Miracle-dz/UniFAR-FTUs huggingface dataset:
Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is misspecified. In this article, we create a formal theoretical framework for developing statistical models and detecting model misspecification in the presence of incomplete data where maximum likelihood estimates are obtained by maximizing the observable-data likelihood function when the missing-data mechanism is assumed ignorable. First, we provide sufficient regularity conditions on the researcher’s complete-data model to characterize the asymptotic behavior of maximum likelihood estimates in the simultaneous presence of both missing data and model misspecification. These results are then used to derive robust hypothesis testing methods for possibly misspecified models in the presence of Missing at Random (MAR) or Missing Not at Random (MNAR) missing data. Second, we introduce a method for the detection of model misspecification in missing data problems using recently developed Generalized Information Matrix Tests (GIMT). Third, we identify regularity conditions for the Missing Information Principle (MIP) to hold in the presence of model misspecification so as to provide useful computational covariance matrix estimation formulas. Fourth, we provide regularity conditions that ensure the observable-data expected negative log-likelihood function is convex in the presence of partially observable data when the amount of missingness is sufficiently small and the complete-data likelihood is convex. Fifth, we show that when the researcher has correctly specified a complete-data model with a convex negative likelihood function and an ignorable missing-data mechanism, then its strict local minimizer is the true parameter value for the complete-data model when the amount of missingness is sufficiently small. Our results thus provide new robust estimation, inference, and specification analysis methods for developing statistical models with incomplete data.
Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot

Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.
Parametric Representation and Application of Micro-Doppler Characteristics for Cone-Shaped Space Targets

Micro-Doppler characteristics have been widely utilized to discriminate the space targets. In this paper, the micro-Doppler parametric model (mDPM) is presented to fit the micro-Doppler characteristics of cone-shaped space targets. Then, the genetic algorithm-general parameterized time-frequency transform (GA-GPTF) method is proposed to estimate the parameters of mDPM accurately. Additionally, the ratio of the two parameters of mDPM is verified to be directly related to the physical attribute of scattering centers. It can be applied to discriminate the type of scattering centers quantitatively. Finally, the electromagnetic simulation and anechoic chamber experiment verify the accuracy of mDPM and the effectiveness of the scattering center discrimination method.
Kinetic energy density of nearly free electrons. II. Response functionals of the electron density

We present electron-density-based response functionals yielding the non-negative kinetic energy density (KED) of nearly free electron systems. In a previous paper, for a canonical free-electron system perturbed by an external potential, we derived the first- and second-order corrections to the KED as functionals of the potential, providing the response functions in reciprocal space. Here, we formulate the KED response in terms of the electron density by converting the potential-based functionals into density functionals. We also determine the related response of the Pauli KED, which is the KED in excess of the von Weizsäcker KED. We anticipate that the structure of these density functionals will help guide the design of the more sophisticated kinetic energy functionals required for orbital-free density functional theory simulations. We conclude by examining the performance of the first- and second-order density functionals for the KED when applied to electron densities generated from local pseudopotential calculations for Li, Al, and Si crystals.
Span effect on the turbulence nature of flow past a circular cylinder

Turbulent flow evolution and energy cascades are significantly different in two-dimensional (2-D) and three-dimensional (3-D) flows. Studies have investigated these differences in obstacle-free turbulent flows, but solid boundaries have an important impact on the cross-over from 3-D to 2-D turbulence dynamics. In this work, we investigate the span effect on the turbulence nature of flow past a circular cylinder at $Re=10\,000$ . It is found that even for highly anisotropic geometries, 3-D small-scale structures detach from the walls. Additionally, the natural large-scale rotation of the Kármán vortices rapidly two-dimensionalise those structures if the span is 50 % of the diameter or less. We show this is linked to the span being shorter than the Mode B instability wavelength. The conflicting 3-D small-scale structures and 2-D Kármán vortices result in 2-D and 3-D turbulence dynamics which can coexist at certain locations of the wake depending on the domain geometric anisotropy.
Higher Order Statistical Techniques Analysis for Non-linear Systems Evaluation

— The present paper deals with the analysis of non linear system using higher order statistical techniques namely Independent component analysis (ICA). In reality, the data often does not follow a Gaussian distribution and the situation is not as simple as those methods of factor analysis, projection pursuit or PCA assumes. Many real world data sets have super Gaussian Distributions. Hence the probability density of the data is peaked at zero and has many tails, when compared to a Gaussian density of the same variance. This is the starting point of ICA, where we try to find statistically independent components in the general case where the data is non Gaussian. In this paper we had presented the different estimation principles of ICA and their algorithms. The simulation results of ICA were carried out by MATLAB. 
Extensional flow dynamics of polystyrene melt

A linear polystyrene melt with broad molecular weight distribution (CAS 0993-53-6 from Aldrich) was characterized in extensional flow using a filament stretching rheometer. A series of measurements were performed, including (1) the startup of uniaxial extensional flow until steady state; (2) the strain controlled reversed (i.e., biaxial) flow following the startup of uniaxial flow; and (3) stress relaxation (i.e., keeping the strain unchanged) also following the startup of uniaxial flow. The measurements were compared with the model by Narimissa and Wagner [J. Rheol. 60, 625–636 (2016)]. Good agreements at low extension rates and low strain values were observed. Higher deviations were observed at high rates under steady state conditions as well as during stress relaxation and reversed flow.A linear polystyrene melt with broad molecular weight distribution (CAS 0993-53-6 from Aldrich) was characterized in extensional flow using a filament stretching rheometer. A series of measurements were performed, including (1) the startup of uniaxial extensional flow until steady state; (2) the strain controlled reversed (i.e., biaxial) flow following the startup of uniaxial flow; and (3) stress relaxation (i.e., keeping the strain unchanged) also following the startup of uniaxial flow. The measurements were compared with the model by Narimissa and Wagner [J. Rheol. 60, 625–636 (2016)]. Good agreements at low extension rates and low strain values were observed. Higher deviations were observed at high rates under steady state conditions as well as during stress relaxation and reversed flow.
Dynamic Modeling and Analysis of Buck Converter Based Solar PV Charge Controller for Improved MPPT Performance

Maximum power point tracking (MPPT) control is a key functionality in solar photovoltaic (PV)-based power conversion systems. A variety of perturbative MPPT control schemes are available in the literature, many of which are voltage-based techniques wherein the PV bus voltage is perturbed and set to the required level by an appropriate converter control that achieves the MPP tracking. However, a comprehensive plant model of the PV-fed converter system and a systematic control design of the PV bus voltage loop that facilitates the design of the MPPT control is not available in the literature. In this article, a detailed small-signal model is proposed for a single-stage PV-fed buck converter that acts as a battery charge-controller. The effects of parasitic storage elements present in the PV source as well as the interconnecting cable from rooftop are discussed in detail, along with the impact of series–parallel connection of the PV modules for power scaling. A full-order as well as a reduced-order model of the system is proposed and the various relevant transfer-functions are analytically derived. Based on this model and the control to converter input bus voltage transfer-function $\widetilde{v}_{\text{in}}/\widetilde{d}$, a systematic design procedure for the PV bus voltage controller is proposed. It is shown that such a design facilitates the selection of perturbation period of a typical MPPT control algorithm for an improved tracking performance. The steps involved in the system model development and the control design are generalized and can be extended to other converter topologies as well. Active transfer-function measurements and experiments conducted on a 1-kW charge-controller hardware prototype validate the accuracy of the proposed model and the tracking performance of the MPPT control.
Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.
Nutrition and well-being A to Z

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Transformações estruturais e eletrônicas em nanomateriais bidimensionais sob pressão

Com o acelerado avanco nas tecnicas de sintese e caracterizacao dos materiais bidimensionais, ha uma crescente demanda por teorias que possibilitem o estudo e a previsao de tais estruturas. Uma das teorias mais bem sucedidas nessa area e a Teoria do Funcional de Densidade, que permite a descricao de sistemas com celulas unitarias da ordem decentenas de atomos. Buscamos nessa dissertacao estudar as propriedades eletronicas e estruturais de materiais obtidos em laboratorios por colaboradores experimentais. Emprimeiro lugar, estudamos uma transicao metal-semicondutora que acontece ao submeter heteroestruturas de grafeno sobre de nitreto de boro hexagonal a altas pressoes em um experimento de Microscopia de Forca Atomica. Em segundo lugar, estudamos a formacao de estruturas tipo diamante obtidas ao submeter multicamadas de grafeno a altas pressoes em um experimento de espectroscopia Raman. Por ultimo, investigaremos o comportamento de um defeito em tais estruturas, chamado de centro de nitrogenio-vacância, e sua possivel aplicacao em sistemas biologicos e de informacao quântica. Em sintese, obtivemosestruturas por modelos teoricos com comportamentos eletronicos em excelente acordo com os resultados experimentais, tanto para o grafeno sobre nitreto de boro quanto para os filmes finos de diamante. Os resultados teoricos reforcam a viabilidade da utilizacao de nanodiamantes com centro de nitrogenio-vacância, pois apresentam fenomenologia semelhante as que sao obtidas no bulk.
Energy Efficient Three-Phase Utility Interactive Residential Microgrid With Mode Transfer Capabilities at Weak Grid Conditions

This article presents an energy efficient autonomous grid-synchronized photovoltaic (PV) array battery energy storage (BES) based microgrid with power quality improving capabilities under weak grid conditions. The microgrid is developed to supply consumer loads even at disappearance of the weak grid. The BES is not only beneficial on daily energy saving but it is also sized to furnish smoothening facilities to the PV-BES microgrid. A pseudolinear synchronization scheme is used, which is based on extraction of fundamental positive sequence components from the polluted grid voltages. The islanded controller furnishes reliability by maintaining the voltage and frequency and continuous power to the consumer load. The multivariance generalized adaptive controller is proposed to control the microgrid and to improve the power quality aspects. The effectiveness of microgrid is demonstrated experimentally with estimation of phase and frequency at grid synchronization and desynchronization with polluted grid conditions.
An Integrated Optimal Design for Guaranteed Cost Control of Motor Driving System With Uncertainty

In this article, a plant/controller integrated design strategy based on the nested optimization strategy and guaranteed cost control for a motor driving system is developed. Because of the coupling between the plant design and the controller design, the conventional sequential design methods cannot guarantee an overall optimality for the controlled motor driving system. The integrated design strategy proposed in this article aims to tackle this problem by simultaneously optimizing the controller design and plant design. The integrated design objective is to drive the largest load and guarantee the satisfactory robust control performance for reference tracking subject to parametric uncertainties. This is enabled by integrating a guaranteed cost controller into the integrated design strategy and improves the reliability of the control scheme. A novel combined performance index including the plant design objective and the control performance is developed as the integrated design cost function for the motor driving system. A nested optimization strategy is employed so that the optimality of the system can be calculated in an efficient and reliable way. Experimental results demonstrate the effectiveness of the proposed integrated design method.
Mental Health Sequelae in Disaster Workers: Prevention and Intervention

When a disaster imposed by natural forces and/or man-made actions strikes a community, the immediate response of formally designated intervention personnel rarely takes into consideration the emotional impact of their tasks. Moreover, rarely do disaster planning and training deal with the potential for injury that is inherent in rescue and recovery operations [1]. It is assumed that the general training and conditioning afforded by work experience under normal conditions will suffice to protect the worker from both the physical and the mental trauma that may be suffered at a disaster site. Normal precautions against physical injuries and their treatment are considered to be covered by the use of protective clothing and devices, safety regulations in policies and training programs, and instruction in response and first-aid procedures. The potential for physical injury and monitoring of that potential are well-established administrative issues in planning disaster relief. Only recently, however, have administrators given attention to the potential for, and occurrence of, mental trauma that may result from service at a disaster site. Much of the literature recognizes the existence of disaster-induced trauma in rescue and recovery workers (generally in anecdotal reports) and discusses the nature of the job-related stress, but without documenting its magnitude [2]. Efforts to predict and control disaster-induced trauma are severely hampered by lack of a construct for analyzing and assessing events that may adversely affect the ability to perform occupational roles. Despite the considerable body of knowledge regarding stress in the protective services (police, fire, corrections, and emergency medical technicians), there has been no reliable and valid method for directly measuring levels of stress and post-traumatic reactions in workers deployed at specific disaster sites. O'Neill and co-workers [3] have pointed out that stressors are transitory conditions present, especially during a disaster, under abnormal circumstances. Sewell [4] attempted to develop a scale of critical professional life-events experienced by law-enforcement officers in order to predict times of high potential for stress in
Towards a Low-SWaP 1024-Beam Digital Array: A 32-Beam Subsystem at 5.8 GHz

Millimeter-wave communications require multibeam beamforming to utilize wireless channels that suffer from obstructions, path loss, and multipath effects. Digital multibeam beamforming has maximum degrees of freedom compared to analog-phased arrays. However, circuit complexity and power consumption are important constraints for digital multibeam systems. A low-complexity digital computing architecture is proposed for a multiplication-free 32-point linear transform that approximates multiple simultaneous radio frequency (RF) beams similar to a discrete Fourier transform (DFT). Arithmetic complexity due to multiplication is reduced from the fast Fourier transform (FFT) complexity of $\mathcal {O}(N\: \log N)$ for DFT realizations, down to zero, thus yielding a 46% and 55% reduction in chip area and dynamic power consumption, respectively, for the $N=32$ case considered. This article describes the proposed 32-point DFT approximation targeting 1024 beams using a 2-D array and shows the multiplierless approximation and its mapping to a 32-beam subsystem consisting of 5.8 GHz antennas that can be used for generating 1024 digital beams without multiplications. Real-time beam computation is achieved using a Xilinx field-programmable gate array (FPGA) at 120 MHz bandwidth per beam. Theoretical beam performance is compared with measured RF patterns from both a fixed-point FFT and the proposed multiplier-free algorithm and is in good agreement.
Eddy Current Calculation of a High-Speed Slotted Machine With Halbach Magnetization at No-Load Condition

The eddy current and the eddy current loss in permanent magnets (PMs) are important factors in high-speed machine design and performance prediction. The eddy current loss is related to the magnetic field, the shape of magnets, and rotating speed. In this article, the eddy current is analytically calculated with a 2-D method in a high-speed machine with the Halbach magnetization. This analytical method calculates the eddy current with two parts: one part is induced by the instantaneous radial flux density and the other part is induced by instantaneous tangential flux density. The instantaneous radial and tangential flux densities are varying in the PM when the rotor rotates. The flux density in the PM is calculated by the 2-D complex relative permeance method with the SC-Toolbox aid. When the slotted machine operated at no-load condition, the eddy current is analytically calculated in this article based on the instantaneous flux density, and the eddy current distribution in the magnet is compared with the finite-element method (FEM) solutions, the comparison shows that the 2-D analytical method is an effective method for the eddy current and eddy current loss calculation. In the slotted machine with the Halbach magnetization, the eddy current loss increases with the arc angle of the main segment magnet increases, the eddy current loss increases fast at high speeds and cannot be ignored, but Halbach magnetization can reduce it in the high-speed machine as long as the magnetization parameters are chosen properly.
Sustainable Optimization for China’s Hydropower Project Investment Portfolio Using Multiobjective Decision Analysis

The development of renewable energy becomes increasingly important because of exhaustion of fossil energy. Hydropower is one of the most important ways to generate electricity from renewable energy because of its relatively stable output among them. However, hydropower project development with some inherent characteristics is highly susceptible to social and natural environments, which complicates the investment process. For this purpose, this paper proposes a feasible comprehensive optimization model of portfolio investment from the perspective of sustainable development, describing the tradeoff relationship between economic, social, and ecological factors. As a hybrid uncertain NP-hard optimization problem, there are three critical challenges: (1) achieving comprehensive balance between economy, society, and ecology; (2) identifying available multiple conflicting objectives and reasonable constraints; (3) analysing the hybrid uncertain environment. Therefore, a practical problem-oriented multiobjective decision analysis model is established. Then, a multiobjective adaptive particle swarm optimization algorithm is designed to solve the model. Finally, a case study is carried out to verify the practicality of the model and the effectiveness of the improved algorithm. The result demonstrates that the model can be applied as a useful decision-making tool for decision-makers in sustainable hydropower project development.