivity, yet enabling efficient temperature control remains a significant challenge. This study developed and validated a computational fluid dynamics (CFD) model to predict temporal changes in indoor air temperature in response to variable ventilation operations in a commercial broiler house. The model accurately simulated air velocity and airflow distribution for different numbers of tunnel fans in operation, with air-velocity errors ranging from −0.22 to 0.32 m s−1. The predicted airflow rates through inlets and cooling pads showed good agreement with measured values with an accuracy of up to 108.1%. Additionally, the CFD model effectively predicted temperature dynamics, accounting for chicken heat production and ventilation effect. The model successfully predicted the longitudinal temperature gradients and their variations during ventilation cycles, validating its reliability through comparison with experimental data. This study also explored different variable inlet configurations to mitigate the temperature gradient. The variable inlet adjustment showed the potential to relieve the high temperatures but may reduce overall ventilation efficiency or intensify temperature gradients, which confirms the importance of optimising ventilation strategies. This CFD model provides a valuable tool for evaluating and improving ventilation systems and contributes to enhanced indoor microclimates and productivity in poultry houses.
Conventionally Reared Wallon Meat Lambs Carry Transiently Multi-Drug-Resistant Escherichia coli with Reduced Sensitivity to Colistin Before Slaughter
Authors: Dragon Delphine , Jansen Wiebke , Dumont Helene , Wiggers Laetitia , Coupeau Damien , Saulmont Marc , Taminiau Bernard , Muylkens Benoit , Daube Georges
Published: October 2024

Major efforts have been made to reduce the use of colistin in livestock since the discovery of the plasmid-borne mobile colistin resistance (mcr) gene in E. coli a decade ago, to curb the burden of its potential transmission to other bacterial species, spread between animals, humans and the environment. This study explored the longitudinal prevalence and characteristics of colistin-resistant and extended-spectrum beta-lactamase-producing (ESBL) E. coli via in vivo fecal and ex vivo carcass swabs from two batches of conventional indoor and organic outdoor Wallon meat sheep from birth to slaughter in 2020 and 2021. Antimicrobial susceptibility testing via broth microdilution revealed that n = 16/109 (15%) E. coli isolates from conventional meat lamb fecal samples had a reduced colistin sensitivity (MIC = 0.5 μg/mL) and thereof, n = 9/109 (8%) were multi-drug-resistant E. coli, while no resistant isolates were recovered from their carcasses. Sequencing revealed causative pmrB genes, indicating that the reduced sensitivity to colistin was not plasmid-borne. While the sample size was small (n = 32), no colistin-resistant and ESBL-producing E. coli were isolated from the organic meat sheep and their carcasses, potentially due to the different husbandry conditions. Prudent and judicious antimicrobial use and strict slaughter hygiene remain imperative for effective risk management to protect consumers in a sustainable One Health approach.
Bat Ecology and Microbiome of the Gut: A Narrative Review of Associated Potentials in Emerging and Zoonotic Diseases
Authors: Bazzoni Emanuela , Cacciotto Carla , Zobba Rosanna , Pittau Marco , Martella Vito , Alberti Alberto
Published: October 2024

In this review, we tentatively tried to connect the most recent findings on the bat microbiome and to investigate on their microbial communities, that may vary even in conspecific hosts and are influenced by host physiology, feeding behavior and diet, social interactions, but also by habitat diversity and climate change. From a conservation perspective, understanding the potentially negative and indirect effects of habitat destruction on animal microbiota can also play a crucial role in the conservation and management of the host itself. According to the One Health concept, which recognizes an interdependence between humans, animals, and the environment, bat microbiota represents an indicator of host and environmental health, besides allowing for evaluation of the risk of emerging infectious diseases. We noticed that a growing number of studies suggest that animal microbiota may respond in various ways to changes in land use, particularly when such changes lead to altered or deficient food resources. We have highlighted that the current literature is strongly focused on the initial phase of investigating the microbial communities found in Chiroptera from various habitats. However, there are gaps in effectively assessing the impacts of pathogens and microbial communities in general in animal conservation, veterinary, and public health. A deeper understanding of bat microbiomes is paramount to the implementation of correct habitat and host management and to the development of effective surveillance protocols worldwide.
Massa Medicata Fermentata, a Functional Food for Improving the Metabolic Profile via Prominent Anti-Oxidative and Anti-Inflammatory Effects
Authors: Jung Kyung-Mi , Yu Ga-Ram , Kim Da-Hoon , Lim Dong-Woo , Park Won-Hwan
Published: October 2024

Massa Medicata Fermentata (MMF) is a naturally fermented product used to treat indigestion and increase stomach activity in traditional medicine. This study examined the ability of the hydrothermal extract of MMF to scavenge free radicals corresponding to biological oxidative stresses, further protecting essential biomolecules. The anti-inflammatory effects of MMF were evaluated in LPS-induced RAW264.7 macrophages and zebrafish. In addition, the effects of MMF on the body mass index (BMI) and cholesterol accumulation in adult zebrafish fed a high-cholesterol diet (HCD) for three weeks were examined. MMF prevented the DNA and lipid damage caused by oxidative stress, inhibited LDL oxidation, and reduced the expression of cytokines and related proteins (MAPK and NFκB), with prominent anti-oxidative pathway (NRF2-HO-1) activation properties. LPS-induced NO production was reduced, and the increase in BMI and TC caused by the HCD diet was suppressed by MMF in zebrafish embryos or adult zebrafish. The bioactive aglycone of quercetin may be contributing to the mechanisms of systemic effects. MMF has excellent antioxidant properties and is useful for improving inflammation status and metabolic profile, thus highlighting its potential as a healthy, functional food.
Tetramethylpyrazine Analogue T-006 Protects Neuronal and Endothelial Cells Against Oxidative Stress via PI3K/AKT/mTOR and Nrf2 Signaling
Authors: Zhang Guiliang , Liang Zirong , Wang Yuqiang , Zhang Zaijun , Hoi Pui-Man
Published: October 2024

Background: T-006, a novel neuroprotective derivative of tetramethylpyrazine (TMP), exhibits multifunctional neuroprotective properties. T-006 has been shown to improve neurological and behavioral functions in animal models of ischemic stroke and neurodegenerative diseases. The present study aims to further elucidate the mechanisms underlying the protective effects of T-006 against oxidative injuries induced by glutamate or hypoxia. Methods: Mouse hippocampal HT22 cells were used to evaluate the neuroprotective effects of T-006 against glutamate-induced injuries, while mouse brain endothelial bEnd.3 cells were used to evaluate the cerebrovascular protective effects of T-006 against oxygen-glucose deprivation followed by reperfusion (OGD/R)-induced injuries. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry were used to measure cell viability and oxidative stress. Western blot and immunofluorescence analyses of protein expression were used to study cell signaling pathways. Results: T-006 exhibited significant protective effects in both oxidative injury models. In HT22 cells, T-006 reduced cell death and enhanced antioxidant capacity by upregulating mTOR and nuclear factor erythroid 2-related factor 2/Heme oxygenase-1 (Nrf2/HO-1) signaling. Similarly, in bEnd.3 cells, T-006 reduced oxidative injuries and preserved tight junction integrity through Nrf2/HO-1 upregulation. These effects were inhibited by LY294002, a Phosphoinositide 3-kinase (PI3K) inhibitor. Conclusions: T-006 may exert its neuroprotective and cerebrovascular protective effects via the regulation of PI3K/AKT-mediated pathways, which facilitate downstream mTOR and Nrf2 signaling, leading to improved cell survival and antioxidant defenses.
Integrative Human Genetic and Cellular Analysis of the Pathophysiological Roles of AnxA2 in Alzheimer’s Disease
Authors: Ye Lianmeng , Zhao Jiazheng , Xiao Zhengpan , Gu Wenyu , Liu Xiaoxuan , Ajuyo Nuela Manka’a Che , Min Yi , Pei Yechun , Wang Dayong
Published: October 2024

Alzheimer’s disease (AD) is an intractable and progressive neurodegenerative disease. Amyloid beta (Aβ) aggregation is the hallmark of AD. Aβ induces neurotoxicity through a variety of mechanisms, including interacting with membrane receptors to alter downstream signaling, damaging cellular or organelle membranes, interfering with protein degradation and synthesis, and inducing an excessive immune-inflammatory response, all of which lead to neuronal death and other pathological changes associated with AD. In this study, we extracted gene expression profiles from the GSE5281 and GSE97760 microarray datasets in the GEO (Gene Expression Omnibus) database, as well as from the Human Gene Database. We identified differentially expressed genes in the brain tissues of AD patients and healthy persons. Through GO, KEGG, and ROC analyses, annexin A2 (AnxA2) was identified as a putative target gene. Notably, accumulating evidence suggests that intracellular AnxA2 is a key regulator in various biological processes, including endocytosis, transmembrane transport, neuroinflammation, and apoptosis. Thus, we conducted a series of cell biology experiments to explore the biological function of AnxA2 in AD. The results indicate that AnxA2 gene knockdown primarily affects oxidative phosphorylation, cell cycle, AD, protein processing in the endoplasmic reticulum, SNARE interactions in vesicular transport, and autophagy. In SH-SY5Y cells secreting Aβ42, AnxA2 gene knockdown exacerbated Aβ42-induced cytotoxicity, including cell death, intracellular ROS levels, and neuronal senescence, altered cell cycle, and reduced ATP levels, suggesting its critical role in mitochondrial function maintenance. AnxA2 gene knockdown also exacerbated the inhibitory effect of Aβ42 on cell migration. AnxA2 overexpression reduced the inflammatory response induced by Aβ42, while its absence increased pro-inflammatory and decreased anti-inflammatory responses. Furthermore, AnxA2 gene knockdown facilitated apoptosis and decreased autophagy. These results indicated potential pathophysiological roles of AnxA2 in AD.
Leveraging Machine Learning for Sophisticated Rental Value Predictions: A Case Study from Munich, Germany
Authors: Chen Wenjun , Farag Saber , Butt Usman , Al-Khateeb Haider
Published: October 2024

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R2 score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R2 and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector.
Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language
Authors: Jiang Lanlan , Qin Xingguo , Zhang Jingwei , Li Jun
Published: October 2024

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model’s training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation.
An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening
Authors: Zhang Ding , Lv Liangliang , Shao Wen , Tang Jinyuan , Zhao Jiuyue , Zhang Huiyun
Published: October 2024

Introduction: The complex geometry of herringbone gear can lead to uneven surface strengthening, which affects the overall effect of treatment. Methods: A discrete element model (DEM) of shot peening for herringbone gears was developed, incorporating accurate gear surface parameters to study impact characteristics along the tooth profile. A finite element model (FEM) was created for small local units of the gear surface to calculate the residual stress and roughness. Results: There are a large number of low-velocity shots at the root of the gear, and the closer to the top of the gear, the higher the impact velocity of the shots, but the number of impacts also decreases. The surface roughness Sa near the root of the tooth is the smallest, the Sa at the pitch circle is the largest, and the Sa at the top of the tooth is intermediate. However, the residual stress levels at different positions of the tooth surface are not significantly different. Conclusion: The difference in tooth surface roughness of herringbone gear is the synergistic effect of shot impact velocity and shot frequency, but this synergistic effect has no significant effect on the stress after shot peening.
Design and Research of a Virtual Laboratory for Coding Theory
Authors: Aliev Yuksel , Ivanova Galina , Borodzhieva Adriana
Published: October 2024

This article examines the process of creating and researching a virtual laboratory for training students in coding theory. General information about the virtual laboratories is presented. A methodology for conducting comprehensive scientific research is presented, as well as a specialized method for designing and developing the virtual laboratory, including the system’s architecture. The article also presents brief information about the developed system. The results of the experimental research, including a pedagogical experiment aimed at evaluating the influence of the virtual laboratory on the students’ success rate, as well as an analysis of the statistical data on their activity, are discussed in detail. In addition, a survey was conducted to assess the satisfaction of students with the use of the laboratory.
Adaptive Ransomware Detection Using Similarity-Preserving Hashing
Authors: AlMajali Anas , Elmosalamy Adham , Safwat Omar , Abouelela Hassan
Published: October 2024

Crypto-ransomware is a type of ransomware that encrypts the victim’s files and demands a ransom to return the files. This type of attack has been on the rise in recent years, as it offers a lucrative business model for threat actors. Research into developing solutions for detecting and halting the spread of ransomware is vast, and it uses different approaches. Some approaches rely on analyzing system calls made via processes to detect malicious behavior, while other methods focus on the affected files by creating a file integrity monitor to detect rapid and abnormal changes in file hashes. In this paper, we present a novel approach that utilizes hashing and can accommodate large files and dynamically take into account the amount of change within each file. Mainly, our approach relies on dividing each file into partitions and then performing selective hashing on those partitions to rapidly detect encrypted partitions due to ransomware. Our new approach addresses the main weakness of a previous implementation that relies on hashing files, not file partitions. This new implementation strikes a balance between the detection time and false positives based on the partition size and the threshold of partition changes before issuing an alert.
Recovered Foam Impact Absorption Systems
Authors: Marcelino-Sádaba Sara , Benito Pablo , Martin-Antunes Miguel Ángel , Roldán Pedro Villanueva , Veiga Fernando
Published: October 2024

The use of foam materials in environments where they come into contact with individuals often results in deterioration, necessitating periodic replacements to maintain safety and hygiene standards. Foam, a lightweight, porous plastic formed by aggregated bubbles, possesses excellent impact-absorbing properties; however, its inherent porosity and susceptibility to wear present challenges. This project aims to develop a technological application for repurposing spent polyurethane (PU) foam from leisure facilities into effective impact absorption systems. By focusing on the reuse of deteriorated foam materials, this initiative seeks to minimize environmental impact while leveraging their beneficial technical characteristics. Addressing issues related to foam degradation, this project endeavors to create sustainable solutions by reintegrating spent foam into new systems. This innovative approach promotes sustainability while enhancing safety through the provision of high-quality, impact-resistant elements. Ultimately, this work aims to contribute to environmental conservation and the advancement of effective impact protection measures in leisure facilities.
Evaluation of the Effect of the Technical Guidelines for Chilean Milk Dietary Service on the Microbiological and Nutritional Quality of a Powdered Infant Formula
Authors: Bustos-Arriagada Edson , Mendoza Andrea Lizeth Doblado , Mariotti-Celis María Salomé
Published: October 2024

The Chilean Technical Manual for Milk Dietary Service (SEDILE) was evaluated to determine its effectiveness in ensuring the microbiological safety and vitamin D adequacy of a commercially available powdered infant formula (PIF) used in SEDILE. The evaluation focused on whether adherence to the manual’s guidelines positively influenced these factors. Both the PIF and the reconstituted PIF (RIF) were found to be free from Cronobacter sakazakii and Salmonella enterica contamination, with levels of total coliforms and Escherichia coli within acceptable limits. Moreover, the vitamin D content in the formula was within the expected range. These findings suggest that following the Chilean Technical Manual for SEDILE contributes to the microbiological safety and nutritional adequacy of RIF in dietary services.
Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
Authors: Li Chengming , Tao Min , Du Haolun , Wang Ziming , Song Junfeng
Published: October 2024

The pulse laser emission circuit plays a crucial role as the emission unit of time-of-flight (TOF) LiDAR. This paper proposes a nanosecond-level pulse laser diode array drive circuit for LiDAR, primarily aimed at addressing the issue of high-speed scanning drive for the laser diode array at the emission end of solid-state LiDAR. Based on the single pulse laser diode drive circuit, this paper innovatively designs a circuit that includes modules such as a boost circuit, linear power supply, high-speed gate driver, GaN field-effect transistor, and pulse narrowing circuit, realizing an 8-channel laser diode array drive circuit. This circuit can achieve a pulse laser array drive with a single channel operating frequency of greater than 100 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time that does not exceed 1 μs. A field programmable gate array (FPGA) is used to control the operation of this circuit and perform a series of performance tests. Experimental results show that this circuit has a high repetition rate, large output power, a narrow pulse width, and fast switching speeds, making it highly suitable for use in the optical emission module of solid-state LiDAR.
Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
Authors: Fang Qiang , Ma Mingyu , Xiao Dong , Wang Ming , Ning Xiaoqi
Published: October 2024

The extraction of shale gas via horizontal drilling presents considerable challenges, primarily due to the accumulation of cuttings within the annular space, resulting in increased friction, torque, and potential drilling complications. To address this issue, the study proposes an experimental setup aimed at simulating cuttings transport under various operational conditions, with a particular emphasis on gas wells. The methodology encompasses the modulation of the drilling fluid flow rate and the drill’s rotational speed to examine the transport velocity of cuttings. Furthermore, the study analyzes the impact of annular eccentricity on return volume, transport time, and cuttings bed height. Critical initiation velocities for cuttings across different motion modes were also determined, and theoretical calculations were compared with empirical data. The findings indicate that an increased flow rate of drilling fluid and higher rotation speed substantially improve the transport of cuttings, thereby minimizing bed formation, whereas increased eccentricity hinders this process. The results revealed that the theoretical model showed a greater overestimation of the start-up velocity for spherical particles, with average errors ranging from 15.50% to 17.56%. In contrast, the model exhibited better accuracy for non-spherical (flaky) particles, with errors between 8.63% and 9.61%. Under non-rotating conditions, the average error of the model was approximately 8.32%, while the introduction of drill tool rotation increased the average error to 11.94%. These results have the potential to optimize operational parameters in shale gas well drilling and may contribute to the development of specialized borehole purification tools.
Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
Authors: Pérez-Zarate Sergio A. , Corzo-García Daniel , Pro-Martín Jose L. , Álvarez-García Juan A. , Martínez-del-Amor Miguel A. , Fernández-Cabrera David
Published: October 2024

Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer’s images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals.
The Influence of Pelvic Tilt and Femoral Torsion on Hip Biomechanics: Implications for Clinical Assessment and Treatment
Authors: Donati Danilo , Giorgi Federica , Farì Giacomo , Tarallo Luigi , Catani Fabio , Tedeschi Roberto
Published: October 2024

Background: Hip biomechanics are crucial in understanding movement disorders and joint pathologies. Pelvic tilt and femoral torsion are key factors influencing hip function and stability. This review aims to elucidate their effects on acetabular orientation, hip range of motion (ROM), and associated compensatory mechanisms. Methods: A comprehensive search of databases, including PubMed, Scopus, and Pedro, was conducted. Studies were selected based on Population, Concept, and Context (PCC) criteria, focusing on those examining the relationship between pelvic tilt, femoral torsion, and hip biomechanics. Eight studies were included, utilizing methodologies such as CT imaging, musculoskeletal modeling, and gait analysis, covering populations ranging from asymptomatic adults to elderly women with pelvic retroversion. Results: The review identified significant effects of pelvic retroversion on acetabular anteversion and hip extension. Some studies found no correlation between pelvic index (PI) and acetabular orientation, while others reported a linear increase in acetabular anteversion with pelvic retroversion. Subjects with pelvic retroversion showed greater hip extension. Additionally, femoral anteversion was linked to increased internal rotation and altered hip joint contact forces during gait, with changes in hip ROM and force distribution. No relationship was found between femoral and acetabular anteversion in patients with hip osteoarthritis. Conclusions: Pelvic tilt and femoral torsion significantly affect hip biomechanics, influencing acetabular orientation, ROM, and compensatory mechanisms. Future studies should include symptomatic populations to explore these relationships further. These findings emphasize the critical need for individualized clinical assessments and further research on symptomatic populations to enhance our understanding of pelvic tilt and femoral torsion’s impact on hip biomechanics.
Waste Bread as Raw Material for Ethanol Production: Effect of Mash Preparation Methods on Fermentation Efficiency
Authors: Balcerek Maria , Dziekońska-Kubczak Urszula , Pielech-Przybylska Katarzyna , Oleszczak Anna , Koń Magdalena , Patelski Andrea Maria
Published: October 2024

The issue of managing waste bread is a global concern, with significant environmental and the economic implications. The utilisation of waste bread for bioethanol production, employing energy-saving technology, could prevent these consequences and reduce the consumption of traditionally used fossil fuels. The objective of this study was to evaluate the influence of the type of waste bread (wheat and wheat–rye sourdough) and the mash preparation method on the results of alcoholic fermentation and the concentration of selected congeners in the distillates. The highest fermentation efficiency (96% of theoretical) was achieved for both types of bread through the utilisation of the pressureless starch liberation method combined with simultaneous saccharification and fermentation. The separate saccharification of starch resulted in lower process efficiencies (from 85.75 to 88.60% of theoretical). The application of the native starch hydrolysis method (without starch activation) for the fermentation of wheat bread-based mashes exhibited a higher efficiency (87.85% of the theoretical) than that observed for the wheat–rye bread-based mash sample (83.74% of theoretical). All of the obtained spirit distillates exhibited a low concentration of methanol (≤300 mg/L alcohol 100% v/v) and comply with the requirements of the EU regulation for ethyl alcohol of agricultural origin (rectified spirit).
A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems
Authors: Alabdulatif Abdullah , Thilakarathne Navod Neranjan
Published: October 2024

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.