bluuebunny/crossref_metadata_2025 huggingface dataset:
Proof-of-Concept Recirculating Air Cleaner Evaluation in a Pig Nursery
Authors: Evans Jackson O. , Ingle MacKenzie L. , Pan Junyu , Mandapati Himanth R. , Kolar Praveen , Wang-Li Lingjuan , Shah Sanjay B.
Published: October 2024

Low ventilation rates used to conserve energy in pig nurseries in winter can worsen air quality, harming piglet health. A recirculating air cleaner consisting of a dust filter and ultraviolet C (UVC) lamps was evaluated in a pig nursery. It had a recirculation rate of 6.4 air changes per hour, residence time of 0.43 s, and UVC volumetric dose of 150 J·m−3. Reduced ventilation led to high particulate matter (PM) concentrations in the nursery. During the first 9 d, the air cleaner increased floor temperature in its vicinity by 1.9 °C vs. a more distant location. The air cleaner had average removal efficiencies of 29 and 27% for PM2.5 (PM with aerodynamic equivalent diameter or AED < 2.5 µm) and PM10 (PM with AED < 10 µm), respectively. It reduced PM2.5 and PM10 concentrations by 38 and 39%, respectively, in its vicinity vs. a more distant location. The air cleaner was mostly inconsistent in inactivating heterotrophic bacteria, but it eliminated fungi. It trapped 56% of the ammonia but did not trap nitrous oxide, methane, or carbon dioxide. The air cleaner demonstrated the potential for reducing butanoic, propanoic, and pentanoic acids. Design improvements using modeling and further testing are required.
Effects of Metal Oxide Nanoparticles on the Growth and Genotoxicity of Garden Cress (Lepidium sativum L.)
Authors: Mošenoka Aleksandra , Kokina Inese , Plaksenkova Ilona , Jermaļonoka Marija , Sledevskis Eriks , Krasovska Marina
Published: October 2024

The interaction of nano-fertilizers with commercially important crops can be a promising solution to increase both crop yield and quality. This study investigated the effect of iron oxide nanoparticles (Fe3O4 NPs) on four-week-old garden cress (Lepidium sativum L.) seedlings. Iron is an essential micronutrient for plants but is not always available in sufficient quantities, which can lead to chlorosis and even plant death. The seedlings were grown hydroponically, with three concentrations (1 mg/L, 5 mg/L, and 10 mg/L) of the NPs, alongside a control group with no additions. During the experiment, the following methods were employed: measurement of stem and root length, spectrophotometry to determine chlorophyll absorbance and concentration, and the RAPD technique to assess the genotoxicity of Fe3O4 NPs. The study demonstrated a significant increase in the shoot length of cress at all concentrations compared to the control group (p < 0.05; p < 0.01). The light absorption and chlorophyll concentration levels in the experimental groups significantly increased compared to the control group (p < 0.001). Genotoxicity analysis revealed that the genotoxic impact of the NPs on the garden cress genome was only 10%, a statistically insignificant level. The findings suggest that Fe3O4 NPs exhibit low genotoxicity and have the potential to enhance the growth and chlorophyll content of cress seedlings in hydroponic conditions.
Planting Trees on Sandy Saline Soil Increases Soil Carbon and Nitrogen Content by Altering the Composition of the Microbial Community
Authors: Shao Tianyun , Yan Xiao , Ji Kenan , Li Zhuoting , Long Xiaohua , Zhang Yu , Zhou Zhaosheng
Published: October 2024

The remediation and exploitation of sandy saline soils, an underutilized resource, can be enhanced by a greater comprehension of the impact of plants and microorganisms on nutrient cycling. However, there is scant research information on the capacity of different trees and shrubs to improve carbon and nitrogen cycling in saline soils at different depth layers. This study investigated the effect of the trees Zelkova serrata (ZS) and Ligustrum lucidum (LL) and shrub Hibiscus syriacus (HS) on the carbon and nitrogen fractions, soil enzyme activities and microbial communities in sandy saline soils. Planting ZS, LL or HS improved soil quality, increased soil carbon and nitrogen content, changed rhizosphere soil metabolites and enhanced soil enzyme activities and microbial abundance and diversity. Compared to values in the bare soil, the highest reduction in soil salinity was noticed under Zelkova serrata (49%) followed by Ligustrum lucidum (48%). The highest increase in total soil organic carbon (SOC) was noted under Ligustrum lucidum and Hibiscus syriacus (62% each), followed by Zelkova serrata (43%), as compared to levels in the bare soil. In the 0–10 cm soil layer, the total N in bare soil was 298 ± 1.48 mg/kg, but after planting LL, ZS or HS, the soil total N increased by 101%, 56% and 40%, respectively. Compared with that of the bare soil, cbbL sequencing showed that the relative abundance of Bradyrhizobium increased and that of Bacillus decreased due to planting. Similarly, the nifH sequencing results indicated that the relative abundance of Bradyrhizobium and Motiliproteu increased and that of Desulfuromonas and Geoalkalibacter decreased. These findings suggested that soil microorganisms could play a pivotal role in the carbon and nitrogen cycle of saline soils by influencing the content of soil carbon and nitrogen.
Ballistic Performance of Thermoplastic Fiber-Reinforced Metal Laminates Subjected to Impact Loadings
Authors: Zeng Yan , Li Chunguang , Zheng Hong , Liu Yijun
Published: October 2024

This paper aims to predict the damage and fracture behavior of thermoplastic fiber-reinforced metal laminates (TFMLs) under ballistic impact loadings. A dynamic metal constitutive model has been employed and implemented in Abaqus/Explicit through a vectorized user material subroutine (VUMAT). The effects of the Lode angle, temperature, and strain rate are considered in the strength model, while the effects of stress triaxiality, Lode angle, temperature, and strain rate are taken into account in the failure criteria. To assess the validity and superiority of the proposed model, the numerically predicted responses of polypropylene fiber-reinforced metal laminates subjected to varying impact energies were systematically compared with corresponding experimental results. Additionally, a comparative analysis was performed between the numerical simulation results predicted by the present model and those obtained using other constitutive models, such as the Johnson–Cook (JC) constitutive model and the elastoplastic constitutive model. Furthermore, the effect of projectile types on the ballistic performance of TFMLs have been systematically investigated. The findings demonstrate that the failure pattern predicted by the current model closely aligns with the experimental observations, while both the Johnson–Cook (JC) constitutive model and the elastoplastic constitutive model were unable to accurately replicate the experimentally observed failure behavior. This study also reveals that the projectile’s nose shape plays a significant role in influencing the perforation behavior of TFMLs, affecting both the residual velocity and damage.
High-Speed Tomography—A New Approach to Plasma Bulk Velocity Measurement
Authors: Forster Roman , Szulc Michal Jerzy , Schein Jochen
Published: October 2024

The plasma bulk velocity is one of the key parameters describing the behavior of a plasma jet and is relevant for applications such as plasma spraying or electric propulsion. Therefore, different measurement techniques to determine the velocity were developed in the past. This paper presents a novel, non-invasive method for spatially resolved velocity measurements. The method is based on tracking of characteristic features in tomographic reconstructions of the plasma plume. A high-speed image recording system combined with tomographic acquisition is therefore the backbone of this method. The proposed setup captures the object under study from six different directions at a frame rate up to one million frames per second, providing high spatial and temporal resolution. The tomographic reconstructions are then calculated using the ART algorithm to track features in the plasma fluctuations, from which the bulk velocity is determined. The setup was tested with a DC plasma torch operated at reduced pressures in the range of tens of millibars. For the analyzed parameters, the axial velocity reached a maximum of 1061 m/s at a distance of three centimeters from the plasma torch exit and decreased to 919 m/s further downstream at a distance of seven centimeters, which is in good accordance with previous work. Therefore, the proposed diagnostic method can serve as a non-invasive alternative to velocity measurements, providing additional information in the form of a 3D model of the plasma bulk.
Distributed Fire Classification and Localization Model Based on Federated Learning with Image Clustering
Authors: Lee Jiwon , Kang Jeongheun , Park Chun-Su , Jeong Jongpil
Published: October 2024

In this study, we propose a fire classification system using image clustering based on a federated learning (FL) structure. This system enables fire detection in various industries, including manufacturing. The accurate classification of fire, smoke, and normal conditions is an important element of fire prevention and response systems in industrial sites. The server in the proposed system extracts data features using a pretrained vision transformer model and clusters the data using the bisecting K-means algorithm to obtain weights. The clients utilize these weights to cluster local data with the K-means algorithm and measure the difference in data distribution using the Kullback–Leibler divergence. Experimental results show that the proposed model achieves nearly 99% accuracy on the server, and the clustering accuracy on the clients remains high. In addition, the normalized mutual information value remains above 0.6 and the silhouette score reaches 0.9 as the rounds progress, indicating improved clustering quality. This study shows that the accuracy of fire classification is enhanced by using FL and clustering techniques and has a high potential for real-time detection.
Method for Wind–Solar–Load Extreme Scenario Generation Based on an Improved InfoGAN
Authors: Yi Derong , Yu Mingfeng , Wang Qiang , Tian Hao , Wang Leibao , Yan Yongqian , Wu Chenghuang , Hu Bo , Li Chunyan
Published: October 2024

In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.
Prediction Models and Feature Importance Analysis for Service State of Tunnel Sections Based on Machine Learning
Authors: Zhao Debo , Yang Yujia , Cao Chengyong , Liu Bin
Published: October 2024

The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the Federal Highway Administration of the United States to predict the service states of the structural, civil, and non-structural sections of a tunnel, respectively. The results indicate that ensemble learning algorithms such as Light Gradient Boosting Machine (LGBM) and Random Forest outperform Support Vector Machine, Multi-Layer Perceptron, Decision Tree, and K-Nearest Neighbor in solving imbalanced classification problems presented in the NTI database. The machine learning models established using the LGBM algorithm exhibited prediction accuracies of 90.9%, 96.4%, and 77.3% for the structural, civil, and non-structural sections, respectively. The importance sorting of features influencing the tunnel’s service state was then performed based on the LGBM model, revealing that the features with a significant impact on the service states of the structural, civil, and non-structural sections are service time, tunnel length and width, geographic position (longitude and latitude), minimum vertical clearance, annual average daily traffic (AADT), and annual average daily truck traffic (AADTT). Data-driven LGBM models identified human factors such as AADT and AADTT as key features influencing the service states of tunnels’ structural sections, and these factors should be taken into consideration in further research to elucidate the potential physical mechanisms.
Yoga and Swimming—A Symbiotic Approach with Positive Impacts on Health and Athletes’ Performance
Authors: Bucea-Manea-Țoniș Rocsana , Jureschi (Gheorghe) Andreea Natalia , Vasile Luciela
Published: October 2024

Yoga enhances acceptance, compassion, physicality, mental and emotional awareness, and spiritual benefits through breath techniques, postures, and body locks, while swimming improves flexibility, strength, and body awareness. The fusion of yoga and swimming, particularly the aqua yoga asana method, offers a balanced lifestyle for athletes and non-performers, enhancing their performance. Our study examined the feasibility of incorporating yoga and swimming practice into Romanian subjects’ lifestyles, designing a factor analysis in SmartPLS software, based on an online survey. This study assessed participants’ knowledge of yoga’s theory and philosophy, as well as their perceptions of the benefits of swimming practice for social and health issues. Our 250 young swimming athletes train in Bucharest’s sports clubs. According to our study, Romanian participants practice yoga and swimming as often as possible to reduce stress, improve concentration for work-related tasks, and improve joint elasticity, balance, and muscular tone. The high coefficient of path analysis (0.667) proved that those who practice yoga asanas have a high level of awareness and understand the fundamentals of the practice. The second coefficient of path analysis (0.857) shows that those who understand yoga better are convinced of its positive effects on society and their health. Thus, yoga and swimming are substitutes for other approaches in prevention and therapy, making it a beneficial tool for pre-performance swimming.
A Novel Approach to Monitoring the Performance of Carbon-Fiber-Reinforced Polymer Retrofitting in Reinforced Concrete Beam–Column Joints
Authors: Sapidis George M. , Naoum Maria C. , Papadopoulos Nikos A. , Golias Emmanouil , Karayannis Chris G. , Chalioris Constantin E.
Published: October 2024

Due to insufficient transverse reinforcement, the retrofitting of beam–column joints (BCJs) in existing reinforced concrete (RC) frame structures is commonly required to alter their brittle behavior. The construction industry has extensively embraced carbon-fiber-reinforced polymers (C-FRPs) as near-surface-mounted (NSM) reinforcement. Monitoring the performance of C-FRP retrofitting is crucial due to the wide range of factors influencing its effectiveness. A novel methodology has been implemented to assess the efficacy of the C-FRP retrofitting method in this study. This approach was validated through experimental investigation of full-scale BCJs, which were retrofitted with C-FRP ropes and subjected to cyclic loading. Furthermore, piezoelectric lead zirconate titanate (PZT) patches were placed on the NSM C-FRP ropes, and the electro-mechanical impedance (EMI) method was employed to monitor the retrofitting technique’s performance. A combination of the commonly used statistical damage index root mean squared deviation (RMSD) and a hierarchical clustering-based approach (HCA) was used to assess the performance of the C-FRP retrofitting technique. The experimental investigation results strongly indicate the proposed approach’s positive impact on the reliable assessment of C-FRP retrofitting performance. Thus, the proposed approach enhances the safety and resilience of retrofitted BCJs in RC structures.
Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
Authors: Zhang Kai , Li Jinqiu , Wang Bingqian , Meng Haoran
Published: October 2024

Pre-trained language models perform well in various natural language processing tasks. However, their large number of parameters poses significant challenges for edge devices with limited resources, greatly limiting their application in practical deployment. This paper introduces a simple and efficient method called Autocorrelation Matrix Knowledge Distillation (AMKD), aimed at improving the performance of smaller BERT models for specific tasks and making them more applicable in practical deployment scenarios. The AMKD method effectively captures the relationships between features using the autocorrelation matrix, enabling the student model to learn not only the performance of individual features from the teacher model but also the correlations among these features. Additionally, it addresses the issue of dimensional mismatch between the hidden states of the student and teacher models. Even in cases where the dimensions are smaller, AMKD retains the essential features from the teacher model, thereby minimizing information loss. Experimental results demonstrate that BERTTINY-AMKD outperforms traditional distillation methods and baseline models, achieving an average score of 83.6% on GLUE tasks. This represents a 4.1% improvement over BERTTINY-KD and exceeds the performance of BERT4-PKD and DistilBERT4 by 2.6% and 3.9%, respectively. Moreover, despite having only 13.3% of the parameters of BERTBASE, the BERTTINY-AMKD model retains over 96.3% of the performance of the teacher model, BERTBASE.
Harnessing Unsupervised Insights: Enhancing Black-Box Graph Injection Attacks with Graph Contrastive Learning
Authors: Liu Xiao , Huang Junjie , Chen Zihan , Pan Yi , Xiong Maoyi , Zhao Wentao
Published: October 2024

Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries.
A Numerical Evaluation for Estimating the Consequences on Users and Rescue Teams Due to the Fire of an Electric Bus in a Road Tunnel
Authors: Caliendo Ciro , Russo Isidoro , Genovese Gianluca
Published: October 2024

E-mobility is progressively penetrating the European market with the ever-increasing registration of new battery electric vehicles (BEVs). Although BEVs can significantly contribute to achieving the goal of sustainable road transport, they pose new challenges related to the complexity of managing battery fire events, especially in confined spaces such as road tunnels. In this regard, while fires of BEVs with small-sized batteries (i.e., cars and vans) have been widely studied, the consequences of fires involving battery electric buses (BEBs), which are equipped with larger-capacity traction batteries, have not yet been sufficiently investigated. In this context, 3D computational fluid dynamics (CFD) simulations were performed to quantitatively assess the threat that a BEB might pose to the safety of users and rescue teams when it catches fire in a bi-directional road tunnel. In this respect, a comparison was also carried out with the consequences of the fire of a similar internal combustion engine bus (ICEB). Since the environmental conditions inside a tunnel, apart from its geometry, depend on both the traffic flow and type of ventilation, the safety of the users and rescue teams in the event of a BEB fire was evaluated by considering the tunnel under conditions of congested traffic, as well as natural or longitudinal mechanical ventilation. The results showed that the fire of the BEB, compared to that of its ICEB counterpart, worsened the environmental conditions inside the tunnel, especially in terms of toxic gas concentrations. This caused a reduction in the safety level of the users when considering the scenarios of both the naturally and mechanically ventilated tunnel. Moreover, in the case of natural ventilation, the BEB fire was found to cause a higher reduction in the safety level of the rescue teams.
Machine Learning Accelerated Design of High-Temperature Ternary and Quaternary Nitride Superconductors
Authors: Islam Md Tohidul , Liu Qinrui , Broderick Scott
Published: October 2024

The recent advancements in the field of superconductivity have been significantly driven by the development of nitride superconductors, particularly niobium nitride (NbN). Multicomponent nitrides offer a promising platform for achieving high-temperature superconductivity. Beyond their high superconducting transition temperature (Tc), niobium-based compounds are notable for their superior superconducting and mechanical properties, making them suitable for a wide range of device applications. In this work, machine learning is used to identify ternary and quaternary nitrides, which can surpass the properties of binary NbN. Specifically, Nb0.35Ta0.23Ti0.42N shows an 84.95% improvement in Tc compared to base NbN, while the ternary composition Nb0.55Ti0.45N exhibits a 17.29% improvement. This research provides a valuable reference for the further exploration of high-temperature superconductors in diversified ternary and quaternary compositions.
A Theoretical and Experimental Analysis of the Time-Domain Characteristics of a PRBS Phase-Modulated Laser System
Authors: Zhu Yun , Li Ying , Li Dong , Dong Lin , Liu Xuesheng , Yan Anru , Liu Youqiang , Wang Zhiyong
Published: October 2024

Pseudo-Random Binary Sequence (PRBS) phase modulation is an effective method for suppressing the stimulated Brillouin scattering (SBS) effect generated by narrow-linewidth fiber lasers during amplification. We noticed that backward time-domain pulses were generated when using PRBS modulation signals in fiber amplification. In this paper, the time-domain dynamic characteristics of the forward output laser and the backward Stokes light after PRBS phase modulation were studied theoretically. Through analyzing the transient SBS three-wave coupling theory and combining it with the SBS accumulation time constant, we knew that the forward and backward high-intensity pulses were caused by the long dwell time of the PRBS. For this purpose, we provided a new method for suppressing high-intensity pulses caused by a long dwell time; namely, we modified the maximum length sequence (MLS) of PRBS signals to eliminate the long dwell time, took the PRBS-9 signal at 1 GHz as an example, and then used MLS1 modulation and MLS2 modulation to compare them with unoptimized PRBS modulation. The output laser peaks of the MLS1 and MLS2 signals were reduced from ±55% to ±25% and ±10% relative to the original PRBSs, respectively, and the peaks of Stokes light were reduced from 39% to 19% and 11%, respectively. Additionally, we experimentally verified that the rational optimization of the sequence did not reduce the SBS threshold. The results provided a new method for suppressing high-intensity pulses during the amplification of a PRBS phase-modulated laser, which played an important role in the output stability of high-power narrow-linewidth fiber amplifiers.
Techniques for Detecting the Start and End Points of Sign Language Utterances to Enhance Recognition Performance in Mobile Environments
Authors: Kim Taewan , Kim Bongjae
Published: October 2024

Recent AI-based technologies in mobile environments have enabled sign language recognition, allowing deaf individuals to communicate effectively with hearing individuals. However, varying computational performance across different mobile devices can result in differences in the number of image frames extracted in real time during sign language utterances. The number of extracted frames is a critical factor influencing the accuracy of sign language recognition models. If the number of extracted frames is too small, the performance of the sign language recognition model may decline. Additionally, detecting the start and end points of sign language utterances is crucial for improving recognition accuracy, as the period before the start point and after the end point often involves no action being performed. These parts do not capture the unique characteristics of each sign language. Therefore, this paper proposes a technique to dynamically adjust the sampling rate based on the number of frames extracted in real time during sign language utterances in mobile environments, with the aim of accurately detecting the start and end points of the sign language. Experiments were conducted to compare the proposed technique with the fixed sampling rate method and with the no-sampling method as a baseline. Our findings show that the proposed dynamic sampling rate adjustment method improves performance by up to 83.64% in top-5 accuracy and by up to 66.54% in top-1 accuracy compared to the fixed sampling rate method. The performance evaluation results underscore the effectiveness of our dynamic sampling rate adjustment approach in enhancing the accuracy and robustness of sign language recognition systems across different operational conditions.
Grayscale Iterative Star Spot Extraction Algorithm Based on Image Entropy
Authors: Zhao Qing , Liao Jiawen , Zhang Derui , Feng Jia
Published: October 2024

Star trackers are susceptible to interference from stray light, such as sunlight, moonlight, and Earth atmosphere light, in the space environment, resulting in an overall improvement in the star image grayscale, poor background uniformity, low star extraction rate, and high number of false star spots. In response to these challenges, this paper proposes a grayscale iterative star spot extraction algorithm based on image entropy. The implementation of the algorithm is mainly divided into two steps: (1) The algorithm conducts multiple grayscale iterations, effectively utilizing the prior information on the local contrast of star spots to filter out stray light backgrounds to a certain extent. (2) By establishing an inner–outer template, the image entropy algorithm is employed to obtain the real star targets to be extracted, which further suppresses the background clutter and noise. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this algorithm can effectively suppress background stray light, enhance star extraction rates, and reduce the number of false star spots, and it exhibits superior detection performance in complex backgrounds across various scenarios.
Research on Chebyshev Graph Convolutional Neural Network Modeling Method for Rotating Equipment Fault Diagnosis under Variable Working Conditions
Authors: Liao Jige , Deng Yaohua , Xie Xiaobo , Zhang Zilin
Published: October 2024

Given the challenges of rotating equipment fault diagnosis under variable working conditions, including the unbalanced transmission of information during feature extraction, difficulty in capturing both global and local features, and limited generalization across different working conditions, a Chebyshev graph convolutional neural network (ChebyNet) method is proposed to address these issues. First, a symmetry processing mechanism is incorporated into the framework of the ChebyNet to balance the transfer of information between nodes in the graph to ensure the fair and efficient integration of information. Secondly, the wide-area feature extraction capabilities of the ChebyNet and the adaptive nodes of the graph attention network (GAT) are integrated to achieve the comprehensive mining of fault characteristics and accurate characterization of complex interactive relationships. Finally, the node reconstruction task of self-supervised learning and collaborative node classification tasks are used to enhance the model’s ability to capture complex changes in variable working conditions data, significantly improving the generalizability of working conditions. In comparative and cross-validation experiments, the proposed method achieved an average diagnostic accuracy of 99.72%, representing an improvement of up to 17.96% compared to other graph neural network (GNN) models. It significantly enhances the accuracy, stability, and generalization of fault diagnosis. Ablation experiments further validate the effectiveness of the proposed method in improving fault diagnosis performance under variable working conditions.
Research on the Characteristics of Seepage Failure in the Surrounding Rock (Coal) of the Goafs
Authors: Luo Bin , Zhang Chenghang , Zhang Peng , Huo Jiayi
Published: October 2024

During mining, the brittle fracture structure of coal makes it highly susceptible to disturbance, leading to changes in the permeability of the coal seam from non-conductive to water-conductive, which poses a significant threat to the stability and safety of coal pillars in goafs. Therefore, understanding the damage mechanisms of coal during water–rock interactions is crucial for ensuring mine safety. In this paper, based on laboratory seepage tests, the impact of hydrodynamic forces on the microstructure of fissured coal and its subsequent effect on permeability is examined. The study found that increasing confining pressure causes the “closure” of coal fissures, leading to a reduction in permeability. Additionally, during the initial stage of seepage, fine particles within the coal samples are mobilized due to seepage damage, leading to channel blockages and further reductions in permeability. However, as seepage continues, the hydraulic channels eventually open fully, resulting in a sharp increase in permeability. Furthermore, using a two-dimensional fracture seepage model, the study investigated how the scale of fractures in the water-conducting channels influences seepage behavior. A critical fracture width method was proposed to predict permeability surges, offering a new approach for analyzing the stability of coal pillars in mining areas.
Enhancing the Nutritional Value and Preservation Quality of Strawberries through an Optimized Osmotic Dehydration Process
Authors: Ladika Georgia , Tsiaka Thalia , Stavropoulou Natalia A. , Strati Irini F. , Sinanoglou Vassilia J.
Published: October 2024

This study aimed to optimize the osmotic dehydration process of strawberry slices by examining the effects of glycerol concentration, immersion time, and temperature on water loss and solid gain. Additionally, the study explored the use of chokeberry infusion to enhance the total phenolic content of the strawberries, thereby increasing their nutritional value. Using the Box–Behnken design and response surface methodology, the study identified optimal conditions to maximize water loss and solid gain. The findings revealed that high glycerol concentration (60% w/w) and temperature (50 °C), combined with optimal immersion times, significantly influenced water loss and solid gain. Experimental validations confirmed the model’s predictions, showing high prediction accuracy (87.37% to 87.69%) for water loss determination but moderate prediction accuracy (42.80% to 64.72%) for solid gain. The immersion of strawberry slices in a hypertonic osmotic chokeberry infusion for 170–220 min maximized the migration of natural antioxidants. Moreover, the osmotic dehydration process effectively reduced water activity and moisture content, enhancing the strawberries’ shelf stability. Furthermore, the addition of calcium chloride (CaCl2) helped maintain the strawberries’ firmness and color during dehydration. Overall, the optimized osmotic dehydration process preserved the structural integrity and improved the nutritional profile and preservation quality of the strawberries, suitable for preparation of various fruit-based products.
A Prospective Self-Report Survey-Based Cohort Study on Factors That Have an Influence on Tinnitus
Authors: Devos Jana V. P. , Janssen Marcus L. F. , Janssen A. Miranda L. , Hellingman Catharine A. , Smit Jasper V.
Published: October 2024

Background: Limited information is available on factors that affect the burden tinnitus. The aim of this study is to investigate the association between tinnitus burden and demographic, patient-specific and tinnitus characteristics. Secondly, it was examined which variables could predict a change in tinnitus burden after 12 months. Method: In a prospective Dutch cohort of 383 tinnitus patients seeking medical help, tinnitus complaints, demographics, tinnitus characteristics, psychological wellbeing and quality of life were assessed using an online self-report survey at three timepoints (start, 6 months, 12 months). The main outcome variables for tinnitus burden are the Tinnitus Questionnaire (TQ) and Visual Analog Scale (VAS) for tinnitus burden and loudness. Results: Several variables (time, sex, education level, life events, anxiety and depression, sleep issues, tinnitus loudness, hearing impairment and treatment) were significantly associated with tinnitus burden. Additionally, tinnitus burden after 12 months was associated with anxiety, following treatment, sleep issues, negative life events and hearing impairment (increase) and anxiety, total of life events and environmental quality of life (decrease) predicted the tinnitus burden after 12 months. Conclusions: Several factors, such as education level, life events, psychological factors and sleep quality, are related to tinnitus burden and can predict tinnitus burden over time.
Non-Fragile Sampled Control Design for an Interconnected Large-Scale System via Wirtinger Inequality
Authors: Lynnyk Volodymyr , Rehák Branislav
Published: October 2024

A control design for a linear large-scale interconnected system composed of identical subsystems is presented in this paper. The control signal of all subsystems is sampled. For different subsystems, the sampling times are not identical. Nonetheless, it is assumed that a bound exists for the maximal sampling time. The control algorithm is designed using the Wirtinger inequality, and the non-fragile control law is proposed. The size of the linear matrix inequalities to be solved by the proposed control algorithm is independent of the number of subsystems composing the overall system. Hence, the algorithm is computationally effective. The results are illustrated by two examples. The first example graphically illustrates the function of the proposed algorithm while the second one compares with a method for stabilizing a large-scale system obtained earlier, thus illustrating the improved capabilities of the presented algorithm.
Exercise Promotes Hippocampal Neurogenesis in T2DM Mice via Irisin/TLR4/MyD88/NF-κB-Mediated Neuroinflammation Pathway
Authors: Xu Haocheng , Tian Xin , Wang Yuanxin , Lin Junjie , Zhu Baishu , Zhao Chen , Wang Bin , Zhang Xin , Sun Yu , Li Nan , Sun Xun , Zeng Fanxi , Li Mingzhi , Ya Xiquan , Zhao Renqing
Published: October 2024

Neuroinflammation is a major feature of type 2 diabetic mellitus (T2DM), adversely affecting hippocampal neurogenesis. However, the precise mechanism is not fully understood, and therapeutic approaches are currently lacking. Therefore, we determined the effects of exercise on neuroinflammation and hippocampal neurogenesis in T2DM mice, with a specific focus on understanding the role of the irisin and related cascade pathways in modulating the beneficial effects of exercise in these processes. Ten-week exercise significantly decreased T2DM-induced inflammation levels and markedly promoted hippocampal neurogenesis and memory function. However, these positive effects were reversed by 10 weeks of treatment with cyclo RGDyk, an inhibitor of irisin receptor signaling. Additionally, exercise helped reduce the M1 phenotype polarization of hippocampal microglia in diabetic mice; this effect could be reversed with cyclo RGDyk treatment. Moreover, exercise markedly increased the levels of fibronectin type III domain-containing protein 5 (FNDC5)/irisin protein while decreasing the expression of Toll-like receptor 4 (TLR4), myeloid differential protein-88 (MyD88), and nuclear factor kappa-B (NF-κB) in the hippocampus of T2DM mice. However, blocking irisin receptor signaling counteracted the down-regulation of TLR4/MyD88/NF-κB in diabetic mice undergoing exercise intervention. Conclusively, exercise appears to be effective in reducing neuroinflammation and enhancing hippocampal neurogenesis and memory in diabetes mice. The positive effects are involved in the participation of the irisin/TLR4/MyD88/NF-κB signaling pathway, highlighting the potential of exercise in the management of diabetic-induced cognitive decline.
Editorial Board Members’ Collection Series: Biomimetic Design, Constructions and Devices in Times of Change I
Authors: Gebeshuber Ille C.
Published: October 2024

In light of recent global crises, including climate change, species extinction, the COVID-19 pandemic, social upheavals and energy supply challenges, this Special Issue of Biomimetics, entitled “Editorial Board Members’ Collection Series: Biomimetic Design, Constructions and Devices in Times of Change”, aims to explore innovative solutions through biomimetics. This collection features research on various biomimetic applications, such as the peptide-based detection of SARS-CoV-2 antibodies, ergonomic improvements for prolonged sitting, biomimicry industry trends, prosthetic foot functionality and agricultural machinery efficiency. The methods employed include peptide synthesis for diagnostics, simulation software for ergonomic designs, patent analysis for biomimicry trends and engineering discrete element methods for agricultural applications. The findings highlight significant advancements in health diagnostics, ergonomic safety, technological development, prosthetics and sustainable agriculture. The research underscores the potential of biomimetic approaches to address contemporary challenges by leveraging nature-inspired designs and processes. These insights contribute to a broader understanding of how biomimetic principles can lead to adaptive and sustainable solutions in times of change, promoting resilience and innovation across various fields.
Parental Competence and Pornography Use among Hispanic Adolescents
Authors: Calatrava María , Magalhaes Paola Alexandria Pinto de , Vidaurreta Marta , Rivas Sonia , López-Del Burgo Cristina , Belintxon Maider
Published: October 2024

Objectives: This study aims to determine the association between parental competence (warmth, demandingness, and parental education in fortitude and in privacy) and pornography use. Methods: This study presents cross-sectional data from an ongoing international study (YOURLIFE Project) about the opinions and lifestyles of adolescents with respect to affectivity, love, and sexuality. Adolescents (N = 2516) aged 12–15 from Chile, Mexico, Peru, and Spain were included. Multiple logistic regressions were performed to analyze the association between family assets (parental demandingness, warmth, and parental fortitude and privacy education) and pornography use. Results: The results indicated an association between parental warmth and pornography use among boys and girls. Furthermore, privacy education was highly associated with less pornography use only in girls. Parental demandingness and parental fortitude education were not associated with pornography use. Conclusion: Our findings suggest that new educational perspectives including privacy issues should be considered within programs for pornography use prevention among adolescents. Parents should incorporate these variables when discussing pornography with their adolescents and not only focus on filters or demandingness.
Relationship between Experiential Avoidance and Emotional Disturbances in Coping with Disease in Patients with Multiple Sclerosis
Authors: Cuerda-Ballester María , Sancho-Cantus David , Martínez-Rubio David , Proaño-Olmos Belén , García-Pardo María Pilar , de la Rubia Ortí José Enrique
Published: October 2024

Multiple sclerosis (MS) is a neurodegenerative disease that presents with both motor and non-motor symptoms, with anxiety and depression being prominent and potentially exacerbated by negative thoughts. Therefore, the experiential avoidance (EA) exhibited by patients post diagnosis is particularly relevant. This study aimed to measure the degree of EA in patients with MS and determine its relationship with emotional disturbances. A cross-sectional descriptive study was conducted using a sample of 64 patients diagnosed with MS. In October 2018, these patients underwent evaluations of functional and cognitive variables, such as anxiety, depression, and avoidant behaviors towards the disease, using the Expanded Disability Status Scale, Acceptance and Action Questionnaire-II, Self-Compassion Scale Short Form, Five Facet Mindfulness Questionnaire-15, prefrontal symptoms inventory, Beck Depression Inventory II, and State-Trait Anxiety Inventory to assess coping mechanisms in handling the disease. Higher levels of state anxiety (β = 0.79; p < 0.001), trait anxiety (β = 0.82; p < 0.001), and depression (β = 0.62; p < 0.001) were observed in patients with MS as their EA and psychological inflexibility increased. Participants with high self-compassion/self-acceptance tended to have fewer negative thoughts and exhibited better coping with the disease, which may, in turn, affect patterns of psychological rigidity or inflexibility. Dimensions such as kindness and humility could act as positive factors in coping with the disease, whereas self-judgment and isolation are negative elements often associated with avoidant behaviors that hinder effective coping with the illness.
Detecting and Understanding Barriers and Drivers to Advance Systematic Implementation of Resource Circularity in Constructions
Authors: Marchi Lia , Luo Zhengzheng , Gasparini Nicole , Antonini Ernesto , Gaspari Jacopo
Published: October 2024

As the construction sector is one of the most carbon-intensive and resource-intensive industries, the necessity for a transition from a linear to a circular economy is widely acknowledged. Aimed at facilitating the transition, several policy frameworks, operational tools and assessment instruments have been developed in recent decades. Nevertheless, the integration of circularity in the construction sector remains constrained and haphazard, frequently focusing solely on the production phase and neglecting the comprehensive impacts within the overall process. The detected gap between theoretical framework and practical implementation is reflected by the limited coordination between policies and tools, which creates a significant obstacle to the adoption of consistent and effective practices. A dual analysis is conducted, comprising two parallel domains: an investigation of a circular policy theoretical framework in urban environments through a literature review, and an analysis of practice-oriented tools through resilience assessment and green building rating systems. As a result, common ground and shared targets are identified between the two scopes, as well as contrasts and inconsistencies that require further attention. These are classified according to their role as barriers or drivers of change, and recommendations for synergistic improvement between policies and tools are provided.
Fiber Reinforced Polymer (FRP) Composites for Construction
Authors: Guo Rui , Wang Bo , Yang Muye , He Weidong , Zhang Chuntao
Published: October 2024

The development and application of new materials is one of the main driving forces of technical development in the field of civil engineering [...]
Effect of Corrosion on Fatigue Failure of Composite Girders with Corrugated Web on Steel Bottom Plate
Authors: Han Pulu , Wang Genhui , Jin Xuejun
Published: October 2024

Corrosive environments can adversely affect the fatigue performance of bridges and other building structures. In order to determine the influence of corrosion on the fatigue failure of concrete composite girders with a corrugated web on a steel bottom plate (hereinafter referred to as CGCWSB), a scaled model test was conducted on a CGCWSB with a span of 30 m, which served as the structural prototype. Through the model test, theoretical analysis, and numerical simulation, the influence of uniform corrosion and pitting corrosion on the fatigue failure of the CGCWSB was determined, and the propagation law of pitting fatigue crack was determined. The results show that (1) the uniform corrosion caused the stress of the CGCWSB to become larger and the performance of the CGCWSB was reduced, the stress growth of the test girder after corrosion was about 10%, the corrosion rate was 9%, the pitting unevenness coefficient was 1.25, and the relative corrosion life was 26.34 years; (2) the fatigue failure of the non-corroded girder belongs to the weld fatigue failure, and the fatigue failure of the corroded girder was the coexistence of weld fatigue failure and pitting fatigue failure; (3) uniform corrosion did not create a new fatigue source, but it did result in the test girder’s fatigue failure ahead of time. Pitting corrosion did, however, create a new fatigue source; (4) an exponential correlation was present between the propagation length of a pitting crack and the number of equal load cycles. The ultimate failure mode of a pitting fatigue crack was when the crack length reached the thickness of the plate and the component was torn and destroyed; (5) following corrosion, the fatigue life of the test girder was found to be reduced by 10.65%, which suggests that salt corrosion had a significant impact on the fatigue life of the composite girder. This research work can provide a reference for the design and promotion of the use of the CGCWSB.