Scientific Programming Research Articles

Assessing Teachers’ Knowledge of How to Use Computer Programming in Science and Technology Education

Assessing Teachers’ Knowledge of How to Use Computer Programming in Science and Technology Education

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example

ObjectiveIn order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing.MethodsThis study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model.ResultsWe have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas.ConclusionThe model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

Integrating Generative AI in Data Science Programming: Group Differences in Hint Requests

Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners’ use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance (N=45) and those without AI assistance (N=44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances data science education and research by exploring the influence of AI assistance during complex data science problem-solving. We discuss implications and identify paths for future research.

Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting

Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here we report on an evidence-based redesign of an introductory Python programming course, taught fully remotely in 2020 in the School of Oceanography at the University of Washington. Key components included a flipped structure, activities infused with active learning, an individualized final research project, and a focus on creating an accessible learning environment. Cloud-based notebooks were used to teach fundamental Python syntax as well as functions from packages widely used in climate-related disciplines. By analyzing quantitative and qualitative data from surveys, online learning platforms, student work, assessments, and a focus group, we conclude that the instructional design facilitated learning and supported self-guided scientific inquiry. Students with less or no prior exposure to coding achieved similar success as peers with more experience, an outcome likely mediated by higher engagement with course resources. We believe that the constructivist approach to teaching introductory programming and data literacy that we present could be broadly applicable across the earth sciences and in other scientific domains.

BIBLIOMETRIC ANALYSIS OF INNOVATION AND ENTREPRENEURSHIP EDUCATION IN CHINA: BASED ON CNKI DATABASE (2014-2023)

Innovation and entrepreneurship education is the engine and driving force to promote social and economic development and advancement. China's innovation and entrepreneurship education has been researched and developed for more than 20 years. In order to grasp the current situation, hotspots, trends and future of innovation and entrepreneurship education research in China, 1330 sample literatures in CNKI database are used as the research object, and bibliometric methods are adopted to visualise, analyse and compare the big data of the literature in terms of the annual volume of published papers, research topics, disciplinary distributions, authors of the published papers, journals of published papers, institutions of the published papers, high citations, downloads, and categories of sources, etc. The results are as follows. The results found that: China's innovation and entrepreneurship education research disciplines are mainly divided into higher education and vocational education; journals such as Education and Career, Science and Technology in Chinese Colleges and Universities, and Secondary School Politics Teaching Reference are the main dissemination carriers; Peking University Core is the main journal source; Northeast Normal University, Xiamen University, and Wenzhou Medical University are the main publishing institutions; and the National Educational Science Programming Project, and the National Social Science Foundation are the main funding funds.

Harnessing open science practices to teach ecology and evolutionary biology using interactive tutorials.

Open science skills are increasingly important for a career in ecology and evolutionary biology (EEB) as efforts to make data and analyses publicly available continue to become more commonplace. While learning core concepts in EEB, students are also expected to gain skills in conducting open science to prepare for future careers. Core open science skills like programming, data sharing, and practices that promote reproducibility can be taught to undergraduate students alongside core concepts in EEB. Yet, these skills are not always taught in biology undergraduate programs, and a major challenge in developing open science skills and learning EEB concepts simultaneously is the high cognitive load associated with learning multiple disparate concepts at the same time. One solution is to provide students with easily digestible, scaffolded, pre-formatted code in the form of vignettes and interactive tutorials. Here, we present six open source teaching tutorials for undergraduate students in EEB. These tutorials teach fundamental ecological concepts, data literacy, programming (using R software), and analysis skills using publicly available datasets while introducing students to open science concepts and tools. Spanning a variety of EEB topics and skill levels, these tutorials serve as examples and resources for educators to integrate open science tools, programming, and data literacy into teaching EEB at the undergraduate level.

The Politicization of Climate Science: Media Consumption, Perceptions of Science and Scientists, and Support for Policy

Differential media treatment of climate change, including conservative media’s tendency to reject the anthropogenic climate change scientific consensus, has reinforced polarized perceptions of climate change. Studies have found differences in coverage patterns and in perceptions among those relying more heavily on conservative rather than liberal or moderate media. This scholarship has been limited by narrow measurements of media exposure, climate-related outcomes, and the mechanism of effects. We analyzed nationally representative US data (N = 1,181) using measures that included not only reported use of mainstream print, cable, and social media captured in past research, but also science programming, as well as far-right, alternative-health, and Christian media. On average, participants relied more heavily on centrist and liberal media, followed by Fox News and social media. The results corroborate findings associating exposure to centrist media with pro-climate attitudes, and conservative media, including Fox News with the opposite views. Use of far-right outlets was associated with the lowest levels of belief in anthropogenic climate change, perceptions of personal threat, and support for climate-friendly policy. Reliance on science media was associated with pro-climate views. Most associations were mediated via perceptions of science and scientists (using the Factors Associated with Self-Presentation of Science, FASS scale).

Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch

Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch

Modeling and Analysis of Driven Shaft with Composite Materials

This study deals with the review of the optimization of drive shafts using ANSYS. The substitution of composite material over regular steel material for the drive shaft has expanded the upsides of the outline because of its high particular firmness and quality. The drive shaft is the primary part of the drive arrangement of a vehicle. The utilization of traditional steel for assembling drive shafts has many impediments, for example, low particular firmness and quality. Regular drive shaft is made up of two sections to build its major common twisting recurrence. A two-piece drive shaft expands the heaviness of the drive shaft which is not alluring in the present market. Numerous strategies are accessible at display for the plan advancement of basic frameworks and these strategies are in view of scientific programming procedures including slope look and direct inquiry. These techniques expect that the plan factors are persistent. Be that as it may, in useful basic building improvement, all the plan factors are discrete. This is because of the accessibility of segments in standard sizes and imperatives because of development and assembling rehearses. This paper talks about the past work done on composite drive shafts utilizing ANSYS

Multiverse Notebook: Shifting Data Scientists to Time Travelers

Computational notebook environments are popular and de facto standard tools for programming in data science, whereas computational notebooks are notorious in software engineering. The criticism there stems from the characteristic of facilitating unrestricted dynamic patching of running programs, which makes exploratory coding quick but the resultant code messy and inconsistent. In this work, we first reveal that dynamic patching is a natural demand rather than a mere bad practice in data science programming on Kaggle. We then develop Multiverse Notebook, a computational notebook engine for time-traveling exploration. It enables users to time-travel to any past state and restart with new code from there under state isolation. We present an approach to efficiently implementing time-traveling exploration. We empirically evaluate Multiverse Notebook on ten real-world tasks from Kaggle. Our experiments show that time-traveling exploration on Multiverse Notebook is reasonably efficient.

An Instrument to Evaluate 4-H Cloverbud STEM Programming

Evaluation data is needed to demonstrate the impact of 4-H science, technology, engineering, and mathematics (STEM) programming on children and youth. However, collecting evaluation data from cloverbuds (ages 5-7) is particularly challenging given their developmental age. We adapted an observational Cloverbud evaluation tool to measure basic life skills, essential elements of positive youth development experiences, and, unique to this tool, STEM specific experiences. We developed the STEM items using educational science standards allowing Extension to document STEM in addition to youth development outcomes. Doing so, may increase the adoption of 4-H STEM programming by teachers in the school setting.

Influence of Supplementary Oxide Layer on Solar Cell Performance

The increasing use of solar energy for electricity production has led to a directly proportional growth in the production of solar cells. Photovoltaic (PV) performance of silicon solar cells can be improved by using more efficient technologies, optimizing processes, and changing behavior in order to reduce operational costs and greenhouse gas emissions. In order to propose solutions for commercial solar cell production with better performance, this article presents an experimental assessment on Supplementary Oxide Layers (SOLs) that are deposited on the surface of a solar cell absorber layer. SOLs are typically used to improve the performance of solar cells by passivating surface defects, reducing recombination losses, and improving the electrical contact between the absorber layer and the metal electrodes. The obtained solar cells are tested under natural sunlight conditions, following a variable dynamic electronic charge profile. The experimental results along with the corresponding I-V and P-V curves, are assessed according to the process parameters, the lighting parameters, and the dynamic load scenario. SOLs have been shown to improve the Power Conversion Efficiency (PCE) of solar cells considerably. The proposed method for increasing the energy efficiency of solar cells can be applied to any type of commercial solar cell and it is easy to implement at the industrial or research level by controlling process parameters. The integration of the whole process, i.e. development of precursor solutions, deposition of thin films, and testing of electrical properties is another contribution of the current study, along with its interdisciplinary character, which involves materials science, electronics, and software programming.

Optimal Investment Strategy for α-Robust Utility Maximization Problem

In reality, investors are uncertain about the dynamics of risky asset returns. Therefore, investors prefer to make robust investment decisions. In this paper, we propose an α-robust utility maximization problem under uncertain parameters. The investor is allowed to invest in a financial market consisting of a risk-free asset and a risky asset. The uncertainty about the expected return rate is parameterized by a nonempty set. Different from most existing literature on robust utility maximization problems where investors are generally assumed to be extremely ambiguity averse because they tend to consider only expected utility in the worst-case scenario, we pay attention to the investors who are not only ambiguity averse but also ambiguity seeking. Under power utility, we provide the implicit function representations for the precommitted strategy, equilibrium strategy of the open-loop type, and equilibrium strategy of the closed-loop type. Some properties about the optimal trading strategies, the best-case and worst-case parameters under three different kinds of strategies, are provided. Funding: This work was supported by National Natural Science Foundation of China [Grants 12071147, 12171169, 12271171, 12371470, 71721001, 71931004, 72371256], the Shanghai Philosophy Social Science Planning Office Project [Grant 2022ZJB005], Fundamental Research Funds for the Central Universities [Grant 2022QKT001], the Excellent Young Team Project Natural Science Foundation of Guangdong Province of China [Grant 2023B1515040001], the Philosophy and Social Science Programming Foundation of Guangdong Province [Grant GD22CYJ17], the Nature Science Foundation of Guangdong Province of China [Grant 2022A1515011472], and the 111 Project [Grant B14019].

Prospects of using a practice oriented approach in teaching smart technologies: systematic review

The article is devoted to the prospects of teaching using a practice- oriented approach to the study of smart technologies in the preparation of future computer science teachers. Currently, the trend of production development, changes in society, technical and technological progress require new results from the higher education system. Therefore, the issue of professionally trained, competent specialists ready to find solutions to the problems they will face in the future is considered one of the most pressing tasks facing universities. The relevance of the research lies in the analysis of the results of scientific empirical research conducted using a practice-oriented method of training future specialists in universities. The practice-oriented method of training provides stages of interaction with all subjects of the educational process, joint solution of educational tasks, and modeling of situations. A distinctive feature of this method from others is not only the practical development of tasks, but also their correlation with real life and focus on solving problems. During the study and generalization of scientific literature, the results of research aimed at studying such fields of knowledge as computer science, design and teaching programming technologies using a practice-oriented approach were obtained. The PRISMA method was applied to scientific research that was received by filtering information from the database. Based on the collected data, an analysis of the information and the methods used was carried out, as well as the methods and techniques that were used in accordance with the subject of the study were analyzed. The study showed that the use of a practice-oriented approach makes it possible to get better results in the training of future specialists.

Deep Integration Practice of Intelligent Information Technology and Art Design Education under the Background of Scientific Programming

Deep Integration Practice of Intelligent Information Technology and Art Design Education under the Background of Scientific Programming

Retracted: Visual Analysis of English Teaching Model Based on Scientific Programming

Retracted: Visual Analysis of English Teaching Model Based on Scientific Programming

Risk assessment of computer network information using a proposed approach: Fuzzy hierarchical reasoning model based on scientific inversion parallel programming

Abstract Background Computer networks are involved in many fields such as business, education, marketing, government, and tourism in several forms. Technologies related to security protection and improvement of information integrity are used and developed for computer networks intruded on by unauthorized people and help save their confidentiality. Methods To improve the risk identification of computer networks, this manuscript combined a fuzzy hierarchical reasoning model with the scientific inversion parallel programming method to study the risk of computer networks. Moreover, this article defined and analyzed a d-order neighborhood message propagation algorithm. A d-order neighborhood parallel message propagation algorithm using the Gaussian graph model was proposed. Results The risk of the computer network was analyzed using the proposed method resulting in better protection effectiveness. Conclusion The simulations showed that the proposed algorithm could effectively detect risks and improve the security of the computer network.

Uncovering heterogeneous effects in computational models for sustainable decision-making

Computational modeling is frequently incorporated into environmental decision-making in order to capture inherently complex relationships and system dynamics. The complexity of such models often lies in various heterogeneous effects that arise due to the interaction of different input factors or due to designed structural variation in the model. In the past, various sensitivity analysis approaches have been implemented in attempts to identify essential decision factors. However, existing sensitivity analysis methods fail to capture critical information in the presence of heterogeneous effects. In this paper, the recently introduced simulation decomposition (SimDec) visualization method is extended to include quantitative sensitivity analysis. The framework is tested on several decision-making problems and is shown to capture heterogeneous behavior. A formal definition and classification of heterogeneous effects for computational models is introduced. The framework is open-sourced in a variety of scientific programming languages.

More than Just the Facts: Contributions of Subject Matter Experts to Out-of-School Time Youth Programs

Background Out-of-school time (OST) science, technology, engineering, and math (STEM) programming has positive impacts for youth, and the importance of engineering design activities and principles within STEM programming is well-supported by research. Including subject matter experts (SMEs) in these programs enriches the experience of youth participants. Public libraries are widely accessible to the public, and their role in the OST arena is expanding. Library staff can boost the quality of STEM learning experiences by partnering with SMEs. Purpose Subject matter experts (SME) contribute more than just their expertise. Between 2018 and 2019, a group of diverse professional engineers and librarians co-facilitated engineering activities at six U.S. libraries serving traditionally underserved populations. This study investigates the alignment in active learning preferences between youth, library staff, and professional engineer volunteers. Method The study’s design implemented the Framework for the Observation and Categorization of Instructional Strategies (FOCIS), which identifies seven different types of learning activities: collaborating, competing, performing, discovering, making, teaching, and caretaking. The FOCIS helped researchers evaluate the learning activity preferences of youth participants, engineers, and librarians. Results The analysis showed that the learning activity preferences of youth and engineers were much more closely aligned than those of librarians. Conclusions Alignment between youth-program participants and SME program facilitators has the potential to enrich the program experience and foster active engagement.

Алгоритмы дискретной фильтрации на основе модифицированной взвешенной ортогонализации Грама –Шмидта для дискретных стохастических систем с мультипликативными и аддитивными шумами

Discrete-time stochastic systems with multiplicative and additive noises describe a wide class of mathematical models of complex systems, for example, industrial-technological, energy, economic, telecommunication, aerospace systems, etc. An important class of algorithms for processing measure ment information in complex systems are Kalman-type discrete-time filtering algorithms. Purpose. Construction of new discrete-time filtering algorithms for discrete-time linear systems with multiplicative and additive noises based on numerically stable modified weighted Gram – Schmidt orthogonalization (MWGS). Methodology. The methods of computational linear algebra were used, namely, the direct procedu re of MWGS-orthogonalization, the theory of Kalman filtering, methods of scientific programming in MATLAB. Findings. New LD-algorithms for discrete-time filtering in covariance and informational form for discrete-time stochastic systems with multiplicative and additive noises are constructed. The algorithms have an extended array form allowing updates for all necessary filter values. The method employs numerically stable modified weighted Gram – Schmidt orthogonalization. Algebraic equiva lence of LD-filters to Kalman-type covariance and informational algorithms for linear discrete time stochastic systems with multiplicative and additive noises is proved. The conducted numerical experiments have shown the effectiveness of the proposed algorithms using the example of solving the problem of parametric estimation of a model of almost rectilinear motion, as well as their savings in computation time compared to previously constructed UD filters. Value. New discrete-time filtering LD-algorithms can be used as a reliable computational alterna tive to the Kalman-type “standard algorithms” since they have the property being numerically stable to machine round-off errors due to the use of the MWGS orthogonalization computational procedure at each iteration of the algorithm. The results can be used to solve problems of measurement information processing in discrete-time systems with multiplicative and additive noise.