Muhammad Umar Farooq

The association of microfinance and entrepreneurship is increasingly recognized as a tool for the socio-economic development of dawdling regions. By contrast, most of the studies on the micro level have primarily focused on poverty alleviation and spending of loans on living standards and reached conflicting findings. Evidence on the impact of microfinance on entrepreneurial development in different regions has conflicting results. There is growing academic interest in geographical variations and a consensus that geographical disparities exist. This paper investigates the impact of microfinance programs offered by government and non-government organizations on the profitability, employment and sales growth of microenterprises operating in Pakistan. Using a sample of borrowers (treatment group) and respondents on the waiting list (control group) operating microenterprises, we provide evidence that the impact on the profitability of microenterprises is positive, with a higher mark for NGO borrowers. Moreover, the study's novelty compares the microenterprise industry and the findings that using loans in service sector business makes individuals more likely to move from a low-wage labourer to a more profitable entrepreneurship status. Our findings will help policymakers and academics identify the most relevant intervention areas.

The study aims to test and inquire about the nexus between green financing and economic development in the belt-and-road initiative (BRI) member countries with the role of public–private investments. The study used the generalized method of moments (GMM) assessment approach from 1990Q1 to 2018Q4 to evaluate research topicality. The study’s findings have uncovered some hidden insights and presented new insights. In line with this, the study’s findings highlighted that an accurate role of green financing is significant in economic development in BRI economies throughout the research period. The results demonstrate that the GMM method of investigation has distinct validity in establishing the overall significance of results about the role of public–private investments in BRI nations. However, the study results about public–private investments showed a varying impact on nations with high GDP per capita. Moreover, the findings from this research indicate that the development of renewable energy sources is also widely aided by applying environmental regulations. At the same time, factors including population size, technological innovation, industrial structure, and foreign direct investment (FDI) significantly control the relationship between green financing, public–private investments, and economic development. On these findings, the study also recommended practical directions for stakeholders.

In general, the public expenditure on schooling, science, and research and development (R&D) is thought to have a positive effect on the development and sustainability of an economy, but such evidence is lacking in the developing and developed countries, especially in the top 10 CO₂-emitting countries. This study investigates the impact of public spending and green finance on environmental sustainability, using the ordinary least square method and data envelopment analysis, which uses the panel data from selected countries from 2008 to 2018. Results reveal a fluctuating green economic growth index, which was due to the non-serious existence of government policies. More precisely, a 1% increase in gross domestic product (GDP) growth increases the carbon emissions by about 0.40%, whereas the rise in coal consumption decreases environmental efficiency by about 0.88%. We also concluded that 0.95% GDP growth and economic development significantly enhance environmental emissions, whereas 0.5% of renewable energy consumption decreases the negative impact of environmental pollution. Furthermore, a 1% growth in renewable energy consumption improved environmental efficiency by 0.58%. Furthermore, the analysis demonstrates that the public expenditure on human capital and renewable energy (R&D) leads to a productive green economy through labor, and technically advance developmental practices, with varying consequences in distinctive countries.

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

Neutrosophic set (NS) is an extensively used framework whenever the imprecision and uncertainty of an event is described based on three possible aspects. The association, neutral, and nonassociation degrees are the three unique aspects of an NS. More importantly, these degrees are independent which is a great plus point. On the contrary, neutrosophic graphs (NGs) and single-valued NGs (SVNGs) are applicable to deal with events that contain bulks of information. However, the concept of degrees in NGs is a handful tool for solving the problems of decision-making (DM), pattern recognition, social network, and communication network. This manuscript develops various forms of edge irregular SVNG (EISVNG), highly edge irregular SVNG (HEISVNG), strongly (EISVNG), strongly (ETISVNG), and edge irregularity on a cycle and a path in SVNGs. All these novel notions are supported by definitions, theorems, mathematical proofs, and illustrative examples. Moreover, two types of DM problems are modelled using the proposed framework. Furthermore, the computational processes are used to confirm the validity of the proposed graphs. Furthermore, the results approve that the decision-making problems can be addressed by the edge irregular neutrosophic graphical structures. In addition, the comparison between proposed and the existing methodologies is carried out.

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.