bluuebunny/crossref_metadata_2025 huggingface dataset:
Public perceptions and influencing factors of seasonal influenza vaccine uptake in Makkah region, Saudi Arabia: a cross-sectional study
Authors: Alharthi Mohammed S. , Alshehri Abdullah A. , Baali Fahad H. , Altuwairqi Nawaf Awd , Altalhi Meshari Hassan , Almalki Rayan Azib , Aljuaid Meshal Khalid , Algarni Majed A. , Alzahrani Mohmmed S. , Alorfi Nasser M. , Alruqayb Wadia S.
Published: February 2025

Background

Seasonal influenza vaccination is essential for reducing the risk and impact of influenza. Makkah region in Saudi Arabia, a destination for millions of pilgrims during Hajj and Umrah, presents a unique context for this study. Therefore, this research investigates the prevalence and influencing factors of influenza vaccine uptake among the public in Makkah region, Saudi Arabia.

Method

This cross-sectional study was conducted in Saudi Arabia between February and June 2024. The main researcher developed the questionnaire, which was reviewed by five academics and then piloted with 20 individuals for validation. An online questionnaire was used, targeting residents aged 18 and over with internet access. A snowball sampling method was applied. Data were collected via Google Forms and analyzed using SPSS version 26, employing descriptive statistics such as frequencies, percentages, and means.

Results

The total number of respondents is 450 participants, with a response rate of 4.5%. Most respondents were male (71.1%) and aged between 18 and 30 years (44.5%). Education levels varied, with 57.5% holding a bachelor’s degree. Vaccination uptake showed 65.1% had received the vaccine, with 31.1% confident it is safe. Despite this, 58.8% did not get vaccinated this season. Confidence in the vaccine’s effectiveness was 59.5%, though 41.2% reported breakthrough infections. Accessibility was generally rated easy (57.7%). Social pressure (17.4%), trust in health institutions (36.9%), and COVID-19 concerns (36.7%) significantly influenced vaccination decisions. The likelihood of vaccination next season displayed polarized views.

Conclusion

This study provides significant insights about vaccine hesitancy that may inform future research endeavors and practical applications. Understanding the various factors that impact the adoption of influenza vaccines offers valuable insights for developing targeted interventions and public health policies to improve vaccination rates. This study enhances theoretical understanding and practical strategies to encourage influenza vaccination, thereby improving public health initiatives in the area and beyond.
How does pole length affect lower back muscle activity at different inclines and skiing intensities during double poling?
Authors: Ohlsson Marie Lund , Nilsson Marcus , Swarén Mikael
Published: February 2025

Purpose

This study aimed to investigate how pole length, incline, and skiing intensity affect lower back muscle activation in elite cross-country skiers. This addressing a critical gap in understanding the biomechanical demands and risk of low back pain of double poling (DP).

Method

Eleven elite cross-country skiers performed skiing trials on a treadmill, varying in incline (flat vs. 6°), intensity (two self-selected speeds, training speed and racing speed), and pole lengths. Muscle activity was measured by surface electromyography on the erector spinae thoracic and lumbar muscles, on the left and right side. A motion capture system was used for kinematic analysis of the lower back-pole moment arm and the hip angle during the DP cycle.

Results

Compared to men, female skiers had a significantly higher (p < 0.001), overall muscle activation for the m. erector spinae lumbar on both the left and right side (26% of MVC vs. 15% of MVC, and 28% of MVC vs. 22% of MVC, for the left and right side, respectively). No correlations were found, neither between muscle activation to the lower back-pole moment arm, or to the hip angle.

Conclusion

The findings suggest that female skiers experience significantly higher lumbar erector spinae activation during DP, potentially indicating greater susceptibility to back-related issues. The results also highlight the need to tailor training and right adapted equipment to mitigate lower back stress, especially in flat terrain high-intensity conditions. The asymmetrical muscle activity and gender differences underscores the need for further investigation into biomechanical factors influencing back muscle engagement in cross-country skiing.
Economic analysis of Agaricus bisporus mushrooms production and the perspective of sharing economy
Authors: Schilla Filip , Mumladze Gaga , Soukupova Jana , Smutka Luboš
Published: February 2025

Introduction

Urban agriculture, particularly mushroom cultivation in basements, offers a sustainable solution to climate change, land scarcity, and food security challenges. This article explores the viability of cultivating Agaricus bisporus (white button mushrooms) in basements, while also considering the potential of a sharing economy business model to address these issues.

Methods

We simulate basement and ground-level room conditions, comparing factors such as temperature, humidity, light exposure, and air quality, which are crucial for mushroom growth. Economic calculations are also included to assess costs and profits, allowing for a comparison of feasibility and cost-effectiveness between the two environments.

Results

The basement environment proves more favorable for mushroom cultivation, with stable temperatures and higher humidity reducing energy requirements. Economic analysis shows that basement cultivation has lower initial and operational costs compared to ground-level spaces.

Discussion

Basement cultivation of Agaricus bisporus offers a sustainable solution for urban agriculture, addressing food security and land scarcity. Integrating this practice into a sharing economy model could further enhance its feasibility, contributing to environmental sustainability and economic growth in cities.
Short-term effects of brachycephalic obstructive airway syndrome surgery on fitness and exercise in brachycephalic dogs
Authors: Goossens Juliette , Meyer-Lindenberg Andrea , Zablotski Yury , Schroers Maike
Published: February 2025

Introduction

Brachycephalic obstructive airway syndrome (BOAS) significantly impacts the physical fitness of affected dogs. This study aimed to assess changes in physical fitness, as measured by vital signs and salivary cortisol and vasopressin concentrations, before and after BOAS surgery using a standardized fitness test.

Methods

A prospective clinical study was conducted on 35 brachycephalic dogs, including 13 Pugs, 21 French Bulldogs, and one English Bulldog. A preliminary examination included clinical anamnesis and a general examination. Physical fitness was evaluated using a submaximal treadmill test consisting of three 5-minute runs followed by a recovery phase. Vital signs were monitored throughout the test and saliva samples were taken before, immediately after and 15 min after the test. Eighteen animals underwent surgery due to moderate to severe BOAS symptoms, while 17 animals that were free of symptoms were assigned to the control group.

Results

One month post-OP (post-operatively), dogs that underwent BOAS surgery exhibited significant improvement in physical fitness based on vital signs. However, they remained significantly less fit than the control group. No statistically significant changes were observed in salivary cortisol or vasopressin concentrations before and after surgery.

Discussion

BOAS surgery reduces clinical symptoms and improves physical fitness, but affected dogs continue to exhibit substantial limitations. The consistency of cortisol and vasopressin levels across both groups reinforces the hypothesis of a stress-induced HPA-axis dysfunction, yet the limited number of evaluable samples and external influences suggest that salivary stress hormones alone may not reliably indicate surgical success. Future studies should incorporate additional biomarkers and clinical assessments to better understand the physiological impact of BOAS and its treatment.
Magnetic resonance angiography for cerebral arterial and venous visualization in dogs: a comparative study of time-of-flight, phase-contrast, and time-resolved imaging of contrast kinetics sequences
Authors: Hong Sunghwa , Kim Soyeon , Namgoong Mire , Kwon Minjoo , Yoon Junghee , Choi Jihye
Published: February 2025

This study aims to compare the effectiveness of three magnetic resonance angiography (MRA) sequences—Time-of-Flight (TOF), Phase-Contrast (PC), and Time-Resolved Imaging of Contrast Kinetics (TRICKS)—in visualizing cerebral arteries and veins in dogs. The goal is to determine the most suitable MRA sequence for improving diagnostic accuracy in canine cerebral imaging. Five healthy adult beagles underwent imaging using a 1.5-T MRI system. Quantitative measures, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), were calculated, and qualitative evaluations of vessel delineation and visualization symmetry were performed by blinded observers. The three sequences were statistically compared using Friedman and Wilcoxon signed-rank tests. All dogs completed the imaging protocols without any complications. TOF MRA showed the best results for visualizing cerebral arteries, with the highest SNR and CNR in major arteries like the basilar, rostral, caudal cerebral, and caudal communicating arteries. TRICKS MRA performed better than TOF and PC for venous imaging, achieving higher SNR or CNR in most veins, including the transverse, temporal, straight sinus and dorsal cerebral vein, and had the shortest acquisition time. In qualitative evaluations, TOF MRA demonstrated acceptable or higher vascular visibility in most of brain arteries. All venous structures were significantly better visualized with TRICKS MRA compared to TOF MRA. Interobserver agreement was high, showing consistency in the results. These findings suggest that TOF is best for artery imaging, while TRICKS is ideal for venous imaging and faster procedures. TOF is recommended for clear and detailed imaging of cerebral arteries, while TRICKS is more suitable for imaging veins and situations where faster scans are needed. These results provide a basis for improving MRA techniques in veterinary medicine, helping to make diagnoses more accurate and procedures more efficient in both clinical practice and research.
Numerical Predictions of Low-Reynolds-Number Propeller Aeroacoustics: Comparison of Methods at Different Fidelity Levels
Authors: Huang Guangyuan , Sharma Ankit , Chen Xin , Riaz Atif , Jefferson-Loveday Richard
Published: February 2025

Low-Reynolds-number propeller systems have been widely used in aeronautical applications, such as unmanned aerial vehicles (UAV) and electric propulsion systems. However, the aerodynamic sound of the propeller systems is often significant and can lead to aircraft noise problems. Therefore, effective predictions of propeller noise are important for designing aircraft, and the different phases in aircraft design require specific prediction approaches. This paper aimed to perform a comparison study on numerical methods at different fidelity levels for predicting the aerodynamic noise of low-Reynolds-number propellers. The Ffowcs-Williams and Hawkings (FWH), Hanson, and Gutin methods were assessed as, respectively, high-, medium-, and low-fidelity noise models. And a coarse-grid large eddy simulation was performed to model the propeller aerodynamics and to inform the three noise models. A popular propeller configuration, which has been used in previous experimental and numerical studies on propeller noise, was employed. This configuration consisted of a two-bladed propeller mounted on a cylindrical nacelle. The propeller had a diameter of D=9″ and a pitch-to-diameter ratio of P/D=1, and was operated in a forward-flight condition with a chord-based Reynolds number of Re=4.8×104, a tip Mach number of M=0.231, and an advance ratio of J=0.485. The results were validated against existing experimental measurements. The propeller flow was characterized by significant tip vortices, weak separation over the leading edges of the blade suction sides, and small-scale vortical structures from the blade trailing edges. The far-field noise was characterized by tonal noise, as well as broadband noise. The mechanism of the noise generation and propagation were clarified. The capacities of the three noise modeling methods for predicting such propeller noise were evaluated and compared.
Research on an Efficient Network Advanced Orbiting Systems Comprehensive Multiplexing Algorithm Based on Elastic Time Slots
Authors: Zhu Haowen , Zhang Zhen , Li Zhen , Cheng Jinwei , Jin Zhonghe
Published: February 2025

To address the inadequacies of traditional Advanced Orbiting Systems (AOS) multiplexing algorithms in accommodating the networked and diverse transmission demands of space data, this paper proposes an efficient network AOS integrated multiplexing algorithm based on elastic time slots. The AOS network traffic is categorized into three types based on its characteristics, and a strongly scalable AOS integrated multiplexing model is established, which consists of a packet multiplexing layer, a virtual channel multiplexing layer, and a decision-making layer. For synchronous services, an isochronous frame generation algorithm and a periodic polling virtual channel scheduling algorithm are employed to meet the periodic transmission requirements. For asynchronous non-real-time services, a high-efficiency frame generation algorithm and a uniform queue length virtual channel scheduling algorithm are utilized to satisfy the high-efficiency transmission requirements. For asynchronous real-time services, an adaptive frame generation algorithm based on traffic prediction and a virtual channel scheduling algorithm based on comprehensive channel state are proposed. These algorithms optimize frame generation efficiency and dynamically calculate optimal scheduling results based on virtual channel scheduling status, transmission frame scheduling status, virtual channel priority status, and traffic prediction status, thereby meeting the high dynamics, low latency, and high efficiency transmission requirements. Additionally, a slot preemption-based elastic time slot scheduling strategy is proposed at the decision layer, which dynamically adjusts and optimizes the time slot allocation for the three types of traffic based on the current service request status and time slot occupancy status. Simulation results show that the proposed algorithm not only achieves lower average delay, fewer frame residuals, and higher transmission efficiency, but also maintains high stability under different working conditions, effectively meeting the transmission requirements of various types of space network traffic.
Research on a Potato Leaf Disease Diagnosis System Based on Deep Learning
Authors: Zhang Chunhui , Wang Shuai , Wang Chunguang , Wang Haichao , Du Yingjie , Zong Zheying
Published: February 2025

Potato is the fourth largest food crop in the world. Disease is an important factor restricting potato yield. Disease detection based on deep learning has strong advantages in network structure, training speed, detection accuracy, and other aspects. This article took potato leaf diseases (early blight and viral disease) as the research objects, collected disease images to construct a disease dataset, and expanded the dataset through data augmentation methods to improve the quantity and diversity of the dataset. Four classic deep learning networks (VGG16, MobilenetV1, Resnet50, and Vit) were used to train the dataset, and the VGG16 network had the highest accuracy of 97.26%; VGG16 was chosen as the basic research network. A new, improved algorithm, VGG16S, was proposed to solve the problem of large network parameters by using three improvement methods: changing the network structure of the VGG16 network from “convolutional layer + flattening layer + fully connected layer” to “convolutional layer + global average pooling”, integrating CBAM attention mechanism, and introducing Leaky ReLU activation function for learning and training. The improved VGG16S network has a parameter size of 15 M (1/10 of VGG16), and the recognition accuracy of the test set is 97.87%. This article used response surface analysis to optimize hyperparameters, and the test results indicated that VGG16S, after hyperparameter tuning, had further improved its diagnostic performance. At last, this article completed ablation experiments and public dataset testing. The research results will provide a theoretical basis for the timely adoption of corresponding prevention and control measures, improving the yield and quality of potatoes and increasing economic benefits.
Design and Experiments of Automatic Seedling Separation Device for Vegetable Substrate Block Seedling Transplanter
Authors: Zheng Shuhe , Li Jicheng , Dong Zhenfa , Wang Jufei , Weng Wuxiong , Cui Zhichao , Wang Can , Wu Xinhui
Published: February 2025

To address the critical challenges of low success rates and high seedling damage in automatic transplanters for vegetable substrate block seedlings, this study took cabbage substrate block seedlings as the research object and designed a silica gel wheel–synchronous belt clamping seedling separation device. An experimental platform was constructed to perform a three-factor, three-level orthogonal test, investigating the effects of the wheelbase of the silica gel wheel, the inclination angle of the conveyor belt, and the wheelbase of the silica gel wheel and the synchronous belt on seedling separation success rate and substrate block breakage rate. A quadratic regression model was established to analyze the influence of each factor on the index and to optimize the parameter combination verification test. The results showed that the seedling separation effect was better when the wheelbase of the silica gel wheel was 60.47 mm, the inclination angle of the conveyor belt was 8.67°, and the wheelbase of the silica gel wheel and seedling separation synchronous belt was 39.8 mm. The success rate of seedling separation was 90.21% and the substrate block breakage rate was 6.88% in the field verification test of this parameter combination. When the operating speed is 60 plants/min, there is a higher success rate of seedling separation and a lower substrate block breakage rate. This study explored the conditions for stable seedling separation using the seedling separation device, and provided practical reference for the study of the automatic seedling separation of substrate block seedlings.
Optimizing Straw and Manure Co-Substitution Regimes to Maintain Stable Crop Yields Through Enhanced Soil Stoichiometric Balance
Authors: Li Peipei , Shang Yuanyi , Li Hui , Li Fang , Wang Yi , Zhu Xueqiang , Li Shiying , Wang Xiaolin , Han Yanlai
Published: February 2025

The benefits of partially substituting inorganic fertilizers with organic fertilizers have been extensively acknowledged. However, the key mechanisms behind nutrient transformation and supply for stable crop yields are still not fully understood. Based on an 11-year field experiment with a wheat–maize rotation system, this study explored the advantages of combined straw and manure substitution under various organic substitution regimes. These regimes included an unfertilized control (CK), inorganic nitrogen, phosphorus, and potassium fertilizers (NPK), NPK substituted with straw (NPKS), NPK substituted with manure (NPKM), and NPK substituted with both straw and manure (NPKSM). Compared to NPK and NPKS, NPKM and NPKSM significantly improved wheat yield by 12.8% and 13.8%, respectively. Bulk soil organic carbon (SOC), total nitrogen (TN), available superphosphate (AP), β-glucosidase (βG), urease (URE), and alkaline phosphatase (ALP) were all higher in the NPKM treatment than in the NPKSM treatment. However, compared to NPKM, NPKSM significantly decreased the potential nitrification rate by 31.0% and increased the soil NH4+-N content. Correspondingly, the functional genes of nitrification were also found to be decreased in the NPKSM treatment. In the rhizosphere, most soil factors increased compared to bulk soil, but treatment differences were smaller. However, the differences among treatments were reduced in the rhizosphere. The high amount of manure applied in the NPKM treatment caused excessive soil phosphorus accumulation, reaching over 46.7 mg/kg, resulting in lower N/P and C/P ratios. The soil quality index (SQI), based on soil nutrients, enzymes, functional genes, and C:N:P stoichiometry, was 9.9% higher in NPKSM than in NPKM. Bulk soil SQIs showed stronger correlations with wheat yields than rhizosphere SQIs, highlighting that bulk soil was superior to rhizosphere in predicting crop yield. Partial least squares path modeling showed that C/N, N/P, and C/P ratios strongly influenced SQIs. The NPKSM treatment, which improved soil nutrients, biological factors, and balanced C:N:P stoichiometry, is an effective strategy for sustainable agriculture. Future practices should focus on maintaining stoichiometric balance to sustain soil quality and crop yields.
LRNTRM-YOLO: Research on Real-Time Recognition of Non-Tobacco-Related Materials
Authors: Zhang Chunjie , Yun Lijun , Yang Chenggui , Chen Zaiqing , Cheng Feiyan
Published: February 2025

The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related materials, the model was augmented by incorporating an additional layer dedicated to enhancing the detection of small targets, thereby improving the overall accuracy. Furthermore, an attention mechanism was incorporated into the backbone network to focus on the features of the detection targets, thereby improving the detection efficacy of the model. Simultaneously, for the introduction of the SIoU loss function, the angular vector between the bounding box regressions was utilized to define the loss function, thus improving the training efficiency of the model. Following these enhancements, a channel pruning technique was employed to streamline the network, which not only reduced the parameter count but also expedited the inference process, yielding a more compact model for non-tobacco-related material detection. The experimental results on the NTRM dataset indicate that the LRNTRM-YOLO model achieved a mean average precision (mAP) of 92.9%, surpassing the baseline model by a margin of 4.8%. Additionally, there was a 68.3% reduction in the parameters and a 15.9% decrease in floating-point operations compared to the baseline model. Comparative analysis with prominent models confirmed the superiority of the proposed model in terms of its lightweight architecture, high accuracy, and real-time capabilities, thereby offering an innovative and practical solution for detecting non-tobacco-related materials in the future.
Selenium Nanoparticles (SeNPs) Inhibit the Growth and Proliferation of Reproductive Structures in Phytophthora capsici by Altering Cell Membrane Stability
Authors: López-Gervacio Andrés de Jesús , Qui-Zapata Joaquín Alejandro , Barrera-Martínez Iliana , Montero-Cortés Mayra Itzcalotzin , García-Morales Soledad
Published: February 2025

Selenium nanoparticles (SeNPs) are currently receiving attention for controlling plant pathogenic microorganisms, are expected to be especially effective against the genus Phytophthora, and show high anti-oomycete activity. SeNPs synthesized with plant extracts have shown low toxicity, high bioavailability, and mechanisms of action that alter cellular integrity and damage key components of phytopathogen metabolism, causing denaturation and cell death. The aim of this study was to evaluate the inhibitory activity of SeNPs on mycelial growth and the development of reproductive structures in Phytophthora capsici in vitro. Different concentrations of SeNPs (0 to 400 µg/mL) in culture media were used to analyze mycelial growth, sporangium formation, zoospores, and germination of the germ tube. To explain the changes in morphology and development of P. capsici, increased relative conductance and activation of glycerol synthesis were related to osmotic stress and damage to membrane permeability. In addition, SeNPs inhibited the production of exopolysaccharides (EPSs), which are compounds associated with pathogen virulence. A lower accumulation of its biomass evidences alterations in the oomycete growth. The percentage of inhibition of mycelial growth increased with higher SeNP concentrations and incubation time, reaching 100% growth inhibition at 300 and 400 µg/mL. A concentration-dependent reduction in the number of spores, sporangia, and zoospore germination was observed. Concentrations of 50 and 100 µg/mL of SeNPs reduced biomass production by 30%. The increase in glycerol levels indicated an osmoregulatory response to SeNP-induced stress. Also, the increase in electrical conductivity suggested plasma membrane damage, which supports the potential of SeNPs as antifungal agents by inducing cell disruption and structural damage in P. capsici. These results provide new knowledge on the in vitro mechanism of action of SeNPs against P. capsici and offer a new biological alternative for the control of diseases caused by oomycetes.
A Fused Multi-Channel Prediction Model of Pressure Injury for Adult Hospitalized Patients—The “EADB” Model
Authors: Barghouthi Eba’a Dasan , Owda Amani Yousef , Owda Majdi , Asia Mohammad
Published: February 2025

Background: Pressure injuries (PIs) are increasing worldwide, and there has been no significant improvement in preventing them. Traditional assessment tools are widely used to identify a patient at risk of developing a PI. This study aims to construct a novel fused multi-channel prediction model of PIs in adult hospitalized patients using machine learning algorithms (MLAs). Methods: A multi-phase quantitative approach involving a case–control experimental design was used. A first-hand dataset was collected retrospectively between March/2022 and August/2023 from the electronic medical records of three hospitals in Palestine. Results: The total number of patients was 49,500. A balanced dataset was utilized with a total number of 1110 patients (80% training and 20% testing). The models that were developed utilized eight MLAs, including linear regression and support vector regression (SVR), logistic regression (LR), random forest (RF), gradient boosting (GB), K-nearest neighbor (KNN), decision tree (DT), and extreme gradient boosting (XG boosting) and validated with five-fold cross-validation techniques. The best model was RF, for which the accuracy was 0.962, precision was 0.942, recall was 0.922, F1 was 0.931, area under curve (AUC) was 0.922, false positive rate (FPR) was 0.155, and true positive rate (TPR) was 0.782. Conclusions: The predictive factors were age, moisture, activity, length of stay (LOS), systolic blood pressure (BP), and albumin. A novel fused multi-channel prediction model of pressure injury was developed from different datasets.
Lactobacillus plantarum Alters Gut Microbiota and Metabolites Composition to Improve High Starch Metabolism in Megalobrama amblycephala
Authors: Qian Linjie , Lu Siyue , Jiang Wenqiang , Mu Qiaoqiao , Lin Yan , Gu Zhengyan , Wu Yeyang , Ge Xianping , Miao Linghong
Published: February 2025

The aim of this study was to explore the effects of adding Lactobacillus plantarum (LAB) to a high-starch diet on glucose and lipid metabolism, gut microbiota, and the composition of metabolites in Megalobrama amblycephala. This experiment was equipped with three isonitrogenous and isoenergetic feeds as control group (LW), high starch group (HW), and high starch with LAB group (HP). A total of 180 experimental fish (13.5 ± 0.5 g) were randomly divided into three treatments, and three floating cages (1 m × 1 m × 1 m) were set up for each treatment. A total of 20 fish per net were kept in an outdoor pond for 8 weeks. The results showed that both the HW and HP groups had an altered structure and a reduced diversity of gut microbiota. LAB increased the abundance of Cetobacterium and the ratio of Firmicutes/Bacteroidota and decreased PC (16:1/20:5) and taurochenodeoxycholic acid levels. LAB promoted the expression of genes related to the intestinal bile acid cycle (fxr, hmgcr, rxr, shp and hnf4α) and inhibited the expression of pparβ and g6pase (p < 0.05). LAB reduced the expression of genes related to transported cholesterol (lxr and ldlr) (p < 0.05) in the liver. In conclusion, LAB addition could regulate the gut microbiota disorders caused by high starch levels, promote cholesterol metabolism, produce bile acids, and reduce lipid deposition.
Crude Protein Degradation Kinetics of Selected Tropical Forages in Buffalo Using NorFor In Situ Standards
Authors: Riaz Roshan , Bilal Rana Muhammad , Hassan Mahmood Ul , Todaro Massimo , Gannuscio Riccardo , Inal Fatma , Haque Muhammad Naveed Ul , Tahir Muhammad Naeem
Published: February 2025

This study aimed to evaluate the crude protein (CP) degradation kinetics and degradability (CPD) of cereal and legume fodder species grown at two geographically distant locations. Ten forage species, comprising six cereals (barley, maize, millet, oats, sorghum, and wheat) and four legumes (berseem, jantar, lucerne, and mustard), were evaluated to determine the effects of forage family, species, and location of growth on CP degradation fractions and effective CPD. The forage crops were cultivated under uniform agronomic practices at two distinct agro-ecological locations and were harvested at the booting stage (cereals) and 50% flowering stage (legumes). Dried and ground samples were incubated in the rumen of four Nili-Ravi buffalo fitted with rumen cannula. The incubation periods utilized in the experiment were 0, 4, 8, 16, 24, and 48 h, and a 4 × 2 × 2 split-plot design was employed. The results showed that the CP degradation fractions and CPD were significantly affected by forage family, species, and location of growth. Wide variations in degradation kinetics and degradability existed among and within the cereal and legume fodders, with wheat and jantar ranked at the top. Legume forages had larger soluble fractions, smaller potentially degradable fractions, and rapid rates and extent of degradation of dietary proteins than cereal forages. The cooler climatic conditions at location 2 increased the rapidly degradable protein fraction and overall CPD, whereas the warmer climatic conditions enhanced the slowly degradable protein fractions, thereby reducing the overall protein degradability in tropical forages. It was quite evident that some fodder species, such as maize among the cereal fodders and mustard among the legume fodders, remained quite non-responsive to the effects of the climatic conditions. A moderately positive and linear relationship between the rate of degradation and CPD was established for cereals, whereas a strongly positive and quadratic relationship was established for legume fodders. In conclusion, forage species, family, and location of growth significantly affected the degradation fractions and degradability of tropical cereal and legume fodders.
Naringin Suppresses CoCl2-Induced Ferroptosis in ARPE-19 Cells
Authors: Yang Yuchang , Liu Manting , Dong Xiaoxv , Bai Jie , Shi Wenjuan , Zhu Qian , Liu Juan , Wang Ziheng , Yi Lisa , Yin Xingbin , Ni Jian , Qu Changhai
Published: February 2025

Hypoxic damage to retinal pigment epithelial (RPE) cells and subsequent neovascularization are key factors in the pathogenesis of branch retinal vein occlusion (BRVO). Naringin (NG), a naturally occurring flavanone glycoside, has demonstrated significant antioxidant and anti-neovascular activities. However, the regulatory effects and mechanisms of NG on ferroptosis in BRVO are yet to be explored. Our study aimed to investigate the protective effects of NG on RPE cells under hypoxic stress and to elucidate the underlying molecular mechanisms. Our findings revealed that NG significantly reduced cytotoxicity induced by cobaltous chloride (CoCl2) and also inhibited vascular proliferation in the retina, thereby attenuating choroidal neovascularization. NG pretreatment largely countered the overproduction of reactive oxygen species (ROS) and malondialdehyde (MDA) triggered by hypoxic damage, while also restoring levels of the antioxidants glutathione (GSH) and superoxide dismutase (SOD). Furthermore, NG pretreatment significantly activated the expression of hypoxia-inducible factor-1 alpha (HIF-1α) and its downstream heme oxygenase-1 (HO-1) and NADPH dehydrogenase (NQO1). In conclusion, NG not only inhibits neovascularization but also alleviates inflammation in RPE cells by modulating the HO-1/GPX4 pathway to inhibit ferroptosis. These findings highlight the potential of NG as a promising therapeutic agent for the treatment of BRVO.
Research on Anchor Cable Force Detection Technology Based on Magnetic Intensity Distribution of Permanent Magnet
Authors: Jiang Haifei , Hu Anwen , Zhang Yiyong
Published: February 2025

The anchor cables of slopes are affected by long-term environmental corrosion, geotechnical creep, and adverse weather, resulting in gradual loss of tensile force, which can lead to structural failure and subsequent safety accidents. The authors of this paper conducted research based on the magnetic induction density distribution characteristics of permanent magnets, including model derivation, theoretical simulation, and indoor experiments, aiming to propose a new anchor cable force monitoring technology with high sensitivity, strong applicability, and good stability. Based on the molecular circulation model and the Biot–Savart law, the analytical expression of the spatial magnetic field distribution of a rectangular permanent magnet was derived and, combined with the stress–strain relationship characteristics of anchor cables, a theoretical model for the relationship between anchor cable tensile force and magnetic induction density was established. MATLAB (R2018b) was used to simulate and analyze the spatial magnetic field distribution and the force–magnetism relationship. The analysis showed that the magnetic induction density along the central axis of the permanent magnet approximately exhibited a symmetrical quadratic curve distribution, and its value was significantly negatively correlated with the anchor cable force. Based on this, a new anchor cable force monitoring technology was proposed, and an indoor experimental platform was established. The indoor experimental studies further confirmed the negative correlation between force and magnetism (i.e., as the tensile force increases, the magnetic induction strength decreases, and as the tensile force decreases, the magnetic induction strength increases). The fitting results of the force–magnetism curve show that a quadratic function can better describe the correspondence between magnetic induction density and anchor cable force. Reproducibility analysis of the experimental data showed low dispersion in magnetic induction values under various design loads, along with good stability, validating the effectiveness and applicability of the proposed anchor cable force monitoring technology.
Fine-Tuning Large Language Models for Ontology Engineering: A Comparative Analysis of GPT-4 and Mistral
Authors: Doumanas Dimitrios , Soularidis Andreas , Spiliotopoulos Dimitris , Vassilakis Costas , Kotis Konstantinos
Published: February 2025

Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question–answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models’ abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency.
Prediction, Prevention, and Control of “Overall–Local” Coal Burst of Isolated Working Faces Prior to Mining
Authors: Zhang Ming , Yang Shiji
Published: February 2025

Ensuring the accurate prediction, prevention, and control of coal bursts in isolated working faces is crucial for ensuring safe mining operations. Coal bursts are typically caused by the accumulation of stress and energy released in coal seams and the overlying strata. This study focuses on the 76 isolated working faces at Shanxi Wuyang Mine, employing a combination of theoretical analysis, numerical simulation, and field monitoring. Through theoretical analysis, the study examines the influence of the spatial structure of the overlying strata on support stress and develops corresponding estimation functions. Additionally, bearing strength calculation formulas under varying confining pressures are derived. Numerical simulations are used to validate the effectiveness of borehole stress relief, while field monitoring further confirms the accuracy of the proposed model, leading to the development of the “overall–local” coal burst prediction method. The results demonstrate that the proposed method effectively assesses coal burst risks and, based on different coal burst types, recommends borehole stress relief and roof deep-hole blasting as primary mitigation strategies. These methods were successfully applied to the 76 isolated working faces at Wuyang Mine, yielding conclusions of overall stability with localized instability. This study provides new insights into coal burst prediction theory and offers practical guidance for preventive engineering in isolated working faces, demonstrating substantial engineering applicability.
Discreetly Exploiting Inter-Session Information for Session-Based Recommendation
Authors: Sun Jian , Wang Zihan , Wu Gang , Wang Haotong , Qiao Baiyou , Han Donghong
Published: February 2025

Limited intra-session information has constrained the performance of early Graph Neural Network (GNN)-based session-based recommendation (SBR) models. To address this issue, researchers have increasingly incorporated inter-session information to improve next-item prediction. Although such additional information can provide valuable cues, it may also introduce undesirable interference. However, existing approaches often fail to effectively extract relevant information while filtering out extraneous interference, largely owing to incomplete modeling of inter-session dependencies. Specifically, inter-session connections have been examined merely at the item level, overlooking more intricate distinctions and associations between sessions. Moreover, current methods rely exclusively on similarity as the metric for evaluating inter-session dependencies, overlooking their multidimensional complexity. In this work, we propose a GNN-based SBR model that discreetly exploits inter-session information (DEISI). To overcome the first limitation, we introduce factor-level inter-session dependencies using disentangled representation learning, enabling the capture of finer-grained interactions. To address the second limitation, we design a novel metric named “stability” to complement similarity, providing an additional perspective on inter-session relationships. Consequently, DEISI constructs more detailed and reliable inter-session dependencies. Extensive experiments on three benchmark datasets demonstrate the superior performance of DEISI over state-of-the-art models.
The Sensitivity Analysis of Parameters in the 1D–2D Coupled Model for Urban Flooding
Authors: Tang Zuohuai , Chu Junying , Zhou Zuhao , Zhou Tianhong , Yuan Kangqi
Published: February 2025

The ongoing changes in climate and the rapid pace of urbanization are contributing to an alarming increase in the prevalence of urban flooding, which is having a profound impact on the quality of life for residents and the smooth functioning of urban areas. The 1D–2D coupled model is an effective tool for simulating the process of urban flooding, thereby providing a scientific basis for urban planning, flood prevention, and mitigation strategies. The values of numerous parameters within the model not only influence the computational efficiency but also influence the precision of the simulation outcomes. It is of particular significance to ascertain the sensitivity of model parameters. In this study, a 1D–2D coupled model of urban flooding was constructed, and a parameter sensitivity analysis was conducted using the modified Morris method and the Sobol method in two ways, with the amount of waterlogging as the target. The findings indicate that the minimum infiltration rate is the most sensitive parameter in the local sensitivity analysis, whereas the Manning coefficient of the permeable surface area is the most sensitive in the global sensitivity analysis. The research outcomes can facilitate the optimization of the model parameters and enhance the precision and dependability of the model predictions, thereby providing more accurate data support for urban flooding early warning and emergency response.
Dynamic Response and Settlement Cause Analysis of Roadbed–Soft Clay Foundation System Under Traffic Vibration Loads
Authors: Xia Fan , Xia Tangdai , Wang Zihao
Published: February 2025

Roadbeds constructed on soft clay foundations often experience substantial post-construction settlement under long-term traffic loading. To investigate this phenomenon, numerical simulations were conducted to study the vibration response of various roadbed–foundation systems subjected to pavement vibration loads, and the propagation patterns of vibration waves within these systems were analyzed. Vibration reconsolidation tests were performed to examine the reconsolidation behavior of soft clays under vibration loads (cyclic loads without overall compressive or tensile tendencies) following initial consolidation under static loads. The influence of vibration load frequency and amplitude on the reconsolidation effect was briefly analyzed. By combining the analysis of the consolidation mechanism of soft clays under vibration loads, this study investigated the reasons for the large post-construction settlement of roadbed–soft clay foundation systems under traffic vibration loads from both wave propagation and vibration perspectives. Based on the focus on the equilibrium state reached by the soil under vibration loads, the vibration reconsolidation test represents a new research approach. In addition to deepening the understanding of the post-construction settlement phenomenon of foundations under traffic loads, it can also provide guidance for practical engineering applications such as foundation treatment and sludge solidification using vibration loads, generating potential economic benefits.
Model-Based Optimization of the Field-Null Configuration for Robust Plasma Breakdown on the HL-3 Tokamak
Authors: He Muwen , Yang Bin , Chen Yihang , Xu Xinliang , Zhu Xiaobo , Yang Jiaqi , Sun Jiang , Liu Panle , Li Bo , Ji Xiaoquan
Published: February 2025

This paper introduces a self-consistent field-null optimization algorithm of a poloidal magnetic field that precisely accounts for the influence of vacuum vessel eddy currents. Building on existing poloidal field (PF) coil currents, the algorithm can refine these waveforms to achieve various target field-null configurations. Firstly, based on the TokSys toolbox, a response model, including the PF coils and vacuum vessel circuits for the HL-3 tokamak, is developed under the MATLAB® and Simulink™ framework. The resistivity parameters of the model are calibrated using experimental data obtained from single-coil discharge tests. Subsequently, an iterative method was employed to simultaneously solve the dynamic field-null optimization problem within a specified spatial region and precisely account for the effect of passive eddy currents. Typically, B⊥≤1 G within a large area can be obtained with this iterative scheme, which can be stably sustained for over 15 milliseconds to ensure the robustness of breakdown. Finally, a low-pass filtered PID controller is applied to the model to achieve precise control of the PF coils currents, confirming the feasibility of implementing the proposed algorithm in real experiments.
Retinitis Pigmentosa Classification with Deep Learning and Integrated Gradients Analysis
Authors: Ferreira Hélder , Marta Ana , Machado Jorge , Couto Inês , Marques João Pedro , Beirão João Melo , Cunha António
Published: February 2025

Inherited retinal diseases (IRDs) are genetic disorders affecting photoreceptors and the retinal pigment epithelium, leading to progressive vision loss. Retinitis pigmentosa (RP), the most common IRD, manifests as night blindness, peripheral vision loss, and eventually central vision decline. RP is genetically diverse and can be categorized into non-syndromic and syndromic. Advanced imaging technologies such as fundus autofluorescence (FAF) and spectral-domain optical coherence tomography (SD-OCT) facilitate diagnosing and managing these conditions. The integration of artificial intelligence in analyzing retinal images has shown promise in identifying genes associated with RP. This study used a dataset from Portuguese public hospitals, comprising 2798 FAF images labeled for syndromic and non-syndromic RP across 66 genes. Three pre-trained models, Inception-v3, ResNet-50, and VGG-19, were used to classify these images, obtaining an accuracy of over 80% in the training data and 54%, 56%, and 54% in the test data for all models. Data preprocessing included class balancing and boosting to address variability in gene representation. Model performance was evaluated using some main metrics. The findings demonstrate the effectiveness of deep learning in automatically classifying retinal images for different RP-associated genes, marking a significant advancement in the diagnostic capabilities of artificial intelligence and advanced imaging techniques in IRD.