Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
Polymeric nanocomposites are an emerging research topic, as they improve fiber-reinforced composites’ thermo-mechanical and tribological properties. Nanomaterials improve electrical and thermal conductivity and provide excellent wear and friction resistance to the polymer matrix material. In this research work, a systematic study was carried out to examine the tensile and hardness properties of a carbon fiber epoxy composite comprising nano-sized Al2O3 and SiC fillers. The study confirms that adding nano-fillers produces superior tensile and hardness properties for carbon fiber-reinforced polymer composites. The amount of filler loading ranged from 1, 1.5, 1.75, and 2% by weight of the resin for Al2O3 and 1, 1.25, 1.5, and 2% for SiC fillers. The maximum tensile strength gain of 29.54% and modulus gain of 2.42% were noted for Al2O3 filled composite at 1.75 wt.% filler loading. Likewise, enhanced strength gain of 25.75% and 1.17% in modulus gain was obtained for SiC-filled composite at 1.25 wt.% filler loading, respectively. The hardness property of nano-filled composites improved with a hardness number of 47 for nano-Al2O3 and 43 for nano-SiC, respectively, at the same filler loading.
We developed a novel multiobjective markdown system and deployed it across many merchandising units at Walmart. The objectives of this system are to (1) clear the stores’ excess inventory by a specified date, (2) improve revenue by minimizing the discounts needed to clear shelves, and (3) reduce the substantial cost to relabel merchandise in the stores. The underlying mathematical approach uses techniques such as deep reinforcement learning, simulation, and optimization to determine the optimal (marked-down) price. Starting in 2019, after six months of extensive testing, we implemented the new approach across all Walmart stores in the United States. The result was a high-performance model with a price-adjustment policy tailored to each store. Walmart increased its sell-through rate (i.e., the number of units sold during the markdown period divided by its inventory at the beginning of the markdown) by 21% and reduced its costs by 7%. Benefits that Walmart accrues include demographics-based store personalization, reductions in operating costs with limited numbers of price adjustments, and a dynamic time window for markdowns.
The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.
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