The role of corporate social responsibility and government incentives in installing industrial wastewater treatment plants: SEM-ANN deep learning approach.

Contaminated industrial wastewater is one of the severe causes of health diseases. The wastewater treatment trend in developing countries is less, and governments are not strictly pursuing the wastewater management protocols adopted by firms. To study the intention to install wastewater treatment plants at the firm level and provide policy suggestions to the developing countries' governments, we have collected a cross-sectional dataset from manufacturing firms. We have presented an integrated model based on the theory of planned behaviour and tested our proposed model by implying SEM-ANN deep learning techniques. Results revealed that Environmental knowledge, installation cost, firm reputation, and corporate social responsibility positively influence firm management's intention to install wastewater treatment plants, but awareness of the risk associated with contaminated wastewater has a negative influence. On the other hand, Government incentives do not influence the process. In addition, our study has found that firm size moderates the relationship between explained and exploratory variables. Our study provided valuable insight into the available literature and provided some policy suggestions to developing countries' governments to cope with water scarcity and health issues caused by contaminated industrial wastewater.

Synthesis, characterization and evaluation of anti-arthritic and anti-inflammatory potential of curcumin loaded chitosan nanoparticles.

Curcumin is a phytochemical isolated from the dried rhizome of Curcuma longa L. family Zingiberaceae which possesses versatile biological activities and has hydrophobic properties. The current study was conducted to fabricate, and optimize curcumin loaded chitosan and Sodium tripolyphosphate (STPP) nanoparticles (NPs) to improve the bioavailability of curcumin. NPs were fabricated employing the Ionic gelation method. Four formulations were developed based on the selected variables like STPP and chitosan concentration, rotations per minute (rpm), temperature, and pH of chitosan solution. NPs were characterized for morphology, drug-polymer compatibility, yield, particle size, encapsulation efficiency, release behavior, anti-inflammatory and anti-arthritic activity compared to curcumin and standard drug. Fourier transform infrared spectroscopy (FTIR) shows nanoparticle compatibility. The maximum yield was 60%. Entrapment efficiency ranged from 45 to 65%. Curcumin NPs and standard drug 600 µg/ml shows 59% and 70% anti-inflammatory activity by HRB membrane stabilization method respectively which are greater than curcumin alone whereas anti-arthritic activity by protein denaturation method which is also comparable to standard drug and greater than curcumin was 66 and 70% respectively. Statistical analysis shows the mean significant difference at p ≤ 0.05. The study concluded that curcumin-loaded chitosan and STPP NPs formulated successfully by the Ionic gelation method, which increased curcumin absorption leading to a reduced dosing rate and improved patient compliance.

A moderated mediation model to predict the adoption intention of renewable wind energy in developing countries.

Developing countries are deprived of resources, and their economies are dwindling. Energy shortfall is one of the leading issues in developing countries that not only shatter economies but is the leading cause of depletion of natural resources and environmental pollution. There is an urgent need to shift to renewable energy sources to save economies and natural resources and to save our ecological system. Aiming for this, we have collected a cross-sectional data set to study the household intentions of shifting to wind energy and analysed the moderated mediation interactions of the variable to better understand socio-economic and personal factors. 840 responses were analysed using smart-PLS 4.0; results revealed that cost value and social influence directly relate to renewable energy adoption. Environmental knowledge directly influences attitude towards the environment, and health consciousness influences perceived behavioural control. Results also revealed that social influence strengthens the indirect relationship between awareness of renewable energy and its adoption, while it weakens the indirect relationship between health consciousness and renewable energy adoption.

An integrated model of UTAUT2 to understand consumers' 5G technology acceptance using SEM-ANN approach.

It has been a decade since the first extensive study on the internet's adoption and use was conducted. Circumstances have changed in the last decade internet has become an essential need for every human being. Socio-psychological, economic, and personal factors play a significant role in shaping human behaviour. But their role in shaping consumer behaviour toward 5G is still unexplored. In order to determine the impact of socio-psychological elements on 5G technology adoption intention, the study integrated curiosity, perceived value, functional value, and environmental awareness into UTAUT2 and analyzed how they interact. Instead of relying on linear models, this study employed a dual-stage SEM-ANN approach because customers' decision-making process to adopt new technology is complex. Valid responses from 840 respondents were collected, investigated, and ranked using the deep learning ANN approach. All predictors were found statistically significant except social influence. ANN sensitivity analysis revealed that newly integrated predictors (environmental awareness, curiosity) are surprisingly the most important predictors, followed by facilitating conditions and perceived satisfaction. SEM-ANN hybrid two-step deep learning approach explained 83.6% variance higher than the baseline model (UTAUT2). The study improved UTAUT2 by adding new variables and expanding its canvas to predict user technology adoption. This will show how consumers react to 5G services and help telecoms grow into new markets.

Digital Students' Satisfaction With and Intention to Use Online Teaching Modes, Role of Big Five Personality Traits.

During the COVID-19 pandemic, online teaching modes were found vital to continue students' learning process, but sustainable implementation of online teaching models is an area of concern for policymakers. Psychiatrists are also eager to know students' behavior toward learning and modes of teaching during COVID-19. We have drawn a model based on the big five personality traits to study students' satisfaction with online teaching modes and their adoption intentions toward online teaching modes. We have collected data from 718 bachelor's and master's level students from four different universities. We have applied the SEM-ANN dual-stage approach to test personality traits' influence and ranked them based on their normalized importance. The results revealed that agreeableness, conscientiousness, neuroticism, and openness positively influence students' satisfaction with online teaching models, but that extraversion negatively influences their satisfaction. Agreeableness, extraversion, and neuroticism positively impact, but openness negatively influences. Conscientiousness does not affect adoption intention. Furthermore, agreeableness is the most significant, and conscientiousness is the least important factor for students to adopt online teaching modes. The findings of the study have useful perceptiveness for educational policymakers, academics, and psychiatrists.