bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement of crop production in controlled environment agriculture through breeding
Authors: Bhattarai Krishna , Ogden Andrew B. , Pandey Sudeep , Sandoya Germán V. , Shi Ainong , Nankar Amol N. , Jayakodi Murukarthick , Huo Heqiang , Jiang Tao , Tripodi Pasquale , Dardick Chris
Published: January 2025

Controlled environment agriculture (CEA) represents one of the fastest-growing sectors of horticulture. Production in controlled environments ranges from highly controlled indoor environments with 100% artificial lighting (vertical farms or plant factories) to high-tech greenhouses with or without supplemental lighting, to simpler greenhouses and high tunnels. Although food production occurs in the soil inside high tunnels, most CEA operations use various hydroponic systems to meet crop irrigation and fertility needs. The expansion of CEA offers promise as a tool for increasing food production in and near urban systems as these systems do not rely on arable agricultural land. In addition, CEA offers resilience to climate instability by growing inside protective structures. Products harvested from CEA systems tend to be of high quality, both internal and external, and are sought after by consumers. Currently, CEA producers rely on cultivars bred for production in open-field agriculture. Because of high energy and other production costs in CEA, only a limited number of food crops have proven themselves to be profitable to produce. One factor contributing to this situation may be a lack of optimized cultivars. Indoor growing operations offer opportunities for breeding cultivars that are ideal for these systems. To facilitate breeding these specialized cultivars, a wide range of tools are available for plant breeders to help speed this process and increase its efficiency. This review aims to cover breeding opportunities and needs for a wide range of horticultural crops either already being produced in CEA systems or with potential for CEA production. It also reviews many of the tools available to breeders including genomics-informed breeding, marker-assisted selection, precision breeding, high-throughput phenotyping, and potential sources of germplasm suitable for CEA breeding. The availability of published genomes and trait-linked molecular markers should enable rapid progress in the breeding of CEA-specific food crops that will help drive the growth of this industry.
DH2-dependent trans-acting siRNAs regulate leaf and lemma development in rice
Authors: Tang Jun , Li Tianye , Gao Yuanzhuo , Li Xinghang , Huang Ziheng , Zhuang Hui , He Guanghua , Luo Hongfa , Li Yunfeng
Published: January 2025

In most crops, the development of lateral organs such as leaves and floral organs plays important roles in the architecture of plants and grains and then determines the yield. Establishment of polarity in these lateral organs is one of the most critical events for their morphogenesis. However, the molecular mechanisms regarding this in rice remain unclear. Here, we isolated two allelic mutants named degenerated hull 2-1 and degenerated hull 2-2 (dh2-1 and dh2-2) in rice, exhibiting abaxially rolled leaves and rod-shaped lemmas. DH2 encoded the relatively conservative ARGONAUT 7 (AGO7) protein in plants and expressed in the lateral organs including leaf and floral organs. In addition, the overexpression lines of DH2 showed adaxially rolled leaves. Next, it was proved that DH2 was involved in the synthesis of tasiR-ARFs, the expression level of which was decreased sharply in lateral organs of dh2 mutants. Then, it was found that the expression of OsARF2, OsARF3, OsARF14, and OsARF15, the potential targets of tasiR-ARFs, was increased in lateral organs of dh2 mutants. However, it was not expected that the results of in situ hybridization showed that the four ARF genes were not expressed in WT lemma, whereas they were all ectopically expressed in rod-shaped lemma in dh2 mutants. Meanwhile, tasiR-ARFs were expressed in the whole lemma but not in the abaxial side. This means that there was no opposite expression of tasiR-ARFs and ARFs in the adaxial–abaxial of lemma. Therefore, according to our data, we believe that the pathway of OsAGO7–tasiR-ARFs in rice was more likely involved in the development of the whole lemma, not only the abaxial side, by restricting the ectopic expression of OsARFs in the whole lemma, which was different from that in the lateral organs of Arabidopsis.
Ethnic disloyalty or federal loyalty? A case of contesting Baloch representation in postcolonial Pakistan
Authors: Khoso Shahal
Published: January 2025

This article captures the representation of the Baloch ethnic minority in Pakistan’s multinational federation. The research establishes a taxonomy of Baloch representatives and their contesting ideological positions by analysing a sample of news articles published in Pakistan’s national and international newspapers between 2006 and 2024. This approach provides fresh insights into the complex dynamics of minority representation for the Baloch. The study classifies the contesting groups of Baloch representatives into three clusters: (a) The federalists, (b) The nationalists, and (c) The secessionists. Not restricting its scope to periodisation, the research further investigates if there are linkages between the British colonial view of the Baloch, their treatment as a belligerent ethnic group and the submission of their ethnic loyalty in the postcolonial federation of Pakistan. Using the qualitative content analysis technique, the research attaches themes emerging from the analysed discourse of Baloch representatives from the news articles. Moreover, the article scrutinises each representative group’s understanding of how they perceive themselves and other representatives of the Baloch minority within the federation – in the context of the broader Baloch question. The research also aims to provoke thought within federalism theory: if representatives of an ethnic minority group show more allegiance to the federal centre than the minority they represent, is it a case of federal loyalty or ethnic disloyalty?
Integrating emotional vocabulary in EFL education: a model for enhancing emotional intelligence in pre-service EFL teachers
Authors: Lasekan Olusiji , Godoy Margot , Méndez-Alarcón Claudia
Published: January 2025

This study investigates the role of emotional vocabulary in fostering emotional intelligence (EI) among pre-service EFL teachers, focusing on the Headway series. The general objective was to quantify, categorize, and classify emotion vocabulary across proficiency levels and to develop a systematic teaching model to enhance EI in pre-service teachers. A mixed-methods approach was employed: quantitative analysis identified and categorized emotional vocabulary using established lexicons, and qualitative thematic analysis aligned this vocabulary with key EI components such as self-awareness, emotional regulation, and empathy. To develop the systematic teaching model, a design-based research methodology was utilized, incorporating iterative refinement based on empirical findings and expert feedback. Grounded in Goleman’s Emotional Intelligence Theory, the study revealed a progressive increase in emotional vocabulary, with more complex emotions introduced at advanced levels. Positive emotions were more frequent at beginner levels, while negative and neutral emotions increased at higher stages, supporting self-regulation and empathy development. The systematic teaching model proposed in the study addresses key EI components such as self-awareness, social skills, and emotional regulation by scaffolding emotional vocabulary instruction across proficiency levels. This model provides a structured framework to equip educators with the emotional and linguistic tools necessary for effective classroom management and enhanced student outcomes. This research holds pedagogical implications for teacher training programs, recommending that emotional vocabulary and EI be integrated into pre-service education to improve classroom management and student outcomes.
Optimizing the role of pharmacists at the primary healthcare centres in Indonesia through an integrated information system
Authors: Yuliasih Nur , Fatinah Yasmin , Abdulah Rizky , Suwantika Auliya A.
Published: January 2025

Introduction

The role of pharmacists at primary healthcare centers (PHCs) in Indonesia still needs long-term improvement. Enhancing interprofessional collaboration through data-driven collaboration is essential to strengthening cooperation. This study aimed to identify the need for an integrated information system to enhance the role of pharmacists at PHCs in Indonesia.

Methods

A desk review was applied as the initial step to analyze the role of pharmacists at the PHCs in Indonesia. Furthermore, a qualitative study was conducted using Walt and Gilson’s health policy analysis framework. Semi-structured interviews were conducted in four sections (context, content, process, and actors) with consideration of selected key respondents. All interviews were transcribed verbatim and then analyzed using Braun and Clarke’s thematic analyses, ensuring a comprehensive understanding of the situation.

Results

We identified two significant challenges in optimizing the use of integrated systems at the PHCs to increase the role of pharmacists. Considering context-content-process-actors, implementing integrated pharmaceutical care standards relies significantly on human resources and infrastructures. Key challenges related to human resources are a limited number of human resources, a high workload, and insufficient use of working tools. We found several challenges regarding infrastructure, such as network connectivity issues, non-integrated systems or applications, and suboptimal benefits from the current systems.

Conclusion

Various systems or applications in PHC involve reporting to the Ministry of Health, but detailed integration of these systems needs to be achieved immediately. According to informants, the criteria for desired applications are crucial to optimizing the integrated system, using it, and streamlining tasks for pharmacists at PHCs. Common expectations include an integrated system for monitoring drug usage and orders. Apart from usefulness, network connectivity must be assured for accessibility by all parties.
Advancing health equity: evaluating AI translations of kidney donor information for Spanish speakers
Authors: Garcia Valencia Oscar A. , Thongprayoon Charat , Jadlowiec Caroline C. , Mao Shennen A. , Leeaphorn Napat , Budhiraja Pooja , Khoury Nadeen , Pham Justin H. , Craici Iasmina M. , Gonzalez Suarez Maria L. , Cheungpasitporn Wisit
Published: January 2025

Background

Health equity and access to essential medical information remain significant challenges, especially for the Spanish-speaking Hispanic population, which faces barriers in accessing living kidney donation opportunities. ChatGPT, an AI language model with sophisticated natural language processing capabilities, has been identified as a promising tool for translating critical health information into Spanish. This study aims to assess ChatGPT’s translation efficacy to ensure the information provided is accurate and culturally relevant.

Methods

This study utilized ChatGPT versions 3.5 and 4.0 to translate 27 frequently asked questions (FAQs) from English to Spanish, sourced from Donate Life America’s website. The translated content was reviewed by native Spanish-speaking nephrologists using a standard rubric scale (1–5). The assessment focused on linguistic accuracy and cultural sensitivity, emphasizing retention of the original message, appropriate vocabulary and grammar, and cultural relevance.

Results

The mean linguistic accuracy scores were 4.89 ± 0.32 for GPT-3.5 and 5.00 ± 0.00 for GPT-4.0 (p = 0.08). The percentage of excellent-quality translations (score = 5) in linguistic accuracy was 89% for GPT-3.5 and 100% for GPT-4.0 (p = 0.24). The mean cultural sensitivity scores were 4.89 ± 0.32 for both GPT-3.5 and GPT-4.0 (p = 1.00). Similarly, excellent-quality translations in cultural sensitivity were achieved in 89% of cases for both versions (p = 1.00).

Conclusion

ChatGPT 4.0 demonstrates strong potential to enhance health equity by improving Spanish-speaking Hispanic patients’ access to LKD information through accurate and culturally sensitive translations. These findings highlight the role of AI in mitigating healthcare disparities and underscore the need for integrating AI-driven tools into healthcare systems. Future efforts should focus on developing accessible platforms and establishing guidelines to maximize AI’s impact on equitable healthcare delivery and patient education.
Packaging of organic food—the dilemma of consumers’ internal and external motives
Authors: Nifatova Olena , Bliumska-Danko Kseniia , Charreire-Petit Sandra , Danko Yuriy
Published: January 2025

This study provides empirical insights to clarify consumers’ motives for choosing organic food and the importance of the packaging of organic food in shaping consumers’ perceptions of food organicity. Based on a survey of 497 consumers of organic food in Ukraine, we investigate the extent to which organic packaging can serve as an indicator of external (altruistic) consumers’ expectations and whether the environmental friendliness of consumer choice is related to their expectations of sustainability (greenness) of organic food at all stages of their life cycle. We have found that there is no clear distinction between internal and external consumer motives in the perception of food organicity due to the perception of packaging organicity as part of food organicity. However, organic packaging can be an indicator of external (altruistic) consumer motives, subject to the limitations that organic packaging is less related to health care and more to altruistic motives. Consumers driven by external motives may expect produce to be organic at all stages of their life cycle and are more likely to rely on the producer’s commitment to environmental responsibility. Organic producers should consider this in their marketing strategies to meet consumer expectations and avoid the risk of greenwashing. Our results can serve as a starting point for further exploration of ways to foster mutual understanding between consumers and producers regarding shared environmental responsibility.
The adoption of sustainable practices in livestock production systems in tropical Andean Páramos
Authors: Ramírez-Gómez Carlos Julián , García-Cuervo Andrés , Rodriguez-Espinosa Holmes
Published: January 2025

The geographical areas of the Andean paramo are considered strategic and highly important ecosystems both for their ecosystem services and for being a great support for society based on what water resources imply. However, these ecosystems have been affected by livestock production, which is one of the main activities carried out in these areas. This study highlights the importance of approaching farmers who inhabit these areas from an approach of adopting sustainable technologies and practices in livestock farming so that they can contribute to the conservation of the environment. The study has two main objectives (1) to develop the concept of technology adoption in agriculture, starting from its main categories and approach variables until reaching its relationship with the adoption of sustainable and conservation practices in livestock farming, and (2) provide a conceptual basis that interrelates adoption, livestock and páramo areas as a geographic environment. Finally, this proposed framework can serve as a reference to carry out empirical analysis seeking to test various hypotheses.
The long-term outcome of children with VP shunt and hydrocephalus: motor developmental outcome and QOL of patients with hydrocephalus is associated with the number of revisional procedures but is not impacted by the type of the valve
Authors: Wendling-Keim Danielle S. , Luz Hannah , Kren Elena , Muensterer Oliver , Lehner Markus
Published: January 2025

Purpose

Despite constant advances in ventriculo-peritoneal shunt systems, pediatric patients with hydrocephalus may present with neurodevelopmental delay. Therefore, we performed a study including a questionnaire, which aimed to analyze parameters that may have an impact on the cognitive function and quality of life of the pediatric patient with hydrocephalus.

Methods

In this retrospective study, we included 81 patients aged 0–17 with hydrocephalus who were treated with a ventriculo-peritoneal shunt at a single institution. Demographic data, etiology of the hydrocephalus, type of valve implanted, any revision procedures and any complications were analyzed and the neurodevelopmental outcome, epilepsy and quality of life were assessed using a questionnaire sent to these patients. Statistical analysis was performed using SPSS. The significance level was set at p ≤ 0.05.

Results

Questionnaires were sent to 81 patients who were treated at our institution over a mean retrospective study period of 18 years. Of these, 30 questionnaires were completed by the patients themselves or with the support of their families and included in the study. The etiology of the hydrocephalus as a non-controllable parameter did not affect the cognitive and motor development as well as the occurrence of epilepsy, cephalalgia and the quality of life. However, the number of revisions had a significant (p = 0.041) impact on the motor development of the child. The mean Wellbeing Five score was better with 19.63 in the group with no revisions whereas the score was 12.2 in the group with more than two revisional procedures. The type of the valve (adjustable or not adjustable) as a controllable parameter did not change any of the tested parameters (p > 0.05).

Conclusion

The number of revisional procedures significantly affected the motor development of pediatric patients with a ventriculo-peritoneal (VP) shunt calling for further research to improve VP shunt systems as well as surgical procedures like endoscopic third ventriculo-cisternostomy (ETV) in the future.
SA-Net: Leveraging Spatial Correlations Spatial-Aware Net for Multi-Perspective Robust Estimation Algorithm
Authors: Shao Yuxiang , Zhou Longyang , Li Xiang , Feng Chunsheng , Jin Xinyu
Published: January 2025

Robust estimation aims to provide accurate and reliable parameter estimations, particularly when data are affected by noise or outliers. Traditional methods like random sample consensus (RANSAC) struggle with handling outliers because they treat all observations as equally important. A series of advanced deep learning methods have recently emerged, which use deep learning techniques to estimate the probability of each sample being selected, prioritizing higher confidence for observations that are closer to the ground truth model. However, optimizing solely based on proximity to the ground truth model does not guarantee higher-quality estimations. Meanwhile spatial relationships between the data points in the minimum sampled set also influence the accuracy of the final estimated model. To address these issues, we propose Spatial-Aware Net (SA-Net), a dual-branch neural network that integrates both confidence and spatial encodings. SA-Net employs a confidence distribution encoder to learn the confidence distribution and a spatial distribution encoder to capture spatial correlations between point features. By incorporating multi-perspective sampling, the minimum sample set can be selected based on different spatial distributions in the output of the neural network, and applying Chamfer Loss constraints, our approach improves model optimization and effectively mitigates suboptimal solutions. Extensive experiments demonstrate that SA-Net outperforms the state of the art across various real-world robust estimation tasks.
Improving Anaerobic Digestion Efficiency of Animal Manure Through Ball Milling Pretreatment
Authors: Xu Meicai , Uludag-Demirer Sibel , Liu Yan , Liao Wei
Published: January 2025

Anaerobic digestion (AD) can offer a promising pathway for converting animal waste into biogas. This process improves waste management practices while generating renewable energy. However, the lignocellulosic structure of animal manure, particularly in dairy and cattle manure, hinders digestion efficiency and limits biogas yield. This study investigates the application of ball milling as a pretreatment strategy to enhance the anaerobic digestion of dairy manure. By reducing particle size and disrupting lignocellulosic structures, ball milling increases the bioavailability of organic matter, thus promoting microbial conversion and boosting biogas production. Experimental results reveal that 1 h ball milling pretreatment increases biogas and biomethane production by more than 20% compared to untreated manure. Furthermore, microbial community analysis indicates that anaerobic microbes remain largely unaffected by ball milling pretreatment, unlike the changes observed with activated carbon addition. These findings suggest that ball milling is a practical, adaptable, and scalable pretreatment method to enhance the anaerobic digestion efficiency of dairy manure. It offers a sustainable solution for improved manure management and biogas production.
Comprehensive Sampling and Detection Strategies for the Field Surveillance of Tomato Brown Rugose Fruit Virus
Authors: Zhao Xinru , Xu Yanan , Xu Xinyi , Zhou Hui , Shi Juan , Yang Changkai , Zhou Xueping , Yang Xiuling
Published: January 2025

Tomato brown rugose fruit virus (ToBRFV) poses a significant threat to tomato production. Effective and accurate detection is critical for limiting the introduction and spread of ToBRFV. In this study, the impact of tomato planting patterns, growth stages, and cultivar variability on ToBRFV levels in tomatoes from distinct greenhouses and open fields were comprehensively analyzed. The results indicated that ToBRFV is detectable in asymptomatic tissues, regardless of artificial agroinoculation or natural infection. Additionally, higher viral levels were observed in newly emerging leaves and in fruits and sepals compared to old leaves. For tomato fruits infected with ToBRFV, the viral level in the mesocarp is higher than in the other interior parts, and no correlation was found between viral levels and the color of fruit lesions. Based on these results, it is recommended that new leaves and sepals should be given priority for testing of ToBRFV from tomato seedlings to the color turning stage, and that fruits and sepals are suggested to be collected at the full ripeness stage of tomato plants. This study underscores the importance of regular detection and optimal sampling beyond symptom observation in the surveillance of ToBRFV.
Identification of Mango Cultivars’ Resistance Against Red Spider Mite: Impact of Climate Elements on Resistance Performance
Authors: Liang Xiao , Xu Xuelian , Liu Ying , Wu Chunling , Wu Mufeng , Chen Qing
Published: January 2025

The use of resistant plants is recognized as an environmentally friendly measure for mite control. Oligonychus mangiferus, known as the mango red spider mite (MRSM), is a dangerous pest for mango production. To date, the resistance levels of the mango germplasms against the MRSM remain largely unknown. Furthermore, the environmental factors potentially influencing resistance performance have been seldom discussed. To fill those knowledge gaps, this study aimed to identify the resistance level of twelve mango cultivars against the MRSM. Based on three rounds of greenhouse and five seasons of field tests, cultivars with distinct resistant levels were identified. When exploring the climate impact, we found that for the susceptible cultivars, precipitation is the primary external environment factor altering the resistance performance, while temperature presents a secondary effect, and air humidity did not show a significant impact on MRSM resistance. By contrast, MRSM-resistant cultivars were not prone to be affected by changing climate conditions. Furthermore, yield tests indicated that the resistant cultivars can better reduce the yield losses compared with the susceptible ones. This study illustrated the climate element-driven effect on mango tree resistance performance against the MRSM, which can provide insight into insect pest management under changing climate conditions.
Effect of Feeding Frequency on the Growth, Body Composition, and Intestinal Health of Hybrid Grouper (Epinephelus fuscoguttatus♀ × E. lanceolatu♂) Fed a High-Fat Diet
Authors: Huang Weibin , Yang Shipei , Cai Wenshan , Huang Wanting , Liu Yansheng , Li Shuaipeng , Zhou Menglong , Tan Beiping , Dong Xiaohui
Published: January 2025

This experiment was to investigate the effects of feeding frequency on the growth performance, body composition, and intestinal health of hybrid grouper (Epinephelus fuscoguttatus♀ × E. lanceolatu ♂). Fifty-six days of feeding with four different feeding frequencies (1 time/day, 2 times/day, 3 times/day, and 4 times/day) were conducted on groupers with an initial body weight of 11.51 ± 0.02 g. The results show the following: (1) Weight gain rate (WGR) and specific growth rate (SGR) of the groupers in the 1 time/day group were significantly lower than in other groups (p < 0.05). (2) Superoxide dismutase (SOD) had the lowest value in the 1 time/day group, significantly lower than the 2 times/day and 4 times/day groups, catalase showed an upward trend, and the 4 times/day group was significantly greater than the other groups (p < 0.05). The total antioxidant capacity (T-AOC) and glutathione peroxidase (GPX) activities in the 1 time/day group were significantly lower than in the other groups (p < 0.05). (3) The increase in feeding frequency led to a significant increase in the expression levels of cat and il-6 (p < 0.05). In summary, appropriate feeding frequency can promote growth and enhance the antioxidant capacity of the fish’s gut. We recommend a best feeding frequency of 2 times/day.
Genomically Selected Genes Associated with a High Rate of Egg Production in Puan Panjiang Black-Bone Chickens
Authors: Miao Xiaomeng , Huang Zhiying , Liu Jia , Zhang Li , Feng Yulong , Zhang Yalan , Li Diyan , Ning Zhonghua
Published: January 2025

Puan Panjiang black-bone chickens are renowned for their distinctive traits, deep black coloration, and high-quality protein content, making them a focus of genetic research due to their unique egg-laying abilities. In this study, 110 Puan Panjiang black-bone chickens were used to investigate the effects of natural and artificial selection influencing egg production. Whole-genome resequencing data from red junglefowl (RJF) and high-egg-production (HEP) and low-egg-production (LEP) groups of Puan Panjiang black-bone chickens revealed significant genetic variants associated with egg production traits. Additionally, transcriptome analysis of 47 samples from ovary stroma, small white follicles (SWFs), small yellow follicles (SYFs), and liver tissues from 6 HEP and 6 LEP groups identified differentially expressed genes. Notably, differences in egg production were linked to small yellow follicles rather than ovary stroma or SWFs. Key candidate genes, including TRIM7, CASR, SPTBN5, GAL1, ZP1, IL4I1, and CCL19, were identified as potential contributors to egg-laying performance. This study underscores the genetic diversity within this breed and provides valuable insights for future breeding programs to enhance egg production, supporting the sustainable development of this local resource.
Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial Attacks
Authors: Abbasi Maryam , Váz Paulo , Silva José , Martins Pedro
Published: January 2025

The rise of deepfakes—synthetic media generated using artificial intelligence—threatens digital content authenticity, facilitating misinformation and manipulation. However, deepfakes can also depict real or entirely fictitious individuals, leveraging state-of-the-art techniques such as generative adversarial networks (GANs) and emerging diffusion-based models. Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. This study focuses on subsets of frames extracted from the DeepFake Detection Challenge (DFDC) and FaceForensics++ videos to evaluate three convolutional neural network architectures—XCeption, ResNet, and VGG16—for deepfake detection. Performance metrics include accuracy, precision, F1-score, AUC-ROC, and Matthews Correlation Coefficient (MCC), combined with an assessment of resilience to adversarial perturbations via the Fast Gradient Sign Method (FGSM). Among the tested models, XCeption achieves the highest accuracy (89.2% on DFDC), strong generalization, and real-time suitability, while VGG16 excels in precision and ResNet provides faster inference. However, all models exhibit reduced performance under adversarial conditions, underscoring the need for enhanced resilience. These findings indicate that robust detection systems must consider advanced generative approaches, adversarial defenses, and cross-dataset adaptation to effectively counter evolving deepfake threats.
Analyzing Crowd Emotional Contagion in Metro Emergencies Through the Lens of the Weber–Fechner Law: Predictions Based on Computational Techniques Applied to Science
Authors: Wu Wangqiang , Zhang Ying , Liu Hongda
Published: January 2025

The spread of panic can swiftly trigger group behaviors, leading to public security incidents and significant social hazards. Increasing attention is being paid to the impact of human psychology and behavior on the evolution and management of emergencies. Drawing on the Weber–Fechner Law, we proposed an emotional contagion model to explore the dynamics of crowd panic during metro emergencies, focusing on the interplay of emotional levels and stimuli. Key influencing factors such as crowd density, personality traits, official interventions, and evacuation rates are analyzed. Additionally, a case study is conducted to validate the model’s effectiveness in quantifying emotions and characterizing the emotional contagion of crowd panic. Numerical results reveal that the initial intensity of panic stimuli significantly impacts peak panic levels, while contagion duration plays a minor role. Panic intensifies with increased crowd density, with sensitive individuals being more susceptible to extreme emotions, escalating negative contagion. Official intervention proves crucial in mitigating panic, though its effect is transient in enclosed environments. Evacuation rate minimally affects emotional contagion during the train’s motion but becomes pivotal post-arrival. Highly panicked passengers evacuate quickly, necessitating timely interventions to prevent secondary panic on platforms. This highlights the importance of immediate, effective control measures to manage panic dynamics and ensure public safety.
Optimization of Microwave-Assisted Extraction Process for Production of Polyphenol-Rich Crude Extract from Cinnamomum iners Leaves
Authors: Tanruean Keerati , Luangkamin Suwaporn , Srisurat Thanarak , Bunmusik Wasitthi , Suttiarporn Panawan
Published: January 2025

Cinnamomum iners Reinw. ex Blume has long been recognized as a plant with food and medicinal uses. This study was designed to optimize the MAE process to produce a high-value, polyphenol-rich crude extract from cinnamon leaves (PCL). The primary goal was to apply response surface methodology (RSM) with a face-centered central composite design (FCCD) to identify the ideal conditions for microwave-assisted extraction (MAE). Key factors such as the MAE time, microwave power, and solid-to-liquid ratio were examined to produce a polyphenol-rich crude extract from C. iners leaves. The resulting extracts were assessed for extraction yield, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity. The results showed that MAE using a methanol solvent had a significant impact on antioxidant compound levels. The R2 values for all responses, yield, TPC, TFC, and DPPH radical scavenging activity were 0.9497, 0.9494, 0.9199, and 0.9570, respectively, indicating that the developed quadratic polynomial models were accurate and suitable for analyzing MAE parameter interactions. The optimum MAE parameters were determined to be an MAE time of 25 min, microwave power of 214.24 W, and plant leaf–solvent ratio of 1:195.76 g/mL. In these optimized MAE conditions, the predicted extraction yield, TPC, TFC, and IC50 of DPPH scavenging were 18.56%, 22.86 mg GAE/g, 13.89 mg QE/g, and 83.30 µg/mL, respectively. The enhanced efficiency of MAE comes from microwave-induced heating, which disrupts cell walls for faster compound release, making it more effective and time-efficient than traditional HRE for polyphenol extraction. This study demonstrated that polyphenols can be efficiently extracted from C. iners using MAE, producing a valuable extract with potential as a natural preservative in food and a skin-protective, anti-aging ingredient in cosmetics.
Reliability Analysis of Vehicle Braking System Based on Hyperellipsoidal Dynamic Bayesian Network
Authors: Tian Yingjie , Wen Jing , Zheng Shubin
Published: January 2025

Brake systems are subjected to various factors such as wear and fatigue over a long period of time. They bring a great challenge to the reliability analysis of the braking system. In this paper, the hyperellipsoid model is used and the hyperellipsoid dynamic Bayesian network model is constructed. The model synthesizes the strengths of the dynamic Bayesian network with the precise constraint capability of the superellipsoid model, offering a novel methodology for the reliability analysis of the braking system. In order to verify the validity of the super ellipsoidal dynamic Bayesian network model, this paper applies it to the reliability analysis of the braking system and compares it with the traditional interval Bayesian network. The results show that the hyperellipsoidal dynamic Bayesian network model enhances the range accuracy of the probability intervals by 54.06%. This solves the conservatism problem of interval Bayesian networks. This suggests that the method improves the accuracy of brake system reliability analysis.
Advances in Foot Biomechanics and Gait Analysis
Authors: Sánchez-Gómez Rubén
Published: January 2025

As we conclude this Special Issue dedicated to the ‘Advances in Foot Biomechanics and Gait Analysis’, we reflect on the significant strides made in understanding the complexities of foot mechanics and their impact on human movement [...]
Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
Authors: Wang Hua , Pawara Pornntiwa , Chamchong Rapeeporn
Published: January 2025

Video deblurring faces a fundamental challenge, as blur degradation comprehensively affects frames by not only causing detail loss but also severely distorting structural information. This dual degradation across low- and high-frequency domains makes it challenging for existing methods to simultaneously restore both structural and detailed information through a unified approach. To address this issue, we propose a wavelet-based, blur-aware decoupled network (WBDNet) that innovatively decouples structure reconstruction from detail enhancement. Our method decomposes features into multiple frequency bands and employs specialized restoration strategies for different frequency domains. In the low-frequency domain, we construct a multi-scale feature pyramid with optical flow alignment. This enables accurate structure reconstruction through bottom-up progressive feature fusion. For high-frequency components, we combine deformable convolution with a blur-aware attention mechanism. This allows us to precisely extract and merge sharp details from multiple frames. Extensive experiments on benchmark datasets demonstrate the superior performance of our method, particularly in preserving structural integrity and detail fidelity.
Evaluating Pipeline Inspection Technologies for Enhanced Corrosion Detection in Mining Water Transport Systems
Authors: Tuninetti Víctor , Huentemilla Matías , Gómez Álvaro , Oñate Angelo , Menacer Brahim , Narayan Sunny , Montalba Cristóbal
Published: January 2025

Water transport pipelines in the mining industry face significant corrosion challenges due to extreme environmental conditions, such as arid climates, temperature fluctuations, and abrasive soils. This study evaluates the effectiveness of three advanced inspection technologies—Guided Wave Ultrasonic Testing (GWUT), Metal Magnetic Memory (MMM), and In-Line Inspection (ILI)—in maintaining pipeline integrity under such conditions. A structured methodology combining diagnostic assessment, technology research, and comparative evaluation was applied, using key performance indicators like detection capability, operational impact, and feasibility. The results show that GWUT effectively identifies surface anomalies and wall thinning over long pipeline sections but faces depth and diameter limitations. MMM excels at detecting early-stage stress and corrosion in inaccessible locations, benefiting from minimal preparation and strong market availability. ILI provides comprehensive internal and external assessments but requires piggable pipelines and operational adjustments, limiting its use in certain systems. A case study of critical aqueducts of mining site water supply illustrates real-world technology selection challenges. The findings underscore the importance of an integrated inspection approach, leveraging the complementary strengths of these technologies to ensure reliable pipeline integrity management. Future research should focus on quantitative performance metrics and cost-effectiveness analyses to optimize inspection strategies for mining infrastructure.
Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
Authors: Gao Jianping , Liu Ningbo , Li Haotian , Li Zhe , Xie Chengwei , Gou Yangyang
Published: January 2025

In the complex and stochastic traffic flow, ensuring safe driving requires improvements in perception and decision-making. This paper proposed a decision-control method that leveraged the scene perception and understanding capabilities of semantic segmentation networks and the stable convergence strategies of Deep Reinforcement Learning (DRL) algorithms to achieve more accurate and effective autonomous driving decision-control. Perception features obtained from cameras and sensors equipped with a semantic segmentation model were used as input for the intelligent agent. DRL algorithms were employed to update decisions based on reward feedback. Experimental results on the CARLA simulation platform demonstrated that the semantic segmentation network effectively identified obstacles, vehicles, and drivable areas, providing high-quality perception data input for the intelligent agent’s decision-making model. Compared to the original algorithms, the proposed Double Deep Q-Network-Semantic Segmentation (DDQN-SS) and Proximal Policy Optimization-Semantic Segmentation (PPO-SS) increased the reward value by approximately 25% and enhanced driving stability by 14.2% and 28.5%, respectively, enabling more stable and precise decision-control during driving. The method proposed in this paper has better improved the decision-control performance of PPO and DDQN in complex scenarios.
Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
Authors: Ke Xizheng , Han Xu , Liang Jingyuan , Wang Rui
Published: January 2025

The propagation of a beam in atmospheric turbulence causes phase fluctuations due to random variations in the atmospheric refractive index, leading to wavefront distortions. This paper analyzes the mechanisms of wavefront phase changes caused by atmospheric turbulence under different weather conditions and transmission distances. Local wavefront distortions are analyzed using Gaussian curvature, and wavefront distortions are assessed using peak-to-valley values, root mean square values, and the mean square error of the wavefront distortions. Additionally, the effects of different wavelengths and temperatures on wavefront distortions are studied. The experimental results show that the positive and negative Gaussian curvature peak values decrease in the order of snowy day (0.530, −0.850) μm−1, heavy rain (0.345, −0.447) μm−1, dust storm (0.412, −0.057) μm−1, light rain (0.297, −2.75 × 10−3) μm−1, sunny (0.154, −0.3 × 10−3) μm−1, and cloudy (0.107, −0.1 × 10−3) μm−1, with local distortions also decreasing in this order. The peak-to-valley values, root mean square values, and mean square error of the wavefront distortions decrease in the order of heavy rain (129.41 μm, 31.82 μm, 55.18 μm2), dust storm (74.1 μm, 18.84 μm, 51.40 μm2), snowy day (72.09 μm, 17.50 μm, 49.49 μm2), light rain (70.03 μm, 17.11 μm, 37.69 μm2), sunny (57.23 μm, 16.50 μm, 21.84 μm2), and cloudy (52.8 μm, 16.12 μm, 14.40 μm2). Shorter wavelengths exhibit greater phase fluctuations than longer wavelengths, and the degree of distortion increases with temperature. This study lays a theoretical foundation and provides experimental evidence for optical transmission in atmospheric turbulence.
Study on the Fire Spread Characteristics of High-Rise Building Facades Under Strong Wind Conditions Based on the Combination of WRF and CFD
Authors: Yang Shi , Li Yanfeng , Su Zhihe , Li Junmei
Published: January 2025

The spread of fires on the facades of high-rise buildings is highly influenced by atmospheric wind conditions, particularly in strong wind environments. A strong wind environment refers to the situation where the wind speed reaches level 6 or above, or the wind speed is between 10.8 m/s and 13.8 m/s. We conducted an in-depth study of the characteristics of flame spread on the facades of high-rise buildings under strong wind conditions. A nested coupling method based on WRF (Weather Research and Forecasting) and CFD (computational fluid dynamics) software (Ansys Fluent 2021) was used. The mesoscale meteorological simulation software WRF was utilized to obtain regional airflow variation data within a radius of 2 km around the high-rise building. Subsequently, these data were coupled with the CFD software (Ansys Fluent 2021) to simulate and obtain realistic wind field data within a 400 m range around the building. Finally, these realistic wind field data were used for FDS (Fire Dynamics Simulator) fire simulations and model experiments to accurately replicate building fire scenarios under strong wind conditions. The results indicate that using grid nesting for boundary condition division would help to improve the accuracy of fire spread characteristics on the facades of high-rise buildings under strong wind conditions. For a high-rise building, both headwinds and tailwinds promote vertical and horizontal flame spread, with a more significant impact on vertical flame spread speed. Side winds enhance horizontal flame spread but inhibit vertical flame spread. These findings provide a reference for the effective design of fire protection systems for the facades of high-rise buildings.
Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
Authors: Sabah Shafiya , Dhou Salam
Published: January 2025

Respiratory motion serves as a major challenge during treatment of lung cancer patients using radiotherapy. In this work, an image-based method is presented to extract a respiratory signal directly from Cone Beam CT (CBCT) projections. A dense optical-flow method is used to acquire motion vectors between successive projections in each dataset, followed by the extraction of the dominant motion pattern by application of linear kernel Principal Component Analysis (PCA). The effectiveness of the method was tested on three patient datasets and the extracted breathing signal was compared to a ground-truth signal. The average phase shift was observed to be 1.936 ± 0.734 for patient 1, 1.185 ± 0.781 for patient 2 and 1.537 ± 0.93 for patient 3. Moreover, a 4D CBCT image was reconstructed, considering the respiratory signal extracted, using the proposed method, and compared to that reconstructed considering the ground-truth respiratory signal. Results showed that a minimal difference was found between the image reconstructed using the proposed method and the ground-truth in terms of clarity, motion artifacts and edge sharpness.
Effect of Blue–Green Infrastructure in Mitigating Microenvironmental Heat Islands: Field- and Simulation-Based Insights
Authors: Ming Tingzhen , Hu Yiwei , Shi Tianhao , Li Yuewen , Hu Shanjiang , Yang Di , Lv Bing , Peng Chong , Chen Yanhua
Published: January 2025

Urban heat island (UHI) effects, intensified by urbanization and environmental changes, are critical challenges to urban thermal comfort and sustainability. This study investigates the combined role of water bodies and vegetation in mitigating microclimatic heat island effects within a campus area, focusing on their cooling impacts and interactions. Using field measurements and numerical simulations, the research evaluates the cooling effects and wind flow modifications induced by greenery and water bodies in the surrounding environment. The findings demonstrate that both vegetation and water bodies provide a significant cooling effect, reducing temperatures by 0–6 K in downstream regions, with the impact being more pronounced closer to the blue–green spaces. Furthermore, the combined application of water bodies and vegetation offers enhanced cooling effects; however, their influence on the thermal environment exhibits a nonlinear relationship. These results underscore the importance of strategic blue–green infrastructure planning in mitigating UHI effects and optimizing urban thermal environments.
Advanced Data Classification Framework for Enhancing Cyber Security in Autonomous Vehicles
Authors: Neupane Shiva Ram , Sun Weiqing
Published: January 2025

Autonomous vehicles (AVs) have revolutionized the automotive industry by leveraging data to perceive and interact with their environment effectively. Data safety is essential for supporting AV decision-making and ensuring reliability in complex environments. AVs continuously collect data from multiple sources like LiDAR, RADAR, cameras, and ultrasonic sensors to monitor road conditions, traffic signals, and pedestrian movements. An effective data classification framework is crucial for managing vast amounts of information and securing AV systems against cyber threats. This paper proposes a comprehensive framework for AV data classification, categorizing data by sensitivity, usage, and source. By integrating a review of the literature, real-world cases, and practical insights, this study introduces a novel data classification model and explores sensitivity criteria. The findings aim to assist industry stakeholders in creating secure, efficient, and sustainable AV ecosystems.