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In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

Today, many industries are implementing creative approaches in response to increasing environmental awareness. It is of great importance to answer the question of whether the military sector, one of the most important sectors, can support renewable energy (RE) adaptation. This study aims to examine how military spending affects the supply of RE in 27 Organization for Economic Cooperation and Development (OECD) nations as well as the regulatory function of factors such as innovation, international trade and oil prices between 1990 and 2021.

The study examines the effects of military spending, income, green innovation, international trade, oil prices and the human development index on the supply of RE using various econometric approaches, which are the cointegration test, moments quantile regression and robustness test.

The findings demonstrate that all factors, excluding military spending, quite likely affect the expansion of the renewable supply. Military spending negatively influences the RE supply; specifically, a 1% increase in military spending results in a 0.88 reduction in the renewable supply. In addition, whereas income elasticity, trade and human development index in OECD nations are higher in the last quantiles of the regression than in the first quantiles, the influence of military spending and innovation on renewable supply is about the same in all quantiles.

OECD nations must consider the practical implications, which are essential to assess and update the military spending of OECD countries from a green energy perspective to transition to clean energy. Based on the study’s overall findings, the OECD countries should incorporate the advantages of innovation, economic growth and international trade into their clean energy transition strategies to lessen the impact of military spending on renewables.

The study aims to fill a gap in the literature regarding the role of military expenditures in the RE development of an OECD country. In addition, the results of the methodological analysis can be used to guide policymakers on how military spending should be in the field of RE.

This study aims to present the perspectives of heterodox economics and Islamic economics on environmental economics, as an alternative to mainstream economics, which takes economic growth as its main objective and argues that environmental problems will largely disappear when economic growth is achieved.

In this study, there was no intention to conduct a detailed analysis of heterodox economic models and Islamic economics. Instead, the approaches to the “environment,” which can be considered as an urgent need of the planet, were evaluated, and the inadequate proposals of the mainstream economics’ environmental approach were theoretically criticized and heterodox economics and Islamic economics were proposed as an alternative model.

Heterodox and Islamic economics offer alternative models of development prioritizing social and ecological justice to address environmental problems, which is in contrast to mainstream economics’ narrow focus on market mechanisms and individual rationality. Thus, engaging in more dialogue in the context of the environment is inevitable for both schools, considering the vast geography inhabited by Muslims and the proposed heterodox economic policies, and moreover, these approaches are modeled for the first time.

This article presents a synthesis of Islamic economics and heterodox thinking in contrast to mainstream economic policy, highlighting their similarities and differences and providing a more comprehensive understanding of the complexities and potential solutions of environmental problems. To the best of the authors’ knowledge, this approach has not been previously explored, making it an original contribution to the literature.

This paper aims to investigate the impact of political stability on corruption by drawing upon a sample encompassing both developed and developing European and Asian countries.

The dataset, sourced from the Refinitiv database, spans from July 2014 to May 2022. Panel data techniques, specifically pooled estimation and dynamic panel data [generalized method of moments (GMM)] are employed. The analysis encompasses both fixed and random effects models to capture country-specific cross-sectional effects. To validate our findings, we perform a robustness test by including in the investigation four control variables, namely poverty, type of governance, economic freedom and inflation. To test heterogeneity, the dataset is further divided into two distinct subsamples based on the countries’ locations.

Empirical findings substantiate that political stability (viewed as the risk of government destabilization) has a positive and significant impact on corruption in all analyzed samples of European and Asian countries, though some differences are observed in various subsamples. When we take into account the control variables, these analysis results are robust.

This research provided a panel data analysis with GMM, while other empirical methodologies could also be used, like the difference-in-difference approach. However, our results should be validated by extending the time and the sample to a worldwide sample and using alternative measures of corruption and political stability. Moreover, our focus was on a linear and unidirectional relationship between the considered variables, but it would be interesting to test in our further research a non-linear and bidirectional correlation between them. Furthermore, we have introduced in the robustness test only four economic variables, but to consolidate our findings, we plan to include socioeconomic and demographic variables in future studies.

These outcomes imply that authorities should be aware of the necessity of implementing anti-corruption policies designed to establish effective agencies and enforcement structures for combating systemic corruption, to improve the political environment and the quality of institutions and to apply coherent economic strategies to accelerate economic growth because higher political stability and sustainable development determine a decrease in levels of corruption.

At the microeconomic level, the survival of organizations may be in danger from new types of corruption and money laundering. Therefore, in order to prevent financial harm, the top businesses worldwide should respond to instances of corruption through strengthened supervisory procedures. This calls for the creation of a mechanism inside the code of conduct where correct reporting of suspected situations of corruption would have a prompt procedure to be notified of. To avoid corruption in operational procedures, national plans and policies should be developed by government officials, executives and legislators on a national level, as well as by senior management and the board of directors on an organizational level. This might lower organizations' extra corruption-related expenses, assure economic growth and improve global welfare.

A novel feature of our research resides in its broad examination of a sizable sample of European and Asian countries regarding the nexus between corruption and political stability. The paper also investigates a less explored topic in economic literature, namely the impact of political stability on corruption. Furthermore, the study depicts policy recommendations, outlining effective and reasonable measures aimed at improving the political landscape and combating corruption.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

This study aims to contribute to the social media agility literature by examining the impact of perceived social media agility on customer purchases. More specifically, this study seeks to reveal whether perceived social media agility positively affects customer purchases. Furthermore, this study examines the moderating roles of social media self-efficacy and social anxiety to increase the model's explanatory power. That is, this study investigates whether social media self-efficacy positively moderates the impact of perceived social media agility on customer purchases. Similarly, this study examines whether social anxiety negatively moderates the impact of perceived social media agility on customer purchases.

An online survey was conducted on Qualtrics platforms to test the research hypotheses. To test the main effect, a linear regression was used. To test moderating relationships, PROCESS Macro Model 1 was used. Finally, the moderating effects were probed with the Johnson–Neyman technique to gain further insights into the interaction effects.

The study results show that when customers perceive a brand as agile on social media platforms, they are more willing to buy the goods/services of the brand. Notably, individuals who are high on social media self-efficacy (relative to low on it) display more willingness to purchase the brand's products/services. However, customers who are high on social anxiety (relative to low on it) are less willing to purchase the brand's products/services.

This study examines the effect of perceived social media agility on customer purchases while accounting for the moderating role of perceived social media self-efficacy and social anxiety. The results provide noteworthy theoretical and managerial contributions.

This paper aims to explore and review the potential of robotic rehabilitation as a treatment approach for Alzheimer’s disease (AD) and its impact on the health and quality of life of AD patients.

The present discourse endeavors to provide a comprehensive overview of extant scholarly inquiries that have examined the salience of inhibitory mechanisms vis-à-vis robotic interventions and their impact on patients with AD. Specifically, this review aims to explicate the contemporary state of affairs in this realm by furnishing a detailed explication of ongoing research endeavors. With the objective of elucidating the significance of inhibitory processes in robotic therapies for individuals with AD, this analysis offers a critical appraisal of extant literature that probes the intersection of cognitive mechanisms and assistive technologies. Through a meticulous analysis of diverse scholarly contributions, this review advances a nuanced understanding of the intricate interplay between inhibitory processes and robotic interventions in the context of AD.

According to the review papers, it appears that implementing robot-assisted rehabilitation can serve as a pragmatic and effective solution for enhancing the well-being and overall quality of life of patients and families engaged with AD. Besides, this new feature in the robotic area is anticipated to have a critical role in the success of this innovative approach.

Due to the nascent nature of this cutting-edge technology and the constrained configuration of the mechanized entity in question, further protracted analysis is imperative to ascertain the advantages and drawbacks of robotic rehabilitation vis-à-vis individuals afflicted with Alzheimer’s ailment.

The potential for robots to serve as indispensable assets in the provision of care for individuals afflicted with AD is significant; however, their efficacy and appropriateness for utilization by caregivers of AD patients must be subjected to further rigorous scrutiny.

This paper reviews the current robotic method and compares the current state of the art for the AD patient.

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.

The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).

This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.

This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.

Reverse logistics (RL) has become integral in modern supply chains, with many companies investing in circular economy (CE), a recuperative and effective industrial economy. The traditional linear model triggered many negative environmental consequences such as climate change, ocean pollution, loss of biodiversity and land degradation. The development of RL strategies that support the transition between RL to CE is crucial. The purpose of this paper is to connect RL with CE in the context of Industry 4.0 and develop a hierarchal structure to explore the relationship between RL and CE critical success factors in the context of Industry 4.0.

This study used both qualitative and quantitative approach. Literature review in collaboration with the Delphi method is used to identify and validate critical success factors. Then, the ISM-based model and MICMAC method were used to determine the relationship between CE and RL success factors and its driving and dependence power.

This study result shows that waste reduction, skilled employees and expert's involvement and top management commitment and support will provide guidelines and paths for implementing CE and RL, leading to the competitiveness of a firm.

The findings provide managerial insight, particularly useful to third-party logistics companies' managers who are looking to implement RL and CE, to help prioritize where to invest company resources to generate prime difference. Furthermore, this study also identified Industry 4.0 technologies, which would tackle top identified critical success factors within the hierarchical model such as block chain and digital platforms.

This paper contributes to the literature by exploring the connection between RL and CE in the context of Industry 4.0 that determines the critical success factors enabling sustainable inter-firm collaboration.