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Three-dimensional surface topography analysis of die-sinking electrical discharge machined eco-friendly beryllium-free copper alloys

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International Journal of Advanced Manufacturing technology 2025年第7期138卷 2911-2953页

作者： Vijayan, Vipin Saju, Kaiprappady Kunchu Sundaresan, Jithin Division of Mechanical Engineering School of Engineering Cochin University of Science and Technology Thrikkakkara Kerala Kochi682022 India Department of Mechanical Engineering Muthoot Institute of Technology and Science Varikoli Kerala Ernakulam682308 India Business Information Systems Program Information Systems Department School of Computing and Creative Arts Bina Nusantara University Senayan DKI Jakarta Jakarta10270 Indonesia

Beryllium-copper alloys, the most widely used copper alloys, are utilised extensively in diverse sectors, including the electrical, electronics, instrumentation, metallurgy, aerospace, automotive, petrochemical, machinery manufacturing, and die and mould-making industries. The main drawback of these alloys is that they produce the toxic component, beryllium oxide, which can lead to a chronic lung disease known as berylliosis. Beryllium-free copper alloys, such as copper-nickel-silicon-chromium, are eco-friendly, less costly, and possess properties similar to those of beryllium-copper alloys. Hence, they are now replacing beryllium-copper alloys in the applications mentioned earlier. Due to their high strength and hardness, these alloys are often fabricated into components using unconventional machining techniques, such as electrical discharge machining. Electrical discharge machining is particularly advantageous in industrial applications where precisely controlled random surface textures on these alloys are required. However, despite the industrial significance, research on the electrical discharge machining process of copper-nickel-silicon-chromium alloys is scarce. Therefore, the current work aims to address this research gap by conducting an experimental investigation of the random surfaces generated on copper-nickel-silicon-chromium alloy components after the die-sinking electrical discharge machining process through a comprehensive three-dimensional surface topography analysis. Three-dimensional surface topography parameters overcome the drawbacks of two-dimensional roughness parameters by considering the majority of surface points. This work investigates the effects of input factors, including electrode material, dielectric fluid material, flushing condition, and current on nearly all relevant areal texture (3D) parameters. ANOVA is performed to study the level of significance of each input parameter. The regression analyses reveal that current is the most si

关键词： Beryllium powder metallurgy

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Modelling Population-Level Hes1 Dynamics: Insights from a Multi-framework Approach

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Bulletin of Mathematical Biology 2025年第6期87卷 1-31页

作者： Menz, Gesina Engblom, Stefan Division of Scientific Computing Department of Information Technology Uppsala University Uppsala 751 05 Sweden Science for Life Laboratory Department of Information Technology Uppsala University Uppsala Sweden

Mathematical models of living cells have been successively refined with advancements in experimental techniques. A main concern is striking a balance between modelling power and the tractability of the associated mathematical analysis. In this work we model the dynamics for the transcription factor Hairy and enhancer of split-1 (Hes1), whose expression oscillates during neural development, and which critically enables stable fate decision in the embryonic brain. We design, parametrise, and analyse a detailed spatial model using ordinary differential equations (ODEs) over a grid capturing both transient oscillatory behaviour and fate decision on a population-level. We also investigate the relationship between this ODE model and a more realistic grid-based model involving intrinsic noise using mostly directly biologically motivated parameters. While we focus specifically on Hes1 in neural development, the approach of linking deterministic and stochastic grid-based models shows promise in modelling various biological processes taking place in a cell population. In this context, our work stresses the importance of the interpretability of complex computational models into a framework which is amenable to mathematical analysis. © The Author(s) 2025.

关键词： 34C60 34F10 60J20 92B25 92C15 Cellular synchronisation Fate decision Genetic oscillator Neurogenesis Pattern formation Primary: 92-10 Secondary: 34A33

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Bioconvection Cross nanofluid flow with Cattaneo-Christov heat and mass fluxes past plate, wedge and cone

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Multiscale and Multidisciplinary Modeling, Experiments and Design 2025年第6期8卷 1-20页

作者： Shah, Muhammad Zameer Khan, Noor Saeed Khan, Waris Boukhris, Imed Kouki, Marouan Agha, Afnan Al Garalleh, Hakim Al Al-Buriahi, M.S. Division of Science and Technology Department of Mathematics University of Education Lahore54000 Pakistan Department of Mathematics and Statistics Hazara University Mansehra Pakistan Central Labs King Khalid University Abha AlQura’aP.O. Box 960 Saudi Arabia Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department of Physics Sakarya University Sakarya Turkey

This study examines the bioconvection flow of a Cross nanofluid containing gyrotactic microorganisms over three geometrical configurations: a flat plate, a wedge, and a cone. The significance of this research lies in its incorporation of the Cattaneo-Christov heat and mass flux theory, which accounts for thermal relaxation effects-critical in optimizing heat and mass transfer processes in bioengineering and industrial applications. The study aims to analyze the combined effects of radiative heat transfer, thermophoresis, heat generation/absorption, magnetic field strength and other embedded parameters on fluid behavior. The research employs the Homotopy Analysis Method (HAM) to obtain analytical solution for velocity, temperature, nanoparticle concentration, and gyrotactic microorganism density distribution. The key findings indicate that heat transfer is most efficient over the cone, whereas the wedge exhibits the highest mass transfer rate and microbial concentration. The study further highlights the influence of governing parameters such as buoyancy ratio, relaxation time, and external forces on fluid behavior. These results contribute to a deeper understanding of nanofluid dynamics in biomedical and industrial applications, including microbial-enhanced oil recovery and nanofluid-based drug delivery systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： SARS

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Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models

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IEEE Access 2025年 13卷 71503-71520页

作者： Alshraideh, Mohammad Al-Dhaqm, Arafat Alshammari, Ahmad Alshraideh, Abedalrahman Alshraideh, Bahaaldeen Mohamed Al-Fayoumi, Bayan Nasser, Maged Artificial Intelligence Department University of Jordan Amman11942 Jordan Lusail University Information Technology College Lusail Qatar Subang Jaya47500 Malaysia Northern Border University Faculty of Computing and Information Technology Department of Computer Sciences Rafha91911 Saudi Arabia East Midlands Deanery NHS England NottinghamDE74 2SA United Kingdom Division of Urology Department of Special Surgery University of Jordan Amman Jordan Universiti Teknologi PETRONAS Computer and Information Sciences Department Seri Iskandar32610 Malaysia

Recurrent fragility fractures are a big challenge in managing bone health, especially in older adults and people with osteoporosis or related disorders. The prediction of such fractures depends on the overall understanding of various clinical and lifestyle factors contributing to bone fragility. The goal of this analysis is to carry out advanced predictive analytics related to the problem of recurrent fragility fractures concerning several key features about each patient: age, sex, body mass index, physical activities, smoking status, and several others: T-score, along with biomarkers such as Vitamin D3, calcium levels, and the rest. Indeed, the results show that the features most indicative of recurrent fractures include age, T-score, physical activity, and glucocorticoid usage;greater emphasis has been laid on bone mineral density and lifestyle factors. The detailed feature importance analysis showed that age and T-score have the highest important values for fracture recurrence prediction, followed by physical activity and glucocorticoid treatment. The study finds that the proposed models achieved an accuracy of 91.3% (LLaMA 7GB) and 90.2% (Gamma 7GB), with ROC AUC scores of 93% and 92%, respectively. The study highlights that age, T-score, and glucocorticoid usage were the most significant predictive factors. These findings enable improved clinical decision-making for fracture prevention. This model can facilitate the identification of high-risk patients with recurrent fragility fractures by clinicians, thus allowing for targeted intervention and personalized treatment to reduce the risk of future fractures. The findings contribute to the development of fracture prediction, incorporating a wide array of clinical data that, in turn, will improve patient outcomes and their quality of life. © 2013 IEEE.

关键词： Predictive analytics

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models

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IEEE Transactions on Image Processing 2025年 34卷 2463-2472页

作者： Aung, Aye Phyu Phyu Wang, Xinrun Wang, Ruiyu Chan, Hau An, Bo Li, Xiaoli Senthilnath, J. Singapore138632 Singapore Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore KTH Royal Institute of Technology Division of Robotics Perception and Learning Stockholm100 44 Sweden University of Nebraska-Lincoln School of Computing LincolnNE68588 United States Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore A STAR Centre for Frontier AI Research Singapore138632 Singapore

In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. The same concept can be applied to AT with attacker and classifier as players. Training these models is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as training algorithms for both GAN and AT have a large-scale strategy space. Extending our preliminary model DO-GAN, we propose the methods to apply the double oracle framework concept to Adversarial Neural Architecture Search (NAS for GAN) and Adversarial Training (NAS for AT) algorithms. We first generalize the players' strategies as the trained models of generator and discriminator from the best response oracles. We then compute the meta-strategies using a linear program. For scalability of the framework where multiple network models of best responses are stored in the memory, we prune the weakly-dominated players' strategies to keep the oracles from becoming intractable. Finally, we conduct experiments on MNIST, CIFAR-10 and TinyImageNet for DONAS-GAN. We also evaluate the robustness under FGSM and PGD attacks on CIFAR-10, SVHN and TinyImageNet for DONAS-AT. We show that all our variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective base architectures. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

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Operations Research Forum 2025年第1期6卷 1-27页

作者： Alagarsamy, Saravanan Nagarajan, D. Govindaraj, Vishnuvardhan Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Rajiv Gandhi Salai (OMR) Tamilnadu Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India Division of Data Science of School of Computing Science and Education VIT Bhopal University Kothrikalan Madhya Pradesh Sehore 466114 India

Accurate brain tumor prediction is essential when approaching the field of health care;wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crow Search (CS) Interval Type-II Fuzzy Logic System (IT2FLS) Magnetic Resonance Imaging (MRI) Tumor segmentation

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Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Hevesli, Muhammet Seid, Abegaz Mohammed Erbad, Aiman Abdallah, Mohamed Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Mobile edge computing (MEC)-enabled air-ground networks advance 6G wireless networks by utilizing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications like multimedia and Metaverse services, which demand high computational resources and strict quality of service (QoS) guarantees, specifically in terms of latency and efficient task queue management. However, IoTDs often face constraints in energy and computational power, requiring efficient queue management and task scheduling to maintain QoS. To address these challenges, UAVs and HAPS are deployed to supplement the computational limitations of IoTDs by offloading tasks for distributed processing. Due to UAVs' resource limitations, particularly in terms of power and coverage area, HAPS are used to enhance their capabilities and extend coverage. Overloaded UAVs may relay tasks to HAPS, creating a multi-tier computing system. This paper addresses the overall energy minimization problem in the MEC-enabled air-ground integrated network (MAGIN) by optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization problem is highly complex due to the nonconvex and nonlinear nature of this hierarchical system, which traditional methods cannot effectively solve. To tackle this, we reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents. We propose a novel variant of multi-agent proximal policy optimization (MAPPO) with Beta distribution (MAPPO-BD) to solve this problem. Extensive simulations show that MAPPO-BD significantly outperforms other baselines, achieving superior energy savings and efficient resource management in MAGIN, while adhering to constraints related to queue delays and edge computing capabilities. © 2025 IEEE.

关键词： Quality of service

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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A hybrid boundary integral-PDE approach for the approximation of the demagnetization potential in micromagnetics

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Advances in Computational Mathematics 2025年第3期51卷 1-26页

作者： Arjmand, Doghonay Calzada, Víctor Martínez Department of Information Technology Division of Scientific Computing Uppsala University Uppsala Sweden Departamento de Ingeniería Aeronáutica Universidad Politécnica Metropolitana de Hidalgo Hidalgo Mexico

The demagnetization field in micromagnetism is given as the gradient of a potential that solves a partial differential equation (PDE) posed in Rd. In its most general form, this PDE is supplied with continuity condition on the boundary of the magnetic domain, and the equation includes a discontinuity in the gradient of the potential over the boundary. Typical numerical algorithms to solve this problem rely on the representation of the potential via the Green’s function, where a volume and a boundary integral terms need to be accurately approximated. From a computational point of view, the volume integral dominates the computational cost and can be difficult to approximate due to the singularities of the Green’s function. In this article, we propose a hybrid model, where the overall potential can be approximated by solving two uncoupled PDEs posed in bounded domains, whereby the boundary conditions of one of the PDEs are obtained by a low cost boundary integral. Moreover, we provide a convergence analysis of the method under two separate theoretical settings: periodic magnetization and high-frequency magnetization. Numerical examples are given to verify the convergence rates. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Demagnetization potential Elliptic PDEs in infinite domains Long range interactions Micromagnetism

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