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Embedded System for Interactive Pneumatic Hand Rehabilitation: Real-Time Gaming Interface with Cognitive Stimulation for Motor Recovery

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IEEE Access 2025年 13卷 74470-74484页

作者： Ghobadi, Narges Kinsner, Witold Szturm, Tony Sepehri, Nariman University of Manitoba Price Faculty of Engineering Department of Mechanical Engineering MB Canada University of Manitoba Price Faculty of Engineering Department of Electrical & Computer Engineering MB Canada University of Manitoba College of Rehabilitation Sciences Rady Faculty of Health Sciences MB Canada

Restoring fine motor skills in individuals with upper extremity sensory-motor post-stroke impairments necessitates repetitive, task-specific exercises to promote functional recovery. Given the substantial time commitment required for therapy, rehabilitation tools must be not only effective, but also engaging by adopting a game-based approach to mitigate the monotony of prolonged repetitive exercises. This paper presents a user-friendly finger-thumb mechanism designed to support the index and middle fingers as well as the thumb, specifically for patients with hand injuries. The device establishes a wireless connection to a gaming platform enabling patients to engage with computer games in real-time through purposeful thumb and finger movements. This connectivity is established through two Raspberry Pi boards utilizing a server-client network. Additionally, the device incorporates assistive or resistive forces during gameplay to adjust the level of assistance or challenge based on the individual’s motor control proficiency. With a minimal data transfer delay of 10 ms, and a 50 ms delay for game event updates, patients can seamlessly participate in the gaming experience and modify events in real-time through the newly developed wearable device. In the experiments, assistive mode achieved a 100% success rate, while resistive mode dropped to 37-45%. Movement errors varied across modes, with assistive mode showing the lowest errors, indicating more accurate and consistent performance. © 2013 IEEE.

关键词： Pneumatics

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Forecasting renewable energy for microgrids using machine learning

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Discover Applied Sciences 2025年第5期7卷 1-28页

作者： Sudasinghe, Piyumi Herath, Damayanthi Karunarathne, Isiwara Weeratunge, Hansani Jayasuriya, Lahiru Department of Mechanical Engineering University of Peradeniya Peradeniya Sri Lanka Department of Computer Engineering University of Peradeniya Peradeniya Sri Lanka Department of Mechanical Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Electrical and Electronic Technology Faculty of Technology Rajarata University of Sri Lanka Mihintale Sri Lanka

Evaluated ML-based renewable energy forecasting models by implementing 1-D CNN and LSTM models using real-world data. Proposed 1-D CNN performs better than LSTM and baseline models, achieving higher accuracy and computational efficiency. Accurate forecasting of PV and wind energy generation enhances grid stability, reduces backup power dependency, and supports sustainable energy integration. © The Author(s) 2025.

关键词： Wind power integration

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A novel forecasting model for time series using optimized interval division and fuzzy relationships

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Neural Computing and Applications 2025年第5期37卷 3869-3885页

作者： PhamToan, Dinh Vo-Van, Tai Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam College of Natural Science Can Tho University Can Tho City Viet Nam

This study builds an effective forecasting model for time series based on significant improvements of the fuzzy clustering algorithm. Firstly, we use the universal set, which is the percentage change between two consecutive time points, and divide it into unequal intervals using the automatic clustering algorithm that adjusts the number of clusters. Next, we propose the networks so that establish the fuzzy relationship of the elements in the universal set and the intervals based on the improved inverse fuzzy number. Finally, a new principle for future forecasting is built based on these two improvements. The proposed model details the steps and is clearly illustrated through a specific numerical. The convergence of the proposed algorithm is also considered and addressed. The proposed model has demonstrated effectiveness by outperforming many other models applied to the M3 dataset with 3,003 series, the M4 dataset with 100,000 series, and the well-known benchmark dataset. The proposed model is also applied to forecast the number of people infected and deceased due to COVID-19 in Southeast Asian countries. This application also demonstrates the advantages of the proposed model over many existing models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

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Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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A novel approach with vision-language models for custom e-commerce product listings

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Multimedia Tools and Applications 2025年 1-30页

作者： Huynh Ngoc Nhu, Y. Nguyen, Quoc-Dung Kingkan, Cherdsak Deakin University Burwood Melbourne Australia School of Engineering and Technology Asian Institute of Technology Khlong Nueng Thailand Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam

This study introduces an innovative approach to enhancing e-commerce product listings through subject-driven text-to-image generation, leveraging advanced AI technologies. Focused on transforming consumer first impressions, it blends personalized visual styles with online retail needs, striking a balance between standardization and customization. The research develops a unique method for image synthesis, improving upon existing AI models such as DreamBooth and Textual Inversion. This work not only equips online sellers with dynamic visual tools but also significantly enriches AI applications in e-commerce, offering both practical and academic contributions to the field. Our proposed model is evaluated based on various numerical and human-based evaluation metrics. The experimental results show that our model achieves a significant performance compared to other baseline models. Our model is further analyzed and discussed under correlation analysis, visual quality assessment, and ablation study to ensure its practical applicability and user satisfaction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Marketplaces

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Lightweight and Roubust Key Agreement for Securing IIoT-Driven Flexible Manufacturing Systems

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IEEE Internet of Things Journal 2025年第11期12卷 17197-17209页

作者： Hammad, Muhammad Badshah, Akhtar Almeer, Mohammed A. Waqas, Muhammad Song, Houbing Chen, Sheng Han, Zhu GIK Institute of Engineering Sciences and Technology Faculty of Mechanical Engineering Topi23640 Pakistan University of Malakand Department of Software Engineering Dir Lower18800 Pakistan University of Bahrain Computer Engineering Department College of Information Technology 32038 Bahrain University of Greenwich Faculty of Engineering and Science Centre for Sustainable Cybersecurity United Kingdom Edith Cowan University School of Engineering PerthWA6027 Australia Department of Information Systems BaltimoreMD United States University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-Or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrate that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages. © 2025 IEEE.

关键词： Flexible manufacturing systems

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer

arXiv

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arXiv 2025年

作者： Zare, Hadi Abbasi, Mostafa Ahang, Maryam Najjaran, Homayoun Department of Electrical and Computer Engineering Faculty of Engineering and Computer Science University of Victoria Victoria Canada Department of Mechanical Engineering Faculty of Engineering and Computer Science University of Victoria Victoria Canada

Purpose - In Business Process Management (BPM), accurate prediction of the next activities is vital for operational efficiency and decision-making. Current Artificial Intelligence (AI)/Machine Learning (ML) models struggle with the complexity and evolving nature of business process event logs, balancing accuracy and interpretability. This paper proposes an entropy-driven model selection approach and DAW-Transformer, which stands for Dynamic Attribute-Aware Transformer, to integrate all attributes with a dynamic window for better accuracy. Design/methodology/approach - This paper introduces a novel next-activity prediction approach that uses process entropy to assess the complexity of event logs and dynamically select the most suitable ML model. A new transformer-based architecture with multi-head attention and dynamic windowing mechanism, DAW-Transformer, is proposed to capture long-range dependencies and utilize all relevant event log attributes. Experiments were conducted on six public datasets, and the performance was evaluated with process entropy. Finding - The results demonstrate the effectiveness of the approach across these publicly available datasets. DAW-Transformer achieved superior performance, especially on high-entropy datasets such as Sepsis exceeding Limited window Multi-Transformers by 4.69% and a benchmark CNN-LSTM-SAtt model by 3.07%. For low-entropy datasets like Road Traffic Fine, simpler, more interpretable algorithms like Random Forest performed nearly as well as the more complex DAW-Transformer and offered better handling of imbalanced data and improved explainability. Originality/ value - This work’s novelty lies in the proposed DAW-Transformer, with a dynamic window and considering all relevant attributes. Also, entropy-driven selection methods offer a robust, accurate, and interpretable solution for next-activity prediction. © 2025, CC BY-NC-SA.

关键词： Administrative data processing

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Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

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Energy engineering 2025年第1期122卷 153-166页

作者： Rahool Rai Fareed Hussain Mangi Kashif Ahmed Sudhakar Kumaramsay Faculty of Mechanical and Automotive Engineering Universiti Malaysia Pahang Al-Sultan AbdullahPekan26600PahangMalaysia Faculty of Engineering Science&Technology Hamdard UniversityKarachi74600Pakistan Electrical Engineering Department SUKKUR IBA UniversitySukkur65200Pakistan Berlin School of Business and Innovation BSBI Faculty of Computer Science and InformaticsBerlin12043Germany Automotive Engineering Centre University Malaysia Pahang Al Sultan AbdullahPekan26600PahangMalaysia Centre for Research in Advanced Fluid&Processes(Fluid Centre) Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)Paya BesarKuantan26300PahangMalaysia

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

关键词： PV module efficiency water film thickness reynolds number CFD analysis PV/T renewable energy

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Analysis of Signal Amplitude of Violins with Modified Physical Parameters 27th

Analysis of Signal Amplitude of Violins with Modified Physic...

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27th International Conference on Interactive Collaborative Learning, ICL 2024

作者： Stanciu, Mariana Domnica Nauncef, Alina Maria Năstac, Silviu Marian Amariei, Nicusor Departament of Mechanical Engineering Transilvania University of Braşov B-dul Eroilor 29 Brasov500036 Romania Faculty of Music Transilvania University of Braşov Brasov500360 Romania Faculty of Engineering and Agronomy "Dunarea de Jos" University of Galati Braila Romania Faculty of Electrical Engineering and Computer Science Transilvania University of Braşov B-dul Eroilor 29 Brașov500036 Romania

ISBN: (纸本)9783031835193

The analysis of the signal amplitude from different angles in an anechoic chamber is an essential practice in the field of acoustics, especially in the context of musical instruments such as the violin. Anechoic chambers are specially designed spaces to absorb the reflections of sound waves, providing a controlled and stable environment in which accurate and repeatable measurements of sound sources can be made. The recorded acoustic signals were analyzed by obtaining the acoustic spectra in the form of amplitude spectra and continuous frequency spectra. Starting from the idea that the timbre of the violin is formed by the number and intensity of the harmonics, and the factors that influence the timbre are the number of harmonics;the position of the harmonics in their series;harmonic intensity;the position of the foundation on the height scale;fundamental intensity;the noise factor that accompanies the musical sound, in the paper different categories of violins from anatomical features of resonance wood point of view were compared. The results highlighted the fact that the anatomical structure of the wood used for the violin body is one of the factors influencing the acoustic quality of violins, but not decisive. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anechoic chambers

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