bluuebunny/crossref_metadata_2025 huggingface dataset:
MPE-HRNetL: A Lightweight High-Resolution Network for Multispecies Animal Pose Estimation
Authors: Shen Jiquan , Jiang Yaning , Luo Junwei , Wang Wei
Published: October 2024

Animal pose estimation is crucial for animal health assessment, species protection, and behavior analysis. It is an inevitable and unstoppable trend to apply deep learning to animal pose estimation. In many practical application scenarios, pose estimation models must be deployed on edge devices with limited resource. Therefore, it is essential to strike a balance between model complexity and accuracy. To address this issue, we propose a lightweight network model, i.e., MPE-HRNet.L, by improving Lite-HRNet. The improvements are threefold. Firstly, we improve Spatial Pyramid Pooling-Fast and apply it and the improved version to different branches. Secondly, we construct a feature extraction module based on a mixed pooling module and a dual spatial and channel attention mechanism, and take the feature extraction module as the basic module of MPE-HRNet.L. Thirdly, we introduce a feature enhancement stage to enhance important features. The experimental results on the AP-10K dataset and the Animal Pose dataset verify the effectiveness and efficiency of MPE-HRNet.L.
Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack
Authors: Cheng Zhijian , Chen Guanjun , Li Xiao-Meng , Ren Hongru
Published: October 2024

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker’s perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.
Autonomous Landing Strategy for Micro-UAV with Mirrored Field-of-View Expansion
Authors: Cheng Xiaoqi , Liang Xinfeng , Li Xiaosong , Liu Zhimin , Tan Haishu
Published: October 2024

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker’s center point relative to the MAV’s body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs.
Breast Lesion Detection for Ultrasound Images Using MaskFormer
Authors: Anand Aashna , Jung Seungho , Lee Sukhan
Published: October 2024

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound’s limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer’s superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model’s ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale.
CRASA: Chili Pepper Disease Diagnosis via Image Reconstruction Using Background Removal and Generative Adversarial Serial Autoencoder
Authors: Si Jongwook , Kim Sungyoung
Published: October 2024

With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on a Convolutional Neural Network (CNN), but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA’s methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through GrabCut. It shows a high performance through an image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images, instead of the actual input, as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed a higher performance than previous research, and image-based scores showed the best performance.
Long-Term Passenger Flow Forecasting for Rail Transit Based on Complex Networks and Informer
Authors: Li Dekui , Du Shubo , Hou Yuru
Published: October 2024

With the continuous growth of urbanization, passenger flow in urban rail transit systems is steadily increasing, making accurate long-term forecasting essential for optimizing operational scheduling and enhancing service quality. However, passenger flow forecasting becomes increasingly complex due to the intricate structure of rail transit networks and external factors such as seasonal variations. To address these challenges, this paper introduces an optimized Informer model for long-term forecasting that incorporates the influences of other stations based on complex network theory. Compared to the ARIMA, LSTM, and Transformer models, this optimized Informer model excels in processing large-scale complex transit data, particularly in terms of long-term forecasting accuracy and capturing network dependencies. The results demonstrate that this forecasting approach, which integrates complex network theory with the Informer model, significantly improves the accuracy and efficiency of long-term passenger flow predictions, providing robust decision support for urban rail transit planning and management.
Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm
Authors: Faruqui Nuruzzaman , Achar Sandesh , Racherla Sandeepkumar , Dhanawat Vineet , Sripathi Prathyusha , Islam Md. Monirul , Uddin Jia , Othman Manal A. , Samad Md Abdus , Choi Kwonhue
Published: October 2024

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization.
Phase-Shifted Fiber Bragg Grating by Selective Pitch Slicing
Authors: Robalinho Paulo , Piaia Vinícius , Soares Liliana , Novais Susana , Ribeiro António Lobo , Silva Susana , Frazão Orlando
Published: October 2024

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others. The optical output signal consists of the overlap between the FBG reflection and the reflection at the fiber-cleaved tip. This new fiber optic device has been studied as a vibration sensor, allowing for the characterization of this sensor in the frequency range of 150 Hz to 70 kHz. How the phase shift in the FBG can be controlled by changing the length of the last pitch is also shown. This device can be used as a filter and a sensing element. As a sensing element, we will demonstrate its application as a vibration sensor that can be utilized in various applications, particularly in monitoring mechanical structures.
Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification
Authors: Sun Ming , Zhang Jie , He Xiaoou , Zhong Yihe
Published: October 2024

The hyperspectral image (HSI) classification task is widely used in remote sensing image analysis. The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. However, they cannot well utilize the sequential properties of spectral features and face the challenge of increasing computational cost with the increase in network depth. To address these shortcomings, this paper proposes a novel network with a CNN-Mamba architecture, called DBMamba, which uses a bidirectional Mamba to process spectral feature sequences at a linear computational cost. In the DBMamba, principal component analysis (PCA) is first used to extract the main features of the data. Then, a dual-branch CNN structure, with the fused features from spectral–spatial features by 3D-CNN and spatial features by 2D-CNN, is used to extract shallow spectral–spatial features. Finally, a bidirectional Mamba is used to effectively capture global contextual information in features and significantly enhance the extraction of spectral features. Experimental results on the Indian Pines, Salinas, and Pavia University datasets demonstrate that the classification performance surpasses that of many cutting-edge methods, improving by 1.04%, 0.15%, and 0.09%, respectively, over the competing SSFTT method. The research in this paper enhances the existing knowledge on HSI classification and provides valuable insights for future research in this field.
Cardiovascular Response and Locomotor Demands of Elite Basketball Referees During International Tournament: A Within- and Between-Referee Analysis
Authors: Pojskić Haris , Užičanin Edin , Suárez-Iglesias David , Vaquera Alejandro
Published: October 2024

There is little knowledge about within- and between-referee variation (WBRV) in cardiovascular responses (CVR) and locomotor game demands (LMD). Thus, the primary aim of this study was to assess the WBRV of CVR and LMD in male basketball referees during elite international games in preparation [e.g., warm-up (WU) and re-warm-up (R-WU)] and active game phases. The secondary aim was to explore quarter-by-quarter differences in CVR and LMD. Thirty-five international male referees took part in this study (age, 40.4 ± 5.4 years; body height, 184.9 ± 5.7 cm; body weight, 85.1 ± 7.5 kg; BMI, 24.0 ± 1.7 kg × m−2; fat%, 18.8 ± 4.7% and VO2max, 50.4 ± 2.2 L × kg−1 × min−1. In total, 76 games (e.g., 228 officiating cases) were analyzed during the FIBA elite men’s competition. They officiated 4.5 games on average (range 3–9 games). Each referee used the Polar Team Pro system to measure CVR [e.g., heart rate (HR), time spent in different HR intensity categories] and LMD (e.g., distance covered, maximal and average velocity, and number of accelerations). Results showed that the referees had bigger WBRV during the active and preparation (e.g., W-U than R-WU) phase when variables of higher CVR and LMD intensity were observed (e.g., time spent at higher HR zones, distance covered in higher speed zones). The WBRV, CVR, and LMD were higher during WU than R-WU. Moreover, the referees had a lower CVR and LMD in the second half. In conclusion, the referees should establish and follow consistently a game-to-game preparation routine and attempt to spread their on-court preparation time equally within the crew. A half-time preparation routine should be improved to re-establish a sufficient activation level similar to that achieved in pre-game preparation.
Predicting the Wear Amount of Tire Tread Using 1D−CNN
Authors: Park Hyunjae , Seo Junyeong , Kim Kangjun , Kim Taewung
Published: October 2024

Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study.
YOLO-DHGC: Small Object Detection Using Two-Stream Structure with Dense Connections
Authors: Chen Lihua , Su Lumei , Chen Weihao , Chen Yuhan , Chen Haojie , Li Tianyou
Published: October 2024

Small object detection, which is frequently applied in defect detection, medical imaging, and security surveillance, often suffers from low accuracy due to limited feature information and blurred details. This paper proposes a small object detection method named YOLO-DHGC, which employs a two-stream structure with dense connections. Firstly, a novel backbone network, DenseHRNet, is introduced. It innovatively combines a dense connection mechanism with high-resolution feature map branches, effectively enhancing feature reuse and cross-layer fusion, thereby obtaining high-level semantic information from the image. Secondly, a two-stream structure based on an edge-gated branch is designed. It uses higher-level information from the regular detection stream to eliminate irrelevant interference remaining in the early processing stages of the edge-gated stream, allowing it to focus on processing information related to shape boundaries and accurately capture the morphological features of small objects. To assess the effectiveness of the proposed YOLO-DHGC method, we conducted experiments on several public datasets and a self-constructed dataset. Exceptionally, a defect detection accuracy of 96.3% was achieved on the Market-PCB public dataset, demonstrating the effectiveness of our method in detecting small object defects for industrial applications.
UAV Geo-Localization Dataset and Method Based on Cross-View Matching
Authors: Yao Yuwen , Sun Cheng , Wang Tao , Yang Jianxing , Zheng Enhui
Published: October 2024

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual–real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric.
PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation
Authors: Zhou Peng , Fang Hong , Wu Gaochang
Published: October 2024

Defects in photovoltaic (PV) panels can significantly reduce the power generation efficiency of the system and may cause localized overheating due to uneven current distribution. Therefore, adopting precise pixel-level defect detection, i.e., defect segmentation, technology is essential to ensuring stable operation. However, for effective defect segmentation, the feature extractor must adaptively determine the appropriate scale or receptive field for accurate defect localization, while the decoder must seamlessly fuse coarse-level semantics with fine-grained features to enhance high-level representations. In this paper, we propose a Progressive Deformable Transformer (PDeT) for defect segmentation in PV cells. This approach effectively learns spatial sampling offsets and refines features progressively through coarse-level semantic attention. Specifically, the network adaptively captures spatial offset positions and computes self-attention, expanding the model’s receptive field and enabling feature extraction across objects of various shapes. Furthermore, we introduce a semantic aggregation module to refine semantic information, converting the fused feature map into a scale space and balancing contextual information. Extensive experiments demonstrate the effectiveness of our method, achieving an mIoU of 88.41% on our solar cell dataset, outperforming other methods. Additionally, to validate the PDeT’s applicability across different domains, we trained and tested it on the MVTec-AD dataset. The experimental results demonstrate that the PDeT exhibits excellent recognition performance in various other scenarios as well.
Physical Frailty Prediction Using Cane Usage Characteristics during Walking
Authors: Toda Haruki , Chin Takaaki
Published: October 2024

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (p < 0.001, r = 0.36; p < 0.001, r = 0.29; p < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (p = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments.
BGF-YOLOv10: Small Object Detection Algorithm from Unmanned Aerial Vehicle Perspective Based on Improved YOLOv10
Authors: Mei Junhui , Zhu Wenqiu
Published: October 2024

With the rapid development of deep learning, unmanned aerial vehicles (UAVs) have acquired intelligent perception capabilities, demonstrating efficient data collection across various fields. In UAV perspective scenarios, captured images often contain small and unevenly distributed objects, and are typically high-resolution. This makes object detection in UAV imagery more challenging compared to conventional detection tasks. To address this issue, we propose a lightweight object detection algorithm, BGF-YOLOv10, specifically designed for small object detection, based on an improved version of YOLOv10n. First, we introduce a novel YOLOv10 architecture tailored for small objects, incorporating BoTNet, variants of C2f and C3 in the backbone, along with an additional small object detection head, to enhance detection performance for small objects. Second, we embed GhostConv into both the backbone and head, effectively reducing the number of parameters by nearly half. Finally, we insert a Patch Expanding Layer module in the neck to restore the feature spatial resolution. Experimental results on the VisDrone-DET2019 and UAVDT datasets demonstrate that our method significantly improves detection accuracy compared to YOLO series networks. Moreover, when compared to other state-of-the-art networks, our approach achieves a substantial reduction in the number of parameters.
Facial Expression Recognition-You Only Look Once-Neighborhood Coordinate Attention Mamba: Facial Expression Detection and Classification Based on Neighbor and Coordinates Attention Mechanism
Authors: Peng Cheng , Sun Mingqi , Zou Kun , Zhang Bowen , Dai Genan , Tsoi Ah Chung
Published: October 2024

In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). NCAMamba combines the background information reduction capabilities of Mamba, the local neighborhood relationship understanding of neighborhood attention, and the directional relationship understanding of coordinate attention. The resulting FER-YOLO-NCAMamba model, when applied to two unaligned FER benchmark datasets, RAF-DB and SFEW, obtains significantly improved mean average precision (mAP) scores when compared with those obtained by other state-of-the-art methods. Moreover, in ablation studies, it is found that the NCA module is relatively more important than the Visual State Space (VSS), a version of using Mamba for image processing, and in visualization studies using the grad-CAM method, it reveals that regions around the nose tip are critical to recognizing the expression; if it is too large, it may lead to erroneous prediction, while a small focused region would lead to correct recognition; this may explain why FER of unaligned faces is such a challenging problem.
ETFT: Equiangular Tight Frame Transformer for Imbalanced Semantic Segmentation
Authors: Jeong Seonggyun , Heo Yong Seok
Published: October 2024

Semantic segmentation often suffers from class imbalance, where the label ratio for each class in the dataset is not uniform. Recent studies have addressed the issue of class imbalance in semantic segmentation by leveraging the neural collapse phenomenon in conjunction with an Equiangular Tight Frame (ETF). While the use of ETF aids in enhancing the discriminability of minor classes, class correlation is another crucial factor that must be taken into account. However, managing the balance between class correlation and discrimination through neural collapse remains challenging, as these properties inherently conflict with one another. Moreover, this control is established during the training stage, resulting in a fixed classifier. There is no guarantee that this classifier will consistently perform well with different input images. To address this problem, we propose an Equiangular Tight Frame Transformer (ETFT), a transformer-based model that jointly processes the features and classifier using ETF structure, and dynamically generates the classifier as a function of the input for imbalanced semantic segmentation. Specifically, the classifier initialized with the ETF structure is jointly processed with the input patch tokens during the attention process. As a result, the transformed patch tokens, aided by the ETF structure, achieve discriminability between classes while preserving contextual correlation. The classifier, initially structured as an ETF, is adjusted to incorporate the correlation information, benefiting from the attention mechanism. Furthermore, the learned classifier is combined with the fixed ETF classifier, leveraging the advantages of both. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods for imbalanced semantic segmentation on both the ADE20K and Cityscapes datasets.
MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images
Authors: Wang Manlin , Ma Xiaoshuang , Zheng Taotao , Su Ziqi
Published: October 2024

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China’s Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately.
The Impact of a Congested Match Schedule (Due to the COVID-19 Lockdown) on Creatine Kinase (CK) in Elite Football Players Using GPS Tracking Technology
Authors: Garcia-Unanue Jorge , Hernandez-Martin Antonio , Viejo-Romero David , Manzano-Carrasco Samuel , Sanchez-Sanchez Javier , Gallardo Leonor , Felipe Jose Luis
Published: October 2024

The aim was to analyse the consequences of a congested schedule (due to the COVID-19 lockdown) on creatine kinase (CK) in elite football players using GPS tracking technology. A total of 17 elite football players were monitored in training and competition with a global positioning system. Variables including total distance, high-intensity distance, and distance acceleration and deceleration were analysed. Different measurements of serum creatine kinase (CK) concentration were performed on match day (MD) and at 24 h (MD+1), 48 h (MD+2), and 72 h (MD+3) after each match to study the muscle damage of each individual player caused during the match. The results showed a significant increase in physical demands in training (in relative terms regarding the match) at MD+3 compared to MD+1 and MD+2. Furthermore, at +72 h, CK decreases to a value almost the same as that before MD. On the other hand, the players with lower demands for high-intensity actions in the match showed a higher reduction in the concentration of CK at MD+3 compared to MD+1 and MD+2 (p < 0.05). It became evident that players with high-intensity demand and a high number of accelerations and decelerations need more time to assimilate the match load and can remain in a state of muscle fatigue for up to 3 days. In addition, a congested schedule can lead to a state of chronic fatigue in elite football players, limiting physical performance and possibly increasing the potential risk of injury for football players.
Utilizing Zn(Cu/Cr)Al-Layered Double Hydroxide-Based Photocatalysts for Effective Photodegradation of Environmental Pollutants
Authors: Despotović Vesna , Hadnađev-Kostić Milica , Vulić Tatjana , Bognár Szabolcs , Karanović Đurđica , Tot Nataša , Šojić Merkulov Daniela
Published: October 2024

Layered double hydroxides (LDHs) and their derived mixed oxides are emerging as a promising class of biocompatible inorganic lamellar nanomaterials. The detailed structure and textural characteristics of the synthesized LDH-based materials were examined using X-ray diffraction, Fourier transform infrared spectroscopy, and N2 adsorption/desorption isotherm. This study explored the removal efficiency of pharmaceutical tolperisone hydrochloride (TLP), as well as the herbicides quinmerac (QUI) and clomazone (CLO) from water, using dried and calcined LDH-based photocatalysts under simulated solar irradiation and UV irradiation. A higher removal efficiency was observed using UV irradiation, for all substrates. The most effective removal was achieved using ZnAl photocatalysts thermally treated at 100 °C (ZnAl 100) and 500 °C (ZnAl 500). The highest removal rates were observed in the TLP/ZnAl 100 and QUI/ZnAl 100 systems, achieving ~79% and ~86% removal after 75 min of treatment under UV. In contrast, the CLO/ZnAl 100 and CLO/ZnAl 500 systems achieved ~47% removal of CLO. Furthermore, this study investigated the role of reactive species to elucidate the mechanisms of photodegradation under UV. It was found that in the degradation of TLP and QUI in the presence of ZnAl 100 and ZnAl 500, the superoxide anion radical played the most important role.
From Conflict to Cooperation: Norwegian Labour Market Institutions in the Making
Authors: Reiersen Jon
Published: October 2024

Moving a relationship of widespread conflicts and distrust into a virtuous circle of trust and cooperation is challenging, yet the case examined in this article shows that it is possible. A problematic start can indeed lead to a positive outcome. In the early twentieth century, Norway experienced the highest levels of labour conflict in Europe. Class conflicts were intense and often violent, with deep mistrust between the parties in the labour market. This situation changed markedly when the Norwegian Employers’ Confederation and the Norwegian Confederation of Trade Unions signed the Basic Agreement in 1935. This agreement not only led to a lasting decline in labour conflicts, but it also marked a crucial early step towards a system of close collaboration between labour and capital in Norway. By focusing on the intimate relationship between beliefs, trust, and cooperation, this article explores how the labour market parties transitioned from widespread conflict and open struggle to a system of peaceful negotiations and compromises. Changes in beliefs enabled the parties to coordinate on a new cooperative equilibrium, an equilibrium that continues to characterise Norway’s labour market today.
Pain Candidate Genes 5-HTTLPR and COMT Affect Anxiety and Mood in Japanese Ballet Dancers: A Cross-Sectional and Longitudinal Study
Authors: Yatabe Kanaka , Ashikaga Kohei , Muroi Ryota , Somemura Shu , Takemoto Masahiro , Yudoh Kazuo , Miyano Hisao , Fujiya Hiroto
Published: October 2024

The balance of mental, physical, and technical aspects is essential in improving ballet performance. Ballet dancers’ emotional and behavioral characteristics vary, even under identical stress conditions. This study aimed to investigate the association between the pain candidate genes 5-HTTLPR and COMT and anxiety in Japanese ballet dancers. Participants were 18 youth elite ballet students with professional aspirations (Y-Elite) and 16 dancers in a professional ballet company (Pro). We administered psychological questionnaires, the State-Trait Anxiety Inventory (STAI) and Brunel Mood Scale (BRUMS), to participants under the following four different stress conditions: standard practice day, cast decision day, rehearsal day, and one week before competition day. In addition, the genotypes of 5-HTTLPR and COMT Val158Met were examined. The distribution of 5-HTTLPR was not different between Y-Elite and Pro dancers, although one of the COMT genotypes was different. Y-Elite dancers had higher trait anxiety scores than Pro dancers for these genotypes before competition (ps < 0.03), although no significant association was observed between both genotypes and scores on the STAI across conditions. Their moods were significantly different through the four conditions (p < 0.004). Pro dancers’ moods were also more stable than those of the Y-Elite dancers in the presence of pain. The results indicate that 5-HTTLPR and COMT play a crucial role in dancers’ anxiety and mood during pain (ps < 0.05). Pro dancers are probably predicted by their lower neuroticism and mood scores and their better adaptation to stress than Y-Elite dancers. The 5-HTTLPR and COMT genes may be influencing the sensitivity to the environment. Youth elite ballet dancers need to understand the relationship between pain and physical activity from an early stage.
Effects of Apnea-Induced Hypoxia on Hypoalgesia in Healthy Subjects
Authors: Mendoza-Arranz Cristian , López-Rebenaque Omar , Cabrera-López Carlos Donato , López-Mejías Alejandro , Fierro-Marrero José , DeAsís-Fernández Francisco
Published: October 2024

Introduction: Exercise-induced hypoalgesia is a phenomenon in which exercise bouts induce a reduction in pain sensitivity. Apnea training involves similar characteristics that could potentially induce hypoalgesia. Objectives: The objectives of this study are to explore the effect of apnea training on hypoalgesia; assess the correlation between conditioned pain modulation (CPM) response and apnea-induced hypoalgesia; and examine the association between hypoalgesia with hypoxemia, and heart rate (HR) during apnea. Methods: A randomized controlled trial was conducted comparing a walking protocol employing intermittent apnea compared with normal breathing in healthy volunteers. Hypoalgesia was tested with pressure pain thresholds (PPTs) and CPM. Oxygen saturation (SpO2) and HR were also tested. Results: Relevant but not significant changes were detected in the thumb (MD = 0.678 kg/cm2), and tibialis (MD = 0.718 kg/cm2) in favor of the apnea group. No significant differences were detected in CPM. The apnea group presented lower SpO2, but HR values similar to those of the control group during the intervention. Basal CPM and intrasession hypoxemia significantly correlated with the PPT response. However, HR did not correlate with the PPT response. Conclusions: The current results suggest a trend, though not statistically significant, toward an improvement in the PPT in favor of apnea training compared to normal breathing. Nevertheless, subjects who presented greater basal CPM and lower oxygen saturation during the session presented a greater PPT response, suggesting the possibility of mediators of response. Future investigations should clarify this phenomenon.
Spatiotemporal Variability of Soil Erosion in the Pisha Sandstone Region: Influences of Precipitation and Vegetation
Authors: Yang Zhenqi , Guo Jianying , Qin Fucang , Li Yan , Wang Xin , Li Long , Liu Xinyu
Published: October 2024

The Pisha sandstone area, situated in the upper and middle reaches of the Yellow River in China, is characterized by severe soil and water erosion, making it one of the most critical regions on the Loess Plateau. The rugged terrain and exposed bedrock complicate management efforts for this area, posing challenges for accurate forecasting using soil erosion models. Through an analysis of terrain, vegetation, and precipitation impacts on soil erosion, this study offers theoretical support for predicting soil erosion within the exposed Pisha sandstone area of the Loess Plateau. This has substantial implications for guiding water and soil conservation measures in this region. Focusing on China’s exposed sandstone area within the Geqiugou watershed, temporal and spatial changes in vegetation cover and land use from 1990 to 2020 were analyzed. The result shows that, from 1990 to 2020, the grassland area has exhibited a consistent downward trend, with successive reductions of 64.86% to 59.46%. The area of low vegetation cover witnessed a significant decline of 59.29% in 2020 compared to that in 1990. The moderate erosion area decreased from 84.52 to 57.17 km2. The significant reduction in soil and water loss can be attributed to the expansion of forest and grassland areas, with the implementation of the Grain for Green project serving as a key policy driver for facilitating this expansion. This study provided a good example of combining rainfall with vegetation coverage to fast estimation soil erosion. A mathematical relationship between the vegetation rainfall coupling index (RV) and soil erosion was established with strong fitting effects, enabling estimation of the soil erosion volume under varying slope conditions within Pisha sandstone areas. The main focus of future soil and water conservation in the Pisha sandstone area should be on effectively managing the channel slope and minimizing exposed bedrock areas through a combination of slope cutting, the application of anticorrosive materials, and the implementation of artificial vegetation planting.
How Does Digital Economy Influence Green Mobility for Sustainable Development? Moderating Effect of Policy Instruments
Authors: Yin Xingmin , Zhang Jing , Zheng Xiaochen
Published: October 2024

The role of green mobility as a low-carbon lifestyle in carbon reduction and sustainable development cannot be ignored. The digital economy effectively promotes green mobility for sustainable energy use in the broader setting of the significant data era and sustainable development. This study utilizes the panel data of 264 cities in China from 2011 to 2021 to construct a two-way fixed-effects regression model to analyze the impact of the digital economy on residents’ green mobility and the indirect impact mechanism of the two policy tools, a low-carbon transportation pilot and carbon emissions trading, from theoretical and empirical aspects. The results show that digital economic development helps promote residents’ green mobility. In addition, the implementation of low-carbon transportation pilots and carbon trading policies has strengthened the role of the digital economy in promoting green mobility. The findings remain after introducing robustness tests such as “smart city” pilots as exogenous shock policies. A heterogeneity study suggests that the effect of the digital economy on green mobility for residents is more significant in economically developed and human capital-rich areas. This study contributes to the literature by providing empirical evidence on the role of the digital economy in promoting sustainable urban transportation and by demonstrating the moderating effects of policy instruments, thereby offering practical insights for policymakers aiming to reduce urban pollution and enhance sustainable development.
The Spatiotemporal Dynamics of Temperature Variability Across Mts. Qinling: A Comparative Study from 1971 to 2022
Authors: Hao Chengyuan , He Sunan
Published: October 2024

Analyzing the spatiotemporal patterns of atmospheric temperature in sensitive areas is critically important for understanding the broader implications of global climate change, which remains a prominent topic in geosciences. It also plays a crucial role in advancing sustainable development. This study utilized daily minimum, maximum, and mean temperature data from twelve meteorological stations across the South and North Mts. Qinling (Qinling Mountains). Employing trend analysis, the Mann–Kendall mutation test, and Morlet wavelet analysis, we explored the predominant temperature trends and characteristics from 1971 to 2022. Our findings revealed consistent inter-annual warming trends in both regions, with more rapid temperature increases in the North compared to the South. Notably, significant shifts occurred in 2003 for both mean and minimum temperatures in the North, while the maximum and minimum temperature values were recorded in the 2010s and 1980s, respectively. Both regions exhibited a primary temperature fluctuation cycle of 28 years. Seasonally, the strongest warming effects appeared in spring, with the weakest in autumn, and moderate effects in winter and summer, indicating that spring contributes most significantly to regional warming. Monthly analysis showed positive temperature trends across all months, with higher rates in the North. The weakening temperature boundary effect of the Mts. Qinling suggested a weakening North–South division, particularly highlighted by the northward shift of the 1 °C isotherm curve for the coldest month, moving away from the previously observed 0 °C isotherm. This northward shift highlights the differential warming rates between the northern and southern regions. Overall, the analysis confirms a robust warming trend, with notable fluctuations in January’s temperatures since 1998, suggesting the Mts. Qinling’s emerging role as a climatic divider in the Chinese Mainland. This introduces new challenges for regional ecosystems, agricultural production, and water resource management, highlighting the pressing need to advance regional sustainable development in the face of climate change.
Quality Evaluation and Optimization of Idle Goods Swap Platform Based on Grounded Theory and Importance–Performance Analysis
Authors: Wang Xiaoke , Wang Zhaohui , Li Hengtao
Published: October 2024

Given economic downturns and environmental concerns, promoting innovative consumption patterns is urgently needed. Swap is a promising collaborative consumption pattern that redistributes ownership of goods without using money. In the past, no research has focused on swap platform quality due to a lack of empirical resources. Therefore, this study aims to construct theoretical methods for evaluating and optimizing the quality of swap platforms. Analyzing consumer characteristics and motivations, the study creates a user profile that guides swap platforms’ market positioning. Then a quality evaluation model for swap platforms is constructed to identify areas for improvement. Data were collected from core users of a start-up swap platform in Shanghai, including 20 in-depth interviews and 180 survey questionnaires. The findings reveal that the main consumers in Shanghai’s swap market are highly educated women aged 20 to 40, with upper-middle incomes. They are motivated by functional, charitable, economic, and hedonic factors. Using grounded theory and the IPA method, this study developed a quality evaluation model for swap platforms, consisting of 18 indicators that clearly distinguish areas of weakness and strengths. This study provides valuable insights into the methodological research of quality evaluation for swap platforms and offers practical suggestions for their optimization.
A Sustainable Vision for Technical Education 4.0 of Post COVID-19
Authors: EL-Nwasany Rabab I. , Bakr Ali F. , Fathi Amira A.
Published: October 2024

The impacts of industry 4.0 have been reflected in education around the world as countries compete to formulate educational policies based on innovation and digitization in order to adapt to the IR 4.0. In this regard, a new approach to technical education 4.0 has been formulated. It seeks to integrate technical education with the technology developments that define the Industrial Revolution 4.0, emphasizing the importance of this alignment in promoting sustainable economic growth and fostering inclusive development. The paper highlights the strategies and techniques implemented in the design of technical education 4.0 school buildings, arriving at legislative and design recommendations for creating school buildings that are compatible with sustainable education, meet the needs of industry 4.0 building practices, and are supported by modern technologies. By using an integrated approach in terms of new design needs and new education needs that adapt to modern pandemics and prevent any diseases in the future, this research aims to present a new model for technical education 4.0 in the pandemic era in line with the sustainable development goals in the Education Strategy 2030. This study discusses the problems existing in the current situation of technical education schools in Egypt and comes up with a new model that is compatible with new design and educational needs. It is structured using new design strategies for technical education 4.0 schools based on design patterns for 21st century schools that meet industry 4.0 needs and address the COVID-19 pandemic.