Statistical & dynamical multiple-scale predictability of the North Pacific ocean

Prolonged ocean surface warming in North Pacific, such as those extreme events known as marine heatwaves, could lead to significant impact on coastal ecosystems. As such, predicting the North Pacific sea surface temperatures days to weeks to months or even years in advance, especially prolonged marine heatwaves and coastal variability, helps provide guidance to decision makers to understand the future ecosystem variation and to utilize the adverse situation to their benefits. Therefore, it is of vital importance to construct credible and effective ocean forecast systems on various spatial and temporal scales. While statistical models capture the large-scale dynamics and provide forecast skill comparable to the state-of-the-art climate models, regional dynamical models are necessary to resolve high resolution coastal processes and to improve coastal prediction skill. Thus, this thesis combined the use of a Linear Inverse Model (LIM) and the Regional Ocean Modeling System (ROMS), a widely-used empirical model and a commonly-accepted dynamical ocean model, to understand the North Pacific extremes and to evaluate North Pacific forecast on multiple spatial and temporal scales. This includes: (1) using LIM to analyze the statistical behaviors, progression, and prediction of marine heatwaves in Northeast Pacific; (2) using LIM to explore the prediction of North Pacific coastal systems and the impact of tropical versus extratropical Pacific on the prediction; (3) using a multi-scale nesting configuration of ROMS to resolve coastal processes and to explore the near-real time forecast skill around Pt Sal, California; (4) using the fine resolution grid of ROMS to quantify the impact of different forcings, including initial conditions, boundary forcings and atmospheric surface forcings, on the near-real time forecast.Ph.D

Analysis and Modeling of Temporal Features in Data Streams from Multiple Wearable Devices

Time is a vitally important issue in the coordination of multiple wearable devices. Theoretically, wearable applications should require data streams to be synchronized with the necessary degree of precision. However, in the available applications, this critical issue has not been well considered. Actually, time discrepancies exist among data streams, resulting in certain decrease of data analysis and fusion accuracy. The study of time discrepancy is rarely found in the literature, and there is no specific model to describe temporal features. In this dissertation, we first analyze several temporal issues in multi-wearable system and the source of time discrepancy. Then, by taking into account temporal features, we propose two typical models, which provide statistical methods for describing time discrepancy and its distribution. Furthermore, the accuracy of the models is verified by a set of experiments. Finally, we demonstrate the application of the proposed models through a case study, in which the adaptive frequency strategy is adopted. Experimental results show that the strategy can not only guarantee the completeness of the data, but also reduce redundancy compared with the static frequency method. Our models and experiments of time discrepancy can be a basis for further research on the time synchronization of data from multiple wearable devices

Study of anti-cancer effect of winter worm and summer grass on Mcf-7 human breast cancer cells

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on July 9, 2009)Thesis (M.S.) University of Missouri-Columbia 2008.Winter worm and summer grass (WWSG) is one of the most valued traditional Chinese medicines for fighting cancer, increasing longevity, and improving immunity. It consists of the entomopathogenic fungus Cordyceps sinensis and its natural lepidopteran host Hepialus armoricanus. Using the water extract of Cordyceps militaris, a sibling species of C. sinensis cultivated on an artificial host the silkworm Bombyx mori pupae, we have found that the C. militaris extract inhibited growth of MCF-7 human breast cancer cells in a dose- and time-dependent manner. The inhibitory effect of the C. militaris extract on MCF-7 cells was via an apoptosis cascade by inducing the expression of pro-apoptotic genes and by suppressing anti-apoptotic marker genes. Moreover, the C. militaris extract also inhibits DNA methyltransferase transcription, suggesting that the reduced cancer suppressor gene methylation might lead to the recovery of tumor-suppressor gene expression and eventually to the inhibition of tumor cell growth.Includes bibliographical reference

Synthesis and Reactivity of the [NCCCO]– Cyanoketenate Anion

Cyanoketene is a fundamental molecule that is actively being searched for in the interstellar medium. Its deprotonated form (cyanoketenate) is a heterocumulene that is isoelectronic to carbon suboxide whose structure has been the subject of debate. These research questions are hampered by a lack of useful synthetic pathways to these molecules. We report the first synthesis of the cyanoketenate anion in [K(18-crown-6)][NCCCO] (1) as a stable molecule on a multigram scale in excellent yields (>90%). The structure of this molecule is probed crystallographically and computationally. We also explore the protonation of 1, and its reaction with triphenylsilylchloride and carbon dioxide. In all cases, anionic dimers are formed. The cyanoketene could be synthesized and crystallographically characterized when stabilized by a N-heterocyclic carbene. The cyanoketenate is a very useful unsaturated building block containing N, C and O atoms that can now be explored with relative ease and will undoubtedly unlock more interesting reactivity

CellTradeMap: Delineating trade areas for urban commercial districts with cellular networks

Understanding customer mobility patterns to com-mercial districts is crucial for urban planning, facility manage-ment, and business strategies. Trade areas are a widely appliedmeasure to quantify where the visitors are from. Traditionaltrade area analysis is limited to small-scale or store-level studiesbecause information such as visits to competitor commercialentities and place of residence is collected by labour-intensivequestionnaires or heavily biased location-based social media data.In this paper, we propose CellTradeMap, a novel district-leveltrade area analysis framework using mobile flow records (MFRs),a type of fine-grained cellular network data. CellTradeMap ex-tracts robust location information from the irregularly sampled,noisy MFRs, adapts the generic trade area analysis frameworkto incorporate cellular data, and enhances the original trade areamodel with cellular-based features. We evaluate CellTradeMap ona large-scale cellular network dataset covering 3.5 million mobilephone users in a metropolis in China. Experimental results showthat the trade areas extracted by CellTradeMap are aligned withdomain knowledge and CellTradeMap can model trade areaswith a high predictive accuracy

Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.Comment: Accepted by ASE202