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An innovative approach to classify meniscus tears by reducing vision transformers features with elasticnet approach

Journal of Supercomputing 2025年第4期81卷 1-29页

作者： Genç, Hasan Koç, Canan Yüzgeç Özdemir, Esra Özyurt, Fatih Elazig Fethi Sekin City Hospital Elazig Turkey Faculty of Engineer Software Engineering Firat University Elazig Turkey Faculty of Engineering and Architecture Computer Engineering Bingöl University Elazig Turkey

Meniscal tears, a prevalent orthopedic condition caused by abrupt knee movements, excessive load, or injury, require an accurate diagnosis for effective treatment. This study investigates the vision transformer (ViT) models' efficacy in automated classification of meniscus pathologies. It also explores how feature reduction using the ElasticNet method can improve classification accuracy and computational efficiency. The study utilized MRI scans from a dataset comprising 5000 images collected from clinical cases. Initially, classification was performed using EfficientNet and SqueezeNet architectures. Subsequently, feature extraction was conducted using ViT models, generating a feature set of 1000 dimensions. ElasticNet was employed to reduce features before reclassification using support vector machines (SVM). Model performance was evaluated based on accuracy, precision, sensitivity, and specificity. The ViT_base_32 model achieved a classification accuracy of 99.9% with a processing time of 1.2 s. Feature reduction via ElasticNet significantly enhanced classification performance while maintaining high precision, sensitivity, and specificity. These improvements demonstrate the effectiveness of combining ViT models with ElasticNet to diagnose meniscal tears. The findings highlight the potential of vision transformer models, in conjunction with ElasticNet, to provide rapid and highly accurate diagnostic assistance for meniscal injuries. This methodology shows promise for application to other medical diagnostic domains, offering valuable advancements in healthcare technology. © The Author(s) 2025.

关键词： Medical imaging

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Understanding the effect of cathodic reaction on corrosion-enhanced erosion in sand-entraining electrolyte

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材料科学技术（英文版） 2025年第6期209卷 103-116页

作者： Qiliang Zhang Long Hao Wanbin Chen Yi Huang Yunze Xu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian 116024China State Key Laboratory for Corrosion and Protection Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China

In this study,the pure erosion behaviour of pure iron and its erosion-corrosion behaviour under different anodic polarization currents were investigated in various cathodic reactions(oxygen reduction,hydrogen ion reduction,and water reduction)using a cylindrical stirring *** corrosion-enhanced erosion(C-E)rates were determined for each *** results revealed that pure iron displayed similar pure erosion behaviour across all three cathodic *** the cathodic reactions involve hydrogen ion reduction or water reduction,the erosion-corrosion of pure iron manifested as uniform damage,with the reduction in hardness being the main cause of the C-E in this ***,in the case of oxy-gen reduction reaction as the cathodic reaction,the erosion-corrosion presented as pitting damage,with the reduction in hardness resulting from localized concentration of anodic current and the formation of easily worn protruding flaky iron structures at the edges of the pits as the main mechanism of the ***,linear and exponential relationships were found between the C-E rate and the anodic current density for uniform damage and pitting damage,***,the concept of surface equivalent hardness was proposed,along with the establishment of a mathematical model for surface equivalent hardness based on the relationships between the C-E rate and the anodic current *** the surface equivalent hardness enables the evaluation of the erosion rate on material surfaces considering the coupled effect.

关键词： Corrosion-enhanced erosion Cathodic reaction Pitting damage Uniform damage Hardness

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Understanding and probing progression of localized corrosion on inner walls of steel pipelines:an overview

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Journal of Iron and Steel Research International 2025年第1期32卷 1-18页

作者： Ming-yu Wang Hai-yuan Yao Yong-fei Liu Ye-sen Zhu Wan-bin Chen Yun-ze Xu Yi Huang School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian 116024LiaoningChina China National Offshore Oil Corporation Research Institute Co. Ltd.Beijing 100028China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024LiaoningChina

Corrosion poses a major threat to the safety of transportation ***,it is crucial to have an in-depth understanding of corrosion mechanisms in pipeline steels for the effective management of pipeline *** research on corrosion mechanisms relies on the use of efficient and reliable corrosion monitoring and analysis *** advancements in corrosion monitoring techniques specifically designed for the localized corrosion monitoring were aimed to be introduced,and a comprehensive overview of recent progress in understanding the localized corrosion mechanisms in pipeline steels was *** on the different corrosive environments encountered,the localized corrosion issues inside pipelines are classified into two categories:localized corrosion primarily influenced by electrochemical processes and localized corrosion controlled by both electrochemical and mechanical ***,a thorough analysis of the synergistic effects between micro-cell and macro-cell currents,as well as the interplay of mechanics and electrochemistry is ***,recommendations for future research on the mechanisms of internal localized corrosion in pipelines are provided.

关键词： Pipeline steel Localized corrosion Corrosion type Corrosion monitoring Corrosion mechanism

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CNN-based cancer prediction scheme using 5G-assisted federated learning for healthcare Industry 5.0

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Alexandria engineering Journal 2025年 126卷 131-142页

作者： Kumari, Aparna Patadia, Divya Tanwar, Sudeep Pau, Giovanni Alqahtani, Fayez Tolba, Amr Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad382481 India Faculty of Engineering and Architecture Kore University of Enna Enna94100 Italy Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia

Healthcare Industry 5.0 has witnessed a transformative change by incorporating Artificial Intelligence (AI), Internet-of-things (IoT), and genomics to provide individualized and patient-centered healthcare. It prioritizes comprehensive health, proactive strategies for chronic diseases like tumors, cancer, etc., and effective cooperation among participants. Much work has been done for cancer prediction using machine learning and deep learning approaches. However, it has yet to be fully explored to protect data privacy and security by limiting data sharing. So, this paper proposes a Convolutional Neural Networks (CNN)-based Cancer Prediction scheme, i.e., CNN-CPS within the context of Healthcare Industry 5.0. The proposed scheme harnesses the capabilities of 5G-assisted Federated Learning (FL), a revolutionary method for predicting cancer occurrences with data privacy, security, and limiting data sharing. Then, it also leverages model data from dispersed systems to boost efficiency. By capitalizing on the speed and efficiency of 5G networks and the collaborative nature of FL, precise cancer predictions can be attained, all while upholding the confidentiality of sensitive data. This integration of technologies has the potential to significantly reshape cancer diagnosis and therapeutic approaches in the Industry 5.0 landscape. Here, 5G can enable real-time data interchange across remote data centers with rapid transmission of data capabilities and low-latency connectivity, enabling more effective and precise model training. Hence, this study examines the FL to predict breast cancer and combines the optimizer weights from several clients to continually enhance the performance of the overall model. The experimental results show the efficacy of the CNN-CPS compared to the existing approaches based on several parameters like low latency (40.77% improvement), accuracy (75%), and loss (0.54). © 2025 The Authors

关键词： Federated learning

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Effects of water and nitrogen coupling on the plant growth and quality of greenhouse tomatoes at the first flowering and fruiting stages

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International Journal of Agricultural and Biological engineering 2025年第1期18卷 181-190页

作者： Danyan Chen Hailing Li Xinyi Wang Jingze Ma Shilong Li Xiaorui Jiang Aijun Dai Hao Yu Yingying Zhang Ya Liu College of Horticulture Jinling Institute of TechnologyNanjing 210038China College of Forestry Nanjing Forestry UniversityNanjing 210037China College of Horticulture and Landscape Architecture Yangzhou UniversityYangzhou 225009JiangsuChina College of Resources and Environment Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Software Engineering Jinling Institute of TechnologyNanjing 211169China

Good growth of tomato at the early growing stages is the key to final yield formation,for which water(W)and nitrogen(N)applications are two necessary *** this study,two irrigating systems(W1,W2)and three N applications(N1,N2,N3)were interacted(W×N)to plant the cherry tomato variety‘‘Jinling Meiyu’’in greenhouse.W1(reduced irrigation)and W2(normal irrigation)had a 7:9 irrigated ratio based on former research.N1,N2,and N3 were set at 100%,80%,and 60%normal N application,*** tomato plant height(PH),stem circumference(SC),number of leaves(NL),number of first order fruits(NF),the single fruit weight(SFW),contents of fruit Vitamin C(VC)and soluble sugar(SS),fresh weights of root(RW),leaf(LW),and plant stem(PSW),as well as leaf chlorophyll fluorescence value(SPAD),temperature(T),humidity(RH),and nitrogen content(N)were investigated at the first flowering and fruiting *** results showed that W×N had significant impacts on early plant growth and fruit quality of tomato.W2N2 obviously received the largest values of tomato PH(152.5 cm),SC(4.1 cm),NF(11 fruits/plant),and LW(45.0 g/plant),but obtained the lowest VC(9.71 mg/kg)and SS(2.40%).However,W1N3 had the largest values of leaf RH(56.9%),N contents(14.23 mg/g),and VC(16.29 mg/kg),with NF also at 11.0 fruits/plant.W2N1 significantly had the highest RW(14.4 g/plant),PSW(71.8 g/plant),and SFW(21.3 g/fruit).W2N3,W1N1,and W1N2 obtained the most NL(103.7 pieces/plant),SS(4.06%),and leaf SPAD(36.85),*** correlation analysis results showed PH negatively significantly correlated with NF(p<0.05).The leaf SPAD positively significantly correlated with PH(p<0.05)and RH(p<0.01),but negatively significantly correlated with SC(p<0.05)and T(p<0.01).Moreover,leaf N content also had a positive significant correlation with PH(p<0.05),and an extremely positive significant correlation with RH and SPAD(p<0.01).However,it negatively significantly correlated with SC(p<0.01)and T(p<0.05).Significantly,VC ha

关键词： greenhouse tomato water and nitrogen interaction leaf growth SPAD value nitrogen

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Energy-efficient modeling in WSN-assisted IoT based on software defined network

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Computing 2025年第6期107卷 1-35页

作者： Hajian, Elham Khayyambashi, Mohammad Reza Movahhedinia, Naser Computer Engineering Department Faculty of Engineering University of Bojnord Bojnord Iran Department of Computer Architecture Faculty of Computer Engineering University of Isfahan Isfahan Iran Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

Consolidating the Internet of Things (IoT) and software Defined Networks (SDN) has been a great concern among researchers. In IoT, Wireless Sensor Network (WSN) is important communication component. Due to the large volume of data generated in IoT and the limitations of WSN, load distribution in these networks is a serious challenge. The Base Station (BS) in these networks may experience a lot of delay due to high processing. Also, data of applications in these networks must inform to users with the lowest delay. Therefore, in addition to load distribution, reducing response time is also one of the factors that must be considered. Therefore, load distribution among BS’s seems to be crucial Considering several BSs and their relationships with the network nodes and preventing them from overloading through load distribution may solve the problem. This can be implemented by applying the common nodes that belong to several BSs. That is, to reduce the load of the overloaded BS, the common node in the cluster corresponding to BS and the other BSs is selected to send common node load to the other BSs. The common node is selected through the load-balancing node-finder algorithm. Load transfer is done through the forwarding node, which is specified in the proposed routing process. The Colored Petri Nets (CPN) s are applied to implement the proposed method. Here, queue length, residual energy, nodes, BSs, and delay time are simulated in three scenarios. The results show that most of the nodes are applied in the proposed algorithm to implement load balancing between the nodes and BSs. The results show the proposed SDN-based algorithm reduces the residual energy to 18%, the queue length to 9.5%, and the delay to 35%.

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Short-term offshore wind speed forecasting approach based on multi-stage decomposition and deep residual network with self-attention

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engineering Applications of Artificial Intelligence 2025年 146卷

作者： Acikgoz, Hakan Korkmaz, Deniz Department of Software Engineering Faculty of Engineering Architecture and Design Kahramanmaras Istiklal University Kahramanmaras46050 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and Natural Sciences Malatya Turgut Ozal University Malatya44210 Turkey

Wind energy is one of the widely used renewable energy systems. Wind speed forecasting is used to produce of wind energy and to ensure the sustainability of the power system. However, offshore wind speed forecasting is a challenging task with complex variables and highly nonlinear temporal dynamics of the ocean. This paper proposes a hybrid and robust offshore wind speed forecasting approach based on multi-stage decomposition, deep convolutional neural network (CNN), and extreme learning machine (ELM). Unlike conventional preprocessing for forecasting of renewable energy problems, the proposed approach combines two efficient decomposition methods as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and ensemble empirical mode decomposition (EEMD). This method can decompose high-frequency and low-frequency components of the wind speed. While high-frequency components are decomposed with the EEMD, low-frequency components are directly sent to the ELM model. The obtained mode functions from the EEMD are then fed to the designed network for forecasting. The CNN model is constructed with the deep residual network and self-attention (SA) mechanism to improve the network performance. In the comparative evaluations, while other approaches give lower forecasting performance between 0.8233 and 2.1885 for the root mean square error (RMSE), the proposed method presents the lowest RMSE value as 0.5400. The experimental results show that the proposed method exhibits more accurate and robust forecasting performance compared with other model combinations and deep learning models. © 2025 Elsevier Ltd

关键词： Wind forecasting

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Time Domain Analysis for Motions of Ships With the Effects of Steady Flow Using a Rankine Panel Method

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Journal of Offshore Mechanics and Arctic engineering 2025年第4期147卷 041203页

作者： Zhou, Bo Wu, Zhifei Wang, Yu Han, Xiaoshuang Zhang, Teng Zhang, Guiyong State Key Lab of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian 116024 China Marine Engineering College Dalian Maritime University Dalian 116026 China

A novel numerical approach, leveraging the 3D time domain Rankine panel method, has been introduced to simulate ship motions while accounting for the effects of ship wave. This method treats ship wave as steady flow, enabling a more accurate representation of physical phenomena. The underlying numerical formulation used to solve the ship wave potential is comprehensively derived, establishing the unsteady disturbance potential's boundary value problem and incorporating the effects of steady motion. Wave forces and motion responses of various ship forms were computed and compared with experimental data and results from other numerical techniques. The findings reveal that calculations based on uniform stream exhibit significantly larger errors than those considering double body flow or ship wave. For the Wigley I and S175 models, the discrepancies between double body flow and ship wave predictions are relatively minor, with ship wave calculations demonstrating slightly higher accuracy. In contrast, for the intricate KRISO Container Ship (KCS) model, where ship wave is more pronounced, the error margin for ship wave calculations relative to experimental results remains within 21.9%, while errors for double body flow calculations can reach up to 50.2%. This highlights the superior accuracy of ship wave-based calculations for the KCS model. Overall, the proposed method effectively captures the influence of steady flow and demonstrates significant advantages in computing pronounced ship wave generated during realistic ship motion.

关键词： Ships ship motion Rankine panel method Waves Flow (Dynamics) hydrodynamics wave mechanics and wave effects computational fluid dynamics ship wave Boundary-value problems steady flow

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Enhancing Elderly Care with Socially Assistive Robots: A Holistic Framework for Mobility, Interaction, and Well-Being

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IEEE Access 2025年 13卷 82698-82717页

作者： Naseer, Fawad Khan, Muhammad Nasir Tahir, Muhammad Addas, Abdullah Kashif, Hasnain Beaconhouse International College Computer Science and Software Engineering Department Faisalabad 38000 Pakistan Government College University Lahore Electrical Engineering Department Lahore Pakistan Sir Syed University of Engineering and Technology Department of Software Engineering Karachi Pakistan Prince Sattam Bin Abdulaziz University College of Engineering Department of Civil Engineering Alkharj 11942 Saudi Arabia Faculty of Architecture and Planning King Abdulaziz University Landscape Architecture Department P.O. Box 8 0210 Jeddah 21589 Saudi Arabia University of South Asia Department of Computer Science Lahore Pakistan

The global elderly population is projected to double by 2050, creating challenges in mobility, social isolation, and cognitive decline. Socially Assistive Robots (SARs) offer a promising solution, yet gaps remain in integrating mobility assistance with cognitive and emotional support. The research developed a comprehensive framework for SARs, emphasizing user-centered design, technological innovation, and cultural adaptability. A mixed-methods approach was employed with 200 participants aged 60+, from diverse settings, including rehab centers, retired groups, old age communities and nursing homes. Results demonstrated a 33.3% improvement in mobility scores and a 40% increase in social engagement. Health metrics revealed a 22% reduction in falls, a 15% rise in physical activity, and a 90% user satisfaction rate. Gender and age differences were evident, with males aged 71+ showing higher mobility improvements (35.6%) and females aged 71+ reporting the highest satisfaction (93%). Long-term data indicated sustained benefits, with participants retaining 80% of mobility gains and social engagement levels 38% above baseline after six months. These findings highlight the importance of culturally sensitive and user-centered SAR designs in promoting elderly well-being. The research offers a robust foundation for integrating SARs into elderly care systems, emphasizing adaptability, sustainability, and ethical considerations for future implementation. © 2013 IEEE.

关键词： Cognitive support elderly care human-robot interaction (HRI) mobility enhancement quality of life socially assistive robots (SARs) user-centered design

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