Assessing and predicting the students’ systems thinking preference: multi-criteria decision making and machine learning

The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an approach that helps individuals better understand and effectively solve modern complex systems problems by encouraging holistic thinking. Systems thinking consists of two approaches holistic and reductionist views. This dissertation aims to study college engineering and non-engineering students’ preference for holistic thinking versus reductionist thinking, their ranking to the systems thinking dimensions, and whether this preference varies depending on demographics and general factors. Additionally, this study investigates the possibility of predicting the students’ preference for holistic thinking. The study uses the multi-criteria decision-making method, the Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process to determine the student’s preferences, and uses statistical analysis such as independent sample t-test and ANOVA to evaluate the factors. Also, the study uses machine learning classification models such as Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Trees, voting classifiers, Bagging, and Random Forest to predict and evaluate the most predicting model. The results of the dissertation conclude that overall students prefer the reductionist approach and report the students’ preference towards dimensions of complexity, independence, uncertainty, systems worldview, and flexibility and the ranking difference based on some factors. Lastly, the results show that the students’ preference for holistic thinking can be predicted with a 77% accuracy using the Random Forest classifier

Disentangling coastal groundwater level dynamics on a global data set

This study aims to identify common hydrogeological patterns and to gain a deeper understanding of the underlying similarities and their link to physiographic, climatic, and anthropogenic controls of coastal groundwater. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8,000 groundwater hydrographs, and pattern recognition, classification, and explanation using machine learning techniques and SHapley Additive exPlanations (SHAP). Overall, four different GWL dynamics patterns emerge, independent of the different seasons, time series lengths, and periods. We show in this study that similar GWL dynamics can be observed around the world with different combinations of site characteristics, but also that the main factors differentiating these patterns can be identified. Three of the identified patterns exhibit high short-term and interannual variability and are most common in regions with low terrain elevation and shallow groundwater depth. Climate and soil characteristics are most important in differentiating these patterns. This study provides new insights into the hydrogeological behavior of groundwater in coastal regions and guides systematic and holistic groundwater monitoring and modelling, motivating to consider various aspects of GWL dynamics when, for example, estimating climate-driven GWL changes &ndash; especially when information on potential controls is limited.</p

Protein Superfamily Classification using Computational Intelligence Techniques

The problem of protein superfamily classification is a challenging research area in Bioinformatics and has its major application in drug discovery. If a newly discovered protein which is responsible for the cause of new disease gets correctly classified to its superfamily, then the task of the drug analyst becomes much easier. The analyst can perform molecular docking to find the correct relative orientation of ligand for the protein. The ligand database can be searched for all possible orientations and conformations of the protein belonging to that superfamily paired with the ligand. Thus, the search space is reduced enormously as the protein-ligand pair is searched for a particular protein superfamily. Therefore, correct classification of proteins becomes a very challenging task as it guides the analysts to discover appropriate drugs. In this thesis, Neural Networks (NN), Multiobjective Genetic Algorithm (MOGA),and Support Vector Machine (SVM) are applied to perform the classification task.Adaptive MultiObjective Genetic Algorithm (AMOGA), which is a variation of MOGA is implemented for the structure optimization of Radial Basis Function Network (RBFN). The modification to MOGA is done based on the two key controlling parameters such as probability of crossover and probability of mutation. These values are adaptively varied based upon the performance of the algorithm, i.e., based upon the percentage of the total population present in the best non-domination level. The problem of finding the number of hidden centers remains a critical issue for the design of RBFN. The most optimal RBF network with good generalization ability can be derived from the pareto optimal set. Therefore, every solution of the pareto optimal set gives information regarding the specific samples to be chosen as hidden centers as well as the update weight matrix connecting the hidden and output layer. Principal Component Analysis (PCA) has been used for dimension reduction and significant feature extraction from long feature vector of amino acid sequences.In two-stage approach for protein superfamily classification, feature extraction process is carried in the first stage and design of the classifier has been proposed in the second stage with an overall objective to maximize the performance accuracy of the classifier. In the feature extraction phase, Genetic Algorithm(GA) based wrapper approach is used to select few eigen vectors from the PCA space which are encoded as binary strings in the chromosome. Using PCA-NSGA-II (non-dominated sorting GA), the non-dominated solutions obtained from the pareto front solves the trade-off problem by compromising between the number of eigen vectors selected and the accuracy obtained by the classifier. In the second stage, Recursive Orthogonal Least Square Algorithm (ROLSA) is used for training RBFN. ROLSA selects the optimal number o

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Technology for large space systems: A bibliography with indexes (supplement 22)

This bibliography lists 1077 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between July 1, 1989 and December 31, 1989. Its purpose is to provide helpful information to the researcher or manager engaged in the development of technologies related to large space systems. Subject areas include mission and program definition, design techniques, structural and thermal analysis, structural dynamics and control systems, electronics, advanced materials, assembly concepts, and propulsion