Taxi Utilization Rate Maximization by Dynamic Demand Prediction: A Case Study in the City of Chicago

The explosive popularity of transportation network companies (TNCs) in the last decade has imposed dramatic disruptions on the taxi industry, but not all the impacts are beneficial. For instance, studies have shown taxi capacity utilization rate is lower than 50% in five major U.S. cities. With the availability of taxi data, this study finds the taxi utilization rate is around 40% in June 2019 (normal scenario) and 35% in June 2020 (COVID 19 scenario) in the city of Chicago, U.S. Powered by recent advances in the deep learning of capturing non-linear relationships and the availability of datasets, a real-time taxi trip optimization strategy with dynamic demand prediction was designed using long short-term memory (LSTM) architecture to maximize the taxi utilization rate. The algorithms are tested in both scenarios?normal time and COVID 19 time?and promising results have been shown by implementing the strategy, with around 19% improvement in mileage utilization rate in June 2019 and 74% in June 2020 compared with the baseline without any optimizations. Additionally, this study investigated the impacts of COVID 19 on the taxi service in Chicago.

Does human resource system strength help employees act proactively? The roles of crisis strength and work engagement

Many empirical studies have elucidated the antecedents and psychological mechanisms of employees' proactive behaviors. However, there is limited knowledge about how a human resource (HR) system helps employees proactively adjust to their changing work environment. Drawing on social exchange theory and event system theory, we developed a theoretical model to examine whether, how, and when perceptions of the HR system strength impact employee proactive behavior during crises. Results from a three-wave time-lagged survey of 305 employees in 65 teams in eight Chinese companies indicate that HR system strength creates a strong situation by alleviating employees' uncertainty about how to behave during crises, which stimulates employees' work engagement and subsequent proactive behaviors. Moreover, employees' perceptions of HR system strength are more likely to influence work engagement when employees perceive the COVID-19 crisis as more severe. We discuss the theoretical and practical implications of the findings and outline important future research directions. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Remote Work Optimization with Robust Multi-channel Graph Neural Networks (preprint)

The spread of COVID-19 leads to the global shutdown of many corporate offices, and encourages companies to open more opportunities that allow employees to work from a remote location. As the workplace type expands from onsite offices to remote areas, an emerging challenge for an online hiring marketplace is how these remote opportunities and user intentions to work remotely can be modeled and matched without prior information. Despite the unprecedented amount of remote jobs posted amid COVID-19, there is no existing approach that can be directly applied. Introducing a brand new workplace type naturally leads to the cold-start problem, which is particularly more common for less active job seekers. It is challenging, if not impossible, to onboard a new workplace type for any predictive model if existing information sources can provide little information related to a new category of jobs, including data from resumes and job descriptions. Hence, in this work, we aim to propose a principled approach that jointly models the remoteness of job seekers and job opportunities with limited information, which also suffices the needs of web-scale applications. Existing research on the emerging type of remote workplace mainly focuses on qualitative studies, and classic predictive modeling approaches are inapplicable considering the problem of cold-start and information scarcity. We precisely try to close this gap with a novel graph neural architecture. Extensive experiments on large-scale data from real-world applications have been conducted to validate the superiority of the proposed approach over competitive baselines. The improvement may translate to more rapid onboarding of the new workplace type that can benefit job seekers who are interested in working remotely.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins.

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

Analysis of early renal injury in COVID-19 and diagnostic value of multi-index combined detection (preprint)

Objectives The aim of the study was to analyze the incidence of COVID-19 with early renal injury, and to explore the value of multi-index combined detection in diagnosis of early renal injury in COVID-19. Design The study was an observational, descriptive study. Setting This study was carried out in a tertiary hospital in Guangdong, China. Participants 12 patients diagnosed with COVID-19 from January 20, 2020 to February 20, 2020. Primary and secondary outcome measures The primary outcome was to evaluate the incidence of early renal injury in COVID-19. In this study, the estimated glomerular filtration rate (eGFR), endogenous creatinine clearance (Ccr) and urine microalbumin / urinary creatinine ratio (UACR) were calculated to assess the incidence of early renal injury. Secondary outcomes were the diagnostic value of urine microalbumin (UMA), 1-microglobulin (A1M), urine immunoglobulin-G (IGU), urine transferring (TRU) alone and in combination in diagnosis of COVID-19 with early renal injury. Results While all patients had no significant abnormalities in serum creatinine (Scr) and blood urea nitrogen (BUN), the abnormal rates of eGFR, Ccr, and UACR were 66.7%, 41.7%, and 41.7%, respectively. Urinary microprotein detection indicated that the area under curve (AUC) of multi-index combined to diagnose early renal injury in COVID-19 was 0.875, which was higher than UMA (0,813), A1M (0.813), IGU (0.750) and TRU (0.750) alone. Spearman analysis showed that the degree of early renal injury was significantly related to C-reactive protein (CRP) and neutrophil ratio (NER), suggesting that the more severe the infection, the more obvious the early renal injury. Hypokalemia and hyponatremia were common in patients with COVID-19, and there was a correlation with the degree of renal injury. Conclusions Early renal injury was common in patients with COVID-19. Combined detection of UMA, A1M, IGU, and TRU was helpful for the diagnosis of early renal injury in COVID-19.