Smart Retail, Replaces All? Some? : Different Influence of Amazon Go to Local Restaurant Industry.

Amazon Go, the pioneering smart retailer, has been opening physical stores in metropolitan areas of the USA, and seductively distracted customers from adjacent competitors by provisioning quick-and-easy service. This study focuses on how the appearance of the smart retailer affects adjacent competing businesses. We constructed a panel dataset with various features and reviews of restaurants from Yelp.com, and created two dummies, , one if the restaurant is in a certain radius of a smart retailer and zero outside, and , one after the introduction and zero before. By using Difference-in-Difference estimation, we find that (1) negative impacts on the adjacent restaurants after Amazon Go compared to non-adjacent and before the appearance, and (2) less negative impact on adjacent fine-dining restaurants than fast-food restaurants. After Amazon Go, customers’ sentiments about the adjacent restaurants have changed more negatively. This paper may provide businesses with useful implications for their strategies

The signaling effect of group-type profile pictures in the sharing economy: The case of Airbnb

While Airbnb hosts may publish various details of their property on the online platform to persuade travelers to make bookings, they choose to post limited information about themselves, except their profile pictures. Based on the signaling and uncertainty reduction theories, we focus on the impact of host profile pictures on bookings and hypothesize that (1) the presence of a profile picture induces the travelers to trust the host more, (2) the number of people in a picture, a proxy for sociality in trustworthiness, increases bookings, and (3) these two impacts are intensified for properties in risky neighborhoods. Collecting profile pictures of 14,799 hosts on Airbnb, we utilized a deep learning-based face detection technique to extract the number of different faces in a profile and ran random effects models to test our hypotheses. This study is unique in its using archival data to show the impact of profile pictures on bookings

Inventory as a Driver of Demand: The Case of Blockbuster Online Rental System

Word-of-Mouth (WOM) has been recognized as one of the most influential resources of information transmission especially for the experience goods. It has been shown that a higher volume of WOM has a positive effect on increasing the demand for a certain product. In addition to the WOM-sales interdependence, there is also dependence between sales and inventory. It is a well-studied concept that sales are affected by the inventory. An inadequate inventory management may result with lost sales in two aspects: One is the loss of the demand (direct effect) because of unavailability of the product in the inventory, and the other one is the loss of the possible demand which would have been created through WOM (indirect effect). The sales-inventory dependence becomes more important when the seller cannot replenish its inventory after determining the initial stock level for a product. In this study, we consider an online DVD rental system. We make an empirical analysis of movie rental dynamics. Considering the WOM, we find that a larger initial capacity of a given movie title results in an increase in the number of rentals

Attenuation of Vegetation and Snow on RF Wireless Communication

In this study, the attenuation in mobile communication due to snow and vegetation is simulated by using Discrete Propagation Model. The study includes simulation results according to different frequencies of propagating waves and moisture content of various vegetation and snow types. Results are compared with other studies

The Role of CD200 and CD43 Expression in Differential Diagnosis between Chronic Lymphocytic Leukemia and Mantle Cell Lymphoma

Objective: Atypical chronic lymphocytic leukemia (CLL) is most frequently confused with mantle cell lymphoma (MCL). Several markers may contribute to the diagnosis of CLL. However, there is no consensus on which markers are needed to be used in flow cytometry for the diagnosis of CLL. The aim of the present study was to investigate the role of CD43 and CD200 markers in the differential diagnosis between CLL and MCL. Materials and Methods: To address this issue, 339 consecutive patients with CLL and MCL were included in the flow cytometry lymphoproliferative disease panel for evaluation of CD43 and CD200 expressions, but not in the Matutes scoring system. Results: CD200 was expressed in 97.3% of atypical CLL cases, whereas it was dimly expressed in only 6.1% of MCL cases. CD43 expression was 95.7% in atypical CLL cases. In the MCL cases, its expression rate was 39.4%. Conclusion: CD43 and CD200 were found to be more valuable markers than CD22, CD79b, and FMC7. CD43 and CD200 could also be considered as definitive markers in atypical CLL patients, for whom the Matutes scoring system remains ineffective

Cross-border issues and technology and management solutions during COVID-19

Critical cross-border issues have emerged during the COVID-19 pandemic, especially pertaining to security, supply chain, and education, which has led to several new challenges for management. The balance between potential risks and economic benefits has attracted the attention of both industry and academia. Hence, we invited three panelists to participate in the 2021 Association for Information Systems (AIS) Special Interest Group (SIG) on Information Systems in Asia Pacific (ISAP) workshop. The suggested solutions include the right Internet approach, multi-national cooperation to develop flexible global operations, and people’s education (especially refugees) to mitigate risks. These solutions encompass three levels, i.e., technology, management, and society

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. Results: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). Conclusions: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm

SARcopenia Assessment in Hypertension: The SARAH Study.

© 2022 Wolters Kluwer Health, Inc.Objectives: The aims of the study were to investigate the relationship between sarcopenia and renin-angiotensin system-related disorders and to explore the effects of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on muscle mass/function and physical performance. Design: This multicenter, cross-sectional study was performed using ISarcoPRM algorithm for the diagnosis of sarcopenia. Results: Of the 2613 participants (mean age = 61.0 ± 9.5 yrs), 1775 (67.9%) were hypertensive. All sarcopenia-related parameters (except chair stand test in males) were worse in hypertensive group than in normotensive group (all P < 0.05). When clinical/potential confounders were adjusted, hypertension was found to be an independent predictor of sarcopenia in males (odds ratio = 2.403 [95% confidence interval = 1.514-3.813]) and females (odds ratio = 1.906 [95% confidence interval = 1.328-2.734], both P < 0.001). After adjusting for confounding factors, we found that all sarcopenia-related parameters (except grip strength and chair stand test in males) were independently/negatively related to hypertension (all P < 0.05). In females, angiotensin-converting enzyme inhibitors users had higher grip strength and chair stand test performance values but had lower anterior thigh muscle thickness and gait speed values, as compared with those using angiotensin II receptor blockers (all P < 0.05). Conclusions: Hypertension was associated with increased risk of sarcopenia at least 2 times. Among antihypertensives, while angiotensin-converting enzyme inhibitors had higher muscle function values, angiotensin II receptor blockers had higher muscle mass and physical performance values only in females