Background: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs. Objective: This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs. Methods: Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs. Results: A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. Conclusions: The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

In the oil, gas, and water industries, in-pipe robots have shown promising solutions to reach the inaccessible parts of pipeline networks for condition assessment, leak detection, and fluid quality monitoring. However, the pipeline networks have different sizes and configurations, and there are long lines of pipes that make the operation of in-pipe robots challenging. In this paper, we first categorize the mechanical systems of in-pipe robots and classify them. We then define four missions performed by the in-pipe robots: localization, mapping, navigation, and inspection. Next, we present the core methods used for each mission and list the studies that use the presented methods and the way they are implemented. Since image processing approach is a common way to accomplish task(s) for an in-pipe mission, the team decided to provide a comprehensive categorization of image processing techniques and the required tools. We also list the sensors used in in-pipe robots which would help the researchers select the appropriate sensor or sensor module for a specific technique.

(1) Background: Physiological responses, such as heart rate and heart rate variability, have been increasingly utilized to monitor, detect, and predict mental stress. This review summarizes and synthesizes previous studies which analyzed the impact of mental stress on heart activity as well as mathematical, statistical, and visualization methods employed in such analyses. (2) Methods: A total of 119 articles were reviewed following the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Non-English documents, studies not related to mental stress, and publications on machine learning techniques were excluded. Only peer-reviewed journals and conference proceedings were considered. (3) Results: The studies revealed that heart activities and behaviors changed during stressful events. The majority of the studies utilized descriptive statistical tests, including t-tests, analysis of variance (ANOVA), and correlation analysis, to assess the statistical significance between stress and non-stress events. However, most of them were performed in controlled laboratory settings. (4) Conclusions: Heart activity shows promise as an indicator for detecting stress events. This review highlights the application of time series techniques, such as autoregressive integrated moving average (ARIMA), detrended fluctuation analysis, and autocorrelation plots, to study heart rate rhythm or patterns associated with mental stress. These models analyze physiological data over time and may help in understanding acute and chronic cardiovascular responses to stress.

This paper introduces a sub-leasing type of round-trip carsharing to enhance its efficiency for both customers and companies. This innovative type of carsharing provides new temporary stations and allows customers to access both vehicles in the company's stations and vehicles already reserved but parked and unused in other locations. The evaluation is performed using simulation with available demand datasets. Results show that the proposed model significantly increases the acceptance rate of the reservations, enhances the availability and accessibility of the company's vehicles, and reduces the needed fleet size. Hence, it provides many benefits for both companies and customers. Sensitivity analyses quantify the expected improvement by assuming a higher acceptable walking distance for customers and by increasing the flexibility of the customers’ reservation time.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.