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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.

The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.

The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.

Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.

The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.

This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.

This paper aims to investigate the photocatalytic activity of zinc doped MAO-TiO₂ films under the optimum MAO treatment condition.

The coating was prepared by micro arc oxidation, and the influence of doping on the properties of the coating was also investigated.

The results show that the BET surface area is 78.25±0.03m²/g, total pore area is 76.32 ± 0.04m²/g, and the total pore volume is 0.2135 ± 0.0004cm³/g. The degradation ratio of the film electrode with Zn-doped in methyl orange solution is up to 94%. When the react circles is 10 times, the degradation ratio is up to more than 85% and remains steady. With the different reaction conditions, these kinetics of the reactions show some different formulas.

A kinetic equation for photocatalytic activity is established.

This paper aims to explore the role of digital inclusive finance (DIF) in influencing household tourism consumption, whether this influence differs between households with different characteristics and determining the intermediate mechanisms that influence the relationship.

The conceptual framework of this study was designed on the basis of the research on DIF in residential consumption practices. The China Household Finance Survey (CHFS) and the Peking University DIF Index were used in the study, which included four years of unbalanced panel data from 25 provinces in China. A fixed effects model was used to validate the conceptual framework and hypothesis testing.

Both hypothesis paths proposed in this study were supported. Results of this study show that DIF has a significant contribution to household tourism consumption and shows a positive impact in terms of both breadth of coverage and depth of use, and that Internet usage is an important mediating mechanism for DIF to promote household tourism consumption. Thus, the use of DIF as a tool can have a positive impact on tourism consumption.

Results of this study will help researchers and tourism businesses understand the relationship and mechanisms at play between DIF and household tourism consumption and leverage financial tools to drive tourism revival. However, the lack of third-country data for comparative analysis may render the conclusions inapplicable to every economy.

This study is the first to examine the relationship between DIF and household tourism consumption, using an “individual + time + region” fixed effects model to conduct specific empirical tests.

Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19 coronavirus quickly became a global crisis. This crisis has added a large amount of waste to urban waste. The purpose of this study is to create cooperation between municipal waste collector contractors.

Thus, a mathematical model is proposed under uncertain conditions, which includes the volume of municipal waste and infectious waste including personal protective equipment and used equipment for patients. To reduce total costs, the results are evaluated with four cooperative game theory methods such as Shapley value, t value, core center and least core. Ultimately, the saved cost by cooperation in each coalition is allocated fairly among the contractors. Finally, a comparison was made between the solution methods based on the value of the objective function and the solution time.

The results indicate that the proposed cooperative method increases cost savings and reduces the fine of residual waste. Therefore, it can be mentioned that this kind of cooperation would finally result in more incentives for contractors to form larger coalitions. Genetic algorithms were used to solve the large-scale model.

The proposed model boosts the current understanding of waste management in the COVID-19 pandemic. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

The purpose of this paper is to make a comprehensive analysis of 354 publications about Pythagorean fuzzy sets (PFSs) from 2013 to 2020 in order to comprehensively understand their historical progress and current situation, as well as future development trend.

First, this paper describes the fundamental information of these publications on PFSs, including their data information, annual trend and prediction and basic features. Second, the most productive and influential authors, countries/regions, institutions and the most cited documents are presented in the form of evaluation indicators. Third, with the help of VOSviewer software, the visualization analysis is conducted to show the development status of PFSs publications at the level of authors, countries/regions, institutions and keywords. Finally, the burst detection of keywords, timezone review and timeline review are exported from CiteSpace software to analyze the hotspots and development trend on PFSs.

The annual PFSs publications present a quickly increasing trend. The most productive author is Wei Guiwu (China). Wei Guiwu and Wei Cun have the strongest cooperative relationship.

The implication of this study is to provide a comprehensive perspective for the scholars who take a fancy to PFSs, and it is valuable for scholars to grasp the hotspots in this field in time.

It is the first paper that uses the bibliometric analysis to comprehensively analyze the publications on PFSs. It can help the scholars in the field of PFSs to quickly understand the development status and trend of PFSs.