Estimating parameters in the presence of many nuisance parameters

This paper considers estimation of parameters for high-dimensional time series with the presence of many nuisance parameters. In particular we are interested in data consisting of p time series of length n, with p to be as large or even larger than n. Here we consider the composite-likelihood estimation and the profile quasi-likelihood estimation. The asymptotic properties of these methodologies are investigated. Simulations are used to illustrate our both of these methods and explore the performance of these methods

On the Feasibility of Steering Swallowable Microsystem Capsules Using Computer-Aided Magnetic Levitation

Swallowable capsule endoscopy is used for non-invasive diagnosis of some gastrointestinal (GI) organs. However, control over the position of the capsule is a major unresolved issue. This study presents a design for steering the capsule based on magnetic levitation. The levitation is stabilized with the aid of a computer-aided feedback control system and diamagnetism. Peristaltic and gravitational forces to be overcome were calculated. A levitation setup was built to analyze the feasibility of using Hall Effect sensors to locate the in- vivo capsule. CAD software Maxwell 3D (Ansoft, Pittsburgh, PA) was used to determine the dimensions of the resistive electromagnets required for levitation and the feasibility of building them was examined. Comparison based on design complexity was made between positioning the patient supinely and upright

Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.</p

Influence of concentration-dependent material properties on the fracture and debonding of electrode particles with core–shell structure

Core–shell electrode particle designs offer a route to improved lithium-ion battery performance. However, they are susceptible to mechanical damage such as fracture and debonding, which can significantly reduce their lifetime. Using a coupled finite element model, we explore the impacts of diffusion-induced stresses on the failure mechanisms of an exemplar system with an NMC811 core and an NMC111 shell. In particular, we systematically compare the implications of assuming constant material properties against using Li concentration-dependent diffusion coefficient and partial molar volume. With constant material properties, our results show that smaller cores with thinner shells avoid debonding and fracture regimes. When factoring in a concentration-dependent partial molar volume, the maximum values of tensile hoop stress in the shell are found to be significantly lower than those predicted with constant properties, reducing the likelihood of fracture. Furthermore, with a concentration-dependent diffusion coefficient, significant barriers to full electrode utilisation are observed due to reduced lithium mobility at high states of lithiation. This provides a possible explanation for the reduced accessible capacity observed in experiments. Shell thickness is found to be the dominant factor in precluding structural integrity once the concentration dependency is accounted for. These findings shed new light on the performance and effective design of core–shell electrode particles

Consumer E-Service Evaluation in Hong Kong Online Music Subscription Service Industry

This study investigates into factors affecting the success of E-service using a research model grounded on the Updated DeLone and McLean Information System Success Model (DeLone & McLean, 2003). Fourteen factors originated from four constructs, i.e., system quality, information quality, service quality, and vendor dimensions, are included in our research model. Using the online music subscription industry in Hong Kong as the platform of our investigation, we examine the associations between these four constructs and customer preference in the online music subscription service industry in Hong Kong. We collected data from 135 college students from Hong Kong to test our model using the Analytical Hierarchy Process (AHP). We show that each E-business success construct in our model has different levels of importance in E-service success in the online music subscription service industry. Our findings provide decision makers of E-business firms with useful insights to enhance their E-service quality

Nature-inspired flow-fields and water management for PEM fuel cells

Flow-field design is crucial to polymer electrolyte membrane fuel cell (PEMFC) performance, since non-uniform transport of species to and from the membrane electrode assembly (MEA) results in significant power losses. The long channels of conventional serpentine flow-fields cause large pressure drops between inlets and outlets, thus large parasitic energy losses and low fuel cell performance. Here, a lung-inspired approach is used to design flow-fields guided by the structure of a lung. The fractal geometry of the human lung has been shown to ensure uniform distribution of air from a single outlet (trachea) to multiple outlets (alveoli). Furthermore, the human lung transitions between two flow regimes: 14-16 upper generations of branches dominated by convection, and 7-9 lower generations of space-filling acini dominated by diffusion. The upper generations of branches are designed to slow down the gas flow to a rate compatible with the rate in the diffusional regime (Pé ~ 1), resulting in uniform distribution of entropy production in both regimes. By employing a three-dimensional (3D) fractal structure as flow-field inlet channel, we aim to yield similar benefits from replicating these characteristics of the human lung. The fractal pattern consists of repeating “H” shapes where daughter “H’s” are located at the four tips of the parent “H”. The fractal geometry obeys Murray’s law, much like the human lung, hereby leading to minimal mechanical energy losses. Furthermore, the three-dimensional branching structure provide uniform local conditions on the surface of the catalyst layer as only the outlets of the fractal inlet channel are exposed to the MEA. Numerical simulations were conducted to determine the number of generations required to achieve uniform reactant distribution and minimal entropy production. The results reveal that the ideal number of generations for minimum entropy production lies between N = 5 and 7. Guided by the simulation results, three flow-fields with N = 3, 4 and 5 (10 cm2 surface area) were 3D printed via direct metal laser sintering (DMLS), and experimentally validated against conventional serpentine flow-fields. The fractal flow-fields with N = 4 and 5 generations showed ~20% and ~30% increase in performance and maximum power density over serpentine flow-fields above 0.8 A cm-2 at 50% RH. At fully humidified conditions, though, the performance of fractal N = 5 flow-field significantly deteriorates due to flooding issues. Another defining characteristic of the fractal approach is scalability, which is an important feature in nature. Fractal flow-fields can bridge multiple length scales by adding further generations, while preserving the building units and microscopic function of the system. Larger, 3D printed fractal flow-fields (25 cm2 surface area) with N = 4 are compared to conventional serpentine flow-field based PEMFCs. Performance results show that fractal and serpentine flow-field based PEMFCs have similar polarization curves, which is attributed to the significantly higher pressure drop (~ 25 kPa) of large serpentine flow-fields compared to fractal flow-fields. However, such excessive pressure drop renders the use of a large scale serpentine flow-field prohibitive, thus favouring the fractal flow-field. A major shortcoming of using fractal flow-field is, though, susceptibility to flooding in the gas channels due to slow gas velocity. This problem has led to the development of a nature-inspired water management mechanism that draws inspiration from the ability of the Thorny Devil (Australian lizard) to passively transport liquid water across its skin using capillary pressure. We have recently integrated this strategy with the fractal N = 4 flow-fields and verified the viability of the strategy using neutron imaging at Helmholtz-Zentrum Berlin (HZB). Implementation of this water management strategy is expected to circumvent remaining problems of high-generation fractal flow-fields

Overseas Tourist Movement Patterns in Beijing: The Impact of the Olympic Games

Tourist movement patterns are essential for tourism planners to make decisions on tourism product development, destination planning and marketing. The 2008 Beijing Olympic Games have a great impact on the tourism industry in Beijing, especially on tourism flows and movements. In order to understanding changes of tourist movement patterns during the Games, the paper used content analysis method to analyze 500 trip diaries gathered from 6 different websites from January 2001 to April 2009. The paper also utilized social network analysis software Ucinet 6 and NetDraw to map itineraries and analyze overseas tourist movement patterns in Beijing. As a result, the paper summarized the changes of main tourism attractions and main tourist movement patterns in Beijing during the Olympic periods

A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells

The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.</p

Photo control of transport properties in disorderd wire; average conductance, conductance statistics, and time-reversal symmetry

In this paper, we study the full conductance statistics of disordered one dimensional wire under the application of light. We develop the transfer matrix method for periodically driven systems to analyze the conductance of large system with small frequency of light, where coherent photon absorptions play important role to determine not only the average but also the shape of conductance distributions. The average conductance under the application of light results from the competition between dynamic localization and effective dimension increase, and shows non-monotonic behavior as a function of driving amplitude. On the other hand, the shape of conductance distribution displays crossover phenomena in the intermediate disorder strength; the application of light dramatically changes the distribution from log-normal to normal distributions. Furthermore, we propose that conductance of disordered systems can be controlled by engineering the shape, frequency and amplitude of light. Change of the shape of driving field controls the time-reversals symmetry and the disordered system shows analogous behavior as negative magneto-resistance known in static weak localization. A small change of frequency and amplitude of light leads to a large change of conductance, displaying giant-opto response. Our work advances the perspective to control the mean as well as the full conductance statistics by coherently driving disordered systems.Comment: 12 figure