Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-Ranking and Adaptive Learning and Extension

The goal of this research project is the realization of an artificial intelligence-driven lightweight domain knowledge search framework that returns a domain knowledge structure upon request with highly relevant web resources via a set of domain-centric re-ranking algorithms and adaptive ontology learning models. The re-ranking algorithm, a necessary mechanism to counter-play the heterogeneity and unstructured nature of web data, uses augmented queries and a hierarchical taxonomic structure to get further insight into the initial search results obtained from credited generic search engines. A semantic weight scale is applied to each node in the ontology graph and in turn generates a matrix of aggregated link relation scores that is used to compute the likely semantic correspondence between nodes and documents. Bootstrapped with a light-weight seed domain ontology, the theoretical platform focuses on the core back-end building blocks, employing two supervised automated learning models as well as semi-automated verification processes to progressively enhance, prune, and inspect the domain ontology to formulate a growing, up-to-date, and veritable system.\\ The framework provides an in-depth knowledge search platform and enhances user knowledge acquisition experience. With minimum footprint, the system stores only necessary metadata of possible domain knowledge searches, in order to provide fast fetching and caching. In addition, the re-ranking and ontology learning processes can be operated offline or in a preprocessing stage, the system therefore carries no significant overhead at runtime

Grid-Aware On-Route Fast-Charging Infrastructure Planning for Battery Electric Bus with Equity Considerations: A Case Study in South King County

The transition from traditional bus fleets to zero-emission ones necessitates the development of effective planning models for battery electric bus (BEB) charging infrastructure. On-route fast charging stations, distinct from on-base charging stations, present unique challenges related to safe operation and power supply capacity, making it difficult to control grid operational costs. This paper establishes a novel framework that integrates the bus route network and power network, which leverages the inter-dependency between both networks to optimize the planning outcomes of on-route BEB charging stations in South King County. The problem is formulated as a mixed-integer second-order cone programming model, aiming to minimize the overall planning cost, which includes investments in charging equipment, power facility, and grid operation. Furthermore, fairness measurements are incorporated into the planning process, allowing for the consideration of both horizontal transit equity and vertical transit equity based on different zone merging criteria within the county's existing census tracts. The results of this planning model offer valuable insights into achieving both economic efficiency and social justice in the design of on-route charging facilities for BEBs in South King County.Comment: 18 pages, 16 figure

Model Risk and Market Risk in Derivative Trading

Figlewski & Green (1999) develop a methodology to assess the model risk and market risk faced by a financial institution that follows two option-trading strategies: writing standard European options, pricing them by Black-Scholes model with volatilities forecasted from historical data, and carrying the position to expiration, either with or without delta hedging. Specifically, they try to examine the impact of volatility forecasting errors on returns and standard deviation of returns of above two trading strategies. The purpose of this paper is to test the robustness of this methodology. First, we replicated their methodology with the same S&P 500 data (Jan 1976-Dec 1991) used in the paper. Then we updated the results with recent S&P 500 data (Jan 1992- Dec 2003), and applied this methodology on NASDAQ with data from Jan 1992 to Dec 2003. Our robustness testing results indicate that Figlewski & Green‟s methodology is quite robust for different period and different market, since we can draw similar conclusions from our testing results

Avoiding China\u27s capital market: Evidence from Hong Kong-listed P-Chips and Red-Chips

The purpose of this paper is to explore the puzzle of why so many Chinese firms eschew listings in China. Hundreds of firms founded in China have reorganized themselves as overseas corporations and listed on the Hong Kong Stock Exchange. These firms are called Red-chips if they are state-owned enterprises (SOEs) and Pchips if they are not state-owned (non-SOEs). To examine the rationale behind the listing decisions of P-chips and Red-chips, we compare the characteristics of Red-chips (P-chips) with SOEs (non-SOEs) listed on China stock exchanges. We find that SOEs are more likely to list in China. Moreover, while we do not observe any significant difference between the performance of Hong Kong-listed and mainland-listed SOEs, we find non-SOEs that are listed in Hong Kong are significantly more profitable than those listed in China. We then explore three possible explanations for why Chinese firms, especially non-SOEs, may prefer to be listed in Hong Kong: (1) to facilitate personal wealth transfers out of China, (2) to increase access to debt capital, and (3) to facilitate more efficient stock price formation. We find that all three of these explanations have statistical support

Chemistry and material science at the cell surface

Cell surfaces are fertile ground for chemists and material scientists to manipulate or augment cell functions and phenotypes. This not only helps to answer basic biology questions but also has diagnostic and therapeutic applications. In this review, we summarize the most recent advances in the engineering of the cell surface. In particular, we focus on the potential applications of surface engineered cells for 1) targeting cells to desirable sites in cell therapy, 2) programming assembly of cells for tissue engineering, 3) bioimaging and sensing, and ultimately 4) manipulating cell biology.National Institutes of Health (U.S.) (Grabt R03DE019191)American Heart Association (Grant 0970178N

Bone-Inspired Materials by Design: Toughness Amplification Observed Using 3D Printing and Testing

Inspired by the fact that nature provides multifunctional composites by using universal building blocks, the authors design and test synthetic composites with a pattern inspired by the microstructure of cortical bone. Using a high-resolution multimaterial 3D printer, the authors are able to manufacture samples and investigate their fracture behavior in mechanical tests. The authors’ results demonstrate that the bone-inspired design is critical for toughness amplification and balance with material strength. The failure modes of the authors’ synthetic composites show similarities with the cortical bone, like crack deflection and branching, constrained microcracking, and fibril bridging. The authors’ results confirm that our design is eligible to reproduce the fracture and toughening mechanism of bone

Optimization of Composite Fracture Properties: Method, Validation, and Applications

A paradigm in nature is to architect composites with excellent material properties compared to its constituents, which themselves often have contrasting mechanical behavior. Most engineering materials sacrifice strength for toughness, whereas natural materials do not face this tradeoff. However, biology's designs, adapted for organism survival, may have features not needed for some engineering applications. Here, we postulate that mimicking nature's elegant use of multimaterial phases can lead to better optimization of engineered materials. We employ an optimization algorithm to explore and design composites using soft and stiff building blocks to study the underlying mechanisms of nature's tough materials. For different applications, optimization parameters may vary. Validation of the algorithm is carried out using a test suite of cases without cracks to optimize for stiffness and compliance individually. A test case with a crack is also performed to optimize for toughness. The validation shows excellent agreement between geometries obtained from the optimization algorithm and the brute force method. This study uses different objective functions to optimize toughness, stiffness and toughness, and compliance and toughness. The algorithm presented here can provide researchers a way to tune material properties for a vast number of engineering problems by adjusting the distribution of soft and stiff materials.BASF. North American Center for Research on Advanced MaterialsAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Memory Management Using Tab Discard and Reload Prediction

Browsers and other multi-tab applications discard tabs when there is insufficient memory. When a tab has been discarded, the user is forced to reload the tab to continue interaction. Selection of tabs to discard can be based on simple heuristics; however, such selection can lead to discarding tabs that the user is likely to use. Incorrectly discarded tabs are disruptive to users. This disclosure describes the use of machine learning techniques to generate more accurate predictions to select the tab to be discarded. Selectively discarding tabs in this manner can improve memory management while also providing a better user experience