Title: |
Design and Testing of a Refrigeration System with a Heat Recovery Device for Energy Saving |
Authors: |
Cong Vinh Ngu |
Source: |
International Journal of Latest Engineering Research and Applications, pp 01 - 07, Vol 10 - No. 11, 2025 |
Abstract: |
To improve energy efficiency in industrial refrigeration systems, a heat recovery device was designed and installed on a model system. Based on theoretical analysis, the system’s performance was evaluated under two operating conditions: with and without heat recovery. After the model was fabricated, experiments were conducted to measure inlet and outlet parameters over specific time intervals and operating conditions. The collected data were analyzed and compared using simulation methods to identify key factors influencing performance. Results showed that incorporating a heat recovery device increased the coefficient of performance (COP) from 3,0 to 3,44 and reduced the actual ice-making time by approximately 15 minutes compared to the system without heat recovery. This demonstrates a significant reduction in power consumption, with experimental results consistent with theoretical predictions. |
Keywords: |
Refrigerant, Temperature, Pressure, Heat recovery device, Experimental results |
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Title: |
Better Understanding the Loyalty Strategies of Commercial Banks in Côte d'Ivoire: An Exploratory Qualitative Study |
Authors: |
Coulibaly Issa |
Source: |
International Journal of Latest Engineering Research and Applications, pp 08 - 14, Vol 10 - No. 11, 2025 |
Abstract: |
The objective of this research is to apprehend through an exploratory qualitative study, the loyalty practices of commercial banks in Côte d'Ivoire.
The thematic content analysis of the accounts of interviews with 7 bankers shows that customer loyalty is achieved according to a customer relationship management approach in which the main actor is the customer advisor. Based on customer segmentation carried out mainly on the basis of income, banks set up loyalty programs in order to optimizecustomer relations and improve financial performance. |
Keywords: |
Relationship Marketing, Loyalty Practices, Exploratory Qualitative Research, Customer Relationship Management, Commercial Banks |
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DOI: |
10.56581/IJLERA.10.11.08-14 |
Title: |
A Generative AI Framework for High-Accuracy Brain Lesion Detection in MRI |
Authors: |
Franchini Roberto, Bianco Andrea |
Source: |
International Journal of Latest Engineering Research and Applications, pp 15 - 22, Vol 10 - No. 11, 2025 |
Abstract: |
Recent advancements in generative artificial intelligence (AI) are transforming non-invasive diagnosis in medical imaging, particularly in brain lesion detection through Magnetic Resonance Imaging (MRI). Traditional radiological analysis requires expert interpretation and manual lesion delineation, which are time-consuming and subject to observer variability. Generative AI offers an automated alternative that enhances diagnostic accuracy through image synthesis, augmentation, and segmentation.Models such as Generative Adversarial Networks (GANs) and diffusion-based architectures can simulate realistic MRI scans, recover missing anatomical information, and highlight subtle pathological features. These systems learn mappings between healthy and diseased tissues, generating high-fidelity synthetic data useful for both diagnosis and model training. Moreover, generative models help overcome the limitation of small labeled datasets by producing anatomically consistent synthetic images.This study presents a generative AI-based framework for automated brain lesion recognition and classification, integrating preprocessing, lesion-focused data augmentation, and hybrid discriminative–generative modeling. The approach prioritizes robustness and interpretability through explainable modules and uncertainty estimation. Results show that generative learning improves segmentation and classification accuracy, especially for rare lesions, underscoring its potential as a core technology for the next generation of precise, non-invasive diagnostics. |
Keywords: |
AI based framework, Brain lesion detection, Generative Adversarial Networks, Generative artificial intelligence, Magnetic Resonance Imaging. |
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DOI: |
10.56581/IJLERA.10.11.15-22 |
Title: |
AI-Augmented Integral Transform Techniques for Solving Nonlinear Differential Equations in Engineering |
Authors: |
Amarender Reddy Kommula, Dr. Sonu Gupta, Dr. Akhilesh Kumar Mishra |
Source: |
International Journal of Latest Engineering Research and Applications, pp 23 - 25, Vol 10 - No. 11, 2025 |
Abstract: |
This work introduces a modern computational approach that blends classical integral transforms with artificial intelligence (AI) to solve nonlinear differential equations commonly seen in engineering. Traditional transforms—such as Laplace, Fourier, and Mellin—are powerful, but they often struggle with nonlinear or irregular systems. To address these limitations, we propose an AI-enhanced transform method that automatically learns and adjusts transformation parameters, improving accuracy and stability. Using MATLAB and Python simulations, the method is tested on heat transfer, structural vibration, and nonlinear fluid flow problems. Results show faster convergence, reduced numerical error, and broader applicability when compared to conventional transform techniques. |
Keywords: |
Integral Transform, Laplace-Mellin Transform, Artificial Intelligence, Numerical Methods, Engineering Applications, Adaptive Algorithms. |
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DOI: |
10.56581/IJLERA.10.11.23-25 |
Title: |
Standard Predetermined Time on Line 4 of the Sub Assembly Area in an Aerospace Company |
Authors: |
Arnulfo Aurelio Naranjo Flores, Ernesto Ramírez Cárdenas, Iván Francisco Rodríguez Gámez, Alicia Navarro Hernández |
Source: |
International Journal of Latest Engineering Research and Applications, pp 26 - 33, Vol 10 - No. 11, 2025 |
Abstract: |
This study aimed to determine the workload of operators on a production line in an aerospace-sector company, with the goal of increasing operational efficiency by at least 17%. The methodology involved analyzing the process under study, determining the cycle time, calculating the takt time, conducting workload balancing among operators, and evaluating overall efficiency. The main results demonstrated a reduction in staffing from 14 to 11 operators, yielding a 17% decrease in labor costs and a 17.48% improvement in efficiency. It is concluded that the post-balancing scenario produced a positive outcome, indicating a satisfactory enhancement in the efficiency of the subassembly line. |
Keywords: |
Aerospace, Cycle time, Efficiency, Takt time, workload |
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DOI: |
10.56581/IJLERA.10.11.26-33 |
