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Predictive modeling of MXene-based solar absorbers using a deep neural network

Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 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： Time domain analysis

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AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis 3

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahendran, A. Nalla, Sridhar Babu Trivedi, Lalit Mohan Mohankumar, S. Williams, D.R. Mohan Renuka, Manne Sona College of Arts and Science Department of Computer Science Salem India Sr University Department of Computer Science and Engineering Hyderabad India Moradabad Institute of Technology Department of Ash Mathematics Moradabad India Erode Sengunthar Engineering College Department of Computer Science and Engineering Perundurai India College of Computing and Information Sciences University of Technology and Applied Science-Sohar Department of Information Technology Sohar Oman Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331508456

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data. © 2025 IEEE.

关键词： Patient treatment

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Sequential Solution Strategies for the Cahn-Hilliard-Biot Model

Sequential Solution Strategies for the Cahn-Hilliard-Biot ...

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European Conference on Numerical mathematics and Advanced Applications, ENUMATH 2023

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin Adrian Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Bergen Norway

ISBN: (纸本)9783031861680

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational efficiency

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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Modified Wild Horse Optimizer for system reliability-redundancy allocation problems with cold- standby redundancy strategy

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International Journal of System Assurance engineering and Management 2025年 1-15页

作者： Pant, Sangeeta Singh, Manoj K. Kumar, Anuj Chaube, Shshank Ram, Mangey Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Maharashtra Pune 412115 India School of Computer Science Engineering and Technology Bennett University Greater Noida 201310 India School of Computer Science Engineering & Applications D. Y. Patil International University (DYPIU) Akrudi Maharashtra Pune 411044 India Department of Physical Sciences Jaypee University Anoopshahr Bulandshahr 203390 India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

The design of a reliable system depends greatly on several key factors, including the allocation of reliability to individual components, the use of redundancy, and the allocation of redundant components to enhance overall system reliability. This article proposes a Modified Wild Horse Optimizer (MWHO) for solving system reliability-redundancy allocation problems (RRAPs) with cold-standby redundancy strategy. The cold-standby redundancy is a common technique used to enhance system reliability by adding backup components that remain idle until a failure occurs. However, creating a trade-off between the number of redundant components and their reliabilities with the objective of maximizing overall system reliability remains a challenging problem. Since components can malfunction even when transitioning from standby to active mode, any issues caused by imperfect switching are also taken into account when evaluating the system's performance. The proposed MWHO algorithm incorporates a novel search strategy and a new update mechanism to deal with mixed-integer non-linear RRAPs. The proposed MWHO algorithm is demonstrated to be effective for solving system RRAPs with cold-standby redundancy strategy, outperforming other optimization algorithms in solution quality and convergence speed to yield higher system reliability values. © The Author(s) under exclusive licence to The Society for Reliability engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.

关键词： Cold-standby redundancy strategy Imperfect switching Metaheuristics Modified Wild Horse Optimizer Reliability-redundancy allocation problems

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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A Bicriterion Concentration Inequality and Prophet Inequalities for k-Fold Matroid Unions 16

A Bicriterion Concentration Inequality and Prophet Inequalit...

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16th Innovations in Theoretical computer Science Conference, ITCS 2025

作者： Alon, Noga Gravin, Nick Pollner, Tristan Rubinstein, Aviad Wang, Hongao Weinberg, S. Matthew Zhang, Qianfan Department of Mathematics Princeton University NJ United States Schools of Mathematics and Computer Science Tel Aviv University Tel Aviv Israel Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China Department of Management Science and Engineering Stanford University CA United States Department of Computer Science Stanford University CA United States Department of Computer Science Purdue University West LafayetteIN United States Department of Computer Science Princeton University NJ United States

ISBN: (纸本)9783959773614

We investigate prophet inequalities with competitive ratios approaching 1, seeking to generalize k-uniform matroids. We first show that large girth does not suffice: for all k, there exists a matroid of girth ≥ k and a prophet inequality instance on that matroid whose optimal competitive ratio is12. Next, we show k-fold matroid unions do suffice: we provide a prophet inequality with competitive ratio (Formula presented) for any k-fold matroid union. Our prophet inequality follows from an online contention resolution scheme. The key technical ingredient in our online contention resolution scheme is a novel bicriterion concentration inequality for arbitrary monotone 1-Lipschitz functions over independent items which may be of independent interest. applied to our particular setting, our bicriterion concentration inequality yields "Chernoff-strength" concentration for a 1-Lipschitz function that is not (approximately) self-bounding. © Noga Alon, Nick Gravin, Tristan Pollner, Aviad Rubinstein, Hongao Wang, S. Matthew Weinberg, and Qianfan Zhang.

关键词： Choquet integral

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The effect of elemental contents on the dielectric permittivity of nickel laterite ores at 2.5 GHz 12

The effect of elemental contents on the dielectric permittiv...

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12th International Conference on Physics and Its Applications, ICOPIA 2024

作者： Agusu, La Muhammad, A.J. Sulhajib, S. Darusman, L.M. Sudiana, I.N. Safiuddin, L.O. Iwamoto, T. Terui, Y. Karya, I.P.A. Nakagawa, K. Nishiumi, T. Asano, T. Mitsudo, S. Ishikawa, Y. Fujii, Y. Tatematsu, Y. Department of Physics Faculty of Mathematics and Natural Sciences Halu Oleo University Kendari Indonesia Department Chemistry Faculty of Mathematics and Natural Sciences Halu Oleo University Kendari Indonesia Department of Applied Physics Faculty of Engineering University of Fukui Fukui Japan Research Center for Development of Far Infrared Region University of Fukui Fukui Japan

The rising demand for nickel, a critical component in Li-ion battery cathodes, highlights the need for sustainable nickel processing methods that minimize carbon emissions. However, current pyrometallurgical and hydrometallurgical techniques for nickel processing yield significant CO2 emissions, necessitating a transition to greener technologies. The electrical permittivity constant is a crucial parameter in microwave processing technology. This study investigates the dielectric permittivity of nickel laterites, measured from 50 samples with nickel content ranging from 0.9% to 2.2%. The nickel content was quantified using X-ray fluorescence (XRF), while the permittivity constants (real part, Ε’, and imaginary part, Ε") were determined using the cavity perturbation method at a frequency of 2.5 GHz. The results revealed Ε’ values ranging from 2.8 to 4.1, with loss tangents (tan δ = Ε"/Ε’) varying from 0.047 to 0.108. Notably, Ε’ and tan δ exhibited positive correlations with increasing nickel and iron contents, while the presence of other elements such as Si, Mg, Al, Co, and Mn negatively impacted both parameters. These findings suggest that microwave heating presents a viable alternative for nickel processing across a wide range of nickel contents, offering the potential for lower carbon emissions compared to traditional methods. The correlation between permittivity constants and elemental composition provides critical insights for optimizing microwave processing in nickel smelting, positioning this technology as a promising candidate for sustainable mineral processing. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Nickel smelting

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Density-Clustering Aggregation for Personalized Federated Learning With AI-Enabled Aerial and Edge Computing in UAVs

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IEEE Internet of Things Journal 2025年第9期12卷 11220-11232页

作者： Chien, Wei-Che Lin, Chih-Hsun Zhu, Tianli Dai, Cheng Garg, Sahil Mukherjee, Amrit National Dong Hwa University Department of Computer Science and Information Engineering Hualien City974301 Taiwan Sichuan University College of Computer Science Sichuan Chengdu610017 China École de Technologie Supérieure Electrical Engineering Department MontrealH3C 1K3 Canada Applied Science and Technology Dubai United Arab Emirates Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India University of South Bohemia Faculty of Science Department of Computer Science České Budějovice370 05 Czech Republic

This research introduces the density-clustering-based aggregation for personalized federated learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for uncrewed aerial vehicles (UAVs). The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in automatically determining cluster numbers using EPS neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning (ML) tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning (FL) forward in complex and dynamic settings. © 2014 IEEE.

关键词： Federated learning

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Long-term stability from adaptive auto-calibration in mid-infrared sensing instruments 25

Long-term stability from adaptive auto-calibration in mid-in...

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Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications XXV 2025

作者： Alexis, Al Anderson, London Khan, Mohammad A. Division of Physics Engineering Mathematics and Computer Science Optical Science Center for Applied Research Delaware State University 1200 N. Dupont Hwy DoverDE19901 United States

ISBN: (纸本)9781510683686

Mid-infrared laser-based sensing is widely used for the detection and quantification of trace gases for environmental monitoring, industrial process control, defense, and security applications. However, such laser spectroscopy-based field instruments require frequent calibration to correct for long-term drifts and precision. In many situations, such calibrations may be redundant and may not be effective in monitoring instrument errors or discriminating "real"environmental effects with instrument drifts. This paper shows that physics and diffusion-based models can be developed to predict gradual or rapid changes in density at given temperatures and appropriate source characterizations. We used a 3.3μm, laser-based instrument to demonstrate an adaptive and auto-calibration methodology to account for drifts during field deployments and an adaptive method to validate environmental changes, e.g., plume detection, diurnal variations, and flux emissions. We field operation and implementation of adaptive auto-calibration. The instruments are used for the detection of methane and water vapor, but an adaptive auto-calibration technique can be employed for any laser sensing instrument. © 2025 SPIE.

关键词： Laser spectroscopy

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