Transport Equations for Oscillating Neutrinos

We derive a suite of generalized Boltzmann equations, based on the density-matrix formalism, that incorporates the physics of neutrino oscillations for two- and three-flavor oscillations, matter refraction, and self-refraction. The resulting equations are straightforward extensions of the classical transport equations that nevertheless contain the full physics of quantum oscillation phenomena. In this way, our broadened formalism provides a bridge between the familiar neutrino transport algorithms employed by supernova modelers and the more quantum-heavy approaches frequently employed to illuminate the various neutrino oscillation effects. We also provide the corresponding angular-moment versions of this generalized equation set. Our goal is to make it easier for astrophysicists to address oscillation phenomena in a language with which they are familiar. The equations we derive are simple and practical, and are intended to facilitate progress concerning oscillation phenomena in the context of core-collapse supernova theory.Comment: 13 pages; Submitted to Physical Review

An Approach to Finding Parking Space Using the CSI-based WiFi Technology

With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot

Htra1 Expression In Humanized Arms2 Knock-In Mouse Models

Age-related macular degeneration (AMD) is the most common cause of central vision loss in developed world. Two genes on chromosome 10q26, maculopathy susceptibility 2 (ARMS2) and HtrA serine peptidase 1 (HTRA1) were identified as candidate genetic factors of AMD. However, due to the high linkage disequilibrium across the locus as well as the inconsistent functional findings regarding the two genes, it is difficult to distinguish the causative gene that confer the risk of AMD. To provide insight of the functional roles of ARMS2 and HTRA1 in the pathogenesis of AMD, we investigated the regulation relationship between the two genes in vivo. To overcome the difficulty that ARMS2 gene only exists in higher primates, we generated humanized ARMS2 knock-in mice. Human ARMS2 cDNA was inserted into the corresponding locus upstream the Htra1 gene in mouse genome. Decline of Htra1 gene expression was found in the cortex of ARMS2 KI mice using RT-qPCR technique. Our finding indicates the presence of ARMS2 gene upstream Htra1 may manifest negative regulation effect upon Htra1 expression. This new evidence revealed the complexity of how the two genes might work together in the causal pathway of AMD. Furthermore, we provided a novel and valuable animal model that could facilitate further research of AMD pathogenesis

Probabilistic Reliability Analysis of the Water-energy Nexus Using Monte Carlo Simulation

Nowadays, with the development of science and technologies, our modern society is more and more dependent on the reliable performance of the critical infrastructures. Both water systems and power systems are national critical infrastructure supporting our daily life and the development of economic growth. These two types of systems are highly interconnected and complex networks, which consist of various system elements. Similarly, the core function of water and power system is to deliver satisfactory quality water and power to consumers, and at the same time it should satisfy all the demands at all load points. The reliable performance of these critical infrastructure is becoming more and more important. Therefore, it is very urgent to develop a comprehensive reliability evaluation algorithm to quantify the reliability of these critical systems. When it comes to quantitatively assessing reliability of the facility infrastructure, there is a need to develop a comprehensive method to consider a comprehensive set of variables and uncertainties such as the random failures of mechanical components, the amount of water demands, the power supply reliability, maintenance scheduling, and so forth. The rapidly growing urban population is also a great challenge to the aging drinking water distribution networks. The water facilities are aging and in need of expensive repairs. Therefore, this thesis will aid in making informed decisions on infrastructure repair, maintenance, and staffing planning when the available budgets are limited. This thesis proposes a probabilistic reliability evaluation methodology for water distribution systems considering the impact of power supply reliability based on the sequential Monte Carlo simulation (MCS), which can guide cost-effective preventative measures before system failures. A previously developed C++ software tool is used to help perform the simulation. The probabilistic reliability assessment algorithm can be appropriately applied for both the electric power systems and water distribution system is due to the similar stochastic system nature and modeling manner of the system elements. First, the reliability characteristic of each system component in electric power system can be modeled by a two-state model (i.e., up state and down state). Then, the probability of failure for each component can be calculated and a chronological operating sequence can be further determined based on the sequential Monte Carlo Simulation. Likewise, the reliability models for the water distribution system components can be represented using this method. All these similarities result in the similar reliability assessment procedure. The commonly used deterministic criteria in industrial circles lacked the ability to model and quantify the stochastic nature of system behaviors such as the mechanical failure of system elements. Besides the uncertainties come from water distribution system itself, power supply may also affect the performance of the water distribution network and system reliability. Therefore, the two systems are interactive and physically connected. The purpose of this study is to develop a suitable algorithm to evaluate the water sector and power system as an integrated Water-Energy Nexus (WEN) system. This thesis proposes an integrated, probabilistic reliability evaluation method for the WEN model based on the sequential Monte Carlo Simulation. In the proposed evaluation procedure, both mechanical failures and hydraulic analysis are taken into consideration. Case studies are performed base on a representative water-energy nexus system to demonstrate the effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed probabilistic methodology is appropriate to integrated quantitative reliability modeling and assessment of coupled critical infrastructures (i.e., electrical power networks and water distribution networks) by incorporating the emerging smart grid technologies such as electrical microgrids

PASCAL: A Learning-aided Cooperative Bandwidth Control Policy for Hierarchical Storage Systems

Nowadays, the Hierarchical Storage System (HSS) is considered as an ideal model to meet the cost-performance demand. The data migration between storing tiers of HSS is the way to achieve the cost-performance goal. The bandwidth control is to limit the maximum amount of data migration. Most of previous research about HSS focus on studying the data migration policy instead of bandwidth control. However, the recent research about cache and networking optimization suggest that the bandwidth control has significant impact on the system performance. Few previous work achieves a satisfactory bandwidth control in HSS since it is hard to control bandwidth for so many data migration tasks simultaneously. In this paper, we first give a stochastic programming model to formalize the bandwidth control problem in HSS. Then we propose a learning-aided bandwidth control policy for HSS, named \Pascal{}, which learns to control the bandwidth of different data migration task in an cooperative way. We implement \Pascal{} on a commercial HSS and compare it with three strong baselines over a group of workloads. Our evaluation on the physical system shows that \Pascal{} can effectively decrease 1.95X the tail latency and greatly improve throughput stability (2X $\downarrow$ throughput jitter), and meanwhile keep the throughput at a relatively high level

Decreased intracellular zinc in human tumorigenic prostate epithelial cells: a possible role in prostate cancer progression

BACKGROUND: Zinc plays important roles in maintaining normal function of the prostate and in development of prostate malignancy. It has been demonstrated that prostate malignant epithelial cells contain much less cellular zinc than the surrounding normal epithelial cells. However, the pathway(s) which leads to lower zinc accumulation in malignant prostate epithelial cells is poorly understood. In this study, the zinc homeostatic features of two human prostate epithelial cell lines (non-tumorigenic, RWPE1, and tumorigenic, RWPE2) were investigated. Effects of over-expression of ZIP1 in RWPE2 on cell proliferation and apoptosis were also studied. RESULTS: RWPE2 accumulated less intracellular zinc than RWPE1 due to the decreased zinc uptake activity. The mRNA expression of ZIP1 and ZIP3 in RWPE1 and RWPE2 was comparable. However, the protein expression of ZIP1 in RWPE2 was lower than that in RWPE1. ZIP3 was detected in a lysosomal compartment of RWPE2 while no ZIP3 was detected in the same compartment of RWPE1. Over-expression of ZIP1 in RWPE2 resulted in an elevation of intracellular zinc concentration and suppression of cell growth of RWPE2 due to the increased apoptosis. CONCLUSION: These findings suggest that tumorigenic prostate epithelial cells accumulated less intracellular zinc than non-tumorigenic prostate epithelial cells. The reduction in capacity for accumulation of intracellular zinc in tumorigenic prostate epithelial cells may be caused by the decrease in the ZIP1 protein expression and the intracellular redistribution of ZIP3 in RWPE2. RWPE1 and RWPE2 are excellent cellular models to study the association of intracellular zinc levels with prostate cancer progression

Optimization and control of a dual-loop EGR system in a modern diesel engine

Focusing on the author's research aspects, the intelligent optimization algorithm and advanced control methods of the diesel engine's air path have been proposed in this work. In addition, the simulation platform and the HIL test platform are established for research activities on engine optimization and control. In this thesis, it presents an intelligent transient calibration method using the chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm. It is a model-based optimization approach. The test results show that the proposed method could locate the global optimal results of the controller parameters within good speed under various working conditions. The engine dynamic response is improved and a measurable drop of engine fuel consumption is acquired. The model predictive control (MPC) is selected for the controllers of DLEGR and VGT in the air-path of a diesel engine. Two MPC-based controllers are developed in this work, they are categorized into linear MPC and nonlinear MPC. Compared with conventional PIO controller, the MPC-based controllers show better reference trajectory tracking performance. Besides, an improvement of the engine fuel economy is obtained. The HIL test indicates the two controllers could be implemented on the real engine