Data-driven System Design in Service Operations

The service industry has become an increasingly important component in the world's economy. Simultaneously, the data collected from service systems has grown rapidly in both size and complexity due to the rapid spread of information technology, providing new opportunities and challenges for operations management researchers. This dissertation aims to explore methodologies to extract information from data and provide powerful insights to guide the design of service delivery systems. To do this, we analyze three applications in the retail, healthcare, and IT service industries. In the first application, we conduct an empirical study to analyze how waiting in queue in the context of a retail store affects customers' purchasing behavior. The methodology combines a novel dataset collected via video recognition technology with traditional point-of-sales data. We find that waiting in queue has a nonlinear impact on purchase incidence and that customers appear to focus mostly on the length of the queue, without adjusting enough for the speed at which the line moves. We also find that customers' sensitivity to waiting is heterogeneous and negatively correlated with price sensitivity. These findings have important implications for queueing system design and pricing management under congestion. The second application focuses on disaster planning in healthcare. According to a U.S. government mandate, in a catastrophic event, the New York City metropolitan areas need to be capable of caring for 400 burn-injured patients during a catastrophe, which far exceeds the current burn bed capacity. We develop a new system for prioritizing patients for transfer to burn beds as they become available and demonstrate its superiority over several other triage methods. Based on data from previous burn catastrophes, we study the feasibility of being able to admit the required number of patients to burn beds within the critical three-to-five-day time frame. We find that this is unlikely and that the ability to do so is highly dependent on the type of event and the demographics of the patient population. This work has implications for how disaster plans in other metropolitan areas should be developed. In the third application, we study workers' productivity in a global IT service delivery system, where service requests from possibly globally distributed customers are managed centrally and served by agents. Based on a novel dataset which tracks the detailed time intervals an agent spends on all business related activities, we develop a methodology to study the variation of productivity over time motivated by econometric tools from survival analysis. This approach can be used to identify different mechanisms by which workload affects productivity. The findings provide important insights for the design of the workload allocation policies which account for agents' workload management behavior

日中両言語における受動文体系の対照研究: 事象構造の観点から

取得学位:博士（文学）, 授与番号:人博甲第12号, 授与年月日:平成23年3月22日, 授与大学:金沢大学著者名の[陸]は簡体字を活字体で表記

NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review

Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene representation has taken the field of Computer Vision by storm. As a novel view synthesis and 3D reconstruction method, NeRF models find applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Since the original paper by Mildenhall et al., more than 250 preprints were published, with more than 100 eventually being accepted in tier one Computer Vision Conferences. Given NeRF popularity and the current interest in this research area, we believe it necessary to compile a comprehensive survey of NeRF papers from the past two years, which we organized into both architecture, and application based taxonomies. We also provide an introduction to the theory of NeRF based novel view synthesis, and a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section

Simvastatin suppresses the DNA replication licensing factor MCM7 and inhibits the growth of tamoxifen-resistant breast cancer cells

Acquired tamoxifen resistance (TamR) remains a major challenge in breast cancer endocrine therapy. The mechanism of acquiring tamoxifen resistance remains elusive, and no effective drugs are available. In this investigation, we determined that the expression of the DNA damage marker γH2AX is upregulated under minichromosome maintenance protein 7 (MCM7) knockdown in phospho Ser807/811-retinoblastoma protein (p-Rb) defect cells. In addition, the expression of p-Rb was lower in TamR cells than in parental cells, and the expression of γH2AX was significantly upregulated when MCM7 was knocked down in TamR cells. Simvastatin, an agent for hypercholesterolemia treatment, activated the MCM7/p-RB/γH2AX axis and induced DNA damage in TamR cells, especially when combined with tamoxifen. Finally, in vitro and in vivo experiments demonstrated that simvastatin combined with tamoxifen increased TamR cell apoptosis and inhibited xenograft growth. In conclusion, simvastatin may suppress TamR cell growth by inhibiting MCM7 and Rb and subsequently inducing DNA damage

Simvastatin and Atorvastatin inhibit DNA replication licensing factor MCM7 and effectively suppress RB-deficient tumors growth

Loss or dysfunction of tumor suppressor retinoblastoma (RB) is a common feature in various tumors, and contributes to cancer cell stemness and drug resistance to cancer therapy. However, the strategy to suppress or eliminate Rb-deficient tumor cells remains unclear. In the present study, we accidentally found that reduction of DNA replication licensing factor MCM7 induced more apoptosis in RB-deficient tumor cells than in control tumor cells. Moreover, after a drug screening and further studies, we demonstrated that statin drug Simvastatin and Atorvastatin were able to inhibit MCM7 and RB expressions. Further study showed that Simvastatin and Atorvastatin induced more chromosome breaks and gaps of Rb-deficient tumor cells than control tumor cells. In vivo results showed that Simvastatin and Atorvastatin significantly suppressed Rb-deficient tumor growth than control in xenograft mouse models. The present work demonstrates that ‘old' lipid-lowering drugs statins are novel weapons against RB-deficient tumors due to their effects on suppressing MCM7 protein levels

Self-Assembled Porous-Reinforcement Microstructure-Based Flexible Triboelectric Patch for Remote Healthcare.

Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However, wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges. Here, a flexible triboelectric patch (FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porous-reinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa-1 under a pressure range of 0-5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa-1. The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare (IoH) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring

Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification.

The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5 V kPa-1 with a detection limit of 0.14 Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%