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Health concerns and government restrictions during the COVID-19 pandemic caused a sharp increase in telecommuting (i.e., doing paid work at home or possibly an alternate worksite). In addition to reducing vehicle miles traveled (VMT), decreasing energy use, and lowering emissions of air pollutants and greenhouse gases (GHG), telecommuting may offer numerous other co-benefits, including increasing the worker pool, decreasing time and costs associated with travel, improving work-life balance, and decreasing stress. It may also stimulate greater use of non-motorized and active modes of travel (e.g., walking, biking, taking transit).

Concerns about the environmental impacts of transportation have made reducing vehicle miles traveled (VMT) a policy priority. One way to decrease VMT is to decrease the length of commuting trips, and to get commuters out of their private vehicles. Unfortunately, the average one-way commute keeps getting longer in the U.S., increasing from 25.1 to 27.6 minutes between 2005 and 2019. The percentage of work trips made by private vehicle has also soared, jumping from 66.9 percent in 1960 to 84.8 percent in 2019. As commuting typically occurs during traffic peaks, it is a major contributor to congestion and air pollution.

Oil and gas pipelines are exposed to harsh operating conditions that facilitate their susceptibility to complex corrosion mechanisms. This affects their integrity and results in failure with associated consequences. Capturing these complex corrosion phenomena requires a robust approach. This study proposes the application of a dynamic probabilistic model to capture the key influential factors that contribute to the complex under‐deposit corrosion (UDC) mechanism in oil and gas pipelines. The Bayesian network model assesses the pipeline's susceptibility (degradation rate) to the UDC, capturing parametric dependencies. The predicted corrosion rates are input data for the corrosion propagation prediction. Three semi‐empirical corrosion propagation models are used for a comparative assessment to establish the degree of susceptibility given the prevalent influential factors and model parameters. The proposed approach is tested on an offshore pipeline, and the degree of impact of the key influential parameters is predicted. The result shows a percentage increase in the degradation rate by 18.7%, 33.2%, 35.8%, and 63.4%, respectively, for the various interaction scenarios. The present approach offers an adaptive and robust technique that would provide an early warning guide on the rate of pipeline degradation to aid integrity management for offshore assets suffering from deposit corrosion. [ABSTRACT FROM AUTHOR]

Customer engagement in value co-creation is a key strategy to enhance company performance and strategic advantage. Although frontline employees play an important role in customer engagement - particularly in high contact services such as hotels - little research has tried to understand how employees view the value co-creation process. Hence, this study explores how hotel employees view the value co-creation process in hotels, by considering the motivations and roles of consumers and providers, and the outcomes of customer engagement in the value co-creation process. Analysis of qualitative data from an exploratory focus group with experts and context interviews with hotel employees finds mostly similarities in the way experts and employees view the value co-creation process but also some differences. Employee interviews reveal systematic attempts to engage customers in value co-creation only in high-end hotels. An overwhelming consensus is identified among participants that co-creation has a critical influence in enhancing customer experience and hotel performance. [ABSTRACT FROM AUTHOR]

Objective: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real‐time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine‐learning framework that will give more accurate predictions for IDC and metastasis cancer classification. Methods: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large‐scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding. Results: It is evident that using machine learning techniques significantly (15%–25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC. Conclusions: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]