A Boundary Construction Algorithm for a Complex Planar Point Set

It is difficult to extract the boundary of complex planar points with nonuniform distribution of point density, concave envelopes, and holes. To solve this problem, an algorithm is proposed in this paper. Based on Delaunay triangulation, the maximum boundary angle threshold is introduced as the parameter in the extraction of the rough boundary. Then, the point looseness threshold is introduced, and the fine boundary extraction is conducted for the local areas such as concave envelopes and holes. Finally, the complete boundary result of the whole point set is obtained. The effectiveness of the proposed algorithm is verified by experiments on the simulated point set and practical measured point set. The experimental results indicate that it has wider applicability and more effectiveness in engineering applications than the state-of-the-art boundary construction algorithms based on Delaunay triangulation.

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Computational Drug Repositioning aims at ranking and selecting existing drugs for use in novel diseases or existing diseases for which these drugs were not originally designed. Using vast amounts of available omic data in digital form within an in silico screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of Disease Active Subnetwork construction algorithms. DrugMerge uses differential transcriptomic data from cell lines/tissues of patients affected by the disease and differential transcriptomic data from drug perturbation assays, in the context of a large gene co-expression network. Experiments with four benchmark diseases (Asthma, Rheumatoid Arthritis, Prostate Cancer, and Colorectal Cancer) demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Our method is competitive with the state-of-the-art tools such as CMAP (Connectivity Map). Application of DrugMerge to COVID-19 data found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge is able to mimic human expert judgment

Activities in the Secondary School

With the same methodology of the previous chapter, in this chapter there is an outline of vertical path about geometric topics typical of the secondary school. Of course, the algorithm and computational aspect in the MatCos 3.X environment are more developed with respect to classical arguments, surely of interest. In particular, the presentation of conics in both the Euclidean and Cartesian plan is emphasized, based on construction algorithms by points, which can be easily implemented in the MatCos 3.X programming environment. Even solid geometry, or in three dimensions, will be characterized by effective construction algorithms of the solid figures presented. Some of these algorithms are general in nature.

Inferring Future Landscapes: Sampling the Local Optima Level

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this “super-sampling”: a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.