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Analyzing the data collected from different sources requires unpaired data analysis to account for the absence of correspondence between the random variable Y and the covariates $ \boldsymbol {X} $ X. Several attempts have been made to analyze continuous Y, but it may follow a discrete distribution, which previous methodologies have overlooked. To address these limitations, we propose Poisson matching quantiles estimation (PMQE), the first unpaired data analysis method designed to examine the discrete Y and the unpaired continuous covariates $ \boldsymbol{X} $ X. Using their order statistics, the PMQE method matches the linear combination of random variables $ \boldsymbol{\beta} ^{T} \boldsymbol{X} $ β T X to $ {\rm log}(Y) $ log (Y). We further improve the performance of the proposed method by $ \ell _1 $ ℓ 1 penalizing $ \boldsymbol{\beta} $ β , leading to the PMQE LASSO. An effective algorithm and simulation results are presented, along with the convergence results. We illustrate the practical application of PMQE using real data. [ABSTRACT FROM AUTHOR]

Clinical trial endpoints are often bounded outcome scores (BOS), which are variables having restricted values within finite intervals. Common analysis approaches may treat the data as continuous, categorical, or a mixture of both. The appearance of BOS data being simultaneously continuous and categorical easily leads to confusions in pharmacometrics regarding the appropriate domain for model evaluation and the circumstances under which data likelihoods can be compared. This commentary aims to clarify these fundamental issues and facilitate appropriate pharmacometric analyses. [ABSTRACT FROM AUTHOR]

A discrete variable topology optimization method of internally finned ducts in heat exchangers for efficient thermal performance is proposed. Fully developed convective heat transfer (FDCHT) model, which has been extensively employed and well checked in practical thermal engineering applications, is considered here. Under the uniform wall temperature boundary condition, the energy equation of the FDCHT model can be mathematically formulated as a generalized eigenvalue equation, and the total thermal resistance reflecting the thermal performance can be related to the eigenvalue. The well-known eigenvalue optimization formulation and sensitivity analysis is applied to this optimization problem. Significantly, the physical reality, such as precise Nusselt number, is maintained by using the discrete variable method, i.e., Sequential Approximate Integer Programming. Here, only the 0–1 densities denoted to solid and fluid are involved so that blurry intermediate zones and interpolation schemes are avoided. The practical engineering conditions of fixed pumping power and fixed fluid volume flow rate are discussed under the unified framework, respectively. Several designs of internally finned ducts without any blurry zone are obtained. The optimized design conforms to the three-dimensional precise Conjugate Heat Transfer calculation. Numerical results show that contrary to the design method by predefined heat transfer coefficient, the proposed method can automatically obtain the optimal shape and spacing of fins since the spatially varying effect of convective heat transfer has been achieved. [ABSTRACT FROM AUTHOR]

Quantum satellite networking is central to enabling quantum key distribution (QKD) and quantum-state transfer. Delving into the synergistic relations of quantum technology and satellite communications, we elucidate on continuous variable and discrete variable QKD schemes, and the strategic selection of satellite orbits for optimal QKD. The prospects and possibilities of the integration of software-defined networking into this paradigm showcases a fusion of traditional and quantum communication methods. In the context of continuous variable quantum systems, a comparative discussion on Gaussian and non-Gaussian paradigms is presented, emphasizing their distinct characteristics crucial for satellite-assisted quantum networks. Moreover, we underscore the role of quantum computing in bolstering the security of satellite information networks, emphasizing the importance of delay reduction for the connection of Internet of Things (IoT) devices to satellite networks. Lastly, we propose potential domains ripe for future exploration in this burgeoning field.

Quantum key distribution (QKD) is now moving toward a scalable and secure QKD network, which establishes secret keys among network users. The continuous-variable (CV) and discrete-variable (DV) QKD systems are currently being developed in parallel and are all used in QKD networks. We propose here a hybrid QKD network, in which the CV QKD system is used to build metropolitan QKD networks, and the DV QKD system is used to connect metropolitan QKD networks. The hybrid QKD network takes advantage of high secret key rates for CV QKD systems and long distance for DV QKD systems. We also present a feasible hybrid measurement-device-independent (MDI) QKD network, which combines both CV and DV MDI QKD systems. The presented hybrid QKD networks meet the low cost and compact requirement of a realistic QKD network and provide a feasible solution for future real QKD networks by combining the advantages of both CV and DV QKD systems. [ABSTRACT FROM AUTHOR]

Exposure–response modeling is important to optimize dose and dosing regimens in clinical drug development. While primary clinical trial endpoints often have few categories and thus provide only limited information, sometimes there may be additional, more informative endpoints. Benefits of fully incorporating relevant information in longitudinal exposure–response modeling through joint modeling have recently been shown. This manuscript aims to further investigate the benefit of joint modeling of an ordered categorical primary endpoint with a related near-continuous endpoint, through the sharing of model parameters in the latent variable indirect response (IDR) modeling framework. This is illustrated by analyzing the data collected through up to 116 weeks from a phase 3b response-adaptive trial of ustekinumab in patients with psoriasis. The primary endpoint was based on the 6-point physician's global assessment (PGA) score. The Psoriasis area and severity Index (PASI) data, ranging from 0 to 72 with 0.1 increments, were also available. Separate and joint latent variable Type I IDR models of PGA and PASI scores were developed and compared. The results showed that the separate PGA model had a substantial structural bias, which was corrected by the joint modeling of PGA and PASI scores. [ABSTRACT FROM AUTHOR]

This study presents a robust hybrid meta-heuristic optimization algorithm by merging Modified Colliding Bodies Optimization and Genetic Algorithm that is called GMCBO. One of the inabilities of Colliding Bodies Optimization (CBO) is collapsing into the trap of local minima and not finding global optima. In this paper, to rectify this weak point, at first, some modifications are accomplished on the CBO process and then by using the concept of the genetic algorithm able to enhance the convergence rate, establishing a balance between the feature exploration and exploitation processes, the increasing power of finding global optimal design and escaping of local optimal. For evaluating the performance of the proposed method, the optimal design of laminated composite materials has been considered. Compare the results of structural analysis with GMCBO and other optimization methods shows a high convergence rate and its ability to find the global optimal solution of the proposed algorithm for structural optimization problems.

Soil erosion is a major and costly environmental hazard worldwide. Obtaining a thorough understanding of the soil erosion process is essential for erosion control. The high erodibility of the saline‐sodic soil in coastal areas causes rills to develop rapidly and erosion patterns change substantially during the rainfall process. Understanding the temporal and spatial dynamics of the soil surface morphology is crucial for revealing the mechanism behind the severe soil erosion process, but simulating these dynamics with many existing empirical or physical models is difficult. Cellular automata (CA) provides a simple alternative approach with high computational efficiency for modelling the complex patterns of soil erosion evolution using local interactions rules. However, previously established CA models poorly simulated the lateral erosion. By dividing the runoff amount into a discrete variable of every constituent cell and considering the runoff flow velocity when calculating the flow direction, the CA model developed here, based on discrete physical variables (CADV), could simulate soil erosion rate dynamics and rill morphological development with low relative errors. The variations in the simulated sediment yield rates during rainfall events were consistent with the experimental measurements, and the relative errors of cumulative sediment yields between the simulated and measured values were lower than 5% from the 38th to 60th min (the end of rainfall event) during the rainfall. The morphological characteristics of slope erosion with relative errors in rill length, rill width, and rill depth were lower than 5% at specific times during rainfall events. Specifically, regarding the rill width parameter, the relative errors of the maximum and average rill widths were both lower than 5% when the rainfall event ended at the 60th min. The results show that the CADV is an appropriate tool for describing the soil erosion process and improving our understanding of the erosion process characteristics of saline‐sodic soil slopes in a coastal region of China. Highlights: Cellular automata based on discrete variables (CADV) improves soil erosion simulation.CADV simulates soil loss and rill morphological development with low relative errors.CADV improves insight into the mechanism of soil erosion in coastal area.CADV provides a new approach to improve soil erosion modelling with cellular automata. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, it was aimed to examine the probability problems which were posed based on continuous sample space and discrete sample space. Participants of the study consisted of 48 university students selected by using the convenience sampling method. Eight tasks related to continuous and discrete variables were designed by the researcher. Eight problems posed by each student in accordance with these tasks, their reflection papers and interviews conducted with them constituted the data of the study. Problems were analysed according to appropriateness, solvability, context, applicability, language and variable type dimensions. Findings revealed that most solvable problems were in real-life context, applicable and had clear language. However, most of the unsolvable problems had missing information. Inappropriate problems either were not generated depending on the task instructions or suitable variables were not used in them. Students had difficulties particularly in posing continuous sample space problems. It was concluded that problem posing was an effective practice for students since it enhanced their knowledge about probability and helped them to gain insight into problem posing. [ABSTRACT FROM AUTHOR]

A discrete variable optimization algorithm is presented based on peloton dynamics that occur during bicycle racing. Peloton dynamics are mainly attributable to the physical capacity of cyclists, energy saving by the coupling effects of drafting, and the capacity for cyclists to pass others. It also includes cooperating with other cyclists by changing positions inside the peloton, competitors' positions, and their relative energy levels. The algorithm's performance is tested on nine discrete benchmark truss structures subjected to multiple loading conditions. The performance of the peloton dynamics optimization (PDO) algorithm is compared with other optimization algorithms based on the success rate (ability to find the best solution) and computational effort (number of structural analyses required). Other metaheuristic methods generally consist of different components, and these components have several parameters that need to be set manually. As a result, it is not uncommon that these parameters need to be tuned to just the right values to obtain better results and are often specific to the problem to be solved. For the present algorithm, only two parameters are needed: the number of cyclists (solutions) and the maximum number of iterations. Results demonstrate that the performance of the PDO algorithm in terms of finding near optimum solutions and convergence behavior is comparable or better than various alternative optimization methods but with less user-specific parameter settings. [ABSTRACT FROM AUTHOR]

Modal regression is as efficient as mean regression when the random error follows normal distribution, and is robust to the presence of outliers or skewed distributions. Due to such advantages as efficiency and robustness, it has been widely applied in different fields, such as medicine, economics, environment and so on. However, most existing literature based on modal regression are assumed that the covariates are continuous, but discrete variable is very common in practice. In this paper, inspired by the idea of modal regression, an adaptive estimation procedure is proposed for nonparametric models with mixed discrete and continuous regressors. Under some mild assumptions, the limiting behavior of the proposed approach can be established, and a simply feasible estimation algorithm is provided in practice. To illustrate the performance of our proposed method, some numerical results are presented in simulation part. Finally, the proposed method is applied to analyze the GDP data of OECD and non-OECD countries. [ABSTRACT FROM AUTHOR]

The proportion of electric vehicles in vehicle manufacturing is increasing, but with the endurance mileage of the battery improved, the collision safety problem of electric vehicles is becoming more prominent. To solve a multi-objective robust design problem with discrete variables for vehicle collision design, we propose the Grey-Taguchi robust optimization method. The Grey-Taguchi method transforms the multi-objective functions into a single grey relation grade sequence instead of the signal-noises ratio used in Taguchi and selects the optimal combination of design variables predicted by the minimum design of experimental and the analysis of means. For the vehicle crashworthiness problem, the Grey-Taguchi method can converge to the Pareto front with several iterations. The mass of frame, maximum lateral intrusion, and peak deceleration after the optimization are decreased 14.5 %, 32.3 %, and 17.8 %, respectively. The Grey-Taguchi robust optimization method can not only enhance the robustness of the optimization design but also improve the lightweight design performance and crashworthiness. The proposed method is considered promising for complex engineering design problems with multi-objectives and discrete design variables. [ABSTRACT FROM AUTHOR]

Based on the theoretical basis of quantum computation, entanglement has been further explored as one of the key resources. An outstanding problem in quantum computation is the detecting of entanglement, but no closed-form algorithm exists. According to an advanced matrix rearrangement approach for detecting multipartite quantum states, we give some examples to verify the effectiveness and practicability of this new criterion. Meanwhile, this paper shows how techniques from the quantum neural network can be utilized to detect entanglement. Two models, known as the discrete-variable and the continuous-variable quantum neural network, are applied to solve the separability problem. We demonstrate the utility that such a discrete-variable quantum neural network can be trained to detect the entanglement with great exactness. And the complexity of network and computation is greatly reduced. Besides, the continuous-variable quantum neural network is applied for detecting two-qumode Gaussian state. Compared with the results of the traditional neural network, this deep quantum neural network demonstrates its capability and adaptability as well.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The eagle strategy algorithm is combined with particle swarm optimization in this paper. The new algorithm, denoted as the ES-PSO, is implemented by interfacing Etabs structural analysis codes. ES-PSO is used to optimize the RC ribbed floor system, including floor and underground garage roof. By considering the effects of reinforcement, the principle of virtual work is applied to calculate the deflections of components. Construction cost is taken as the objective function and the constraint conditions are required to satisfy. Accordingly, the optimal layout, the optimal sections of the beams and slabs and the corresponding reinforcements are obtained for different column grids. In this investigation, the RC ribbed floor system is optimized according to the Chinese standard, whose column grids are 8.4 m and 8.4 m. The performance of the ES-PSO algorithm is good enough, which can be applied to practical engineering. The paper can also provide a basis for subsequent optimization design of monolithic structures. [ABSTRACT FROM AUTHOR]

According to the equivalent mapping relation of decision variables,the constrained discrete optimization problems for gear transmission design are transformed into nonlinear constrained non- negative integer programming problems（ CNIPPs） and a discrete differential evolution（ DDE） algorithm is used to solve these problems. An index of average gene distance is introduced to evaluate quantitatively the population diversity. On this basis,this work presents an adaptive escape strategy in which an opposite- based learning operator is employed to generate new individuals to overcome the drawback that the basic DDE algorithm easily traps into local optimal regions for solving discrete optimization problems. Thus this study embeds the escape strategies in DDE algorithm,adopts feasibility rules to handle constraints,and forms to an escape DDE（ EDDE）algorithm for solving CNIPPs. The proposed EDDE algorithm is applied to approach a real case of gear transmission optimization and an index of relative comprehensive performance is presented to compare several algorithms on optimization performances. The experimental and analytical results show that this novel algorithm has good robustness and reliability and is better than compared ones in term of the comprehensive index. Furthermore,the obtained result is better than one of the published literature and the corresponding gear mass is decreased by27%.

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that plug-in bandwidths are relatively small. Several empirical examples show that the plug-in bandwidths are typically similar in magnitude to their cross-validated counterparts.

This paper is an outgrowth of previous works devoted to the application of non monotone direct search methods (DSMs) to locate the global minimum of an objective function subjected to bounds on its variables defined in the Euclidean space. This paper proves that DSMs can be easily adapted for solving models with discrete variables, as long as these variables are regularly spaced along each coordinate. Convergence is established without imposing additional conditions on the objective function, but the construction of the search directions plays a fundamental role. Moreover, function evaluations are carried out only on discrete feasible points, avoiding spurious computations on non feasible points. The paper describes a pseudo code and a preliminary code written in C, which is applied to solve a discretized version of a continuous problem with encouraging results. [ABSTRACT FROM AUTHOR]

The fireworks algorithm (FWA), a newly introduced metaheuristic algorithm, has exhibited remarkable performance in a variety of optimization problems. However, FWA still has some shortcomings that need to be addressed. In the present study, an improved fireworks algorithm (IFWA) is proposed to mitigate the drawbacks of the original algorithm. In the IFWA, the possibility of interaction among different solutions during the optimization process is provided. Moreover, a strategy is considered to decrease the computational effort of the algorithm. The IFWA is used to deal with the discrete structural optimization problems of steel trusses and frames. The results demonstrate the efficiency of the proposed IFWA compared with other well-known metaheuristics in the literature, in terms of the optimum solutions and the convergence rate. [ABSTRACT FROM AUTHOR]

Exposure-response modeling is important to optimize dose and dosing regimen in clinical drug development. The joint modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript presents the results of joint modeling of continuous and ordered categorical endpoints in the latent variable IDR modeling framework through the sharing of model parameters, with an application to the exposure-response modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose-response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The parameter-less population pyramid (P3) is a recent evolutionary computation algorithm proposed for black box optimization. Shown to be efficient for a variety of benchmark problems, P3 replaces the conventional constant population model with expanding sets of expanding populations. We investigated how this new metaheuristic optimization algorithm would transfer to optimize large-scale tower structure problems involving different constraints: geometric and mechanical. P3 is examined by optimizing two discrete tower design problems, 26-story and 35- story tower structures. The performance of P3 is compared with other well-known evolutionary algorithms for black-box optimization including random restart hill climbing, parameter-less hierarchical Bayesian optimization algorithm, differential evolution, and a modified genetic algorithm. The results show that does P3 not only finds the best final solutions, but it also reaches high quality solutions much faster than the other algorithms This fast optimization is vital for the tedious and large-scale structural engineering problems. Finally, the unique search features used in the P3 and the implications for future studies are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a probabilistic power flow (PPF) method considering continuous and discrete variables (continuous and discrete power flow, CDPF) for power systems. The proposed method—based on the cumulant method (CM) and multiple deterministic power flow (MDPF) calculations—can deal with continuous variables such as wind power generation (WPG) and loads, and discrete variables such as fuel cell generation (FCG). In this paper, continuous variables follow a normal distribution (loads) or a non-normal distribution (WPG), and discrete variables follow a binomial distribution (FCG). Through testing on IEEE 14-bus and IEEE 118-bus power systems, the proposed method (CDPF) has better accuracy compared with the CM, and higher efficiency compared with the Monte Carlo simulation method (MCSM). [ABSTRACT FROM AUTHOR]

All the linear functionalsandassociated to the-coherent pairs, have been given by Area et al. (2000). From these linear functionalsandare introduced bilinear functionals,, but only for the-coherent pairshaving a support. Five kinds of-coherent pairs given by Area et al. are concerned. They imply Charlier polynomials or Meixner polynomials, and they depend on a set of parameters. This paper is devoted to the study of the positivity ofin order to obtain Markov–Bernstein inequalities The Markov–Bernstein constantis equal towhereis the smallest zero of a polynomial of degreensatisfying a three term recurrence relation. The five kinds of three term recurrence relation are obtained. The behavior ofis studied for a part of the variation of the parameters which characterize the-coherent pairs. [ABSTRACT FROM AUTHOR]

Based on the basic principle of firefly algorithm and the corresponding improvement strategy, the topology optimization for truss structure is discussed. In order to make the algorithm be applied to discrete variable optimization problems, the original firefly locations and the location updating formula are expressed discretely. Moreover, to overcome the problem that the searching speed is slow at the beginning of the optimization, the attractiveness item is improved. Based on the above improvement strategies, the improved firefly algorithm which has faster optimization speed and better optimization effect is proposed. And then, an optimization method for the design of truss structures based on the improved firefly algorithm is proposed. In this method, the ground structure method is used to generate the initial topology, the bar sections are set as the topological variables, and the total weight is set as the optimization objective. Finally, some topology optimization examples of plane truss and spatial truss show the feasibility and efficiency of the topology optimization method. [ABSTRACT FROM AUTHOR]

The Bayesian network (BN) method has been increasingly applied in predicting earthquake-induced liquefaction. Nonetheless, the existing BN models treat all factors of liquefaction, including discrete and continuous variables, as discrete variables using different discretization approaches in the prediction of earthquake-induced liquefaction. Information loss may occur in the discretization process of the continuous variables, which reduces the predictive accuracy of the BN model. To tackle this issue, a shear wave velocity (V s) database is taken as an example in this study for developing mixed continuous-discrete BN models to improve the predictive accuracy of earthquake-induced liquefaction. First, the discrete and continuous variables are distinguished and the continuous variables are tested whether they approximately obey the Gaussian distribution. Second, discrete variables (e.g., liquefaction potential as binary variables) and continuous variables are simultaneously considered in structural modeling. Then, the conditional linear Gaussian distribution and Markov chain Monte Carlo approaches are used to construct two hybrid BN models. A 10-fold cross-validation test is used to demonstrate that the performance of the hybrid BN models is better than those of the discrete BN models or other methods such as logistic regression, artificial neural network, support vector machine, and naïve Bayes. The hybrid BN models are applied to the 2010–2011 Canterbury earthquake sequence to demonstrate their generalizability. This study finally discusses the difference in information loss and computational cost between the discrete and hybrid BN models. • A construction framework of the mixed continuous-discrete BN model is proposed. • The CLG and MCMC simulation approaches are adopted in parameter learning of the BN models. • The performance of the hybrid BN models is compared with other models for liquefaction prediction. • The information loss is compounded after multiple continuous variables are discretized. [ABSTRACT FROM AUTHOR]