AN ALGORITHM FOR IMPACTING SOFT STRUCTURES

Impact among soft structures is often difficult to model because of the geometrical non-linearity involved. There are a number of previous studies of the contact dynamics of rigid bodies, but few has focused on soft structures so far. This thesis models impact between soft structures without any restraint on their geometries. The goal is to simulate the dynamics involved among soft structures during an impact process. This has been done through designing and implementing an contact algorithm that uses the finite element method along with a three-dimensional solid element to solve the fundamental time integration problem. Modeling of the contact force is the core part and major challenge for the design of the algorithm. In general, the contact algorithm has been implemented in a clear and easy-to-understand style and the program features a comprehensive list of output and its compatibility with other service programs within the same simulation environment Three test systems have been designed to check the robustness of the algorithm. Although these testing systems can not represent all kinds of structures in the real world, the design of them ensures that they\u27re able to represent a number of generic cases where soft structures come into contact. The results have shown that the algorithm designed works very well in terms of handling these impact systems. The algorithm developed here has been validated under some generic cases, but it\u27s just a start and there are still much work remained to be done for the perfection of it. Details of some improvements are discussed at the end of this thesis

Interlayer ferroelectric polarization modulated anomalous Hall effects in four-layer MnBi2Te4 antiferromagnets

Van der Waals (vdW) assembly could efficiently modulate the symmetry of two-dimensional (2D) materials that ultimately governs their physical properties. Of particular interest is the ferroelectric polarization being introduced by proper vdW assembly that enables the realization of novel electronic, magnetic and transport properties of 2D materials. Four-layer antiferromagnetic MnBi2Te4 (F-MBT) offers an excellent platform to explore ferroelectric polarization effects on magnetic order and topological transport properties of nanomaterials. Here, by applying symmetry analyses and density-functional-theory calculations, the ferroelectric interface effects on magnetic order, anomalous Hall effect (AHE) or even quantum AHE (QAHE) on the F-MBT are analyzed. Interlayer ferroelectric polarization in F-MBT efficiently violates the PT symmetry (the combination symmetry of central inversion (P) and time reverse (T) of the F-MBT by conferring magnetoelectric couplings, and stabilizes a specific antiferromagnetic order encompassing a ferromagnetic interface in the F-MBT. We predict that engineering an interlayer polarization in the top or bottom interface of F-MBT allows converting F-MBT from a trivial insulator to a Chern insulator. The switching of ferroelectric polarization at the middle interfaces results in a direction reversal of the quantum anomalous Hall current. Additionally, the interlayer polarization of the top and bottom interfaces can be aligned in the same direction, and the switching of polarization direction also reverses the direction of anomalous Hall currents. Overall, our work highlights the occurrence of quantum-transport phenomena in 2D vdW four-layer antiferromagnets through vdW assembly. These phenomena are absent in the bulk or thin-film in bulk-like stacking forms of MnBi2Te4

Dynamic Energy Management

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.Comment: 63 pages, 15 figures, accompanying open source librar

Self-assembly of H2S-responsive nanoprodrugs based on natural rhein and geraniol for targeted therapy against <i>Salmonella Typhimurium</i>

Salmonellosis is a globally extensive food-borne disease, which threatens public health and results in huge economic losses in the world annually. The rising prevalence of antibiotic resistance in Salmonella poses a significant global concern, emphasizing an imperative to identify novel therapeutic agents or methodologies to effectively combat this predicament. In this study, self-assembly hydrogen sulfide (H2S)-responsive nanoprodrugs were fabricated with poly(α-lipoic acid)-polyethylene glycol grafted rhein and geraniol (PPRG), self-assembled into core–shell nanoparticles via electrostatic, hydrophilic and hydrophobic interactions, with hydrophilic exterior and hydrophobic interior. The rhein and geraniol are released from self-assembly nanoprodrugs PPRG in response to Salmonella infection, which is known to produce hydrogen sulfide (H2S). PPRG demonstrated stronger antibacterial activity against Salmonella compared with rhein or geraniol alone in vitro and in vivo. Additionally, PPRG was also able to suppress the inflammation and modulate gut microbiota homeostasis. In conclusion, the as-prepared self-assembly nanoprodrug sheds new light on the design of natural product active ingredients and provides new ideas for exploring targeted therapies for specific Enteropathogens

<i>Neisseria</i> species as pathobionts in bronchiectasis

Neisseria species are frequently identified in the bronchiectasis microbiome, but they are regarded as respiratory commensals. Using a combination of human cohorts, next-generation sequencing, systems biology, and animal models, we show that bronchiectasis bacteriomes defined by the presence of Neisseria spp. associate with poor clinical outcomes, including exacerbations. Neisseria subflava cultivated from bronchiectasis patients promotes the loss of epithelial integrity and inflammation in primary epithelial cells. In vivo animal models of Neisseria subflava infection and metabolipidome analysis highlight immunoinflammatory functional gene clusters and provide evidence for pulmonary inflammation. The murine metabolipidomic data were validated with human Neisseria-dominant bronchiectasis samples and compared with disease in which Pseudomonas-, an established bronchiectasis pathogen, is dominant. Metagenomic surveillance of Neisseria across various respiratory disorders reveals broader importance, and the assessment of the home environment in bronchiectasis implies potential environmental sources of exposure. Thus, we identify Neisseria species as pathobionts in bronchiectasis, allowing for improved risk stratification in this high-risk group.Published versio

A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China

Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region