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This paper addresses the need for innovative optimization solutions in automotive manufacturing. Through advanced algorithms, we review existing methods and introduce novel ap- proaches tailored to this sector. Our literature review identifies gaps and limitations in current methodologies. We define a specific optimization problem within automotive manufacturing, emphasizing its unique challenges. Our key contributions include: (a) Exploring hybrid optimization algorithms, combining genetic algorithms with simulated annealing for a 15% improvement in convergence speed, (b) Integrating machine learning techniques, resulting in a 20% reduction in optimization error compared to static settings, (c) Incorporating multiobjective optimization, achieving a 25% improvement in simultaneous cost and efficiency optimization, and (d) Proposing dynamic optimization algorithms, reducing decision-making latency by 30% during rapid environmental changes. Case studies demonstrate practical ap- plication, with quantitative results highlighting the superiority of our approaches over traditional methods. Additionally, the data analysis was conducted using Python, contributing to the robustness and accuracy of our findings.

Background: Understanding the microbiota of disease vectors can help for developing new strategies to prevent the transmis­sion of vector pathogens. Ixodes ricinus is one of the most notorious tick vectors with increasing importance in Iran and other parts of the world while there is limited data on its microbiota. This study aimed to use metagenomics for identifying the I. ricinus tick’s microbiota of Iran. Methods: A total of 39 adult ticks were collected from Mazandaran (21 females), Gilan (17 females), and Golestan (1 male). Five tick pools prepared from 39 adults of I. ricinus were subjected to metagenomics analysis. The data were analyzed by targeting the V6 region of the 16S rRNA gene by Illumina 4000 Hiseq sequencing. Results: Among hundreds of intestinal microbiota identified by metagenomics, various pathogenic microorganisms distributed in 30 genera and species including those responsible for tick-borne diseases resided in the genera Coxiella, Rickettsia, and Burkholderia were found. Conclusion: Our results indicated that metagenomics identifies bacteria genera and species which cannot be easily rec­ognized by routine methods. The presence of such pathogenic bacteria indicates the importance of possible zoonotic diseases in this region which could affect public health. These results further substantiate the importance of advanced metagenomics analyses to identify neglected tick-borne pathogens which enable researchers to provide efficient mapping roads for the management of emerging and re-emerging infectious diseases.