Variable Constraints Research Articles

Optimal transfer orbit design of spacecraft with finite thrust based on Legendre pseudospectral method

This article explores the application of the Legendre pseudospectral method to spacecraft orbital transfer with finite thrust optimization problem. Firstly, the model of the orbital transfer optimization control problem was established, while equations of motion were simplified based on some hypotheses. The performance was optimized to minimize the cumulative fuel consumption. The control variable was the thrust attack angle, and terminal state variable constraints included path angle, altitude, and velocity constraints. Then, the optimal control problem was transformed into a nonlinear programming problem (NLP) using the Legendre pseudospectral method. The dynamic optimization problem was transformed into a static parameter optimization problem. The state variables and control variables were selected as the optimal parameters at all collocation nodes. Lastly, the parameter optimization problem was solved using the SNOPT (Sparse Nonlinear Optimizer) software package. The SNOPT software package shows high convergence for a nonlinear programming problem. During the simulation, it was noted that the Legendre pseudospectral method is not sensitive to orbital transfer initial conditions. It was also observed that the optimal solutions of the orbital transfer optimization problem are fairly good in robustness. Therefore, the Legendre pseudospectral method is a viable approach to the spacecraft orbital transfer with a finite thrust optimization problem. The orbit optimization method proposed in this paper can also provide reference and guidance for solving other interplanetary orbital transfer optimization problems.

Modelling Bottlenecks of Bike-Sharing Travel Using the Distinction between Endogenous and Exogenous Demand: A Case Study in Beijing

This paper aims to investigate the internal mechanisms of bottlenecks in bike-sharing travel. We perform kernel density analysis to obtain analysis points and areas designated by buffer areas. Additionally, we improve the spatial lag model through Tobit regression, so as to avoid the interference of autocorrelation and to set reasonable constraints for dependent variables. The proposed model distinguishes between bike-sharing demand determined by land use and other built environmental factors, which helps to define and identify bottlenecks in bike-sharing travel. Based on a Bayesian network fault tree, we define the diagnosis mode of evidence nodes to calculate the posterior probabilities and to determine the most sensitive factors for bottlenecks. We use Beijing city as the case study. The results show that the most sensitive factors that induce bottlenecks in bike-sharing travel are few subway stations, few bus stops, few bus lines, a low density of bike lanes, and more serious home–work separation. The findings presented here can enhance the generation of bike-sharing trips in response to bike-sharing development and contribute to adjusting the urban structure and reconstructing the green infrastructure layout.

Coverage Control for UAV Swarm Communication Networks: A Distributed Learning Approach

Recently, unmanned aerial vehicle (UAV) swarm communication has drawn much attention in search and rescue (SAR) missions owing to its wide wireless coverage and increasing autonomy in navigation. In this article, we consider maximizing the downlink wireless coverage of a UAV swarm in an unknown mission area by controlling the quasistationary deployments of UAVs. Particularly, the stochastic wireless link failures caused by channel fading and noise in UAV-to-UAV communication links are considered in coverage control. Specifically, due to delay sensitivity and onboard energy limitation of UAV-enabled SAR networks, we study a distributed control strategy where swarm UAVs can address the coverage problem by exchanging only the local information. In this case, the wireless coverage problem is divided into several distributed optimization subproblems. However, due to the integer variable and nonlinear constraints, each subproblem is nonconvex and mutually coupling, which makes it difficult to solve via standard convex optimization solvers. Thus, we model the UAV swarm network as an undirected random graph and then solve the optimization subproblems by formulating a UAV swarm wireless coverage game. As per the designed utility function and potential function of the formulated game, existence of the pure Nash equilibrium is discussed and a distributed algorithm is developed to achieve the best Nash equilibrium. We analyze the convergence property and computational complexity of the proposed algorithm. Meanwhile, we analyze effects of the initial learning rate and step size on algorithm performance from both theoretical and simulation results. Simulation results show that the proposed algorithm improves the coverage by around 58% when compared with initial performance.

Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications

Up to date, the existing methods for nonlinear optimization with time-dependent parameters can be classified into two types: 1) static methods are capable of handling inequality constraints but may generate large lagging errors in the solution of the intrinsically time-dependent constrained nonlinear optimization (TDCNO) problem due to the hypothesis of short-time invariance and 2) time-variant methods, e.g., zeroing neural networks, are able to remedy the lagging error but fail to solve the TDCNO problem under inequality constraints. To resolve this contradiction, a noise-suppressing neural dynamics (NSND) model is proposed to solve the TDCNO problem subject to both equality and inequality constraints via the nonlinear complementary problem (NCP) function. The proposed method allows inequality constraints for unknown variables, removes the short-time invariance hypothesis, and further eliminates lagging errors during the solving process in the presence of noises. Besides, the rapid convergence, global stability, and noise processing of the NSND model are verified by the theoretical analyses. Simulation results of illustrative examples, including dimensionality reduction on principal component analyses (PCA) and a robot motion control, show that the NSND model outperforms the existing models for the TDCNO problem.

The role of internal constraints and stylistic congruence on a variant's social impact

AbstractIn natural conversation, multiple factors likely impact the social force of a sociolinguistic variant, yet researchers have tended to examine individual factors in isolation. This paper considers two underexamined factors together—the role of a variable's internal constraints and the role of stylistically congruent surrounding speech—to understand their combined influence on how a single variable's realization is socially interpreted. Focusing on English variable (ING), two accent rating experiments used stimuli varying the grammatical category of (ING) words and varying the stylistic congruence (natural sentences versus spliced stimuli) between (ING) realization and sentence frames. Results indicate that listeners showed sensitivity to (ING)'s internal constraints but only when the congruence between (ING)'s realization and other cues was not disrupted by using spliced stimuli. These findings suggest that internal constraints and stylistic congruence play a role in social signaling, and have methodological implications for the use of splicing.

Progress in Salicylic Acid-Dependent Signaling for Growth-Defense Trade-Off.

One grand challenge for studying plant biotic and abiotic stress responses is to optimize plant growth and plasticity under variable environmental constraints, which in the long run benefits agricultural production. However, efforts in promoting plant immunity are often accompanied by compromised morphological “syndromes” such as growth retardation, sterility, and reduced yield. Such a trade-off is dictated by complex signaling driven by secondary messengers and phytohormones. Salicylic acid (SA) is a well-known phytohormone essential for basal immunity and systemic acquired resistance. Interestingly, recent updates suggest that external environmental cues, nutrient status, developmental stages, primary metabolism, and breeding strategies attribute an additional layer of control over SA-dependent signaling, and, hence, plant performance against pathogens. In this review, these external and internal factors are summarized, focusing on their specific roles on SA biosynthesis and downstream signaling leading to immunity. A few considerations and future opportunities are highlighted to improve plant fitness with minimal growth compensation.

Body composition and grip strength constraints in elite male rink-hockey players of contrasting ethnicity

Rink hockey is a highly specialized and physiological demanding sport with sparse research regarding the game and athletes’ characteristics. A cross-sectional study was developed to characterize the body composition and grip strength of elite male rink hockey players and to establish the relationship between ethnicity on body composition and grip strength. A sample of 100 elite rink-hockey athletes aged 26.59 ± 6.02 participated in the study, comprised of 69 Caucasian male adults aged 27.58 ± 6.44 years and 31 Black African male adults aged 24.39 ± 4.27. Body composition was assessed by anthropometric measurements. Static grip strength was assessed with an adjustable dynamometer. Multiple regression analysis was applied to understand which variables constraints body fat percentage (BF) and grip strength. Body mass showed an average of 76.36 ± 9.18 kg for 175.80 ± 5.87 cm of height and BF% of 10.82 ± 5.07%. Maximal right grip strength was 50.91 ± 6.26 kg and 50.27 ± 6.23 kg for left grip strength. Four predictors accounted for 70.01% of the variance of BF%: abdominal circumference (p < 0.001), right thigh circumference (p < 0.001), right calf circumference (p = 0.001) and ethnicity (p = 0.016). Three predictors accounted for 13.1% of the variance of right grip strength: ethnicity (p = 0.013), chronological age (p = 0.024) and right distal thigh circumference (p = 0.014). Results suggest that elite rink hockey athletes have a specific anthropometric identity, which at the elite level may lead to reduced body fat and greater handgrip strength. Ethnicity seems to predict body fat and grip strength in elite rink hockey athletes.

Central-place foraging poses variable constraints year-round in a neotropical migrant

Background“Central-place foragers” are constrained in their habitat selection and foraging range by the frequency with which they need to return to a central place. For example, chick-rearing songbirds that must feed their offspring hourly might be expected to have smaller foraging ranges compared to non-breeding songbirds that return nightly to a roost.MethodsWe used GPS units to compare the foraging behaviour of an aerial insectivorous bird, the purple martin (Progne subis), during the breeding season in three regions across North America, as well as the non-breeding season in South America. Specifically, we tested foraging range size and habitat selection.ResultsForaging range did not vary among regions during breeding (14.0 ± 39.2 km2) and was larger during the nonbreeding period (8840 ± 8150 km2). Purple martins strongly preferred aquatic habitats to other available habitats year-round and in the Amazon commuted from night roosts in low productivity sediment-poor water, where risk of predation was probably low, to daytime foraging sites in productive sediment-rich water sites.ConclusionsWe provide the first estimates for foraging range size in purple martins and demonstrate foraging preference for aquatic habitats throughout two stages of the annual cycle. Understanding foraging constraints and habitat of aerial insectivores may help plan conservation actions throughout their annual cycle. Future research should quantify foraging behaviour during the post-breeding period and during migration.

Decentralized Primal-Dual Proximal Operator Algorithm for Constrained Nonsmooth Composite Optimization Problems over Networks.

In this paper, we focus on the nonsmooth composite optimization problems over networks, which consist of a smooth term and a nonsmooth term. Both equality constraints and box constraints for the decision variables are also considered. Based on the multi-agent networks, the objective problems are split into a series of agents on which the problems can be solved in a decentralized manner. By establishing the Lagrange function of the problems, the first-order optimal condition is obtained in the primal-dual domain. Then, we propose a decentralized algorithm with the proximal operators. The proposed algorithm has uncoordinated stepsizes with respect to agents or edges, where no global parameters are involved. By constructing the compact form of the algorithm with operators, we complete the convergence analysis with the fixed-point theory. With the constrained quadratic programming problem, simulations verify the effectiveness of the proposed algorithm.

Nonlinear variational inequalities with variable regular bilateral constraints in variable domains

Nonlinear variational inequalities with variable regular bilateral constraints in variable domains

OPTIMASI OPTIMAL BINNING MENGGUNAKAN ALGORITMA GENETIKA UNTUK MODEL PENILAIAN KREDIT

Credit scoring in financial institutions/banks is very important in determining whether it is feasible or not to receive borrowed funds. Therefore, credit scoring is the most important part of the company and has a great impact on the company's profits. Therefore, there are very strict regulations in the credit scoring process. This credit evaluation uses customer data collected by filling out forms, customer financial history data, and various other data sources. The Optimal Binning Algorithm is an industry-standard method used by data scientists to build credit scoring models. However, data scientists need to analyze each variable or feature individually to understand how the pattern of each variable affects the goal. When there are hundreds or even thousands of variables, it will take a lot of time, From a business perspective, it usually requires a solution that is as fast and effective as possible. To solve this problem, we propose the implementation of evolutionary algorithms to find the best solution of variable value constraints to form optimal binning. Genetic algorithms as a branch of adaptive evolutionary algorithms commonly used to solve value searches in optimization problems. Through this research, genetic algorithms can solve the problem of determining the limitations in the formation of optimal binning ariables very well. The results of the comparison between the classifier methods provide evidence that Logistic Regression can consistently have stable performance. In addition, it can maintain the performance of the predictive model despite changes in trends in the test data, while other methods get a decrease in performance results. In the Logistics Regression t test data, he received an AUC of 0.71

Power constraint waveform design based on ADMM algorithm for MIMO radar transmit beampattern

To address the problems of high sidelobe and large fluctuation amplitude of the mainside on matching the radar beampattern of multiple-input multiple-output (MIMO) radar, we proposed a transmission beampattern design method based on alternating direction method of multipliers (ADMM) under power limitation and peak-to-average ratio (PAR) constraint. The method used the Least-Most-Rule for the transmitting beamform design, i.e., the ratio of the maximum value of sidelobe to the minimum value of mainlobe as objective function. Meanwhile, the sum of power over the mainside and sidelobe is constrained to match the desired pattern. Then, the log-sum-exp function and Taylor's formula are used to approximate the optimization problem to produce a fourth-order polynomial on the beamform vector; finally, we introduce a dual variable and equation constraints and apply ADMM method to convert the fourth-order polynomial problem into the second-order polynomial problem for solution. In addition, the computational complexity of the proposed method is also analyzed. Two sets of experiments verify the effectiveness of the method in the simulation part.

DN/dS-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution.

One of the most popular measures in the analysis of protein sequence evolution is the ratio of nonsynonymous distance (dN) to synonymous distance (dS). Under the assumption that synonymous substitutions in the coding region are selectively neutral, the dN/dS ratio can be used to statistically detect the adaptive evolution (or purifying selection) if dN/dS > 1 (or dN/dS < 1) significantly. However, due to strong structural constraints and/or variable functional constraints imposed on amino acid sites, most encoding genes in most species have demonstrated dN/dS < 1. Consequently, the statistical power for testing dN/dS = 1 may be insufficient to distinguish between different selection modes. In this paper, we propose a more powerful test, called dN/dS-H, in which a new parameter H, a relative measure of rate variation among sites, was introduced. Given the condition of strong purifying selections at some sites, the dN/dS-H model predicts dN/dS = 1-H for neutral evolution, dN/dS < 1-H for nearly neutral selection, and dN/dS > 1-H for adaptive evolution. The potential of this new method for resolving the neutral-adaptive debates is illustrated by the protein sequence evolution in vertebrates, Drosophila and yeasts, as well as somatic cancer evolution (specialized as the CN/CS-H test).

KMOEA: A Knowledge-Based Multiobjective Algorithm for Distributed Hybrid Flow Shop in a Prefabricated System

In this article, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a 3-D vector, where the factory assignment, machine assignment, operation scheduling, and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global, and local search procedures. Next, an efficient initialization approach is presented, which is embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm.

Intuitive Remote Robotic Nasal Sampling by Orientation Control With Variable RCM in Limited Space

When performing polymerase chain reaction testing, collecting nasopharyngeal swabs from patients is necessary. Hence, the development of a noncontact specimen collection system is desired to avoid the risk of secondary infection from direct contact between the medical personnel and patients. This study aims to develop a master-follower-type remote specimen collection system to achieve infection protection through adequate isolation. A teleoperated robot is used for specimen collection work instead of a human; the teleoperated robot moves remotely, and a cotton swab is grasped and inserted into the nostril by the robot. A remote center of motion (RCM) mechanism was implemented to prevent the cotton swabs from making excessive contact with the nostril during specimen collection. Further, a variable RCM constraint that combines safety and operability is proposed as the conventional RCM control modification. The evaluation results indicated that the variable RCM restraint allowed a quicker and safer collection of specimens than the conventional RCM constraint.

Optimization of wheat flour production processes in a flour mill industry using linear programming

This study applied linear programming method as an optimization package to analyze the operational processes of wheat flour mill industry, with functional models developed using defined variables constraints of wheat flour x_1, baking flour x_2 and semolina x_3 for objective functions. Furthermore, a proficient operational process of wheat flour mill industry based on raw materials requirements and production cost using linear programming method with MATLAB application to optimize the operational process of wheat flour production plant by maximizing raw material resources and minimizing production cost using a modified milling process flow chart for enhanced products yield from the milling of the whole wheat grains, and a monthly analysis of the available wheat resource for production and machine hours operated during each month. The results after computing the average supply of wheat and available machine hours as variables and modelled into linear programming equations for optimization showed increase in semolina production, reduction in wheat flour production and reduction in production cost of baking flour respectively. In addition, the profit generated showed a significant increase in the months of December at ₦164,850.00 (highest) when the machine hours and wheat available were at its peak. Thus, baking flour was found to be the least profitable among the three products monthly and recommendations suggested a reduction in its production cost to enable profitability in its production

Parallel solution of optimal control problems using the graphics processing unit

AbstractThis paper presents an indirect method for solving optimal control problems (OCPs) using graphics processing unit (GPU). The OCPs considered here include control variable inequality constraints (CVICs), state variable inequality constraints (SVICs), and parameters. The necessary conditions of the minimum for the OCPs are written as a boundary value problem with index‐1 differential algebraic equations (BVP‐DAEs). The complementarity conditions associated with those inequality constraints are approximated using Kanzow's smoothed Fisher‐Burmeister formula. The numerical solution for solving the BVP‐DAEs is based on the multiple shooting technique and the DAEs are solved using a single step linearly implicit Runge–Kutta (Rosenbrock–Wanner, ROW) method. A Newton's continuation method is performed to solve the BVP‐DAEs system and the descent direction is found by solving a sparse bordered almost block diagonal (BABD) linear system with a structured orthogonal factorization algorithm. Parallel computing techniques are used to accelerate the code which is implemented using Python and CUDA on GPU. Numerical examples are presented to illustrate the efficiency of the code. The GPU based parallel implementation is shown to be significantly faster than the implementation using central processing unit (CPU) alone.

Post-Communist Junctures, the Left, and Illiberalism: Theory with Evidence from Central and Eastern Europe

While many post-communist European party systems have experienced the rise of anti-establishment parties, not all have been equally vulnerable to challengers attacking both cultural and economic liberalism. This article borrows insights from scholarship on Latin American politics and develops a critical juncture approach to understanding the linkages between economic liberalization and political illiberalism in 15 post-communist democracies during the 1990-2020 period. It argues that contingent political dynamics during post-communist junctures shaped ensuing legacies by conditioning the long-term programmatic positions and variable electoral performance of “traditional” leftist parties. This path-dependent process culminated in variable electoral viability of illiberal parties with incentives for ideological differentiation and organizational capacities to adopt those economic positions that were most likely to be electorally rewarded. By focusing on variable incentives, resources, and constraints, the article challenges previous scholarship and advances and tests a novel theoretical framework based on a probabilistic approach to critical juncture analysis.

Efficient Knowledge Compilation Beyond Weighted Model Counting

AbstractQuantitative extensions of logic programming often require the solution of so called second level inference tasks, that is, problems that involve a third operation, such as maximization or normalization, on top of addition and multiplication, and thus go beyond the well-known weighted or algebraic model counting setting of probabilistic logic programming under the distribution semantics. We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems. As 2AMC is to (algebraic) model counting what forall-exists-SAT is to propositional satisfiability, it is notoriously hard to solve. First level techniques based on Knowledge Compilation (KC) have been adapted for specific 2AMC instances by imposing variable order constraints on the resulting circuit. However, those constraints can severely increase the circuit size and thus decrease the efficiency of such approaches. We show that we can exploit the logical structure of a 2AMC problem to omit parts of these constraints, thus limiting the negative effect. Furthermore, we introduce and implement a strategy to generate a sufficient set of constraints statically, with a priori guarantees for the performance of KC. Our empirical evaluation on several benchmarks and tasks confirms that our theoretical results can translate into more efficient solving in practice.

New Solutions of Nonlinear Equations Constrained by Bounded Variables under Edge Computation

Aiming at the problem of low global convergence and local convergence rate of trust region interior points of bounded variable constrained nonlinear equations, a trust region interior point algorithm for bounded variable constrained nonlinear equations under edge calculation is designed. By constructing the basic function form of nonlinear equations constrained by bounded variables, the boundary of nonlinear equations is determined by Gauss Newton iterative process to ensure the global convergence of changes; solve the original objective function, analyze the trust region subproblem of the unconstrained optimization problem, and generate an acceptable region. The region is generated through two-dimensional example interpretation. The interior points in the acceptable region are determined by the primal dual interior point method, and the interior points in the acceptable region are optimized. With the help of edge calculation, the trust region interior point programming model of bounded variable constrained nonlinear equations is designed to realize the algorithm design. The experimental results show that the designed algorithm can improve the trust region interior point global convergence and local convergence rate of nonlinear equations with bounded variable constraints.