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This paper studies finite sample minimax optimal randomization schemes and estimation schemes in estimating parameters including the average treatment effect, when treatment effects are heterogeneous. A randomization scheme is a distribution over a group of permutations of a given treatment assignment vector. An estimation scheme is a joint distribution over assignment vectors, linear estimators, and permutations of assignment vectors. The key element in the minimax problem is that the worst case is over a class of distributions of the data which is invariant to a group of permutations. First, I show that given any assignment vector and any estimator, the uniform distribution over the same group of permutations, namely the complete randomization scheme, is minimax optimal. Second, under further assumptions on the class of distributions and the objective function, I show the minimax optimal estimation scheme involves completely randomizing an assignment vector, while the optimal estimator is the difference-in-means under complete invariance and a weighted average of within-block differences under a block structure, and the number of treated units is determined by the Neyman allocation.

This article revisits the optimization problems of age-based replacement models for exponential failure distributions. First, we show the steps how an age replacement time goes to an infinite value when the lifetime of a unit has an exponential distribution, and then, we give two discussions of possible replacement times, by introducing a new concept of cost named as deviation cost per unit of time between replacement and failure. Second, similar discussion is given for a random replacement model when the age replacement time is a random variable with a uniform distribution. Third, the models of age-based replacement first and last with deviation costs are discussed when a random replacement is taken into account for an age replacement model. Numerical examples are illustrated to show the results of the analytical discussions. Finally, necessary proofs are provided in the Appendixes.

A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s. The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.

The supply of species gases and removal of liquid water in a PEM fuel cell are performed through gas flow channels. Therefore, optimising the flow channel configurations is critical for maximising performance of fuel cells. In this paper, the ANSYS PEM Fuel Cell Module is used to simulate a PEM fuel cell with an active area of 100 cm2 and four different flow channel configurations: single-channel serpentine, two-channel serpentine, three-channel serpentine, and parallel channel with headers under two set of different operating conditions. Under both sets of operating conditions, the simulation results indicated that 3-channel serpentine configuration has the best performance due to uniform distribution of species gases over the catalyst layer. It was concluded from the simulation results that the pressure-drop along the 3-channel serpentine configuration is less compared to other serpentine channel configurations, which will require less power for the blowing in of the species gases. The results obtained from simulation under more optimal operating conditions (set 2) used by Hwang’s URFC, predicted increase in the performance of all the four channel configurations compared to the set 1 operating conditions due to higher cell temperature and stichometry of inlet gases. Under set 2 operating conditions, the values of pressure drop across all the channel configurations have increased substantially due to higher flow rates of inlet gases.

We investigated the solid–liquid suspension characteristics in the tank with a liquid height/tank diameter ratio of 1.5 stirred by a novel long-short blades (LSB) impeller by the Euler granular flow model coupled with the standard k–e turbulence model. After validation of the local solid holdup by experiments, numerical predictions have been successfully used to explain the influences of impeller rotating speed, particle density, particle size, liquid viscosity and initial solid loading on the solid suspension behavior, i.e. smaller particles with lower density are more likely to be suspended evenly in the liquid with higher liquid viscosity. At a low impeller rotating speed (N), increase in N leads to an obvious improvement in the solid distribution homogeneity. Moreover, the proposed LSB impeller has obvious advantages in the uniform distribution of the solid particles compared with single Rushton turbine (RT), dual RT impellers or CBY hydrofoil impeller under the same power consumption.

For investigating whether the MARSSIM nonparametric test has sufficient statistical power when a site has a specific contamination distribution before conducting a final status survey (FSS), a novel approach was proposed to predict the release probability of the site. Five distributions were assumed: lognormal distribution, normal distribution, maximum extreme value distribution, minimum extreme value distribution, and uniform distribution. Hypothetical radioactivity populations were generated for each distribution, and Sign tests were performed to predict the release probabilities after extracting samples using Monte Carlo simulations. The designed Type I error (0.01, 0.05, and 0.1) was always satisfied for all distributions, while the designed Type II error (0.01, 0.05, and 0.1) was not always met for the uniform, maximum extreme value, and lognormal distributions. Through detailed analyses for lognormal and normal distributions which are often found for contaminants in actual environmental or soil samples, it was found that a greater statistical power was obtained from survey units with normal distribution than with lognormal distribution. This study is expected to contribute to achieving the designed decision error when the contamination distribution of a survey unit is identified, by predicting whether the survey unit passes the statistical test before undertaking the FSS according to MARSSIM.

Let B n ( m ) be a set picked uniformly at random among all m-elements subsets of { 1 , 2 , … , n } . We provide a pathwise construction of the collection ( B n ( m ) ) 1 ⩽ m ⩽ n and prove that the logarithm of the least common multiple of the integers in ( B n ( ⌊ m t ⌋ ) ) t ⩾ 0 , properly centered and normalized, converges to a Brownian motion when both m , n tend to infinity. Our approach consists of two steps. First, we show that the aforementioned result is a consequence of a multidimensional central limit theorem for the logarithm of the least common multiple of m independent random variables having uniform distribution on { 1 , 2 , … , n } . Second, we offer a novel approximation of the least common multiple of a random sample by the product of the elements of the sample with neglected multiplicities in their prime decompositions.

The problem of uniformly placing N points onto a sphere finds applications in many areas. For example, points on the sphere correspond to unit quaternions as well as to the group of rotations SO(3) and the online version of generating uniform rotations (known as "incremental generation") plays a crucial role in a large number of engineering applications ranging from robotics and aeronautics to computer graphics. An online version of this problem was recently studied with respect to the gap ratio as a measure of uniformity. The first online algorithm of Chen et al. was upper-bounded by 5.99 and later improved to 3.69, which is achieved by considering a circumscribed dodecahedron followed by a recursive decomposition of each face. In this paper we provide a more efficient tessellation technique based on the regular icosahedron, which improves the upper-bound for the online version of this problem, decreasing it to approximately 2.84. Moreover, we show that the lower bound for the gap ratio of placing at least three points is the golden ratio, approx. 1.618, and for at least four points is no less than 1.726., Comment: 18 pages, 13 figures

The main objective of this study is to investigate the buckling analysis of CCSs reinforced by CNTs subjected to combined loading of hydrostatic pressure and axial compression resting on the two-parameter elastic foundation (T-P-EF). It is one of the first attempts to derive the governing equations of the CCSs reinforced with CNTs, based on a generalized first-order shear deformation shell theory (FSDST) which includes shell-foundation interaction. By adopting the extended mixing rule, the effective material properties of CCSs reinforced by CNTs with linear distributions are approximated by introducing some efficiency parameters. Three carbon nanotube distribution in the matrix, i.e. uniform distribution (U) and V and X-types linear distribution are taken into account. The stability equations are solved by using the Galerkin procedure to determine the combined buckling loads (CBLs) of the structure selected here. The numerical illustrations cover CBLs characteristics of CCSs reinforced by CNTs in the presence of the T-P-EF. Finally, a parametric study is carried out to study the influences of the foundation parameters, the volume fraction of carbon nanotubes and the types of reinforcement on the CBLs. (C) 2021 China Ordnance Society. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Various canonical piecewise linear (CPWL) function-based models have been widely used for modeling radio frequency (RF) power amplifiers (PAs). In most literatures, the thresholds of CPWL are often selected according to the uniform distribution, and how to select the optimal thresholds is rarely discussed. This letter presents a new iterative method, which updates the thresholds after calculating the model coefficients, and finally searches the optimal thresholds. The experimental results confirmed that the method can keep good linearization performance and significantly reduce the number of thresholds and the number of model coefficients.

Traditionally, ensembles of Slepian-Wolf (SW) codes are defined such that every bin of each $n$-vector of each source is randomly drawn under the uniform distribution across the sets $\{0,1,\ldots,2^{nR_X}-1\}$ and $\{0,1,\ldots,2^{nR_Y}-1\}$, where $R_X$ and $R_Y$ are the coding rates of the two sources, $X$ and $Y$, respectively. In a few more recent works, where only one source, say, $X$, is compressed and the other one, $Y$, serves as side information available at the decoder, the scope is extended to variable-rate S-W (VRSW) codes, where the rate is allowed to depend on the type class of the source string, but still, the random-binning distribution is assumed uniform within the corresponding, type-dependent, bin index set. In this expository work, we investigate the role of the uniformity of the random binning distribution from the perspective of the trade-off between the reliability (defined in terms of the error exponent) and the compression performance (measured from the viewpoint of the source coding exponent). To this end, we study a much wider class of random-binning distributions, which includes the ensemble of VRSW codes as a special case, but it also goes considerably beyond. We first show that, with the exception of some pathological cases, the smaller ensemble, of VRSW codes, is as good as the larger ensemble in terms the trade-off between the error exponent and the source coding exponent. Notwithstanding this finding, the wider class of ensembles proposed is motivated in two ways. The first is that it outperforms VRSW codes in the above-mentioned pathological cases, and the second is that it allows robustness: in the event of a system failure that causes unavailability of the compressed bit-stream from one of the sources, it still allows reconstruction of the other source within some controllable distortion.

The uniform distribution of stresses in flying-wing aircraft improves the aeroelastic flight envelope. In this paper, we document the effect of wing cross-section configuration on the stress distri...

This paper proposes new indices of cascaded multilevel inverters (MLIs) in order to compare the conventional topology with 10-existed new topologies. Reduction in the number of power switches is the main target of these new topologies. This aim is to reduce the total cost of designed inverters, though the mentioned aim is not enough to design new inverters. Thus, in this study, four comparative indices including sources power uniform distribution index (SPUDI), switches current uniform distribution index (SCUDI), switches loss uniform distribution index (SLUDI), and switches voltage uniform distribution index (SVUDI), are proposed. In addition, it is shown that in most of the new topologies, reducing the number of switches can lead to lowered values of the proposed indices, after which non-uniform power, current, loss, and voltage pattern are obtained. The results prove that mere emphasis on the number of switches, along with the exclusion of either of the proposed indices will cause significant problems, where cost reduction may be underachieved. Based on new indices, the advantages of conventional topology over new ones are presented in this study.

The power of features considerably influences the classification performance of remote sensing scene classification (RSSC). Recently, deep convolutional neural networks (DCNNs) have been used to extract powerful scene features. Nevertheless, confusion and overlap still occur in the feature space, leading to inaccurate RSSC. To alleviate this problem, we propose a novel deep metric learning loss function incorporated into a sphere loss to enhance the discrimination of feature representations. Inspired by two representative loss functions (i.e., angular loss and center loss), the proposed sphere loss learns a unique cluster center for each class in a remote sensing scene. Because the cluster centers and features are restricted by an introduced geometrical constraint, the intraclass distance of features decreases, while the interclass distance increases. Moreover, we introduce a spatial constraint, i.e., a uniformity coefficient on different cluster centers, which causes the centers to form a uniform distribution that maximizes the interclass distances between features. Extensive analysis and experiments on three commonly used RSSC data sets consistently show that, compared with state-of-the-art methods, the proposed sphere loss can effectively learn discriminative feature representations and significantly improve RSSC.

The attenuation of coherent noise plays a crucial role in reflection seismology but still poses some technical challenges. The multiple reflection-refraction (MRR) is one of the main coherent noises in land seismic surveys. The Cadzow filter can effectively attenuate incoherent noise. But it struggles in attenuating coherent noise. After keeping reflection events relatively horizontal, some researchers randomly rearrange the trace orders of the input data to realign coherent noise in an incoherent position so that the Cadzow filter can treat the realigned coherent noise as incoherent noise. Accordingly, a random-derangement-based FX Cadzow filter is proposed to attenuate MRRs in 2-D common-shot gathers based on the linear feature of MRR events and randomization of limited trace orders of the input data. In practice, after linear-move-out (LMO) with an estimated dip, the input data are divided into several small windows to obtain a group of relatively horizontal MRR events. Then, these windows are randomized and filtered one by one. Due to the limited trace orders of each window, there are many possible rearrangements after randomization. Some rearrangements may lead to undesirable filtering performance. To obtain the rearrangements that lead to good filtering performance, each window's trace orders are randomly deranged by resampling from the input orders with uniform distribution. A 2-D synthetic data experiment shows the influence of different rearrangements on the filtering performance. Both 2-D synthetic data and 2-D field data demonstrate that the proposed method outperforms the 2-D FK filter to attenuate both aliased and nonaliased MRRs.

Band sampling (BSam) is an innovative concept for hyperspectral imaging, which is derived from signal sampling in communications/signal processing as well as sampling theory in information theory. It is quite different from band selection (BSel) in several aspects. First of all, BSam ``samples'' bands with a given fixed BSam rate (BSamR) as opposed to BSel, which ``selects'' appropriate bands according to the number of bands to be selected. Second, BSam requires no specific means of sampling bands compared to BSel, which requires a specific rule to select bands such as band prioritization (BP) criteria or band search strategies. Third, BSam samples bands without prior band knowledge or specific applications in contrast to BSel, which considers certain bands more significant than other bands according to various applications. Two strategies are developed for BSam. One is from information theory. Under a completely blind and unknown environment, the maximum entropy is achieved by the uniform distribution. This suggests that one best strategy for BSam is uniform band sampling (UBSam). Another strategy is random BSam (RBSam) analogous to random signal sampling in compressive sensing (CS) where the bands sampled by RBSam are random but are not deterministic like the bands selected by BSel. Interestingly, UBSam and RBSam generally perform better than custom-designed BSel methods. To illustrate its potential utility of BSam in various applications, target detection, anomaly detection, and image classification are studied through extensive experiments for demonstration.

Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multi-scenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by k-means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influ18 ences of each uncertain parameter on the optimal configuration and economy of the IES.

In this paper, the experimental and numerical investigations of both swirling hybrid rocket motors without and with the design of regenerative cooling (RC hereafter), using 90%-wt H 2 O2 and polypropylene (PP) as the liquid oxidizer and the solid fuel, respectively, were performed and compared. We implemented a new design of regenerative cooling using the oxidizer to transfer the heat generated from combustion. A series of ground horizontal hot-fire tests, which include the catalytic-bed only and the motor without/with RC were performed to measure the propulsion performance related parameters. The resulting C* efficiency of the RC design with catalytic-bed only remained in comparison with the one without RC. With a similar initial combustion chamber geometry, the RC implementation resulted in a better engine Isp efficiency from 96.3 to 98.6 with a lower O/F ratio from 6.49 to 5.94. In addition, the topologies of the burned fuel grain surface were optically measured by a 3D laser scanner. The resulting variation of regression rates between the upstream and downstream fuel for the chamber with the RC was smaller than case without the RC. Moreover, a simplified semi-empirical computational fluid dynamics (CFD) model based on the measured regression rates was constructed for investigating the physical phenomenon inside the hybrid rocket combustion chamber. The simulations show that a higher swirling number in the later portion of the port was found for the RC case. By concerning about the different O/F ratios and port size of the burned fuel, a series of simplified numerical CFD investigation with a fixed O/F and a fixed port size varying the regression rates from upstream to downstream fuel systematically with the same average region rate was performed to complement the semi-empirical CFD simulations. The results showed that there were two distinct regions including an upstream non-classical region dominated by the gas impingement of gas and a downstream classical region caused by the swirling effect of gases. In the non-classical region, the calculated distributions of the swirling numbers of the former are essentially the same for all the cases no matter what the magnitude of the regression rate is, while in the classical region higher swirling numbers are found for the case of more uniform distribution of regression rates. Heat generated near the upstream of the fuel grain was transferred more efficiently through the design of RC, which makes the resulting fuel regression more uniform than that without RC. The resulting swirling efficiency in the RC case was higher than that without RC. The HRM with RC hence had a better engine Isp efficiency with a lower O/F ratio.

We propose a Quantum inspired Hash Function using controlled alternate quantum walks with Memory on cycles (QHFM), where the $j$th message bit decides whether to run quantum walk with one-step memory or to run quantum walk with two-step memory at the $j$th time step, and the hash value is calculated from the resulting probability distribution of the walker. Numerical simulation shows that the proposed hash function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions based on quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property; and theoretical analysis indicates that the time and space complexity of the new scheme are not greater than those of its peers. The good performance of QHFM suggests that quantum walks that differ not only in coin operators but also in memory lengths can be combined to build good hash functions, which, in turn, enriches the construction of controlled alternate quantum walks.

Channel estimation (CE) is a crucial phase in wireless communication systems, especially in cell-free (CF) massive multiple input multiple output (M-MIMO) since it is a dynamic wireless network. Therefore, this work is introduced to study CE for CF M-MIMO system in the uplink phase, wherein the performance of different estimators are evaluated, discussed, and compared in various situations. We assume the scenario in which each access point has prior knowledge of the channel statistics. The phase-aware-minimum mean square error (PA-MMSE) estimator, the non-phaseaware-MMSE (NPA-MMSE) estimator, and the least-squares estimator are the three estimators which are exploited in this work. Besides, we consider the Rician fading channel in which the line-of-sight path is realized with a phase-shift that models the users’ mobility where the considered phase-shift follows a uniform distribution. On the other hand, the mean-squared error metric is employed in order to evaluate the performance of each estimator, where an analytical and simulated result is provided for the PA-MMSE estimator and the NPA-MMSE estimator in order to assert our numerical results.

The development of ultrastrong maraging stainless steels (MSSs) is always in high demand. However, traditional high-strength MSSs generally exhibit early plastic instability with a low uniform strain since the precipitated nanoparticles are non-coherent with the body-centered-cubic (BCC) lath martensitic matrix. Here, we design a novel ultrahigh strength MSS (Fe-5.30Cr-13.47Ni-3.10Al-1.22Mo-0.50W-0.23Nb-0.03C-0.005B, wt.%) using a cluster formula approach. A fabulous microstructure consisting of a uniform distribution of high-density coherent B2-NiAl nanoprecipitates (3−5 nm) in BCC martensitic matrix was successfully obtained. This alloy has not only an exceedingly high ultimate tensile strength of 2.0 GPa, but also a decent uniform elongation of 4.2%−5.1%, which is almost triple of the value observed in existing MSSs. We present an in-depth discussion on the origins of ultrahigh strength and uniform plastic strain in the new alloy to validate our design strategy and further offer a new pathway to exploit high-performance MSSs.

We develop a formal framework to analyse a monopoly’s problem when demand is determined by a Poisson-distribution and the valuations of the buyers are draws from a common distribution. The buyers have unit demand, and the good in question is discrete. The monopoly has to make its quantity and pricing decisions before demand is realised. We determine sufficient conditions for the monopoly’s pricing decision to be unique, and we demonstrate the difference to the planner’s problem by numerical examples. We also study an economy with a fixed number of buyers assuming that the valuations are draws from the uniform distribution. When the economy grows in the limit one recovers the standard case of a monopoly with linear demand and constant marginal costs.

In the design of magnetless multitoothed doubly salient (DS) machine, most multitoothed structures are regarded as uniform distribution, while the influence of multitoothed distribution on performance is ignored. This article investigates the design of magnetless DS machine with consideration of multitoothed distribution. Analytical expressions, including magnetomotive force, permeance modulation, air-gap flux density, flux linkage, and back electromotive force, are derived to reveal the mechanism of multitoothed distribution. Then, different multitoothed distribution models are optimized through a genetic algorithm combined with a finite-element method for maximum torque and reduced torque ripple. It is found that with the consideration of multitoothed distribution design, a ~20% torque improvement is achieved in the machine. Finally, the effectiveness of the theoretical analysis and multitoothed distribution design is validated by finite-element simulation and experiments.

The aim of this paper is to propose a demountable shear wall (DSW) system for multistory precast concrete buildings. For this purpose, instead of a large wall panel, several slender walls are used by connecting to adjacent beams. To evaluate the efficiency of the DSW system, nonlinear cyclic and time history analyses are performed on eighteen 4-, 8- and 12-story models, including three conventional shear wall models and fifteen DSW models with various wall numbers. The results indicate that for DSW system, when using more walls with a smaller width, the structural deformation tends from flexural-type to almost shear-type. This result prevents the plastic strain from concentrating on the columns and walls of the first story and causes the damage to be uniformly distributed on the walls of all stories. The DSW models exhibit hysteretic behavior without significant strength degradation at high drift levels. Also, DSW models with more walls, while increasing the ductility factor (up to 30%) and creating a more uniform distribution of drifts, reveal less value of maximum floor acceleration (up to 48%) and story shear (up to 53%), compared to conventional shear wall models.

The coating is one of the most important components that affect the efficiency of the pavements. The uniform distribution of coating has received less attention in pavement design and implementatio...

This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates. In the proposed approach, the DFNN model is developed based on a data set containing 20 000 samples of damage scenarios, obtained via finite element (FE) simulation, of the FG-CNTRC plates. The elemental modal kinetic energy (MKE) values, calculated from natural frequencies and translational nodal displacements of the structures, are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output. The state-of-the art Exponential Linear Units (ELU) activation function and the Adamax algorithm are employed to train the DFNN model. Additionally, in order to enhance the performance of the DFNN model, the mini-batch and early-stopping techniques are applied to the training process. A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer. The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution (UD) and functionally graded-V distribution (FG-VD). Furthermore, the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated. Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

We study intersections of semigroup orbits in polynomial dynamics with multiplicative subgroups and its approximations, extending results of Ostafe and Shparlinski (Orbits of algebraic dynamical systems in subgroups and subfields. Number theory: diophantine problems, Uniform Distribution and Applications. pp 347–368, 2010).

Processing of Al-MMC incorporates liquid state processing, specifically, stir casting owing to its simplicity, flexibility and ease of processing. Achieving uniform distribution of reinforcement particles and making the casting process continuous is a tall challenge for the researchers. In the present investigation, a simulation based optimization of various parameters for newly developed continuous casting setup was carried out. Computational simulations were accomplished by Ansys-CFD software. Simulation of various aspects like design, flow behavior, temperature profile, etc., has been studied for optimization of casting process. After analyzing flow pattern of liquid in different conditions, optimized geometry and casting parameters have been proposed. The proposed designed and casting parameters were experimentally validated to synthesize Al-MMC. The experimental result showed that the optimized simulation results can produce sound MMCs with minimum cast defect. Hence, this economical continuous casting process can cast high quality Al MMCs in industrial scale.

Highly efficient, durable, and economically viable electrocatalysts for oxygen reduction reaction (ORR) is central for energy conversion and storage devices such as fuel cells and metal–air batteri...

The channel capacity of noncoherent reception of multi-level one-sided amplitude-shift keying (ASK), which is an asymmetric constellation, in Rayleigh fading with receive diversity and energy detection is considered. The asymmetries result in capacity achieving input probability distributions, that is, a priori probability distributions, that deviate from uniformity. An analytical expression for the mutual information in terms of a single integral is derived, and from it the set of equations, which can be solved to obtain the optimum or capacity achieving input probabilities, is obtained. High and low signal-to-noise ratio (SNR) approximations of the optimum input probabilities and the capacity are derived next. Furthermore, a logarithmic upper bound on the mutual information is obtained. Numerical results confirm that the uniform distribution of input probabilities is not capacity achieving. For example, with average SNR per symbol per branch of 6 dB, the relative deviation of the mutual information (with uniform input distribution) from the capacity is nearly 20% for 4-level ASK with one transmit diversity branch and two receive diversity branches. Furthermore, the derived high and low SNR approximations to the capacity are shown to be reasonably accurate.

Radioactive aerosols that arise from natural sources and nuclear accidents can be a long-term hazard to human health. Despite the heterogeneous particle deposition in the respiratory tract, uniform aerosol doses have long been assumed in respiratory radiation dosimetry predictions, such as in the compartment and uniform distribution models. It is unclear how these deposition patterns affect internal radiation doses, which are critical in the health assessment of radioactive hazards. This work seeks to quantify the radio-dosimetry sensitivity to initial deposition patterns by comparing computational and compartment/uniform models. A new approach was developed to implement the compartment model into voxel phantoms (e.g. VIP-man) for radiation dosimetry. The calculated radiation fluence, energy deposition density and organ doses were compared to those obtained from coupling computational fluid-particle dynamics (CFPD) with Monte Carlo radiation transport and to those obtained from uniform source distribution approximation. The results show that the source particle distribution within the respiratory system substantially influences the radiation dosimetry distribution. The compartment and uniform models underestimated aerosol deposition in the crania ridge, leading to lower doses in the trachea and surrounding organs. For 0.5 MeV gammas, the CFPD-Monte Carlo N-particle (MCNP) model predicted a tracheal dose twice that of the compartment model and four times the uniform model. For 1 MeV betas, the CFPD-MCNP-predicted tracheal dose is 2.6 times that of the compartment model and 14 times the uniform model. Compared to the compartment/uniform models, the CFPD approach predicted a 50% lower beta dose in the lung but higher beta doses in the heart (six times), liver (four times) and stomach (2.5 times). It is suggested that including compartments for the lung periphery and tracheal carina ridge may improve the dosimetry accuracy of compartment models.

One method of predicting stock prices is to use the time series analysis method. In this method, a linear prediction model is made to see patterns from historical stock price data to assess future prices. The stock data used in this study is the daily stock data of PT. Telkom and PT. Indosat in 2020-2021. Autoregressive (AR) model is a time series model that is often used with the assumption that its volatility does not change with time (Homoscedastic). After analyzing the AR Model(1) data for the stock data of PT. Telkom and PT. Indosat has a non-independent error, therefore the AR(1)-N.GARCH(1,1) time series model construction was carried out to model the error (ϵ_(i,t)). Furthermore, the error of the AR(1)-N.GARCH(1,1) model is independent of t, so it can be modeled using Copula. After the Copula model was applied to the data and obtained the value of the fit of the Gaussian Copula distribution error model. From the values generated from the Gaussian Copula C({ϵ_(i,t) }_(t=1)^T ),T=1,2,…, and approximates a uniform distribution. So the stock data of PT. Telkom and PT. It can be said that Indosat is not suitable to be modeled with the Gaussian Copula.

In this study, Modified Monarch Butterfly Optimization Algorithm (M2BO) is proposed by modeling stochastic processes in Monarch Butterfly Optimization (MBO) algorithm with different random distribution functions. The proposed M2BO algorithm has been firstly tested with benchmark functions and the results have been compared with the literature and classical MBO algorithm. In order to analyze the performance of the proposed M2BO algorithm for the real engineering problem, the feedback gain matrix (K matrix) for the control of the 3 Degree of Freedom (3 DOF) Hover system has been optimized. The results have been compared with classical MBO, DSO (Discrete Stochastic Optimization), and SMDO (Stochastic Multi-parameter Divergence Optimization) optimization algorithms. The obtained results have been compared on 3 DOF Hover simulation models and real-time 3 DOF Hover experimental sets. The performance of the proposed M2BO algorithm in benchmark and real engineering problem tests has been shown theoretically and experimentally. Thus, it has been shown that the performance of algorithms can be increased without changing the basic philosophy of algorithms by modeling stochastic processes in algorithms with random distributions other than a uniform distribution. In addition to these, it has been determined that distribution function-based contributions can be applied to many of these algorithms.

Designing a generative model to synthesize realistic tabular data is of great significance in data science. Existing tabular data generative models have difficulty in handling complicated and diverse marginal distribution types due to the gradient vanishing problem, and these models pay little attention to the correlation between attributes. We propose a method that improves the generative adversarial network (GAN) with inverse cumulative distribution function for tabular data synthesis. This method first transforms continuous columns into uniform distribution data by using the cumulative distribution function, which can alleviate the gradient vanishing problem in model training. Then the method trains GAN with the transformed data, where the discriminator with label reconstruction function is presented to model the correlation among attributes accurately by introducing an auxiliary supervised task to help the correlations extraction. After that, we train a neural network for each continuous column to perform the inverse transformation of generated data into the target distribution, thereby the synthetic data is obtained. Experiments on simulated and real-world datasets show that our method compares favorably against the state-of-the-art methods in modeling tabular data.

In this paper, important functional parameters of solid oxide fuel cells are identified by introducing a novel high-speed optimization method, namely adaptive chaotic grey wolf optimization algorithm. The suggested optimization method is obtained by combining the adaptive grey wolf optimization and chaotic grey wolf optimization algorithms. The chaotic algorithm is applied to the basic grey wolf optimization to achieve higher convergence speed, keep the population's diversity, and provide an initial population with uniform distribution. Besides, a nonlinear convergence factor is defined for balancing the global and local exploration abilities. Employing the improved convergence factor resulted in a new version of the grey wolf optimization algorithm, namely adaptive grey wolf optimization algorithm. Adaptive chaotic grey wolf optimization algorithm adopts the advantages of both chaotic grey wolf optimization and adaptive grey wolf optimization methods simultaneously. The adaptive grey wolf optimization algorithm is applied to a 5 kW dynamic tubular stack. The results of the simulation report the lowest values of mean squared error, higher accuracy, higher robustness, and high convergence speed for the adaptive grey wolf optimization algorithm compared to some well-known optimization methods. Besides, the proposed method shows a good agreement with experimental results with lower computational difficulty.

There are several algorithms for determining control forces of active devices in civil engineering structures. Uniform distribution of deformations theory is a structural concept which mainly focuses on equalizing deformations in order to promote the utilization of ductility capacity of structural elements thereby mitigating damages to a structure. In common active control algorithms, deformation uniformity and reducing damages are barely noted. In this research, a new active control algorithm regarding uniform deformation theory will be proposed. Uniform distribution of Deformation Control (UDC) algorithm consists of a massless optimized Virtual Added linear structure with Pinned connections (VAP) which is added to a desired original structure. This VAP can be treated as an added stiffness matrix. Once the parameters of the VAP were optimized in a manner that the desired responses were obtained from the new integrated structure, then, the new integrated structure is developed and analyzed in each time step. Finally, the VAP beams axial forces are extracted and applied to the original structure as control forces via active control devices such as active tendons. This proposed control algorithm is simple and effective in reducing responses especially drifts, making more uniform thereby reducing structural damages as well as decreasing the required capacity of actuators and the power consumption eventually. High potential and promising performance of the proposed UDC algorithm will be represented through numerical investigation in an office building example. The results are compared with Nonlinear Instantaneous Optimal Control (NIOC) algorithm as a very common active control algorithm for linear and nonlinear structures.

A mathematical model of diffusion of admixtures in a porous body with randomly distributed spherical pores of different radii is proposed. The skeleton of the body is modeled by closely packed spheres of different radii admitting the possibility of introduction of several effective diameters. We deduce a differential equation of mass transfer for the entire body with regard for the jump discontinuities of the unknown function and its derivative at the random interfaces. A new integrodifferential equation equivalent to the obtained initial-boundary-value problem is constructed and its solution is found in the form of a Neumann series. We perform the quantitative analysis of the process of mass transfer of admixtures in a porous layer under the condition of a uniform distribution of the phases. It is shown that the larger the volume fraction of porous inclusions in the body, the lower the means concentration of migrating substances.

In this study, a performance-based optimisation framework is proposed for optimal cross-sectional distribution of steel moment resisting frames (MRFs) subject to earthquake excitations. For the first time, the concept of uniform distribution of damage (UDD) is adopted for the complex optimisation of moment resisting steel frames under dynamic earthquake excitations. To enhance the computational efficiency of the optimisation process, a novel adaptive algorithm is proposed, where the power functions change based on the demand to capacity ratio of the structural elements. The framework is then used for optimisation of 3, 5, 7, 10 and 15-storey steel MRFs under a set of representative earthquake records. Maximum plastic rotations and strength-based demand to capacity ratios are considered as the main performance parameters for deformation-controlled and force-controlled members, respectively. The results indicate that the proposed method is very efficient at improving the seismic performance of the designed structures in only a few steps by satisfying all the prescribed performance targets. It is shown that, for the same performance level, the optimum design frames require up to 38% less structural-weight compared to their code-based design counterparts. The average storey shear force distributions of the optimum frames under the design spectrum-compatible earthquakes are then used to obtain more efficient lateral load patterns for seismic design of steel MRFs. It is shown that by adopting the proposed load pattern in the conventional seismic design process, the frames exhibit significantly (up to 70%) less global damage compared to the similar frames designed based on code suggested load pattern.

The frequency veering of a metal porous rotating cantilever twisted plate with twist angle and stagger angle is investigated. Metal porous materials may have the characteristics of gradient or uniform distribution along the thickness direction. Based on the classical shell theory, considering the influence of centrifugal force produced by high-speed rotation, the free vibration equations of a rotating cantilever twisted plate are derived. Through the polynomial function and Rayleigh–Ritz method, the natural frequencies and mode shapes of the metal porous cantilever twisted plate in both static and rotating states are derived. The accuracy of the present theory and calculation results is confirmed by a comparison between them and the results available from the literature and those obtained from Abaqus. The influences of the thickness ratio, porosity, twist angle, stagger angle and rotational velocity on the frequency veering and mode shape shift of the rotating cantilever twisted plate with porous material under three different distributions are analyzed. It should be mentioned that the frequency veering accompanied by mode shape shift occurs in both static and dynamic states.

UDC 511.311We prove that the ordinates of the nontrivial zeros of the Lerch zeta-function are uniformly distributed modulo one.

Vanadium redox flow batteries (VRFBs) are one of the emerging energy storage techniques that have been developed with the purpose of effectively storing renewable energy. Due to the lower energy density, it limits its promotion and application. A flow channel is a significant factor determining the performance of VRFBs. Performance excellent flow field to ensure uniform distribution of electrolytes and increases the overall performance of the battery. In order to better explore the influence of the flow field on the transmission characteristics of the electrolyte, novel variable cross-section flow field is designed to analyze its impact on battery performance. The influence of flow field with and without flow field, different flow field configurations, and variable cross-section on battery performance was analyzed emphatically. The main contribution of this study are to make a comparative analysis of the existing channel design methods and analyze the advantages and disadvantages of different design methods and existing problems. It provides reference for the design and optimization of VRFBs in the future.

Parabolic trough collector stands out as the most proven and widely adopted technology for concentrating solar power. A simple mathematical model based on dividing the trough’s aperture and the surface of the absorber into small segments is developed herein. The model is used to investigate the influence of the design and operational parameters on the distribution of the concentrated solar flux over. In this study, eight absorbers with different geometries are considered and the effect of the rim angle, absorber’s geometry, and operational mode on the distribution of the concentrated solar flux over their surfaces have been addressed. For all absorbers, it has been found that increasing the rim angle results in reducing the intensity of the concentrated solar flux with increase in its coverage area. The distribution of the concentrated solar flux over the surface of the parabolic shaped absorber (ABS.6) is found to be uniform over the portion of the surface located between its two rims. It has also been demonstrated that the operation under free absorber mode can help in redistributing the concentrated solar flux over the absorber surface resulting in a more uniform distribution compared to that over the surface of the fixed absorber. From the combined geometries of the investigated absorbers two optimal shapes are obtained and are found to perform much better than the circular absorber ABS.1.

This letter investigates the downlink integrated low earth orbit (LEO) satellite-terrestrial millimeter-wave decode-and-forward relay network, where the satellite and terrestrial relays locate in uniform distribution over the spherical cap surface of the LEO and in Poisson point process over the ground, respectively. In order to obtain fine-grained information on the coverage performance, the meta distribution (MD) of signal-to-interference-to-noise ratio (SINR) is adopted for network performance evaluation, which provides the probability that certain percentage of satellite-relays-user links are able to reach a target SINR threshold. The closed-form and analytical expression of the ${b}$ -th moments of the complementary cumulative distribution functions (CCDFs) of SINR for the satellite-to-relay link and relay-to-user link are then derived, respectively. In addition, we further give the analytical MD expression via the beta approximation. Finally, numerical results are provided to validate our analytical results.

LPN (learning parity with noise) problem is a good candidate for post-quantum cryptography which enjoys simplicity and suitability for weak-power devices. Dottling et al. (ASIACRYPT 2012) initiated the first secure public key encryption (PKE) under the low-noise LPN assumption. Kiltz et al. (PKC 2014) proposed a simpler and more efficient scheme using double-trapdoor technique from the same assumption. Both schemes abide the decoding failure rate (DFR) 2 − Θ ( k ) (k is the security parameter) and there exists CPA/CCA2-secure PKE with squared-exponential DFR 2 − Θ ( k 2 ) from constant-noise LPN (Yu and Zhang, CRYPTO 2016). In this work, we give a positive answer with squared-exponential DFR in the low-noise setting. More precisely, we first introduce a variant (VxLPN) of the low-noise Exact LPN (xLPN, proposed by Jain et al. at ASIACRYPT 2012 and used as building block in commitments and zero-knowledge proofs), where the coefficient matrix A follows the uniform distribution over { 0 , 1 } q × n ( n = Θ ( k 2 ) , q = Θ ( n ) ), the secret x is sampled from B μ n ( B μ is the Bernoulli distribution with noise rate μ = Θ ( 1 q ) ), and the noise e follows a column vector distribution uniform over { z ∈ { 0 , 1 } q : | z | = q μ } . A series of reductions show that VxLPN is at least as hard as the standard LPN for the same noise rate μ. We then construct from the VxLPN CPA/CCA2 secure PKE schemes with squared-exponential DFR 2 − Θ ( k 2 ) which share the common structure extrinsically with Kiltz et al. and Yu-Zhang schemes. The secret key(s) in our schemes are simply sampled from the Bernoulli distribution, and comparatively, the secret key(s) in Yu-Zhang schemes must be chosen from a tailored version of Bernoulli distribution (along with the coefficient matrix A that follows a distribution D λ n × n = U n × λ ⋅ U λ × n induced by multiplying two random matrices in the public key, λ = Θ ( log 2 ⁡ n ) ) in order to guarantee the correctness of their schemes. Consider the performance on 128-bit security level, our CCA2-secure scheme only holds 117.79 MB public keys, 67.31 MB secret keys and 10.15 KB ciphertexts, and thus is more efficient than the schemes of Dottling et al. and Kiltz et al. ((14.53 GB, 14.48 GB, 14.06 KB) and (161.78 MB, 92.45 MB, 13.60 KB) respectively).

In this paper, numerical study of flow field in the new design cyclones with five different wall temperature profiles are investigated. The new design cyclone is based on the idea of improving cyclone collection efficiency and pressure drop by increasing the vortex length. In this paper, the five wall temperature profiles are as follows: (A) cooling with uniform distribution, (B) without temperature change, (C) heating with uniform distribution, (D) incremental linear heating, (E) reduction linear heating. Results are compared in new design and conventional cyclones. The Reynolds averaged Navier–Stokes equations with Reynolds stress turbulence model (RSM) are solved. The Eulerian-Lagrangian computational procedure is used to predict particles tracking in the cyclones. The velocity fluctuations are simulated using the Discrete Random Walk (DRW). Results show that generally, heating the bottom zone of the cyclones can improve the collection efficiency and reduce the pressure drop while heating the top zone of the cyclones marginally affects the flow field. Moreover, cooling the cyclones reduces the efficiency and causes a higher pressure drop. Among five different wall temperature profiles, C and E profiles can increase the efficiency about 8% and profile C reduces the pressure drop by about 9%. The mentioned values in different conditions including particle diameter, flow rate, etc. can be different.

Fabrication of MWCNT-reinforced nanocomposites with uniform distribution is still remaining as a challenge. Even for researchers who achieved uniform distribution in powder, boundary agglomerations...

A two-parameter Gamma-Uniform distribution was recently introduced as a prominent alternative in modeling bounded phenomena. Unfortunately, however, its maximum likelihood estimators (MLEs) are fou...

The North East (NE) of India is an earthquake (EQ) prone zone and has experienced considerable damages in the past due to high intensity EQs. With rapidly growing infrastructure, the NE India demands extensive and accurate seismic hazard analysis (SHA) in order to minimize the damages caused by frequency seismic activity. The region has complex tectonic setting with varying seismicity. The entire NE region (except Sikkim) is classified amongst the highest seismic hazard region in the country. However, it is characterized by an inhomogeneous seismicity distribution. The present work attempts seismic source characterization of the region. So far, very few works have attempted source zonation of NE, where the outcomes were primarily based on visual understanding of different parameters alone. Thus, the accuracy of endeavors based on subjective accounts can be questionable. Consequently, such attempts did not ensure uniform distribution of past EQs and seismic activity. Thus, the prime objective of source characterization is defeated. The aforementioned limitation is addressed in the current study. As a first step, a hierarchical clustering method is applied based on which nine clusters, each having uniform distribution of past EQs, were identified. Secondly, using homogenized and declustered EQ catalogue, seismic activity variations based on least square method (LSM) and maximum likelihood method (MLM) are studied. Further, based on hierarchical clustering methodology, source zones having same seismic activity within them are identified. Finally, based on the present work, the entire NE is divided into eleven source zones each having uniform EQ distribution as well as seismic activity. These findings will be helpful in understanding the relative contribution of different parts of NE in controlling regional as well as site-specific seismic hazard scenario in the future.

This article analyzes existing installations for active ventilation of grain (further in the article:AVG) in containers (bunkers, silos), identifies their main disadvantages. Experimental study has been carried out to assess the technological efficiency of existing installations for AVGduring stationary storage in a container. As a result of the analysis of the obtained results of the experimental study, the cause of the inhomogeneous temperature field inside the grain mound was revealed due to the uneven distribution of the air flow (working agent) in the intergrain space. When designing the air distribution channel of the installation, the need to comply with the law of conservation of mass for a continuous medium, for example, an air flow, was not taken into account. A ventilated bunker with a rational design of an installation for active ventilation of grain has been proposed. The principal difference between the bunker and the existing ones lies in the method of uniform distribution of the air flow inside the grain mound. The installation for the implementation of this method includes two channels designed for injecting air into the mound and removing the exhaust air together with the evaporated moisture when drying the grain using the active ventilation method. The law of conservation of mass is observed due to the cone-shaped design of the air distribution channels, as a result of which a uniform temperature field and humidity are created in the intergrain space of the mound.