Machine Learning for Scientific Data Analysis

Over the last few years, machine learning has revolutionized countless areas and fields. Nowadays, AI bears promise for analyzing, extracting knowledge, and driving discovery across many scientific domains such as chemistry, biology, and genomics. However, the specific challenges posed by scientific data demand to adapt machine learning techniques to new requirements. We investigate machine learning-driven scientific data analysis, focusing on a set of key requirements. These include the management of uncertainty for complex data and models, the estimation of system properties starting from low-volume and imprecise collected data, the support to scientific model development through large-scale analysis of experimental data, and the machine learning-driven integration of complementary experimental technologies.

Mathematical simulation analysis of optimal testing of shot puter's throwing path

Objective To study the mathematical simulation analysis of shot-putter throwing optimal path. Methods Shot put was simplified as a parabolic motion of a particle, the corresponding mathematical model was established, and the mathematical relationship between the throwing distance and the initial velocity of shot put, the shooting Angle and the shooting height was defined. Results The fitting formula between shooting speed and shooting Angle was obtained by using the fitting method, and the quantitative relationship between them and the ideal shooting Angle was identified. Conclusion The mathematical principle of shot put is revealed through the process of building a model from simple to complex. However, there are still many problems to be solved, among which the height problem is a complex one. At the present level, it is not possible to find a reasonable height, because it involves many factors. However, the development of grey mathematics will provide a beneficial attempt for it to establish a reasonable and scientific model.

Pre-service physics teachers’ mental models about the electric field

This paper identifies pre-service physics teachers’ mental models of the concept of the electric field. The models were determined by means of five contexts all of which were supported with sets of experiments. The contexts examined were (1) the effect of the electric field on the insulator, (2) the comparison of the conductor and insulator in the electric field, (3) the effect of the electric field on the neutral conductor and insulator, (4) the effect of the electric field on the conductor liquid, and (5) the effect of the conductor and insulators materials forming a closed surface on the electric field. Semi-structured interviews related to the contexts were conducted with the 22 pre-service physics teachers. The data collected throughout the interviews were put to content analysis and thus, the pre-service teachers’ mental models were identified. In total, six mental models were identified. One model was a scientific model (Scientific Model of the Electric Field (SMEF)) and five of which were unscientific models (Magnetic-Based Field Model (MBFM), Mechanical Wave Model (MWM), Material Independent Field Model (MIFM), Force-Free Field Model (FFFM) and Force-Based Field Model (FBFM)) were identified. It became apparent as a result of document analysis that several unscientific mental models were also included in resource books. Approximately one and a half years later, almost all students were interviewed again about the contexts so as to find whether or not their models were permanent or not. Following the interviews, their mental models were found to be quite permanent and to be time-independent.

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

Artificial Neural Network Based shafts surface pressures Analysis

This paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.

High School Students’ Epistemic Cognition and Argumentation Practices during Small-Group Quality Talk Discussions in Science

For high school students to develop scientific understanding and reasoning, it is essential that they engage in epistemic cognition and scientific argumentation. In the current study, we used the AIR model (i.e., Aims and values, epistemic Ideals, and Reliable processes) to examine high school students’ epistemic cognition and argumentation as evidenced in collaborative discourse in a science classroom. Specifically, we employed a qualitative case study approach to focus on four small-group discussions about scientific phenomena during the Quality Talk Science intervention (QTS), where students regularly received explicit instruction on asking authentic questions and engaging in argumentation. In total, five categories of epistemic ideals and five categories of reliable processes were identified. Students demonstrated more instances of normative epistemic ideals and argumentative responses in the discussions after they received a revised scientific model for discussion and explicit instruction on argumentation. Concomitantly, there were fewer instances of students making decisions based on process of elimination to determine a correct scientific claim. With respect to the relationship of epistemic cognition to authentic questioning and argumentation, the use of epistemic ideals seemed to be associated with the initiation of authentic questions and students’ argumentation appeared to involve the use of epistemic ideals.

The Interplay Between Affective Processing and Sense of Agency During Action Regulation: A Review

Sense of agency is the feeling of being in control of one's actions and their perceivable effects. Most previous research identified cognitive or sensory determinants of agency experience. However, it has been proposed that sense of agency is also bound to the processing of affective information. For example, during goal-directed actions or instrumental learning we often rely on positive feedback (e.g., rewards) or negative feedback (e.g., error messages) to determine our level of control over the current task. Nevertheless, we still lack a scientific model which adequately explains the relation between affective processing and sense of agency. In this article, we review current empirical findings on how affective information modulates agency experience, and, conversely, how sense of agency changes the processing of affective action outcomes. Furthermore, we discuss in how far agency-related changes in affective processing might influence the ability to enact cognitive control and action regulation during goal-directed behavior. A preliminary model is presented for describing the interplay between sense of agency, affective processing, and action regulation. We propose that affective processing could play a role in mediating the influence between subjective sense of agency and the objective ability to regulate one's behavior. Thus, determining the interrelation between affective processing and sense of agency will help us to understand the potential mechanistic basis of agency experience, as well as its functional significance for goal-directed behavior.

Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering

The development of scientific predictive models has been of great interest over the decades. A scientific model is capable of forecasting domain outcomes without the necessity of performing expensive experiments. In particular, in combustion kinetics, the model can help improving the combustion facilities and the fuel efficiency reducing the pollutants. At the same time, the amount of available scientific data has increased and helped speeding up the continuous cycle of model improvement and validation. This has also opened new opportunities for leveraging a large amount of data to support knowledge extraction. However, experiments are affected by several data quality problems since they are a collection of information over several decades of research, each characterized by different representation formats and reasons of uncertainty. In this context, it is necessary to develop an automatic data ecosystem capable of integrating heterogeneous information sources while maintaining a quality repository. We present an innovative approach to data quality management from the chemical engineering domain, based on an available prototype of a scientific framework, SciExpeM, which has been significantly extended. We identified a new methodology from the model development research process that systematically extracts knowledge from the experimental data and the predictive model. In the paper, we show how our general framework could support the model development process, and save precious research time also in other experimental domains with similar characteristics, i.e., managing numerical data from experiments.