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Enhancing HIV Drug Resistance Prediction Using Bidirectional LSTM Neural Networks

Procedia computer science 2025年 258卷 2888-2898页

作者： Poojitha D Tanmay Darak Ushnish Samaddar Vasavi C S Karthikeyan B Divya B. Korlepara Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham 560035 India Department of Artificial Intelligence Amrita School of Artificial Intelligence Bengaluru Amrita Vishwa Vidyapeetham 560035 India Department of Embedded Technology School of Electronics Engineering Vellore Institute of Technology Vellore 632014 India Divison of Physics School of Advanced Sciences Vellore Institute of Technology Chennai 600127 India

One major obstacle that humanity faces today is the ability of viruses to develop resistance to antibiotics or other drugs used in treating the diseases. In the current study, we utilise a specifically developed Bidirectional Long Short-Term Memory (LSTM) neural network to identify HIV drug resistant strains in an effort to help clinicians identify the most appropriate treatment options so that the development new resistant strains is prevented as much as is possible. By incorporating the Bidirectional LSTM model, temporal dependency analysis is improved and deepened in both forward and backward directions, improving the model’s performance and interpretability. The accuracy of the model is measured by MAE and R² where the result from MAE is 16.151 of PI (Protease Inhibitors), 14.703 of NRTI (Nucleoside Reverse Transcriptase Inhibitors) and 18.815 of NNRTI (Non-Nucleoside Reverse Transcriptase Inhibitors) while result from R² is 0.914 of PI, 0.942 of NRTI and 0.885 of NNRTI. Consequently, these results suggest the custom model may be useful to refine drug resistance predictions to progress the accuracy of HIV treatment.

关键词： Bidirectional LSTM neural networks resistant strains genetic sequences bioinformatics Protease Inhibitors Nucleoside Reverse Transcriptase Inhibitors Non-Nucleoside Reverse Transcriptase Inhibitors

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Venturing ChatGPT's lens to explore human values in software artifacts: a case study of mobile APIs

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Behaviour and Information Technology 2025年

作者： Mougouei, Davoud Rafi, Saima Fahmideh, Mahdi Mougouei, Elahe Khan, Javed Ali Dam, Hoa Khanh Nurwidyantoro, Arif Chaudron, Michel School of Information Technology Deakin University Burwood Australia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Business University of Southern Queensland Springfield Australia Faculty of Computer Engineering Islamic Azad University Esfahan Iran School of Physics Engineering & Computer Science University of Hertfordshire Hertfordshire United Kingdom School of Computing and IT University of Wollongong Wollongong Australia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Software is designed for humans and must account for their values. However, current research and practice focus on a narrow range of well-explored values, e.g. security, overlooking a more comprehensive perspective. Those exploring a broader array of values rely on manual identification, which is labour-intensive and prone to human bias. Moreover, existing methods offer limited reliability as they fail to explain their findings. In this paper, we propose leveraging the reasoning capabilities of Large Language Models (LLMs) for automated inference about values. This allows for not only detecting values but also explaining how they are expressed in the software. We aim to examine the effectiveness of LLMs, specifically ChatGPT (Chat Generative Pre-Trained Transformer), in automated detection and explanation of values in software artifacts. Using ChatGPT, we investigate how mobile APIs align with human values based on their documentation. Human evaluation of ChatGPT's findings shows a reciprocal shift in understanding values, with both ChatGPT and experts adjusting their assessments through dialogue. While experts recognise ChatGPT's potential for revealing values, emphasis is placed on human involvement to enhance the accuracy of the findings by detecting and eliminating convincing but inaccurate explanations provided by the language model due to potential hallucinations or confabulations. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Groupware

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Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images

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Cognitive Neurodynamics 2025年第1期19卷 1-13页

作者： Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San Dash, Pratyusa Mohapatra, Manoranjan Sabut, Sukanta Sensing and Computing Lab School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Mathematics Physics and Computing University of Southern Queensland Springfield Central QLD Australia National Heart Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Radio-Diagnosis Kalinga Institute of Medical Science Bhubaneswar India

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 (“ICH” class) and 1648 (“Normal” class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Autoencoder CAE-DNN Deep learning ICH detection Intracerebral hemorrhage (ICH) Non-contrast CT

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AC/DC hybrid microgrid energy management optimization as a decisive factor towards De-Carbonization and rational integration of electrical self-generating units using three-objective grey wolf optimization algorithm-power to X and renewable energies solutions-

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International Journal of Hydrogen Energy 2025年 138卷 1116-1130页

作者： Ouassima El Qouarti Tamou Nasser Ahmed Essadki Youssef Akarne High National School of Arts and Crafts (ENSAM) Mohammed V University in Rabat Rabat 10100 Morocco High National School for Computer Science and Systems Analysis (ENSIAS) Mohammed V University in Rabat Rabat Morocco School of Applied and Engineering Physics University Mohammed VI Polytechnic Ben Guerir 43150 Morocco

Tertiary hierarchical control of AC/DC Hybrid Microgrids (HMGs) or Energy Management System (EMS) is a widely discussed literature topic. It aims to provide well-performing systems grid interaction according to internal and external constraints, such as electricity market prices and utility grid requirements as macro-decisive vectors, and the MG local demand and generation as micro-decisive factors. In this paper, our objective is to perform the tertiary control on a grid-tied AC/DC HMG that incorporates renewable energies, distributed generation using H 2 Proton Exchange Membrane Fuel Cell (PEMFC), along with batteries and H 2 electrolysis, in order to minimize the operational cost of the MG, maximize the use of renewable energies, and reduce the C O 2 emissions based on variable wind/solar irradiation profiles and loads curves that reflect a real industrial consumption behavior. The Multi-Objective Grey Wolf Optimization (MOGWO) algorithm was performed to tune the fitness functions of this three-scale optimization problem, in order to obtain the best operational scenario for the studied system operation and storage scheduling with the help of MATLAB/Simulink simulation tool. The results approve the performance of the applied method in maximizing the use of renewable energy (84 %). They enable a reliable system through optimal power dispatch while providing a diverse Pareto front, facilitating a balanced trade-off between HMG C O 2 emissions and cost reductions.

关键词： Battery management systems

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In-situ dynamic spatial reconfiguration of nanoplasmonics using photothermal-shock tweezers

arXiv

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arXiv 2025年

作者： Zhu, Runlin Gu, Zhaoqi Shen, Tianci Liu, Yifei Shi, Zhangxing Linghu, Shuangyi Gu, Fuxing Laboratory of Integrated Opto-Mechanics and Electronics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Physics Guizhou University Guiyang550025 China

Dynamic reconfiguration is crucial for nanoplasmonic structures to achieve diversified functions and optimize performances;however, the dynamic reconfiguration of spatial arrangements remains a formidable technological challenge. Here, we showcase in-situ dynamic spatial reconfiguration of plasmonic nanowire devices and circuits on dry solid substrates, by harnessing a photothermal-shock tweezers platform. Owing to its versatility, nanoscale precision, real-time operation, and large external output force, the multimodal platform enables dexterous fine-tuning of positions, overlap lengths, and coupling distances and orientations of discrete components in situ. Spatial position-dependent optical properties that have not been reported before or are challenging to achieve through traditional micro/nanomanipulation are easily tuned and observed, such as the intensity evolution of axial photon-plasmon coupling from near field to far field, and the resonant mode evolution of photonic cavity-plasmonic cavity coupling from weak to strong. We also employ the nanorobotic probe-based operation mode to optimize the side-mode suppression ratios of single-mode lasers and the intensity splitting ratios of 3-dB couplers. Our results are general and applicable to materials of almost any size, structure, and material type, as well as other narrow or curved micro/nano-waveguide surfaces, which opens new avenues for reconfigurable nanoplasmonic structures with dynamically tunable spatial features. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanowires

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Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning

Enhancing Energy Efficiency in Wireless Sensor Networks usin...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Sandeep Raskar Gulshan Dhasmana M. Lakshminarayana Harshal Patil Bhagya Shree S Natrayan L Department of Artificial Intelligence and Data Science Terna Engineering College Nerul Navi Mumbai Maharashtra India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Bengaluru Karnataka India School of Computer Science & Engineering IILM University Greater Noida Uttar Pradesh India Department of Physics Brindavan College of Engineering Bengaluru Karnataka India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations.

关键词： Measurement Wireless sensor networks Translation Scalability Deep reinforcement learning Routing Energy efficiency Real-time systems Synchronization Optimization

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Strategies to enhance THz harmonic generation combining multilayered,gated,and metamaterial-based architectures

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光：科学与应用（英文版） 2025年第2期14卷 356-365页

作者： Ali Maleki Moritz B.Heindl Yongbao Xin Robert W.Boyd Georg Herink Jean-Michel Ménard Department of Physics University of OttawaOttawaON K1N 6N5Canada Experimental Physics Ⅷ-Ultrafast Dynamics University of BayreuthBayreuth 95447Germany Iridian Spectral Technologies Ltd OttawaON K1G 6R8Canada Department of Physics University of OttawaOttawaON K1N 6N5Canada School of Electrical Engineering and Computer Science University of OttawaOttawaON K1N 6N5Canada Institute of Optics and Department of Physics and Astronomy University of RochesterRochesterNY 14627USA Department of Physics University of OttawaOttawaON K1N 6N5Canada School of Electrical Engineering and Computer Science University of OttawaOttawaON K1N 6N5Canada

Graphene has unique properties paving the way for groundbreaking future *** large optical nonlinearity and ease of integration in devices notably makes it an ideal candidate to become a key component for all-optical switching and frequency conversion *** the terahertz(THz)region,various approaches have been independently demonstrated to optimize the nonlinear effects in graphene,addressing a critical limitation arising from the atomically thin interaction ***,we demonstrate sample architectures that combine strategies to enhance THz nonlinearities in graphene-based *** achieve this by increasing the interaction length through a multilayered design,controlling carrier density with an electrical gate,and modulating the THz field spatial distribution with a metallic metasurface *** study specifically investigates third harmonic generation(THG)using a table-top high-field THz *** measure THG enhancement factors exceeding thirty and propose architectures capable of achieving a two-order-of-magnitude *** findings underscore the potential of engineered graphene-based structures in advancing THz frequency conversion technologies for signal processing and wireless communication applications.

关键词： metamaterial strategies generation harmonic architectures based combining enhance gated multilayered

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Self-configuring high-speed multi-plane light conversion

arXiv

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arXiv 2025年

作者： Rocha, José C.A. Būtaitė, Unė G. Carpenter, Joel Phillips, David B. Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD4072 Australia

Multi-plane light converters (MPLCs) – also known as linear diffractive neural networks – are an emerging optical technology, capable of converting an orthogonal set of optical fields into any other orthogonal set via a unitary transformation. MPLC design is a non-linear problem typically solved by optimising a digital model of the optical system. However, inherently high levels of design complexity mean that even a minor mismatch between this digital model and the physically realised MPLC leads to a severe reduction in real-world performance. Here we address this challenge by creating a self-configuring free-space MPLC. Despite the large number of parameters to be optimised (typically tens of thousands or more), our proof-of-principle device converges in minutes using a method in which light only needs to be transmitted in one direction through the MPLC. Two innovations make this possible. Firstly, we devise an in-situ optimisation algorithm combining wavefront shaping with the principles of wavefront matching that would conventionally be used to inverse-design MPLCs offline in simulation. Secondly, we introduce a new MPLC platform incorporating a microelectromechanical system (MEMS) phase-only light modulator – allowing rapid MPLC switching at up to kiloHertz rates. Our scheme automatically accounts for the physical characteristics of all system components and absorbs any unknown misalignments and aberrations into the final design. We demonstrate self-configured MPLCs capable of mapping random orthogonal speckle input fields to well-defined Laguerre-Gaussian and Hermite-Gaussian output modes, as well as universal mode sorters. Our work paves the way towards large-scale ultra-high-fidelity fast-switching MPLCs and diffractive neural networks, which promises to unlock new applications in areas ranging from optical communications to optical computing and imaging. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavefronts

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Tunable Raman activity and enhanced piezoelectricity of AlGeZ2 (Z= N, P, and As) monolayers: A first-principles calculation

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Ceramics International 2025年第14期51卷 19562-19567页

作者： Vu, Tuan V. Vi, Vo T.T. Phuc, Huynh V. Anh, Nguyen P.Q. Kartamyshev, A.I. Hieu, Nguyen N. Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh city Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh city Viet Nam Faculty of Basic Sciences University of Medicine and Pharmacy Hue University Hue Viet Nam Division of Physics School of Education Dong Thap University Cao Lanh Viet Nam Faculty of Electronics and Telecommunications Saigon University 273 An Duong Vuong Ward 3 District 5 Ho Chi Minh city Viet Nam Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Faculty of Natural Sciences Duy Tan University Da Nang550000 Viet Nam

In this work, we theoretically design a series of two-dimensional (2D) materials with eight stacked sublayers AlGeZ2 (or Al2Ge2Z4) (Z= N, P, and As) and explore their structural, vibrational, piezoelectric, electronic, and transport properties via first-principles prediction. It is demonstrated that AlGeZ2 monolayers exhibit high structural stability by analyzing their phonon dispersions and ab initio molecular dynamics tests. Further, we also examine the vibrational characteristics and Raman activity of AlGeZ2 by using the finite displacement technique. At equilibrium, AlGeZ2 monolayers are found to be semiconductors with a moderate bandgap. AlGeZ2 semiconductors exhibit superior mechanical features with 2D in-plane stiffness up to 382.83 N/m (AlGeN2). The piezoelectricity examinations indicate that we can consider AlGeZ2 as piezoelectric semiconductors with in-plane piezoelectric coefficient d11 ranging from −0.22 to −0.44 pm/V. Importantly, AlGeZ2 semiconductors exhibit very high electron mobility, up to 3.71×103 cm2V−1s−1 (AlGeAs2), which is suitable for applications in flexible nanoelectronics. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Piezoelectricity

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Bi-chromatic intensity squeezing using four-wave mixing in 85Rb vapor

Optica Quantum

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Optica Quantum 2025年第1期3卷 72-77页

作者： Ziqi Niu Jianming Wen Chuanwei Zhang Shengwang Du Irina Novikova Department of Physics William & Mary Williamsburg Virginia 23187 USA Department of Electrical and Computer Engineering Binghamton University Binghamton New York 13902 USA Department of Physics Washington University in St. Louis St. Louis Missouri 63105 USA Department of Physics The University of Texas at Dallas Richardson Texas 75080 USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA

We experimentally investigate four-wave mixing (FWM) in a diamond interaction scheme using 85 Rb vapor, and identify the optimal conditions for joint amplification and relative intensity squeezing of two optical fields: one near the 85 Rb D 1 optical transition ( λ = 794.6 nm) and the other in the telecom O-band ( λ = 1324 nm). We achieved a reduction of relative intensity noise by up to 2.6 ± 0.4 dB compared with the shot noise level, signifying the non-classical quantum correlations. The observed level of intensity squeezing is primarily limited by the available pump laser power, which constrains the achievable FWM gain. Numerical simulations show good agreement with the experimental results.

关键词： High power lasers Nonlinear optical materials Numerical simulation Optical fields Shot noise Squeezed states

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