Path planning with the derivative of heuristic angle based on the GBFS algorithm.

Robots used in extreme environments need a high reactivity on their scene. For fast response, they need the ability to find the optimal path in a short time. In order to achieve this goal, this study introduces WA*DH+, an improved version of WA*DH (weighted A* with the derivative of heuristic angle). In some path planning scenes, WA*DH cannot find suboptimal nodes with the small inflation factor called the critical value due to its filtering method. It is hard to develop a new filtering method, so this study inflated the suboptimality of the initial solution instead. Critical values vary in every path planning scene, so increasing the inflation factor for the initial solution will not be the solution to our problem. Thus, WA*DH + uses the GBFS algorithm with the infinitely bounded suboptimal solution for its initial solution. Simulation results demonstrate that WA*DH + can return a better solution faster than WA*DH by finding suboptimal nodes in the given environment.

Fine-Tuning of Nonlinear Optical Contrasts of Hexaphyrin-Based Molecular Switches Using Inverse Design.

In the search for new nonlinear optical (NLO) switching devices, expanded porphyrins have emerged as ideal candidates thanks to their tunable chemical and photophysical properties. Introducing meso-substituents to these macrocycles is a successful strategy to enhance the NLO contrasts. Despite its potential, the influence of meso-substitution on their structural and geometrical properties has been scarcely investigated. In this work, we pursue to grasp the underlying pivotal concepts for the fine-tuning of the NLO contrasts of hexaphyrin-based molecular switches, with a particular focus on the first hyperpolarizability related to the hyper-Rayleigh scattering (ß HRS ). Building further on these concepts, we also aim to develop a rational design protocol. Starting from the (un)substituted hexaphyrins with various π-conjugation topologies and redox states, structure-property relationships are established linking aromaticity, photophysical properties and ß HRS responses. Ultimately, inverse molecular design using the best-first search algorithm is applied on the most favorable switches with the aim to further explore the combinatorial chemical compound space of meso-substituted hexaphyrins in search of high-contrast NLO switches. Two definitions of the figure-of-merit of the switch performance were used as target objectives in the optimization problem. Several meso-substitution patterns and their underlying characteristics are identified, uncovering molecular symmetry and the electronic nature of the substituents as the key players for fine-tuning the ß HRS values and NLO contrasts of hexaphyrin-based switches.

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach.

In the past few years, classification has undergone some major evolution. With a constant surge of the amount of data gathered from different sources, efficient processing and analysis of data is becoming difficult. Due to the uneven distribution of data among classes, data classification with machine-learning techniques has become more tedious. While most algorithms focus on major data samples, they ignore the minor class data. Thus, the data-skewing issue is one of the critical problems that need attention of researchers. The paper stresses upon data preprocessing using sampling techniques to overcome the data-skewing problem. Here, three different sampling techniques such as Resampling, SpreadSubSampling, and SMOTE are implemented to reduce this uneven data distribution issue and classified with the K-nearest neighbor algorithm. The performance of classification is evaluated with various performance metrics to determine the efficiency of classification.

Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique.

(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification. Thus, some scholars believe that how to select the informative genes for downstream classification is more important than how to impute MVs. However, most feature-selection (FS) algorithms need beforehand imputation, and the impact of beforehand MV imputation on downstream FS performance is seldom considered. (2) Method: A modified chi-square test-based FS is introduced for gene-expression data. To deal with the challenge of a small sample size of gene-expression data, a heuristic method called recursive element aggregation is proposed in this study. Our approach can directly handle incomplete data without any imputation methods or missing-data assumptions. The most informative genes can be selected through a threshold. After that, the best-first search strategy is utilized to find optimal feature subsets for classification. (3) Results: We compare our method with several FS algorithms. Evaluation is performed on twelve original incomplete cancer gene-expression datasets. We demonstrate that MV imputation on an incomplete dataset impacts subsequent FS in terms of classification tasks. Through directly conducting FS on incomplete data, our method can avoid potential disturbances on subsequent FS procedures caused by MV imputation. An experiment on small, round blue cell tumor (SRBCT) dataset showed that our method found additional genes besides many common genes with the two compared existing methods.

Molecular Property Optimizations with Boundary Conditions through the Best First Search Scheme.

There is an increasing interest in applying quantum chemistry to rationally design novel compounds with some desired characteristics. Furthermore, many applications require more than one property to be optimal. In this Concept, several inverse design strategies, based on the discrete best first search scheme, are introduced that allow for the simultaneous optimization of multiple properties or the optimization of the most vital target property with constraints for secondary properties. A detailed assessment of the different optimization techniques is carried out, and special attention is paid to improve the cost efficacy and performance by tuning the process parameters. Our suggested protocol allows for a more successful optimization routine when additional boundary conditions are desired.