Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

The aim of this study is to investigate the effect of tourist resources, conditions and opportunities of sacral tourism in Kazakhstan using panel data (time series and cross-sectional) regression analysis for a sample of 14 regions of Kazakhstan observed over the period from 2004 to 2022. The article presents an overview of modern methods of assessment of the tourist and recreational potential of sacral tourism, as used by national and foreign scientific works. The main focus is on the method of estimating the size and effectiveness of the tourist potential, which reflects the realization and volume of tourist resources and their potential. The overall results show a significant positive effect in that the strongest impact on the increase in the number of tourist residents is the proposed infrastructure and the readiness of regions to receive tourists qualitatively. This study is expected to be of value to firm managers, investors, researchers, and regulators in decision- making at different levels of government.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

Through a comparative investigation of the function of socialist realism in the drama and law of Kenya, Nigeria, and South Africa, this research investigates the decolonization of neo-colonial hegemonies in Africa. Using the drama and legal systems of Kenya, Nigeria, and South Africa as comparative case studies, the research explores how African societies can challenge and demolish oppressive systems of domination sustained by colonial legacies and contemporary neo-colonial forces. Relying on the Socialist Realism and Critical Postcolonial theoretical frameworks which both support literary and artistic genre that encourages social and political transformation, the research deploys the case study analysis, comparative literature analysis and focused group discussion methods. Data obtained are subjected to content and thematic analysis. The study emphasizes how important the relationship between the legal and artistic worlds is to the fight against neo-colonialism. It further reveals the transformational potential of socialist realism as a catalyst for social change by looking at themes of resistance, social justice, and the amplifying of disadvantaged voices in drama and legal discourse. The research contributes to ongoing discussions about de-neo-colonization through this comparative case study, and emphasizes the role socialist realism plays in overthrowing neo-colonial hegemonies. The study sheds light on the distinct difficulties and opportunities these nations—and indeed, all of Africa—face in their pursuit of decolonial justice by examining the experiences of Kenya, Nigeria, and South Africa.

Analysis of the factors influencing the price of carbon emissions trading in China and its time-varying characteristics is essential for the smooth operation of the carbon trading system. We analyse the time-varying effects of public concern, degree of carbon regulation, crude oil price, international carbon price and interest rate level on China’s carbon price through SV-TVP-VAR model. Among them, the quantification of public concern and the degree of carbon emission regulation is based on microblog text and government decisions. The results show that all the factors influencing carbon price are significantly time-varying, with the shocks of each factor on carbon price rising before 2019 and turning significantly thereafter. The short-term shock effect of each factor is more significant compared to the medium- and long-term, and the effect almost disappears at a lag of six months. Thanks to public environmental awareness, low-carbon awareness and the progress of carbon market management mechanisms, public concern has had the most significant impact on carbon price since 2019. With the promulgation of relevant management measures for the carbon market, relevant regulations on carbon emission accounting, financing constraints, and carbon emission quota allocation for emission-controlled enterprises have become increasingly mature, and carbon price signals are more sensitive to market information. The above findings provide substantial empirical evidence for all stakeholders in the market, who need to recognize that the impact of non-structural factors on the price of carbon varies over time. Government intervention also serves as a key aspect of carbon emission control and requires the introduction of relevant constraints and incentives. In particular, emission-controlling firms need to focus on the policy direction of the carbon market, and focus on the impact of Internet public opinion on business production while reducing carbon allowance demand and energy dependence.