International Journal of Pioneering Technology and Engineering

Optimization of the Desktop CPU's Straight Heatsink via CFD Simulation by Solidworks Flow Simulation

2025-01-25T11:32:19+03:00

The straight heatsink is one of the most common heat transfer components used in desktop CPUs to manage the heat generated by the microprocessor. The study aimed to find the optimal fin numbers of the straight heatsink for three different fin thicknesses and compare the masses at these points. For the analysis, the present study used Solidworks^® software to create CAD models and perform the CFD simulation. It was found that each of the three different fin thicknesses had a turning point at which the microprocessor’s temperature was at its minimum. The weight of the heatsink was also measured at those turning points. Specifically, the heatsinks with 1 millimeter, 1.5 millimeters, and 2 millimeters thickness had a microprocessor temperature of about 83.52 degrees Celsius, 86.50 degrees Celsius, and 89.25 degrees Celsius, with the weight of approximately 307.80 grams, 388.80 grams, and 448.2 grams. Overall, a 1-millimeter fin thickness with 21 fins configuration for this study was best under the criteria of minimum microprocessor temperature and minimum heatsink mass. Thus, this study successfully demonstrated that optimization of mass and fin thickness of the heatsink was possible to provide better thermal management of the microprocessors of a desktop’s CPU. This study is significant for this era because it provides a panacea for minimum material cost, lightweight, and minimum microprocessor temperature.

Usability in learning management systems: A mixed-method analysis

2025-04-24T20:08:50+03:00

Usability analysis in software has a critical role in creating useful products for users and obtaining the necessary feedback. Learning Management Systems (LMSs) are among the most frequently used software in distance education. The usability of these systems is crucial for both student success and the quality of the service offered. This study examines LMS usability through faculty perceptions at a state university, employing a mixed-methods design. Quantitative data were collected using the System Usability Scale (SUS) from 109 faculty members, while qualitative insights were gathered from semi-structured interviews with nine participants. Statistical and thematic content analyses were employed to interpret and compare results. The quantitative analysis yielded an average SUS score of 63.85 ± 16, indicating moderate usability concerns. Based on findings from both the SUS responses and interview data, several recommendations were proposed, such as enhancing system infrastructure, simplifying the interface, improving instructional guidance, strengthening interaction features, optimizing file management, refining notification systems, addressing character encoding issues, and streamlining listing and reporting functions. The results underscore the importance of a user-centered development approach, incorporating participatory design principles. Future research should track how faculty adapt to LMS updates over time. We hope these findings will guide future usability studies.

Skin Cancer Cell Detection using Image Processing

2025-04-30T08:50:08+03:00

Early diagnosis and precise detection of skin cancer represent a global health priority since this disease remains highly dangerous while being among the most frequent ones. This research investigates the effectiveness of deep learning techniques, specifically Convolutional Neural Networks (CNN) and the VGG16 architecture, for skin cancer detection and classification. The study works with images from the International Skin Imaging Collaboration (ISIC) while employing resizing and augmentation preprocessing to boost its model performance. We evaluate the proposed model using precision, recall, and F1-score metrics to ensure accurate classification. The proposed CNN model achieved 87% validation accuracy, outperforming the VGG16 model, which attained 65% accuracy. Experimental results highlight the potential of AI-driven models in improving diagnostic accuracy, demonstrating their significance in medical image analysis and early skin cancer detection.

Hydrogen generation from sodium borohydride via natural materials

2025-05-03T19:47:10+03:00

In this study, the catalytic activity of starch, cellulose and coffee were investigated in the dehydrogenation of sodium borohydride. The hydrogen generation rates of starch, cellulose and coffee were measured as 4.0, 6.7 and 60 ml H₂ min^-1 g^-1 and the activation energies of the reactions were calculated as 27.4, 17.1 and 14.5 kJ mol^-1 for starch, cellulose and coffee respectively. The study showed that natural sources could be used directly as catalysts in the dehydrogenation of chemical hdyrides.

Underwater Modulation Classification Using Discrete Wavelet Transform and Genetic Algorithm

2025-06-02T23:40:25+03:00

Underwater wireless optical communication systems face significant challenges due to the heterogeneous nature of the underwater environment and the attenuation of optical signals caused by absorption and scattering. These effects restrict the data transfer capacity and transmission distance, resulting in communication errors. Different modulation techniques are used to minimize the effects of these parameters. Automatic modulation classification plays a critical role in terms of effective management of spectrum resources. In this study, underwater wireless optical communication channels are modulated with different modulation techniques, and the signals are transformed into the discrete wavelet space, resulting in approximation and detail coefficients that are used as feature vectors for training machine learning algorithms. In addition, optimized classification features are determined for different signal-to-noise ratios and different transmission distances using the genetic algorithm. The results show that the approximation and detail coefficient energies provide higher classification performance in the classification of modulated signals according to statistical features such as mean, variance, and standard deviation. According to simulation results, an average classification accuracy of 82% has been obtained using the proposed discrete wavelet transform and genetic algorithm-based technique, which demonstrates high classification accuracy for noisy underwater channels.