Growing worldwide waste management challenges have prompted research efforts for sustainable solutions, especially in green energy technologies. This study highlights robotics’ critical role in modern waste management by putting forth a novel strategy incorporating cutting‐edge technologies. The research aims to create an effective waste monitoring system to measure the amounts of methane gas and organic waste at disposal sites. The main component of this method is a 3D‐printed spider robot driven by a servo motor with solid and flexible claws. This robotic system communicates gas level readings to the ThingSpeak platform on the Internet of Things, while it navigates waste disposal sites autonomously. The study also investigates how to develop robotic systems more economically and efficiently by using a portable, low‐power microcontroller, such as the Arduino Nano. The study uses a rigorous testing methodology to assess the performance and viability of the microcontroller and robotic system. The results are expected to emphasise a practical and economic development approach while offering insightful information on developing sustainable waste management technologies.

The goal of this study is to create machine learning models that use a big dataset to predict Autism Spectrum Disorder (ASD). To achieve optimal performance, a number of algorithms were employed and refined, including Support Vector Machines (SVM), Random Forest, XGBoost, Multi-Layer Perceptron (MLP) and a hybrid model that combines MLP and SVM. To evaluate the durability of the model's performance, the study used cross-validation and hyperparameter tuning techniques. Measures such as memory, accuracy, precision and F1-score have been employed to assess how well the models predict ASD. It's interesting to note that the RBF kernel did quite well in the grid search using the SVM model. All models produced good findings, with test set accuracies ranging from 87-97%. With 97% accuracy on the testing set, the CatBoost algorithm demonstrated excellent performance. Additionally, the hybrid MLP + SVM model demonstrated the potential benefits of combining different approaches by doing well on both the training and testing sets. Additionally, a Flask application was made to provide a straightforward user interface for the machine learning models that were learned. For those with ASD or who are at risk, this application generates predictions based on user input and provides tailored recommendations and interventions. The work highlights the potential for developing useful tools to support ASD diagnosis and intervention, as well as the effectiveness of machine learning techniques in ASD prediction. The robustness and applicability of the existing models may be strengthened by more research and validation on bigger and more varied datasets

The world is facing an enormous challenge to provide sufficient quantity of clean energy to its burgeoning population. Energy is a fundamental asset for enabling socio-economic development and poverty eradication in any country. Nepal has been suffering from the chaotic energy crisis for about a decade now despite having a potential for generating 43,000 MW of hydroelectricity, 2,100 MW of solar power, and 3,000 MW of wind power. A normal life of its citizens is being crippled with long hours of load shedding and never ending queues in front of the gas stations reflecting the state of the energy-hungry country. This paper gives a perspective on the energy crisis in Nepal in order to point out how serious the problem is and also presents some ways how to obtain an immediate relieve through the use of green energy commodities. In addition, the strategic advantages to potential investors are explored that encourage investments into national priority projects facilitated by the progressive policies of the Government of Nepal.

The economy of South Asia is experiencing growth, yet it faces constraints due to heavy reliance on fossil fuels and frequent power outages. Access to diverse energy sources, particularly electricity, is crucial for sustaining this growth. One feasible solution involves neighbouring countries engaging in the trade of renewable electrical energy. Hydropower stands as one of the many energy sources available in South Asia. However, sectorial constraints pose significant challenges to energy trade initiatives. This study utilises the Analytic Hierarchy Process (AHP) to evaluate Nepal’s readiness and identify obstacles to its cross-border energy trade with India and Bangladesh. A comprehensive analysis of these obstacles is imperative for formulating effective strategies and policies. Additionally, this study offers recommendations for enhancing preparedness and resolving issues related to energy trading, which may apply to similar cross-border situations. This study ranks energy trading obstacles with neighbouring nations using the AHP, offering key insights for stakeholders and policymakers. Using a non-probabilistic purposeful sampling technique, 25 expert respondents from the energy sector and prominent academicians were selected as part of the data collection procedure. At every level of the interview process, their perspectives were invaluable in guaranteeing a thorough and rigorous investigation.