Set Of Solutions Research Articles

ATM-R: An Adaptive Tradeoff Model With Reference Points for Constrained Multiobjective Evolutionary Optimization.

The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and well-distributed feasible solutions. To achieve this goal, a delicate tradeoff must be struck among feasibility, diversity, and convergence. However, balancing these three elements simultaneously through a single tradeoff model is nontrivial, mainly because the significance of each element varies in different evolutionary phases. As an alternative approach, we adapt distinct tradeoff models in various phases and introduce a novel algorithm named adaptive tradeoff model with reference points (ATM-R). In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from "the tradeoff between feasibility and diversity" to "the tradeoff between diversity and convergence." This transition is instrumental in discovering an adequate number of feasible regions and accelerating the search for feasible Pareto optima in succession. In the feasible phase, ATM-R places an emphasis on balancing diversity and convergence to obtain a set of feasible solutions that are both well-converged and well-distributed. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a diverse set of benchmark test functions and real-world problems demonstrate that ATM-R is effective. When compared to eight state-of-the-art constrained multiobjective optimization evolutionary algorithms, ATM-R consistently demonstrates its competitive performance.

Dyeing of Mixed Cotton and Polyester Fabrics with New Dyes—Complexes of Collagen with Transition Metal Ions

Uniform, stable, one-step dyeing of textile fabrics made from a mixture of natural and synthetic fibers using traditional methods remains problematic to this day. As an alternative to traditional methods, a one-step method for dyeing mixed cotton and polyester fabrics with a new natural dye—a complex of collagen and [Formula: see text] and [Formula: see text] ions—was proposed for the first time. Dyeing of pre-prepared cotton, polyester, and cotton–polyester fabrics is carried out by dipping in an aqueous dye solution, squeezing, drying, and heat setting. The influence of the mass ratio of dye components, fixation temperature, and pH of the solution on the degree of dye fixation was determined. A degree of sorption of dyes (14–25%) from the solution onto the surface of fabrics was established, which after heat fixation decreases slightly (1–3%). During washing processes, the copper complex is almost completely washed out from polyester and blended fabrics and remains in small quantities (2.4–3.8%) on the surface of cotton fabrics. After water washing, the degree of fixation of the collagen–chrome complex in fabrics is as follows: in cotton—8–10%, in polyester—10–12%, and in cotton–polyester—8–9%. The color coordinates of cotton and cotton–polyester fabrics hardly change before and after washing, but for polyester fabric, the changes are significant. The mechanism of interaction of the complex dye with the fibers was studied using the Fourier transform infrared method, and the morphology and distribution of the dye on the surface of the samples using the SEM-EDS method. The dye binds to cellulose through ionic and coordination bonds, and to polyester through joint melting. Therefore, unlike pure polyester fabric, cotton and cotton–polyester fabrics demonstrated high color fastness to washing and light fastness. The use of production waste, the exclusion of synthetic dyes and harmful chemicals, and single-stage dyeing of the material reduce the environmental burden.

Technology Use for Home-Based Stroke Rehabilitation in Switzerland From the Perspectives of Persons Living With Stroke, Informal Caregivers, and Therapists: Qualitative Interview and Focus Group Study.

Stroke is a leading cause for long-term disability, requiring both inpatient and outpatient rehabilitation and self-training in the home environment. Technology-based tools are gradually gaining acceptance as additional and suitable options for extending the rehabilitation process. While the experiences of persons living with stroke, therapists, and informal caregivers with respect to technology use have already been investigated in other countries, this topic is underexplored in the Swiss context. We aimed to explore the experiences and needs of persons living with stroke, informal caregivers, and therapists in using technology-based tools in a home environment for stroke rehabilitation in Switzerland. This study followed a qualitative descriptive methodology, including semistructured interviews and focus group discussions. We applied a deductive template analysis alongside the accessibility, adaptability, accountability, and engagement framework to analyze the qualitative data sets for technology-assisted solutions for poststroke rehabilitation. We collected the experiences and needs of persons living with stroke (7/23, 30%), informal caregivers (4/23, 17%), and therapists (occupational and physical therapists; 12/23, 52%). The 4 categories we used to organize the analysis and results were accessibility to quality rehabilitation, adaptability to patient differences, accountability or compliance with rehabilitation, and engagement with rehabilitation. Persons living with stroke stated that they use various tools within their rehabilitation process depending on their specific needs. They felt that there is a plethora of tools available but sometimes felt overwhelmed with the selection process. Informal caregivers indicated that they generally felt underserved and insufficiently informed throughout the rehabilitation process. They reported that they use technology-based tools to support their relatives affected by stroke in becoming more independent. Therapists appreciate the numerous possible applications of technology-based tools in rehabilitation. At the same time, however, they express dissatisfaction with the lack of clarity in Switzerland regarding cost coverage, recommendations, and training opportunities. Persons living with stroke, informal caregivers, and therapists in Switzerland reported varied and unique experiences and needs with the use of technology-based tools in outpatient stroke rehabilitation. Written recommendations, the assumption of financial costs, and the provision of information and education could foster increased confidence in the use of technology-based tools for patients and therapists.

Multi-Objective Optimization Methods for Train Load Planning in Seaport Container Terminals

In this paper we study the planning problem of loading trains in seaport container terminals, which consists in assigning containers to the wagon slots. The train load can be realized considering different objectives, in this work we focus on two very important objectives for terminal managers, that are the minimization of the number of reshuffles in the storage area and the minimization of the total distance covered by the handling equipment between the storage area and the wagons. To address this problem, some multi-objective methods are investigated in the paper, based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint approach and the lexicographic algorithm, allowing to find different sets of efficient solutions to be provided to train planners. The experimental tests, devised to compare the proposed multi-objective methods, show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint method integrated with a lexicographic approach is the most efficient choice since it allows to obtain a higher number of efficient solutions in shorter computational times. This method can be applied in decision support systems for train planners in order to allow them to decide the train load in an efficient way. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The automation and the process optimization in seaport container terminals is a relevant issue for terminal managers in order to face the strong competitiveness in the logistics and shipping sector and the new emerging problems due to the presence of mega vessels. Among the different operations to be optimally managed in a terminal, the loading of trains is becoming more and more significant, also due to the increase of freight traffic by rail, seen as a cheaper and more sustainable option compared with road transport. The optimal assignment of containers present in a storage area to the wagon slots is a crucial decision in order to reduce operational times and costs in a container terminal. In this paper, we address the train load planning problem considering two important objectives to be minimized, i.e. the number of reshuffles and the distance covered by the handling equipment. The multi-objective methods proposed in the paper are devised to find different efficient solutions in short computational times. The proposed algorithms are intended to be implemented within a decision support tool in which the solutions found are shown to the train planner who chooses the preferred option depending on personal evaluations and on the basis of information coming in real time.

Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity

Reversed-phase (RP) liquid chromatography is an important tool for the characterization of materials and products in the pharmaceutical industry. Method development is still challenging in this application space, particularly when dealing with closely-related compounds. Models of chromatographic selectivity are useful for predicting which columns out of the hundreds that are available are likely to have very similar, or different, selectivity for the application at hand. The hydrophobic subtraction model (HSM1) has been widely employed for this purpose; the column database for this model currently stands at 750 columns. In previous work we explored a refinement of the original HSM1 (HSM2) and found that increasing the size of the dataset used to train the model dramatically reduced the number of gross errors in predictions of selectivity made using the model. In this paper we describe further work in this direction (HSM3), this time based on a much larger solute set (1014 solute/stationary phase combinations) containing selectivities for compounds covering a broader range of physicochemical properties compared to HSM1. The molecular weight range was doubled, and the range of the logarithm of the octanol/water partition coefficients was increased slightly. The number of active pharmaceutical ingredients and related synthetic intermediates and impurities was increased from four to 28, and ten pairs of closely related structures (e.g., geometric and cis-/trans- isomers) were included. The HSM3 model is based on retention measurements for 75 compounds using 13 RP stationary phases and a mobile phase of 40/60 acetonitrile/25 mM ammonium formate buffer at pH 3.2. This data-driven model produced predictions of ln α (chromatographic selectivity using ethylbenzene as the reference compound) with average absolute errors of approximately 0.033, which corresponds to errors in α of about 3 %. In some cases, the prediction of the trans-/cis- selectivities for positional and geometric isomers was relatively accurate, and the driving forces for the observed selectivity could be inferred by examination of the relative magnitudes of the terms in the HSM3 model. For some geometric isomer pairs the interactions mainly responsible for the observed selectivities could not be rationalized due to large uncertainties for particular terms in the model. This suggests that more work is needed in the future to explore other HSM-type models and continue expanding the training dataset in order to continue improving the predictive accuracy of these models. Additionally, we release with this paper a much larger data set (43,329 total retention measurements) at multiple mobile phase compositions, to enable other researchers to pursue their own lines of inquiry related to RP selectivity.

Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand.

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The Q -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

Design of Na3MnZr(PO4)3/Carbon Nanofiber Free-Standing Cathodes for Sodium-Ion Batteries with Enhanced Electrochemical Performances through Different Electrospinning Approaches.

The NASICON-structured Na3MnZr(PO4)3 compound is a promising high-voltage cathode material for sodium-ion batteries (SIBs). In this study, an easy and scalable electrospinning approach was used to synthesize self-standing cathodes based on Na3MnZr(PO4)3 loaded into carbon nanofibers (CNFs). Different strategies were applied to load the active material. All the employed characterization techniques (X-ray powder diffraction (XRPD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), thermal gravimetric analysis (TGA), and Raman spectroscopy) confirmed the successful loading. Compared to an appositely prepared tape-cast electrode, Na3MnZr(PO4)3/CNF self-standing cathodes demonstrated an enhanced specific capacity, especially at high C-rates, thanks to the porous conducive carbon nanofiber matrix. Among the strategies applied to load Na3MnZr(PO4)3 into the CNFs, the electrospinning (vertical setting) of the polymeric solution containing pre-synthesized Na3MnZr(PO4)3 powders resulted effective in obtaining the quantitative loading of the active material and a homogeneous distribution through the sheet thickness. Notably, Na3MnZr(PO4)3 aggregates connected to the CNFs, covered their surface, and were also embedded, as demonstrated by TEM and EDS. Compared to the self-standing cathodes prepared with the horizontal setting or dip-drop coating methods, the vertical binder-free electrode exhibited the highest capacity values of 78.2, 55.7, 38.8, 22.2, 16.2, 12.8, 10.3, 9.0, and 8.5 mAh/g at C-rates of 0.05C, 0.1C, 0.2C, 0.5C, 1C, 2C, 5C, 10C, and 20C, respectively, with complete capacity retention at the end of the measurements. It also exhibited a good cycling life, compared to its tape-cast counterpart: it displayed higher capacity retention at 0.2C and 1C, and, after cycling 1000 cycles at 1C, it could be further cycled at 5C, 10C, and 20C.

Constructing a MOEA approach for product form Kansei design based on text mining and BPNN

Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design.

Propagation of anisotropic Gabor singularities for Schrödinger type equations

We show results on propagation of anisotropic Gabor wave front sets for solutions to a class of evolution equations of Schrödinger type. The Hamiltonian is assumed to have a real-valued principal symbol with the anisotropic homogeneity a(λx,λσξ)=λ1+σa(x,ξ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$a(\\lambda x, \\lambda ^\\sigma \\xi ) = \\lambda ^{1+\\sigma } a(x,\\xi )$$\\end{document} for λ>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda > 0$$\\end{document} where σ>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma > 0$$\\end{document} is a rational anisotropy parameter. We prove that the propagator is continuous on anisotropic Shubin–Sobolev spaces. The main result says that the propagation of the anisotropic Gabor wave front set follows the Hamilton flow of the principal symbol.

Three-stage multi-modal multi-objective differential evolution algorithm for vehicle routing problem with time windows

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

Identification of Real and Complex Solution Varieties and Their Singularities Defined by Loop Constraints of Linkages Using the Kinematic Tangent Cone

Abstract The configuration space (c-space) of a mechanism is the real-solution variety of a set of loop closure constraints, which is therefore the chief object in kinematic analysis. Singularities of this variety (referred to as c-space singularities) are singular configurations of the mechanism. In addition, a mechanism may exhibit other kinematic singularities that are not visible from the differential geometry of the c-space (referred to as hidden singularities). Such situations were analyzed by investigating the local geometry of the c-space and its corank stratification. It has been shown recently that hidden singularities and shakiness can be attributed to the fact that complex solution branches intersect with the c-space, i.e., with real-solution branches. This paper employs the kinematic tangent cone to identify local solution branches. While the kinematic tangent cone is an established generally applicable concept, which gives rise to a computational (numeric and symbolic) algorithm, it has yet only been applied to analyzing the real-solution set. Application of the method is shown for several examples. Further, the algebraic aspects are briefly elaborated.

Regret Analysis of Repeated Delegated Choice

We present a study on a repeated delegated choice problem, which is the first to consider an online learning variant of Kleinberg and Kleinberg, EC'18. In this model, a principal interacts repeatedly with an agent who possesses an exogenous set of solutions to search for efficient ones. Each solution can yield varying utility for both the principal and the agent, and the agent may propose a solution to maximize its own utility in a selfish manner. To mitigate this behavior, the principal announces an eligible set which screens out a certain set of solutions. The principal, however, does not have any information on the distribution of solutions nor the number of solutions in advance. Therefore, the principal dynamically announces various eligible sets to efficiently learn the distribution. The principal's objective is to minimize cumulative regret compared to the optimal eligible set in hindsight. We explore two dimensions of the problem setup, whether the agent behaves myopically or strategizes across the rounds, and whether the solutions yield deterministic or stochastic utility. We obtain sublinear regret upper bounds in various regimes, and derive corresponding lower bounds which implies the tightness of the results. Overall, we bridge a well-known problem in economics to the evolving area of online learning, and present a comprehensive study in this problem.

Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization

Optimizing multiple conflicting black-box objectives simultaneously is a prevalent occurrence in many real-world applications, such as neural architecture search, and machine learning. These problems are known as expensive multi-objective optimization problems (EMOPs) when the function evaluations are computationally or financially costly. Multi-objective Bayesian optimization (MOBO) offers an efficient approach to discovering a set of Pareto optimal solutions. However, the data deficiency issue caused by limited function evaluations has posed a great challenge to current optimization methods. Moreover, most current methods tend to prioritize the quality of candidate solutions, while ignoring the quantity of promising samples. In order to tackle these issues, our paper proposes a novel multi-objective Bayesian optimization algorithm with a data augmentation strategy that provides ample high-quality samples for Pareto set learning (PSL). Specifically, it utilizes Generative Adversarial Networks (GANs) to enrich data and a dominance prediction model to screen out high-quality samples, mitigating the predicament of limited function evaluations in EMOPs. Additionally, we adopt the regularity model to expensive multi-objective Bayesian optimization for PSL. Experimental results on both synthetic and real-world problems demonstrate that our algorithm outperforms several state-of-the-art and classical algorithms.

An integrated model of supply chain resilience considering supply and demand uncertainties

AbstractThe complexity of the global supply chain has increased dramatically over the past few decades as a result of uncertainty caused by various factors. This paper studies the optimal strategy for supply chain resilience models considering supply disruptions and demand fluctuations. We present two‐stage stochastic programming models based on different scenarios, including a risk‐neutral model that considers the expected total cost, a risk‐averse model that considers the conditional value‐at‐risk measure, and a responsiveness model that considers the service level. We also propose multiobjective mathematical programming that considers all three models simultaneously and suggests the solution approach. Finally, we present the results of computational experiments and demonstrate how to cope with uncertainty through flexibility and redundancy. We offer a set of nondominated solutions from the multiobjective model and derive managerial insights, which suggest a decision‐making strategy between the disruption risk, expected total cost, and service level.

Optimal Control Related to Weak Solutions of a Chemotaxis-Consumption Model

In the present work we investigate an optimal control problem related to the following chemotaxis-consumption model in a bounded domain Ω⊂R3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega \\subset {\\mathbb {R}}^3$$\\end{document}: ∂tu-Δu=-∇·(u∇v),∂tv-Δv=-usv+fv1Ωc,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} \\partial _t u - \\Delta u = - \ abla \\cdot (u \ abla v), \\quad \\partial _t v - \\Delta v = - u^s v + f \\,v\\, 1_{\\Omega _c}, \\end{aligned}$$\\end{document}with s≥1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$s \\ge 1$$\\end{document}, endowed with isolated boundary conditions and initial conditions for (u, v), being u the cell density, v the chemical concentration and f the control acting in the v-equation through the bilinear term fv1Ωc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f \\,v\\, 1_{\\Omega _c}$$\\end{document}, in a subdomain Ωc⊂Ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _c \\subset \\Omega $$\\end{document}. We address the existence of optimal control restricted to a weak solution setting, where, in particular, uniqueness of state (u, v) given a control f is not clear. Then by considering weak solutions satisfying an adequate energy inequality, we prove the existence of optimal control subject to uniformly bounded controls. Finally, we discuss the relation between the considered control problem and two other related ones, where the existence of optimal solution can not be proved.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound’s aggregation propensity.Graphical

Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision

In the 21st century, the software industry has achieved great development. The development complexity and volume of software projects are also continuously increasing. The design of software engineering supervision network plans is becoming increasingly important. In response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms, the Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of multi-objective particle swarm optimization algorithm are improved to enhance the convergence and distribution of the algorithm. Traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. A multi-objective model based on resources, duration, cost, and quality is established for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions are consistent with the actual theoretical Pareto frontier curves. The proposed method is applied to engineering project examples. 10 solutions that meet the schedule requirements are obtained. Most engineering projects have a quality of over 80%, which verifies the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model has the ability to consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential.

Parametric Semidefinite Programming: Geometry of the Trajectory of Solutions

In many applications, solutions of convex optimization problems are updated on-line, as functions of time. In this paper, we consider parametric semidefinite programs, which are linear optimization problems in the semidefinite cone whose coefficients (input data) depend on a time parameter. We are interested in the geometry of the solution (output data) trajectory, defined as the set of solutions depending on the parameter. We propose an exhaustive description of the geometry of the solution trajectory. As our main result, we show that only six distinct behaviors can be observed at a neighborhood of a given point along the solution trajectory. Each possible behavior is then illustrated by an example. Funding: This work was supported by OP RDE [Grant CZ.02.1.01/0.0/0.0/16_019/0000765].

Investigation of soliton solutions to the Peyrard-Bishop-Deoxyribo-Nucleic-Acid dynamic model with beta-derivative

This study purposes to extract some fractional analytical solutions of the Peyrard-Bishop-Deoxyribo-Nucleic-Acid ([Formula: see text]-PBDNA) dynamic model with the beta-derivative by the unified Riccati equation expansion method (UREEM). Furthermore, we examine the role of various parameters of the fractional model on the soliton dynamic. The research focuses on computational biophysics and materials science, examining the impact of various parameters on the fractional model. This paper contributes to understanding soliton solutions and the [Formula: see text]-PBDNA dynamic model, demonstrating the applicability of the UREEM method to various fractional models. Some soliton solutions of the model are successfully generated by applying the UREEM. Implementing the UREEM, we take a fractional wave transformation to convert the model into a nonlinear ordinary differential equation. So, a linear equation system is generated. After the system is solved, the soliton solutions are gained for the appropriate solution sets. Finally, 3D, 2D and contour graphs of diverse solutions are depicted at suitable values of parameters. In addition, this paper presents 3D, 2D and contour graphs of various solutions with suitable parameter values. The results are beneficial for interpreting the model in future work and confirm that UREEM is effectively applicable to diverse fractional models, coupled with a comprehensive graphical analysis of how different parameters influence these solutions.

Study on flexible job shop scheduling problem considering energy saving

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation.