DEVELOPMENT OF MICROBIAL ELECTROCHEMICAL SENSOR FOR TOXICITY SCREENING OF INFLUENT WASTEWATER

Ph.DDOCTOR OF PHILOSOPH

Learning Bayesian networks with ancestral constraints

Abstract We consider the problem of learning Bayesian networks optimally, when subject to background knowledge in the form of ancestral constraints. Our approach is based on a recently proposed framework for optimal structure learning based on non-decomposable scores, which is general enough to accommodate ancestral constraints. The proposed framework exploits oracles for learning structures using decomposable scores, which cannot accommodate ancestral constraints since they are non-decomposable. We show how to empower these oracles by passing them decomposable constraints that they can handle, which are inferred from ancestral constraints that they cannot handle. Empirically, we demonstrate that our approach can be orders-of-magnitude more efficient than alternative frameworks, such as those based on integer linear programming

Application of Zebrafish Models in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic, recurrent, and remitting inflammatory disease with unclear etiology. As a clinically frequent disease, it can affect individuals throughout their lives, with multiple complications. Unfortunately, traditional murine models are not efficient for the further study of IBD. Thus, effective and convenient animal models are needed. Zebrafish have been used as model organisms to investigate IBD because of their suggested highly genetic similarity to humans and their superiority as laboratory models. The zebrafish model has been used to study the composition of intestinal microbiota, novel genes, and therapeutic approaches. The pathogenesis of IBD is still unclear and many risk factors remain unidentified. In this review, we compare traditional murine models and zebrafish models in terms of advantages, pathogenesis, and drug discovery screening for IBD. We also review the progress and deficiencies of the zebrafish model for scientific applications

Electric-field Control of Magnetism with Emergent Topological Hall Effect in SrRuO3 through Proton Evolution

Ionic substitution forms an essential pathway to manipulate the carrier density and crystalline symmetry of materials via ion-lattice-electron coupling, leading to a rich spectrum of electronic states in strongly correlated systems. Using the ferromagnetic metal SrRuO3 as a model system, we demonstrate an efficient and reversible control of both carrier density and crystalline symmetry through the ionic liquid gating induced protonation. The insertion of protons electron-dopes SrRuO3, leading to an exotic ferromagnetic to paramagnetic phase transition along with the increase of proton concentration. Intriguingly, we observe an emergent topological Hall effect at the boundary of the phase transition as the consequence of the newly-established Dzyaloshinskii-Moriya interaction owing to the breaking of inversion symmetry in protonated SrRuO3 with the proton compositional film-depth gradient. We envision that electric-field controlled protonation opens a novel strategy to design material functionalities

Ductile fracture and microstructure of a bearing steel in hot tension

Ductile fracture, such as micro-cavities and micro-voids, inevitably exist and evolve under tensile stress state in metal forming. Ductile fracture sways the mechanical performance of 52100 bearing steel. It is necessary to investigate the influences of strain rate and deformation temperature on both ductile fracture and microstructure evolution. Uniaxial hot tension tests were performed, in which specimens were stretched to failure in the temperatures range from 950 C to 1160 C and in the strain rates range from 0.01 /s to 1.0 /s. Specimens metallographies have been explored after hot tension. Experimental results show that the peak stress decreases when deformation temperature increases and strain rate decreases. The critical strain of stress–strain relationships increases when strain rate increases. Fracture morphology is severe at higher deformation temperatures and lower strain rates. Hot tension deformation capacity is worst at 1160 C and a strain rate of 0.01 /s, has been caused by a larger and coarser grain structure

Ductile fracture and microstructure of a bearing steel in hot tension

382-388Ductile fracture, such as micro-cavities and micro-voids, inevitably exist and evolve under tensile stress state in metal forming. Ductile fracture sways the mechanical performance of 52100 bearing steel. It is necessary to investigate the influences of strain rate and deformation temperature on both ductile fracture and microstructure evolution. Uniaxial hot tension tests were performed, in which specimens were stretched to failure in the temperatures range from 950 °C to 1160 °C and in the strain rates range from 0.01 /s to 1.0 /s. Specimens metallographies have been explored after hot tension. Experimental results show that the peak stress decreases when deformation temperature increases and strain rate decreases. The critical strain of stress–strain relationships increases when strain rate increases. Fracture morphology is severe at higher deformation temperatures and lower strain rates. Hot tension deformation capacity is worst at 1160 °C and a strain rate of 0.01 /s, has been caused by a larger and coarser grain structure

K-LITE: Learning Transferable Visual Models with External Knowledge

Recent state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. This free form of supervision ensures high generality and usability of the learned visual models, based on extensive heuristics on data collection to cover as many visual concepts as possible. Alternatively, learning with external knowledge about images is a promising way which leverages a much more structured source of supervision. In this paper, we propose K-LITE (Knowledge-augmented Language-Image Training and Evaluation), a simple strategy to leverage external knowledge to build transferable visual systems: In training, it enriches entities in natural language with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that can understand both visual concepts and their knowledge; In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models. We study the performance of K-LITE on two important computer vision problems, image classification and object detection, benchmarking on 20 and 13 different existing datasets, respectively. The proposed knowledge-augmented models show significant improvement in transfer learning performance over existing methods.Comment: Preprint. The first three authors contribute equall