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Accurate solar photovoltaic models (SPVM) are critical for optimizing solar photovoltaic (PV) capacity to convert sunlight into electricity. The simulation and design of PV systems rely on estimating unknown constraints from solar photovoltaic (SPV) cells. Each parameter plays a crucial task in the output properties of an SPV under actual environmental conditions. Optimizing the unknown constraints of the SPVM is not an easy task due to the nonlinear characteristics of the PV cell. This study aims to develop a novel metaheuristic algorithm, enhanced dynamic inertia particle swarm optimization (EDIPSO) algorithm with velocity clamping, to establish all the seven and five constraints of the two-diode model (TDM) and one-diode model (ODM).

In complex parameter spaces, the conventional particle swarm optimization (PSO) approach typically leads to poor convergence because it fails to balance exploration and exploitation. The proposed approach is an EDIPSO with velocity clamping to minimize the possibility of overshooting possible solutions and improve stability. Velocity clamping is also used to prevent particle velocities from rising over specified limitations. Beginning the process with a large inertia weight to promote exploration and progressively decreasing it to improve exploitation, leading to a thorough analysis of the search space. The algorithm is implemented to investigate the accuracy of estimated constraint values of RTC-France (RTC-F) solar cell, Photo watt-PWP 201 SPV module (PWP 201 SPV), KC 200GT SPV module (KC 200 GT SPV) for ODM and TDM.

The proposed approach is used to extract the seven and five constraints of the TDM and ODM under standard test conditions for three different SPV modules. Thorough simulation and statistical analysis indicate that the EDIPSO with velocity clamping may outperform other cutting-edge optimization algorithms exclusively regarding accuracy, computational time and reliability.

An enhanced dynamic inertia PSO is suggested for determining the parameters of the TDM and ODM in SPV modules. This method specifically accounts for the recombination saturation current within the p–n junction’s depletion region, without overlooking or assuming away any parameters, thereby achieving greater accuracy. When comparing the estimated constraints of TDM and ODM for various SPVs, EDIPSO almost precisely aligns the data from the proposed model with the practical data. Thus, the proposed method for calculating the SPV model parameter may exhibit to be a feasible and efficient solution.

Across the globe, cities have contemplated practical measures to handle sustainable development issues which vary from environmental, social and to economic problems. Despite this, Africa has continued to fall behind in the implementation of smart city development. Yet, one of Africa's most cherished goals is achieving sustainable human settlements to keep up with the present and future urbanisation rates. The backbone to achieving sustainable human settlements is having efficient, well-capacitated, municipal systems and committed public servants. African Governments, however, continue to have failing municipalities with corruption being rife in both small towns and cities, yet there are chances to evolve and become smarter. Blockchain technology is a novel and disruptive innovation that has the potential to empower smart cities by providing a platform for interoperability, coordination and governance among multiple smart city initiatives and actors. However, the adoption of blockchain technology also faces several challenges and barriers to its adoption and implementation in smart cities, especially in Africa, where there is lack of awareness, regulation, infrastructure and access to ICT. This chapter examines the current state, opportunities and challenges in the adoption of blockchain technology in smart city development in Africa, a continent that faces multiple urbanisation issues, such as poverty, inequality, environmental degradation and lack of infrastructure. As such, the study adopts two theories, the diffusion of innovation (DOI) and the technology–organisation–environment (TOE) framework to view the use and opportunities placed by adopting blockchain technology through municipal stakeholders and citizens to enhance smart city development.

Consumers are increasingly shopping online, and they want reliable and trustworthy information about the quality of goods and all members of the distribution chain. Blockchain and gamification are two technologies that can drive digital transformation across various industries. The current study investigates the application of blockchain and gamification in emerging modern markets. Gamification is a viable method for familiarising individuals with blockchain technology. It is an example of an idea that might be utilised to build a setting as a bargaining strategy. The goal of this research is to investigate how gamification components might be used to engage individuals in blockchain technology and foster a positive attitude towards future use. In this review article, we did a thorough study of the research articles published between 2018 and 2023 on various web search engines for scholarly literature and academic resources. This comprehensive review investigates the incorporation of blockchain technology and gamification in emerging markets. Blockchain, known for its decentralised and secure nature, has the potential to transform a wide range of industries, including marketing. Similarly, gamification has grown in favour as a method of engaging and motivating customers. Marketers may use the benefits of both approaches to build unique and effective marketing campaigns by integrating blockchain and gamification. This review focuses on the digital transformation with focus on major concepts, benefits, problems and real-world applications of using blockchain and gamification into modern marketing techniques in emerging markets.

In a context where the promotion of a more sustainable agriculture is clearly aimed at, the paradoxical combination of sustainable agricultural practices (SAP) with chemical pesticides use instead of biological pest management techniques is recurrent in Sub-Saharan African (SSA) countries like Burkina Faso. Chemical pesticides are harmful to the environment and health. This paper aims to analyze the role of farmer education on the mode of adoption of SAP.

An endogenous treatment effect model is used with survey data on 1,898 rural households in Burkina Faso.

The results show a positive causal effect of farmer education on sustainable and chemical pesticide-free agriculture adoption.

Formal education appears to be general. This research could be extended to consider the role of training or extension services. More detailed results, focusing on spatial effects, could reinforce those of the present research.

Unlike previous studies, this paper addresses for the first time the paradoxical behavior of combining SAP with chemical pesticides use. It shows that farmer education contributes to explain it and is therefore a determining factor for a more sustainable agriculture.

Peer-to-peer lending has emerged as a promising alternative investment avenue globally. This study explores the variables influencing investors' intention in peer-to-peer lending. Studying these factors is crucial for inspiring investor motivation, which leads to the growth of peer-to-peer lending platforms in India.

The research utilizes information gathered from 293 investors of the Rang de platform through a questionnaire. It employs ordinal logistic regression to assess how demographic characteristics, investor experience, social projects and investment factors influence the study’s outcomes.

The research indicates that the education qualification and income level of the investors are significant demographic variables in their investment decisions. Moreover, social projects, the experience of the investors and investment factors affect their intent to invest more in peer-to-peer lending projects.

It helps stakeholders and borrowers of crowdfunding platforms think about these aspects when creating plans to bring in and keep investors. This approach also enhances the openness and dependability of lending processes.

This research sheds light on Indian investors' behavior toward peer-to-peer lending platforms and includes new investment factors like project categories and impact partners that other researchers did not identify.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and text-based corporate innovation based on a sample of India’s ESG-disclosed companies from financial year 2011–2012 to 2021–2022. Further, it endeavors to investigate the moderating role of heightened climate policy uncertainty (CPU) in this relationship.

To verify these hypotheses, the authors first construct a corporate innovation index for India using a sophisticated natural language processing model on each firm-year’s management discussion and analysis reports. Next, the authors use a panel fixed effects model to examine how ESG performance impacts corporate innovation and its moderating and mediating components.

Empirical evidence suggests higher ESG performance bolsters text-based corporate innovation. After addressing endogeneity issues with the system GMM estimator and two-stage least square IV, incorporating additional control variables and using alternative innovation measurement, the baseline results remain unchanged. Next, the authors find this link is mediated by reducing information asymmetry, financial constraints and managerial myopia. The authors also observe that increased CPU favorably moderates the ESG-innovation nexus. Additionally, the heterogeneity research shows that ESG only positively impacts innovation in specific industries and firms in their growth and mature life cycle phases.

The results demonstrate that sustainable and ethical business practices can foster corporate innovation. Thus, this study may provide valuable insight for investors, managers and policymakers.

To the best of the authors’ knowledge, this is the first study to examine the relationship between ESG performance and text-based corporate innovation using a machine learning model.

Dietary diversity (DD) is a proxy indicator of nutrient adequacy and has been propagated as a health beneficial component of dietary behavior. This study aims to understand DD and its association with lipid indices among Indian Kho-Kho players.

In all, 50 Indian Kho-Kho players aged between 16 years and 31 years belonging to different regions of India were recruited. Dietary diversity score (DDS; food groups/day) and food variety score (FVS; number of food items/day) were considered to assess the DD. The United Nations Food and Agricultural Organization food frequency questionnaire was used as a tool. Serum total cholesterol (TC), triglycerides (TG), LDL, VLDL and HDL were measured.

The DDS and FVS showed that athletes were consuming approximately 7 of 9 food groups (Mean ± SD: 6.5 ± 1.3) and 27 varieties of foods (Mean ± SD:27.0 ± 8.6) from 9 food groups per day. This study could not find significant differences for DDS and FVS among athletes belonging to different regions. FVSs of organ meat, eggs and spices and condiments were significantly positively associated with TG (p < 0.036, p < 0.020 and p < 0.001) and VLDL (p < 0.054, p < 0.021 and p < 0.001). LDL was inversely significantly associated with FVS of cereals, white root and tubers (p < 0.032).

DDS and FVS were not associated with lipid indices among Kho-Kho players. However, individual FVS of organ meat, eggs and spices and condiments may increase TG and VLDL. Optimum consumption of cereals and root tubers may help to maintain LDL levels among Kho-Kho players.

It was found that daily consumption of organ meat and eggs may increase TG and VLDL, and optimum consumption of cereals and white roots and tubers may help to maintain LDL. High energy and fat foods may act as mediators along with spices and condiments in increasing TG and VLDL.

This paper aims to explore how corporate diversification influences corporate cash holdings (CCH) in India. It also assesses CCH behavior and its determinants during crisis, stability and recovery periods.

The study uses the system generalized method of moments (System GMM) on 1684 non-financial firms listed on the National Stock Exchange during 2002–2022. Further analyses are carried out for group-affiliated firms based on investment cash flow sensitivity, agency costs and debt capacity.

The findings show that firm diversification (at the group level) results in a roughly 6% fall in cash ratio, thereby implying that diversified firms (diversified business groups) hold lower cash levels than specialized firms. The reduced cash balances are attributed to the financially unconstrained nature of diversified business groups, higher debt capacity, good governance behavior and active internal markets. Additionally, the authors observe a time-varying cash policy for diversified firms, with mean cash holdings being 2% higher during crisis periods. Moreover, the findings reveal that the inverse relationship between diversified firms and CCH is less pronounced for unrelated diversified firms (2% fall in cash ratio) than related diversified firms (5% fall in cash ratio).

To the best of the authors’ knowledge, this is the first study to examine the relationship between corporate diversification and CCH in India during both crisis and non-crisis periods. The authors’ research uniquely uses System GMM on a large sample and differentiates between related and unrelated diversification.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.