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The frequency and intensity of massive natural disasters are increasing significantly with climate change. In response, the need and interest in disaster response systems are increasing. One of the disaster response's most important activities is delivering relief resources. This process has distinct characteristics from the general logistics environment, such as uncertainty in information transmission and the importance of deadlines. To enhance the efficiency of this process, a new distributed emergency logistics system is proposed by focusing on emergency logistics for the delivery of relief resources. In the proposed system, to overcome the uncertainty in information transmission, the distributed agent architecture which is applied to facility management and vehicle planning is adopted. In this system, each vehicle as an agent and facility gathers up-to-date information and generates its plan; then, the generated plans are coordinated through communication between vehicles. The proposed algorithm was evaluated through simulation experiments based on the Korean urban environment.
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http://dx.doi.org/10.1038/s41598-025-91024-w | DOI Listing |
J Med Microbiol
September 2025
Alberta Precision Laboratories Public Health Lab, Edmonton, Alberta, Canada.
For thousands of years, parasitic infections have represented a constant challenge to human health. Despite constant progress in science and medicine, the challenge has remained mostly unchanged over the years, partly due to the vast complexity of the host-parasite-environment relationships. Over the last century, our approaches to these challenges have evolved through considerable advances in science and technology, offering new and better solutions.
View Article and Find Full Text PDFGenome Biol
September 2025
Department of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, 90089, USA.
Background: Recent advances in high-throughput sequencing technologies have enabled the collection and sharing of a massive amount of omics data, along with its associated metadata-descriptive information that contextualizes the data, including phenotypic traits and experimental design. Enhancing metadata availability is critical to ensure data reusability and reproducibility and to facilitate novel biomedical discoveries through effective data reuse. Yet, incomplete metadata accompanying public omics data may hinder reproducibility and reusability and limit secondary analyses.
View Article and Find Full Text PDFNucleic Acids Res
September 2025
School of Software, Shandong University, Jinan 250101, Shandong, China.
Spatial transcriptomics (ST) reveals gene expression distributions within tissues. Yet, predicting spatial gene expression from histological images still faces the challenges of limited ST data that lack prior knowledge, and insufficient capturing of inter-slice heterogeneity and intra-slice complexity. To tackle these challenges, we introduce FmH2ST, a foundation model-based method for spatial gene expression prediction.
View Article and Find Full Text PDFForensic Sci Int Synerg
December 2025
DNA Analysis Laboratory, Natural Sciences Research Institute, University of the Philippines Diliman, Quezon City 1101 Philippines.
Massively parallel sequencing (MPS) has caused a paradigm shift in forensic DNA analysis by enabling simultaneous examination of multiple genetic markers with higher resolution. Despite its growing importance, adoption in the 11 Southeast Asian countries remains limited. This paper reviews MPS implementation in forensic DNA laboratories across the region and discusses key adoption challenges.
View Article and Find Full Text PDFCrit Care Res Pract
August 2025
Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sepsis remains one of the leading causes of morbidity and mortality worldwide, particularly among critically ill patients in intensive care units (ICUs). Traditional diagnostic approaches, such as the Sequential Organ Failure Assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria, often detect sepsis after significant organ dysfunction has occurred, limiting the potential for early intervention. In this study, we reviewed how artificial intelligence (AI)-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can aid physicians.
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