XR and mental wellbeing: state of the art and future research directions for the Metaverse.

Introduction: The purpose of this study is to provide an overview of extant research regarding XR technology and its effect on consumer wellbeing. With the hopes of informing marketing practitioners on XR consumer psychology, in preparation for the Metaverse. Methods: To achieve the above aim, two types of analysis took place. Firstly, a bibliometric analysis was conducted which was then followed by a framework-based structured literature review. The latter entailed an analysis of 81 articles evaluated from a positive psychological approach. Findings: Following the TCCM framework, the analysis revealed the most common psychological theories demonstrating potential avenues for XR to impact consumer wellbeing. Moreover, researchers found preliminary links between, theory, characteristics, and contexts. Giving a preliminary description of how theory manifests into reality. Finally, the overview of extant literature was used to propose new avenues for future research pertaining to marketing, the Metaverse, and consumer effects. Conclusion: In conclusion, the paper provides stakeholder insights which can ensure minimal consumer risk and sustainable use of the XR technology and Metaverse. While addressing the need for more research that uncovers the psychological effects of emerging technologies, so to prepare for the Metaverse. This is especially important when considering the current upsurge of these technologies and the uncertainties associated with their novelty and the idea of an 'always on' consumer.

Impact of chart image characteristics on stock price prediction with a convolutional neural network.

Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.

When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning.

One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.

Installation of an artificial vegetating island in oligomesotrophic Lake Paro, Korea.

After cut off of inflowing water, Lake Paro, an oligomesotrophic lake lost littoral zone, an important region for the aquatic ecosystem. For the first step of restoration, the artificial vegetation island was installed. The concentration of nutrients in lake water was not sufficient for the growth of macrophyte as total phosphate was ranged from 58 to 83 µg L(-1). In order to overcome this problem, the hydrophobic substratum for bacterial attachment was selected as buoyant mat material of the artificial vegetation island. In this medium, total phosphate and total nitrogen were ranged from 190 to 1,060 µg L(-1) and from 4.9 to 9.1 mg L(-1), respectively. These concentrations were high enough for macrophytes growth. After launching 1,800 m(2) of AVI in Lake Paro, the macrophytes, Iris pseudoacorus and Iris ensata, grew well after five years of launching without the addition of fertilizer. Furthermore, fishes were plentiful under the artificial vegetation island, and ducks were observed on the artificial vegetation island. Bacteria using sunlight as energy source and self-designed ecotechnology can be used as an alternative method for the restoration of disturbed littoral zone in oligo-mesotrophic lakes.