This paper presents an approach that utilizes waste heat from the exhaust of Biomass Cookstoves (BCS), which is dominantly used for food grilling in tropical Asian countries, Thailand's fish grilling stoves were used as a case study. The study involves designing and comparing computationally the exergy quantities from four types of spiral heat exchangers— (H01, H02, H03, and H04) - using Computational Fluid Dynamics (CFD) within a flow rate range of 0.5-2.5 l/min with heat transfer oil as the working fluid. Subsequently, the economic feasibility was analyzed using the Net Present Value (NPV) and Internal Rate of Return (IRR) methods, and the carbon dioxide emissions to the atmosphere were evaluated across four helical coil configurations, C01, C02, C03, and C04. The results from the CFD analysis under specified conditions demonstrate that the helical heat exchanger can reclaim up to 2900W of exergy from BCS exhaust, which is transferred to water used by coffee machines for commercial use. This system can reduce electricity consumption for water heating by 38.48%, equating to a cost savings of 22,311.81 THB (621.50 USD) per year, with a maximum NPV of 20,195.49 THB (562.55 USD). The investment in upgrading BCS can be recouped within 4.6 years. On the environmental conservation front, using waste heat from BCS exhaust can also reduce carbon dioxide emissions to the atmosphere by up to 3,606.04 kg CO2e per year per stove. The findings from this analysis can help restaurant operators make informed decisions on investing in BCS improvements and aligning business operations with environmental conservation efforts.

This paper presents a novel approach for lane detection using a Convolutional Neural Network with Line Detection (CNN-LD) methodology, aimed at enhancing the accuracy and efficiency of road lane recognition. The proposed model leverages advanced pre-processing techniques, including distortion correction, color space transformation, and noise reduction, to prepare input images for effective feature extraction. The methodology incorporates edge detection using Sobel filters and the Hough transform for precise lane identification. A comprehensive dataset of 4,000 annotated images captured under diverse lighting conditions—daytime, low light, and night-time—was utilized to train and evaluate the model. The CNN-LD framework demonstrated superior performance, achieving an accuracy of 98.92% and an F1-Score of 97.90%, significantly outperforming traditional methods. The integration of the Kanade–Lucas–Tomasi (KLT) tracker ensures robust lane tracking, even in challenging environments. Experimental results indicate that the proposed approach effectively addresses common issues in lane detection, such as variations in visibility and road conditions. This research contributes to the development of intelligent transportation systems, providing a reliable solution for autonomous driving applications. Future work will focus on improving model robustness against adverse weather conditions and integrating multimodal data for enhanced lane detection capabilities.

This study investigated the communicated themes in journal articles of e-learning. The aim was to reveal the common issues and patterns of communication as written in the published articles of e-learning. A corpus of 152 journal articles in computer-based language learning was used from reputable journals in Computer Science between the years of 2017-2023. The results showed the themes of mobile learning, distance learning, situated learning, blended learning, teaching, and learning, as well as informal learning to be recurring in the past three years. In addition, e-learning for language was always focused on learning vocabulary. In conclusion, despite the development of technology in e-learning, human factors were identified to be at the center of discussion. Future implications included the use of e-learning as an organic technology for language learning in the future.

Artificial Intelligence (AI) has emerged as a transformative force in engineering systems, offering advanced decision-making capabilities that enhance system reliability and cybersecurity. This study examines the implications of AI-driven models, focusing on their ability to improve operational efficiency and safeguard against cyber threats. Using descriptive and inferential statistical methods, the research evaluates the performance of various AI models, including neural networks, support vector machines, and decision trees. Results indicate significant improvements in system uptime, reduced failure events, and enhanced cybersecurity breach detection, with neural networks demonstrating superior accuracy and reliability. However, challenges such as system complexity and data management highlight the need for optimized designs and robust cybersecurity frameworks. This research emphasizes the critical role of model selection and ethical considerations in deploying AI for engineering systems, paving the way for more resilient and efficient technological advancements.

As artificial intelligence, particularly language models like ChatGPT, become more advanced and prevalent, it's crucial to understand how this technology is shaping the way the younger generations are learning and acquiring the English language.As the field of artificial intelligence progresses at a rapid pace, marked by significant advancements exemplified by language models such as ChatGPT, the importance of delving deeper into the repercussions of this technological innovation on the learning processes of today's youth becomes increasingly evident. Specifically, understanding how AI impacts the acquisition and development of the English language among younger generations is crucial in light of the expanding presence and complexity of tools like ChatGPT. The far-reaching implications these technologies bear on education and language acquisition cannot be overstated, underscoring the need for educators and language enthusiasts to grasp the transformative effects of AI on contemporary language learning techniques and vocabulary assimilation.The evolving technological landscape presents a dual-edged sword of challenges and opportunities for those involved in education and language studies. It is imperative to develop a holistic comprehension of how AI is reshaping language learning approaches and enriching vocabulary acquisition practices in the present era. By scrutinizing the ways in which advanced language models influence learning environments for the youth, valuable insights are gleaned into the shifting paradigms of language acquisition and proficiency enhancement. Thus, a vigilant and analytical observation of the changing role of AI, particularly through language models like ChatGPT, in fortifying the foundation of English language education for today's young learners is essential. A nuanced examination of how AI technology intersects with language acquisition processes equips us to adapt educational methodologies effectively, leveraging the advantages of these advancements while resolving any emerging hurdles. Maintaining a well-informed perspective on the influence of AI, especially language models such as ChatGPT, on the linguistic development of young students is integral to optimizing the utilization of this technology. By doing so, we enhance their English language learning journeys, ensuring that they benefit from enriched experiences and achieve superior outcomes in their linguistic proficiency.

This research aims to analyze heat transfer behavior and evaluate the potential for reducing carbon dioxide emissions of elliptical cross-section helical coil heat exchangers. The study employs experimental testing and computational fluid dynamics (CFD) simulations under constant surface temperature conditions. The investigation focuses on the influence of physical characteristics, particularly the percent ovality (δ) and the hydraulic diameter to coil diameter ratio ????ℎ???????????????????? on heat transfer efficiency.Empirical results demonstrate that helical coils with high δ values (66.67%) and smaller surface areas exhibit higher heat transfer coefficients and Nusselt numbers. A mathematical model in the form of ????????=0.0327????????0.8595????????0.4 was developed, achieving a correlation coefficient of 99.04%. Furthermore, exergy analysis reveals that the optimized design can reduce carbon dioxide emissions by up to 175.54 kg per year per unit, assuming 8-hour daily operation.These findings can be applied to the development of high-efficiency, environmentally friendly air conditioning systems for buildings and residences, aligning with greenhouse gas emission reduction targets set by the Paris Agreement.

The interacting boson model-1 has been used to calculate the reduced electric transition probability B(E2) ↓ of even-even 122-130Te (Tellurium) isotopes with even neutrons from N = 70 to 78. The three-three boson interactions are also formed in the Hamiltonian from Casimir invariant operators. The parameters of best fit to measure the data are used from the experimental value of B (E2; 21+ → 01+) for even-even 122-130Te isotopes. The theoretical values are good in agreement especially with the experimental ones. The branching ratios B (E2; 41+ → 21+) / B (E2; 21+ → 01+) is less than 2 represents U (5) symmetry in 122-130Te isotopes.

The adoption of social media among tribal women in the Visakhapatnam District represents a significant shift in the digital landscape, highlighting both opportunities and challenges in bridging the digital divide. This research paper examines the factors influencing the adoption and usage of social media by tribal women, focusing on the socio-economic, cultural, and technological barriers that persist in this marginalized community. The study analyzes the extent to which tribal women in Visakhapatnam District engage with social media platforms, their motivations for use, and the impact on their social and economic empowerment. Findings suggest that while social media serves as a vital tool for communication, information sharing, and community building, several impediments, such as limited digital literacy, inadequate infrastructure, and socio-cultural restrictions, hinder full participation. Moreover, the research highlights the positive outcomes of social media usage, including increased awareness of rights and resources, enhanced self-expression, and greater opportunities for economic activities. However, it also points to potential risks, such as exposure to misinformation and privacy concerns. The paper concludes with recommendations for policymakers, community leaders, and stakeholders to implement culturally sensitive digital literacy programs, improve digital infrastructure, and create supportive environments that foster safe and meaningful engagement with social media. By addressing these gaps, the study aims to contribute to the ongoing discourse on digital inclusion and gender equality among tribal communities in India.