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Enhancement of Near-Infrared Light Absorption via Bottom c-Si Surface Structuring of Perovskite/c-Si Tandem Solar Cells
No full textSchool of Energy and Chemical Engineering (Energy Engineering)clos
Impact of TonEBP in myeloid cells on neuroinflammation and obesity-induced insulin resistance
Get PDFDepartment of Biological SciencesTonicity-responsive enhancer-binding protein (TonEBP, also called NFAT5) is stress protein that mediates cellular responses via pleiotropic actions. In stress conditions, TonEBP regulates transcription of many genes using different mechanisms: transcription factor, transcriptional suppressor or cofactor. During my degree, I investigated impact of TonEBP in myeloid cells on neuroinflammation and obesity-induced insulin resistance. Inflammation is biological response to harmful stimuli but also largely considered as contributor to disease development. Neuroinflammation is related with neurodegenerative disease showing memory loss and cognitive deficits. In the brain, Inflammatory stimulus prime microglia cell. This results in a constant production of inflammatory cytokines and chemokines (NO, TNF-??, and IL-1??) by these cellsin turn, the cytokines and chemokines maintain activation of the primed cells. This process results in a vicious circle, and finally causing neuron loss and neurodegeneration. Transcription factors NF-??B and AP-1 are key mediators of inflammation associated with many inflammatory diseases including AD. Here we report that TonEBP promotes neuroinflammation and neuronal cell death through microglial activation. In the microglia cell line BV2, TonEBP deficiency reduced LPS-induced expression and secretion of pro-inflammatory cytokines TNF-?? and IL-1?? in association with decreased activity of both NF-??B and AP-1. This was due to a reduced assembly of pro-inflammatory transcriptional complex which consisted of TonEBP, NF-??B, AP-1, and p300. As expected, myeloid-specific TonEBP deletion blunted the LPS-induced microglia activation and neuroinflammation. Cerulenin, a small molecule which disrupted the assembly of the pro-inflammatory transcriptional complex, suppressed the LPS-induced activation of microglia and memory loss in association with alleviation of neuronal cell death. The incidence of obesity worldwide has increased drastically during recent decades. Obesity is associated with increased risk of insulin resistance, type 2 diabetes. Visceral adipose tissue expansion is associated with chronic low-grade inflammation and metabolic dysfunction characterized by progressive accumulation of immune cells. Here, we show that myeloid cell-specific TonEBP depletion reduced inflammation and insulin resistance in mice with high-fat diet-induced obesity, but did not affect adiposity. This phenotype was associated with a reduced accumulation and a reduced M1-like/M2-like ratio of macrophagesdecreased expression of inflammatory factors related to insulin resistanceand enhanced insulin sensitivity in epididymal white adipose tissue and the liver. TonEBP expression in macrophages was elevated by concentrations of palmitate and gut-derived endotoxin found in obese individuals, and Sp1 was identified as a central regulator of TonEBP induction. TonEBP in macrophages promotes obesity-associated systemic insulin resistance and inflammation, and downregulation of TonEBP may induce a healthy metabolic state during obesity. In conclusion, TonEBP in myeloid cells is a major factor in the immune response that accelerates disease development. Thus, TonEBP is a promising therapeutic target for neuroinflammation and obesity-induced insulin resistance.ope
Investigation of Single Atom Embedded GaN Monolayers for Efficient and Selective CO2 Electroreduction
Get PDFDepartment of ChemistryThe concentration of carbon dioxide (CO2) in our atmosphere has increased drastically over the past few decades. Considering that CO2 contributes significantly to the greenhouse effect and is credited with causing global warming, stabilizing and reducing its concentration is important. Electrochemical reduction of CO2 into useful products using renewable electricity is acknowledged as a potential solution for reducing the atmospheric levels of CO2. Single-atom catalysts (SACs) recently have shown great potential for the electrochemical conversion of CO2 to valuable products. Herein, by means of density functional theory (DFT) computations and computational hydrogen electrode (CHE) model, we report a descriptor-based design principle to investigate the potential of 3d/4d and 5d transition metal (TM) single atoms (SAs) embedded gallium nitride (GaN) monolayers as electrocatalysts for CO2 reduction reaction (CO2RR). We show that both initial CO2 adsorption and activation are hugely influenced by intricate interplay between local coordination environment, surface charge density and electronic structure properties of catalysts. Based on this analysis, we readily identified 15 TM-SACs which are capable of activating the inert CO2 molecule to form carbon dioxide radical anion (CO2??????). Our mechanistic analysis and descriptor-based screening approach predicted the Mn/Rh/Os/Ir-SAC to be the most promising CO2RR electrocatalysts. Remarkably, the proposed lowest free energy pathway analysis indicated that the Rh-SAC has the best activity towards formation of HCOOH with the limiting potential (UL) of -0.42 V, while the Ir-SAC exhibits a better catalytic activity for CH4 production with a UL of -0.48 V and a selectivity of 97% against the competing HER process. Our findings provide a deep insight into designing highly selective and efficient CO2RR electrocatalysts.ope
Mathematical Analysis on Spatio-Temporal Criminal Patterns
Get PDFDepartment of Mathematical SciencesMathematics has been used in many areas, and in particular to analyze numerous social phenomena in the world in great detail. Mathematical modeling has been used to model certain crimes and/or their phenomena. In addition, crime data analyses have been performed and used for crime prevention and control. In this study, we initially modeled crimes based on the concept of ???population biology.??? We cat-egorized crimes as felonies or misdemeanors and modified the changes in the population of each crime according to the characteristics of each offender, length of detention, recidivism rate, and arrest rate. We aimed to parameterize their characteristics and analyze the dynamics of the changes. In particular, we aimed to reflect the criminology theories of the ???broken window effect??? and ???crime school effect??? in our model. Through this modeling, we identified the equilibrium point and analyzed the stability of the equilibrium point when each effect was present. Then, we analyzed the phenomena according to the changes in the parameters related to the crimes and compared them with what was reflected in the real world using a bifurcation analysis. We then collected the criminal temporal and spatio-temporal data and used dynamic mode decomposition (DMD) to analyze the important factors of each data type. Furthermore, we calculated the key factors through DMD, compared the characteristics obtained from the temporal and spatio-temporal data with those from the original data, and analyzed the relationships obtained from each characteristic.
Based on the results of the crime dynamics, we concluded that misdemeanors must be considered to reduce the number of felonies from the perspective of police resources, as the aforementioned broken window effect and crime school effect are related to the dynamics of moving in and out of prison. We also analyzed the temporal and spatio-temporal criminal data using DMD. For the temporal data, we verified the predictions of the future states using DMD and the decay, growth, or oscillation of each type of crime. Moreover, we connected the DMD mode to crime hotspots and analyzed the environmental characteristics of the spatial factors.ope
Overtaking in Autonomous Racing with Online Refinement of Opponent Behavior Prediction using Gaussian Process
Get PDFDepartment of Mechanical EngineeringThis paper addresses an overtaking strategy in autonomous head-to-head racing, by virtue of a learningbased prediction to the opponent vehicle???s behavior. The existing prediction approaches either rely on prior model or off-line learning for opponent behavior, whose accuracy diminishes when the opponent in real racing exhibits different driving style. Motivated by this concern, we proposes an online learningbased prediction algorithm that can adapt to the opponents??? different driving style and refine the prediction during the race. Resorting to Gaussian Process (GP) regressor as the baseline learning model, we leverage several techniques to reduce the data size and computation cost of GP, making the algorithm suitable for online learning and prediction refinement in real time. The effectiveness of the proposed algorithm is demonstrated with different simulation scenarios and compared with the other algorithms in terms of prediction accuracy, computation efficiency, and success rate of overtaking maneuver.ope
POEM : Polarization of Embeddings for Out-of-Distribution Generalization
No full textGraduate School of Artificial Intelligenceclos
Studies on diagnostic systems dedicated to coupled dynamics of charged particle beams
Get PDFDepartment of PhysicsUnderstanding the dynamics of charged particle beam is of importance in the accelerator facility. With extensive physics and engineering, various diagnostic systems were developed and used to characterize the dynamic features of the charged particle beam. Recently, as the demands for the high-intensity beam are increasing, the dynamics of the particle beam is becoming complicated such that the degree of freedom are inter-correlated. Such a coupling between the degrees of freedom affects the accelerator performance by degrading/improving the beam quality such as the emittance/current. Moreover, for certain experimental purposes,
the dynamics of the particle beam is coupled through the accelerator. Accordingly, developing the diagnostic system to determine the features on the coupling of beam is of great interest.
Because the existing systems such as the quadrupole scan method, the slit-scan method, etc., are limited for use to the particle beam moving under the uncoupled dynamics, specialized systems are required for the coupled dynamics. This dissertation contributes to the developments of the diagnostic system for characterizing the features of coupled beam with the proposed two independent systems.
First, it dedicates to the differential virtual pepper-pot (DVPP) system, which aims to characterize the phase space of the coupled beam. The reconstruction algorithm developed for the phase space, the numerical simulations to optimize the structure of the system for compromise of the measurement uncertainty, and the experimental demonstration using the laser beam are explained.
Second, it pursues to determine the transverse spatial coupling and the transverse-longitudinal coupling in the non-destructive manner. The transverse spatial distribution of the beam is intentionally coupled through the multiple skew quadrupole magnets to enhance the measurement precision for the J-PARC muon g-2/EDM experiments. The transverse-longitudinal coupling is the key for improving the X-ray free electron laser performance at the XFEL facility. The multiple stripline-based system is optimized with the numerical simulation. It is then tested with the electron beam to demonstrate the determination of those couplings through the non-destructive manner at the photo-injector accelerator facility of PAL e-LABs. For the PAL e-LABs operation, both the software and the hardware systems are extensively prepared to produce the electron beam as desired conditions. EPCIS-based control systems are built to operate the entire experimental systems of the e-LABs facility including the electron source, microwave and diagnostic equipments. The microwave RF systems are then conditioned to facilitate the high power operation mode.ope
Sustaining a full plastron on nano-grass-covered micro-trench superhydrophobic surfaces in high-speed flows of open water
No full textThis paper studies the sustainability of plastrons on superhydrophobic (SHPo) surfaces made of longitudinal micro-trenches covered by nano-grass with the main interest on hydrodynamic friction drag reduction in high-speed flows of open water, which represent the operating conditions of common watercraft. After revising the shear-driven drainage model to address the air diffusion for SHPo surfaces, the existing theories are combined to reveal the trends of how the immersion depth, air saturation level and shear stress affect the maximum attainable plastron length. Deviations from the theories by the dynamic effect at the two ends of the trench, the interfacial contaminations and turbulent fluctuation are also discussed. A combinatorial series of well-defined SHPo trench surfaces (4 cm x 7 cm in size with varying trench widths, depths, lengths and roughnesses) is microfabricated and attached underneath a 4 m long motorboat on seawater in turbulent flows up to 7.2m s(-1) (shear rate similar to 83 000 s(-1) and friction Reynolds number similar to 5500). Because the plastron can provide a substantial slip only while its air-water interfaces are pinned (or only slightly depinned) at the trench top, two underwater cameras are employed to differentiate the pinned (and slightly depinned) interfaces from the depinned (and no) interfaces. In addition to achieving pinned plastrons on 6 cm long trenches aligned to high-speed flows in open water, the experimental results corroborate the theoretical estimations, supporting the design of SHPo surfaces for field applications
STDP-based Associative Memory Formation and Retrieval
No full textSpike-timing-dependent plasticity (STDP) is a biological process in which the precise order and timing of neuronal spikes affect the degree of synaptic modification. While there has been numerous research focusing on the role of STDP in neural coding, the functional implications of STDP at the macroscopic level in the brain have not been fully explored yet. In this work, we propose a neurodynamical model based on STDP that renders storage and retrieval of a group of associative memories. We showed that the function of STDP at the macroscopic level is to form a ???memory plane??? in the neural state space which dynamically encodes high dimensional data. We derived the analytic relation between the input, the memory plane, and the induced macroscopic neural oscillations around the memory plane. Such plane produces a limit cycle in reaction to a similar memory cue, which can be used for retrieval of the original input
All-Green Solvent and Additive Combination Enables Efficient Nonfullerene Organic Solar Cells via Sequential Deposition Strategy
No full textThe performance of organic solar cells mainly depends on the morphology of the active layer. Traditional solution-processed active layers have poor performance due to the random distribution of donor and acceptor materials during solution processing. In addition, halogenated solvents and additives widely used in traditional fabrication processes will have a huge impact on human health and the environment. Herein, an all-green solvent and additive combination strategy is proposed to assist the morphology control of the active layer with sequential deposition (SD) technique. Ultimately, with adopting benzyl viologen (BV) additive as well as ethyl alcohol treatment, PM6:Y6-2BrO-based devices yield a power conversion efficiency of 14.3%, higher than those of the control device (13.24%) and the device solely with BV additives (13.62%). The result demonstrates the feasibility of SD technique to finely control the morphology of active layer in future all-green industrial production