bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing
Authors: Yang Xiaofei , Zhou Hao , Li Qiao , Fu Xueliang , Li Honghui
Published: February 2025

Potato is a major food crop in China. Its development and nutritional state can be inferred by the content of chlorophyll in its canopy. However, the existing study on applying feature extraction and optimization algorithms to determine the canopy SPAD (Soil–Plant Analytical Development) values of potatoes at various fertility stages is inadequate and not very reliable. Using the Pearson feature selection algorithm and the Competitive Adaptive Reweighted Sampling (CARS) method, the Vegetation Index (VI) with the highest correlation was selected as a training feature depended on multispectral orthophoto images from unmanned aerial vehicle (UAV) and measured SPAD values. At various potato fertility stages, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) inversion models were constructed. The models’ parameters were then optimized using the Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA). The findings demonstrated a higher correlation between the feature selected VI and SPAD values; additionally, the optimization algorithm enhanced the models’ prediction accuracy; finally, the addition of the fertility stage feature considerably increased the accuracy of the full fertility stage in comparison to the single fertility stage. The models with the highest inversion accuracy were the CARS-SSA-RF, CARS-SSA-XGBoost, and Pearson-SSA-XGBoost models. For the single-fertility and full-fertility phases, respectively, the optimal coefficients of determination (R2s) were 0.60, 0.66, and 0.87, the root-mean-square errors (RMSEs) were 2.63, 3.23, and 2.39, and the mean absolute errors (MAEs) were 2.00, 2.75, and 1.99.
Does “Zero Growth Policy” Affect Environmental Productivity of Wheat Production in China?
Authors: Ma Yuan
Published: February 2025

Recently, many environmental regulations (ER) have been enacted to achieve the agricultural green development goal. After the introduction of the “Zero Growth Policy (ZGP)”, compound fertilizers kept increasing, albeit three fertilizers—N fertilizer, P fertilizer, and K fertilizer—decreased after the launch of the ZGP. In this case, whether the amounts of N, P, and K actually reduced is uncertain and ambiguous, and the effects of the ZGP are difficult to identify. This paper aims to measure environmental productivity and examine the specific effects of the ZGP on China’s wheat production. Based on the province-level data of 14 provinces from 2010 to 2022, environmental efficiency and environmental productivity of wheat production are measured and decomposed. The empirical results indicate that the nutrient-oriented efficiency is low (0.50), and 40% of the current nutrients can be reduced to become environmentally efficient after being technically efficient. Besides, the ZGP can significantly affect nutrient-oriented productivity change (NTFPC). Only when the growth rates of both fertilizer application and total nutrients contained in different types of fertilizers are decreased can the NTFPC be improved and agricultural pollution be reduced. Additionally, improving the environment quality and enhancing the social well-being of farmers are beneficial to increasing NTFPC.
Evaluating the Level of Total Mercury Present in the Soils of a Renowned Tea Production Region
Authors: Xu Jinghua , Xie Ruijia , Liu Liping , Huang Zhiqun
Published: February 2025

Total mercury pollution in oolong tea garden soils was comprehensively investigated in this study. Soil samples were collected from 146 villages in a famous oolong tea production area. The total mercury content in the soils ranged from 0.025 to 0.296 mg/kg, with a median of 0.105 mg/kg. According to the Soil Accumulation Index Method, 67.81% of samples were pollution-free, 31.51% had pollution levels from none to moderate, and 0.68% were moderately polluted. The PMF model revealed that natural geochemical processes were the main mercury source, contributing 72.4%, with some from transportation, coal combustion, and industrial activities. Most values were below the HQ threshold, suggesting low non-carcinogenic risk from mercury in most soils. Further research is needed to understand mercury’s bioaccumulation in tea leaves and assess short- and long-term exposure risks for a better understanding of its long-term impacts on the tea industry and human health.
Turning Waste Wool into a Circular Resource: A Review of Eco-Innovative Applications in Agriculture
Authors: Camilli Francesca , Focacci Marco , Dal Prà Aldo , Bortolu Sara , Ugolini Francesca , Vagnoni Enrico , Duce Pierpaolo
Published: February 2025

Agriculture significantly impacts the environment in terms of greenhouse gas emissions, soil nutrient depletion, water consumption, and pollution and waste produced by intensive farming. Wool has great potential and can be a valuable resource for agriculture due to its high nitrogen, carbon, and sulfur content and good water absorption and retention properties, benefiting soil carbon storage and fertility, as well as decreasing the risk of water contamination due to the slow decomposition and nitrogen release. This review aims to provide an overview of bio-based solutions that can benefit agroecosystems as a circular bioeconomy practice. Raw wool and wool hydrolysate are the most common applications, but also wool pellets, wool compost, and wool mats are interesting treatments for plant growing. Waste wool showed positive effects on soil fertility by primarily increasing nitrogen and sulfur content. Improved water retention capacity and microbial activity were also recorded in several studies. The use of wool as mulching is effective for weed control. Attention to the plant species tested aimed at identifying the most promising cultivations in terms of treatment efficiency, possibly lowering environmental impact on the agroecosystem. To eco-design and scale-up processes that strengthen the circular use of wool into widespread practices, further research should be encouraged in conjunction with environmental impact assessments and economic evaluations.
Antimicrobial, Anti-Biofilm, Anti-Quorum Sensing and Cytotoxic Activities of Thymbra spicata L. subsp. spicata Essential Oils
Authors: Barak Timur Hakan , Eryilmaz Mujde , Karaca Basar , Servi Huseyin , Kara Ertekin Simge , Dinc Muhittin , Ustuner Hatice
Published: February 2025

Background/Objectives: Essential oils of Thymbra spicata subsp. spicata are known for their rich phytochemical content and bioactive properties. This study aimed to evaluate the antimicrobial, anti-biofilm and anti-quorum sensing, as well as the cytotoxic activities of T. spicata subsp. spicata essential oils (TS-EO1 and TS-EO2) obtained from two different localities in Türkiye, along with a detailed chemical composition analysis. Methods: TS-EO1 and TS-EO2 were obtained by the hydrodistillation method and analyzed using Gas Chromatography—Mass Spectrometry (GC-MS) to determine their phytochemical profiles. Antimicrobial activities were assessed against Gram-positive and Gram-negative bacteria, and fungal strains were assessed using the broth microdilution method. Anti-biofilm and anti-quorum sensing activities were evaluated using Pseudomonas aeruginosa PAO1 and Chromobacterium violaceum ATCC 12472, respectively. Cytotoxic properties were tested on four cell lines (A549, MCF-7, U87MG, and L929) using the MTT assay. Results: Both essential oil samples were rich in carvacrol (54.3% and 54.1%), followed by p-cymene and γ-terpinene. The essential oils exhibited significant antimicrobial activity, particularly against Staphylococcus aureus (6.25 mg/mL) and Candida parapsilosis (0.20 mg/mL). Sub-MIC concentrations significantly inhibited biofilm formation and quorum sensing. Both samples showed moderate cytotoxic properties against human cancer cell lines, particularly A549 (IC50: 116.3 and 134.4 μg/mL, respectively). Conclusions: This study showed that T. spicata subsp. spicata essential oils have significant antimicrobial, anti-biofilm, and anti-quorum sensing properties against various bacteria and fungi, along with moderate cytotoxic effects, indicating their medicinal and pharmaceutical potentials. This is the first study which revealed anti-biofilm and anti-quorum sensing properties of T. spicata essential oils to our knowledge.
Bioactive Compounds and Antioxidant Efficacy of Djulis (Chenopodium formosanum) Leaves: Implications for Sustainable Cosmeceutical Development
Authors: Chiu Chien-Shan , Chan Yung-Jia , Wu Yan-Zhen , Lu Wen-Chien , Chiang Po-Yuan , Li Po-Hsien
Published: February 2025

Chenopodium formosanum (djulis), well known for its vivid color variation during diverse senescence stages, has attracted attention for its perceived health benefits and antioxidant capacity. Djulis leaves, often discarded as biowaste after harvesting, were evaluated for their potential as a source of antioxidant compounds. The current study analyzes the physicochemical and antioxidant activities of red, green, and yellow djulis leaf extracts across various senescence stages to probe their prospective utility in cosmeceuticals. Various plant compounds including total carbohydrates, phenolics and flavonoids, chlorophyll a and b, and betaxanthins and betacyanins were measured using spectrophotometric techniques. Antioxidant potential was assessed using DPPH, FRAP, CUPRAC, TEAC, and DMPD assays. Green djulis leaves displayed elevated total carbohydrate and chlorophyll levels, whereas red djulis leaves exhibited heightened phenolic, flavonoid, betaxanthin, and betacyanin content, indicating its suitability for cosmeceutical applications. Antioxidant evaluations revealed substantial disparities among the extracts, with red djulis leaf extract demonstrating superior antioxidant activity in most assays. These findings revealed the distinct antioxidant profiles of djulis leaf extracts influenced by color and senescence stage. These findings advance our understanding of the bioactive attributes of djulis leaves and their potential for incorporation into functional products.
Origin and Variety Identification of Dried Kelp Based on Fluorescence Fingerprinting and Machine Learning Approaches
Authors: Suzuki Kana , Akiyama Rikuto , Llave Yvan , Matsumoto Takashi
Published: February 2025

Accurate labeling of the origin of food ingredients is essential to ensure quality and safety; however, establishing a reliable identification method remains an urgent task. The origin and variety of dried kelp are generally identified based on their morphological characteristics; however, they are difficult to distinguish unless experts are involved. In addition, genetically close varieties have almost no differences in their base sequences; therefore, the accuracy of conventional identification methods using genetic analysis is limited. This study aimed to develop a system for identifying the origin and variety of dried kelp using fluorescence fingerprint data obtained by fluorescence spectroscopy and a convolutional neural network (CNN). The fluorescence characteristics of dried kelp were measured in the range between 250 and 550 nm. The obtained fluorescence fingerprint data were converted into image data and analyzed using a CNN model implemented in Python, TensorFlow, and Keras. Unlike conventional methods that rely on morphological characteristics and genetic analyses, by combining fluorescence spectroscopy and CNN, a high identification accuracy of 98.86% was achieved even for genetically close varieties. These results highlight the excellent potential of fluorescent fingerprints in identifying the origin and variety of food and are believed to contribute to preventing food fraud and quality control.
Predominance Preferential Selection for Minimizing Surplus Parts in the Selective Assembly of a Flow Production System
Authors: Shin Kanghyeon , Son Hyobin , Jin Kyohong
Published: February 2025

This paper presents the Predominance Preferential Selection (PPS) algorithm, an advanced approach to optimizing selective assembly in flow production systems. PPS enhances matching efficiency and reduces surplus parts by prioritizing component selection based on dimensional similarity. Unlike the Density-Based Prioritization (DBP) approach, PPS improves process capability and computational efficiency while maintaining dimensional diversity within slot structures. By grouping components with similar dimensions, PPS minimizes search space, achieving a matching rate increase from 95.11% to 97.09% and reducing computational time by 83%. Validation with real-world process data from a precision bearing manufacturer confirmed a 40% reduction in surplus parts, demonstrating its effectiveness. Although a slight decrease in process capability index (Cpk) was observed, the benefits of improved matching rates and efficiency outweigh this drawback. PPS is highly scalable and adaptable to multi-slot row structures, making it suitable for various flow production systems. This study contributes to advancing selective assembly methodologies, offering a computationally efficient and adaptable solution for precision manufacturing.
A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks
Authors: Jing Tengyun , Liu Cuiyin , Chen Yuanshuai
Published: February 2025

In recent years, with the development of deep learning technologies, Vision Transformers combined with Convolutional Neural Networks (CNNs) have made significant progress in the field of single-image super-resolution (SISR). However, existing methods still face issues such as incomplete high-frequency information reconstruction, training instability caused by residual connections, and insufficient cross-window information exchange. To address these problems and better leverage both local and global information, this paper proposes a super-resolution reconstruction network based on the Parallel Connection of Convolution and Swin Transformer Block (PCCSTB) to model the local and global features of an image. Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. Additionally, this paper designs a fusion module to integrate the global and local information extracted by CNNs. The experimental results show that the proposed network effectively balances SR performance and network complexity, achieving good results in the lightweight SR domain. For instance, in the 4× super-resolution experiment on the Urban100 dataset, the network achieves an inference speed of 55 frames per second under the same device conditions, which is more than seven times as fast as the state-of-the-art network Shifted Window-based Image Restoration (SwinIR). Moreover, the network’s Peak Signal-to-Noise Ratio (PSNR) outperforms SwinIR by 0.29 dB at a 4× scale on the Set5 dataset, indicating that the network efficiently performs high-resolution image reconstruction.
Knowledge Integration in Steel Microstructure Analysis Using Unsupervised Image Segmentation and Supervised Machine Learning Techniques
Authors: Jażdżewski Tomasz , Hallo Filip , Korpała Grzegorz , Regulski Krzysztof
Published: February 2025

This study presents a methodology for analyzing bainitic microstructures in steel using image segmentation techniques and machine learning methods. Images of steel microstructures were processed using a superpixel segmentation algorithm, which generated image segments based on grayscale intensity. The histograms of values in grayscale of those segments served as input for classification models such as decision trees, random forests, and the KNN algorithm. Experimental results demonstrated that this method enables effective microstructure classification, including the identification of segments containing bainite. A comparison of algorithms revealed the superiority of random forests in terms of stability and accuracy. Results also show that small segments provide better final results. The obtained results indicate the potential for further development of this method using more advanced neural networks for automated steel microstructure analysis.
Risk Evaluation of Hazardous Chemical Road Transportation Accidents Based on a Combined Empowerment-Cloud Model
Authors: Xu Haoyu , Liu Yong , Cheng Zhihui , Xiang Qianqian , Wang Wenhe
Published: February 2025

To improve the accuracy of the risk evaluation results of hazardous chemical road transportation systems, an accident risk evaluation model based on the combined weight-cloud model is proposed to address the problems of ambiguity and randomness in the risk factors for hazardous chemical road transportation accidents. First, incorporating the risk factor identification results, the risk evaluation index system for hazardous chemical road transportation accidents is established. On the basis of determining the subjective and objective weights of the indices by using the analytic hierarchy process and entropy weight method, the comprehensive weights of the evaluation indices are calculated by the linear weighting method. Then, the weighting results are combined with the cloud model to calculate the risk level of the evaluation indices at all levels and to output the results in the form of a risk cloud diagram to visually determine the risk level of hazardous chemical road transportation. Finally, the model is applied to evaluate the accident risk in an example. The results demonstrate that the model is both practical and applicable in the evaluation of the risk of hazardous chemical road transportation accidents. The evaluation results can provide a valuable reference for hazardous chemical road transportation enterprises to prevent accidents.
A Novel Design of Dual-Band Circularly Polarized Microstrip Patch Antenna for Unmanned Aerial Vehicle Applications
Authors: Almehmadi Abdulaziz S. , Aldhaheri Rabah W.
Published: February 2025

This article presents a new design for a dual-band circular polarization microstrip patch antenna that can be used in unmanned aerial vehicle (UAV) applications. The proposed antenna consists of an etched circular shape on the radiator side of the antenna with multiple slots and stubs. The bottom side comprises a partial ground plane with multiple horizonal, vertical and square slots. These shapes on the front and bottom sides of the antenna are used to keep the resonant frequencies, impedance bandwidth and axial ratio (AR) at the desired values. The antenna operation is within the WiFi frequency bands, achieving maximum gains of 5.01 and 5.27 dBi at 2.4 and 5 GHz, respectively. Circular polarization (CP) is effectively realized through the implementation of opposite truncated corners and intentionally located stubs. The 3 dB axial ratio bandwidth (ARBW) is significantly enhanced, while a defected ground structure (DGS) is utilized to further improve the bandwidth and gain. The optimized antenna has overall dimensions of 40 × 40 × 1.6 mm3 and demonstrates a wide −10 dB reflection bandwidth of 5.38% (2.396–2.525 GHz) and 9.26% (4.91–5.38 GHz), along with a broad 3 dB axial ratio bandwidth (ARBW) of 380 MHz (2.29–2.67 GHz) and 80 MHz (5–5.08 GHz). The proposed antenna is fabricated using a low-cost FR-4 substrate with a dielectric constant of 4.4 and a loss tangent of 0.02. The fabricated antenna is experimentally characterized to verify the design concept as well as to validate the simulation results. It is found that the experimental measurements correlate very well with the simulation results. A comparison with comparable designs in the literature shows that the proposed antenna provides a higher gain with a relatively reduced size.
Virtual Cultural Experiences for the Elderly: Formative Studies and First Findings Regarding Cultural Content
Authors: Antoniou Angeliki , Vassilakis Costas , Kantianis Georgios , Sylaiou Stella , Kolokithas George
Published: February 2025

In societies with aging populations, the active participation of elders in all aspects of societal life is crucial. Cultural heritage provides a rich vehicle for engaging elders, stimulating both cognitive and affective responses, and keeping human brains active. This study focused on gathering requirements for creating content for virtual cultural experiences. Specifically, participants were shown various images and a film, and their self-reported thoughts and emotions were collected. The images were chosen by experts for the specific emotions they could trigger. The same images were analyzed with sentiment analysis software and finally given to the elders to express their emotions. The results of sentiment analysis, the analysis of experts, and the data from the participants were compared, showing differences between the perceived emotions. Regarding the historical film, the participants discussed the main emotions they experienced. The results were analyzed to extract guidelines for the content creation of VR applications.
Application of Long-Distance Drilling and Blasting Technology to Prevent Rock Bursts in High-Level Roofs
Authors: Gu Qianyue , Cao Anye , Zhao Weiwei , Yang Yao , Xue Chengchun , Hao Qi
Published: February 2025

In view of the high-level, thick, and hard roof in a mine in Shaanxi, it is difficult for existing technology to solve the problem of frequent rock bursts, which are caused by the direct weakening of the whole underground layer. In this paper, a technology for preventing rock bursts using the long-distance drilling and blasting of a thick and hard roof in a high drilling field is proposed. The authors used theoretical analyses, numerical simulations, and other research methods to analyze the mechanisms of pressure relief and load reduction achieved by this technology, determined its layout parameters and layers, and carried out engineering practices in 2412 working faces in a mine in Shaanxi. The results show that the long-distance drilling and blasting technology can achieve the aim of unloading the pressure drop load by arranging a high-level drilling field to achieve the whole-layer presplitting of the thick and hard roof above the working face. According to the orthogonal test method, when using long-distance drilling and blasting under the condition of a high-level roof, the choice of the blasting layer is the biggest factor affecting the change in overburden subsidence. Using the identification basis of the main control disaster causing the layer of overburden, it was determined that 52~67 m above the coal seam of the 2412 working faces was the blasting layer. According to the periodic weighting interval of the working face and the development radius of the fractures in the blasting surrounding rock, the blast hole spacing was determined to be 30 m. After long-distance drilling and blasting, the frequency and energy of micro seismic events were reduced, the entry deformation was reduced compared with the common roof deep-hole blasting technology, and the pressure relief effect of the long-distance drilling and blasting technology was better. These research conclusions can provide theoretical support for the prevention and control of rock bursts during mining production under similar conditions by reducing the load and the unloading pressure on thick and hard roof layers that are difficult to unload from the source.
Understanding the Unexplored: A Review on the Gap in Human Factors Characterization for Industry 5.0
Authors: Ricci Alessia , Ronca Vincenzo , Capotorto Rossella , Giorgi Andrea , Vozzi Alessia , Germano Daniele , Borghini Gianluca , Di Flumeri Gianluca , Babiloni Fabio , Aricò Pietro
Published: February 2025

The integration of neurophysiological techniques into Industry 5.0 represents a transformative approach to assessing human factors in real-world operational settings. This study presents a systematic review of existing literature to evaluate the application of neurophysiological methods in assessing cognitive and emotional states, such as workload, stress, attention, and trust, within industrial environments. A total of X peer-reviewed articles published between 2018 and 2024 were analyzed following a structured methodology. The findings reveal that EEG (45%), eye-tracking (30%), EDA (20%), and ECG (15%) are the most frequently adopted techniques for monitoring cognitive and emotional responses. Additionally, 60% of the studies focused on stress and workload assessment, while only 25% examined trust and collaboration in human–robot interaction, highlighting a gap in comprehensive teamwork analysis. Furthermore, only 35% of the studies validated their approaches in real-world industrial settings, emphasizing a significant limitation in ecological validity. The review also identifies that multimodal integration remains underexplored, with just 15% of studies combining multiple neurophysiological signals for a more holistic assessment. These results indicate a growing but still fragmented research landscape, with clear opportunities for expanding real-world applications, improving methodological standardization, and fostering interdisciplinary collaboration. Future research should prioritize validation in dynamic, real-life work environments and explore the synergistic potential of multimodal neurophysiological approaches to enhance human-centred industrial systems.
Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
Authors: Liakonis Vasileios , Papananos Yannis , Dielacher Franz , Wojnowski Maciej , Hartner Walter
Published: February 2025

In this paper, the performance, advantages and challenges of polymer microwave fibers (PMF) for sub-THz links are evaluated first. Then, a simple and elegant transceiver-integrated circuit (IC) and PMF wireless coupling scheme is presented. The proposed solution utilizes an advanced IC packaging technology to implement a Vivaldi antenna-in-package (AiP). The antenna is designed to provide lateral radiation and excellent directivity, so the proposed solution is very simple, compact, robust and cost-efficient: the IC readily connects to the PMF and the coupling is merely achieved by the packaged IC, without the need for any extra interface. The system operates at around 140 GHz, achieving a coupling loss of just 3.5 dB.
Numerical Simulation of Crack Propagation in Rocks with Random Pores Based on Improved Smoothed Particle Hydrodynamics Method
Authors: Sun Wei , Ren Xuhua , Zhang Jixun , Xu Hesi
Published: February 2025

This study aims to explore the influence of random pore characteristics inside rock mass on the fracture mechanical properties of rock under tensile stress. By means of numerical simulation based on the improved smoothed particle hydrodynamics (SPH) method, a specific kernel function approximate integral interpolation form and discrete particle superposition expression form are constructed to handle physical processes. The maximum tensile stress criterion and fracture marker ω are introduced to improve the traditional smooth kernel function for dealing with crack propagation. Meanwhile, the center and radius information of circular pores are generated using random numbers to create a rock model with random pores. The research results show that in terms of crack propagation morphology, as the pore percentage increases, the crack gradually changes from a straight propagation slightly disturbed by pores to an overall fragmentation propagation with frequent branching and coalescence; when the pore size increases, the crack propagation changes from a complex network-like shape frequently disturbed by small pores to a relatively simple through fracture controlled by key nodes of large pores. In terms of the stress–strain law, the increase in pore percentage leads to a decrease in the elastic modulus and peak strength of the rock and a weakened post-peak ductility; when the pore size increases, the elastic modulus first decreases and then increases, the peak strength changes similarly, and the post-peak characteristics change from complex fluctuations to a stable transition. The conclusion indicates that the pore percentage and size have a significant and complex influence on the mechanical properties of the rock.
The Role of Motion Capture Analysis in Evaluating Postoperative Functional Outcomes in Adolescent Idiopathic Scoliosis: A Systematic Review
Authors: De Salvatore Sergio , Brigato Paolo , Palombi Davide , Oggiano Leonardo , Sessa Sergio , Longo Umile Giuseppe , Costici Pier Francesco
Published: February 2025

Introduction: This systematic review evaluates the application of motion capture analysis (MCA) in assessing postoperative functional outcomes in adolescent idiopathic scoliosis (AIS) patients treated with spinal fusion. Material and Methods: A comprehensive search of PubMed, Scopus, Embase, and Cochrane Library was conducted for studies published between January 2013 and September 2024. Eligible studies included original research examining AIS patients’ post-spinal fusion, specifically assessing kinematic outcomes via MCA. Key outcomes included gait parameters, range of motion (ROM), and trunk–pelvic kinematics. Results: Nine studies comprising 216 participants (81.5% female), predominantly with Lenke 1 and 3 curve types. MCA revealed significant improvements in gait symmetry, stride length, and trunk–pelvic kinematics within one year of surgery. Enhanced mediolateral stability and normalized transverse plane motion were commonly observed. However, persistent reductions in thoracic–pelvic ROM and flexibility highlight postoperative limitations. Redistributing mechanical loads to adjacent unfused segments raises concerns about long-term compensatory mechanisms and risks for adjacent segment degeneration. Conclusions: While spinal fusion effectively restores coronal and sagittal alignment and improves functional mobility, limitations in ROM and dynamic adaptability necessitate targeted rehabilitation. Future research should standardize MCA methodologies and explore motion-preserving surgical techniques to address residual functional deficits.
An Improved Small Target Segmentation Model Based on Mask Dino
Authors: Yang Jun , Chen Xu , Guan Yun , Hu Yixuan , Ge Gang
Published: February 2025

To address the issue of low segmentation accuracy for small objects in the Mask Dino segmentation method, we propose an improved small object segmentation model called FFMask Dino. Initially, we introduce scaled cosine attention and the log-cpb method into the Swin Transformer backbone network. Subsequently, by adjusting the network structure, we enhance the feature extraction process, which helps the model maintain generalization across different datasets and reduces the risk of overfitting. Lastly, we propose the FFPN module to optimize the pathways for feature fusion and transmission. The improved FPN reduces unnecessary computations, accelerates model inference speed, and integrates multi-scale feature details and high-level semantic information to complement object features, thereby enhancing model segmentation accuracy. Experimental results demonstrate that the improved segmentation model achieves a mean Intersection over Union (mIoU) of 42.15% on the ADE20K dataset for semantic segmentation tasks, representing a 0.96% increase compared to the Mask Dino method. On the CoCo dataset for instance segmentation tasks, with the Swin Transformer backbone, the Mask AP and Box AP are 47.10 and 52.60, respectively, showing improvements of 1% and 1.3% over the Mask Dino method. With the ResNet-50 backbone, the Mask AP and Box AP are 40.00 and 44.10, respectively, with improvements of 0.5% and 0.9% over the Mask Dino method. For the CoCo dataset’s panoptic segmentation tasks, with the Swin Transformer backbone, the PQ is 54.95, showing a 0.4% increase over the Mask Dino method. With the ResNet-50 backbone, the PQ is 46.93, showing a 0.9% increase over the Mask Dino method. These results effectively demonstrate the improved accuracy and precision of Mask Dino in segmenting small objects across various segmentation tasks.
Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
Authors: Bu Fanqiang , Wei Wencong , Wang Xingguo , Li Xiaogao , Shen Guolang , Ma Chengwen , Tang Guoxing
Published: February 2025

In this study, a detection method utilizing nonlinear ultrasonic guided waves is presented to tackle the difficulties in detecting localized damage and weakening in bonded composite structures. For a three-layer structure made of polystyrene, acrylic resin, and aluminum plate, dispersion equations for ultrasonic guided waves were developed using the spring model and wave equation. The A1-S1 mode was selected by examining the material parameters’ influence on the adhesive layer’s dispersion curves. The finite element method was employed to simulate the propagation characteristics of ultrasonic guided waves within the composite structure. The error between the theoretically calculated and simulated group velocities was less than 5.15%. As the propagation distance increased, both the nonlinearity coefficient and the amplitude of the second-order harmonic showed an upward trend. This indicates a significant accumulation effect at the second harmonic of nonlinear guided waves. Compared to without adhesive layer weakening, localized and overall weakening resulted in higher amplitudes of the second-order harmonic. Experimental testing of ultrasonic guided waves was conducted to investigate the nonlinear properties of the composite structure. The error between the experimentally measured and theoretically calculated group velocities was less than 6.96%. The experimental results corroborated the propagation accumulation effect of the second-order harmonic amplitude.
Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
Authors: Xiao Yonghong , Li Guolong , Wei Lu , Ding Jing , Zhang Zhen
Published: February 2025

As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method’s adaptability across various regions.
Handgrip Strength as an Indicator of Overall Strength and Functional Performance—Systematic Review
Authors: Szaflik Piotr , Zadoń Hanna , Michnik Robert , Nowakowska-Lipiec Katarzyna
Published: February 2025

Reduced muscle strength can affect daily activities, so monitoring is essential. Methods such as isokinetic dynamometers assess maximum muscle force, while hand dynamometers measure hand grip strength. Research has investigated hand grip strength as a predictor of upper, lower and total strength, but the results remain unsystematic. The aim of this study was to systematize the current state of knowledge to test whether hand grip strength can be used as an indicator of overall strength and functional performance. A review of studies from 2000 to 2023 was conducted using: ScienceDirect, PubMed and GoogleScholar. The focus was on articles that examined the relationship between hand grip strength and muscle torque. The following keywords, entered in different configurations, were used in the search: ’hand grip’, ’muscle strength’, ’correlation’ and ’association’. A total of 22 studies were selected for analysis, involving 752 healthy adults, 30 physically active people, 120 ill adults, 2965 older people and 112 ill older people. The analysis looked at associations between hand grip strength and muscle moments of the trunk, limbs and knee joint. The results from the literature indicate that the value of the correlation between hand grip strength and muscle torque varies (from low to high) depending on the test method or group. Therefore, caution should be exercised when using hand grip strength as the sole measure of overall strength and functional performance.
Long-Term Quantitative Analysis of the Temperature Vegetation Dryness Index to Assess Mining Impacts on Surface Soil Moisture: A Case Study of an Open-Pit Mine in Arid and Semiarid China
Authors: Liu Bin , Liu Xinhua , Wan Huawei , Ma Yan , Lu Longhui
Published: February 2025

High-intensity coal mining significantly impacts the surrounding soil moisture (SM) through water seepage, artificial watering for dust suppression, and geomorphological changes, which will lead to ecological degradation. This study explores the impact of open-pit mines on surface SM in an arid–semiarid open-pit mine area of China over the period from 2000 to 2021. Using the temperature vegetation dryness index (TVDI), derived from the Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) feature space, this paper proposes a method—the TVDI of climate factor separation (TVDI-CFS)—to disentangle the influence of climate factors. The approach employs the Geographically and Temporally Weighted Regression (GTWR) model to isolate the influence of temperature and precipitation, allowing for a precise quantification of mining-induced disturbances. Additional techniques, such as buffer analysis and the Dynamic Time Warping (DTW) algorithm, are used to examine spatiotemporal variations and identify disturbance years. The results indicate that mining impacts on surface SM vary spatially, with disturbance distances of 420–660 m and strong distance decay patterns. Mining expansion has increased disturbance ranges and intensified cumulative effects. Inter-annual TVDI trends from 2015 to 2021 reveal clustered disturbances in alignment with mining directions, with the largest affected area in 2016. These findings provide a systematic valuable insights for ecological restoration and sustainable environmental management in mining-affected areas.
Harnessing Spatial-Frequency Information for Enhanced Image Restoration
Authors: Park Cheol-Hoon , Choi Hyun-Duck , Lim Myo-Taeg
Published: February 2025

Image restoration aims to recover high-quality, clear images from those that have suffered visibility loss due to various types of degradation. Numerous deep learning-based approaches for image restoration have shown substantial improvements. However, there are two notable limitations: (a) Despite substantial spectral mismatches in the frequency domain between clean and degraded images, only a few approaches leverage information from the frequency domain. (b) Variants of attention mechanisms have been proposed for high-resolution images in low-level vision tasks, but these methods still require inherently high computational costs. To address these issues, we propose a Frequency-Aware Network (FreANet) for image restoration, which consists of two simple yet effective modules. We utilize a multi-branch/domain module that integrates latent features from the frequency and spatial domains using the discrete Fourier transform (DFT) and complex convolutional neural networks. Furthermore, we introduce a multi-scale pooling attention mechanism that employs average pooling along the row and column axes. We conducted extensive experiments on image restoration tasks, including defocus deblurring, motion deblurring, dehazing, and low-light enhancement. The proposed FreANet demonstrates remarkable results compared to previous approaches to these tasks.
Dynamic Modeling and Analysis of a Flying–Walking Power Transmission Line Inspection Robot Landing on Power Transmission Line Using the ANCF Method
Authors: Jia Wenxing , Lei Jin , Qin Xinyan , Jin Peng , Zhang Shenting , Tao Jiali , Zhao Minyu
Published: February 2025

To enhance the safety of hybrid inspection robots (HIRs) landing on power transmission lines (PTLs) with inclination and flexibility, this research derives a coupled dynamic model for a developed flying–walking power transmission line inspection robot (FPTLIR) to analyze the dynamic behavior of the FPTLIR during the landing process. The model uses the absolute nodal coordinate formulation (ANCF) for the dynamics of the PTL and the Hunt–Crossley theory for the contact model, integrating these components with the Euler–Lagrange method. A modular simulation was conducted to evaluate the effects of different landing positions and robot masses. An experimental platform was designed to evaluate the landing performance and validate the model, which confirms the method’s accuracy, with a mean relative Z-displacement error of 0.004. Simulation results indicate that Z-displacement decreases with increased landing distance, with the farthest point showing only 34.4% of the Z-displacement observed at the closest point. Conversely, roll increases, with the closest point exhibiting 3.7% of the roll at the farthest point. Both Z-displacement and roll are directly correlated with the robot’s mass; the lightest robot’s Z-displacement and roll are 9.2% and 12.8% of those of the heaviest robot, highlighting the safety advantage of lighter robots. This research enables precise analysis and prediction of the system’s responses during the landing process, providing significant insights for safe landing and design.
An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning
Authors: Alnami Hani , Mohzary Muhammad , Assiri Basem , Zangoti Hussein
Published: February 2025

The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection.
Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
Authors: Kim Hyeong Jin , Cho Jin-Rae
Published: February 2025

The rapid growth of wind energy has necessitated the development of advanced materials to address the increasing structural demands of wind turbine blades. Graphene platelets (GPLs) have garnered attention as a promising reinforcement material due to their outstanding mechanical properties, such as high strength and low density. This study investigates the fatigue life of wind turbine blades reinforced with GPLs, benchmarking their performance against conventional fiberglass blades. A finite element model of a 5 MW wind turbine blade was developed to evaluate stresses within the blade structure. The traditional fiberglass blade was modeled based on the SNL 61.5 m design by Sandia National Laboratories, while the GPL-reinforced composite (GPLRC) blade was designed by substituting fiberglass with GPLRCs. Material properties of the GPLRCs were determined using the rule of mixtures and the Halpin–Tsai micromechanics model. Wind speed data were randomly sampled following the probability distribution observed at European wind farms, and corresponding aerodynamic loads were computed using blade element momentum theory. Finite element analyses were performed to derive stress time histories, and fatigue life was predicted using the S-N curve approach, incorporating the Goodman diagram and the Palmgren–Miner rule. The results reveal that while GPLRC-reinforced blades exhibit some limitations in fatigue performance compared to traditional fiberglass blades, potential solutions for improving their durability are proposed, highlighting avenues for further research and optimization in the application of GPLRCs to wind turbine blades.
Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications
Authors: Tsangaratos Paraskevas , Ilia Ioanna , Spanoudakis Nikolaos , Karageorgiou Georgios , Perraki Maria
Published: February 2025

The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three CNN architectures—VGG-16, Xception, and MobileNet V2—designed to identify multiple minerals within a single frame and output probabilities for various mineral types, including Pyrite, Aragonite, Quartz, Obsidian, Gypsum, Azurite, and Hematite. Among these, MobileNet V2 demonstrated exceptional performance, achieving the highest accuracy (98.98%) and the lowest loss (0.0202), while Xception and VGG-16 also performed competitively, excelling in feature extraction and detailed analyses, respectively. Gradient-weighted Class Activation Mapping visualizations illustrated the models’ ability to capture distinctive mineral features, enhancing interpretability. Furthermore, a stacking ensemble approach achieved an impressive accuracy of 99.71%, effectively leveraging the complementary strengths of individual models. Despite its robust performance, the ensemble method poses computational challenges, particularly for real-time applications on resource-constrained devices. The application of this methodology in Mineral Quest, an educational Python-based game, underscores its practical potential in geology education, mining, and geological surveys, offering an engaging and accurate tool for real-time mineral classification.