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A software bug is some sort of a fault in the source code or a computer program. These bugs work in unusual and unintended ways which is a serious problem for a programmer and the company. Detecting bugs in software has been tried and tested through multiple means, the most recent of which is Machine Learning algorithms. Using a revolutionary dataset consisting of real software code snippets of the C language into key values, we were able to train the algorithms based on numerical parameters. This in turn simplified our algorithmic process. Furthermore, we run multiple classification algorithms to gain precision, recall and Fn scores, and improve upon these scores using key hyperparameter tuning techniques. Our observations revealed an increase in accuracy and were able to create an end module which can directly take the source code as an input from which the metrics and features are extracted and give the output if the code has a software bug or not.

Nowadays, electric vehicles (EVs) significantly affect transportation as they provide a more environmentally friendly alternative to traditional fossil-fueled automobiles. Electric vehicles, which depend on energy stored in batteries, significantly contribute to environmental preservation and comply with worldwide efforts to tackle climate change. However, the growing demand for electric vehicles causes traditional power grids under pressure emphasizing the necessity of establishing a suitable infrastructure for charging electric vehicles. Charging stations are becoming increasingly critical since they allow for the recharging of electric vehicles and play a significant role in stabilizing the power system. In order to optimize charging station infrastructure with multiple servers, the current research incorporates a Markovian queueing modeling approach. The primary objective of the study is to address queue management concerns and boost overall productivity. Considering the real-world challenges, a queue-based stochastic model for multi-server EV systems and individual feedback strategies is developed. Subsequently, a transition state diagram is provided by balancing the input-output rates between the adjacent states. Next, the system of Chapman-Kolmogorov differential-difference equations is formulated to help understand mathematical modeling better. The matrix method is employed to demonstrate the state probability distribution in equilibrium. The infographics are utilized and incorporated for better visualization of the research findings. For a better understanding from an individual's point of view, numerous managerial insights are provided. Lastly, several concluding remarks and future perspectives are provided that can help decision-makers and practitioners to construct and analyze economic strategies based on EV management systems.

The circular economy is progressively a way towards a sustainable society. The shift from linear to circular business models concerns the entrepreneurs to adopt new practices. The purpose of this research is to propose circular business models to make the worlds three of the most wasteful industries food, plastic, and fashion more sustainable. This paper is developed based on an extensive literature review and case studies. The proposed framework analyses the challenges of these three industries and prescribes relevant business models and sustainable practices. The findings in circular economy business models suggest elements of businesses that can be adopted by entrepreneurs in any industry to create a circular activity. This research paper gives the entrepreneurial spirit with business tools to achieve sustainability ambition.