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The Large Language Models on Biomedical Data Analysis: A Survey

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT

arXiv

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arXiv 2025年

作者： Mei, Zihao Li, Jianhao Zhang, Bolin Wang, Chong Guo, Lijun Li, Guoqi Qian, Jiangbo Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing100045 China

With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high-frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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Enhanced nonlinear sliding mode control technique for wind power generation systems application: Theoretical design and comparative study

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年 11卷

作者： El Fadili, Yattou Boumhidi, Ismail Computer Science Signal Automation and Cognitivism Laboratory Physics Department Faculty of Sciences Dhar El Mahraz (FSDM) University of Sidi Mohamed Ben Abdellah (USMBA) Fez Morocco

Wind power generation systems (WPGSs) have emerged as a vital source of clean energy, appreciated for their renewable nature and zero fuel costs. However, controlling these systems is challenging due to their nonlinear dynamics, unpredictable disturbances, parameter uncertainties, and rapid wind speed fluctuations. To address these challenges, robust control strategies are essential. This study presents a novel robust nonlinear controller for regulating the electromagnetic torque of horizontal-axis, variable-speed WPGSs with three blades connected to the grid. The proposed controller is designed to enhance system efficiency and profitability by maximizing electricity production, reducing torque ripples, eliminating peak overshoots, and achieving precise setpoint tracking with optimal transient performance. The controller combines sliding mode (SM) control with fractional calculus (FC) to exploit the benefits of both methods. SM control, known for its effectiveness in controlling nonlinear systems, stabilizes the system and ensures finite-time convergence to the desired state. The incorporation of fractional-order operators into the sliding surface introduces greater flexibility, leveraging the long-term memory properties of FC to enhance system stability and robustness while mitigating chattering effects. The stability of the proposed controller is rigorously validated through Lyapunov theory. The simulation phase is carried out in MATLAB under various wind conditions and operating scenarios demonstrate the controller's superior performance. The results show its ability to reduce chattering phenomena, improve power quality, and minimize high peaks in the input controller by achieving reductions of 38.0856 KN.m in the first test and 280.2039 KN.m in the second test. Additionally, this controller ensures system stability, with a Lyapunov function standard deviation of 0.0105 and 0.1755 for the first and second tests, respectively. The controller also achieves a high e

关键词： Boundary layer technique fractional integral operator Lyapunov stability theory maximum power point tracking (MPPT) Sliding mode control (SMC) Variable-speed wind power generation systems

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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Short-Term Load Forecasting Based on Graph Convolution and Dendritic Deep Learning

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IEEE Transactions on Network science and Engineering 2025年

作者： Zhang, Chunyang Yu, Yang Zhang, Tengfei Song, Keyu Wang, Yirui Gao, Shangce Nanjing University of Posts and Telecommunications College of Automation & College of Artificial Intelligence Nanjing210023 China Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Key Laboratory of Mobile Network Application Technology Zhejiang315211 China University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan

Short-term load forecasting (STLF) is a significant task to the planning, operation and control of future power systems. The increasing number of devices connected to the system has led to more complex characteristics and forms of load, which has brought considerable difficulties to the relevant methods in achieving higher load prediction accuracy and reliability. In this regard, this study proposes a deep learning model that combines graph convolutional network (GCN), gated recurrent unit (GRU), and dendritic neural model (DNM) to forecast electric load more accurately. Firstly, the sample load data is constructed into graph data with individual time steps as nodes. A GCN is used to extract the hidden features while allowing a full communication between the time steps feature data. A GRU is then used to capture the time-dependent relationship of the data. Finally, a dendritic layer instead of a fully connected layer is used as the output to integrate data features in depth. Experiments are conducted to verify the validity of the proposed model and compared it with several effective deep learning models, including CNN_LSTM, Transformer and Kolmogorov-Arnold Networks (KAN). The results show a significant improvement in prediction compared to the baseline models, with mean absolute percentage error(MAPE) of 1.62% and 3.98%, coefficient of determination(R2) of 0.983 and 0.928 respectively on two load datasets at different levels of aggregation, nationally and regionally. © 2013 IEEE.

关键词： Convolutional neural networks

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Quantum Complex-Valued Self-Attention Model

arXiv

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arXiv 2025年

作者： Chen, Fu Zhao, Qinglin Feng, Li Tang, Longfei Lin, Yangbin Huang, Haitao Faculty of Innovation Engineering Macau University of Science and Technology 999078 China College of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China College of Computer Engineering College Jimei Univeristy Xiamen361021 China

The self-attention mechanism has revolutionized classical machine learning, yet its quantum counterpart remains underexplored in fully harnessing the representational power of quantum states. Current quantum self-attention models exhibit a critical limitation by neglecting the indispensable phase information inherent in quantum systems when compressing attention weights into real-valued overlaps. To address this fundamental gap, we propose the Quantum Complex-Valued Self-Attention Model (QCSAM), the first framework that explicitly leverages complex-valued similarities between quantum states to capture both amplitude and phase relationships. Simultaneously, we enhance the standard Linear Combination of Unitaries (LCUs) method by introducing a Complex LCUs (CLCUs) framework that natively supports complex-valued coefficients. This framework enables the weighting of corresponding quantum values using fixed quantum complex self-attention weights, while also supporting trainable complex-valued parameters for value aggregation and quantum multi-head attention. Experimental evaluations on MNIST and Fashion-MNIST demonstrate our model’s superiority over recent quantum self-attention architectures including QKSAN, QSAN, and GQHAN, with multi-head configurations showing consistent advantages over single-head variants. We systematically evaluate model scalability through qubit configurations ranging from 3 to 8 qubits and multi-class classification tasks spanning 2 to 4 categories. Through comprehensive ablation studies, we establish the critical advantage of complex-valued quantum attention weights over real-valued alternatives. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Minimization of Nonlinear Energies in Python Using FEM and Automatic Differentiation Tools 15th

Minimization of Nonlinear Energies in Python Using FEM and...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Béreš, Michal Valdman, Jan Institute of Geonics of the Czech Academy of Sciences Studentská 1768 Ostrava708 00 Czech Republic Department of Applied Mathematics VSB - Technical University of Ostrava 17. listopadu 15/2172 Ostrava708 00 Czech Republic Institute of Information Theory and Automation of the Czech Academy of Sciences Pod vodárenskou věží 4 Prague18208 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic

ISBN: (纸本)9783031857027

This contribution examines the capabilities of the Python ecosystem to solve nonlinear energy minimization problems, with a particular focus on transitioning from traditional MATLAB methods to Python’s advanced computational tools, such as automatic differentiation. We demonstrate Python’s streamlined approach to minimizing nonlinear energies by analyzing three problem benchmarks - the p-Laplacian, the Ginzburg-Landau model, and the Neo-Hookean hyperelasticity. This approach merely requires the provision of the energy functional itself, making it a simple and efficient way to solve this category of problems. The results show that the implementation is about ten times faster than the MATLAB implementation for large-scale problems. Our findings highlight Python’s efficiency and ease of use in scientific computing, establishing it as a preferable choice for implementing sophisticated mathematical models and accelerating the development of numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Python

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TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules

arXiv

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arXiv 2025年

作者： Zhang, Ruoyu Wang, Lulu He, Yi Pan, Tongling Yu, Zhengtao Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Key Laboratory of Computer Technologies Application Kunming650504 China Hongyun Honghe Group Honghe Cigarette Factory Kunming650202 China

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs;however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs’ inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs’ contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： Information filtering

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Temperature stability analysis of a Ti:LiNbO3 optical waveguide electric field sensor

引用

Applied Optics 2025年第13期64卷 3693-3701页

作者： Wei Song Manling Dong Xiaokuo Kou Zhenyu Zhan Jiahong Zhang Qihong Ding Shaoqi He Qian Chen Jing Zhang Yingna Li State Grid Henan Electric Power Company Zhengzhou Henan 450000 China State Grid Henan Electric Power Research Institute Zhengzhou Henan 450052 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming 650504 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming 650504 China

The output static optical power of the developed $y$ -cut $z$ -propagation ${\rm Ti}{:}{{\rm LiNbO}_{3}}$ optical waveguide electric field sensor has been tested under the temperature changes from 5°C to 35°C. The results show that the static optical power has decreased for 866.6 µW, corresponding to a change of 32.79° in the static operating point ${\varphi _B}$ and an electric field measurement error exceeding 10%. Based on the finite element method, the light wave modes in the optical waveguide have been simulated. The results reveal that there is a small ${E_z}$ component in both the quasi-TE and TM modes, causing ${\varphi _B}$ to be affected not only by the thermal expansion effect but also by the thermal-optic effect. It has been calculated that the effects of thermal-optic and thermal expansion on ${\varphi _B}$ are approximately ${-}{0.3301}^\circ {\rm /}^\circ {\rm C}$ and 0.0087°/°C, respectively, resulting within 5–35°C, the static optical power variation is approximately ${-}{267.94}\;\unicode{x00B5} {\rm W}$ , with a rate of change is ${-}{8.93}\;\unicode{x00B5} {\rm W/}^\circ {\rm C}$ . Furthermore, the influence of temperature on the birefringence of the sensor output polarization-maintaining fiber (PMF) has been considered and analyzed. It has been found that the phase difference of the slow and fast light waves changes by ${-}{3.050}\;{\rm rad/K}$ when the length of the PMF is 1488 mm. As a result, considering the effects of thermo-optical and thermal expansion as well as the influence of the PMF, the sensor output static optical power changes ${-}{868.86}\;\unicode{x00B5} {\rm W}$ , with temperature changes from 5°C to 35°C, which is consistent with the experiment results.

关键词： Effective refractive index Finite element method Pockels effect Slow light Temperature sensors Thermal effects

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FSIW Cavity-Based Self-Packaging Filtering Slot Antennas with Wide-Stopband and High Frequency Selectivity

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IEEE Transactions on Antennas and Propagation 2025年

作者： Zhou, Xin Zhang, Gang Tam, Kam-Weng Pang, Ziyu Zhang, Zhuowei Lin, Huawei Hong, Jiasheng University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Nanjing Normal University School of Electrical and Automation Engineering Nanjing210046 China Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing210046 China Heriot-Watt University School of Engineering and Physical Sciences Department of Electrical Electronic and Computer Engineering EdinburghEH14 4AS United Kingdom

This article proposes a novel design approach for miniaturized, highly selective, self-packaged, and wide-stopband filtering slot antennas based on C- and T-type folded substrate integrated waveguide (C-/T-FSIW) cavities. Firstly, two forms of FSIW cavity are explored and analyzed. A new coupled C-type half-mode FSIW (C-HMFSIW) cavities are developed. A filtering antenna is proposed, employing a C-HMFSIW cavity for feeding and a slot-loaded C-FSIW cavity for radiation. By introducing cross-coupling between different resonators, a radiation null (RN) is achieved. To further enhance bandwidth and frequency selectivity, a new antenna configuration is developed using a C-HMFSIW cavity for feeding and a slot-loaded T-FSIW cavity for radiation. After loading the radiation slot, the T-FSIW cavity serves as two parts: one as the main radiator and the other as a parasitic radiator opposite to it. This configuration increases antenna bandwidth and generates an additional RN. Interestingly, adjusting the position of the radiation slot allows the RN to switch between the upper and lower stopbands. The antennas proposed in this work exhibit good stopband performance and compact size. Three filtering antenna prototypes are designed, fabricated, and measured, showing consistent results between measurements and simulations. © 1963-2012 IEEE.

关键词： Radiators

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