Evaluation of thermodynamic simulation (FactSage) for the interpretation of the presence of phases and the firing behavior of triaxial ceramics

The present work provides an evaluation of the thermodynamic calculation, using the FactSage software, which allows the study of the interaction of a system of various oxides. It started with the examination of the crystalline phases present in the raw materials indicated by the FactSage and compared with the analysis by XRD. Subsequently, the crystalline phases and the amount of liquid phase predicted by the software at different firing temperatures were analyzed. These results were compared with experimental data and records from the literature. The FactSage showed consistent results, managing to indicate the presence of the main phases in the raw materials; however, the actual and predicted secondary phases did not always coincide. The type of phase in the firing was correctly indicated, tridymite (stable phase above 867 °C), mullite and liquid phase, but their quantity in relation to the firing temperature was quite different from the experimental data.     

Automated formal analysis of temporal properties of Ladder programs

International Journal on Software Tools for Technology Transfer - Programmable Logic Controllers are industrial digital computers used as automation controllers in manufacturing processes. The...     

A novel transfer learning approach for the classification of histological images of colorectal cancer

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation.

     

Indoor air quality assessment in dwellings with different ventilation strategies in Nunavik and impacts on bacterial and fungal microbiota

Indoor air quality is a major issue for public health, particularly in northern communities. In this extreme environment, adequate ventilation is crucial to provide a healthier indoor environment, especially in airtight dwellings. The main objective of the study is to assess the impact of ventilation systems and their optimization on microbial communities in bioaerosols and dust in 54 dwellings in Nunavik. Dwellings with three ventilation strategies (without mechanical ventilators, with heat recovery ventilators, and with energy recovery ventilators) were investigated before and after optimization of the ventilation systems. Indoor environmental conditions (temperature, relative humidity) and microbiological parameters (total bacteria, Aspergillus/Penicillium, endotoxin, and microbial biodiversity) were measured. Dust samples were collected in closed face cassettes with a polycarbonate filter using a micro-vacuum while a volume of 20 m³ of bioaerosols were collected on filters using a SASS3100 (airflow of 300 L/min). In bioaerosols, the median number of copies was 4.01 × 10³ copies/m³ of air for total bacteria and 1.45 × 10¹ copies/m³ for Aspergillus/Penicillium. Median concentrations were 5.13 × 10⁴ copies/mg of dust, 5.07 × 10¹ copies/mg, 9.98 EU/mg for total bacteria, Aspergillus/Penicillium and endotoxin concentrations, respectively. The main microorganisms were associated with human occupancy such as skin-related bacteria or yeasts, regardless of the type of ventilation.