Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress–strain curves were fitted to Duncan–Chang hyperbolic models. An artificial neural network (NN) scheme was able to anticipate the value of the model parameters for all these PSDs, with an accuracy similar to the precision of the experiment and even when the NN was trained with a few hundred DEM simulations. The estimations were indeed more accurate than those given by multiple linear regressions (MLR) between the model parameters and common geotechnical and statistical descriptors derived from the PSD. This was achieved in spite of the presence of noise in the training data. Although the results of this massive simulation are limited to specific systems, ways of packing and testing conditions, the NN revealed the existence of hidden correlations between PSD of the macroscopic mechanical behavior.

Towards forming a socio-ecological action model for urban open spaces’ design in New Cairo, Egypt

Under the supervision of UN-Habitat, the Egyptian General Organization of Physical Planning started its first phase of “Promoting Better Quality and More Manageable Public Spaces Project, 2021” that targets enhancement and development of open spaces quality in New Cairo, Egypt. This project is functioning under three main objectives: (1) recognize the most occupied urban open spaces in New Cairo, (2) identify the required community needs in these urban open spaces, and (3) evaluate quality and suitability of these open spaces for public usage. In this paper, we are attempting to achieve the 2nd objective addressed previously by laying hands on hidden correlations among socio-ecological community needs. This is achieved in two phases; the first phase is mainly concerned with adapting thematic analytical method to tackle multiple correlations while reviewing literature, while the second phase is focusing on conducting a pilot study survey in East Academy district to validate the previously concluded socio-ecological correlations. Also findings indicate that East-Academy’s open spaces have strong correlations with multiple socio-ecological attributes and that ten urban qualities showed the highest positive measures. These correlations, in the future, can be used to establish an intervention action model.

Privacy preserving association rule hiding using border based approach

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

The physical fitness of college students can be evaluated scientifically based on the data of physical education (PE). This paper firstly relies on the Apriori algorithm to mine the hidden correlations between the physical fitness indices from the PE data on college students, and identify the indices closely associated with the physical fitness of college students. Then, the Apriori algorithm was improved to reduce the time complexity of association rule mining. Based on the improved algorithm, it was learned that the correlation coefficients of several indices surpassed the minimum support of 0.2 and minimum confidence of 0.7, reflecting their important impacts on physical fitness. Thus, physical fitness of college students is significantly influenced by speed, endurance, flexibility, and vital capacity, but not greatly affected by height and weight. The research results provide an important guide for the test and curriculum designs of PE for college students.

Computational target fishing by mining transcriptional data using a novel Siamese spectral-based graph convolutional network

Computational target fishing aims to investigate the mechanism of action or the side effects of bioactive small molecules. Unfortunately, conventional ligand-based computational methods only explore a confined chemical space, and structure-based methods are limited by the availability of crystal structures. Moreover, these methods cannot describe cellular context-dependent effects and are thus not useful for exploring the targets of drugs in specific cells. To address these challenges, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. Using a benchmark set, the model achieved impressive target inference results compared with previous methods such as Connectivity Map and ProTINA. More importantly, the powerful generalization ability of the model observed with the external LINCS phase II dataset suggests that the model is an efficient target fishing or repositioning tool for bioactive compounds.

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.