Review Article
Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science
Ke-Lin Zhao,
Qing-Xu Li*
Issue:
Volume 9, Issue 2, June 2024
Pages:
17-30
Received:
6 April 2024
Accepted:
22 April 2024
Published:
29 April 2024
Abstract: With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed.
Abstract: With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impac...
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Research Article
Qualitative Assessment of Water Sources in Pench Tiger Reserve, Maharashtra
Gajanan Khadse*,
Sandip Narnaware
Issue:
Volume 9, Issue 2, June 2024
Pages:
31-36
Received:
1 April 2024
Accepted:
24 April 2024
Published:
2 July 2024
Abstract: To ascertain the quality of water used for drinking purpose, by the consumers and wildlife, water quality monitoring of the groundwater and surface water sources was conducted in Pench Tiger Reserve area in Maharashtra. The fecal streptococci (FS) and fecal coliforms (FC) ranged from 0-5 CFU/100ml and 0-62 CFU/100ml respectively. The bacterial contamination in groundwater may be because of improper source protection and possibility of enroute contamination. Geogenic background has major impact on groundwater quality in Pench with gneisses and basaltic type of formation which reflect on the physicochemical water quality parameters. Water quality parameters like turbidity, fluoride, iron and bacterial counts are above the permissible limit in some water sources making the water unsuitable for drinking purpose. The high turbidity in the groundwater samples is attributed to the presence of iron precipitates. Study area is covered with red ferruginous soils which are rich in iron and contributes to iron content in groundwater. Appropriate treatment is required to reduce down the iron and fluoride concentrations followed by disinfection so that the water quality parameters fulfill BIS guidelines for potability of water. The correlation and regression analysis among different water quality parameters helps in establishing relationships among water quality parameters drawing inferences about them. Water quality monitoring at regular interval with suitable treatment measures is very much essential to provide safe drinking water to the consumers.
Abstract: To ascertain the quality of water used for drinking purpose, by the consumers and wildlife, water quality monitoring of the groundwater and surface water sources was conducted in Pench Tiger Reserve area in Maharashtra. The fecal streptococci (FS) and fecal coliforms (FC) ranged from 0-5 CFU/100ml and 0-62 CFU/100ml respectively. The bacterial cont...
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