Journal of Engineering, Technology, and Applied Science (JETAS)

Factors Influencing the Efficiency of Solar Energy Systems

2024-12-31T16:13:21+00:00

The efficiency of solar panels is significantly influenced by temperature and irradiance, which are crucial in solar energy conversion. As temperatures rise, solar panel efficiency typically decreases due to increased electrical resistance, resulting in lower output voltage and power production. This efficiency loss is quantified by the temperature coefficient, indicating the drop per degree Celsius above 25°C. Advanced cooling systems and optimal thermal management can mitigate these effects. Irradiance, the sunlight intensity reaching the panels, directly affects electricity generation. While higher irradiance increases efficiency by providing more photons for conversion, it can also raise temperatures, negatively impacting performance. Solar panels achieve maximum efficiency under optimal irradiance and moderate temperatures, typically 1000 W/m² at 25°C. Variations in irradiance due to geographical location, time of day, and weather conditions cause fluctuations in power output. Efficient system design must consider local irradiance patterns and utilize tracking systems to maintain optimal panel orientation. To optimize efficiency, innovative methods such as advanced materials, cooling techniques, and smart tracking systems are employed. Additionally, integrating energy storage solutions and predictive analytics helps manage environmental impacts. Proper design, installation, and maintenance strategies are crucial for maximizing solar panel efficiency and lifespan under varying conditions. Understanding the interplay between temperature and irradiance is essential for advancing solar energy technologies, and enhancing their reliability and effectiveness in diverse environments.

Blockchain Adoption in Ireland's Financial Sector and its Regulatory Challenges and Implementation Opportunities

2024-12-28T03:31:21+00:00

This research explores the adoption of blockchain technology within Ireland’s financial sector, focusing on its implementation, challenges, and potential. The study adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather data from a range of financial institutions and key stakeholders. A stratified sampling technique is used to select institutions based on size, including large multinational banks, mid-sized banks, and fintech startups. Purposive sampling targets key experts such as blockchain developers, regulatory bodies, and industry leaders for interviews. Surveys collect data on blockchain adoption levels, benefits, and challenges, while interviews provide deeper insights into the perspectives of stakeholders. The research findings reveal that blockchain adoption is still in its early stages but growing steadily, with larger multinational banks leading the way. Key benefits identified include enhanced transparency, security, and efficiency, while challenges include regulatory uncertainty, technological compatibility, and cybersecurity concerns. The study concludes that while blockchain holds transformative potential for Ireland’s financial sector, broader adoption is hindered by these barriers. Recommendations include increased collaboration between financial institutions, regulators, and blockchain developers, as well as clearer regulations to foster innovation while addressing security and compliance concerns.

Enhancing Industrial Sustainability in Uzbekistan through Solar Energy Adoption in Reducing Costs and Carbon Emissions

2024-12-28T03:31:19+00:00

This study evaluates the impact of solar energy adoption on industrial sustainability in Uzbekistan, focusing on both economic and environmental outcomes. A mixed-methods approach was employed, combining quantitative data from energy savings, carbon emissions reduction, and operational cost savings with qualitative analysis from interviews and field observations across three villages: Mirzo-Ulugbek, Karakol, and Fergana. The results indicate that higher adoption of solar energy, particularly in Mirzo-Ulugbek, led to significant improvements, including a 30% reduction in energy use, a 40% decrease in carbon emissions, and a 25% saving in operational costs. In contrast, Karakol, with minimal solar adoption, showed much lower reductions. The study also highlights the challenges of widespread adoption, such as high initial investment, technological limitations in integrating solar systems, and regulatory obstacles. Data were analyzed using comparative analysis, with a focus on identifying the barriers and benefits specific to Uzbekistan’s industrial context. The findings suggest that while solar energy offers substantial potential for economic and environmental sustainability, overcoming financial and infrastructural challenges is essential for its broader adoption. Future research should explore financing models, policy development, and international case studies to further facilitate solar integration into Uzbekistan’s industrial sectors

Characteristics and Applications of Bionanosilica from Betung Bamboo Leaves

2024-12-28T03:31:16+00:00

Nanoparticles are materials that are currently widely used in research due to their novelty and the growing number of suitable applications. Silica nanoparticles can be produced by synthesizing using several methods such as melting, coprecipitation, sol-gel, and ultrasonication. The aim of this study is to determine the most appropriate synthesis method for the production of SiO₂ nanoparticles to optimize the quality of physical properties of fast-growing wood. The synthesis of SiO2 nanoparticles used in this study utilized three different methods: acid isolation method (F1), sol-gel method (F2), and reflux method (F3). Characterization of SiO2-NPs was performed using particle size analyzer (PSA), X-ray diffraction analysis (XRD), and Fourier transform infrared spectroscopy (FTIR). The results of PSA analysis showed that the acid isolation method produced the smallest SiO2 particle size compared to the sol-gel and reflux methods. The zeta pontential value in each method shows that the particles produced are unstable because the potential zeta value produced is around -10 mV to -30 mV. The results of FTIR and XRD analysis show that the synthesized material is a SiO₂ compound with a cristobalite phase. Application of the material on jabon wood through impregnation showed an improvement in physical properties, including an increase in WPG, density, and BE, especially in the sol-gel method (F2).

Brand Logos Recognition System Using Image Processing for Food and Beverage Brands

2024-12-28T03:31:13+00:00

This study investigates the development of a Brand Logo Recognition (BLR) system employing Convolutional Neural Networks (CNNs), specifically designed for the food and beverage industry in Ipoh. Accurate logo recognition is vital for businesses to strengthen brand identity, monitor consumer engagement, and mitigate the misuse of counterfeit logos. Existing systems often encounter challenges related to variations in logo design, image quality, and lighting conditions. To address these issues, the research adopts a hybrid methodology that integrates the Machine Learning Life Cycle and the Software Development Life Cycle (SDLC), utilizing an iterative Agile development framework. The system incorporates CNN models for feature extraction and classification, complemented by Single Shot Detector (SSD) algorithms for object detection. A curated dataset of food and beverage logos underwent preprocessing techniques, including resizing, normalization, and augmentation, to enhance the model’s generalization capabilities. Empirical results demonstrate high accuracy in detecting and classifying logos across diverse conditions, underscoring the effectiveness of the CNN-SSD architecture. The proposed system offers practical applications for marketing analytics and consumer research, empowering local businesses to refine branding strategies and improve customer engagement. Future research directions include the exploration of multi-label classification, real-time processing, and the integration of advanced methodologies, such as generative adversarial networks (GANs), for counterfeit logo detection. This study emphasizes the transformative potential of AI-driven logo recognition systems in revolutionizing marketing practices and supporting small and medium-sized enterprises (SMEs).