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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.

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]

Background: Major depressive disorder (MDD) is a common and debilitating mental illness characterized by persistent feelings of sadness, hopelessness, and a lack of interest in daily activities. The objective of this study was to investigate whether levels of macrophage inflammatory protein-1β (MIP-1β) and macrophage chemoattractant protein-2 (MCP-2) in the blood were associated with the pathophysiology and development of MDD compared to healthy controls (HCs). Methods: This case-control study was conducted involving 50 MDD patients and 38 HCs. We performed a comprehensive assessment to match age, sex, BMI, and socio-demographic profile between the groups. The study excluded participants with chronic infection, inflammatory diseases, coexisting psychiatric disorder, history of liver and kidney diseases, and individuals who are under antipsychotic medications. A professional psychiatrist diagnosed MDD patients and evaluated HCs based on the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria. The severity of depression was assessed using the Hamilton Depression (Ham-D) rating scale. Commercially available enzyme-linked immunosorbent assay (ELISA) kits were used to quantify the serum MIP-1β and MCP-2 levels. Results: The results indicated elevated serum MIP-1β levels (207.73±24.24 pg/ml) in MDD patients compared to HCs (58.77±9.14 pg/ml). This difference in concentration is positively correlated with severity of disease symptoms (r = 0.451; p<0.001). Similarly, the levels of MCP-2 were found to be elevated in patients compared to controls (143.61±19.92 vs. 56.84±4.02 pg/ml; p = 0.003), with a positive correlation with the Ham-D scores (r = 0.373; p = 0.004). Conclusion: According to this study, elevated levels of MIP-1β and MCP-2 may be associated with the pathophysiology and development of MDD. These increased serum MIP-1β and MCP-2 levels could be used as risk assessment tools for MDD. The present findings urge further research and the development of therapeutic and diagnostic approaches for depression. [ABSTRACT FROM AUTHOR]

Among various metal additive manufacturing (AM) technologies, L-PBF is known for fabricating intricate components. However, due to step edges and powder particle attachments, attaining a good surface finish is challenging, especially on downskin surfaces. Contour scanning has potential to improve surface quality because such scanning may dominate the surface formation of sloped features. This study evaluates the effects of pre- and post-contouring strategies on the sloped downskin surfaces fabricated using a commercial L-PBF system with Ti6Al4V powder. L-PBF parts printed at inclination angles 30°, 45° and 60° were investigated. A double-contouring approach with varying processing conditions was employed and surface characteristics were analyzed using data acquired by white light interferometry. The average surface roughness, Sa, surface skewness, Ssk, and percentage area of powder particles attached onto surfaces were statistically evaluated. The lowest Sa obtained for pre- and post-contoured samples is 14.08 µm and 18.88 µm, respectively. For both strategies, the combination of a low laser power and a high scan speed on the interface of downskin surface and underneath powder results in smoother surfaces. However, while comparing both strategies, pre-contouring gives better surface finish for samples built at similar processing conditions, with a difference of nearly 5 µm in Sa. [ABSTRACT FROM AUTHOR]