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This study aims to analyze the two-dimensional ferrofluid flow in porous media. The effects of changes in parameters such as permeability parameter, buoyancy parameter, Reynolds and Prandtl numbers, radiation parameter, velocity slip parameter, energy dissipation parameter and viscosity parameter on the velocity and temperature profile are displayed numerically and graphically.

By using simplification, nonlinear differential equations are converted into ordinary nonlinear equations. Modeling is done in the Cartesian coordinate system. The finite element method (FEM) and the Akbari-Ganji method (AGM) are used to solve the present problem. The finite element model determines each parameter’s effect on the fluid’s velocity and temperature.

The results show that if the viscosity parameter increases, the temperature of the fluid increases, but the velocity of the fluid decreases. As can be seen in the figures, by increasing the permeability parameter, a reduction in velocity and an enhancement in fluid temperature are observed. When the Reynolds number increases, an increase in fluid velocity and temperature is observed. If the speed slip parameter increases, the speed decreases, and as the energy dissipation parameter increases, the temperature also increases.

When considering factors like thermal conductivity and variable viscosity in this context, they can significantly impact velocity slippage conditions. The primary objective of the present study is to assess the influence of thermal conductivity parameters and variable viscosity within a porous medium on ferrofluid behavior. This particular flow configuration is chosen due to the essential role of ferrofluids and their extensive use in engineering, industry and medicine.

Due to the high penetration of social media and mobile devices in the recent decade, especially with the coronavirus, digital media tools have become a priority for marketing managers. Digital content marketing (DCM) is one of the crucial ingredients of the digital marketing strategy of businesses, which proposes value to the audience through brand-related and relevant content. The tourism industry is also trapped in the digital wave and has witnessed fundamental changes in how customers communicate. The growth of investment in DCM in this industry to introduce tourist attractions and acquire tourists calls for more research to explore multiple aspects of these initiatives' implementation. Despite the importance of DCM, there is no clear understanding of its implementation's various components. Therefore, the primary goal of the current study is to design a new comprehensive framework of DCM implementation that integrates its antecedents, process, and consequences in the tourism industry.

The mixed method was applied to achieve the research goal. The initial criteria and main components of the framework were identified with a comprehensive literature review to develop the framework. To enrich the initial criteria, some semi-structured interviews with experts were conducted; then, the extracted criteria and sub-criteria were prioritized and weighted using the quantitative best-worst method (BWM).

The results indicate that the proposed integrated framework contains three categories of antecedents, processes, and consequences and 12 main concepts. The weights and ranks of the extracted concepts and their sub-criteria are calculated using BWM.

The proposed framework helps managers have a big picture of the DCM strategy to successfully implement and consider the multiple dimensions of such initiatives. The proposed framework provides actionable insight for digital marketing decision-makers to manage such projects effectively and plan appropriate actions for progress.

A review of content marketing reveals that there are few studies conducted that integrate the components of the DCM implementation process, including antecedents, process, and consequences. This research is one of the first in the field of DCM implementation in the tourism industry to fill this theoretical gap. The main contribution of this research is to design a new integrated framework for DCM implementation that offers a holistic view of antecedents, process, and consequences.

Acrylamide (AA) is predominantly used as a synthetic substance within various industries. However, AA is also recognized as a carcinogen. Zinc oxide nanoparticles (ZnO-NPs) are becoming increasingly attractive as medical agents. However, to the knowledge, the effects of ZnO-NPs on preventing cytotoxicity with AA have not been reported. Therefore, this study aims to determine the protective effects of ZnO-NPs against the cytotoxicity caused by AA.

MTT assay was used to determine the cytotoxicity. Reactive oxygen species (ROS) formation, carbonyl protein, malondialdehyde (MDA) and glutathione (GSH) were measured and analyzed statistically.

The findings observed that the presence of 200 µM AA led to a substantial reduction in cell viability (p < 0.001). However, ZnO-NPs restored cell viability at 50 and 100 µM concentrations (p = 0.0121 and p = 0.0011, respectively). The levels of ROS were significantly reduced (p = 0.001 and p = < 0.001) to 518 ± 47.57 and 364 ± 47.79, respectively, compared to the AA group. The levels of GSH were significantly increased (p = 0.004 and p = 0.002) to 16.9 ± 1.3 and 17.6 ± 0.5, respectively, compared to the AA group. The levels of MDA were significantly decreased (p = 0.005, p < 0.001 and p < 0.001) when compared to the AA group, as were the levels of carbonyl protein (p = 0.009 and p < 0.002) in comparison to the AA group.

In summary, the outcomes of this research indicate that ZnO-NPs played a role in inhibiting AA-induced oxidative stress and cytotoxicity.

The purpose of the study is to develop a cloud supporting model for green computing. In today's contemporary world, information technology (IT) plays a significant role. Because of the rapid growth of the IT business and the high level of greenhouse gas emissions, salient data centers are increasingly considering green IT techniques to reduce their environmental impacts. Both developing and underdeveloped countries are widely adopting green infrastructure and services over the cloud because of its cost-effectiveness, scalability and guaranteed high uptime. Several studies have investigated the fact that cloud computing provides beyond green information and communication technology (ICT) services and solutions. Therefore, anything offered over clouds also needs to be green to reduce the adverse influence on the environment.

This paper examines the rationale for the use of green ICT in higher education and finds crucial success variables for the implementation of green ICT on the basis of an analysis of chosen educational organizations and interviews with key academic experts from the Universities of Ethiopia, in general, and BuleHora University, in particular.

Finally, this paper described the design and development of a green cloud selection supporting model for green ICTs in higher educational institutions that helps cloud service customers choose the most green cloud-based ICT products as well as services.

This study may be a significant source of new information for green ICT design and implementation in higher education institutions to preserve the environment and its impact on human life.

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.

The study aims to verify and establish the result of the most suitable optimization approach for higher performance and lower emission of a variable compression ratio (VCR) diesel engine. In this study, three types of test fuels are taken and tested in a variable compression ratio diesel engine (compression ignition). The fuels used are conventional diesel fuel, e-diesel (85% diesel-15% bioethanol) and nano-fuel (85% diesel-15% bioethanol-25 ppm Al₂O₃). The effect of bioethanol and nano-particles on performance, emission and cost-effectiveness is investigated at different load and compression ratios (CRs). The optimum performance and lower emission of the engine are evaluated and compared with other optimization methods.

The test engine is run by diesel, e-diesel (85% diesel-15% bioethanol) and nano-fuel (85% diesel-15% bioethanol-25 ppm Al₂O₃) in three different loadings (4 kg, 8 kg and 12 kg) and CR of 14, 16 and 18, respectively. The optimum value of energy efficiency, exergy efficiency, NO_X emission and relative cost variation are determined against the input parameters using Taguchi-Grey method and confirmed by response surface methodology (RSM) technique.

Using Taguchi-Grey method, the maximum energy and exergy efficiency, minimum % relative cost variation and NO_X emission are 24.64%, 59.52%, 0 and 184 ppm, respectively, at 4 kg load, 18 CR and fuel type of nano-fuel. Using RSM technique, maximum energy and exergy efficiency are 24.8% and 62.9%, and minimum NO_X emission and % cost variation are 208.4 ppm and –6.5, respectively, at 5.2 kg load, 18 CR and nano-fuel. The RSM is suggested as the most appropriate technique for obtaining maximum energy and exergy efficiency, and minimum % relative cost; however, for lowest possible NO_X emission, the Taguchi-Grey method is the most appropriate.

Waste rice straw is used to produce bioethanol. 4-E analysis, i.e. energy, exergy, emission and economic analysis, has been carried out, optimized and compared.

The purpose of this study is to review cyberbullying incidents among students in higher education institutions (HEIs). Cyberbullying has become a threat to students' wellbeing as it penetrates one life due to the pervasive availability of digital technologies.

Through a bibliometric analysis, this study analyzes 361 journal publications from the Web of Science (WoS) based on bibliographic coupling and co-word analysis.

Significant themes were found related to cyberbullying in HEIs, particularly related to the impact and determinants of cyberbullying on students. Bibliographic coupling produces three clusters on the current research fronts, while co-word analysis produces four clusters on the prediction of future trends. Implications of this phenomenon warrant comprehensive intervention by the HEIs management to dampen its impact on students' wellbeing. Findings would enhance the fundamental understanding through science mapping on the prevalent and potential incidence of cyberbullying.

Crucial insights will benefit the government, HEIs’ management, educators, scholars, policymakers and parents to overcome this dreadful phenomenon of cyberbullying. Several managerial interventions and mitigation strategies are proposed to reduce and control the occurrence of cyberbullying.

This study presents a bibliometric review to uncover the knowledge structure of cyberbullying studies in HEIs.