We address the problem of deciding how many positions to set aside for military recruits undergoing training. Within a cap on the total number of military members, we vary the ratio between positions allocated to the training pipeline versus those required in the trained effective establishment. This is done with the goal of determining the extent to which given ratios are sustainable. We use a Markovian model of the training pipeline, with parameters derived from historical personnel data. Through Monte Carlo simulation, we predict how often a given ratio allows the required trained force to be fully generated, as well as the surplus of trained personnel, it is expected to generate. We extend our previous work in this area by considering an alternative Human Resources policy that uncaps the training pipeline. Our modelling results have informed ongoing initiatives to optimize the force mix and structure of the Canadian Armed Forces.
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http://dx.doi.org/10.1007/s42979-022-01122-z | DOI Listing |
Biochim Biophys Acta Mol Basis Dis
January 2025
MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China; Key Laboratory of Alkene-carbon Fibres-based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou 215123, China; Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou 215123, China. Electronic address:
Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes.
View Article and Find Full Text PDFCurr Opin Struct Biol
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, United Kingdom.
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modelling of the CDRH3 loop, where inaccuracies are potentially worse due to a bias in structural data towards antibody-antigen complexes. This class imbalance provides a challenge for deep learning models trained on this data, potentially limiting generalisation to unbound forms.
View Article and Find Full Text PDFViruses
December 2024
Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania.
Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Energy and Power Engineering, Xihua University, Chengdu 610039, China.
Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years.
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