In-Store Media and Channel Management

In this paper, we study the interesting and complicated effects of retailer in-store media on distribution channel relationships. With the help of advanced technology, retailers can open in-store media in their stores and allow manufacturers to advertise through the instore media. We show that opening in-store media is a strategic decision for a retailer, and a retailer may strategically subsidize manufacturers on their advertising through instore media to better coordinate the channel. Even when in-store media is more effective than commercial media (i.e., radio, TV, newspaper, etc.), a retailer may still charge an advertising rate lower than commercial media does. We also show that the benefit of instore media to a retailer can be a U-shaped curve of manufacturer bargaining power, and a retailer may introduce in-store media only when manufacturer bargaining power is either very high or very low, but not intermediate. With manufacturer competition, a retailer can strategically use in-store media to ration excessive advertising between manufacturers, achieving better channel coordination. When manufacturers are asymmetric with pre-advertising brand awareness, a retailer has incentive to subsidize manufacturers whose brand awareness is higher. We also find that retailer in-store media can benefit social welfare even when in-store media is less effective than commercial media. However, if in-store media effectiveness is very low, a retailer may introduce instore media for its own benefit with the sacrifice on social welfare.in-store media; advertising; distribution channel; channel coordination; retailing

Towards Automated Machine Learning on Imperfect Data for Situational Awareness in Power System

The increasing penetration of renewable energy sources (such as solar and wind) and incoming widespread electric vehicles charging introduce new challenges in the power system. Due to the variability and uncertainty of these sources, reliable and cost-effective operations of the power system rely on high level of situational awareness. Thanks to the wide deployment of sensors (e.g., phasor measurement units (PMUs) and smart meters) and the emerging smart Internet of Things (IoT) sensing devices in the electric grid, large amounts of data are being collected, which provide golden opportunities to achieve high level of situational awareness for reliable and cost-effective grid operations.To better utilize the data, this dissertation aims to develop Machine Learning (ML) methods and provide fundamental understanding and systematic exploitation of ML for situational awareness using large amounts of imperfect data collected in power systems, in order to improve the reliability and resilience of power systems.However, building excellent ML models needs clean, accurate and sufficient training data. The data collected from the real-world power system is of low quality. For example, the data collected from wind farms contains a mixture of ramp and non-ramp as well as the mingle of heterogeneous dynamics data; the data in the transmission grid contains noisy, missing, insufficient and inaccurate timestamp data. Employing ML without considering these distinct features in real-world applications cannot build good ML models. This dissertation aims to address these challenges in two applications, wind generation forecast and power system event classification, by developing ML models in an automated way with less efforts from domain experts, as the cost of processing such large amounts of imperfect data by experts can be prohibitive in practice.First, we take heterogeneous dynamics into consideration, especially for ramp events. A Drifting Streaming Peaks-over-Threshold (DSPOT) enhanced self-evolving neural networks-based short-term wind farm generation forecast is proposed by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events, based on which different neural networks are trained to learn different dynamics of wind farm generation. As the efficacy of the neural networks relies on the quality of training datasets (i.e., the classification accuracy of ramp and non-ramp events), a Bayesian optimization based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. Next, we address the challenges of event classification due to the low-quality PMU measurements and event logs. A novel machine learning framework is proposed for robust event classification, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Moreover, with the small number of good features, we need much less training data to train a good event classifier, which can address the challenge of insufficient and imbalanced training data, and the training time is negligible compared to neural network based approaches. Based on the proposed framework, we developed a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data. Subsequently, we address the challenge of insufficient training labels. The real-world PMU data is often incomplete and noisy, which can significantly reduce the efficacy of existing machine learning techniques that require high-quality labeled training data. To obtain high-quality event logs for large amounts of PMU measurements, it requires significant efforts from domain experts to maintain the event logs and even hand-label the events, which can be prohibitively costly or impractical in practice. So we develop a weakly supervised machine learning approach that can learn a good event classifier using a few labeled PMU data. The key idea is to learn the labels from unlabeled data using a probabilistic generative model, in order to improve the training of the event classifiers. Experimental results show that even with 95\% of unlabeled data, the average accuracy of the proposed method can still achieve 78.4\%. This provides a promising way for domain experts to maintain the event logs in a less expensive and automated manner. Finally, we conclude the dissertation and discuss future directions

Distribution Channel Choice and Divisional Conflict in Remanufacturing Operations

We consider a firm consisting of two divisions, one responsible for designing and manufacturing new products and the other responsible for remanufacturing operations. The firm will sell these new and remanufactured products either directly to the consumer (direct selling) or through an independent retailer (indirect selling). Our study demonstrates that a firm’s organizational structure can affect its marketing decisions. Specifically, a decentralized firm with separate manufacturing and remanufacturing divisions can benefit from indirect selling with higher firm profit, supply chain profit, and total consumer demand than direct selling. Moreover, this structure also induces a remanufacturable product design. In contrast, a centralized firm in which the manufacturing and remanufacturing divisions are consolidated is intuitively better off by choosing direct selling than indirect selling. Furthermore, we show that, surprisingly, when the focal firm sells through an independent retailer, a decentralized internal structure can result in higher supply chain profit than a centralized internal structure. We further investigate the case of dual dedicated channels and conclude that, while direct selling of remanufactured products and indirect selling of new products can better induce a remanufacturable product design and higher supply chain profit, it is not in the best interest of the firm in terms of total sales and firm profit

BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning

BackgroundVenous thromboembolism (VTE) is characterized by high morbidity, mortality, and complex treatment. A VTE knowledge graph (VTEKG) can effectively integrate VTE-related medical knowledge and offer an intuitive description and analysis of the relations between medical entities. However, current methods for constructing knowledge graphs typically suffer from error propagation and redundant information.MethodsIn this study, we propose a deep learning-based joint extraction model, Biaffine Common-Sequence Self-Attention Linker (BCSLinker), for Chinese electronic medical records to address the issues mentioned above, which often occur when constructing a VTEKG. First, the Biaffine Common-Sequence Self-Attention (BCsSa) module is employed to create global matrices and extract entities and relations simultaneously, mitigating error propagation. Second, the multi-label cross-entropy loss is utilized to diminish the impact of redundant information and enhance information extraction.ResultsWe used the electronic medical record data of VTE patients from a tertiary hospital, achieving an F1 score of 86.9% on BCSLinker. It outperforms the other joint entity and relation extraction models discussed in this study. In addition, we developed a question-answering system based on the VTEKG as a structured data source.ConclusionThis study has constructed a more accurate and comprehensive VTEKG that can provide reference for diagnosing, evaluating, and treating VTE as well as supporting patient self-care, which is of considerable clinical value

Weather Support for the 2008 Olympic and Paralympic Sailing Events

The Beijing 2008 Olympic and Paralympic Sailing Competitions (referred to as OPSC hereafter) were held at Qingdao during August 9–23 and September 7–13 2008, respectively. The Qingdao Meteorological Bureau was the official provider of weather support for the OPSC. Three-dimensional real-time information with high spatial-temporal resolution was obtained by the comprehensive observation system during the OPSC, which included weather radars, wind profile radars, buoys, automated weather stations, and other conventional observations. The refined forecasting system based on MM5, WRF, and statistical modules provided point-specific hourly wind forecasts for the five venues, and the severe weather monitoring and forecasting system was used in short-term forecasts and nowcasts for rainstorms, gales, and hailstones. Moreover, latest forecasting products, warnings, and weather information were communicated conveniently and timely through a synthetic, speedy, and digitalized network system to different customers. Daily weather information briefings, notice boards, websites, and community short messages were the main approaches for regatta organizers, athletes, and coaches to receive weather service products at 8:00 PM of each day and whenever new updates were available. During the period of OPSC, almost one hundred people were involved in the weather service with innovative service concept, and the weather support was found to be successful and helpful to the OPSC

Comparison of Diagnostic Performance of Three-Dimensional Positron Emission Mammography versus Whole Body Positron Emission Tomography in Breast Cancer

Objective. To compare the diagnostic performance of three-dimensional (3D) positron emission mammography (PEM) versus whole body positron emission tomography (WBPET) for breast cancer. Methods. A total of 410 women with normal breast or benign or highly suspicious malignant tumors were randomized at 1 : 1 ratio to undergo 3D-PEM followed by WBPET or WBPET followed by 3D-PEM. Lumpectomy or mastectomy was performed on eligible participants after the scanning. Results. The sensitivity and specificity of 3D-PEM were 92.8% and 54.5%, respectively. WBPET showed a sensitivity of 95.7% and specificity of 56.8%. After exclusion of the patients with lesions beyond the detecting range of the 3D-PEM instrument, 3D-PEM showed higher sensitivity than WBPET (97.0% versus 95.5%, P = 0.913), particularly for small lesions (<1 cm) (72.0% versus 60.0%, P = 0.685). Conclusions. The 3D-PEM appears more sensitive to small lesions than WBPET but may fail to detect lesions that are beyond the detecting range. This study was approved by the Ethics Committee (E2012052) at the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China). The instrument positron emission mammography (PEMi) was approved by China State Food and Drug Administration under the registration number 20153331166

Internet Retailing as a Marketing Strategy

We analyze the incentives for incumbent bricks-and-mortar firms and new entrants to start an online retail channel in a differentiated goods market. To this end we set up a two-stage model where firms first decide whether or not to build the infrastructure necessary to start an online retail channel and then compete in prices using the channels they have opened up. Consumers trade-off the convenience of online shopping and the ease to compare prices, with online uncertainties. Without a threat of entry by a third pure online player we find that for most parameter constellations firms' dominant strategy is not to open an online retail channel as this cannibalizes too much on their conventional sales. As the cannibalization effect is not present for a pure Internet player, we show that these firms will start online retail channels under a much wider range of parameter constellations. The threat of entry may force incumbent bricks-and-mortar firms to deter entry by starting up an Internet retail channel themselves. We also show that a low cost of building up an online retail channel or online shopping conveniences may not be to the benefit of online shopping as the strategic interaction between firms may be such that no online retail channel is built when the circumstances seem to be more favourable