Biju Patnaik University of Technology

To understand how software quality improves during testing, SRGMs play a significant role. SRGMs are even more critical, especially when human learning, debugging practices, and uncertainties in the testing environment influence fault detection processes. Traditional SRGMs often assume ideal or simplified conditions, which may not fully reflect real testing scenarios. To address this gap, this study proposes two SRGMs that consider imperfect debugging, S-shaped learning pattern, and uncertainty modeled through a Weibull distribution. These additions allow the models to capture fault detection behaviors more realistically. The performance of the proposed models is evaluated using three datasets and compared with some well-known SRGMs. Results show that the proposed models perform competitively across all datasets. Although no single model dominates across all cases, the results indicate that combining learning and uncertainty provides flexibility and enhanced predictive ability in various testing environments.

Purpose: Digital entrepreneurship in the green markets served as the research topic to understand market potential, together with the facing of obstacles and financial aspects of sustainable business operations. Design/Methodology/Approach: Survey questionnaires were used for data collection to obtain information from operators who are focused on digital implementation within the green sector. Findings: The findings demonstrate that digital tools can create better business efficiency and sustainable measurement capabilities while expanding markets. The main obstacles faced by businesses are their elevated expenses and restrictive regulations in combination with resource shortages. Limitations: As the study relies on surveys, the responses may be subject to bias and perception of respondents, and the sample size and composition were limited to only early and mid-stage green enterprises. Originality/Value: This study with original data offers insights into how digitalisation transforms green entrepreneurship through innovation, which promotes sustainability. Keywords: Digital Entrepreneurship; Sustainability Impact; Green Market; Digital Tools; Green Initiatives; Carbon Footprints. Citation: Hota, S. L., Mohanty, N., Sahoo, S. R. and Panda, A. (2026): Digital Entrepreneurship for Sustainability: Opportunities in Emerging Green Market. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, Nos. 1-2, pp. 29-49.

Machine learning (ML) has become a commodity in every sphere of routine life today. It is a subfield of artificial intelligence, broadly defined as the ability of a machine to imitate intelligent human behavior. It has become a standard tool in every field of science, research, technology, and engineering. Many ML applications are changing people's lives at unprecedented speed and scale. Both AI and ML have become popular subjects of research interest in the current era as they are the building blocks of data science. Like humans, machines can make intelligent decisions by learning from their past experiences. Evolved way back from 1950 to the current age of the Fourth Industrial Revolution (4IR), ML has progressed like a giant in the field of data science. ML intelligently prioritizes the analysis of the wealth of data, processes them to build models, and develops the corresponding smart and automated applications. Various learning algorithms, like supervised, unsupervised, semi-supervised, and reinforcement learning, are key methods in ML technology for data analysis. Deep learning is a part of machine learning which can also intelligently analyze the wealth of data on a large scale. This chapter describes the basics of ML, its evolution, and subsequent implementation in different research fields like cybersecurity, healthcare, agriculture, and communication. But more specifically, it dives into the 5G and beyond wireless communication field.

The paper proposes addressing maritime environment challenges, including high attenuation, multi-path fading, and complex topographical constraints. Here, the first hop utilizes power line communication (PLC) to transmit data over existing electrical wiring in the control centre (CC), ensuring reliable communication. The second hop employs unmanned aerial vehicle (UAV) based re-configurable intelligent surface-assisted radio frequency (RIS-RF) technology, which leverages to enhance signal strength and directivity in the challenging marine atmosphere. The final hop utilizes underwater acoustic communication (UAC) to transmit data through the underwater environment, overcoming the high attenuation and limited bandwidth associated with acoustic signals. The PLC, RF, and UAC links are distributed with log-normal, Rayleigh and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {F}$</tex-math></inline-formula> distributions, respectively. The modulation schemes considered here are binary modulation and rectangular quadrature amplitude modulation (RQAM). We evaluate the decode-and-forward (DF) relay-based three-hop system's performance in terms of signal-to-noise ratio (SNR), average bit error probability (ABEP), and outage probability (OP) with a closed-form expression. The expression contains special functions like Meijer-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> and Fox-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> functions. The study further incorporates active RIS-assisted RF links and demonstrates their performance advantage over passive RIS configurations. The results demonstrate the viability and efficiency of the hybrid PLC/RIS-RF/UAC communication system, highlighting its potential to significantly improve data transmission reliability and efficiency in marine applications.

In modern economies, entrepreneurship is a primary driver of economic growth, innovation, and job creation. Government policies are critical for creating an environment that promotes the growth and sustainability of entrepreneurial ventures. This article examines how institutional frameworks, financial aid, regulations, and government activities promote entrepreneurship. Policies that simplify company procedures, make finance available, encourage talent development, and invest in infrastructure can help to reduce new business barriers. Government programs promoting inclusive entrepreneurship, innovation, and small and medium-sized businesses (SMEs) help to develop the entrepreneurial climate. The paper underlines how successful government efforts encourage economic growth, competitiveness, and social advancement while also encouraging the formation of new firms.

Requirement for electrical power generation in the developing countries is growing day by day. The major source of power generation is coal. The availability of coal is big concern. Smaller countries intend to procure electrical power from larger or developed countries. This trading of electrical power makes the power network more complex. This work is focusing on economic operation of such generators with better security.