<br />
<b>Warning</b>:  Undefined array key 12 in <b>/home/ijaems/public_html/issue_feed.php</b> on line <b>18</b><br />
<br />
<b>Warning</b>:  Undefined array key 12 in <b>/home/ijaems/public_html/issue_feed.php</b> on line <b>23</b><br />
<?xml version='1.0' encoding='UTF-8'?><rss version='2.0'><channel><title>Volume 12 Number 5 (September )</title>
		<link>http://ijaems.com/</link>
		<description>Open Access international Journal to publish research paper</description>
		<language>en-us</language>
		<date>September </date><item>
		<title>Evaluating the Impact of the Skills Training Course on the Operation and Maintenance of the Rice Processing System (RPS) on the Operational Efficiency and Asset Sustainability of Farmers’ Cooperatives and Associations in Nueva Ecija, Philippines</title>
		<description>This study evaluated the operational, technical, and cognitive impact of a five-day Skills Training Course on the Operation and Maintenance of the Rice Processing System (RPS) administered across Nueva Ecija, Philippines. Using an evaluative mixed-methods concurrent triangulation design combined with a one-group pre-test/post-test quasi-experimental approach, the study monitored fifteen (15) purposively selected beneficiaries (12 Farmers&#039; Cooperatives and Associations and 3 Local Government Units) spanning Level 1, 2, and 3 facilities, alongside forty (40) individual operators. Quantitative records covering one full cropping season before and after the intervention were subjected to a paired-samples t-test at alpha = 0.05, supplemented by qualitative focus group discussions. Empirical results for the operational cohort (n = 10) revealed a marked increase in mean Milling Recovery Rate (MRR), rising from a baseline of 58.67% to a post-training average of 64.34% (+5.67 percentage points), bringing facilities into the 63.0-65.0% designed operating range of modern multi-pass systems. Unplanned facility downtime dropped by 78.75%, from 16.00 to 3.40 hours per month, while moisture tracking accuracy improved from 62.40% to 91.80%. Individual technical knowledge assessments (n = 30 paired records) showed a statistically significant mean score expansion from 12.87 to 14.77 out of 25 items, t(29) = 3.738, p = 0.0007, with secondary analysis identifying learning variances attributable to cognitive overload in the most technically complex competency areas. Post-test scores correlated strongly with both the Milling Efficiency Index, r(8) = .742, p = .014, and the Asset Sustainability Index, r(8) = .795, p = .006. Contextual audits revealed that five (5) distributed facilities were structurally constrained from immediate operationalization due to a lack of active three-phase power grid connectivity or ongoing construction backlogs. Post-training survey metrics confirmed a distinct cultural transition away from reactive breakdown frameworks toward checklist-driven preventive maintenance routines, with the asset sustainability composite score advancing from 1.77 to 4.40 out of 5.00. The study concludes that targeted capacity building is a necessary prerequisite for sustainable postharvest mechanization, and recommends that national line agencies institutionalize mandatory operator certification and enforce stricter inter-agency utility grid coordination prior to machinery distribution.</description>
		<link>http://ijaems.com/detail/evaluating-the-impact-of-the-skills-training-course-on-the-operation-and-maintenance-of-the-rice-processing-system-rps-on-the-operational-efficiency-and-asset-sustainability-of-farmers-cooperatives-and-associations-in-nueva-ecija-philippines/</link>
		<author>Irish Ramos Arambulo, Christopher M. Ladignon, Lorinda E. Pascual</author>
		<pdflink>http://ijaems.com/upload_images/issue_files/1IJAEMS-10820263-Evaluating.pdf</pdflink>
                
		</item><item>
		<title>Integration of AI-Assisted Development Tools into Modern Software Delivery Workflows and Their Impact on Engineering Productivity</title>
		<description>AI-assisted development tools now enter routine software delivery through code completion, repository-aware coding assistants, automated pull request (PR) comments, and AI-supported testing. Their effect on productivity depends on the workflow stage in which engineers use them, the quality controls around generated output, and the way senior developers preserve ownership of design decisions. This article develops an analytical model for integrating AI code generation and AI-driven pull request review into modern software delivery. The study uses comparative source analysis, conceptual synthesis, typologization, and analytical generalization based on ten recent academic and industry-facing publications on large language models in software engineering, GitHub Copilot, automated review, hallucinations, package risks, and security attacks. The article separates measured evidence from practitioner-reported operational estimates. It identifies where AI shortens implementation work, where it adds review burden, and which governance mechanisms protect code quality, onboarding, mentoring, dependency control, and release stability.</description>
		<link>http://ijaems.com/detail/integration-of-ai-assisted-development-tools-into-modern-software-delivery-workflows-and-their-impact-on-engineering-productivity/</link>
		<author>Michael Rainesh</author>
		<pdflink>http://ijaems.com/upload_images/issue_files/2IJAEMS-10920261-Integration.pdf</pdflink>
                
		</item></channel>
</rss>