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A machine learning and neural network approach for classifying multidrug-resistant bacterial infections

Healthcare Analytics 2025年 7卷 100388-100388页

作者： Mengsiri, Preeda Ungcharoen, Ratchadaporn Gertphol, Sethavidh Biomedical Data Science Program Faculty of Science Kasetsart University Thailand Faculty of Public Health Kasetsart University Chalermphrakiat Sakon Nakhon Province Campus Thailand Department of Computer Science Faculty of Science Kasetsart University Thailand

Antimicrobial resistance (AMR) represents a major public health challenge, significantly complicating infection prevention and treatment. This study employs machine learning and neural network techniques to classify multidrug-resistant Gram-negative bacterial (MDR-GNB) infections using electronic health records from 624 patients at Thatphanom Crown Prince Hospital in Thailand. We compared several algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM), with the MLP model exhibiting the highest accuracy and specificity. Performance was further enhanced by integrating feature selection methods such as Sequential Forward Selection (SFS), Recursive Feature Elimination with Cross-Validation (RFE-CV), and SelectKBest with data augmentation techniques, including ADASYN and SMOTE variants. Utilizing SHapley Additive exPlanations (SHAP) provided valuable insights into the most influential predictors for MDR-GNB. Notably, the MLP model achieved an AUC of 0.70, surpassing prior studies and highlighting its potential to advance clinical decision-making in managing MDR-GNB infections. © 2025 The Authors

关键词： Electronic health records Gram-negative bacteria Machine learning Multidrug resistance Neural network

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding

arXiv

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arXiv 2025年

作者： Elhenawy, Mohammed Ashqar, Huthaifa I. Rakotonirainy, Andry Alhadidi, Taqwa I. Jaber, Ahmed Tami, Mohammad Abu CARRS-Q and Centre for Data Science Queensland University of Technology Kelvin GroveQLD4059 Australia Data Science and AI Department Arab American University P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University New YorkNY10027 United States

Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analysis on the Honda Scenes dataset, which contains a collection of about 80 hours of annotated driving videos capturing diverse real-world road and weather conditions, our study highlights the robustness of CLIP models in learning visual concepts from natural language supervision. Results also showed that fine-tuning the CLIP models, such as ViT-L/14 and ViT-B/32, significantly improved scene classification, achieving a top F1-score of 91.1%. These results demonstrate the ability of the system to deliver rapid and precise scene recognition, which can be used to meet the critical requirements of Advanced Driver Assistance Systems (ADAS). This study shows the potential of CLIP models to provide scalable and efficient frameworks for dynamic scene understanding and classification. Furthermore, this work lays the groundwork for advanced autonomous vehicle technologies by fostering a deeper understanding of driver behavior, road conditions, and safety-critical scenarios, marking a significant step toward smarter, safer, and more context-aware autonomous driving systems. © 2025, CC BY.

关键词： Advanced driver assistance systems

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： Mobile edge computing

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Momentum-Based Distributed Economic Dispatch of Smart Grids in Time-Varying Directed Networks

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IEEE Transactions on Network science and Engineering 2025年

作者： Zhang, Keke Lu, Qingguo Zhou, Qixing Li, Huaqing Xia, Dawen Huang, Tingwen Chongqing University College of Computer Science China Southwest University College of Electronic and Information Engineering China Kaili University College of Microelectronics and Artificial Intelligence College of Big Data Engineering China Texas A&M University at Qatar Science Program Doha23874 Qatar

In this paper, we investigate the economic dispatch problem of smart grids in time-varying directed networks. The EDP essentially revolves around optimizing the distribution of generation power amongst multiple generators, with the aim of fulfilling load demands at the lowest possible total generation cost, while strictly conforming to the constraints imposed by the local generation capacity. For a faster resolution of the EDP, we propose an efficient distributed accelerated economic dispatch algorithm that incorporates a unified momentum acceleration strategy into the push-pull deviation tracking approach. The involved acceleration strategy under specific momentum parameters includes two well-known acceleration strategies, i.e., the heavy-ball and Nesterov acceleration strategies, which is more flexible and provides additional improvements in convergence. We present rigorously theoretical proof of linear convergence to the optimal dispatch with explicit bounds for step size and momentum parameters. Finally, to verify the effectiveness of our algorithm and the correctness of the theoretical analysis, we conduct simulations of diverse EDP studies in smart grids. © 2013 IEEE.

关键词： Smart power grids

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Adaptive barrier function terminal sliding mode control for nonlinear systems subject to uncertain bounded perturbations

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Ain Shams Engineering Journal 2025年第7期16卷

作者： Wang, ZiKai Hsia, Kuo-Hsien Mobayen, Saleh Mofid, Omid Skruch, Pawel China Bachelor Program in Intelligent Robotics National Yunlin University of Science and Technology Douliou640301 Taiwan of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan Computer Science Department University of Tulsa OK India Department of Automatic Control and Robotics AGH University of Krakow Poland

This research proposes a tracking control strategy for perturbed and uncertain nonlinear systems by incorporating an adaptive barrier function within a terminal sliding mode control framework. The proposed approach leverages terminal sliding mode control, integrated with the global convergence principle, to ensure global finite-time tracking performance. Additionally, the adaptive barrier function mechanism is employed to effectively mitigate the impact of system perturbations and uncertainties, enhancing overall robustness and stability in nonlinear system control. The key findings and advantages of the proposed method are summarized as: (1) The use of terminal sliding mode control enhances the system's robustness;(2) Since the controller derivative is continuous except for a discontinuous sign function, integrating the barrier function-based control signal effectively minimizes chattering;(3) By incorporating an adaptive barrier-tuning function, the method ensures robustness against system uncertainties, disturbances, and nonlinearities while mitigating the chattering effect;(4) The proposed method improves tracking accuracy and reduces chattering caused by switching laws;(5) This novel approach holds significant potential for real-world applications, particularly in tracking control scenarios involving input saturations and time delays in uncertain nonlinear systems. Additionally, the proposed approach is highly adaptable for practical applications as it employs two adaptation rules to handle uncertainties and disturbances with unknown upper bounds under specific conditions. Numerical simulation and experimental validation on a rotary inverted pendulum system demonstrate the effectiveness and feasibility of the proposed control scheme for the trajectory tracking control of the real nonlinear system, which confirms the compatibility of the proposed method in real applications. © 2025 The Author(s)

关键词： Control nonlinearities

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Innovative biomarker exploration in ASD: Combining Graph Neural Networks and permutation testing on fMRI data

引用

NeuroImage: Reports 2025年第2期5卷 100249-100249页

作者： Wang, Donglin Ding, Wandi Department of Mathematical Sciences Computational and Data Science Program Middle Tennessee State University 1301 E Main St Murfreesboro 37132 TN United States

This study employed Graph Neural Networks (GNNs), specifically an unsupervised GNN, to extract node embeddings from brain regions in both Autism Spectrum Disorder (ASD) and control groups. The objective was to identify potential biomarkers by analyzing node embeddings extracted from a graph model based on functional Magnetic Resonance Imaging (fMRI) data. Permutation tests were conducted to identify regions with significant differences in their embeddings between the two groups. Our results revealed several regions exhibiting significant differences, including the cerebellum, temporal lobe, and occipital lobe. These findings align with previous studies on ASD. Moreover, novel regions such as Vermis_3, Vermis_4_5, Fusiform areas, Parietal, and Cuneus were identified, emphasizing the need for further investigation. This study underscores the potential of GNNs in analyzing brain networks for ASD biomarker discovery. The identified regions warrant additional validation and exploration to understand their association with specific domains of ASD symptoms. Our approach presents a promising avenue to advance the diagnosis of ASD and to improve our understanding of its underlying neural basis. © 2025 The Authors

关键词： Autism Spectrum Disorder (ASD) Biomarker Graph Neural Networks (GNNs) Permutation test

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Correction: Learning distribution-free anchored linear structural equation models in the presence of measurement error

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Journal of the Korean Statistical Society 2025年 1-2页

作者： Chung, Junhyoung Ahn, Youngmin Shin, Donguk Park, Gunwoong Department of Statistics Seoul National University Seoul Republic of Korea Institute for Data Innovation in Science Seoul National University Donguk Shin Seoul Republic of Korea Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea

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Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective

arXiv

引用

arXiv 2025年

作者： Sharma, Buddha Nath Rai, Anish Luwang, Salam Rabindrajit Nurujjaman, Md. Majhi, Sushovan Department of Physics National Institute of Technology Sikkim Sikkim737139 India AlgoLabs Chennai Mathematical Institute 603103 India Data Science Program George Washington University WashingtonDC20052 United States

The paper presents a comprehensive causality analysis of the US stock and commodity markets during the COVID-19 crash. The dynamics of different sectors are also compared. We use Topological data Analysis (TDA) on multidimensional time-series to identify crashes in stock and commodity markets. The Wasserstein Distance (WD) shows distinct spikes signaling the crash for both stock and commodity markets. We then compare the persistence diagrams of stock and commodity markets using the WD metric. A significant spike in the WD between stock and commodity markets is observed during the crisis, suggesting significant topological differences between the markets. Similar spikes are observed between the sectors of the US market as well. Spikes obtained may be due to either a difference in the magnitude of crashes in the two markets (or sectors), or from the temporal lag between the two markets suggesting information flow. We study the Granger-causality between stock and commodity markets and also between different sectors. The results show a bidirectional Granger-causality between commodity and stock during the crash period, demonstrating the greater interdependence of financial markets during the crash. However, the overall analysis shows that the causal direction is from stock to commodity. A pairwise Granger-causal analysis between US sectors is also conducted. There is a significant increase in the interdependence between the sectors during the crash period. TDA combined with Granger-causality effectively analyzes the interdependence and sensitivity of different markets and sectors. © 2025, CC BY.

关键词： Financial markets

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