bluuebunny/crossref_metadata_2025 huggingface dataset:
The Function of Myostatin in Ameliorating Bone Metabolism Abnormalities in Individuals with Type 2 Diabetes Mellitus by Exercise
Authors: Zhong Chenghao , Zeng Xinyu , Yi Xiaoyan , Yang Yuxin , Hu Jianbo , Yin Rongbin , Chen Xianghe
Published: February 2025

Purpose: The molecular mechanisms involved in bone metabolism abnormalities in individuals with type 2 diabetes mellitus (T2DM) are a prominent area of investigation within the life sciences field. Myostatin (MSTN), a member of the TGF-β superfamily, serves as a critical negative regulator of skeletal muscle growth and bone metabolism. Current research on the exercise-mediated regulation of MSTN expression predominantly focuses on its role in skeletal muscle. However, due to the intricate and multifaceted mechanical and biochemical interactions between muscle and bone, the precise mechanisms by which exercise modulates MSTN to enhance bone metabolic disorders in T2DM necessitate additional exploration. The objective of this review is to systematically synthesize and evaluate the role of MSTN in the development of bone metabolism disorders associated with T2DM and elucidate the underlying mechanisms influenced by exercise interventions, aiming to offer novel insights and theoretical recommendations for enhancing bone health through physical activity. Methods: Relevant articles in Chinese and English up to July 2024 were selected using specific search terms and databases (PubMed, CNKI, Web of Science); 147 studies were finally included after evaluation, and the reference lists were checked for other relevant research. Results: Myostatin’s heightened expression in the bone and skeletal muscle of individuals with T2DM can impede various pathways, such as PI3K/AKT/mTOR and Wnt/β-catenin, hindering osteoblast differentiation and bone mineralization. Additionally, it can stimulate osteoclast differentiation and bone resorption capacity by facilitating Smad2-dependent NFATc1 nuclear translocation and PI3K/AKT/AP-1-mediated pro-inflammatory factor expression pathways, thereby contributing to bone metabolism disorders. Physical exercise plays a crucial role in ameliorating bone metabolism abnormalities in individuals with T2DM. Exercise can activate pathways like Wnt/GSK-3β/β-catenin, thereby suppressing myostatin and downstream Smads, CCL20/CCR6, and Nox4 target gene expression, fostering bone formation, inhibiting bone resorption, and enhancing bone metabolism in T2DM. Conclusion: In the context of T2DM, MSTN has been shown to exacerbate bone metabolic disorders by inhibiting the differentiation of osteoblasts and the process of bone mineralization while simultaneously promoting the differentiation and activity of osteoclasts. Exercise interventions have demonstrated efficacy in downregulating MSTN expression, disrupting its downstream signaling pathways, and enhancing bone metabolism.
Earthwork Traceability Management System Using Compaction History and Dump Truck Sensing Data
Authors: Takao Atsushi , Yabuki Nobuyoshi , Otsuka Yoshikazu , Hirai Takashi
Published: February 2025

The productivity of the construction industry is about half that of the manufacturing industry, and the labor shortage in the construction industry is serious; therefore, improving productivity using information and communication technology (ICT) is an urgent issue. In addition, in civil engineering works, the number of projects that handle multiple types of soil and sand is increasing due to the recycling of construction waste soil; thus, traceability management is important to ensure quality. This paper presents a system that uses sensing on soil-transporting dump trucks and ICT to record which soil was piled up where with the aim of improving the efficiency of traceability management in earthwork construction. This system automatically creates traceability data by linking sensing data and data from the compaction management system via an application. This eliminates the need to record and manage the earthwork location, which was previously required manually to create traceability data, and reduces the labor and manpower required for traceability management. The created traceability data are automatically assigned attribute information such as the construction date and soil information; consequently, they can be used to check the construction history in the future.
From Optimism to Risk: The Impact of Climate Change on Temperature Sums in Central Europe
Authors: Minárik Martin , Kišš Vladimír , Ziernicka-Wojtaszek Agnieszka , Prčík Martin , Čimo Ján , Mikulová Katarína
Published: February 2025

This study examines the impact of climate change on agricultural productivity in Slovakia, the Czech Republic, and Poland, focusing on temperature sums influencing the growing season. Using meteorological data from 2001 to 2020, the research analyses the onset and termination of temperatures ≥5 °C (growing season). Temperature sums for two periods (2001–2010, 2011–2020) were calculated and future temperature projections under three scenarios (+1.5 °C, +2.6 °C, +3.6 °C) were developed. Results indicate regional variation in temperature sums, with 69% of the area falling in the 2900–3100 °C range, and Poland showing the highest percentage (81%). In the second decade of the 21st century, temperature sums shifted to the 3100–3300 °C range, affecting 63% of the region. The projections indicate a substantial increase in temperature sums, with the most optimistic scenario (+1.5 °C) leading to the dominance of the 3700–3900 °C range. The warmest areas (West Pannonian Basin), show a temperature sum of 4900–5100 °C. The comparison of predicted and observed temperature sums for 2011–2020 shows a minimal error (±3% in Slovakia and ±4% in Poland and the Czech Republic), confirming the projections. These findings highlight the importance of adaptive strategies in agriculture, particularly fruit farming, to mitigate the climate change effects.
An Empirical Evaluation of Neural Network Architectures for 3D Spheroid Segmentation
Authors: Oudouar Fadoua , Bir-Jmel Ahmed , Grissette Hanane , Douiri Sidi Mohamed , Himeur Yassine , Miniaoui Sami , Atalla Shadi , Mansoor Wathiq
Published: February 2025

The accurate segmentation of 3D spheroids is crucial in advancing biomedical research, particularly in understanding tumor development and testing therapeutic responses. As 3D spheroids emulate in vivo conditions more closely than traditional 2D cultures, efficient segmentation methods are essential for precise analysis. This study evaluates three prominent neural network architectures—U-Net, HRNet, and DeepLabV3+—for the segmentation of 3D spheroids, a critical challenge in biomedical image analysis. Through empirical analysis across a comprehensive Tumour Spheroid dataset, HRNet and DeepLabV3+ emerged as top performers, achieving high segmentation accuracy, with HRNet achieving 99.72% validation accuracy, a Dice coefficient of 96.70%, and a Jaccard coefficient of 93.62%. U-Net, although widely used in medical imaging, struggled to match the performance of the other models. The study also examines the impact of optimizers, with the Adam optimizer frequently causing overfitting, especially in U-Net models. Despite improvements with SGD and Adagrad, these optimizers did not surpass HRNet and DeepLabV3+. The study highlights the importance of selecting the right model–optimizer combination for optimal segmentation.
Application of Thulium Fiber Laser in Plastic Surgery
Authors: Borisenko Anastasiya S. , Sharobaro Valentin I. , Avdeev Alexey E. , Ahmed Alsheikh Yousif M. , Bairamova Anna S. , Gafurova Olga A. , Smolyannikova Vera A.
Published: February 2025

We present our experience of using the UROLASE+ surgical system, based on thulium laser technology, in the context of plastic surgery. Our work with the thulium fiber laser for soft tissue dissection in vitro, ex vivo, and in vivo is described, and the positive outcomes of using the thulium fiber laser, which has not previously been employed in aesthetic surgery of the face and body, are demonstrated in this study. Integration of the thulium fiber laser may enhance safety and facilitate tissue dissection through more precise interventions, reduced blood loss, and improved clinical outcomes. This is particularly relevant for patients who have undergone cosmetic procedures leading to fibrotic changes in soft tissues, which complicate inter-tissue dissection when using traditional methods.
Induced Chirality in CuO Nanostructures Using Amino Acid-Mediated Chemical Bath Deposition
Authors: Jabreen Lama , Mastai Yitzhak
Published: February 2025

This study explored the controlled formation of chiral copper(II) oxide (CuO) crystals using chiral amino acids as chirality-inducing agents. Utilizing chemical bath deposition (CBD) as the fabrication method, we achieved simple, reproducible synthesis suitable for industrial-scale applications. Our characterization of the induced chirality through high-performance liquid chromatography (HPLC), circular dichroism (CD), and isothermal titration calorimetry (ITC) revealed distinctive chiral features. These findings not only advance our understanding of chirality control in inorganic nanostructures but also establish CBD as a viable technique for the large-scale production of chiral materials.
MoSe2 as Electrode Material for Super-Capacitor, Hydrogen Evolution, and Electrochemical Sensing Applications: A Review
Authors: Vignesh Shanmugam , Ramkumar Ramya , Suganthi Sanjeevamuthu , Kumar Praveen , Ahmad Khursheed , Kim Woo Kyoung , Oh Tae Hwan
Published: February 2025

In the past few years, metal chalcogenides have received extensive consideration because of their excellent physicochemical belongings. Particularly, molybdenum selenide (MoSe2) is a promising metal dichalcogenide which possesses decent optical, electrical, and chemical properties and can be explored for a variety of applications. MoSe2 has been extensively used for several applications such as energy storage and sensing. Since the energy crisis is one of the major challenges of today’s world, super-capacitors and hydrogen evolution are promising energy technologies that may benefit the global world in the future. Thus, researchers have been motivated towards the strategy and fabrication of electrode materials for super-capacitors and hydrogen evolution applications. MoSe2 is a multifunctional material, and previous years have witnessed the rapid growth in the publication of MoSe2-based electrode materials for super-capacitors, hydrogen evolution, and electrochemical sensing applications. Thus, it is of great significance to merge the previous reports into a single review article on MoSe2-based modified electrode materials for super-capacitors, hydrogen evolution, and electrochemical sensing applications. Therefore, we have compiled the previous reports on the design and fabrication of MoSe2 and electrodes based on its composites for super-capacitors, hydrogen evolution, and electrochemical sensing applications. It is believed that this article may benefit the researchers working in the research field of super-capacitors, hydrogen evolution, and electrochemical sensing applications.
A Low-Cost Optomechatronic Diffuse Optical Mammography System for 3D Image Reconstruction: Proof of Concept
Authors: Rivera-Fernández Josué D. , Hernández-Mendoza Alfredo , Fabila-Bustos Diego A. , de la Rosa-Vázquez José M. , Hernández-Chávez Macaria , de la Rosa-Gutierrez Gabriela , Roa-Tort Karen
Published: February 2025

Background: The development and initial testing of an optomechatronic system for the reconstruction of three-dimensional (3D) images to identify abnormalities in breast tissue and assist in the diagnosis of breast cancer is presented. Methods: This system combines 3D reconstruction technology with diffuse optical mammography (DOM) to offer a detecting tool that complements and assists medical diagnosis. DOM analyzes tissue properties with light, detecting density and composition variations. Integrating 3D reconstruction enables detailed visualization for precise tumor localization and sizing, offering more information than traditional methods. This technological combination enables more accurate, earlier diagnoses and helps plan effective treatments by understanding the patient’s anatomy and tumor location. Results: Using Chinese ink, it was possible to identify simulated abnormalities of 10, 15, and 20 mm in diameter in breast tissue phantoms from cosmetic surgery. Conclusions: Data can be processed using algorithms to generate three-dimensional images, providing a non-invasive and safe approach for detecting anomalies. Currently, the system is in a pilot testing phase using breast tissue phantoms, enabling the evaluation of its accuracy and functionality before application in clinical studies.
Proposed Novel Heart Failure Biomarkers and Their Association with Length of Hospital Stay and Mortality: A Retrospective Observational Pilot Study
Authors: Cristescu Liviu , Iancu Dragos-Gabriel , Marusteri Marius-Stefan , Tilea Ioan , Varga Andreea
Published: February 2025

Background/Objectives: Chronic heart failure (CHF) remains a significant global health burden, with high morbidity, prolonged hospitalizations, and increased mortality. Traditional biomarkers such as NT-proBNP provide prognostic value; however, novel biomarker ratios may enhance risk stratification. This study evaluated the predictive utility of the NT-proBNP-to-albumin ratio (NTAR), red cell distribution width-to-eGFR ratio (RGR), and red cell distribution width-to-fibrinogen ratio (RFR) for hospital length of stay (LOS), extended hospitalization (ELOS), in-hospital mortality, and 6-month all-cause mortality. Methods: A retrospective observational pilot study was conducted on 382 CHF admissions (2022–2024) with comprehensive laboratory assessment. Biomarker performance was assessed through uni- and multivariate logistic regression, receiver operating characteristic curve, and Cox proportional hazards stepwise methods of analyses for refining predictive models. Results: NTAR and RGR emerged as significant predictors of hospitalization outcomes. NTAR demonstrated a moderate correlation with prolonged LOS (r = 0.45, p < 0.001) and was an independent predictor of ELOS (AUC = 0.697, OR = 2.438, p < 0.001), outperforming NT-proBNP. Additionally, NTAR significantly predicted in-hospital mortality (AUC = 0.768, OR = 4.461, p < 0.001) and 6-month all-cause mortality (AUC = 0.766, OR = 4.185, p < 0.001). RGR was the strongest predictor of in-hospital mortality (AUC = 0.785, HR = 2.18, p = 0.005), highlighting its role in renal dysfunction and erythropoietic alterations in CHF. The RFR observed prognostic value was minimal. Conclusions: In our study, NTAR and RGR offered valuable prognostic value underscoring the interplay of cardiac stress, nutritional status, and renal function in CHF prognosis. Further multicenter validation is warranted for these biomarkers.
Comparing the Effectiveness of Human Extracted Teeth and Plastic Teeth in Teaching Dental Anatomy
Authors: Thune Noora Helene , Kristensen Anna Tostrup , Sehic Amer , Haabeth Brox Julie Marie , Utheim Tor Paaske , Hammer Hugo Lewi , Khan Qalbi
Published: February 2025

Objectives: A thorough knowledge of tooth morphology, encompassing the detailed structural complexities, is essential for the practice of dental hygienists in all aspects of their profession. The aim of this study was to assess the efficacy of two instructional approaches in tooth morphology education, by analyzing the performance of dental hygienist students trained with human extracted teeth compared to those educated with plastic teeth models. Methods: This study included two cohorts of undergraduate dental hygienist students: a control group (n = 27) trained using human teeth, and an experimental group (n = 34) trained using plastic teeth models. Each group underwent two consecutive practical exams where they identified all 32 permanent teeth and 8 deciduous molars. Initially, students were tested on the training material that they were assigned (either extracted human teeth or plastic teeth), and, subsequently, they were tested using the alternative material. Both the number and patterns of identification errors were recorded and analyzed. Paired t-tests were used to compare error rates between real and plastic teeth for students trained on either plastic or real teeth, unpaired t-tests were conducted to assess differences in performance between students trained on plastic versus real teeth when tested on both tooth types, and Fisher’s exact tests were employed to examine variations in error proportions across maxillary and mandibular tooth categories. Results: The control group recorded a mean of 6.41 errors per student (total of 173 errors), with three students (11.1%) failing by committing over 12 errors. Their performance improved to a mean of 5.44 errors (total of 147 errors) when tested on plastic teeth, although the improvement was not statistically significant (p = 0.20). Conversely, the experimental group demonstrated high accuracy on plastic teeth, with 19 out of 34 students (55.9%) achieving perfect scores and a total of only 50 errors (mean, 1.47). Their performance, however, declined when tested on real teeth, escalating to a total of 354 errors, with 32 students (94.12%) making more errors on real teeth than on plastic, resulting in a significant increase in errors to an average of 10.41 per student (p < 0.001). Conclusions: This study demonstrates that students perform best when tested on the materials that they initially were trained with, showing that real teeth provide better educational outcomes than plastic models. This advantage underscores the importance of using natural teeth when learning dental anatomy.
Antibiotic Prescription for the Prevention of Postoperative Complications After Third-Molar Extractions: A Systematic Review
Authors: De Angelis Nicola , Denegri Lorenzo , Miron Ioana Cristina , Yumang Catherine , Pesce Paolo , Baldi Domenico , Delucchi Francesca , Bagnasco Francesco , Menini Maria
Published: February 2025

Background: Third-molar extractions are common procedures often complicated by infections and alveolitis. The use of antibiotics as prophylaxis to prevent these complications is debated due to potential risks and side effects. Therefore, the aim of the present systematic review was to determine the efficacy of antibiotic prescription for the prevention of these complications. Methods: A comprehensive literature search was conducted in MEDLINE/PubMed, Cochrane, and SCOPUS databases up until June 2024. The focused question was “Does the antibiotic prescription influence the incidence of postoperative complications following third-molar extractions in healthy patients?” Systematic reviews assessing complications after third-molar extractions were included. Results: A total of 16 studies were included, revealing that antibiotic use significantly reduces infection risk and dry socket incidence compared to no prescription. Amoxicillin–clavulanic acid was particularly effective. Conclusions: Antibiotics, especially amoxicillin–clavulanic acid, are effective in preventing postoperative infections and alveolitis after third-molar extraction. However, their administration should be carefully considered to balance benefits against potential risks. Evidence supports the judicious use of antibiotics in dental surgery to optimize patient outcomes, minimizing possible adverse effects and the risk of developing antibiotic resistance.
Breaking Barriers in Orthodontics: An Experimental Study on How Stabilization Discs Improve Mini-Implant Outcomes
Authors: Panaite Tinela , Romanec Cristian Liviu , Iacob Mihnea , Balcos Carina , Savin Carmen , Olteanu Nicolae Daniel , Vieriu Raluca-Maria , Alice Chehab , Zetu Irina Nicoleta
Published: February 2025

Background/Objectives: The stabilization disc (SD) for orthodontic mini-implants is a novel device designed to enhance anchorage stability and minimize the risk of mini-implant mobility. The disc features a flat structure with four prongs and is crafted from biocompatible materials such as titanium or stainless steel. It provides additional support to mini-implants by improving force distribution and reducing stress concentration around the insertion site. This study aims to evaluate the biomechanical performance of mini-implants with an SD compared to without-SD mini-implants, with a specific focus on their ability to maintain anchorage under orthodontic loading conditions. Methods: A finite element analysis (FEA) model was created for a commercially available mini-implant (2.0 mm in diameter and 12 mm in length). The mandible’s anatomical structure was reconstructed in 3D from computed tomography (CT) scans using SpaceClaim software 2023.1. To simulate real-world orthodontic conditions, forces of 10 N were applied at an angle of 30°. This retrospective study explores the role of SDs in enhancing mini-implant stability by reducing displacement and optimizing stress distribution. The evaluation included analyzing von Mises stress, cortical bone deformation, and mini-implant movement under simulated orthodontic loading. Results: The results demonstrate that the SD significantly reduces maximum total displacements by over 41% and redistributes von Mises stresses more evenly across the mini-implant and surrounding bone. Cortical bone stress and deformation were reduced in cases utilizing the SD, indicating enhanced implant stability and durability. Conclusions: The stabilization disc enhances mini-implant stability by improving stress distribution and reducing deformation without requiring permanent implant modifications. Its adaptability makes it a valuable solution for managing variable bone density and high orthodontic forces, offering a promising advancement in orthodontic anchorage.
Multistable Synaptic Plasticity Induces Memory Effects and Cohabitation of Chimera and Bump States in Leaky Integrate-and-Fire Networks
Authors: Provata Astero , Almirantis Yannis , Li Wentian
Published: February 2025

Chimera states and bump states are collective synchronization phenomena observed independently (in different parameter regions) in networks of coupled nonlinear oscillators. And while chimera states are characterized by coexistence of coherent and incoherent domains, bump states consist of alternating active and inactive domains. The idea of multistable plasticity in the network connections originates from brain dynamics where the strength of the synapses (axons) connecting the network nodes (neurons) may change dynamically in time; when reaching the steady state the network connections may be found in one of many possible values depending on various factors, such as local connectivity, influence of neighboring cells etc. The sign of the link weights is also a significant factor in the network dynamics: positive weights are characterized as excitatory connections and negative ones as inhibitory. In the present study we consider the simplest case of bistable plasticity, where the link dynamics has only two fixed points. During the system/network integration, the link weights change and as a consequence the network organizes in excitatory or inhibitory domains characterized by different synaptic strengths. We specifically explore the influence of bistable plasticity on collective synchronization states and we numerically demonstrate that the dynamics of the linking may, under special conditions, give rise to co-existence of bump-like and chimera-like states simultaneously in the network. In the case of bump and chimera co-existence, confinement effects appear: the different domains stay localized and do not travel around the network. Memory effects are also reported in the sense that the final spatial arrangement of the coupling strengths reflects some of the local properties of the initial link distribution. For the quantification of the system’s spatial and temporal features, the global and local entropy functions are employed as measures of the network organization, while the average firing rates account for the network evolution and dynamics. In particular, the spatial minima of the local entropy designate the transition points between domains of different synaptic weights in the hybrid states, while the number of minima corresponds to the number of different domains. In addition, the entropy deviations signify the presence of chimera-like or bump-like states in the network.
Human Comfort and Environmental Sustainability Through Wetland Management: A Case Study of the Nawabganj Wetland, India
Authors: Avishek Kirti , Singh Pranav Dev , Kanungo Abhrankash , Kumar Pankaj , Chakraborty Shamik , Singh Suraj Kumar , Kanga Shruti , Meraj Gowhar , Sajan Bhartendu , Gupta Saurabh Kumar
Published: February 2025

Wetlands play a vital role in ecosystem sustainability by regulating atmospheric temperature and enhancing human comfort levels. This study aims to evaluate the temperature regulation function of the Nawabganj Wetland, Uttar Pradesh (India), a Ramsar site designated in January 2020, located in a semi-arid region vulnerable to increasing heat waves. The primary objective is to assess the wetland’s influence on microclimatic conditions and human thermal comfort across different seasons. Field surveys were conducted to collect temperature, humidity, wind speed, and vegetation data over three consecutive days in each season: 15–17 May 2019 (pre-monsoon), 12–14 August 2019 (monsoon), and 5–7 October 2019 (post-monsoon). The human comfort index was calculated using field data, while vegetation density and frequency were analyzed based on seasonal variations using the quadrant method. The results indicate that the wetland significantly contributes to local temperature reduction and improved comfort levels. Vegetation plays a crucial role in amplifying this cooling effect, particularly during summer when temperatures range from an average low of 23 °C to a high of 40 °C. In winter, temperatures vary between an average low of 6 °C and a high of 22 °C, with a consistently high humidity level of approximately 94%, further influencing microclimatic conditions. The extent of weed cover varied between 10% and 60% from December to May, reflecting seasonal fluctuations in water levels and wetland health. The study highlights the necessity of effective water and vegetation management, especially during summer, to sustain the wetland’s cooling capacity. Integrating wetland-based strategies into urban planning can enhance environmental sustainability, mitigate climate extremes, and improve human well-being in rapidly urbanizing regions.
Transient Analysis of Fault-Induced Delayed Voltage Recovery by Mathematical Modeling of a Three-Phase Induction Motor
Authors: Loaiza-Estrada Sebastian , Mesa-Beleño Juan P. , Lemmel-Vélez Karen , Estrada-Mesa Sergio A. , Valencia-Hernandez Carlos A. , Velasco-Méndez Jose R. , Monsalve-Cadavid Marco A.
Published: February 2025

This paper presents a detailed fifth-order mathematical model of a three-phase induction motor, used in the WECC (Western Electricity Coordinating Council) composite load model. The main objective is to analyze the dynamic behavior of three-phase induction motors during fault-induced delayed voltage recovery (FIDVR) events, a critical phenomenon in power system voltage stability. The model is implemented in MATLAB/Simulink and validated by comparing its results with previous studies, demonstrating its ability to capture transient and subtransient responses under voltage disturbances. Different types of motors (A, B, and C) are analyzed, and variations in load and voltage disturbance magnitudes are studied. The results show that the model is accurate and robust, making it useful for voltage stability studies in systems with high induction motor penetration. However, the model has limitations, such as the lack of consideration for frequency drives and protection systems, suggesting areas for future research.
Defect Detection in Freight Trains Using a Lightweight and Effective Multi-Scale Fusion Framework with Knowledge Distillation
Authors: Ma Ziqin , Zhou Shijie , Lin Chunyu
Published: February 2025

The safe operation of freight train equipment is crucial to the stability of the transportation system. With the advancement of intelligent monitoring technology, vision-based anomaly detection methods have gradually become an essential approach to train equipment condition monitoring. However, due to the complexity of train equipment inspection scenarios, existing methods still face significant challenges in terms of accuracy and generalization capability. Freight trains defect detection models are deployed on edge computing devices, onboard terminals, and fixed monitoring stations. Therefore, to ensure the efficiency and lightweight nature of detection models in industrial applications, we have improved the YOLOv8 model structure and proposed a network architecture better suited for train equipment anomaly detection. We adopted the lightweight MobileNetV4 as the backbone to enhance computational efficiency and adaptability. By comparing it with other state-of-the-art lightweight networks, we verified the superiority of our approach in train equipment defect detection tasks. To enhance the model’s ability to detect objects of different sizes, we introduced the Content-Guided Attention Fusion (CGAFusion) module, which effectively strengthens the perception of both global context and local details by integrating multi-scale features. Furthermore, to improve model performance while meeting the lightweight requirements of industrial applications, we incorporated a staged knowledge distillation strategy on large-scale datasets. This approach significantly reduces model parameters and computational costs while maintaining high detection accuracy. Extensive experiments demonstrate the effectiveness and efficiency of our method, proving its competitiveness compared with other state-of-the-art approaches.
YOLO-FIX: Improved YOLOv11 with Attention and Multi-Scale Feature Fusion for Detecting Glue Line Defects on Mobile Phone Frames
Authors: Ye Tianrun , Huang Shize , Qin Weiwei , Tu Haiyang , Zhang Ping , Wang Yafei , Gao Chunming , Gong Yanli
Published: February 2025

This paper presents YOLO-FIX, an improved intelligent detection model based on YOLOv11, designed to identify glue line defects in mobile phone frames. The model addresses the challenges of complex glue line morphology, background interference, and illumination transformation. YOLO-FIX enhances the extraction of local and global features to optimize the detection accuracy by integrating advanced attention mechanisms and multi-scale feature fusion modules specifically Deformable Large-Kernel Attention (De-formable-LSKA) and Mamba-Like Linear Attention (MLLA). Experimental evaluations demonstrate that YOLO-FIX achieves a mean Average Precision (mAP50) of 95.2%, an 8.6% improvement over the baseline YOLOv11 model while maintaining a real-time detection speed of 189 FPS. It effectively identifies five common defect types: broken glue, wall climbing, glue dropping, single-tip wall climbing, and collapsed glue, showcasing exceptional robustness and generalization across varying production environments. These results affirm YOLO-FIX as a highly accurate and efficient solution for automated defects in industrial applications.
Real-Time Edge Computing vs. GPU-Accelerated Pipelines for Low-Cost Microscopy Applications
Authors: Bueno Gloria , Sanchez-Vargas Lucia , Diaz-Maroto Alberto , Ruiz-Santaquiteria Jesus , Blanco Maria , Salido Jesus , Cristobal Gabriel
Published: February 2025

Environmental microscopy is crucial for analyzing microorganisms, but traditional optical microscopes are often expensive, bulky, and impractical for field use. AI-driven image recognition, powered by deep learning models like YOLO, enhances microscopy analysis but typically requires high computational resources. To address these challenges, we present two cost-effective pipelines integrating AI with low-cost microscopes and edge computing. Both approaches use the OpenFlexure Microscope and Raspberry Pi devices. The first performs real-time inference with a Raspberry Pi 5 and Hailo-8L accelerator, while the second captures images with a Raspberry Pi 4, transferring them to a GPU-equipped desktop for processing. Using YOLOv8, we evaluate their ability to detect phytoplankton species, including cyanobacteria and diatoms. Results show that edge computing enables accurate, efficient, and low-power microscopy analysis, demonstrating its potential for real-time environmental monitoring in resource-limited settings.
Research on Distributed Optimization Scheduling and Its Boundaries in Virtual Power Plants
Authors: Yu Jiaquan , Fan Yanfang , Hou Junjie
Published: February 2025

To improve the operational efficiency of the Virtual Power Plant (VPP) and the effectiveness and reliability of scheduling boundary characterization, this paper proposes a time-decoupled distributed optimization algorithm. First, based on the Lyapunov optimization theory, time decoupling is implemented within the VPP, transforming long-term optimization problems into single-period optimization problems, thereby reducing optimization complexity and improving operational efficiency. Second, the Alternating Direction Method of Multipliers (ADMM) framework is used to decompose the optimization problem into multiple subproblems, combined with a hybrid strategy to improve the particle swarm optimization algorithm for solving the problem, thus achieving distributed optimization for the VPP. Finally, to facilitate intra-day interaction between the VPP and the distribution network, the remaining controllable capacity of the VPP’s devices is used as the spinning reserve to address renewable energy fluctuations. A dynamic scheduling boundary model is constructed by introducing wind and solar fluctuation factors. Based on time decoupling and algorithm improvement, the scheduling boundaries are solved and updated on a rolling basis. Simulation results show that, firstly, the time decoupling strategy based on Lyapunov optimization has an error of less than 3%, and the solving time is reduced by 86.11% after decoupling, significantly improving solving efficiency and validating the feasibility and effectiveness of the time decoupling strategy. Secondly, the hybrid strategy-improved particle swarm optimization algorithm achieves improvements in convergence speed and accuracy compared to other algorithms. Finally, the VPP scheduling boundary and scheduling cost characterization times are 115 s and 6.7 s, respectively, effectively meeting the timeliness of VPP and distribution network interaction while ensuring the safety and reliability of the scheduling boundaries.
Joint Use of Thermal Characterization and Simulation of AlGaN/GaN High-Electron Mobility Transistors in Transient and Steady State Regimes to Estimate the Hotspot Temperature
Authors: Karrame Khalil , Chang Christophe , Nallatamby Jean-Christophe , Colas Maggy , Sommet Raphael
Published: February 2025

Channel temperature has a strong impact on the performance of a microwave power transistor. In this paper, various methods, including electrical measurements, optical analysis, and FEM simulations, were employed to perform both transient and steady-state thermal characterization of GH15 2 × 150 µm transistors from UMS foundry (United Monolithic Semiconductors, France). The transient study allowed for the extraction of thermal time constants according to which the temperature in the transistor changes. The steady-state study provided the hotspot temperature. As the channel is physically inaccessible, direct thermal measurement at the hotspot is not possible, in order to extract the temperature of this hotspot, different measurement methods are combined by simulation, which is calibrated based on the measurements, resulting in a well-adjusted thermal model. This calibrated model enabled the extraction of temperature at any location within the device’s structure, particularly at the hotspot. The measurement and simulation results have shown that the hotspot temperature can be 30% higher than the temperature of the nearest external surface for high dissipated power levels.
Towards Federated Robust Approximation of Nonlinear Systems with Differential Privacy Guarantee
Authors: Yang Zhijie , Yan Xiaolong , Chen Guoguang , Niu Mingli , Tian Xiaoli
Published: February 2025

Nonlinear systems, characterized by their complex and often unpredictable dynamics, are essential in various scientific and engineering applications. However, accurately modeling these systems remains challenging due to their nonlinearity, high-dimensional interactions, and the privacy concerns inherent in data-sensitive domains. Existing federated learning approaches struggle to model such complex behaviors, particularly due to their inability to capture high-dimensional interactions and their failure to maintain privacy while ensuring robust model performance. This paper presents a novel federated learning framework for the robust approximation of nonlinear systems, addressing these challenges by integrating differential privacy to protect sensitive data without compromising model utility. The proposed framework enables decentralized training across multiple clients, ensuring privacy through differential privacy mechanisms that mitigate risks of information leakage via gradient updates. Advanced neural network architectures are employed to effectively approximate nonlinear dynamics, with stability and scalability ensured by rigorous theoretical analysis. We compare our approach with both centralized and decentralized federated models, highlighting the advantages of our framework, particularly in terms of privacy preservation. Comprehensive experiments on benchmark datasets, such as the Lorenz system and real-world climate data, demonstrate that our federated model achieves comparable accuracy to centralized approaches while offering strong privacy guarantees. The system efficiently handles data heterogeneity and dynamic nonlinear behavior, scaling well with both the number of clients and model complexity. These findings demonstrate a pathway for the secure and scalable deployment of machine learning models in nonlinear system modeling, effectively balancing accuracy, privacy, and computational performance.
Small-Target Detection Based on Improved YOLOv8 for Infrared Imagery
Authors: Wang Huicong , Ma Kaijun , Yue Juan , Li Yuhan , Huang Jiaxin , Liu Jie , Li Linhan , Wang Xiaoyu , Cai Nengbin , Gao Sili
Published: February 2025

Infrared small-target detection plays a crucial role in applications such as public safety monitoring. However, it faces significant challenges due to the loss of target features, which weakens detection performance. To tackle this problem, this study proposes an improved infrared small-target detection model based on YOLOv8n. First, the Dual-Path Fusion Downsampling Convolution (WFDC) module enhances the backbone network’s ability to extract fine-grained features of targets, preventing the loss of image details as the depth of the convolutional neural network increases. Second, the Involution and the Coordinate Attention (CA) mechanisms are integrated into the spatial pyramid pooling module, where self-attention and the Involution modules aggregate contextual semantic information over a broader spatial range, enriching channel information at each scale. Finally, deformable convolutions are incorporated into the backbone of the model, enabling better handling of target deformations across various scenarios. Experiments conducted on the SIRST-5K and IRSTD-1K datasets demonstrate that our method outperforms both the baseline YOLOv8 model and several state-of-the-art YOLOv8-based improved detection methods. The results show that, compared to the baseline model, our approach achieves mAP@[0.5:0.95] improvements of 12.3% and 16.4% on the two datasets, respectively. These results highlight the effectiveness of our proposed enhancements in improving detection accuracy and model robustness.
Topology, Control, and Applications of MMC with Embedded Energy Storage: A Brief Review
Authors: Zhang Lidong , Zhu Qionghai , Xiao Huangqing
Published: February 2025

In recent years, with the continuous growth of energy demand and the large-scale deployment of renewable energy sources, the power system’s need for high-capacity power transmission and energy storage systems has increased significantly. In this context, the integration of modular multilevel converters (MMCs) with energy storage (ES) systems has led to the development of the MMC with embedded energy storage systems (ES-MMC), which combines the advantages of both the MMC and the ES system. Over the past few years, research on ES-MMC-related technological issues has emerged rapidly. On this foundation, this paper provides an overview of the ES-MMC in terms of electrical topology, steady-state control strategies, common applications, and the challenges it faces. First, the advantages of various ES interfaces are analyzed, and a comparison on the techno-economic feasibility of different submodules with embedded energy storage is conducted. Then, the main control strategies of distributed ES-MMC are examined from several perspectives. Finally, the paper discusses the advantages of the ES-MMC over traditional solutions in various application scenarios and explores possible future research directions. The final analysis establishes ES-MMC’s advantages over traditional solutions in various application scenarios and explores its potential research prospects.
An Enhanced Algorithm for Detecting Small Traffic Signs Using YOLOv10
Authors: Liu Hongrui , Wang Ke , Wang Yudi , Zhang Ming , Liu Qinghua , Li Wentao
Published: February 2025

Recognizing traffic signs is crucial for autonomous driving systems, as it significantly impacts their safety and dependability. However, challenges like the diminutive size of objects and intricate background environments limit the effectiveness of current object detection models. To improve small traffic sign detection, this research introduces an enhanced detection algorithm built on YOLOv10. First, a custom-designed layer for detecting small objects is integrated into the neck section of the network, enhancing the feature extraction process for these objects. Second, a refined downsampling module, called Triple-Branch Downsampling (TBD), utilizes a multi-branch structure and hybrid pooling strategy to boost feature extraction efficiency within the model. Finally, the loss function is optimized by integrating the Normalized Wasserstein Distance (NWD) and Wise-MPDIoU mechanisms, increasing the accuracy of bounding box matching and regression. The experimental findings indicate that the enhanced algorithm reaches a mAP@0.5 of 84.8%, marking a 4% increase over YOLOv10. The classification accuracy and recall are 73.4% and 82.9%, respectively. Moreover, the parameter count decreases by approximately 10%, while the computational complexity is reduced by around 5%.
Correction: Islam et al. A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks. Electronics 2022, 11, 2813
Authors: Islam Umar , Malik Rami Qays , Al-Johani Amnah S. , Khan Muhammad. Riaz , Daradkeh Yousef Ibrahim , Ahmad Ijaz , Alissa Khalid A. , Abdul-Samad Zulkiflee , Tag-Eldin Elsayed M.
Published: February 2025

In the published article [...]
Picosecond-Level Synchronization over Optical Free Space Link Using White Rabbit
Authors: Zhang Peng , Hou Dong , Liu Ke , Zhou Wenjian , Li Minghong , Fang Lujun
Published: February 2025

White Rabbit (WR) time synchronization has an accuracy up to a sub-nanosecond level. However, the current application scenario of WR is limited to wired transmission links. In this paper, we have proposed a time synchronization technique over a free space link using WR. In the WR-based free space synchronization scheme, we replace the original WDM (Wavelength Division Multiplexing) with single-wavelength transmission to reduce the asymmetry of the path and design a high-power optical transceiver module to improve the transmission power. With the scheme, a free space synchronization experiment with a transmission distance of 50 m is conducted. The experimental results show that the RMS (root mean square) time drift of this free space synchronization system is 20.5 ps over a 24 h period, and the TDEV (Time Deviation) of the time synchronization is 14.3 ps at 1 s and 3.9 ps at 20,000 s. The experiment proves that it will be convenient to complete the free space time synchronization network between clock sites with the proposed technique in the future application of complex environments.
YOLOv5s-Based Lightweight Object Recognition with Deep and Shallow Feature Fusion
Authors: Wang Guili , Liu Chang , Xu Lin , Qu Liguo , Zhang Hangyu , Tian Longlong , Li Chenhao , Sun Liangwang , Zhou Minyu
Published: February 2025

In object detection, targets in adverse and complex scenes often have limited information and pose challenges for feature extraction. To address this, we designed a lightweight feature extraction network based on the Convolutional Block Attention Module (CBAM) and multi-scale information fusion. Within the YOLOv5s backbone, we construct deep feature maps, integrate CBAM, and fuse high-resolution shallow features with deep features. We also add new output heads with distinct feature extraction structures for classification and localization, significantly enhancing detection performance, especially under strong light, nighttime, and rainy conditions. Experimental results show superior detection performance in complex scenes, particularly for pedestrian crossing detection in adverse weather and low-light conditions. Using an open-source dataset from Shanghai Jiao Tong University, our algorithm improves pedestrian crossing-detection precision (AP0.5:0.95) by 5.9%, reaching 82.3%, while maintaining a detection speed of 44.8 FPS, meeting real-time detection requirements. The source code is available at GitHub.
A Systematic Review on Advancement of Image Segmentation Techniques for Fault Detection Opportunities and Challenges
Authors: Rahman Md Motiur , Rahman Saeka , Bhatt Smriti , Faezipour Miad
Published: February 2025

Fault and defect detection are critical for ensuring the safety, reliability, and quality of products and infrastructure across various industries. As traditional manual inspection methods face limitations in efficiency and accuracy, advancements in artificial intelligence, particularly image segmentation, have paved the way for automated and precise fault detection processes. A significant gap exists in current research regarding the integration and comparative analysis of classical and modern segmentation approaches across diverse application domains. This study addresses this gap by providing a systematic review that bridges traditional segmentation techniques with cutting-edge deep learning methodologies. Unlike previous reviews that focus solely on isolated techniques or specific domains, this paper offers a holistic analysis of methodological innovations, application breadth, and emerging trends. Emphasis is placed on the integration of deep learning models, hybrid approaches, and advancements like attention mechanisms and lightweight architectures. Additionally, the review highlights critical challenges and proposes future research directions aimed at enhancing model scalability, robustness, and adaptability. This systematic review addresses gaps in the field and provides useful insights for academia and industry, making it a key reference in fault detection using image segmentation.