Naveen Sharma

Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If such concerns are channeled to the appropriate authorities, it can aid in the prevention and mitigation of potential infrastructural failures. In this paper, we develop an infrastructure ombudsman -- that automatically detects specific infrastructure concerns. Our work considers several recent structural failures in the US. We present a first-of-its-kind dataset of 2,662 social web instances for this novel task mined from Reddit and YouTube.

Through investment, loans, political influence, and migration, China is utilizing an inventive form of neocolonialism on African nations that continues the cycle of underdevelopment. It is a colonialism that does not mirror the tactics and strategies of the Europeans before, but instead creates the façade of partnership and trust for the extraction of natural African wealth through soft power. This study is meant to examine how Sino-African investment is affecting African economies, resources, labor, environment, and diplomacy and argue that it fulfills the requirements of neocolonialism. The methodology for this study is mixed methods, including analytical, applied, and exploratory research. Using secondary sources, such as books, journals, and data sites, the research consists of collecting data and displaying it under different circumstances to support the argument and raise new questions. An example of this is with thesection on labor, where the absence of labor rights is attractive or unattractive depending on the context or point of view of the scenario. Although this creates complexities in the research process, it also opens new inquiries and arguments. Through each section, the attributes of China’s soft power through a continued interest in African resources will show its equivalence as a neocolonial force that keeps African nations dependent on Chinese investment.

In this paper, we analyze some data-related challenges to building resilient and sustainable communities, particularly how to computationally model the social and economical dynamic that exists within a community. To that end, we propose the Community Asset Ontology (CAO) for a knowledge graph that can encapsulate community data as modeled in existing social science literature. We utilize existing information extraction paradigms to map natural language community data to CAO and evaluate the usefulness of such an ontology-based approach compared to a baseline open information extraction approach.

Background Faecal microbiota transplantation (FMT) demonstrates therapeutic potential in ulcerative colitis (UC). Through a prospective, open-label randomised trial (STOP-Colitis pilot) we examined mechanistic insights associated with clinical response to FMT in UC. Methods Thirty adult participants with active UC were randomised to either colonic (n=14) or nasogastric (n=16) route of administration of 8 FMT infusions over 8 weeks. Each patient was matched to an FMT donor. Changes in faecal microbial diversity and composition before/after FMT were analysed with shotgun metagenomics. Stool metabolomics analysis and mucosal immune cell phenotyping was performed. Correlation was evaluated with Kendall’s Tau coefficient. Results Eleven of the 20 patients completing the study achieved a clinical response to FMT. Response to FMT was associated with an increase in alpha diversity (PChristensenellaceae and Lachnospiracae families correlated with both reduced calprotectin and had high engraftment frequency (FDR adjusted PLachnospiraceae, Butyricicoccaceae and Oscillospiraceae families correlated positively with gut homing Tregs and negatively with Th17 cells (FDR adjusted PConclusion We demonstrate significant correlations linking response to FMT and mucosal immunoregulatory shifts with changes in gut microbial diversity and key microbial species (short chain fatty acid producers) in patients with UC.

In this paper, a new method for estimation and removal of decaying dc offset from fault current signal using the Least Median of Squares Regression (LMSR) method is proposed. The fault currents include the decaying component and the mentioned component reduces the speed response and validity of grid protection relays. The proposed algorithm can foresee the dc decaying component and disassemble it from the fault current wave in a period of the wave cycle. as well as it has used from conventional fast Fourier equations to measure the phasor magnitude and fault current wave in grid protection relays which are mean the dc decaying magnitude and time constant is estimating by integrating fault current during one period cycle. The dc decaying component by deducing the ever sampling of a cycle wave in one period. About the fact that the mentioned proposed method creates the best performance, the simulation has been applying in PSCAD/EMTDC. the results of this work illustrate that the proposed method can foresee the component from fault current wave, as well as this method, could apply as phasor derivation in digital protection relays.

Modeling and solving resource allocation and path planning problems often requires extensive computational resources and hardcoded constraint. In this work, with the help of synthetically generated graphs to emulate real-world data, we explore a preliminary framework that uses neural networks and reinforcement learning for optimization in the context of urban planning operations. We show that this model scales better than Google's OR-Tools on large instances (n > 50), and can be trained without the use of distance matrices or explicitly defined constraints. By training on just synthetic data, our model can be applied to different scenarios without the need for retraining,