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Low-loss and broadband silicon-based multimode waveguide crossing at 2 µm using 2-D sub-wavelength holey metamaterials

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Optics Express 2025年第4期33卷 6788-6798页

作者： Ni, Bin Xu, Hao Luo, Chenyang Wu, Shengbao Xiong, Jichuan Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Photonics Information Innovation Center Hebei Provincial Center for Optical Sensing Innovations College of Physics Science and Technology Hebei University Baoding071002 China

Silicon-based multi-mode optical waveguide crossings with small size and high performance are crucial components for building photonic integrated circuits, but almost all the current devices operate around the near-infrared bands of 1.31 µm and 1.55 µm. To further enhance communication capacity, in this paper, a multimode (e.g., TE0-TE2) waveguide crossing operating at mid-infrared waveband of 2 µm, with good performance, is proposed and experimentally demonstrated using a full two-dimensional (2-D) sub-wavelength holey metamaterial (SWHM) waveguide, where the specific distributed 2-D hole arrays are inserted into two orthogonal multi-mode interference waveguides. Here, the introduction of SWHMs offers great design flexibility and tunability to generate the desired refractive index distributions within the crossing structure that enables tight trap of incident multiple TE modes in the through route over a large waveband around 2 µm, thus realizing an effective multimode crossing with low insertion loss, low crosstalk and large bandwidth. Simulation results show that the realized device has a compact size of 12.6 µm × 12.6 µm and its insertion losses are, respectively, 0, TE1, and TE2 modes over an ultra-broad wavelength range of 1700–2200 nm, while the crosstalks are below −32 dB. The experiment results verify that the average insertion losses for the three modes are as low as 0.14 dB, 0.08 dB, and 0.20 dB, respectively, with the corresponding crosstalk below −25 dB at 2 µm. The provided device may find potential applications in on-chip optical communications and computing. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Insertion losses

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Performance of retrofitted eccentrically loaded GFRP-reinforced electronic waste concrete components having synthetic fibers

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Steel and Composite Structures 2025年第2期54卷 139-156页

作者： Raza, Ali Ahmed, Mohd Awais, Muhammad Department of Civil Engineering University of Engineering and Technology Taxila 47050 Pakistan Department of Civil Engineering College of Engineering King Khalid University PO Box 394 Abha61411 Saudi Arabia Center for Engineering and Technology Innovations King Khalid University Abha61421 Saudi Arabia Department of Business Administration Institute of Southern Punjab Multan 66000 Pakistan

These days, poor electronic waste (E-waste) disposal is polluting the environment and posing health risks in developing nations. Prior research neglected post-damage retrofit efficiency in favor of concentrating on the compressive execution of concrete compressive members reinforced with glass fiber reinforced polymer (GFRP). The purpose of this study is to evaluate the mechanical execution of partly broken and retrofitted electronic waste aggregate concrete (EWC) compressive members that have been reinforced with GFRP helix, bars, and synthetic fibers. Twelve circular specimens measuring 1000 mm in height and 250 mm in diameter were retrofitted quickly using carbon fiber-reinforced polymer (CFRP) sheets. Six specimens had steel bars and helix (SSEWC compressive members) and six specimens had GFRP bars and helix (GSEWC compressive members) for main and transverse reinforcement. CFRP sheets applied for retrofit after load application caused a compressive load-carrying capacity loss of up to 30% in the post-ultimate loading stage. Compressive load-carrying capacity, load-deflection curves, compressive deflection, strength index, stiffness index, ductility index, and crack patterns were assessed pre-and post-retrofit for the effects of monotonic axial and eccentric loading, CFRP casing, spiral vertical spacing, synthetic fibers, E-waste concrete, and reinforcement Kind. Comparing the reconstructed SSEWC and GSEWC compressive members to their original counterparts, the results showed improved mechanical execution. © 2025 Techno-Press, Ltd.

关键词： Retrofitting

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Hybrid MNLTP Texture Descriptor and PDCNN-Based OCT Image Classification for Retinal Disease Detection

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Computers, Materials & Continua 2025年第2期82卷 2831-2847页

作者： Jahida Subhedar Anurag Mahajan Shabana Urooj Neeraj Kumar Shukla Department of E&TC Symbiosis Institute of TechnologyPune CampusSymbiosis International(Deemed University)Pune412115India Symbiosis Skills and Professional University School of MechatronicsPune412101India Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Center for Engineering and Technology Innovations King Khalid UniversityAbha61421Saudi Arabia

Retinal Optical Coherence Tomography (OCT) images, a non-invasive imaging technique, have become a standard retinal disease detection tool. Due to disease, there are morphological and textural changes in the layers of the retina. Classifying OCT images is challenging, as the morphological manifestations of different diseases may be similar. The OCT images capture the reflectivity characteristics of the retinal tissues. Retinal diseases change the reflectivity property of retinal tissues, resulting in texture variations in OCT images. We propose a hybrid approach to OCT image classification in which the Convolution Neural Network (CNN) model is trained using Multiple Neighborhood Local Ternary Pattern (MNLTP) texture descriptors of the OCT images dataset for a robust disease prediction system. Parallel deep CNN (PDCNN) is proposed to improve feature representation and generalizability. The MNLTP-PDCNN model is tested on two publicly available datasets. The parameter values Accuracy, Precision, Recall, and F1-Score are calculated. The best accuracy obtained specifying the model’s overall performance is 93.98% and 99% for the NEH and OCT2017 datasets, respectively. With the proposed architecture, comparable performance is obtained with a subset of the original OCT2017 data set and a comparatively smaller number of trainable parameters (1.6 million, 1.8 million, and 2.3 million for a single CNN branch, two parallel CNN branches, and three parallel network branches, respectively), compared to off-the-shelf CNN models. Hence, the proposed approach is suitable for real-time OCT image classification systems with fast training of the CNN model and reduced memory requirement for computations.

关键词： Local ternary pattern texture descriptor optical coherence tomography parallel CNN multiple neighborhood

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Addition of Synthetic Steel Fibers for Enhancing the Mechanical Behavior of Fly Ash-Based Geopolymer Composites

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Iranian Journal of Science and technology - Transactions of Civil engineering 2025年第3期49卷 2679-2691页

作者： Alomayri, Thamer Khan, Qaiser Uz Zaman Raza, Ali Alashker, Yasser Department of Physics Faculty of Applied Science Umm Al-Qura University Makkah 21955 Saudi Arabia Department of Civil Engineering University of Engineering and Technology Taxila Taxila 47050 Pakistan Department of Civil Engineering College of Engineering King Khalid University PO Box 394 Abha 61411 Saudi Arabia Center for Engineering and Technology Innovations King Khalid University Abha 61421 Saudi Arabia

This study investigates the mechanical and structural performance of synthetic steel fiber-reinforced geopolymer concrete (GP) through rheological, compressive, flexural, and ductility tests. The effect of fiber inclusion (0%, 1.0%, 1.50%, and 2.0%) on the slump, compressive strength (CS), stress-strain behavior, and energy absorption capacity was examined. The results indicate that the addition of synthetic steel fibers reduced the slump values, with the plain GP exhibiting the highest workability. The incorporation of fibers enhanced the CS by up to 71% equated with the control specimen, with the optimal performance observed at 1.50% fiber content. However, beyond this limit, a reduction in CS was noted due to casting difficulties. Load-displacement analysis depicted that the fiber reinforcement expressively improved the axial stiffness and post-peak behavior, with the 1.50% fiber-reinforced specimen exhibiting the largest load-displacement curve. Stress-strain curves highlighted an increase in elastic responses and crack bridging capacity, delaying failure. In flexural tests, specimens reinforced with 1.50% fibers showed superior energy absorption and post-peak performance, whereas those with 1% fiber content displayed brittle failure similar to the control mix. The energy absorption capacity of GP improved by up to 223% with fiber reinforcement, demonstrating the potential of synthetic steel fibers in enhancing the toughness and durability of GP composites. These findings suggest that fiber-reinforced GP can be effectively used in structural applications requiring enhanced ductility and energy dissipation characteristics. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Analysis of variance (ANOVA) Compressive strength Energy absorption capacity Flexural performance Geopolymer Synthetic steel fiber

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical Sciences. All rights reserved.

关键词： Electroencephalography

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3-D Unbalanced Coordinate Transformation With Super-Twisting Control and Observation for Unbalance and Disturbance Load Voltage Mitigations in Stand Alone Four-Leg Inverters

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IEEE Access 2025年 13卷 70464-70488页

作者： Chebabhi, Ali Barkat, Said Ziane, Djamal Benbouhenni, Habib Benkhoris, Mohamed Fouad Elbarbary, Z.M.S. Fellah, Mohammed Karim Rekioua, Toufik University of M’sila Electrical Engineering Laboratory Faculty of Technology M’sila28000 Algeria University of Nantes IREENA Laboratory Saint-Nazaire44600 France École Nationale Polytechnique d’Oran Laboratoire LAAS Oran31000 Algeria King Khalid University College of Engineering Department of Electrical Engineering Abha61421 Saudi Arabia King Khalid University Center for Engineering and Technology Innovations Abha61421 Saudi Arabia Djillali Liabes University of Sidi Bel-Abbes ICEPS Laboratory Sidi Bel Abbès22000 Algeria Bejaia06000 Algeria

This paper presents an enhanced dual-loop voltage vector control method for standalone power supply systems using a four-leg voltage source inverter (4LVSI). The proposed method aims to optimize the quality of load voltage and 4LVSI output current waveforms under unbalanced three-phase loads, enhance load voltage disturbance rejection during unbalanced load changes, and improve the system’s dynamic response during transients. Its purpose is to increase the robustness of both the inner 4LVSI output current and outer load voltage control loops against system parameter uncertainties while reducing the number of sensors. In the inner current control loop, a second-order sliding mode control combined with a 3-D unbalanced transformation is proposed. This transformation is adopted herein for its ability to transform the three-phase unbalanced inverter currents into direct current quantities in the synchronous reference frame based on the dynamic estimation of their unbalanced phases and amplitudes. The second-order sliding mode control is adopted in this control loop for its robustness and its ability to reduce the chattering problem and improve the robustness of the inner current control loop against parameter uncertainties. For the outer three-phase load voltage control loop, a proportional-integral controller incorporated with a super-twisting observer for load disturbance observation is designed not only to get rid of sensors but also to ameliorate the load voltage dynamics. It permits to enhance the load disturbance rejection and reliability, as well as decrease the system’s cost and size. In this control loop, the super-twisting sliding mode observer is suggested not only for the load current disturbance estimation but also to build up the current feed-forward control scheme to estimate and reject the load disturbance quickly. The most significant advantages of the designed technique over existing strategies are that it can overcome the control issue of periodic cur

关键词： Disturbance rejection

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Polytree-based adaptive bubble-enriched smoothed polygonal finite element method for dual yield structural design

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engineering with Computers 2025年 1-27页

作者： Lee, Changkye Ho, Phuc L. H. Natarajan, Sundararajan Le, Canh V. Yee, Jurng-Jae Center for Digital Cardiovascular Innovations Miller School of Medicine University of Miami Miami33136 United States School of Civil Engineering and Management International University - VNU-HCMC Ho Chi Minh City Viet Nam Department of Mechanical Engineering Indian Institute of Technology Madras Chennai600036 India Faculty of Civil Engineering HUTECH University Ho Chi Minh City Viet Nam Department of Architectural Engineering Dong-A University Busan49315 Korea Republic of

This study presents an efficient numerical approach, marking the first integration of polytree-based adaptive refinement and bubble-enriched smoothed polygonal finite element methods within the dual-yield structural design framework. The behavior of smoothed polygonal elements in response to volumetric locking, both with and without bubble enhancement, is thoroughly analyzed and numerically validated. Two robust indicators, based on plastic dissipation energy and yield stress fields at the limit state, are developed to guide local mesh refinement, thereby enhancing the performance of the proposed discretization techniques. The essential design parameters of structural yield, including the limit load multiplier and collapse mechanism, are accurately determined with optimal computational efficiency. Numerical investigations demonstrate the effectiveness of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Structural dynamics

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Replacing toxic dyes with photonic crystals for printing applications: simulation study

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Applied Optics 2025年第9期64卷 2203-2207页

作者： Zaky, Zaky A. Alamri, Sagr Al-Dossari, M. Mohamed, D. Zhaketov, V.D. Aly, Arafa H. TH-PPM Group Physics Department Faculty of Sciences Beni-Suef University Beni-Suef62514 Egypt Cairo Egypt Frank Laboratory of Neutron Physics Joint Institute for Nuclear Research Dubna141980 Russia Department of Mechanical Engineering College of Engineering King Khalid University Abha61421 Saudi Arabia Center for Engineering and Technology Innovations King Khalid University Abha61421 Saudi Arabia Department of Physics Faculty of Science King Khalid University Abha62529 Saudi Arabia Moscow Oblast Dolgoprudny Russia

Photonic crystals are proposed for colored printing due to the fantastic geometrical and optical features of photonic crystal structures. This study proposes multilayers to replace toxic dyes with safe, durable printers in the range of visible light. The color of each point on the printed model can be changed by changing the thickness of the layers. Further, the suggested models have demonstrated the practical ability of PhC to manipulate the structural color by tuning the thickness of layers. These properties make it a good choice for 3D printing. Hence, we can substitute dangerous dyes with periodic optical structures in 3D printers to cover 3D shapes. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Photonic crystals

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th International Conference on ENTERprise Information Systems, centerIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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