This study examines the degree of macroeconomic recovery of the Nepal tourism industry after a natural disaster using the autoregressive integrated moving average model (ARIMA). The study investigated the case of Nepal’s earthquakes in 2015 and examined the impact of the earthquakes on tourism inflows and GDP using time series data from 1990 to 2018. The results show that the increasing trend in the number of tourists changes in the post-earthquake period. In particular, the excess in tourist demand by age and purpose of visits after the earthquake indicates natural disaster as a potential reason for a tourism demand boost, often described as dark tourism in literature. This research shows the process of a heritage tourist destination assessing macroeconomic recovery from a natural disaster and fills the gap in the literature regarding purpose-based tourism demand and a link between dark tourism and disaster recovery on a heritage tourism destination.

Various efforts are used to preserve American history including relying on formal education, distributing information (text, video or visual aids) on social channels, displaying artifacts in historical centers or more recently, virtual reality applications posted on a shared medium. However, many of the newly developed applications are designed specifically for dedicated hardware rather than for a broad audience, thus creating a barrier for disseminating cultural values. In this paper, we propose a web-based Augmented Reality (AR) application, namely PalmitoAR, which provides an intuitive way of observing one of the most significant historical Civil War battlefields, Palmito Ranch Battlefield located in Cameron County, Texas. The proposed AR application is designed to resurrect a series of events through (i) a printed map of Palmito Ranch with embedded markers that enables viewers to experience the battle without being present at the site, (ii) a mobile device with a WebGL supported browser that allows 3D contents to be rendered, and (iii) an AR library (A-Frame.io) that enables enthusiasts to recreate similar work. Our methodology strongly relies on the benefits of a simple, robust algorithm for AR marker recognition to position 3D models in a specific context and time. As a result, the proposed AR application is complementary to existing work and provides a seamless experience for a wide range of viewers. We evaluated and improved the application with the help of twenty-six users to gather perspectives on the specific benefits of employing AR in learning about battlefields and reenactment. The technology acceptance model was adapted to access an individual’s acceptance of information technology.

Early discharge or reduced length of stay for inpatient psychiatric patients is related to increased readmission rates and worse clinical outcomes including increased risk for suicide. Trajectories of mental illness outcomes have been identified as an important method for predicting the optimal length of stay but the distinguishing factors that separate trajectories remain unclear. We sought to identify the distinct classes of patients who demonstrated similar trajectories of mental illness over the course of inpatient treatment, and we explore the patient characteristics associated with these mental illness trajectories. We used data ( N = 3,406) from an inpatient psychiatric hospital with intermediate lengths of stay. Using growth mixture modeling, latent mental illness scores were derived from six mental illness indicators: psychological flexibility, emotion regulation problems, anxiety, depression, suicidal ideation, and disability. The patients were grouped into three distinct trajectory classes: (1) High-Risk, Rapid Improvement (HR-RI); (2) Low-Risk, Gradual Improvement (LR-GI); and (3) High-Risk, Gradual Improvement (HR-GI). The HR-GI was significantly younger than the other two classes. The HR-GI had significantly more female patients than males, while the LR-GI had more male patients than females. Our findings indicated that younger females had more severe mental illness at admission and only gradual improvement during the inpatient treatment period, and they remained in treatment for longer lengths of stay, than older males.

Background Observational studies serve as an alternative to randomized experiments, but they can introduce selection bias. Propensity score (PS) methods address this issue by balancing covariates between groups. For the best estimation of PS, all possible and important covariates should be included in the estimation model. However, if too many covariates are included in the estimation model, problems may occur due to the high dimensionality. As the solution, many studies suggested neural networks for high-dimensional data. Methods This study utilized a deep neural network(DNN) and a convolutional neural network (CNN) for PS estimation, which can algorithmically handle nonlinear relationships and interactions of covariates. A simulation was conducted to examine the performance of the DNN and CNN in comparison to logistic regression (LR). Results This study demonstrated that DNN and CNN are feasible and effective as PS estimators, outperforming LR in reducing bias. Specifically, while LR increased bias in the outcome by 17% and could only decrease 5% of covariate imbalance, DNN and CNN were capable of reducing 13% and 16% of bias in the outcome respectively, alongside diminishing 16% and 21% of covariate imbalance. Conclusions Although the application of neural networks requires specialized tools and additional system resources, they offer significant advantages when the precise estimation of treatment is essentially required with many covariates. However, we found that for reliable PS estimation using DNN and CNN, it is imperative to establish and adhere to proper error rate thresholds. Therefore, it is imperative to utilize these machine learning techniques for PS estimation judiciously until appropriate error rates are determined and methods for achieving these error rates are established.