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Effect of Printing Strategies on Mechanical Properties of Tool Steel in Laser Powder Bed Fusion Process

Lasers in Manufacturing and Materials Processing 2025年第1期12卷 147-173页

作者： Kisraoui, Chams Omidi, Narges Dehghan, Shayan Belhadj, Asma Slama, Salma Barka, Noureddine El Ouafi, Abderrazak Department of Mathematics Computer Science and Engineering University of Quebec at Rimouski RimouskiQCG5L 3A1 Canada Tunis Tunisia

The selection of printing strategies in the LPBF process is pivotal, as it significantly impacts residual stress, which in turn affects the mechanical properties and density of the material. Recognizing the importance of optimizing printing strategies to enhance the final residual stress characteristics, this study investigates the effects of different printing strategies on as-manufactured H13 tool steel. Specifically, the research focuses on evaluating the tensile properties, micro-hardness, and density of the LPBF-manufactured samples. Two distinct printing strategies—bidirectional and chess scanning—were employed, with each strategy incorporating four different rotational orientations per layer (0°, 45°, 67°, and 90°). The aim is to understand how these variations influence porosity formation and the resultant mechanical properties of the tool steel, thereby providing insights into optimal printing parameters for improved material performance. Results showed that minimum porosity, maximum relative density, maximum mechanical strength, and maximum hardness were obtainable with the bidirectional printing strategies at rotation angles of 45° and 67°. The chess strategy exhibited more significant manufacturing defects, including extensive cracking and voids, particularly at 45° and 67°, leading to inferior mechanical properties and higher porosity compared to the bidirectional strategy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Selective laser melting

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A Comparative Study of the Analysis Model for Timber Structures: Addressing Nonlinearities and Connection Behaviour

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BioResources 2025年第2期20卷 3587-3603页

作者： Celik, Huseyin Kursat Sakar, Gokhan Isleem, Haytham F. Department of Civil Engineering Izmir University of Economics Izmir Turkey Department of Civil Engineering Dokuz Eylul University Izmir Turkey Department of Computer Science University of York YorkYO10 5DD United Kingdom

Timber has been studied as a material of construction from many perspectives, including strength and durability. Despite many studies showing a good correlation between material models, connection behaviour, and structural modelling, it is still not clear which approach is suitable under what constraints. This study was performed to clarify the problem. The basis for the analysis of timber structures is emphasized in this work in terms of problem dimension, material constitution, and geometrical nonlinearities. The modelling and the idealisation methods of structures are categorised into five different groups and briefly explained. An experiment available in the literature is used as a reference to illustrate modelling capabilities of different techniques, and models from five different groups is used to perform analysis. By comparing the analysis results and the experiment, key notes on the analysis proficiency are highlighted, including failure load, maximum displacement, and failure mode. The results show that most of the errors occurred in the displacements. Furthermore, the divergence between test and analysis results are investigated, and an approximate method for calculating actual displacements is proposed. © 2025, North Carolina State University. All rights reserved.

关键词： Structural Mechanics

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Cross-Layer Management Framework for Enhancing XR-Based System Security in Zero-Trust Wireless Communications

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2011-2024页

作者： Ghourab, Esraa M. Azab, Mohamed Gracanin, Denis Al-Qutayri, Mahmoud Muhaidat, Sami Khalifa University 6G Center Department of Computer Science Abu Dhabi127788 United Arab Emirates Virginia Military Institute Department of Computer and Information Sciences VA United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Khalifa University 6G Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms. © 1983-2012 IEEE.

关键词： Resource allocation

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Beyond Strang:a Practical Assessment of Some Second-Order 3-Splitting Methods

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应用数学与计算数学学报 2025年第1期7卷 95-114页

作者： Raymond J.Spiteri Arash Tavassoli Siqi Wei Andrei Smolyakov Department of Computer Science University of Saskatchewan110 Science PlaceSaskatoon S7N 5K9SKCanada Department of Mathematics and Statistics University of Saskatchewan106 Wiggins RoadSaskatoon S7N 5E6SKCanada Department of Computer Science University of Saskatchewan110 Science PlaceSaskatoon S7N 5K9SKCanada Department of Physics and Engineering Physics University of Saskatchewan116 Science PlaceSaskatoon S7N 5E2SKCanada Department of Physics and Engineering Physics University of Saskatchewan116 Science PlaceSaskatoon S7N 5E2SKCanada

Operator-splitting is a popular divide-and-conquer strategy for solving differential ***,the right-hand side of the differential equation is split into a number of parts that are then integrated *** methods are known that split the right-hand side into two *** approach is limiting,however,and there are situations when 3-splitting is more natural and ultimately more *** second-order Strang operator-splitting method readily generalizes to a right-hand side splitting into any number of *** is arguably the most popular method for 3-splitting because of its efficiency,ease of implementation,and intuitive *** 3-splitting methods exist,but they are less well known,and analysis and evaluation of their performance in practice are *** demonstrate the effectiveness of some alternative 3-splitting,second-order methods to Strang splitting on two problems:the reaction-diffusion Brus-selator,which can be split into three parts that each have closed-form solutions,and the kinetic Vlasov-Poisson equations that are used in semi-Lagrangian plasma *** find alternative second-order 3-operator-splitting methods that realize efficiency gains of 10％-20％over traditional Strang *** analysis for the practical assess-ment of the efficiency of operator-splitting methods includes the computational cost of the integrators and can be used in method design.

关键词： Operator-splitting methods Fractional-step methods Brusselator Vlasov-Poisson equations

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XGBoost-Liver:An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model

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computers, Materials & Continua 2025年第4期83卷 1459-1474页

作者： Sumaiya Noor Salman A.AlQahtani Salman Khan Business and Management Sciences Department Purdue UniversityWest LafayetteIN 47907USA New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11564Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200KPKPakistan

The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and *** diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic *** significant health risks and demand timely diagnosis and treatment to enhance survival ***,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive ***,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and *** study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver *** Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall *** performance of the proposed model has been extensively evaluated through k-fold cross-validation ***,the performance of the proposed model is evaluated using individual and hybrid ***,the XGBoost-Liver model performance is compared to that of commonly used classifier ***,its performance is compared with the existing state-of-the-art computational *** experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.

关键词： Machine learning deep neural network SHAP(SHapley Additive exPlanation) liver disease classifica-tion SMOTE(synthetic minority over-sampling technique)

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Robust 3D point clouds classification based on declarative defenders

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Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Integrating Big Data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices.. © 2025 IEEE.

关键词： Data quality

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LiDAR Image-Based Climate Change Detection with Their Economic Ecosystem Modelling: Artificial Intelligence Application

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 173-181页

作者： Sravani, Chekka Ananth, Prithiviraj Sasikala, C. Kumar, N. Sathish Kumari, Ch D. V. P. Thrimurthulu, V. Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of CSE Sona College of Technology Salem-5 Salem India Department of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of ECE Sri Ramakrishna Engineering College Coimbatore 22 India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad India

Over the past few decades, change detection has been the subject of more research in both close-quarters and distant sensing due to its significance in environment monitoring and database updating. The advancement of sensing technology has made data collecting more accessible and economical, which has increased the amount of data available from different sensing platforms. Studying these atmospheric components has become much easier because of LiDAR technology, which can measure backscattered signals and explore the atmosphere at very high spatial and temporal resolution. This research proposes a novel technique in satellite data security analysis with climate change detection utilizing ML algorithm. Input has been collected as input as satellite data based on security and processed for noise removal as well as normalisation. Processed data has been segmented and classified using SegNet histogram graph cut contour model and transfer AlexNet adversarial Markov model. The experimental analysis has been carried out in terms of data integrity, precision, normalised coefficient, recall and F-1 score. The proposed technique attained data integrity of 98%, F-1 score 95%, PRECISION of 97%, normalised coefficient of 93% and RECALL of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial Markov Climate change detection Graph cut contour model LiDAR technology Machine learning algorithm

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Neural network-based inversion of NMR dispersion profiles for enhanced analysis of food systems

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Neural Computing and Applications 2025年第5期37卷 3957-3972页

作者： Spinelli, Giovanni Vito Evangelista, Davide Hu, Liwei Zama, Fabiana Department of Mathematics University of Bologna Piazza di Porta S. Donato 5 BO Bologna40126 Italy Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 BO Bologna40126 Italy

Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) relaxometry is a powerful non-destructive technique used to study molecular dynamics and structures in various systems, including food products. This study introduces a novel machine learning framework to address the Quadrupole Relaxation Enhancement (QRE) phenomenon in FFC-NMR analysis. The proposed method leverages a pre-trained feed-forward neural network within a coordinate descent optimization algorithm to extract quadrupolar parameters and fit NMR Dispersion (NMRD) profiles. The neural network is trained using a unique model loss function combining L2 loss and predicted quadrupolar component of the NMRD profile accuracy. The approach is validated against a robust optimization method, showing strong concordance and potential for expedited analysis of extensive datasets. This advancement opens new avenues for assessing food quality and authenticity using FFC-NMR relaxometry. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Nuclear quadrupole resonance

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A Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions

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IEEE Robotics and Automation Letters 2025年第6期10卷 5369-5376页

作者： Yang, Zi-Qi Wang, Miaomiao Kermani, Mehrdad R. Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China

In recent years, the focus on developing robot manipulators has shifted towards prioritizing safety in Human-Robot Interaction (HRI). Impedance control is a typical approach for interaction control in collaboration tasks. However, such a control approach has two main limitations: 1) the end-effector (EE)’s limited compliance to adapt to unknown physical interactions, and 2) inability of the robot body to compliantly adapt to unknown physical interactions. In this work, we present an approach to address these drawbacks. We introduce a modified Cartesian impedance control method combined with a Dynamical System (DS)-based motion generator, aimed at enhancing the interaction capability of the EE without compromising main task tracking performance. This approach enables human coworkers to interact with the EE on-the-fly, e.g. tool changeover, after which the robot compliantly resumes its task. Additionally, combining with a new null space impedance control method enables the robot body to exhibit compliant behaviour in response to interactions, avoiding serious injuries from accidental contact while mitigating the impact on main task tracking performance. Finally, we prove the passivity of the system and validate the proposed approach through comprehensive comparative experiments on a 7 Degree-of-Freedom (DOF) KUKA LWR IV+ robot. © 2016 IEEE.

关键词： Human robot interaction

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