Data-driven approach of discovering organic photocatalysts and developing molecular force field by machine learning

Machine learning techniques are becoming more prevalent in chemistry research as they offer an effective approach for handling large, complex chemical datasets generated from high-throughput experiments and molecular simulations. To gain a comprehensive under- standing of datasets, it is crucial to employ efficient methods for data representation and analysis. This PhD project utilized classical machine learning algorithms to effectively visualize high-dimensional chemical data, ascertain connections between chemical struc- ture and properties, facilitate the discovery of novel organic catalysts, and developing a machine learning potential to describe intermolecular interactions

System-Characterized Artificial Intelligence Approaches for Cardiac cellular systems and Molecular Signature analysis

The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully represents an in-vitro cardiac cellular system. The generated data can be used to enhance the understanding and analysis of the system’s behavior. Additionally, a novel AI approach is formulated, which integrates deep learning and GAN techniques for Raman characterization. This approach enables efficient detection of multi-analyte mixtures by leveraging the power of deep learning algorithms and the generation of synthetic data through GANs. Overall, the integration of machine learning, generative artificial intelligence, and multi-physics modeling provides valuable insights and tools for precise prediction and efficient detection in cardiac cellular systems and molecular signature systems

PhD students´day FMST 2023

The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav

Winthrop University Undergraduate Scholarship & Creative Activity 2019

University College and Winthrop University proudly present Undergraduate Scholarship and Creative Activity 2019. This eighth annual University-wide compilation of undergraduate work chronicles the accomplishments of students and faculty mentors from at least 34 academic departments and programs, spanning all five colleges of the university: College of Arts and Sciences (CAS), College of Business Administration (CBA), College of Education (COE), College of Visual and Performing Arts (CVPA) and University College (UC).https://digitalcommons.winthrop.edu/undergradresearch_abstractbooks/1017/thumbnail.jp

2017 - The Twenty-second Annual Symposium of Student Scholars

The full program book from the Twenty-second Annual Symposium of Student Scholars, held on April 20, 2017. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1019/thumbnail.jp

Winthrop University Undergraduate Scholarship & Creative Activity 2020

University College and Winthrop University proudly present Undergraduate Scholarship and Creative Activity 2020. This ninth annual University- wide compilation of undergraduate work chronicles the accomplishments of students and faculty mentors from at least 35 academic departments and programs, spanning all five colleges of the university: College of Arts and Sciences (CAS), College of Business Administration (CBA), College of Education (COE), College of Visual and Performing Arts (CVPA) and University College (UC).https://digitalcommons.winthrop.edu/undergradresearch_abstractbooks/1018/thumbnail.jp

Critical Thinking Skills Profile of High School Students In Learning Science-Physics

This study aims to describe Critical Thinking Skills high school students in the city of Makassar. To achieve this goal, the researchers conducted an analysis of student test results of 200 people scattered in six schools in the city of Makassar. The results of the quantitative descriptive analysis of the data found that the average value of students doing the interpretation, analysis, and inference in a row by 1.53, 1.15, and 1.52. This value is still very low when compared with the maximum value that may be obtained by students, that is equal to 10.00. This shows that the critical thinking skills of high school students are still very low. One fact Competency Standards science subjects-Physics is demonstrating the ability to think logically, critically, and creatively with the guidance of teachers and demonstrate the ability to solve simple problems in daily life. In fact, according to Michael Scriven stated that the main task of education is to train students and or students to think critically because of the demands of work in the global economy, the survival of a democratic and personal decisions and decisions in an increasingly complex society needs people who can think well and make judgments good. Therefore, the need for teachers in the learning device scenario such as: driving question or problem, authentic Investigation: Science Processes