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Second-generation panel unit root and cointegration tests are used because of the cross-sectional dependence between our considered variables. The cross-sectional distributed lag (CS-DL) methodology is performed to estimate our long-run coefficients. Several novel results are highlighted. In the long-run, arms imports increase political stability and economic growth. While military expenditures increase oil exports, arms imports slightly reduce them.  Oil exports increase military expenditures but reduce arms imports. Political stability reduces military expenditures and increases gross domestic product. These oil-exporting Gulf countries are advised to reinforce their military efforts, in particular by planning the production of high-tech weapons, to improve their oil exports and thus their gross domestic product. 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To identify putative causal genes from these data, we performed computational modelling using available genome-wide association data sets for platelet traits. Our model identified a joint collection of genomic features enriched at established platelet trait associations and plausible candidate variants. Additional studies associating variation at these loci with change in gene expression highlighted the\n                  <jats:italic>Tropomyosin 1<\/jats:italic>\n                  (\n                  <jats:italic>TPM1<\/jats:italic>\n                  ) among our top-ranked candidate genes. CRISPR\/Cas9-mediated\n                  <jats:italic>TPM1<\/jats:italic>\n                  knockout in human induced pluripotent stem cells (iPSCs) enhanced hematopoietic progenitor development, increasing total megakaryocyte as well as erythroid cell yields. 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This technology provides an efficient solution to the over-choice problem by quickly combing through large amounts of information and supplying recommendations that fit each user\u2019s individual preferences. It is quickly becoming an integral part of operations, as it yields successful and convenient results. This research provides an enhanced integrated fuzzy logic-based deep learning technique (EIFL-DL) for recent industrial challenges. Extracting useful insights and making appropriate suggestions in industrial settings is difficult due to the fast development of data. Traditional RSs often struggle to handle the complexity and uncertainty inherent in industrial data. To address these limitations, we propose an EIFL-DL framework that combines fuzzy logic and deep learning techniques to enhance recommendation accuracy and interpretability. The EIFL-DL framework leverages fuzzy logic to handle uncertainty and vagueness in industrial data. 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