bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated metabolomic and transcriptomic analysis of Pogostemon cablin shed new light on the complete biosynthesis pathway of pogostone
Authors: Wang Sen , Zeng Zhaoqi , Zhang Qi , Liu Wenhua , Liu Qinjian , Xie Chong , Bei Jinlong , Chen Bingxian , Zhang Aixia
Published: February 2025

Pogostemon cablin (patchouli) is a well-known perennial herbaceous plant for traditional Chinese medicine, and its primary bioactive compounds are patchoulol and pogostone. The biosynthesis pathway of patchouli has been well resolved early, while the biosynthesis pathway of pogostone is still not fully resolved due to the lack of terminal enzyme directly synthesizing pogostone. Here, the present study aims to predict the terminal enzyme of pogostone biosynthesis and reconstruct its most possible complete biosynthesis, through the integrated transcriptomic and metabolomic analysis. The metabolomic and transcriptomic profiles of patchouli leaf were largely different to those of root and stem. Patchoulol analogs like patchoulene and germacrene mainly accumulated in leaf, while pogostone content was much higher in root. Based on the integrated analysis of differentially expressed genes and metabolites, we reconstructed the biosynthesis pathways of patchoulol, and predicted the most likely complete biosynthesis pathway of pogostone. Besides, we identified 29 highly-expressed genes involved in pogostone biosynthesis for the neo-octoploid genome of patchouli, and most of their expression levels were strongly correlated with pogostone content. In particular, patchouli BAHD-DCR acyltransferases (BAHD-DCRs) were phylogenetically distant from but structurally similar to the other known plant BAHD acyltransferases. Most of them possessed the conservative catalysis motif HXXXD, and the catalysis center could bind to the widely recognized substrate molecules of 4-hydroxy-6-methyl-2-pyrone and 4-methylvaleryl-CoA and product molecule of pogostone. Thus, the highly-expressed BAHD-DCRs in patchouli root were proposed to be terminal enzymes directly synthesizing pogostone. The findings here provide more supporting evidence for the medical use of patchouli whole plants, and make an important step forward fully resolving the pogostone biosynthesis pathway. The identified genes involved in pogostone biosynthesis, especially BAHD-DCRs, deserve further investigation and utilization in the synthetic production of pogostone.
The compositions, characteristics, health benefits and applications of anthocyanins in Brassica crops
Authors: Li Xinjie , Wang Fan , Ta Na , Huang Jinyong
Published: February 2025

Brassica crops, well known for their nutritional and medicinal value, encompass a diverse range of species and varieties, many of which are rich in anthocyanins. These flavonoid pigments not only contribute to the vibrant colors of Brassica plants but also possess significant antioxidant, anti-inflammatory, and neuroprotective properties. This review provides an in-depth analysis of the distribution, composition, and health benefits of anthocyanins in Brassica crops, highlighting their potential applications in the food industry and medicine. We discuss the accumulation patterns of anthocyanins in various Brassica tissues, the influence of genetic and environmental factors on their concentration, and the impact of acylation on their stability and biological activities. This review also explores the antioxidant capacity and cardioprotective effects of Brassica anthocyanins, as well as their roles in protecting against hepatic and renal injury and promoting neuroprotection. Furthermore, we examine the use of anthocyanins as natural food colorants and their integration into intelligent packaging for the real-time monitoring of food freshness. Our findings underscore the multifaceted benefits of Brassica anthocyanins, positioning them as key components in the development of functional foods and sustainable food systems.
Influence of perceived parental views of failure on academic resilience among middle school students: a moderated mediation model
Authors: He Wenying , Shi Dasheng
Published: February 2025

Focusing on a sample of middle school students, this study examined the impact of perceived parental views of failure on academic resilience, as well as the mediating role of growth mindset and the moderating role of parental involvement in education. A total of 2,546 Chinese middle school students were recruited from the Beijing, Fujian, and Guizhou regions to complete a questionnaire. The data were analyzed using structural equation modeling (SEM) in SPSS 26.0 and PROCESS. The results indicate that perceived parental positive views of failure significantly and positively predict academic resilience, and this relationship is mediated by growth mindset. Furthermore, parental involvement in education moderates the direct effect within the mediation model. Interestingly, this effect is stronger at high levels of parental involvement in education but is not evident at low levels. Additionally, the moderating effects of specific dimensions of parental involvement in education (emotional, cognitive, and behavioral involvement) differ. Emotional involvement demonstrates the strongest moderating influence, while behavioral involvement shows the weakest. These findings provide theoretical insights and empirical support for developing intervention strategies aimed at enhancing the academic resilience of middle school students through family education.
Perceptions of the determinants of health and cancer: trends in discourse and level of argumentation between girls and boys aged 6 to 18
Authors: Deyra Maéliane , Gay Chloé , Gerbaud Laurent , Berland Pauline , Pizon Frank
Published: February 2025

Aims

To characterize the discourse trends and level of argumentation of girls and boys by analysing the differences in the conceptions and systems of conceptions of children and adolescents aged between 6 and 18.

Materials and methods

This is a multi-centre qualitative study in the human and social sciences, based on data collected in two different phases using two different tools: e.Photoexpression©, which deals with health in general, and Photonarration, which deals with cancer. The aim of this open, exploratory method, which uses photographs, is to gather data on the experiences and knowledge specific to each child and adolescent. The informative value of data from qualitative collection tools, combined with a mixed analysis methodology, enabled us to characterize the differences in perceptions of health determinants and cancer between girls and boys aged between 6 and 18.

Results

4,174 productions were collected from 1,068 children aged 6 to 18, identifying 30 determinants of health and cancer. For all of these results, there were significant gender differences from a very early age: boys focused on leisure activities and physical activity, while girls took a more global view, focusing on the environment, food, emotional aspects of social relationships, hygiene, care, prevention, etc. As they got older, the focus shifted to health determinants. As we get older, we see a change in discourse trends and in the level of argumentation, with girls becoming richer and boys poorer.

Discussion and outlook

The trends in girls’ and boys’ discourse on what, in their view, determines health demonstrate the interest and relevance of adapting the prevention methods used as closely as possible to the conceptions of children and adolescents. The differences observed between boys and girls are a crucial lever that takes into account the specific characteristics of a population. They offer the possibility of taking more effective action, both in the context of interventions aimed at teenagers and in support of decision-making in the context of prevention policies.
An Eye in the Sky — Tracking Ammonia Pollution From Space
Authors: Coheur Pierre , Van Damme Martin , Clarisse Lieven , Whitburn Simon , Franco Bruno , Clerbaux Cathy
Published: February 2025

Ammonia is a chemical commonly found in fertilizers, cleaning products, and even animal waste. While this substance does have some important uses, rising emissions of ammonia in the atmosphere can pose serious risks to human health, plant and animal health, and the environment. Therefore, measuring and tracking ammonia levels in the air is crucial, especially as human activities, such as large-scale farming and industrial processes, release more and more ammonia. In this article, we will tell you about the main sources of ammonia pollution, the dangers it poses to health and the environment, and how scientists are using satellites to monitor ammonia levels from space. By keeping a close eye on ammonia trends, researchers hope to trigger ways to reduce emissions and protect the planet for future generations.
Color me khmao: the effects of social factors on colorism among Khmer women
Authors: Veak Vanessa Lakana
Published: February 2025

Introduction

Given the lack of academic literature on colorism within the Cambodian community and the lack of focus on how colorism influences experiences in this context, this study seeks to analyze how Khmer women’s perceptions of colorism are shaped by their family and community environments.

Methods

The data and methods consist of 40 in-depth qualitative interviews with Khmer women, primarily those living in the United States and Cambodia, with their experiences of colorism analyzed through thematic analysis.

Results

Findings reveal that factors such as family support and cultural connections to Khmer identity interact with relative skin tone within families and broader communities to shape self-esteem and experiences with colorism. Women with darker skin did not necessarily have the lowest self-esteem, especially when they had supportive families and lived in communities where darker skin tones were more prevalent. Conversely, women with lighter skin did not necessarily have the highest self-esteem, especially when their families reinforced color hierarchies and they lived in communities dominated by White or East Asian Americans.

Discussion

By further examining this structural issue, colorism, the study highlights how communities of color can work toward racial and ethnic justice while developing strategies for future generations to challenge and move beyond colorism.
The role of playing position in soccer injury characteristics: evidence from sub-elite athletes
Authors: Thema Makete Thomas , Jacobs Suzanne , van den Berg Linda , Strauss Anita , Mahlangu Mzwandile Prescott
Published: February 2025

This study examines the association between playing position and injury characteristics among sub-elite male soccer players in South Africa. Using a cross-sectional survey, 223 players from four universities were assessed for injury prevalence, type, mechanism, and severity during the 2023 soccer season. Midfielders experienced the highest injury frequency (43.6%), followed by defenders (30.0%), forwards (17.9%), and goalkeepers (8.6%). Lower limb injuries were predominant across all positions (89.6%), with defenders (94.6%) and midfielders (95.1%) at greatest risk, while goalkeepers sustained a significant proportion of upper limb injuries (44.4%) due to their specialized role. Soft tissue injuries were most common among midfielders (78.0%) and defenders (67.6%), whereas goalkeepers reported higher rates of bone-related injuries (66.7%). Defensive actions, such as tackling, accounted for most injuries among defenders (56.8%), while aerial play contributed substantially to goalkeeper injuries. No statistically significant differences in injury severity were found across positions. These findings highlight the influence of playing position on injury characteristics and underscore the need for position-specific injury prevention strategies tailored to sub-elite soccer players.
Efficacy of valgus osteotomy in treating nonunion of femoral neck fractures post-internal fixation
Authors: Xu Haohao , Yang Shenda , Zheng Yuanyuan , Tian Ao , Wu Ruofei , Zhang Xiaoyu , Mao Yujiang
Published: February 2025

Background

Nonunion of femoral neck fractures following internal fixation presents a significant challenge, particularly in young patients. This study evaluated the efficacy of valgus osteotomy in treating femoral neck fracture nonunion after failed internal fixation in young adults.

Methods

A retrospective analysis was conducted including a total of 23 male patients with a mean age of 32.9 years, who underwent valgus osteotomy for femoral neck fracture nonunion. The procedure involved lateral closing wedge osteotomy at the subtrochanteric level, followed by fixation with a dynamic hip screw (DHS). The postoperative follow-up period exceeded one year.

Results

Significant improvements in biomechanical parameters were observed. The mean neck-shaft angle increased from 118.91° to 139.26° (p < 0.001), whereas the Pauwels angle decreased from 62.07° to 46.91° (p < 0.001). The median Harris Hip Score improved from 63 to 96 (p < 0.001). Limb length demonstrated a trend toward improvement, increasing from a median of 73.30–74.50 cm, although this was not statistically significant (p = 0.077). Union was achieved in 22 of 23 patients, with a median healing time of 8.5 months.

Conclusion

Valgus osteotomy is an effective treatment for femoral neck fracture nonunion in young adults, offering improved biomechanics, good union rates, and enhanced functional outcomes. This technique is a viable option for preserving the native hip joint in challenging patients.
Groin Hernia Repair, the History of the Open Pre-Peritoneal Route Towards a Minimally Invasive Approach. Narrative Review
Authors: Soler Marc
Published: February 2025

The history of pre peritoneal groin hernia surgery start only after solving the problems related to asepsis, antisepsis and anesthesia. Fundamental work on the use of a new form of polyethylene to create synthetic meshes was carried out in the 1950s by C. Usher. L. Nyhus was the first to popularize the use of a mesh. But the inventor of the first synthetic prosthesis was Don Eugène Acquaviva in 1944, and the first surgeon to discuss the installation of a pre-peritoneal prosthesis for the treatment of hernias of the groin is Jerome Corti in his thesis in 1949. In the 50 s and 60 s H. Fruchaud had particularly and directly influenced Jean Rives and René Stoppa, and due to the poor results of techniques without prosthesis, particularly for complex hernias Rives and Stoppa techniques were then disseminated with lots of variations, (G. Wantz, J.H. Alexandre, R. D. Kugel….) But the parietalization step was difficult to achieve for many colleagues and the development of endoscopy has made it possible to clearly demonstrate this crucial step in order to properly unroll the prosthesis. Franz Ugahary put up resistance against endoscopy with the Grid Iron technique in 1995, the fist open minimal invasive pre peritoneal approach. In 2004, Pelissier invented a specific semi-rigid prosthesis, which made it possible to codify with colleagues the Trans Inguinal Pre-Peritoneal (TIPP) technique. But it was also necessary to master the step of parietalization of the cord, this is probably why the ONSTEP technique was created in 2005. It is a partially preperitoneal technique without parietalization W. Akkersdick has tackled the challenge with the Trans Rectus sheath Pre Peritoneal (TREPP) technique in 2006, a pure posterior approach. For my part I modified the TIPP technique in 2011 using Ugahary’s dissection principles, the Minimal Open Pre Peritoneal (MOPP) technique was created. It is only in recent years that the literature has provided data about TIPP, TREPP, MOPP, with comparisons with others techniques. Now the new route, preperitoneal, minimal open and minimal invasive has its place in the treatment of groin hernias!
A Variable-Threshold Segmentation Method for Rice Row Detection Considering Robot Travelling Prior Information
Authors: He Jing , Dong Wenhao , Tan Qingneng , Li Jianing , Song Xianwen , Zhao Runmao
Published: February 2025

Accurate rice row detection is critical for autonomous agricultural machinery navigation in complex paddy environments. Existing methods struggle with terrain unevenness, water reflections, and weed interference. This study aimed to develop a robust rice row detection method by integrating multi-sensor data and leveraging robot travelling prior information. A 3D point cloud acquisition system combining 2D LiDAR, AHRS, and RTK-GNSS was designed. A variable-threshold segmentation method, dynamically adjusted based on real-time posture perception, was proposed to handle terrain variations. Additionally, a clustering algorithm incorporating rice row spacing and robot path constraints was developed to filter noise and classify seedlings. Experiments in dryland with simulated seedlings and real paddy fields demonstrated high accuracy: maximum absolute errors of 59.41 mm (dryland) and 69.36 mm (paddy), with standard deviations of 14.79 mm and 19.18 mm, respectively. The method achieved a 0.6489° mean angular error, outperforming existing algorithms. The fusion of posture-aware thresholding and path-based clustering effectively addresses the challenges in complex rice fields. This work enhances the automation of field management, offering a reliable solution for precision agriculture in unstructured environments. Its technical framework can be adapted to other row crop systems, promoting sustainable mechanization in global rice production.
Evaluation of Weed Infestations in Row Crops Using Aerial RGB Imaging and Deep Learning
Authors: Nikolova Plamena D. , Evstatiev Boris I. , Atanasov Atanas Z. , Atanasov Asparuh I.
Published: February 2025

One of the important factors negatively affecting the yield of row crops is weed infestations. Using non-contact detection methods allows for a rapid assessment of weed infestations’ extent and management decisions for practical weed control. This study aims to develop and demonstrate a methodology for early detection and evaluation of weed infestations in maize using UAV-based RGB imaging and pixel-based deep learning classification. An experimental study was conducted to determine the extent of weed infestations on two tillage technologies, plowing and subsoiling, tailored to the specific soil and climatic conditions of Southern Dobrudja. Based on an experimental study with the DeepLabV3 classification algorithm, it was found that the ResNet-34-backed model ensures the highest performance compared to different versions of ResNet, DenseNet, and VGG backbones. The achieved performance reached precision, recall, F1 score, and Kappa, respectively, 0.986, 0.986, 0.986, and 0.957. After applying the model in the field with the investigated tillage technologies, it was found that a higher level of weed infestation is observed in subsoil deepening areas, where 4.6% of the area is infested, compared to 0.97% with the plowing treatment. This work contributes novel insights into weed management during the critical early growth stages of maize, providing a robust framework for optimizing weed control strategies in this region.
Microcrystal Delivery Using a Syringe and Syringe Pump Method for Serial Crystallography
Authors: Nam Ki Hyun
Published: February 2025

Serial crystallography (SX) determines the crystal structures of target molecules at room temperature with minimal radiation damage. During SX data collection, the stable delivery of many microcrystals to the X-ray interaction point is crucial for efficient sample consumption and effective beamtime usage. Most microcrystal delivery techniques for SX require sophisticated devices or specialized techniques, which can be challenging for data collection. This review introduces a straightforward method that delivers microcrystal samples in SX experiments using a commercially available syringe and syringe pump. This method does not require specialized skills for sample delivery and can be tested in the laboratory prior to SX data collection at the beamline. Advantages and disadvantages of this method are also discussed, along with various application cases. This straightforward sample delivery approach is concluded to facilitate efficient SX data collection.
Automated Dataset-Creation and Evaluation Pipeline for NER in Russian Literary Heritage
Authors: Kassab Kenan , Teslya Nikolay , Vozhik Ekaterina
Published: February 2025

Developing robust and reliable models for Named Entity Recognition (NER) in the Russian language presents significant challenges due to the linguistic complexity of Russian and the limited availability of suitable training datasets. This study introduces a semi-automated methodology for building a customized Russian dataset for NER specifically designed for literary purposes. The paper provides a detailed description of the methodology employed for collecting and proofreading the dataset, outlining the pipeline used for processing and annotating its contents. A comprehensive analysis highlights the dataset’s richness and diversity. Central to the proposed approach is the use of a voting system to facilitate the efficient elicitation of entities, enabling significant time and cost savings compared to traditional methods of constructing NER datasets. The voting system is described theoretically and mathematically to highlight its impact on enhancing the annotation process. The results of testing the voting system with various thresholds show its impact in increasing the overall precision by 28% compared to using only the state-of-the-art model for auto-annotating. The dataset is meticulously annotated and thoroughly proofread, ensuring its value as a high-quality resource for training and evaluating NER models. Empirical evaluations using multiple NER models underscore the dataset’s importance and its potential to enhance the robustness and reliability of NER models in the Russian language.
Assessment of Fluoride-Releasing Materials in Remineralization of Adjacent Demineralized Enamel
Authors: Puttipanampai Onsinee , Panpisut Piyaphong , Sitthisettapong Thanya
Published: February 2025

This study aimed to evaluate the ability of fluoride-releasing restorative materials to remineralize artificially demineralized proximal enamel adjacent to class II restorations. Fifty-four demineralized enamel lesions were created on extracted premolar teeth, and the baseline lesion depth (LD) and mineral density (MD) were measured using micro-CT. The samples were randomly assigned to one of four groups, each in contact with a class II restorative material: Filtek Z350 XT™ (FZ), Cention N® (CN), Fuji II LC® (FJ), or Equia Forte® (EQ). Finally, post 14 days of pH cycling, measurements were taken. SEM, EDX, and Raman Imaging Microscopy were additionally performed. The results showed significant reductions in LD for all fluoride-releasing materials (CN, FJ, EQ). EQ and CN showed a significantly higher percentage change in LD than FZ. The MD of adjacent demineralized enamel increased significantly in all groups. There was no significant difference in the percentage change in MD between groups. SEM-EDX images revealed greater mineral deposition in fluoride-releasing materials than FZ. The Ca/P ratio of demineralized enamel adjacent to CN was equal to that of EQ, while FJ demonstrated the highest ratio. In conclusion, fluoride-releasing materials exhibited a reduction in the LD of adjacent demineralized enamel and demonstrated Ca/P ratios higher than FZ. CN may be an alternative restorative material to remineralize adjacent demineralized enamel.
An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
Authors: Liang Kui , Ren Xudong , Yin Hao , Jiang Jiayong , Xiang Liang , Dai Wei
Published: February 2025

In the manufacturing of parts, the machining process is a critical factor for ensuring product quality. Process condition monitoring is essential for this process. Traditional monitoring approaches that usually use static thresholds are inefficient and lag behind process monitoring, which limits the regulation capability of process monitoring. In this paper, a new adaptive process monitoring method based on process state information is proposed. More precisely, two critical issues are addressed. First, adaptive control limits improve the dynamic monitoring ability. Second, designing monitoring strategies for quality trends improves the monitoring efficiency. In this paper, a framework for an adaptive process monitoring method is designed. First, the modulus maxima is calculated to improve the preprocessing method of wavelet threshold denoising. Second, an adaptive monitoring strategy is designed for different quality trends to improve the monitoring efficiency. Third, the real-time adaptive update of the control limits is realized for machining condition monitoring based on the artificial adaptive linear element (ADALINE) neural network. Finally, experiments are conducted to validate the feasibility of the proposed approach, and the results show that the proposed approach has good applicability as needed in practice.
Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
Authors: Naito Takeshi , Nishijima Shota , Nishikawa Yuichiro , Hirano Akira
Published: February 2025

We proposed a novel technique for detecting optical filter shift in ROADMs in optical transmission lines by applying machine learning on DP-16QAM constellation data captured just after Analogue-to-Digital Converters (ADCs) in a digital coherent receiver. For this purpose, we implemented Docker container applications in WhiteBox Cassini to acquire the real-time raw digital data. By using the captured data, we generated CNN models for the detections in off-line processing and used them for real-time detections. As preliminary results, we confirmed the successful detection of optical filter shift in real-time with an accuracy of 51 GHz. To enhance the detection accuracy, we challenged ourselves to reproduce digital coherent constellation data by using a Generative Adversarial Network (GAN) for real-time optical filter shift detection for the first time. By utilizing a GAN, we successfully generated clone data based on actual constellation data. By adding the cloned data onto the actually captured data, we successfully enhanced detection sensitivity to as high as 26 GHz. As a result, we reduced the amount of required data for the high detection accuracy by 68% with the help of GAN-supported data augmentation. Furthermore, we confirmed our augmentation method enables the prediction of faults before they occur by enabling high enough detection sensitivity to detect an optical filter shift before degradation of Bit Error Rates (BERs) appears. This demonstrates the potential of GAN-based data augmentation in optimizing the efficiency and precision of optical network impairment sensing by using captured digital coherent optical signal.
Igneous Rock Occurrence Prediction Based on Seismic Information Inversion and Its Influence Analysis During Coal Mining: A Case Study in Huaibei Coalfield
Authors: Li Juanjuan , Li Fanjia , Fan Yanan , Wang Bo , Fu Tianchi
Published: February 2025

Overlying hard and thick igneous rocks pose numerous potential safety hazards during the exploitation of coal resources. Identifying the spatial distribution of igneous rocks and analyzing their impact on coal mining are a primary research concern. In this study, a coal mine was investigated in depth. Initially, based on the seismic information, the authors predicted the occurrence conditions of igneous rocks in coal measure strata. Subsequently, two models were developed via the UDEC software4.00: one with igneous layers and the other without. Using the simulation results, the change law of stress, the roof abscission layer, and roof strata subsidence in the overburden during coal face advancement were analyzed. Through a comparison of the simulation results, the hazard-causing mechanism of the igneous intrusion was discussed. Consequently, the occurrence of igneous rocks in the overburden is crucial for predicting potential safety hazards, and the seismic inversion method can be considered an effective tool for evaluating overlying igneous strata.
Diagnostic Value of microRNA Signatures for Early and Non-Invasive Detection of Colorectal Cancer: A Systematic Review and Meta-Analysis
Authors: Purnomo Hery Djagat , Permatadewi Cecilia Oktaria , Hutami Hesti Triwahyu , Indiarso Didik , Muniroh Muflihatul
Published: February 2025

Colorectal cancer (CRC) continues to be a primary contributor to the global health burden, and early detection is vital for optimal outcomes. Standard detection techniques, including colonoscopy and fecal occult blood tests, have been confirmed effective yet their invasiveness and poor sensitivity are a limitation. MicroRNA (miRNA) are now recognized as stable non-invasive biomarkers with differential expression in cancerous tissues, but reports have been heterogeneous and studied under different settings. This study, completed based on PRISMA guidelines, was a systematic review of miRNA signature diagnostic accuracy to detect early CRC. Case-case control studies, cross-sectional, and cohort research published between 2014–2024 were identified through the Scopus, PubMed, and Cochrane databases. We extracted diagnostic metrics such as sensitivity and specificity while assessing bias with the ROBINS-e tool and the Newcastle-Ottawa Scale. Meta-analyses showed that miRNA panels have high diagnostic accuracy with a pooled sensitivity of 1.84 (95% CI: 1.48–2.19) and a pooled specificity of 1.43 (95% CI: 1.01–1.85). The accuracy of miRNA-139-3p was the highest among all panels. Meta-regression did not reveal any significant confounders, while publication bias was not detected. These results highlight the miRNA panels’ potential as non-invasive biomarkers in early CRC detection, providing a promising alternative to conventional screening methods, with miRNA-139-3p as the most diagnostically accurate biomarker.
Effective Data Augmentation Techniques for Arabic Speech Emotion Recognition Using Convolutional Neural Networks
Authors: Bouchelligua Wided , Al-Dayil Reham , Algaith Areej
Published: February 2025

This paper investigates the effectiveness of various data augmentation techniques for enhancing Arabic speech emotion recognition (SER) using convolutional neural networks (CNNs). Utilizing the Saudi Dialect and BAVED datasets, we address the challenges of limited and imbalanced data commonly found in Arabic SER. To improve model performance, we apply augmentation techniques such as noise addition, time shifting, increasing volume, and reducing volume. Additionally, we examine the optimal number of augmentations required to achieve the best results. Our experiments reveal that these augmentations significantly enhance the CNN’s ability to recognize emotions, with certain techniques proving more effective than others. Furthermore, the number of augmentations plays a critical role in balancing model accuracy. The Saudi Dialect dataset achieved its best results with two augmentations (increasing volume and decreasing volume), reaching an accuracy of 96.81%. Similarly, the BAVED dataset demonstrated optimal performance with a combination of three augmentations (noise addition, increasing volume, and reducing volume), achieving an accuracy of 92.60%. These findings indicate that carefully selected augmentation strategies can greatly improve the performance of CNN-based SER systems, particularly in the context of Arabic speech. This research underscores the importance of tailored augmentation techniques to enhance SER performance and sets a foundation for future advancements in this field.
Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
Authors: Tai Tzu-Ching , Bao Shiao-Yu Andrew , Boyanapalli Bharath Kumar , Kuo Cheng-Chien , Chang Chien-Kuo
Published: February 2025

This paper investigates partial discharge (PD) characteristics and breakdown behavior in five electrode configurations fabricated from fiberglass circuit boards, including parallel electrode (PE), triangular electrode (TE), right-angled electrode (RE), and two floating electrode (FE) designs with 0.3 and 0.5 mm insulation gaps. The electrodes are tested according to IEC 60270:2000 using a commercial device with eight samples per configuration. Key parameters such as the PD inception voltage (PDIV) and breakdown voltage are measured. The TE configuration exhibited the highest breakdown voltage of 14.9 kV, with a PDIV of 6.13 kV and RPDIV of 8.04 kV, indicating strong dielectric properties. The RE configuration showed a PDIV of 7 kV, RPDIV of 8.5 kV, and a breakdown voltage of 13.3 kV. The FE_0.5 mm sample exhibited surface discharges, whereas the FE_0.3 mm sample experienced breakdown at 18 kV with an average breakdown voltage of 15.3 kV. The results indicate that the electrode geometry and insulation spacing strongly influence PD behavior and breakdown resilience. The phase-resolved partial discharge (PRPD) patterns at PDIV and breakdown provide further understanding of the dielectric stability. These findings offer critical insights into designing insulation systems under electrical stress.
Optimization Algorithm for 3D Smooth Path of Robotic Arm in Dynamic Obstacle Environments
Authors: Ren Li , Kang Yiming , Yang Longfan , Jia Haitao , Wang Shaojiang
Published: February 2025

With the rapid development of industrial automation, robotic arms are increasingly required to operate in dynamic and unstructured environments. Path planning for such systems has become a critical challenge, particularly in scenarios involving dynamic obstacles and complex environments. This paper addresses the challenge of obstacle avoidance path planning for robotic arms in dynamic and complex environments. It introduces a hybrid trajectory planning method that combines the strengths of the rapidly exploring random tree (RRT) and artificial potential field (APF) algorithms. The method divides the process into two phases: initial static path planning and dynamic path replanning. An adaptive angle guidance strategy is proposed to enhance the efficiency of RRT in static environments by improving target directionality and reducing redundant nodes, followed by Catmull–Rom spline interpolation for path smoothing. For dynamic environments, the algorithm dynamically adjusts the weight factors of RRT and APF, enabling rapid local path replanning to avoid both static and dynamic obstacles. Experimental results demonstrate that the proposed method achieves significant improvements in trajectory smoothness, path length, and computational efficiency compared to traditional approaches. This work offers a robust solution for real-time motion planning, contributing to the advancement of robotic arm control in industrial and unstructured settings.
Symmetry and Monotonicity of Solutions to Mixed Local and Nonlocal Weighted Elliptic Equations
Authors: Daiji Yongzhi , Huang Shuibo , Tian Qiaoyu
Published: February 2025

This paper focuses on the symmetry and monotonicity of non-negative solutions to a mixed local and nonlocal weighted elliptic problem. This problem generalizes the ground-state representation of elliptic equations with the Hardy potential. The novelty of this research lies in developing the moving planes method for mixed local and nonlocal equations with a weighted function, thus clarifying the influence of the weighted function on the solution properties.
A Non-Invasive Approach for Facial Action Unit Extraction and Its Application in Pain Detection
Authors: Bouazizi Mondher , Feghoul Kevin , Wang Shengze , Yin Yue , Ohtsuki Tomoaki
Published: February 2025

A significant challenge that hinders advancements in medical research is the sensitive and confidential nature of patient data in available datasets. In particular, sharing patients’ facial images poses considerable privacy risks, especially with the rise of generative artificial intelligence (AI), which could misuse such data if accessed by unauthorized parties. However, facial expressions are a valuable source of information for doctors and researchers, which creates a need for methods to derive them without compromising patient privacy or safety by exposing identifiable facial images. To address this, we present a quick, computationally efficient method for detecting action units (AUs) and their intensities—key indicators of health and emotion—using only 3D facial landmarks. Our proposed framework extracts 3D face landmarks from video recordings and employs a lightweight neural network (NN) to identify AUs and estimate AU intensities based on these landmarks. Our proposed method reaches a 79.25% F1-score in AU detection for the main AUs, and 0.66 in AU intensity estimation Root Mean Square Error (RMSE). This performance shows that it is possible for researchers to share 3D landmarks, which are far less intrusive, instead of facial images while maintaining high accuracy in AU detection. Moreover, to showcase the usefulness of our AU detection model, using the detected AUs and estimated intensities, we trained state-of-the-art Deep Learning (DL) models to detect pain. Our method reaches 91.16% accuracy in pain detection, which is not far behind the 93.14% accuracy obtained when employing a convolutional neural network (CNN) with residual blocks trained on actual images and the 92.11% accuracy obtained when employing all the ground-truth AUs.
Difference Analysis Among Six Kinds of Acceptor Splicing Sequences by the Dispersion Features of 6-mer Subsets in Human Genes
Authors: Si Yangming , Li Hong , Li Xiaolong
Published: February 2025

Identifying the sequence composition of different splicing modes is a challenge in current research. This study explored the dispersion distributions of 6-mer subsets in human acceptor splicing regions. Without differentiating acceptor splicing modes, obvious differences were observed across the upstream, core, and downstream regions of splicing sites for 16 dispersion distributions. These findings indicate that the dispersion value of each subset can effectively characterize the compositional properties of splicing sequences. When acceptor splicing sequences were classified into common, constitutive, and alternative modes, the differences in dispersion distributions for most of the XY1 6-mer subsets were significant among the three splicing modes. Furthermore, the alternative splicing mode was classified into normal, exonic, and intronic sub-modes, the differences in dispersion distributions for most of the XY1 6-mer subsets were also significant among the three splicing sub-modes. Our results indicate that dispersion values of XY1 6-mer subsets not only revealed the sequence composition patterns of acceptor splicing regions but also effectively identified the differences in base correlation among various acceptor splicing modes. Our research provides new insights into revealing and predicting different splicing modes.
Protective Effects of Bromelain in Testicular Torsion-Detorsion: Reducing Inflammation, Oxidative Stress, and Apoptosis While Enhancing Sperm Quality
Authors: Yakut Seda , Karabulut Merve , Koca Recep Hakkı , Erbaş Elif , Özkanlar Seçkin , Gençer Berrin Tarakçı , Kara Adem , Kumar K. J. Senthil
Published: February 2025

Inflammation and increased oxidative stress in testicular tissue are documented side effects of torsion of the testicles. The preventive role of Bromelain (Bro) against testicle torsion-induced ischemia/reperfusion (I/R) injury was investigated in this research. Five groups of six animals each were created: ischemia, Ischemia+Reperfusion (I+R), Ischemia+Reperfusion+Bromelain (I+R+Bro; 10 mg/kg), control (sham), and Bromelain (Bro; 10 mg/kg). An I/R damage resulted from two hours of 720° clockwise twisting of the left testis. Blood samples and epididymal sperm were collected after reperfusion to analyze sperm parameters (recovery, motility, viability, and morphology) and cytokines that promote inflammation (IL-1β, IL-6, and TNF-α). Using Western blotting, testicular tissue was examined for histopathological alterations, antioxidant enzymes (GSH, SOD), lipid peroxidation (MDA), apoptosis, and survival-related proteins (TLR4, Caspase-3, Bcl-2, NRF-2, HO-1, PI3K, mTOR, AKT-1). While raising the activities of GSH and SOD, two antioxidant enzymes, Bro administration dramatically reduced MDA concentrations. The I+R+Bro group had significantly reduced amounts of cytokines that promoted inflammation compared to the I+R group. Bro’s protective properties are also attributed to proteins that are altered by it and participate in the apoptosis and survival of cells. Sperm morphology, motility, and concentration notably improved in the bromelain-treated group, according to spermatological examination. Testicular samples treated with bromelain showed less tissue damage according to histological evaluations than the untreated I+R group. These findings imply that Bro has anti-inflammatory, anti-apoptotic, and antioxidant qualities. It effectively reduces oxidative stress and inflammation by modulating the PI3K/Akt/mTOR and NRF-2/HO-1 pathways, hence minimizing I/R injury.
Functional Investigation of a Novel PIWIL4 Mutation in Nonobstructive Azoospermia During the First Wave of Spermatogenesis
Authors: Wang Xiayu , Du Qian , Li Wanqian , Zou Zhongyu , Wang Chikun , Zhou Yan , Hu Zhibin , Gu Yayun , Li Feng
Published: February 2025

PIWI-interacting RNAs (piRNAs) are small noncoding RNAs that are almost exclusively expressed in germ cells to silence harmful transposons to maintain genome stability. PIWIL4 is guided by its associated piRNAs to transposable elements, where it recruits the DNA methylation apparatus and instructs de novo DNA methylation. Herein, we identified a missense variant of PIWIL4 (c.805 C>T p.R269W) in two infertile males. Homozygous male mice carrying the orthologous knock-in variant displayed elevated transposable element expression and aberrant gene expression during the first wave of spermatogenesis, despite exhibiting normal sperm counts and morphology. Mechanistically, the mutated site altered the piRNA-binding ability of PIWIL4 and led to the derepression of endogenous LINE-1 elements. In summary, we identified a piRNA binding mutation in PIWIL4 that may be involved in human nonobstructive azoospermia.
Signal Differentiation of Moving Magnetic Nanoparticles for Enhanced Biodetection and Diagnostics
Authors: Hwang Kee Young , Brown Dakota , Attanayake Supun B. , Luu Dan , Nguyen Minh Dang , Lee T. Randall , Phan Manh-Huong
Published: February 2025

Magnetic nanoparticles are extensively utilized as markers/signal labelling in various biomedical applications. Detecting and distinguishing magnetic signals from similarly sized moving magnetic nanoparticles in microfluidic systems is crucial yet challenging for biosensing. In this study, we have developed an original method to detect and differentiate magnetic signals from moving superparamagnetic (SPM) and ferrimagnetic (FM) nanoparticles of comparable sizes. Our approach utilizes a highly sensitive magnetic-coil-based sensor that harnesses the combined effects of giant magnetoimpedance (GMI) and an LC-resonance circuit, offering performance superior to that of conventional GMI sensors. Iron oxide nanoparticles, which have similar particle sizes but differing coercivities (zero for SPM and non-zero for FM) or similar zero coercivities but differing particle sizes, flow through the magnetic coil at controlled velocities. Their distinct effects are analyzed through changes in the complex impedance of the sensing system. Our findings provide a unique pathway for utilizing SPM and FM nanoparticles as innovative magnetic markers to identify specific biological entities, thereby expanding their potential applications.
Street Geometry Factors Influencing Outdoor Pedestrian Thermal Comfort in a Historic District
Authors: Lai Bin , Fu Jian-Ming , Guo Cheng-Kai , Zhang Dan-Yin , Wu Zhi-Gang
Published: February 2025

As China’s urbanization progresses, the urban heat island (UHI) effect has become more pronounced, impacting the health of residents and the activity intentions of visitors within historic urban areas. This study focuses on the historic district of the Three Lanes and Seven Alleys Tourist Area (SFQX) in Fuzhou, where simulations were conducted on four representative streets across various times during a typical summer meteorological day. Typological methods were employed to simplify neighborhood modeling, and Phoenics software was utilized to simulate the neighborhood’s wind environment and the outdoor pedestrian thermal comfort index. Aspect ratio (AR), sky view factor (SVF), air velocity (Va), and universal thermal climate index (UTCI) values at specific locations were collected for statistical analysis. The findings reveal that: (1) the N–S orientation exhibits more significant correlations between Va, the UTCI, and street geometry compared to the E–W orientation; (2) the relationship between SVF and the UTCI fluctuates with time; (3) areas with higher AR values, such as medium and deep canyons, offer better thermal comfort for outdoor pedestrians; and (4) at 8:00, the UTCI and wind speed show minimal correlations with street geometry and direction, being predominantly influenced by objective climatic factors. These insights are expected to significantly inform the geometric design and planning of streets in Fuzhou’s historic districts, aiming to create more comfortable outdoor environments for inhabitants and visitors alike.
Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine
Authors: Zhao Yanhua , Zhang Kai , Guo Aojun , Hao Fukang , Ma Jie
Published: February 2025

In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail to predict the erosion rate of concrete (CER) structures accurately. This issue arises from oversimplified assumptions and the failure to account for environmental variations and complex nonlinear relationships between parameters. Consequently, a single traditional model is inadequate for predicting the CER under wind gravel flow conditions in this region. To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R2 value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. Ensuring that the training set comprises at least 80% of the total data volume is crucial for the SVM model’s prediction accuracy. Moreover, erosion time is identified as the most significant factor affecting the CER. An enhanced theoretical erosion model, derived from the Bitter and Oka framework and integrating concrete strength and erosion parameters, was formulated. It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. The SVM model, however, recorded a minimal average relative error of just −0.5%, markedly surpassing these improved theoretical models in terms of prediction accuracy. Theoretical models often rely on simplifying assumptions, such as linear relationships and homogeneous material properties. In practice, however, factors like concrete materials, wind gravel flow, and climate change are nonlinear and non-homogeneous. This significantly limits the applicability of these models in real-world environments. Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. This model is crucial for safeguarding concrete structures in wind gravel flow environments.