bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Machine Learning Modeling Approach for Fatigue Failure of Hydrogen-Transporting Pipelines
Authors: Ahmed Nayem , Ahmed Ramadan , Teodoriu Catalin , Gyaabeng Michael 
Published: February 2025

Summary
The challenge of data scarcity significantly impedes machine learning (ML) applications in specialized domains, such as research on hydrogen embrittlement in carbon steel, posing a significant obstacle to repurposing existing natural gas networks for hydrogen transportation. Hence, this study aims to develop an ML model to predict hydrogen-assisted fatigue failures in hydrogen-transporting gas pipelines.
A novel approach is developed to establish an accurate and widely applicable ML model that leverages data from one set of mechanical tests to improve the prediction of other relevant mechanical parameters. The new approach involves employing data extracted from primary literature sources that conducted the fatigue crack growth (FCG) test. These data are then utilized in an existing ML model, previously trained with tensile test data, to predict the reduction of area (RA) of materials subject to hydrogen embrittlement. Building upon the aforementioned approach, the model-generated RA is further integrated as an input parameter to train a new ML model aimed at predicting FCG rates. In parallel, a conventional ML model is developed that excludes model-calculated RA as an input parameter, focusing solely on fatigue test variables. The traditional approach is undertaken to evaluate the efficacy of incorporating model-generated RA into the model. Data sets from one source (i.e., 96 data points) are completely removed from the database to compare the models. Afterward, model predictions are compared with the removed data set to assess model performance and accuracy.
Testing the models with the removed data sets shows the traditional model has a mean relative error (MRE) of 11.65, while the RA-based model has an MRE of 10.06. Moreover, the model with RA is 15.65% more accurate than the traditional model in terms of MRE after data removal. Furthermore, both models are assessed using standard statistical performance parameters such as the coefficient of determination (R2), and they show similar performance. Despite the similarity in conventional performance metrics for both models, the analysis of maximum error and the scatter in the cross-correlation plots of model predictions clearly indicates that the model incorporating the calculated RA demonstrates closer alignment with actual values. This outcome not only underscores the merit of this innovative approach but also contributes significantly to mitigating data scarcity challenges in the evolving field of hydrogen energy.
Data scarcity challenges in hydrogen embrittlement studies are addressed by developing a novel approach that uses data from one type of mechanical test to improve the prediction accuracy of a model of other relevant mechanical parameters. The method developed in this study can be extended for other ML modeling applications when data scarcity is the limiting factor.
Knowledge and Using of Dry needling by Physiotherapists in Central Africa in the management of Trigger points: the first cross sectional survey in Cameroon
Authors: MUNTESSU Dilane Landry NSANGOU , NGueha Nicodemos , Ghassi Hyacinte Trésor , Mpatoutou Mpatoutou Me , Noumoe David Leonel , Buh Franklin Chu 
Published: February 2025

Abstract

Background
Dry Needling (DN) emerged in the 1970s as a therapeutic breakthrough, proving effective in the treatment of Trigger Points (TP). Despite its worldwide application, there is little data on the knowledge, attitudes and practices (KAP) of physiotherapists in Central Africa, particularly in Cameroon. This study aims to fill this gap by exploring the knowledge, attitudes and practices of Cameroonian physiotherapists in relation to pressure point management. Understanding their current levels of CAP is essential for identifying educational needs and developing strategies to improve DN practice in the region.
Methods
A quantitative, analytical, cross-sectional survey was carried out online in the 10 regions of Cameroon over a six-month period. All practicing physiotherapists with at least one year's work experience and who had given their consent were included. They were asked KAP questions relating to DN and TP. Pearson correlations were performed between KAP domains together and with sociodemographic data, Using Epi Info software version 7.2.5.0.
Results
123 physiotherapists participated. The majority were male (56.1%), with an average age of 29 ± 5 years and a mean year of experience of 5 ± 2 years. The majority of physiotherapists obtained scores < 50% of the total score allocated to the domain for knowledge of TP and DN and practice of DN, thus indicating insufficient knowledge and weak practices. However, they had positive attitudes, i.e., a score > 50%.
Conclusions
This study highlights a significant need for educational initiatives in the field of Trigger Point and Dry Needling among physiotherapists in Cameroon. The results reveal a lack of knowledge and practice, despite a positive attitude towards the technique. Addressing this need for training is essential to improving the quality of patient care and the effectiveness of trigger zone management. The data collected provides a solid basis for the development of targeted training programmes and resources to equip physiotherapists with the necessary skills and knowledge. Such efforts will ultimately contribute to the advancement of physiotherapy practice in Cameroon and Africa as a whole.
Optimized Aircraft Wing Design for Subsonic Flow Regimes
Authors: Banerjee Aayushman , Taneja Sumit 
Published: February 2025

Abstract

Reducing aircraft weight is vital for improving operational efficiency and lowering fuel consumption. This study explores topological optimization (TO) for aircraft wings, focusing on material redistribution to achieve lightweight and structurally efficient designs. Using computational tools, an aluminium wing was optimized, resulting in a 41% weight reduction—from 1.23 kg to 0.72 kg—without compromising structural integrity. The findings demonstrate the potential for Topological Optimization to enhance the performance, reduce costs, and streamline material use in manufacturing. The integration of TO with aerodynamic analysis highlights its capability to redefine traditional design processes, paving the way for innovative solutions in aerospace engineering.
Diffuse Traumatic Brain Injury Induced Stimulator of Interferons (STING) Signaling in Microglia Drives Cortical Neuroinflammation, Neuronal Dysfunction, and Impaired Cognition
Authors: Packer Jonathan M. , Giammo Samantha G. , Wangler Lynde M. , Davis Amara C. , Bray Chelsea E. , Godbout Jonathan P. 
Published: February 2025

Abstract

Neuropsychiatric complications including depression and cognitive impairment develop, persist, and worsen in the years after traumatic brain injury (TBI), negatively affecting life and lifespan. Inflammatory responses mediated by microglia are associated with the transition from acute to chronic neuroinflammation after TBI. Moreover, type I interferon (IFN-I) signaling is a key mediator of inflammation during this transition. Thus, the purpose of this study was to determine the degree to which a microglia-specific knockout of the stimulator of interferons (STING) influenced TBI-induced neuroinflammation, neuronal dysfunction, and cognitive impairment. Here, microglial inducible STING knockout (CX₃CR1Cre/ERT2 x STINGfl/fl) mice were created and validated (mSTING-/-). Diffuse brain injury (midline fluid percussion) in male and female mice increased STING expression in microglia, promoted microglial morphological restructuring, and induced robust cortical inflammation and pathology 7 days post injury (dpi). These TBI-associated responses were attenuated in mSTING-/- mice. Increased cortical astrogliosis and rod-shaped microglia induced by TBI were independent of mSTING-/-. 7 dpi, TBI induced 237 differentially expressed genes (DEG) in the cortex of functionally wildtype (STING+/+) associated with STING, NF- κB, and Interferon Alpha signaling and 85% were attenuated by mSTING-/-. Components of neuronal injury including reduced NeuN expression, increased cortical lipofuscin, and increased neurofilament light chain in plasma were increased by TBI and dependent on mSTING. TBI-associated cognitive tasks (novel object recognition/location, NOR/NOL) at 7 dpi were dependent on mSTING. Notably, the TBI-induced cognitive deficits in NOR/NOL and increased cortical inflammation 7 dpi were unaffected in global interferon-α/β receptor 1 knockout (IFNAR1) mice. In the final study, the RNA profile of neurons after TBI in STING+/+ and mSTING-/- mice was assessed 7 dpi by single nucleus RNA-sequencing. There was a TBI-dependent suppression of cortical neuronal homeostasis with reductions in CREB signaling, synaptogenesis, and oxytocin signaling and increases in cilium assembly and PTEN signaling. Overall, mSTING-/- prevented 50% of TBI-induced DEGs in cortical neurons. Collectively, ablation of STING in microglia attenuates TBI-induced IFN-dependent responses, cortical inflammation, neuronal dysfunction, neuronal pathology, and cognitive impairment.
Danmu, a Traditional Chinese Medicine from the stem of Nauclea officinalis, Alleviates DSS-Induced Colitis by Modulating Inflammatory Cytokines and Gut Microbiota
Authors: Peng Yuxuan , Li Yan , Shen Zhenguo , Lin Yaqin , Lei Xianglan 
Published: February 2025

Abstract

Danmu (DM), derived from the stem of Nauclea officinalis, is a traditional Chinese medicine renowned for its anti-inflammatory and antibacterial properties. However, its potential in treating inflammatory bowel diseases (IBD) remains unexplored. This study investigates the therapeutic effects of DM on dextran sulfate sodium (DSS)-induced colitis in mice, focusing on its modulation of inflammatory cytokines and gut microbiota. Using a DSS-induced colitis model, we evaluated the impact of DM on clinical symptoms, inflammatory cytokine expression, and gut microbiota composition. DM treatment significantly alleviated weight loss, colon shortening, and histopathological damage in DSS-treated mice. Mechanistically, DM suppressed pro-inflammatory cytokines (IL-6, IL-12, IL-17, and TL-1A) while upregulating anti-inflammatory cytokines (IL-13, TGF-β, and IL-2). Furthermore, 16S rRNA sequencing revealed that DM altered gut microbiota composition, reducing Firmicutes and increasing Actinobacteriota, with specific genera (e.g., Bifidobacterium) correlating with IL-6 modulation. These findings suggest that DM ameliorates colitis by regulating inflammatory cytokine expression and reshaping gut microbiota, thereby modulating intestinal immune responses. This study highlights DM as a promising therapeutic candidate for IBD, offering a dual mechanism of action through immune modulation and microbiota regulation.
Nitrogen Transformation Characteristics and Mechanisms of the High-Temperature-Tolerant Heterotrophic Nitrifying Bacterium Aeribacillus pallidus sp. GW-E
Authors: Zhang Yindi , Ma Yongqi , Qi Wenrui , Guan Wenping , Gong Lulin , Xue Yuan , Lin Dong , Sun Likun , Wang Zike 
Published: February 2025

Abstract

High-temperature heterotrophic nitrifying bacteria grow stably at elevated temperatures and exhibit effective nitrification capabilities. These bacteria exhibit remarkable environmental adaptability and have significant potential for practical applications in nitrogen transformation. In this study, the high-temperature-tolerant heterotrophic nitrifying bacterium, Aeribacillus pallidus sp. GW-E, was isolated from a high-temperature aerobic composting pile. The nitrification performance of this strain was evaluated based on its ability to utilize different types of inorganic nitrogen sources. The nitrogen transformation characteristics and mechanisms of strain GW-E were elucidated using nitrogen balance assessments, enzyme-specific activity analysis, and whole-genome sequencing. The results indicated that the utilization rates of NH4+-N (416.31 mg/L), NO3--N (409.68 mg/L), and NO2--N (415.71 mg/L) were 3.79 mg/L/h, 0.71 mg/L/h, and 2.24 mg/L/h, respectively. Whole-genome analysis identified several genes associated with nitrogen metabolism, including amt, npd, nirA, gdhA, glnA, and gltBD. Through this analysis, three nitrogen transformation pathways related to ammonia assimilation, heterotrophic nitrification, and assimilatory nitrate reduction were identified. At the optimal cultural conditions of A. pallidus sp. GW-E, the NH4+-N utilization rate reached 4.33 mg/L/h when the carbon-to-nitrogen (C/N) ratio was 15:1, the temperature was 54°C, and the pH was 8. These results indicate that A. pallidus sp. GW-E exhibits exceptional nitrification performance, ammonia assimilation capacity, and the ability to reduce assimilatory nitrate. This study provides reliable strain resources and scientific evidence for the application of high-temperature heterotrophic nitrifying bacteria.
Addressing risk factors of Santals Adivasis: Policy recommendations for non-communicable diseases in Bangladesh
Authors: Sultana Sharmin , Iqbal Md. Hafiz 
Published: February 2025

Abstract

Background There is increasing evidence that rate of non-communicable diseases (NCDs) vary among different groups. However, studies related to disease burden and its risk factors among ethnic minorities are rare in Bangladesh. To fill this gap, our study explores the prevalence of various risk factors for major NCDs among Santal Adivasis and compares the interrelationship among different groups of risk factors.Methods Employing purposive sampling techniques, data were collected from 389 respondents in Birgonj Upazila of Dinajpur district through a pretested questionnaire. In addition to socio-demographic-anthropometric factors, laboratory diagnoses were conducted following standard techniques. Data were analyzed using univariate regression analysis.Results The prevalence of shared immediate risk factors for NCDs is remarkably low among Santal Adivasis. Among the modifiable intermediate risk factors, the use of tobacco and harmful alcohol consumption from early adulthood poses major risks for cardiovascular diseases in this population. Males appear to be more vulnerable to behavioral risk factors such as smoking and alcohol consumption, while a significantly higher proportion of females suffer from impaired glucose tolerance.Conclution The findings of this study emphasize the importance of targeted policies and interventions to improve health outcomes in this ethnic minority group.
CircoTax: a novel circular representation of differentially abundant microbial ranks
Authors: Gloria Leandro Di , Casbarra Lorenzo , Ramazzotti Matteo 
Published: February 2025

Abstract

One of the most relevant and widely used applications of NGS-based microbial abundance analysis is the discovery of taxa that appear as differentially represented between ecological niches. In the last 10 years several methods have been proposed to accomplish this task. All such analyses invariantly report the differentially represented taxa at the appropriate taxonomic rank as well as a p-value (usually adjusted for multiple testing) and a fold change, representing the confidence on differential call and the amount of variation recorded, usually in log scale, respectively. This work describes CircoTax, an R function implementing a specialized ggplot2 graph to represent a rank-aware collection of differentially abundant taxa in an intuitive and compact radial form. Specifically, the CircoTax builds a circular plot with sectors encoding the taxonomy depth (from kingdom to genus) and, departing from the center, a number of radial bars that reach the appropriate sector and whose color and transparency are proportional to the log fold change intensity and direction. In addition to the manageability in providing custom tables to plot, our implementation also integrates fast and user-friendly ancillary functions which automate both the differential analysis and the display of the results while leaving to the user the choice of further customizations. The visualization of results is both graphically appealing, allowing to accommodate tens of differentially abundant ranks, and biologically informative since the amount of variation, the direction and rank are easily intelligible for each differentially represented taxa at a glance.
ERecommend: A Search scheme for encrypted storage supporting association analysis and active recommendation
Authors: Chen Yuxiang , Dong Guishan , Xu Chunxiang , Liang Yikuan , Hao Yao 
Published: February 2025

Abstract

The recommendation idea is inspired by the user's search habits and experience, there is a demand that the server should provide active recommendation. However, data encryption makes keywords lose their semantics, which hinders the analysis and recommendation function of the server.
In this paper, we study the "search mode" and "access mode" of encrypted data and present a novel ciphertext search model supporting association and recommendation. Specifically, we construct the basic searchable encryption with access control, association analysis and recommendation method in detail. Finally, we conduct an extensive security and performance analysis to ensure ERecommend is secure and feasible.
Application of big data in the network security situation awareness platform of intelligent power monitoring system
Authors: Li Liang , Zhang Jun , Zhang Peng , Cao Liangjing , Zhao Chenxiao 
Published: February 2025

Abstract

INTRODUCTION: The growing complexity of network infrastructures has led to increasingly sophisticated network attacks. Traditional intrusion detection and firewall systems are no longer sufficient to handle these advanced threats. As a result, network situational awareness has emerged as a vital approach to enhance network security, particularly in power information systems. By leveraging big data technologies, network operators can better understand network behavior and perform rapid analyses of diverse data types, enabling effective threat detection and mitigation.
OBJECTIVES: The primary goals of this study are: To develop a robust security situational awareness framework for power information systems. To integrate tools and methods for efficient data collection, processing, storage, analysis, and visualization. To implement scalable and high-performance solutions for detecting and forecasting various network security risks.
METHODS: Data Collection: Tools such as Flume and Kafka are used to collect network attack logs and relevant data.
Data Processing: Frameworks like Apache Spark and Storm are employed for batch and real-time data analysis, ensuring fast and efficient handling of large datasets.
Feature Extraction: A Deep Gated Recurrent Unit (D-GRU) model is used to determine index weights and extract critical features of the network security situation.
Scenario Assessment: A decision matrix is constructed and analyzed using Apache Spark to assess the current state of network security.
RESULTS: The proposed framework demonstrated scalability, ease of deployment, and high availability. These characteristics allowed for effective detection and forecasting of various network security threats, providing an advanced understanding of network behavior and enhancing overall security posture.
CONCLUSION: The integration of big data technologies with network situational awareness offers a significant improvement in detecting and preventing sophisticated network attacks. The proposed framework, which leverages tools like Flume, Kafka, Spark, and D-GRU, provides a comprehensive solution for network security, ensuring robust performance and adaptability to dynamic security challenges. This approach is particularly effective for power information systems, enabling proactive threat mitigation and improved network reliability.
Mathematical Prediction and Optimization of Engineered Properties of Recycled Concrete with Marble Fines using Central Composite Design
Authors: Djedid Tarek , Abbas Messaoud , Zerig Tahar , Belachia Mouloud 
Published: February 2025

Abstract

The reuse of wastes from demolished concrete is now seen as a promising solution for preserving natural resources and protecting the environment. This study aims to assess the influence of varying proportions: 0%, 5%, 10%, 15%, and 20% of marble fines on the behaviour of concrete made of recycled aggregates produced by demolition, using Central Composite Design (CCD) based on the response surface methodology (RSM). Multiple physical and mechanical parameters were examined to determine the optimal amount of fine marble for recycled aggregate concrete (RAC). The results suggested that recycled concrete with marble fines 20% (RC20) shows exceptional quality in terms of fineness modulus, resulting in an improved fresh density and a high drop in plastic during the fresh phase. In parallel, the introduction of 10% and 15% marble fines significantly improves the mechanical properties of hardened RAC. Specifically, RC10 (recycled concrete with 10% marble fines) increases compressive strength by 13.79% and reduces capillary absorption capacity by 46.75% compared to the control (RC0) after 60 days. The incorporation of fines 15% proves sufficient to significantly improve flexural strength at the same hydration time compared to other formulations. Furthermore, this study establishes polynomial correlations using an adjusted value of the coefficient of determination (R-squared) equal to or greater than 0.98, to relate various characteristics of fresh concrete (density and slump) and hardened concrete (porosity and water absorption by immersion) after the addition of marble fines. The proposed RSM model-based CCD validates a promising approach that allows optimal values of compressive strength (35.9 MPa) and flexural strength (4.92 MPa), with a desirability coefficient of approximately 93%.
Two Tabata cycles in a single training set maximize fat oxidation after exercise in male college students with overweight/obesity
Authors: Cheng Zhuoyue , Liu Lixia , Han Yanbai , Wang Hongli , Wang Yongbo , Han Yiming 
Published: February 2025

Abstract

Tabata has been identified to enhance energy expenditure and fat oxidation in humans. The study aims to find an optimal Tabata volume for weight loss. 32 male university students with overweight/obesity participated in three tests. Test I consisted of a single Tabata cycle, Test II consisted of two, and Test III consisted of three. Each cycle was separated by a 10-minute interval, and each test was set as 7 days interval. Gas exchange indices were monitored during the last Tabata cycle of the test and the 30-minute recovery period. Subsequently, fat and glucose oxidation amounts, rates, and energy expenditure were calculated. During the 10-minute recovery period, the fat oxidation amounts of Test II (0.80±0.26 g) and Test III (0.87±0.24 g) were higher than Test I (0.50±0.11 g, p<0.001). There was no significant difference between Test II and Test III. During the 20-minute and 30-minute recovery periods, Test II (3.21±0.50 g; 5.04±1.02 g) showed significantly higher fat oxidation amount than Test I (2.47±0.59 g, p<0.001; 4.41±0.98 g, p<0.05) and Test III (2.80±0.43 g, p<0.001; 4.11±0.96 g, p<0.001), there was no significant difference between Test I and Test III (p>0.05). No significant differences in energy expenditure were observed among the three tests during recovery periods (p>0.05). We conclude that two Tabata cycles show the highest fat oxidation amount with the same energy expenditure amount during the recovery period, which is the optimal Tabata volume for weight loss.