bluuebunny/crossref_metadata_2025 huggingface dataset:
Bats as an Important Source of Antimicrobial-Resistant Bacteria: A Systematic Review
Authors: Soto-López Julio D. , Diego-del Olmo Manuel , Fernández-Soto Pedro , Muro Antonio
Published: December 2024

Background/Objectives: Bats are the second-largest known order of mammals, accounting for about twenty percent of the species described to date. This group has special importance in health and epidemiology because they are considered hosts of a wide range of antimicrobial-resistant human pathogens. Over the past few decades, the emergence of pathogenic bacteria resistant to antimicrobials has been a growing threat to public health, especially given its repercussions such as deaths associated with antimicrobial resistance and economic losses in the healthcare sector. Results: The diversity of antimicrobial-resistant bacteria, the different methodologies in numeric analysis, and the variety of antibiotics reported in this review make it difficult to establish the scope of the effect of bats on the antimicrobial resistance crisis. Methods: In this systematic review, we focus on the existence of antibiotic-resistant bacteria associated with bats and summarize the main findings of studies conducted on the topic to date. Conclusions: Surveillance is essential to control the emergence of resistant bacteria related to bats, which could eventually affect humans, as this is a problem of a ‘One Health’ nature, with effects on human, animal, and environmental health.
Melatonin Ameliorates Organellar Calcium Homeostasis, Improving Endoplasmic Reticulum Stress-Mediated Apoptosis in the Vastus Lateralis Muscle of Both Sexes of Obese Diabetic Rats
Authors: Salagre Diego , Navarro-Alarcón Miguel , González Luis Gerardo , Elrayess Mohamed A. , Villalón-Mir Marina , Haro-López Rocío , Agil Ahmad
Published: December 2024

Endoplasmic reticulum (ER) stress is a crucial factor in the progression of obesity-related type 2 diabetes (diabesity), contributing to skeletal muscle (SKM) dysfunction, calcium imbalance, metabolic inflexibility, and muscle atrophy. The ER and mitochondria together regulate intracellular calcium levels, and melatonin, a natural compound with antioxidant properties, may alleviate these challenges. Our previous research showed that melatonin raises intracellular calcium and preserves muscle structure by enhancing mitochondrial function in obese diabetic rats. This study further explores melatonin’s potential to reduce ER stress in the vastus lateralis (VL) muscle by modulating the unfolded protein response (UPR) and restoring calcium levels disrupted by diabesity. Five-week-old Zücker diabetic fatty (ZDF) rats and lean littermates of both sexes were divided into control and melatonin-treated groups (10 mg/kg/day for 12 weeks). Flame atomic absorption spectrometry results showed that melatonin restored VL intraorganellar calcium homeostasis, increasing calcium levels in mitochondria and reducing them in the ER by raising the activity and expression of calcium transporters in both sexes of ZDF rats. Melatonin also decreased ER stress markers (GRP78, ATF6, IRE1α, and PERK) and reduced pro-apoptosis markers (Bax, Bak, P-JNK, cleaved caspase 3 and 9) while increasing Bcl2 levels and melatonin receptor 2 (MT2) expression. These findings suggest that melatonin may protect against muscle atrophy in obese and diabetic conditions by mitigating ER stress and calcium imbalance, highlighting its therapeutic potential.
Advancements in Antioxidant-Based Therapeutics for Spinal Cord Injury: A Critical Review of Strategies and Combination Approaches
Authors: Shen Yang-Jin , Huang Yin-Cheng , Cheng Yi-Chuan
Published: December 2024

Spinal cord injury (SCI) initiates a cascade of secondary damage driven by oxidative stress, characterized by the excessive production of reactive oxygen species and other reactive molecules, which exacerbate cellular and tissue damage through the activation of deleterious signaling pathways. This review provides a comprehensive and critical evaluation of recent advancements in antioxidant-based therapeutic strategies for SCI, including natural compounds, RNA-based therapies, stem cell interventions, and biomaterial applications. It emphasizes the limitations of single-regimen approaches, particularly their limited efficacy and suboptimal delivery to injured spinal cord tissue, while highlighting the synergistic potential of combination therapies that integrate multiple modalities to address the multifaceted pathophysiology of SCI. By analyzing emerging trends and current limitations, this review identifies key challenges and proposes future directions, including the refinement of antioxidant delivery systems, the development of multi-targeted approaches, and strategies to overcome the structural complexities of the spinal cord. This work underscores the pressing need for innovative and integrative therapeutic approaches to advance the clinical translation of antioxidant-based interventions and improve outcomes for SCI patients.
Effects of Selected Antioxidants on Electroretinography in Rodent Diabetic Retinopathy
Authors: Dutczak Radosław , Pietrucha-Dutczak Marita
Published: December 2024

Electroretinography (ERG) is a non-invasive technique for evaluating the retinal function in various ocular diseases. Its results are useful for diagnosing ocular disorders and assessing disease progression or treatment effectiveness. Since numerous studies are based on animal models, validating the ERG results from animals is pivotal. The first part of this paper presents basic information on the types of ERG tests used on rodents, and the second part describes the recorded functional changes in rodents’ retinas when various antioxidant treatments for diabetic retinopathy were used. Our study showed that among the tests for diabetic retinopathy diagnosis in rodents, full-field ERG is accurate and the most commonly used, and pattern ERG and the photopic negative response of the flash ERG tests are rarely chosen. Furthermore, antioxidants generally protect retinas from functional losses. Their beneficial influence is expressed in the preserved amplitudes of the a- and b-waves and the oscillatory potentials. However, prolonging the drug exposure showed that the antioxidants could delay the onset of adverse changes but did not stop them. Future studies should concentrate on how long-term antioxidant supplementation affects the retinal function.
Distinct Urinary Metabolite Signatures Mirror In Vivo Oxidative Stress-Related Radiation Responses in Mice
Authors: Li Yaoxiang , Bansal Shivani , Singh Baldev , Jayatilake Meth M. , Klotzbier William , Boerma Marjan , Lee Mi-Heon , Hack Jacob , Iwamoto Keisuke S. , Schaue Dörthe , Cheema Amrita K.
Published: December 2024

Exposure to ionizing radiation disrupts metabolic pathways and causes oxidative stress, which can lead to organ damage. In this study, urinary metabolites from mice exposed to high-dose and low-dose whole-body irradiation (WBI HDR, WBI LDR) or partial-body irradiation (PBI BM2.5) were analyzed using targeted and untargeted metabolomics approaches. Significant metabolic changes particularly in oxidative stress pathways were observed on Day 2 post-radiation. By Day 30, the WBI HDR group showed persistent metabolic dysregulation, while the WBI LDR and PBI BM2.5 groups were similar to control mice. Machine learning models identified metabolites that were predictive of the type of radiation exposure with high accuracy, highlighting their potential use as biomarkers for radiation damage and oxidative stress.
Theoretical and Simulation Study of Suction Force in Wall-Climbing Cleaning Robots with Negative Pressure Adsorption
Authors: Zhang Zheng , Yang Shida , Zhang Peixian , Xu Chaobin , Li Bazhou , Li Yang
Published: December 2024

To address the frequent cleaning requirements of casting molds in bridge tower construction, a wall-climbing cleaning robot based on negative pressure adsorption is designed to safely and efficiently replace manual labor for cleaning tasks. The primary focus of this paper is the establishment of a theoretical model for negative pressure adsorption, along with an analysis of potential adhesion hazards. Initially, the robot’s chassis was designed, followed by the development of a theoretical model for the rotational-flow suction unit that incorporates two critical parameters: the number of blades and their thickness. This model was validated through computational fluid dynamics (CFD) and experimental methods. The findings indicate that, with fewer blades, an increase in blade quantity significantly improves the distribution of nonlinear velocity in the z-plane, resulting in a substantial enhancement of suction force up to a certain limit. As the number of blades increases, the thickness of the blades primarily influences the volume of air within the rotating domain, thereby affecting the suction force; thinner blades are preferable. Moreover, this study reveals that square suction units provide greater suction force compared to circular ones, attributable to their superior negative pressure effect and larger adsorption area. The most critical adhesion risk identified is leakage at the edges of the suction unit.
Optimization on Reasonable Construction State for Cable-Stayed Bridge with Steel Box Girder Based on Multi-Objective Programming Algorithm
Authors: He Junbiao , Chai Wenhao , Hu Weiming , Dai Jie , Di Jin , Qin Fengjiang
Published: December 2024

The reasonable construction state of a cable-stayed bridge refers to the state achieved after construction is carried out according to a specific sequence of procedures, leading to the reasonable completion status the bridge. The corresponding construction states at each stage are considered as part of the reasonable construction state. For the optimization of the construction state of cable-stayed bridges with steel box girders, a method combining a multi-objective programming algorithm with a forward iteration method is proposed to determine a reasonable construction state based on the structural characteristics and optimization principles of such bridges. First, a multi-objective programming model was established, taking the bending moments of the main girder and pylon, as well as cable forces, as objective functions. The weighted square sum method, a type of evaluation function method, was then employed to convert the multi-objective programming model into an unconstrained single-objective quadratic programming model. Subsequently, the damped Newton method was utilized to solve the quadratic programming problem. By integrating this algorithm with the forward iteration method, the reasonable construction state of a large-span and double-tower steel box girder cable-stayed bridge was optimized. The influence of different objective functions on the optimization results was analyzed. The findings demonstrate that the proposed method produces a smooth structural configuration under the optimized construction state, with internal forces and normal stresses within a reasonable range. In the completed state derived from this construction state, internal forces, normal stresses, and cable forces are uniformly distributed, while the reactions at transition piers and auxiliary piers exhibit sufficient pressure reserves. The structural state under dead load achieved through this method closely aligns with the desired reasonable completed state.
A New Approach to Designing Advance Stress Release Boreholes to Mitigate Rockburst Hazards in Deep Boring-Machine-Constructed Tunnels
Authors: Xie Zhenkun , Qiu Shili , Li Shaojun , Xiao Yaxun , Zheng Minzong , Kuang Zhihao
Published: December 2024

The use of tunnel boring machines (TBMs) in deep hard rock tunnels disrupts the original stress equilibrium of the rock mass, often resulting in the aggregation and release of a large amount of elastic strain energy, and even leading to rockburst. Under extremely high rockburst proneness conditions, advance stress release boreholes (ASRBs) deployed behind the TBM cutter head can be used to reduce stress concentration levels. However, there is a lack of scientific design methods for the parameters of the ASRB program for TBM tunnels, leading to poor stress release and difficulty in mitigating high-intensity rockburst hazards. This study proposes a parameter design method for ASRBs in the potential rockburst seismic source area of deeply buried hard rock TBM tunnels, including test scheme establishment methods, parameter selection methods, and parameter space relationship and evaluation index establishment methods. A deep tunnel in southwest China was used as an engineering case study to explore the effect of stress release and energy dissipation under different ASRB layout schemes. The results show that the sensitivity of the five important design parameters of ASRBs to the stress release effect is, in descending order, “aperture”, “inclination”, “included angle”, “spacing”, and “length”. A parameter control law for ASRBs is proposed, which confirms their effectiveness in preventing, controlling, and reducing rockburst disasters in deep hard rock TBM tunnels.
Experimental Observations of Heat-Assisted Boycott Effect in Trapezoidal Enclosures
Authors: Apaz Fernando , Ihle Christian F.
Published: December 2024

The present study examined the influence of controlled heat injection on the sedimentation of fine particles in a trapezoidal container, aiming to explore the combined effects of the Boycott effect and convection induced by heating. The experimental design incorporates varying initial particle concentrations (1500 ppm and 3000 ppm) and heat injection levels (0 W, 4.5 W, and 9 W imposed power) to analyze sedimentation dynamics, focusing on concentration distribution patterns and clear water production. The findings reveal complex interactions between heat injection and particle concentration. At 1500 ppm, heat injection shows minimal impact on sedimentation due to particle resuspension. However, at 3000 ppm, particularly with a 9 W heat injection, the sedimentation performance improves significantly during the early stages of the process, achieving an average sedimentation rate approximately 40 % higher than without heat injection and an average clear water generation rate nearly four times greater. These clear water generation rates were determined considering water with particle concentrations below 20 % of the initial concentration (300 ppm for 1500 ppm and 600 ppm for 3000 ppm). A further analysis of the column and row data reveals stratification patterns influenced by heat injection, characterized by distinct horizontal and vertical layers. Additionally, the results suggest that wall temperature distributions are largely unaffected by the initial particle concentration, while clear water production and sedimentation efficiency are highly dependent on heat levels and initial particle density. These results highlight the potential of heat-enhanced sedimentation to improve separation processes in industrial systems. Specifically, they provide valuable insights for optimizing the design and efficiency of lamella settlers, commonly used in water treatment and other particulate separation applications. Future studies will explore the combined use of coagulants and flocculants and the application of these findings to real mixtures, such as mine water or wastewater, to further validate and expand their industrial applicability.
Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
Authors: Zhi Chuanxiao , Zhai Jingyu , Wen Baogang , Li Ning
Published: December 2024

Integrated bearings, characterized by their unique structure, feature an inner ring that is seamlessly integrated with the shaft. This study is based on the theoretical framework of rolling bearing dynamics and considers bearing friction, lubrication, and Hertz elastic contact theory. A dynamic simulation model considering the interaction between the components of the rolling bearing is established. Additionally, a subroutine for calculating the interaction forces between the bearing components was written in C and compiled into a dynamic link library, which was then integrated with the dynamic simulation software. To solve and simulate the dynamics of the integrated bearing model, a sophisticated combination of a refined integration method and the predictor-corrector Adams–Bashforth–Moulton multistep technique was employed. The theoretical analysis offers insights into the vibration characteristics of the integrated bearings across different structural and operational parameters. Results indicate that a judicious selection of parameters, such as the curvature radius ratio of the inner and outer grooves and the gap of the cage pockets, can significantly enhance the bearings’ vibration and noise reduction capabilities. Furthermore, the application of an appropriate axial preload effectively reduces bearing vibrations, and there exists an optimal range of rotational speeds that minimizes these vibrations.
Expectorant Effects of Atractylodes japonica Koidz. ex Kitam Root Extracts on Particulate Matter-Induced Pulmonary Injury in Mice
Authors: Lee Junho , Mansoor Sana , Lee Jeongjun , Chung Hunsuk , Kwon Young-Sam , Bashir Khawaja Muhammad Imran , Choi Jae-Suk , Ku Sae-Kwang
Published: December 2024

This study investigated the growing environmental concern of particulate matter (PM)-induced pulmonary injury and explored novel preventive strategies. In particular, it evaluated the protective effects of Atractylodes japonica Koidz. ex. Kitam root extract (AJ), which is known for its anti-inflammatory and antioxidant properties, against PM2.5-induced subacute pulmonary injuries in Balb/c mice. The experimental design involved administering AJ at a concentration from 400 to 100 mg/kg over a ten-day period, with comparisons made to the mucolytic agent ambroxol hydrochloride (AX). The results revealed that AJ significantly alleviated PM2.5-induced pulmonary injuries, mucus overproduction, and respiratory acidosis in a dose-dependent manner. Notably, body surface redness was reduced by up to 55% at a concentration of 100 mg/kg compared to the control. These effects were evidenced by reduced mRNA expression of the mucus-associated genes MUC5B and MUC5AC and increased concentrations of substance P (up to 475%) and acetylcholine (up to 355%) in the lungs at 400 mg/kg, compared to the intact vehicle control. Particularly, the 400 mg/kg dose of AJ demonstrated comparable effectiveness to AX, highlighting its potent mucolytic and expectorant activities. The results of this study highlight the fact that AJ could act as a promising alternative for respiratory protection, with potential applications as a functional food ingredient. This study substantiates AJ’s role in enhancing respiratory health, emphasizing its capacity as a candidate for further development into therapeutic agents against toxic environmental exposure.
Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems
Authors: Magzymov Daulet , Makhatova Meruyert , Dairov Zhassulan , Syzdykov Murat
Published: December 2024

This paper evaluates the ability of machine learning (ML) algorithms to capture and reproduce complex multiphase behavior in surfactant–oil–water systems. The main objective of the paper is to evaluate the ability of machine learning algorithms to capture complex phase behavior of a surfactant–oil–water system in a controlled environment of known data generated via physical models. We evaluated several machine learning algorithms including decision trees, support vector machines (SVMs), k-nearest neighbors, and boosted trees. Moreover, the study integrates a novel graphical equation-of-state model with ML-generated compositional spaces to test ML’s effectiveness in predicting phase transitions and compares its performance to experimental data and a validated physical model. Our results demonstrate that the cubic SVM has the highest accuracy in capturing key behaviors, such as the shrinking of two-phase regions as salinity deviates from optimal conditions, and performs well even in near-extrapolated scenarios. Additionally, the graphical equation-of-state model aligns closely with both experimental data and the physical model, providing a robust framework for analyzing multiphase behavior. We do not suggest that machine learning models should replace traditional physical models, but rather should complement physical models by extending predictive capabilities, especially when experimental data are limited. This hybrid approach offers a promising method for investigating complex multiphase phenomena in surfactant systems.
A Comparative Study of Convolutional Neural Network and Transformer Architectures for Drone Detection in Thermal Images
Authors: Gutierrez Gian , Llerena Juan P. , Usero Luis , Patricio Miguel A.
Published: December 2024

The widespread growth of drone technology is generating new security paradigms, especially with regard to the unauthorized activities of UAVs in restricted or sensitive areas, as well as illegal and illicit activities or attacks. Among the various UAV detection technologies, vision systems in different spectra are postulated as outstanding technologies due to their peculiarities compared to other technologies. However, drone detection in thermal imaging is a challenging task due to specific factors such as thermal noise, temperature variability, or cluttered environments. This study addresses these challenges through a comparative evaluation of contemporary neural network architectures—specifically, convolutional neural networks (CNNs) and transformer-based models—for UAV detection in infrared imagery. The research focuses on real-world conditions and examines the performance of YOLOv9, GELAN, DETR, and ViTDet in different scenarios of the Anti-UAV Challenge 2023 dataset. The results show that YOLOv9 stands out for its real-time detection speed, while GELAN provides the highest accuracy in varying conditions and DETR performs reliably in thermally complex environments. The study contributes to the advancement of state-of-the-art UAV detection techniques and highlights the need for the further development of specialized models for specific detection scenarios.
Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
Authors: Wei Fangzheng , Guo Zhiyong , Han Qiaoli , Qi Wenkai
Published: December 2024

Icing on wind turbine blades in cold and humid weather has become a detrimental factor limiting their efficient operation, and traditional methods for detecting blade icing have various limitations. Therefore, this paper proposes a non-contact ice volume estimation method based on binocular vision and improved image processing algorithms. The method employs a stereo matching algorithm that combines dynamic windows, multi-feature fusion, and reordering, integrating gradient, color, and other information to generate matching costs. It utilizes a cross-based support region for cost aggregation and generates the final disparity map through a Winner-Take-All (WTA) strategy and multi-step optimization. Subsequently, combining image processing techniques and three-dimensional reconstruction methods, the geometric shape of the ice is modeled, and its volume is estimated using numerical integration methods. Experimental results on volume estimation show that for ice blocks with regular shapes, the errors between the measured and actual volumes are 5.28%, 8.35%, and 4.85%, respectively; for simulated icing on wind turbine blades, the errors are 5.06%, 6.45%, and 9.54%, respectively. The results indicate that the volume measurement errors under various conditions are all within 10%, meeting the experimental accuracy requirements for measuring the volume of ice accumulation on wind turbine blades. This method provides an accurate and efficient solution for detecting blade icing without the need to modify the blades, making it suitable for wind turbines already in operation. However, in practical applications, it may be necessary to consider the impact of illumination and environmental changes on visual measurements.
Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination
Authors: Vieira Manuela , Galvão Gonçalo , Vieira Manuel A. , Vestias Mário , Louro Paula , Vieira Pedro
Published: December 2024

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users.
Microbiological Safety and Health Properties of Marketed Fermented Root Vegetables
Authors: Knez Eliza , Hałasa Rafał , Turecka Katarzyna , Ośko Justyna , Kadac-Czapska Kornelia , Waleron Krzysztof , Grembecka Małgorzata
Published: December 2024

The study aimed to evaluate the antioxidant potential and microbial composition of edible fermented vegetable roots available on the Polish market, including celeriac, carrot, beetroot, radish, and white radish. The microbiological determinations were conducted according to European Standards. The total phenol content (TPC) was measured by the Folin–Ciocalteu (FC) method, while the total antioxidant capacity (TAC) was analyzed by CUPRAC and DPPH assays. The mean recovery for FC, CUPRAC, and DPPH was in the range of 104–105%, 97–102%, and 96–108%, while the precision amounted to 2.3, 2.75, and 5.99, respectively. The average antioxidant potential among all fermented roots decreased in the following order: beetroot > celeriac > radish > carrot > white radish. In the case of microbiological analyses, no bacteria were found in pasteurized products. However, among unpasteurized vegetables, 7 out of 11 products met the FAO/WHO criteria for probiotic foods, as they contained a sufficient number of lactic acid bacteria and lacked Escherichia coli. None of the tested products were contaminated with Bacillus cereus, Salmonella spp., Enterococcus spp., or spore-forming anaerobic bacteria. Moreover, chemometric techniques such as the Kruskal–Wallis test and cluster analysis were used to differentiate samples in view of their antioxidant potential. These analyses demonstrated the similarity of vegetable samples from the Apiaceae and Brassicaceae families while highlighting differences in antioxidant potential compared to samples from the Amaranthaceae family.
Effect of the Bifidobacterium animalis Subsp. Lactis, BB-12® on Cronobacter sakazakii Growth in Infant Formulas with Different Acid-Buffering Capacities
Authors: Batrinou Anthimia , Tsakali Efstathia , Sinanoglou Vassilia J. , Eleni Polyvakidi Maria , Pyrovolou Katerina , Chatzilazarou Arhontoula , Konteles Spyros J.
Published: December 2024

The opportunistic pathogenic bacterium C. sakazakii poses a significant infection risk to infants through contaminated powdered infant formulae (PIFs) when proper hygiene and temperature control are neglected during reconstitution. This study aimed to investigate whether the acid-buffering capacity (ABC) of commercially available PIFs enriched with the probiotic strain Bifidobacterium animalis subsp. lactis (BB-12®) could influence the growth of C. sakazakii. Two PIFs differing in their ABC were reconstituted (RIF), inoculated, and incubated in monoculture and co-culture at 22 °C and 37 °C for 24 h. After 24 h of incubation at 22 °C, regardless of the ABC type of PIF, the population of C. sakazakii in the monoculture was approx. 1.4 log cycles higher than the inoculum, while, in the co-culture, the C. sakazakii count was approx. 0.34 log cycles lower. In contrast, at 37 °C during the 24 h co-culture in the lower ABC infant formula, C. sakazakii was <10 CFU/mL. In all co-culture samples, the pH was significantly lower (p < 0.05) in the PIF with the lower ABC. An analysis of the weak organic acids at 12 and 24 h of incubation revealed that the antimicrobial activity is significantly affected by the final pH value, the type of the weak organic acids, and their ionic–non-ionic ratio, which is formed through the common ion effect.
Immediate Effects of Dynamic Cupping on Shoulder Active Range of Motion of Senior Male Handball Players: A Randomized Controlled Trial
Authors: Cardoso Ricardo , Seixas Adérito , Moreira-Silva Isabel , Azevedo Joana , Duarte Diogo
Published: December 2024

Background: Shoulder injuries are common among handball players and have been associated with adjustments in shoulder mobility with decreased flexibility. The aim of the study is to analyze the immediate effects of dynamic cupping on shoulder active range of motion (AROM) of senior male handball players. Materials and Methods: A total of 80 senior male handball players completed a socio-demographic and clinical questionnaire. They were then randomly divided into two groups: the Intervention Group (IG; n = 40), which received dynamic cupping therapy, and the Control Group (CG; n = 40), which did not receive any intervention. Shoulder AROM, including flexion, extension, abduction, adduction, horizontal adduction, horizontal abduction, internal rotation, and external rotation, was assessed in both groups before the intervention (M0) and after the intervention (M1). Results: After the intervention, the IG showed a statistically significant increase in AROM for all shoulder movements. In contrast, the CG only demonstrated significant improvements in internal rotation (p = 0.042), adduction (p = 0.011), horizontal abduction (p = 0.004), and horizontal adduction (p = 0.005). Additionally, the IG exhibited a statistically significant increase in shoulder AROM across all movements compared to the CG at M1. Conclusions: The findings of this study support that dynamic cupping enhances shoulder AROM in senior male handball players.
Optimal Virtual Power Plant Control Algorithm Considering the Electrical Characteristics of Distributed Energy Resources
Authors: Hyun Seokwoo , Kim Geonho , Park Jungsung , Choi Yunhyuk
Published: December 2024

This study addresses the challenges of grid stability posed by the integration of renewable energy sources, focusing on regions, like Jeju Island, where overgeneration complicates energy management. We introduce a novel sensitivity-based control algorithm for Virtual Power Plants (VPPs) that optimizes the deployment of distributed energy resources (DERs). Unlike conventional curtailment methods, such as Last In, First Out (LIFO) and Pro-rata, which often overlook the nuanced impacts of individual DERs on grid stability, our approach leverages electrical sensitivity analysis. This analysis quantifies the effect of each DER on voltage stability, allowing resources to be grouped and managed based on their specific sensitivity. By deriving the sensitivity matrix from the Jacobian matrix of power flow equations, the algorithm identifies critical DERs that significantly influence grid conditions, enabling more strategic curtailment decisions. The proposed method was validated using real-world data from Jeju Island, demonstrating improved efficiency and reduced curtailment compared to existing techniques. These findings underscore the potential of sensitivity-based control to enhance the efficiency of renewable energy systems, supporting sustainable energy transitions.
Multi-Granularity User Anomalous Behavior Detection
Authors: Feng Wenying , Cao Yu , Chen Yilu , Wang Ye , Hu Ning , Jia Yan , Gu Zhaoquan
Published: December 2024

Insider threats pose significant risks to organizational security, often going undetected due to their familiarity with the systems. Detection of insider threats faces challenges of imbalanced data distributions and difficulties in fine-grained detection. Specifically, anomalous users and anomalous behaviors take up a very small fraction of all insider behavior data, making precise detection of anomalous users challenging. Moreover, not all behaviors of anomalous users are anomalous, so it is difficult to detect their behaviors by standardizing with single rules or models. To address these challenges, this paper presents a novel approach for insider threat detection, leveraging machine learning techniques to conduct multi-granularity anomaly detection. We introduce the Multi-Granularity User Anomalous Behavior Detection (MG-UABD) system, which combines coarse-grained and fine-grained anomaly detection to improve the accuracy and effectiveness of detecting anomalous behaviors. The coarse-grained module screens all of the user activities to identify potential anomalies, while the fine-grained module focuses on specific anomalous users to refine the detection process. Besides, MG-UABD employs a combination of oversampling and undersampling techniques to address the imbalance in the datasets, ensuring robust model performance. Through extensive experimentation on the commonly used dataset CERT R4.2, we demonstrate that the MG-UABD system achieves superior detection rate and precision. Compared to the suboptimal model, the accuracy has increased by 3.1% and the detection rate has increased by 4.1%. Our findings suggest that a multi-granularity approach for anomaly detection, combined with tailored sampling strategies, is highly effective in addressing insider threats.
Optimizing Bioleaching for Printed Circuit Board Copper Recovery: An AI-Driven RGB-Based Approach
Authors: Vives Pons Jordi , Comerma Albert , Escobet Teresa , Dorado Antonio D. , Tarrés-Puertas Marta I.
Published: December 2024

Recovering copper from end-of-life electronics, especially from printed circuit boards, provides significant economic benefits, reduces environmental impact, and supports a circular economy. This case study presents a data-driven approach to predicting copper recovery in the electrolysis stage of a bioleaching process by utilizing RGB sensor readings. We tested nine regression models using RGB values from experimental data. The gradient boosting model, optimized via response surface methodology (RSM), outperformed the others, with predictions matching 84% of observed patterns. These results demonstrate strong predictive capabilities, with scope for further accuracy enhancements. We offer an open-source, web-based digital twin designed specifically to monitor the bioleaching plant, enabling real-time and historical data analysis to support predictive maintenance. Our results underscore the potential to optimize the entire bioleaching process, marking a significant advancement for large-scale copper recovery. This study is the first to investigate predictive bioleaching continuous processes in a semi-industrial e-waste plant using RGB sensors, presenting a novel approach in the field.
The Wavelength Characteristics of Vertical Deformation and a Train Dynamics Simulation of Long-Span, Cable-Stayed Bridges Under Complex Loads
Authors: Pang Zhiqiang , Gao Mangmang , Li Guolong , Yang Jingjing , Yang Fei
Published: December 2024

Ballastless tracks have a high smoothness, but the corresponding laying requirements are strict. Therefore, the maximum span of cable-stayed bridges that can accommodate ballastless tracks is 392 m. For laying ballastless track structures over larger spans, the deformation characteristics of long-span, cable-stayed bridges under complex loads are incompletely understood, and the interaction between them and long-span track–bridge structures is unclear. The influence of the wavelength of the cosine wave on the track–bridge mapping of different orbital structures was explored. The wavelength characteristics of vertical deformation under complex loads were investigated. The track–bridge integrated model for the cable-stayed bridge was established to analyze the mapping relationship between the rail and the bridge and the wavelength characteristics of deformation. Based on the mapping relationships and the wavelength characteristics of deformation, the train–track–bridge dynamics simulation model was simplified. The results show that, when the minimum wavelength of bridge deformation surpassed 6 m, 10 m, and 16 m, the rail deformation in the ballasted track, the longitudinal-connected track, and the unit slab-type ballastless track accurately mirrored the deformation of the bridge. For the span of bridges ranging from 200 m to 600 m, the wavelength of vertical deformation ranged from 21 to 1270 m under complex loads. During local loads, the vertical deformation below the 200 m wavelength constituted a significant proportion near the pie. Considering the influence of the deformation on the train vibration response, the train–bridge dynamic coupling model can be employed to treat the track structure as a load to reduce the complexity of the model and enhance the calculation efficiency.
Evaluating the Potential of Herbal Extracts as Treatment in Immune Thrombocytopenia: A Review of Evidence and Limitations
Authors: Wiggins Russell W. , Woo Jihoo , Cauba John Nicholas , Mito Shizue
Published: December 2024

Immune thrombocytopenia, formerly idiopathic thrombocytopenia purpura (ITP), is an autoimmune disease characterized by the depletion of platelets below 100,000/µL when other causes of thrombocytopenia have been ruled out. It is associated with several infectious pathologies, disease states, and as a known side effect and complication of several drugs and chemotherapies. Standard treatment calls for glucocorticoid-mediated immunosuppression, intravenous immunoglobin transfusion, platelet stimulation, platelet transfusion, and splenectomy in instances of chronic and severe disease. While standard treatments are often effective, some cases prove resistant, and more commonly, some patients are unable to tolerate standard treatment protocols or opt out of surgical intervention. In addition, second-line therapies can be unfeasibly expensive and are associated with side effects themselves. Therefore, for a subset of patients afflicted by immune thrombocytopenia, the exploration of alternative treatment methods is needed in order to ease their burden of disease. Emerging evidence suggests that plant-derived extracts, traditionally used in regions such as Asia and Africa to manage acute thrombocytopenia, hold promise as alternative or adjunctive therapies for the mentioned subset of patients. These natural compounds may provide a cost-effective and less invasive option, potentially bridging gaps in current treatment regimens. We propose these extracts may play a role in fulfilling this deficiency in current treatment protocols. With this review, we aim to characterize and compile evidence that various organic extracts and compounds may be utilized to improve outcomes in these patients. By highlighting their clinical relevance and potential for integration into ITP treatment protocols, this manuscript underscores the importance of expanding the alternative therapies for ITP to improve patient outcomes and reduce treatment burdens.
Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
Authors: Baset Amjad , Jradi Muhyiddine
Published: December 2024

This review explores the novel integration of data-driven approaches, including artificial intelligence (AI) and machine learning (ML), in advancing building energy retrofits. This study uniquely emphasizes the emerging role of explainable AI (XAI) in addressing transparency and interpretability challenges, fostering the broader adoption of data-driven solutions among stakeholders. A critical contribution of this review is its in-depth analysis of innovative applications of AI techniques to handle incomplete data, optimize energy performance, and predict retrofit outcomes with enhanced accuracy. Furthermore, the review identifies previously underexplored areas, such as scaling data-driven methods to diverse building typologies and incorporating future climate scenarios in retrofit planning. Future research directions include improving data availability and quality, developing scalable urban simulation tools, advancing modeling techniques to include life-cycle impacts, and creating practical decision-support systems that integrate economic and environmental metrics, paving the way for efficient and sustainable retrofitting solutions.
Predictive Factors for Hearing Loss in Congenital Cytomegalovirus Infection
Authors: Corazzi Virginia , Musumano Lucia Belen , Migliorelli Andrea , Negossi Laura , Bianchini Chiara , Stomeo Francesco , Pelucchi Stefano , Ciorba Andrea
Published: December 2024

Objectives: The present study aims to identify potential predictive factors for developing sensorineural hearing loss (SNHL) in individuals with congenital Cytomegalovirus (cCMV) infection. Methods: A retrospective study was performed on 50 subjects with cCMV infection (symptomatic and asymptomatic), followed at the Audiology Service of Sant’Anna Hospital (University Hospital of Ferrara). The following data were analyzed: the type of maternal Cytomegalovirus (CMV) infection (primary versus non-primary), time of in utero infection, systemic signs and symptoms or laboratory test anomalies due to cCMV infection, and signs and symptoms of central nervous system (CNS) involvement at birth. In particular, brain ultrasonography and encephalic magnetic resonance imaging (MRI) were evaluated, searching for possible links between imaging findings and SNHL. Results: The statistical analysis showed a significantly higher risk of developing SNHL in subjects with signs and symptoms of CNS involvement at birth (p = 0.009 *). The presence of brain MRI abnormalities significantly influenced the onset of SNHL in patients with symptomatic cCMV infection (p = 0.012 *). Brain ultrasonography, the type of maternal CMV infection, systemic signs/symptoms and laboratory test anomalies at birth, and sex resulted in nonsignificant correlations in the analysis. Conclusions: The presence of neurological symptoms at birth and of detectable abnormalities in brain MRI are predictors of SNHL developing in symptomatic cCMV infection. Further investigation on this topic is necessary.
Lie Bialgebra Structures and Quantization of Generalized Loop Planar Galilean Conformal Algebra
Authors: Yang Yu , Wang Xingtao
Published: December 2024

In this paper, we analyze the Lie bialgebra (LB) and quantize the generalized loop planar-Galilean conformal algebra (GLPGCA) W(Γ). Additionally, we prove that all LB structures on W(Γ) possess a triangular coboundary. We also quantize W(Γ) using the Drinfeld-twist quantization technique and identify a group of noncommutative algebras and noncocommutative Hopf algebras.
Modeling of a Non-Aqueous Redox Flow Battery for Performance and Capacity Fade Analysis
Authors: D’Adamo Mirko , Daub Nicolas , Trilla Lluis , Saez-Zamora Jose A. , Paz-Garcia Juan Manuel
Published: December 2024

This study presents a prototype non-aqueous redox flow battery that advances the capabilities of conventional systems by achieving a wide operational voltage range, high efficiency, and prolonged cycle life. Leveraging the redox pair 10-[2-(2-methoxy ethoxy)ethyl]-10H-phenothiazine and 2-ethylterephthalonitrile, the system delivers a discharge cell voltage ranging from approximately 2.25 V to 1.9 V. To address the economic challenges associated with non-aqueous redox flow batteries, this work explores a cost-efficient design using a symmetric cell architecture and a low-cost, porous separator. To evaluate the feasibility and scalability of this approach, a 2D time-transient reactive transport model is developed, integrating Nernst–Planck electroneutrality principles and porous electrode kinetics. The model is optimized and validated against experimental charge/discharge cycles, accurately predicting voltage behavior. Additionally, the study provides crucial insights into the crossover phenomenon, elucidating the transport dynamics and spatial distribution of active species within the cell. This comprehensive framework establishes a robust foundation for future efforts to scale and optimize non-aqueous redox flow batteries for large-scale energy storage applications, bringing them closer to commercial viability.
An Integrated Na2S−Electrocatalyst Nanostructured Cathode for Sodium–Sulfur Batteries at Room Temperature
Authors: Ma Sichang , Zhu Yueming , Yang Yadong , Li Dongyang , Tan Wendong , Gao Ling , Zhao Wanwei , Liu Wenbo , Liang Wenyu , Xu Rui
Published: December 2024

Room-temperature sodium–sulfur (RT Na–S) batteries offer a superior, high-energy-density solution for rechargeable batteries using earth-abundant materials. However, conventional RT Na–S batteries typically use sulfur as the cathode, which suffers from severe volume expansion and requires pairing with a sodium metal anode, raising significant safety concerns. Utilizing Na2S as the cathode material addresses these issues, yet challenges such as Na2S’s low conductivity as well as the shuttle effect of polysulfide still hinder RT Na–S battery development. Herein, we present a simple and cost-effective method to fabricate a Na2S–Na6CoS4/Co@C cathode, wherein Na2S nanoparticles are embedded in a conductive carbon matrix and coupled with dual catalysts, Na6CoS4 and Co, generated via the in situ carbothermal reduction of Na2SO4 and CoSO4. This approach creates a three-dimensional porous composite cathode structure that facilitates electrolyte infiltration and forms a continuous conductive network for efficient electron transport. The in situ formed Na6CoS4/Co electrocatalysts, tightly integrated with Na2S, exhibit strong catalytic activity and robust physicochemical stabilization, thereby accelerating redox kinetics and mitigating the polysulfide shuttle effect. As a result, the Na2S–Na6CoS4/Co@C cathode achieves superior capacity retention, demonstrating a discharge capacity of 346 mAh g−1 after 100 cycles. This work highlights an effective strategy for enhancing Na2S cathodes with embedded catalysts, leading to enhanced reaction kinetics and superior cycling stability.