bluuebunny/crossref_metadata_2025 huggingface dataset:
Nasopharyngeal Carriage, Antimicrobial Resistance, and Serotype Distribution of Streptococcus pneumoniae in Children Under Five in Lebanon: Baseline Data Prior to PCV13 Introduction
Authors: Rafei Rayane , Zaylaa Mazen , Diab Mohamad , Kassem Issmat I. , El Omari Khaled , Halimeh Fatima B. , El Moujaber Grace , Achour Afaf , Ismail Bassel , Mallat Hassan , Hamze Monzer , Dabboussi Fouad , Osman Marwan
Published: February 2025

Background: The nasopharyngeal carriage of Streptococcus pneumoniae can be the source of transmission between humans and the starting step towards invasive pneumococcal diseases. Data on the carriage of pneumococci in children before and after the pneumococcal conjugate vaccines (PCV) integration in a country are essential for monitoring any change in pneumococcal carriage serotypes and their antimicrobial-resistance profiles. Methods: We investigated the epidemiology of S. pneumoniae carriage among children younger than five years old in Tripoli, Lebanon, in 2016, the same year of integration of PCV13 in the country’s Expanded Program on Immunization. Results: Of 104 participating children, 57 (54.8%) gave a positive culture for S. pneumoniae. Antimicrobial susceptibility testing revealed that 26.3% of isolates were multidrug-resistant. Resistance was detected mainly against oxacillin (77.2%), tetracycline (29.8%), erythromycin (22.8%), trimethoprim-sulfamethoxazole (22.8%), clindamycin (19.3%), minocycline (19.3%), and teicoplanin (1.8%). Serotyping analysis identified 14 distinct serotypes, with only 31.3% and 50% of isolates corresponding to vaccine serotypes covered by PCV13 and PCV20, respectively. The most common serotypes were 11A, 19F, 23A, and those of serogroup 24 (Sg24) accounted for 37.5% of the serotyped isolates. Conclusions: Our findings have revealed the circulation of a pool of pneumococci isolates with high levels of antibiotic resistance and different degrees of likelihood of causing invasive diseases in children under five years old in Tripoli in 2016. The overall limited PCV13 vaccine coverage in this study highlighted the need for vaccines with greater coverage in the immunization programs in Lebanon. Longitudinal national studies investigating the carriage of pneumococci in children are required to further assess the impact of the PCV vaccine on pneumococci carriage in children and steer new vaccine development.
Adaptive Semi-Supervised Algorithm for Intrusion Detection and Unknown Attack Identification
Authors: Li Meng , Luo Lei , Xiao Kun , Wang Geng , Wang Yintao
Published: February 2025

Intrusion detection systems face significant challenges, including the inability to detect unknown threats and imbalances between normal and anomalous traffic. To address these limitations, we propose a semi-supervised intrusion detection algorithm based on GAN with a Transformer backbone for network security in IoT devices. To address the issue of imbalanced normal and anomalous traffic due to the diversity of network behavior and the difficulty that supervised algorithms experience in detecting unknown intrusions, we use only normal traffic as training data. By integrating the self-attention mechanism of Transformers, we leverage their ability to capture long-range dependencies in sequential data, enhancing the core capability of the GAN. The experimental results show that our algorithm achieves an F1-score of 95.2% and a false omission rate (FOR) of 10.7% on the CIC-IDS2017 dataset. On the Kitsune dataset, it attains an F1-score of 83.2% and a FOR of 15.8%. In real-world applications, when the algorithm was deployed on actual vehicle devices, it maintained strong performance with a FOR of 13%, further validating the practical applicability and value of the algorithm.
The Development of Optical Sensing Techniques as Digital Tools to Predict the Sensory Quality of Red Meat: A Review
Authors: Anagnostou Georgios , Ferragina Alessandro , Crofton Emily C. , Frias Celayeta Jesus Maria , Hamill Ruth M.
Published: February 2025

The sensory quality of meat, encompassing the traits of appearance, texture, and flavour, is essential to consumer acceptance. Conventional quality assessment techniques, such as instrumental methods and trained sensory panels, often face limitations due to their destructive and time-consuming nature. In recent years, optical sensing techniques have emerged as a fast, non-invasive, and non-destructive technique for the prediction of quality attributes in meat and meat products, achieving prediction accuracies of over 90%. This review critically examines the potential of optical sensing techniques, such as near-infrared spectroscopy (NIRS), Raman spectroscopy, and hyperspectral imaging (HSI), to inform about the sensory attributes of red meat, aligning with industrial demands for early information on the predicted sensory performance of inventory to support meeting consumer requirements. Recent trends and the remaining challenges associated with these techniques will be described. While technical issues related to spectral data acquisition and data processing are important challenges when considering industrial implementation, overall, optical sensing techniques, in tandem with recent developments in digitalisation and data analytics, provide potential for the online prediction of meat sensory quality in the meat processing industries. Establishing technologies for enhanced information on the product and improved possibilities for quality control will help the industry to meet consumer demands for a consistent quality of product.
Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
Authors: Liang Dingge , Li Junliang , Yin Junping
Published: February 2025

In modern wargaming, accurately predicting the locations of the opponent units is crucial for effective strategy and decision making. However, situational data provided by tactical wargame systems present significant challenges: high redundancy across consecutive frames and extreme data sparsity, with units occupying only a small fraction of the overall map. Traditional convolutional neural networks (CNNs) struggle to extract meaningful patterns from such data. To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. By capturing complex inter-node relationships, our model effectively reduces the impact of data repetition and sparsity, achieving robust location predictions in dynamic and sparse wargaming environments. Experimental results demonstrate that our approach significantly improves the prediction accuracy (the combined use of both the GNN and transformer modules results in a 6.4% average performance boost), highlighting its potential to advance intelligent decision making in wargaming applications.
Thallium Poisoning: Case Report and Scoping Review on Diagnostic Delay and Therapeutic Outcome
Authors: Soave Paolo Maurizio , Chirico Francesco , Pallocchi Matteo , Magnavita Nicola
Published: February 2025

Thallium poisoning, which may be accidental or criminal, presents with a non-specific clinical picture but is rapidly progressive. A delay in diagnosis may cause the appearance of serious, often irreversible, and sometimes fatal lesions. Prompt treatment with Prussian Blue before toxicological confirmation results in immediate improvement in cases of intoxication, without appreciable side effects, and is, therefore, recommended as an “ex juvantibus” strategy in cases of suspected thallium poisoning. A successfully treated case of poisoning is presented as an example of this strategy. An analysis of the contaminated well water the patient had unknowingly drunk subsequently showed pollution over 75 times higher than the potable limit, and plasma levels revealed values 267 times higher than the normal range. All the test results were received when the patient undergoing treatment had improved so much that she had been discharged from hospital. To complete the study, we conducted a scoping review to understand the extent and type of evidence in relation to the latency in the diagnosis of intoxication and health effects. The review of 30 articles covering 115 cases of thallotoxicosis confirmed that early treatment with Prussian Blue offers the best chance of achieving complete recovery.
Exergaming-Based Rehabilitation for Lateral Trunk Flexion in Parkinson’s Disease: A Pilot Study
Authors: Mazzari Laura , Zambon Elena , Tonzar Serena , Martini Miriam , Sabot Raffaele , Galmonte Alessandra , Manganotti Paolo
Published: February 2025

(1) Background: Axial postural deformities represent a more common disabling motor complication in Parkinson’s disease. This study aims to investigate the clinical and neurophysiological effect of a rehabilitation treatment based on exergaming. (2) Methods: A pilot observational study was conducted on nine subjects affected by Parkinson’s disease and lateral trunk flexion, as well as on nine healthy controls with regard to some clinical and neurophysiological outcomes (3) Results: Statistically significant improvements were observed in all clinical assessment outcomes taken in to consideration: Berg balance scale (p = 0.0078), timed up and go tests (p = 0.03), degrees of lateral trunk inclination (p = 0.0039), and anterior/posterior trunk inclination (p = 0.0039). Regarding neurophysiological outcomes, the pressure pain threshold was enhanced and statistically significant in all areas assessed. Moreover, tensiomyography highlighted a statistically significant improvement in the maximal radial displacement of the ipsilateral erector spinae muscles. (4) Conclusions: The clinical and neurophysiological outcomes suggest both peripheral and central effects of exergaming. Peripherally, exergaming seems to lead to a postural trunk correction through a reduction in muscle stiffness in the ipsilateral erector spinae. Centrally, exergaming seems to lead to a central pain modulation through an upregulation of cortical connectivity associated with cognitive tasks. Taken together, these results also indicate that exergaming can be a feasible and enjoyable complement to traditional rehabilitation, potentially enhancing patients’ motivation and adherence.
A Noise Reduction Algorithm for White Noise and Periodic Narrowband Interference Noise in Partial Discharge Signals
Authors: Cao Jiyuan , Wang Yanwen , Zhu Weixiong , Zhang Yihe
Published: February 2025

Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into k optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to 3σ filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field.
Research on Non-Random Vibration Analysis of Concrete Pump Truck Boom Based on Dynamic Excitation
Authors: Zhou Weixin , Xu Wubin , Li Bing , Xiao Yuanbin , Liu Xianyu , Zhang Hanwen , Xu Bo
Published: February 2025

When pouring concrete overhead, a pump truck boom’s vibration has a big effect on how accurately the concrete is poured. This is especially true during fixed-point pouring, where the boom’s vibration is likely to cause the pouring position to deviate, which lowers the quality of the construction. It is difficult to forecast the dynamic reaction of the pump truck boom in a construction setting because of the constantly shifting external factors (wind speed, pipeline stress during pumping, etc.), which makes it difficult to guarantee casting accuracy. This study suggests a non-random vibration analysis technique for pump truck booms based on the interval process theory in order to address this issue. A dynamic excitation analysis method based on rigid–discrete coupling is proposed, taking into account the response influence of the material characteristics in the transportation process. The pumping process of concrete materials in the conveying pipeline is simulated using discrete element simulation technology to determine the system’s stress conditions under pumping conditions. The dynamic response characteristics of the pump truck boom under operating conditions are revealed by using non-random vibration analysis with the mathematical model that has been created based on the particular specifications of the pump truck boom. This study employs the Newmark-β technique for numerical computation to solve the dynamic equations and characterize the displacement response envelope under uncertain system parameter settings. The experimental findings demonstrate that the suggested approach may accurately capture the upper and lower bounds of the boom dynamic response, offering a trustworthy way to assess the dynamic behavior while pumping. The technique can reliably predict the dynamic displacement boundary and control the casting position deviation within a predefined range by accurately predicting the dynamic displacement range of the pump truck’s boom end and efficiently constructing the displacement envelope under uncertain dynamic excitation. For numerical computation, use the Newmark-β algorithm. This outcome confirms the substantial enhancement of the proposed method regarding pouring precision in construction settings, offering a novel solution and technical guidance for vibration control in engineering projects.
Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
Authors: Islam Shama Naz , Mahmud Md Apel
Published: February 2025

In this paper, a new communication protocol is proposed to allow direct communication between internet of things (IoT)-enabled home energy management systems (HEMSs) in a smart microgrid. The direct communication features are an important attribute for decentralised demand management and local energy trading operations in a microgrid equipped with renewable energy resources. The proposed scheme utilises the intermediate HEMSs as relay nodes that forward the sum of the received signals from nearby HEMSs to both ends of the entire network. The scheme can achieve lower latency compared to the cases when HEMSs adopt direct decode−and−forward (DF) or transmit through the central controller. For the proposed protocol, we have analytically obtained expressions for the error probability at different HEMSs, as well as the average bit error rate (BER) to indicate the overall error performance of the microgrid communication. To evaluate the proposed protocol in different channel conditions, numerical simulation is performed. The results demonstrate that the channel conditions between the HEMSs at the middle of the network have a greater impact on the system error performance. Overall, it can be observed that the proposed protocol suffers a lower degradation in error performance in comparison to direct DF when one of the users experiences worse channel conditions.
Cooperative Formation Control of a Multi-Agent Khepera IV Mobile Robots System Using Deep Reinforcement Learning
Authors: Garcia Gonzalo , Eskandarian Azim , Fabregas Ernesto , Vargas Hector , Farias Gonzalo
Published: February 2025

The increasing complexity of autonomous vehicles has exposed the limitations of many existing control systems. Reinforcement learning (RL) is emerging as a promising solution to these challenges, enabling agents to learn and enhance their performance through interaction with the environment. Unlike traditional control algorithms, RL facilitates autonomous learning via a recursive process that can be fully simulated, thereby preventing potential damage to the actual robot. This paper presents the design and development of an RL-based algorithm for controlling the collaborative formation of a multi-agent Khepera IV mobile robot system as it navigates toward a target while avoiding obstacles in the environment by using onboard infrared sensors. This study evaluates the proposed RL approach against traditional control laws within a simulated environment using the CoppeliaSim simulator. The results show that the performance of the RL algorithm gives a sharper control law concerning traditional approaches without the requirement to adjust the control parameters manually.
Baseline Particulate Matter Characteristics and Microbial Composition in Air Samples from Natural and Urban Environments: A First Combined Genomic and Microscopy Analysis
Authors: Pálhalmi János , Niemcewicz Marcin , Krzowski Łukasz , Mező Anna , Szelenberger Rafał , Podogrocki Marcin , Bijak Michal
Published: February 2025

This study examines the differences in particulate matter (PM) properties and microbial compositions between natural and urban environments, providing foundational data for environmental monitoring and biothreat detection. Air samples were collected during the spring and early summer from two distinct locations: a forest/lake area, and an urban parking lot adjacent to a high-traffic roadway. Quantitative phase imaging microscopy and genomic sequencing were employed to characterize particle size distributions, statistical properties, and microbial community structures in these environments. The results revealed significant differences in PM properties between the two locations. Urban air exhibited higher particle concentrations that reflect pollution sources, whereas the natural environment displayed greater variability in particle size and distribution, correlating with diverse biological content. Genomic sequencing showed a lower diversity of microbial communities compared to the forest/lake area but with greater uniformity. To sum up, by integrating optical microscopy and genomic sequencing, this research demonstrates the feasibility of establishing environmental baselines for PM characteristics and bio-component diversity. The findings underscore the potential of combining real-time optical sensing with genomic tools for early biothreat detection and improved environmental monitoring in diverse settings.
Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis
Authors: Duan Ganglong , Du Yutong , Shang Yanying , Xue Hongquan , Zhang Ruochen
Published: February 2025

Short-time traffic flow prediction is essential for intelligent traffic management. By accurately predicting traffic conditions in the near future, it helps to alleviate congestion, improve road efficiency, reduce accidents, and support timely traffic control. Short-time traffic flow exhibits uncertainty and randomness, and this paper proposes an SVR model for short-time traffic flow prediction on non-main and branch roads, using correlations between associated roads to improve accuracy. Association Rule Analysis: First, we use Pearson correlation to identify strongly correlated roads. This step helps in understanding the relationships between different roads and their traffic patterns. SVR Model Construction: Second, based on the identified correlations, we construct an SVR model using traffic data from the target road and its associated roads. The model parameters are optimized using grid search and cross-validation to ensure the best performance. Simulation and Evaluation: Third, we conduct simulation experiments using real traffic data from Xi’an city. The performance of our model is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE). Simulation experiments show that our model outperforms existing methods. Specifically, our model achieved an RMSE of 11.422, an MAE of 7.017, and an MRE of 0.139. In comparison, other models tested in our study, such as LSTM, Random Forest, and Gradient Boosting Decision Tree (GBDT), had higher error values. For instance, the LSTM model had an RMSE of 14.5, an MAE of 8.2, and an MRE of 0.165; the Random Forest model had an RMSE of 13.8, an MAE of 7.8, and an MRE of 0.152; and the GBDT model had an RMSE of 13.2, an MAE of 7.5, and an MRE of 0.148. These results demonstrate that our proposed SVR model, combined with association rules, is highly effective in predicting short-time traffic flow on non-main and branch roads, which are often overlooked in existing research.
The Synergy of Renewable Energy and Desalination: An Overview of Current Practices and Future Directions
Authors: Gevorkov Levon , Domínguez-García José Luis , Trilla Lluis
Published: February 2025

Water is one of the most valuable and essential resources for human life, yet its scarcity has become a pressing global issue exacerbated by climate change and population growth. To address the increasing demand for water driven by urbanization, industrial expansion, tourism, and agricultural needs, many countries are turning to desalination as a viable solution. This study investigates the integration of renewable energy sources (RES) with desalination technologies to enhance both sustainability and efficiency. A comprehensive review of major desalination methods has been conducted, with a particular focus on the application of solar and wind energy. Additionally, the challenges associated with renewable energy-powered desalination, including the need for effective energy storage systems and the inherent volatility of power supply, were explored. Our findings indicate that coupling renewable energy with desalination not only significantly reduces carbon emissions but also enhances the sustainability of water supply systems. The study also emphasizes the importance of emerging technologies, such as hybrid energy storage systems (HESS) and machine learning (ML), in optimizing RES powered desalination processes. Ultimately, this study aims to guide future research and development initiatives, promoting the global adoption of desalination systems powered by renewable energy.
Digital Infrastructure, New Digital Infrastructure, and Urban Carbon Emissions: Evidence from China
Authors: Nie Jiayun , Shen Jia , Ren Xiaohong
Published: February 2025

To advance the global sustainable development agenda and uphold the emission reduction commitments of the Paris Agreement, this study is dedicated to exploring the potential of digital infrastructure construction in fostering carbon emission reductions. Drawing on panel data from 249 cities in China spanning the period from 2010 to 2021, we empirically analyze the impact, mechanisms, and heterogeneous effects of digital infrastructure construction on urban carbon emissions using a two-way fixed-effect model. Furthermore, we delve into the carbon-reducing effects of new-type digital infrastructure construction. The research findings indicate that digital infrastructure construction can significantly decrease urban carbon emissions; it achieves this by enhancing urban green innovation and heightening public environmental awareness, thereby further reducing urban carbon emissions. In city samples located in western regions, with higher government fiscal expenditures and better foundations for new energy utilization, the promotional effect of digital infrastructure construction on urban carbon emission reductions is more pronounced. Additionally, new-type digital infrastructure construction demonstrates a significant reduction in urban carbon emissions, and where new-type digital infrastructure is well developed, the carbon-reducing effect of digital infrastructure is even more evident. This study deepens our understanding of the mechanisms through which digitization empowers carbon emission reductions and the regional variations involved, providing empirical evidence for governments to formulate differentiated policies on digital infrastructure construction and carbon emission reductions. In future research, we plan to expand the scope of our investigation to more countries and regions globally; concurrently, we will conduct an in-depth analysis of the long-term effects of digital infrastructure construction on carbon emissions.
Threshold Effects of the Interaction Between Urban Development and Atmospheric Pollution
Authors: Yuan Xiaoling , Geng Hanyu , Li Zhaopeng
Published: February 2025

Over the past 70 years since the founding of the People’s Republic of China, urban development has achieved remarkable progress but also encountered severe atmospheric pollution, which has become a significant obstacle to high-quality urban development. Understanding the interaction mechanisms between urban development and atmospheric pollution is thus crucial for promoting sustainable urban construction. This paper explores these mechanisms by analyzing the interplay between urban population, industry, space, social development, and pollution through a theoretical framework. Using a simultaneous equations model and the Three-Stage Least Squares (3SLS) method, it examines these relationships and further investigates threshold effects. The findings reveal a nonlinear relationship with significant thresholds: (1) High levels of PM2.5, population size, and industrial agglomeration can shift from exacerbating pollution to enabling governance, though excessive thresholds reverse this trend. (2) PM2.5 mediates the impact of spatial sprawl, environmental regulation, and population dynamics, oscillating between governance and pollution effects. (3) Industrial agglomeration and spatial sprawl show variable impacts on pollution mitigation depending on pollution intensity and urban thresholds. These findings provide critical insights into the intricate dynamics between urban development and atmospheric pollution, emphasizing the importance of adopting differentiated strategies based on specific urban thresholds. Ultimately, this research contributes to the broader goal of harmonizing economic growth, social development, and environmental sustainability in urban areas, serving as a valuable reference for cities worldwide facing similar challenges.
Visible Light Spectroscopy of W14+ Ions in an Electron Beam Ion Trap
Authors: Niu Ben , Chen Zhaoying , Chen Jihui , Li Yanting , Jia Fangshi , Li Bingli , He Zhencen , Xiao Jun , Zou Yaming , Yao Ke
Published: February 2025

In this work, the visible lines of W14+ ions in the wavelength range of 400–650 nm are investigated experimentally and theoretically. The experiments were performed in a low-energy electron beam ion trap. The simulated spectra of W14+ ions (Nd-like) were obtained from atomic structure computations in combination with a collisional–radiative model. Overall, there is a reasonable similarity between the measurements and the results of the simulations, and most of the twelve observed spectral lines associated with W14+ were tentatively identified.
Enhancing the FFT-LSTM Time-Series Forecasting Model via a Novel FFT-Based Feature Extraction–Extension Scheme
Authors: Yemets Kyrylo , Izonin Ivan , Dronyuk Ivanna
Published: February 2025

The importance of enhancing the accuracy of time-series forecasting using artificial intelligence tools is increasingly critical in light of the rapid advancements in modern technologies, particularly deep learning and neural networks. These approaches have already shown considerable advantages over traditional methods, especially due to their capacity to efficiently process large datasets and detect complex patterns. A crucial step in the forecasting process is the preprocessing of time-series data, which can greatly improve the training quality of neural networks and the precision of their predictions. This paper introduces a novel preprocessing technique that integrates information from both the time and frequency domains. To achieve this, the authors developed a feature extraction–extension scheme, where the extraction component focuses on obtaining the phase and amplitude of complex numbers through fast Fourier transform (FFT) and the extension component expands the time intervals by enriching them with the corresponding frequency characteristics of each individual time point. Building upon this preprocessing method, the FFT-LSTM forecasting model, which combines the strengths of FFT and Long Short-Term Memory (LSTM) recurrent neural networks, was enhanced. The simulation of the improved FFT-LSTM model was carried out on two time series with distinct characteristics. The results revealed a substantial improvement in forecasting accuracy compared to established methods in this domain, with about a 5% improvement in MAE and RMSE, thereby validating the effectiveness of the proposed approach for forecasting applications across various fields.
Novel Extreme-Lightweight Fully Convolutional Network for Low Computational Cost in Microbiological and Cell Analysis: Detection, Quantification, and Segmentation
Authors: Ramirez-Quintana Juan A. , Salazar-Gonzalez Edgar A. , Chacon-Murguia Mario I. , Arzate-Quintana Carlos
Published: February 2025

Integrating deep learning into microbiological and cell analysis from microscopic image samples has gained significant attention in recent years, driven by the rise of novel medical technologies and pressing global health challenges. Numerous methods for segmentation and classification in microscopic images have emerged in the literature. However, key challenges persist due to the limited development of specialized deep learning models to accurately detect and quantify microorganisms and cells from microscopic samples. In response to this gap, this paper introduces MBnet, an Extreme-Lightweight Neural Network for Microbiological and Cell Analysis. MBnet is a binary segmentation method based on a Fully Convolutional Network designed to detect and quantify microorganisms and cells, featuring a low computational cost architecture with only 575 parameters. Its innovative design includes a foreground module and an encoder–decoder structure composed of traditional, depthwise, and separable convolution layers. These layers integrate color, orientation, and morphological features to generate an understanding of different contexts in microscopic sample images for binary segmentation. Experiments were conducted using datasets containing bacteria, yeast, and blood cells. The results suggest that MBnet outperforms other popular networks in the literature in counting, detecting, and segmenting cells and unicellular microorganisms. These findings underscore the potential of MBnet as a highly efficient solution for real-world applications in health monitoring and bioinformatics.
Explainable Deep Learning for COVID-19 Vaccine Sentiment in Arabic Tweets Using Multi-Self-Attention BiLSTM with XLNet
Authors: Sweidan Asmaa Hashem , El-Bendary Nashwa , Taie Shereen A. , Idrees Amira M. , Elhariri Esraa
Published: February 2025

The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment analysis resources for the Arabic language. This paper proposes an explainable Deep Learning (DL) approach designed for sentiment analysis of Arabic tweets related to COVID-19 vaccinations. The proposed approach utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network with Multi-Self-Attention (MSA) mechanism for capturing contextual impacts over long spans within the tweets, while having the sequential nature of Arabic text constructively learned by the BiLSTM model. Moreover, the XLNet embeddings are utilized to feed contextual information into the model. Subsequently, two essential Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), have been employed for gaining further insights into the features’ contributions to the overall model performance and accordingly achieving reasonable interpretation of the model’s output. Obtained experimental results indicate that the combined XLNet with BiLSTM model outperforms other implemented state-of-the-art methods, achieving an accuracy of 93.2% and an F-measure of 92% for average sentiment classification. The integration of LIME and SHAP techniques not only enhanced the model’s interpretability, but also provided detailed insights into the factors that influence the classification of emotions. These findings underscore the model’s effectiveness and reliability for sentiment analysis in low-resource languages such as Arabic.
Biomechanical Analysis of Cycle-Tempo Effects on Motor Control Among Jump Rope Elites
Authors: Zhou Qi , Liu Yufeng , Kang Jianguo , Wang Xiuping , Zhang Kai , Shan Gongbing
Published: February 2025

Jump rope is a widely applied basic training technique in various sports, yet it is understudied biomechanically. This study investigates the impact of cycle-tempo-induced motor control changes in elite jump rope athletes, addressing the biomechanical gap of cyclic skill control. The hypothesis posited two accelerations per jump cycle—one in front of and one behind the body—and anticipated that increased cycle frequency would alter the distribution of acceleration time within a cycle. Using 3D motion capture technology, 12 young elite jump rope athletes were analyzed at 100, 140, and 180 revolutions per minute (rpm). The kinematic parameters obtained confirmed the presence of two distinct accelerations per cycle. As tempo increased, the percentage of rear acceleration time increased from 9.58% at 100 rpm to 17.42% at 180 rpm, while front acceleration time decreased from 39.03% at 100 rpm to 31.40% at 180 rpm, along with peak velocities increasing from 12.94 m/s at 100 rpm to 22.74 m/s at 180 rpm significantly (p < 0.01). Rope trajectory analysis indicated a consistent movement pattern across tempos, primarily in the sagittal plane. Variations in skill control revealed shorter contact phases, decreasing from 61.53% at 100 rpm to 48.25% at 180 rpm, as well as a reduced vertical range of motion for the center of gravity (from 0.15 body height at 100 rpm to 0.06 body height at 180 rpm) and feet (from 0.05 body height at 100 rpm to 0.03 body height at 180 rpm) (p < 0.05). Significant reductions were also observed in the flexion/extension range of motion for the hip (from 22.31° at 100 rpm to 3.47° at 180 rpm), knee (from 49.31° at 100 rpm to 9.35° at 180 rpm), and ankle (from 52.99° at 100 rpm to 21.41° at 180 rpm) (p < 0.01). These findings enhance the understanding of motor control adaptations to different tempos and have practical implications for developing coaching programs aimed at optimizing performance, stability, and efficiency in jump rope training.
Implications of Environmental Variations on Saccharina japonica Cultivation in Xiangshan Bay, China
Authors: Bao Yikang , Xu Peng
Published: February 2025

This study took Xiangshan Bay as an example to illustrate the variation characteristics of the physicochemical environments (temperature, salinity, light, nutrients, and currents) during one kelp cultivation cycle. The study was conducted from November 2020 to May 2021 through tracking down observations. Furthermore, the environmental factors were evaluated using suitability functions of kelp growth, aiming to provide references for promoting kelp cultivation in South China. We discussed the self-limiting effect of nutrients in the culture zone. The results showed that the average temperature, salinity, and light intensity during the cruises in Xiangshan Bay kelp farm were characterized by seasonal variations. Temperature was found to be the most critical environmental factor in determining the kelp cultivation window and hence the yield in Xiangshan Bay. The dissolved inorganic nitrogen (DIN) concentrations initially decreased and then increased, while the dissolved inorganic phosphorus (DIP) concentrations remained decreasing along with the kelp cultivation. The surface tide currents were dramatically attenuated by the suspended kelp cultivation, while the quasi-steady circulations which played a key role in nutrient supplementation for kelp cultivation were not weakened by the kelp cultivation. Among the cruises, the suitability indices’ ranges for temperature, salinity, light, and nutrients were 0.02–0.94, 0.96–0.99, 0.97–1, 0.96–0.97 (DIN), and 0.92–0.95 (DIP), respectively. The results of the suitability functions demonstrated that the salinity and light conditions in Xiangshan Bay were very suitable for kelp cultivation and would not cause significant cultivation risks. Although the cultivated kelp could greatly absorb nutrients, the suitability index of nutrients remained adequate even during the late stage of cultivation, indicating the present kelp cultivation scale has not reached the carrying capacity of Xiangshan Bay and there is still much potential for development. To this end, further selective breeding of the thermal tolerance variety has become the key to improving the kelp cultivation performance in Xiangshan Bay. Meanwhile, the self-limiting effects in relation to nutrients are not significant in the Xiangshan Bay kelp farm, but it might be more significant in other kelp farms.
Predicting the Potential Suitable Distribution of Albizia odoratissima (L. f.) Benth. Under Climate Change Based on the Biomod2 Model
Authors: Li Zhiting , Ji Qiaomiao , Yang Yong , Gao Yunfei , Xu Meng , Guan Yali
Published: February 2025

Albizia odoratissima (L. f.) Benth. is a highly valuable tree species, both economically and ecologically. It has great potential for development. To conserve, develop, and use its genetic resources sustainably, it is crucial to evaluate the potential habitat of A. odoratissima and identify the key environmental factors that affect its distribution. In this study, the Biomod2 model was used to predict the potential distribution of A. odoratissima in China, using 65 occurrence points and 11 environmental variables for both current and future climate scenarios. The distribution of A. odoratissima is mainly influenced by three temperature-related factors: bio4, bio8, and bio11. Our prediction shows that the suitable habitats for A. odoratissima are mostly located in several southern provinces of China, totaling 136.98 × 104 km2. Under different climate scenarios, the suitable habitat area is expected to shift northward in the future, although the highly suitable areas will remain relatively stable. These findings have important implications for the conservation, development, and sustainable utilization of A. odoratissima resources.
Bioactive Materials in Vital Pulp Therapy: Promoting Dental Pulp Repair Through Inflammation Modulation
Authors: Qiao Liang , Zheng Xueqing , Xie Chun , Wang Yaxin , Ye Lu , Zhao Jiajia , Liu Jiarong
Published: February 2025

With the paradigm shift towards minimally invasive biologic therapies, vital pulp therapy (VPT) has been receiving increasing attention. Currently, bioactive materials (BMs), including MTAs, Biodentine, Bioaggregate, and iRoot BP Plus, are clinically widely used for the repair of damaged pulp tissue. Emerging evidence highlights the crucial role of inflammation in pulp repair, with mild to moderate inflammation serving as a prerequisite for promoting pulp repair. BMs play a pivotal role in regulating the balance between inflammatory response and reparative events for dentine repair. Despite their widespread application as pulp-capping agents, the precise mechanisms underlying the actions of BMs remain poorly understood. A comprehensive literature review was conducted, covering studies on the inflammatory responses induced by BMs published up to December 2023. Sources were identified through searches of PubMed and MEDLINE databases, supplemented by manual review of cross-references from relevant studies. The purpose of this article is to discuss diverse mechanisms by which BMs may regulate the balance between tissue inflammation and repair. A deeper understanding of these regulatory mechanisms will facilitate the optimization of current pulp-capping agents, enabling the development of targeted regenerative strategies to achieve superior clinical outcomes.
Pulmonary Arterial Hypertension-Induced Reproductive Damage: Effects of Combined Physical Training on Testicular and Epididymal Parameters in Rats
Authors: Assis Mírian Quintão , Leite Luciano Bernardes , Guimarães-Ervilha Luiz Otávio , Adão Rui , Reis Emily Correna Carlo , Natali Antônio José , Machado-Neves Mariana
Published: February 2025

Background/Objectives: Pulmonary arterial hypertension (PAH) affects the pulmonary vasculature and cardiac function. While its impact on target organs has been extensively studied, little is known about its effects on highly vascularized organs, such as those from the male reproductive system. This study explores the impact of PAH on testis and epididymis, evaluating the potential role of combined exercise training as a non-pharmacological strategy to mitigate alterations in these organs. Methods: Male Wistar rats (n = 8/group) were assigned to one of three groups: sedentary control, sedentary PAH, and exercise PAH. PAH was induced by monocrotaline administration (60 mg Kg−1, i.p). The exercise PAH group underwent three weeks of combined physical training, including treadmill aerobic activity and resistance training on a ladder. Testis and epididymis were analyzed histologically, histomorphometrically, and biochemically for antioxidant activity, oxidative stress markers, and sperm parameters. Results: Sedentary PAH animals showed reductions in body and epididymis weight, normal seminiferous tubule percentage, and testicular morphometric parameters. These changes led to disorganized seminiferous tubules and compromised sperm production and sperm count in the testis and epididymis. Combined physical training improved testicular morphometric alterations and increased sperm count in hypertensive animals. Conclusions: PAH negatively affects testicular structure and function, leading to low sperm production. Combined physical training mitigated these effects by preserving testicular architecture and improving reproductive parameters, though it appeared less effective for the epididymis. These findings suggest physical training as a potential therapeutic strategy to protect reproductive health in PAH.
The Many Faces of Protease-Activated Receptor 2 in Kidney Injury
Authors: Oe Yuji , Tanaka Tetsuhiro , Takahashi Nobuyuki
Published: February 2025

Protease-activated receptor 2 (PAR2) is a seven-transmembrane, G-protein-coupled receptor that is activated by coagulation proteases such as factor VIIa and factor Xa and other serine proteases. It is a potential therapeutic target for kidney injury, as it enhances inflammatory and fibrotic responses via the nuclear factor-kappa B and mitogen-activated protein kinase cascades. The body of knowledge regarding the role of PAR2 in kidney disease is currently growing, and its role in various kidney disease models, such as acute kidney injury, renal fibrosis, diabetic kidney disease, aging, and thrombotic microangiopathy, has been reported. Here, we review the literature to better understand the various aspects of PAR2 in kidney disease.
Prevalence and Clinical Correlates of Cerebrovascular Alterations in Fabry Disease: A Cross-Sectional Study
Authors: Di Natale Daniele , Rossi Salvatore , Dalla Zanna Gianmarco , Funcis Antonio , Nicoletti Tommaso Filippo , Sicignano Ludovico Luca , Verrecchia Elena , Romano Angela , Vita Maria Gabriella , Caraglia Naike , Graziani Francesca , Re Federica , Guerrera Gisella , Battistini Luca , Silvestri Gabriella
Published: February 2025

Background/Objectives: Fabry disease (FD) is an inborn error of the glycosphingolipid metabolism with variable kidney, heart, and central nervous system (CNS) involvement. CNS-related FD manifestations include early ischemic stroke and white matter lesions (WMLs) related to cerebral small-vessel disease (CSVD), possibly resulting in cognitive impairment. We studied 40 adult FD patients (17 male) to assess: (i) prevalence of cerebrovascular and cognitive manifestations in FD and their correlation with heart and renal involvement; and (ii) the potential value of serum neurofilament light chain (NfL) levels as an indicator of WMLs in FD. Methods: Patients underwent detailed diagnostic assessment related to FD, also including Mainz Severity Score Index (MSSI), neuropsychological tests, brain MRI to assess WMLs by the modified Fazekas score (mFS), and NfL determination by single-molecule array (SiMoA) (n = 22 FD patients vs. 15 healthy controls). Results: Overall, 4 FD patients had a history of ischemic stroke and 13/32 patients (40.6%) had an mFS ≥ 1. Almost two-thirds of FD patients (27/39, 69.2%) showed impairment on at least one cognitive test. On univariate analysis, only a reduction in estimated glomerular filtration rate was associated with an increased likelihood of having WMLs on brain MRI. Serum NfL levels were higher in FD patients vs. controls, with a trend toward significance (p = 0.08). Conclusions: Mild-to-moderate CSVD is a characteristic brain “signature” in FD patients. Both cardiac and renal involvement correlate with WML load, but only renal involvement appears to be predictive of CNS damage. Brain microvascular damage is associated with mild cognitive impairment in FD, and serum NfL might represent a potential biomarker of CSVD in FD.