Fibre reinforced mortar application for out-of-plane strengthening of schist walls

The aim of the present work is to assess the effectiveness of an innovative strengthening technique for the rehabilitation of masonry buildings deficiently prepared to resist to loading conditions typical of seismic events. This technique is based on the application of outer layers of fibre reinforced mortar (FRM) by spray technology and it is used for increasing the load carrying capacity and deformation ability of masonry elements. For this purpose three almost real scale schist walls prototypes were strengthened and tested. The experimental program is described and the relevant results are presented and discussed. For estimating the properties of the schist walls and FRM taking into account the application conditions, the tested prototypes were simulated with a FEM-based computer program that has constitutive models for the simulation of the nonlinear behaviour of these materials. By using the derived properties, a parametric study was conducted to identify the influence of the FRM properties on the performance of the proposed strengthening system.The author wish to acknowledge CiviTest, Lda (Jesufrei, Portugal) for supporting the experimental program, the sustain provided by INOTEC - Innovative material of ultra-high ductility for the rehabilitation of the built patrimony, QREN project number 23024, and the collaboration of the companies Owens Corning, Exporplas, Sika, Chryso and SECIL for providing, respectively, glass fibres, polypropylene fibres, superplasticizers, Viscous Modifier Agent, and Cement. The authors further wish to acknowledge the Erasmus Plus and Placement Mobility Programs among the University of Ferrara (Italy), the University of Minho (Portugal) and the CiviTest Lda (Portugal) which made this international cooperation possible

Machine Learning Framework for the Prediction of Alzheimer’s Disease Using Gene Expression Data Based on Efficient Gene Selection

In recent years, much research has focused on using machine learning (ML) for disease prediction based on gene expression (GE) data. However, many diseases have received considerable attention, whereas some, including Alzheimer’s disease (AD), have not, perhaps due to data shortage. The present work is intended to fill this gap by introducing a symmetric framework to predict AD from GE data, with the aim to produce the most accurate prediction using the smallest number of genes. The framework works in four stages after it receives a training dataset: pre-processing, gene selection (GS), classification, and AD prediction. The symmetry of the model is manifested in all of its stages. In the pre-processing stage gene columns in the training dataset are pre-processed identically. In the GS stage, the same user-defined filter metrics are invoked on every gene individually, and so are the same user-defined wrapper metrics. In the classification stage, a number of user-defined ML models are applied identically using the minimal set of genes selected in the preceding stage. The core of the proposed framework is a meticulous GS algorithm which we have designed to nominate eight subsets of the original set of genes provided in the training dataset. Exploring the eight subsets, the algorithm selects the best one to describe AD, and also the best ML model to predict the disease using this subset. For credible results, the framework calculates performance metrics using repeated stratified k-fold cross validation. To evaluate the framework, we used an AD dataset of 1157 cases and 39,280 genes, obtained by combining a number of smaller public datasets. The cases were split in two partitions, 1000 for training/testing, using 10-fold CV repeated 30 times, and 157 for validation. From the testing/training phase, the framework identified only 1058 genes to be the most relevant and the support vector machine (SVM) model to be the most accurate with these genes. In the final validation, we used the 157 cases that were never seen by the SVM classifier. For credible performance evaluation, we evaluated the classifier via six metrics, for which we obtained impressive values. Specifically, we obtained 0.97, 0.97, 0.98, 0.945, 0.972, and 0.975 for the sensitivity (recall), specificity, precision, kappa index, AUC, and accuracy, respectively

Morphological and Molecular Studies of Ecto- and Endoparasites Infested Chicken in Ismailia Province, Egypt

The native breed of chicken is one of the most income-producing species in the poultry sector in the Egyptian governorate of Ismailia. Thus, the objective of the current study was to identify the collected parasites using a light microscope and estimate the prevalence and seasonal dynamics of the collected helminths using the newly introduced molecular biology methods. 120 chickens out of 170 recorded (70.59%) prevalence of helminth infestation during the period from September 2021 until the end of August 2022. Four nematodes with a prevalence of 44.12 %, which were Ascaridia galli, Heterakis gallinarum, Subulura brumpti, Trichostrongylus tenuis, and four cestodes with 26.47 %, which were Raillietina tetragona, R. echinobothrida, Hymenolepis carioca, and Choanotaenia infundibulum. Eimeria spp. infestation (11.18%), which were E. tenella, E. maxima, E. mitis, and E. burnetti. Ectoparasites (15.88%) were Echidnophaga gallinacea, Lipeurus caponis, Menopon gallinae, Columbicola columbae, and Dermanyssus gallinae. The identities of the certainly recovered nematode and cestode species were confirmed by the blast test using DNA sequence data. Thus, it is advised to use the molecular approach as the primary methodology for the accurate identification of helminths, particularly in closely related species.

Cellulose nanofibers to assist the release of healing agents in epoxy coatings

Epoxy monomer and amine curing agents were immobilized on cellulose nanofibers (CNF). Obtained epoxy immobilized CNF (EiCNF) and amine curing agent immobilized CNF (AiCNF) were characterized using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The mechanism and nature of interaction in EiCNF and AiCNF were elucidated. While chemical interaction was observed between amine curing agent and CNF, only physical interaction exists between epoxy monomer and CNF. Preliminary investigation of self-healing ability of epoxy coating incorporated in both EiCNF and AiCNF was carried out. The dual healing agents supported on CNF were effective in imparting self-healing ability to epoxy coatings. 1 2017 Elsevier B.V.This paper was made possible by PDRA grant # PDRA1-1216-13014 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.Scopu

Halloysite Nanotube as Multifunctional Component in Epoxy Protective Coating

The current research explores the use of halloysite nanotube as a multifunctional filler in epoxy coating for carbon steel. Epoxy monomer loaded halloysite was incorporated into epoxy coating along with amine hardener immobilized mesoporous silica. The waterproofing, self-healing, anticorrosive abilities, and stability under weathering of the coating were evaluated. The halloysite nanotubes are able to impart better waterproofing property to the coating. The released epoxy monomer encapsulated inside the halloysite cavity upon reaction with amine curing agent immobilized in mesoporous silica recovers the damage and thereby facilitates self-healing in epoxy coating. Apart from offering healing ability to the coating, the halloysite nanotubes are able to protect the coatings for a longer period from severe weathering conditions.Scopu

A comparative study on long term stability of self-healing epoxy coating with different inorganic nanotubes as healing agent reservoirs

Self-healing epoxy coatings were prepared with different nanotubes as reservoirs for epoxy monomer (healing agent). The nanotubes selected for the current study were TiO2 nanotubes with two different tube diameter (TNT1 and TNT2) and naturally occurring hallyosite nanotubes (HNT). These self-healing coatings were subjected to accelerated weathering exposure. The weathering stability of the coatings were observed. The surface morphology, chemical changes and surface roughness were studied as a function of weathering exposure period. These studies confirmed that the long term stability of the coatings highly depend on the nanotube parameters such as nature, surface area and diameter. It was found that the photocatalytic degradation of epoxy matrix with TiO2 nanotubes was prominent in TNT1 filled coating compared with their TNT2 variant. The higher possibility of exposure of epoxy monomer encapsulated inside both HNT and TNT2 facilitated the cure reaction with UV light to create new chains during weathering