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Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

The purpose of the research is to investigate whether corporate social responsibility awareness (pCSR), environmental concerns (EC) and consumer environmental knowledge (CK) will have an impact on sustainable purchase intention (SPI). Furthermore, this paper also contributes to surveying the mediating impact of consumer attitudes (CAs) between intention and the three factors mentioned above.

SmartPLS (version 4.0) structural equation modeling (SEM) and quantitative methods were used to analyze 457 responses from consumers. The survey sample consisted of individuals between the ages of 18 and 34, with a male-to-female ratio of 70 to 30. The study aims to examine and put into practice new directions for manufacturing firms in the fields of fashion, food and consumer products. At the same time, provide more convincing evidence about the use of these fields in the research.

The study showed a favorable link between pCSR, EC, CK and SPI through the proposed hypotheses. The research additionally showed that CAs mediate between the aforementioned variables.

The important and distinctive results of this study encourage both consumers and enterprises to make changes in their perceptions of society. Consumers should gradually change their daily lifestyle by consuming more sustainable products. As a result, this outcome will provide the impetus for manufacturing businesses to alter their operational procedures in order to support the shift from the production of products to more friendly processes, with the help of all levels of management within the business.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.