Programming Environment Research Articles

Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.

Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries. The study utilized RNA-Seq data comprising 52 expression data samples for 54,583 gene transcripts. These samples were categorized into four classes based on the GOLD classification for chemically injured individuals: Severe (n=14), Moderate (n=11), Mild (n=16), and healthy controls (n=11). Data preparation involved examining an Excel file created in the R programming environment using MLSeq and devtools packages. Feature selection was performed using Genetic Algorithm and Simulated Annealing, with Random Forest algorithm employed for classification. Ab initio methods ensured computational efficiency and result accuracy, while molecular dynamics simulation acted as a virtual experiment bridging the gap between experimental and theoretical experiences. A total of 12 models were created, each introducing a list of differentially expressed genes as potential biomarkers. The performance of models varied across group comparisons, with the Genetic Algorithm generally outperforming Simulated Annealing in most cases. For the Severe vs. Moderate group, GA achieved the best performance with an accuracy of 94.38%, recall of 91.64%, and specificity of 97.10%. The results highlight the effectiveness of GA in most group comparisons, while SA performed better in specific cases involving Moderate and Mild groups. These biomarkers were evaluated against the gene expression data to assess their expression changes between different groups of chemically injured individuals. Four genes were selected based on level expression for further investigation: CXCR1, EIF2B2, RAD51, and RXFP2. The expression levels of these genes were analyzed to determine their differential expression between the groups. This study was designed as a computational effort to identify diagnostic biomarkers in basic biological system research. Our findings proposed a list of discriminative biomarkers capable of distinguishing between different groups of chemically injured individuals. The identification of key genes highlights the potential for biomarkers to serve as indicators of chemical injury severity, warranting further investigation to validate their clinical relevance and utility in diagnosis and treatment.

Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods.

DSKIPP: A Prompt Method to Enhance the Reliability in LLMs for Java API Recommendation Task

ABSTRACTIn the realm of software development, selecting the appropriate Java application programming interfaces (APIs) from a vast pool remains a significant challenge for developers. This research addresses this complexity by tackling the limitations of current API recommendation methods, which often struggle to align API suggestions with the specific queries and development contexts. In this paper, we introduce a novel prompt method named DSKIPP (Development Scenario, key Knowledge and Intention's Progressive Prompt), designed to enhance the efficiency of large language models (LLMs) in Java API recommendations. Firstly, we devise an overview of DSKIPP which conducts LLMs through a sequential process: first, inferring the package level, followed by the class level, and ultimately the method level as an API comprises three distinct components at varying levels—package, class and method. Secondly, at each level, DSKIPP assists LLMs in deducing the development scenario associated with a query and the essential key knowledge relevant to that scenario. This approach enables LLMs to gain a more profound contextual understanding of the query's intention. Moreover, during the inference process at the class and method level, we implement a self‐check mechanism enabling LLMs to validate the results and ensure a more reasoned and reliable outcome. To validate the efficiency of DSKIPP, comparison and ablation experiments are both conducted within Java programming environment. The comparison results affirm that our method outperforms the current state‐of‐the‐art technologies in API recommendation tasks, while the ablation results shed light on why DSKIPP can enhance the reliability of API recommendations in LLMs. This research contributes to the field by offering a more reliable and context‐sensitive solution for API recommendation in software development.

The Importance Of The Virtual Learning Environment In Uniandes Postgraduates

This study focused on analyzing the influence of virtual learning environments (VLE) in postgraduate programs, specifically in the fields of Law with a Major in Criminal Procedure Practice and Oral Litigation, and Citizen Security, at the Regional Autonomous University of the Andes. – UNIANDES, in Ecuador. The background revealed a growing interest in the integration of EVA in higher education. The objectives of the research focused on understanding the impact of VLEs on the quality of teaching and the learning process. Regarding the methodology, a descriptive and exploratory qualitative approach was used, observing the interaction of students on the Moodle platform. Synchronous and asynchronous workshops were carried out in the "Research Methodology" and "Graduation Work" modules. The results reflected a high level of success, with 96.15% of students passing their "Scientific Papers." These findings suggest the effectiveness of VLEs in postgraduate academic training. Additionally, variability between cohorts in a program will be observed, highlighting the need for future research on factors that influence student achievement. In conclusion, this study highlights the importance of VLEs in graduate programs, supporting their contribution to effective teaching and learning. The results highlight the need to maintain high quality standards and the relevance of originality in academic works. These findings provide a basis for future research aimed at optimizing the implementation of VLE in postgraduate courses

Teacher Evaluation In Postgraduate Studies: Reflections And Current Projections

The teaching and learning process in postgraduate programs requires that instructors respond to multiple social and individual demands, which lead to the necessity of having up-to-date knowledge in their field of expertise, in teaching methodologies, in technological aspects, in teacher-student relationships, and in different learning approaches, among others. This ensures that postgraduate students maintain an active performance. Therefore, this study aims to present the results of an evaluation of the performance of postgraduate instructors. To this end, qualitative and quantitative methods are used, drawing from various sources of information such as student evaluations of instructors, self-assessments, evaluations by the academic coordinator, and the Postgraduate Directorate. In this context, the applied tool allows for the determination of the weighting of instructor performance in qualitative and quantitative terms, based on the "Generic Model for Evaluating the Learning Environment of Postgraduate Programs." Key points highlighted in the conclusions include the high quality of instructors in their respective fields of expertise, which is notably different from the negative perceptions students have regarding the academic aspects of the faculty. It is concluded that analyzing spaces for reflection within postgraduate classrooms is predominantly related to instructors, and these reflective spaces allow for optimization with high levels of academic rigor in the teaching and learning processes

PROCESSING OF MEASUREMENT RESULTS DURING TESTING THE OBJECT UNDER THE INFLUENCE OF A DIRECT LIGHTNING STRIKE

The current state of the problem of evaluating oscillograms during tests for the action of pulsed currents and voltages are analyzed, and the lack of interpretation tools that would meet the requirements of specialized standards for electromagnetic compatibility testing was shown. The program algorithm has been developed for interpreting the results of testing technical objects for resistance to a direct lightning strike in terms of processing oscillograms of various current components. The use of the trapezoidal method for numerical integration enabled the implementation of the algorithm as software for oscillogram interpretation in the Embarcadero RAD Studio XE8 programming environment. Standardized methods for determining pulse front, rise time, and pulse duration were taken into account during the implementation. In addition, the program allows you to determine the charge value and the integral of the current action in accordance with normalized values. Manual adjustment of the analysis interval was added, allowing the filtering of other current components and "noise" effects. The developed software found practical application in the verification testing of artificial lightning current generator complexes. The correctness of the program operation was confirmed by an empirical method. To obtain oscillograms, a measuring shunt with a range of up to 300 kA, lightning component pulse current generators and a simulated test object were used. The analysis was performed for all four components of artificial lightning, including the shortened constant component C*. Comparison of the interpretation results with "manual" analysis proved the high accuracy of the calculation (deviation up to 3%) with significantly less (5-10 times) time costs. Its extensive capabilities make the tool suitable for high-voltage testing and research in power engineering and related fields.

Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance

Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR). To address these challenges, a crow search algorithm is developed for effective load scheduling within the distribution system. The proposed method minimizes the total operating cost (TOC) and maximizes VR under varying loading conditions and distributed generation (DG) configurations. A case study in Tamil Nadu, India, is conducted using a microgrid composed of three distributed generation sources (DGs), modeled and simulated using the Electrical Transient Analyzer Program (ETAP) environment. The proposed approach is tested under three operational scenarios: grid-connected mode, islanded mode, and grid-connected mode with one DG outage. Results indicate that the crow search algorithm significantly optimizes load scheduling, leading to a substantial reduction in power loss and enhancement in voltage profiles across all scenarios. The islanded mode operation using the crow search algorithm demonstrates a remarkable reduction in TOC and maximizes voltage regulation compared to other modes. The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. The findings showcase the effectiveness of the crow search algorithm in microgrid power management and its potential for application in other real-time power distribution systems.

A closed-box kernel function for numerical simulation of transient heat conduction

A new kernel function, termed the closed-box kernel function, has been developed to address numerical simulation of transient heat conduction in the same medium. Firstly, this method is versatile and not limited to specific industrial scenarios or designated materials. Secondly, the method solves the spatial temperature at each time point only once, eliminating the need for multiple iterations. Thirdly, the method allows for controlled and adjustable simulation speed of heat conduction while maintaining high accuracy. After 24,700 iterations in the transient process, the relative error in temperature values is merely 0.000072. Fourthly, this numerical technique can break data dependency between spatial nodes, making it suitable for parallel computing with Graphics Processing Units (GPUs). The average speedup achieved is 94.0712 times faster compared to Central Processing Unit (CPU) computations. Finally, we provide mathematical examples to verify the correctness and accuracy of this numerical approach and present a computational framework for parallel computing in the Visual Studio (VS) programming environment to demonstrate its practicality in engineering applications.

ADVANCED APPLICATIONS OF PHYSICS-INFORMED NEURAL NETWORKS (PINNS) IN R FOR SOLVING DIFFERENTIAL EQUATIONS

Deep learning, a powerful machine learning technique leveraging artificial neural networks, excels in identifying complex patterns and relationships within data. Among its innovations is the emergence of Physics-Informed Neural Networks (PINNs), which have revolutionized the field of applied mathematics by enabling the solution and discovery of differential equations through neural networks. PINNs address two key challenges: data-driven solutions, where the model approximates the hidden solutions of differential equations with fixed parameters, and data-driven discovery, where the network learns parameters that best describe observed data. This study explores the implementation of PINNs within the R programming environment to solve two differential equations: one with boundary conditions y^'-y=0 with y(0)=0 and y(e)=1 boundaries and the Burgers’ Equation. The research utilizes R libraries, including reticulate for Python integration and torch for neural network operations, to demonstrate the versatility and efficacy of PINNs in addressing both data-centric solutions and parameter discovery. The results showcase the ability of PINNs to handle complex, high-dimensional problems, offering a promising alternative to traditional numerical methods for solving differential equations.

Optimizing Face Recognition with PCA and KNN: A Machine Learning Approach

Face detection and recognition have become critical applications in various fields, including security, identity verification, and human-computer interaction. This paper presents a comprehensive analysis of face detection techniques using Artificial Intelligence (AI), focusing on the integration of PCA and KNN algorithms. PCA is employed to reduce the dimensionality of face image datasets, effectively extracting important features while minimizing data loss. The KNN classifier is used for classification by identifying the closest matching face in a dataset. By applying these techniques to the LFW dataset, we achieved an overall accuracy of 88%, demonstrating the efficacy of this approach for face detection. The methodology involves training the system with face image data, utilizing PCA to project the images onto a lower-dimensional space, and applying KNN to classify the images based on their reduced feature set. The implementation was carried out using Python’s Scikit-learn library, highlighting the ease of combining well-established machine learning algorithms in a straightforward programming environment. Results show that using KNN with an optimal K value of 5, alongside PCA retaining 95% variance, provides a robust and efficient solution for face detection tasks. While this approach achieved significant success, further improvements could be made by integrating advanced classifiers such as CNNs or exploring neural networks for feature extraction. Additionally, real-time performance can be enhanced by optimizing the computational process or leveraging OpenCV for real-world applications.

Log-Based Analysis of Creativity in the Context of Computational Thinking

Computational thinking (CT) and creativity have been recognized as crucial skills for adapting to the current digital era. However, despite being extensively studied over the last few decades, research on their associations has only emerged recently. We report on a study that examined how creativity is manifested in the context of CT, specifically while solving computational problems in an online game-based learning environment for early programming. We took a learning analytics log-based approach to evaluate measures of CT and creativity. We developed a Python algorithm to automatically analyze the logged solutions across four creativity measures. This allowed for an objective, quantitative, multidimensional analysis of 52,438 submissions of N = 111 primary and secondary school students over 85 tasks. We examined the relationships between measures of creativity, game level, and CT, utilized exploratory analysis to investigate how measures of creativity differ across age groups, and explored how these measures characterize students. Our findings suggest that creativity does not decrease throughout the game despite the increased difficulty and its mechanics that penalize creative solutions. We also point out how various dimensions of creativity play different roles in learning. These findings suggest that educators should foster intrinsic motivation and encourage students to explore multiple solution paths to enhance both CT and creativity skills. Researchers should keep investigating mechanisms to assess and enhance creativity in learning environments and explore the influence of personal and contextual factors.

Numerical Solutions of Certain Time-Independent Quantum Mechanics Problems by Using the Python Environment

This paper discussed the digitization of some analytical solutions of the fundamental equation of non-relativistic quantum mechanics, the Schrödinger equation, in the Python programming environment. The main objective of the work is to automate the solution of several known analytically solvable problems of quantum mechanics and ensure their use in more complex problems. For this purpose, the Python programming environment was chosen, which, due to the large number of libraries and flexibility, is currently widely used in solving physical and mathematical problems. In the program we propose, which is available on the GitHub platform, it is solved easily. The problem of determining the probability of the spatial distribution of an electron in a hydrogen-like atom is discussed. The Schrödinger equation is presented in the spherical coordinate system. The wave function, describing the electron's state, quantized energy values, and probability density, is derived analytically. A recursive function was written in Python to handle the series coefficients defining the wave function. Utilizing relevant libraries, the resulting program constructs electron spatial distribution functions for different excitation levels. The paper then examines two one-dimensional tunneling scenarios and provides the tunneling coefficients in analytical form. A Python program was developed to plot the dependence of these coefficients on the particle's coordinate and energy. All the code is available on GitHub.

Strengthening The Profile Of Pancasila Students (P5) And The Profile Of Rahmatan Lil Alamin Students (PPRA) Through The Implementation Of Local Wisdom Exhibition At Mts Al-Maarif 01 Singosari

This study aims to analyze the implementation of a local wisdom exhibition at MTs Al-Maarif 01 Singosari as a means to strengthen the Pancasila Student Profile (P5) and the Rahmatan Lil Alamin Student Profile (PPRA). The background of this research lies in the multidimensional challenges faced by education in Indonesia in shaping a young generation that excels not only intellectually but also possesses strong character aligned with the values of Pancasila and the teachings of Islam as rahmatan lil alamin. The implementation of the Pancasila Student Profile (P5) and the Rahmatan lil Alamin Student Profile (P2RA) programs in schools involves the principal, Civics and Social Studies teachers, and students as key elements. The principal acts as the leader, formulating policies and creating a supportive environment for both programs. Civics and Social Studies teachers serve as facilitators, integrating Pancasila values and the Rahmatan lil Alamin concept into the learning process, guiding students to understand and apply these values in their daily lives. Students, as the main participants, actively engage in these programs to internalize Pancasila values and become individuals who bring benefit to the world. This collaboration is expected to shape students' character, making them morally and socially outstanding individuals. The results of the study indicate that the local wisdom exhibition can be an effective medium for internalizing character values in students, strengthening national identity, and integrating Islamic teachings with local cultural contexts. This research is expected to contribute to the development of a character education model that is more relevant to the socio-cultural realities of students in Indonesia.

Regional mapping of climate variables in Galicia from point samples

Galicia's (NW Spain) diverse climate, driven by various factors, exhibits significant spatial and temporal variability, directly affecting the region's economy and society. Taking this into account, it is of great importance to have climate observation networks that cover most of the territory, and in the case of not having them, to have tools that allow the estimation of climate parameters. The main tools used for the estimation of parameters in unmeasured locations are climate database and the interpolation of data from climate stations, with kriging being one of the most widely used interpolation methods. The evaluation of climatic database is very important to establish how well they estimate the parameters and to ensure that they result in a homogeneous data series. For this purpose, in the present study a comparison was made of point data from the MeteoGalicia weather station network with data extracted from the SIMPA, AEMET and ERA5 databases and with data resulting from interpolation using regression kriging methods. The climatic parameters for which data were used for the study are: mean temperature, precipitation and reference potential evapotranspiration (Eto). To extract the data from SIMPA, ERA5 and AEMET and to interpolate point data from MeteoGalicia, we worked with the statistical programming environment R. After extracting the data or interpolating them, they were compared with the data measured in the MeteoGalicia network of climatological stations using the Nash-Sutcliffe index. The results of this study seek to conclude which method is more effective for estimating each of the parameters studied. In general, the predictions based on interpolation (AEMET and regression kriging) presented the best goodness of fit, while SIMPA also presented good results for some parameters. In general, the parameters with the best estimation results were mean temperature, followed by precipitation and finally potential evapotranspiration.

Study on the implementation of the CAL (Coding as Another Language) curriculum in early childhood education

In recent years, Information and Communication Technologies (ICT) have been increasingly integrated into preschool education, enriching the learning process with valuable information and motivation while capturing young students' interest. Numerous studies emphasize the importance and impact of utilizing ICT in education, introducing new dynamics to learning experiences, particularly as children grow up surrounded by diverse digital stimuli. From an early age, they interact with applications and digital tools, especially touchscreens, building digital experiences that become essential in educational interventions. Rapid technological advancements have led to the development of a wide range of applications that enhance educational processes, significantly influencing how young children learn and process information. Games, mobile devices, interactive whiteboards, and programming applications aid in the development of critical ICT skills, logical thinking, associative reasoning, computational thinking, and programming abilities. Striking the right balance between play and learning creates a rich, stimulating environment that fosters young learners' growth in skills and abilities. Considering the European Union Council's recommendation of May 22, 2018, digital competence is one of the key skills that must be cultivated both within and beyond school throughout life. As future citizens, today's students need skills that will enable them to solve problems effectively. Our digital society demands individuals capable of responding efficiently to challenges across various social contexts. Consequently, teaching students computational thinking and programming skills has become essential. Research demonstrates that preschool children, when faced with problem-solving tasks requiring the creation of code, can develop basic programming and computational thinking skills, such as debugging and understanding sequences. Moreover, in recent years, digital applications specifically designed to teach young children fundamental programming concepts have emerged. While numerous learning environments focus on coding skills, studies on their effectiveness in fostering coding and computational thinking in preschoolers remain limited. As the number of mobile learning applications grows, it is crucial to identify those with genuine educational value, avoiding those marketed as educational without substantive merit. This study describes an educational intervention based on experiential and collaborative teaching principles. The research aims to demonstrate that through the use of the CAL (Coding as Another Language) approach within the ScratchJr programming environment, significant programming and computational thinking skills can be cultivated in a conventional Greek preschool classroom. The teacher and researcher are the same individual, ensuring direct involvement in the educational process.

Hybrid Educational Environment of the University's Engineering Schools for the Implementation of Early Career Guidance for Schoolchildren

The paper considers the issues of developing a technology for creating a hybrid educational environment, including, along with real research laboratories, various virtual laboratories with the possibility of fully immersing schoolchildren in their space, which would increase the level of vocational guidance for schoolchildren, namely: improving their knowledge of professions, interest in engineering areas, confidence in the choice a future career, the development of professionally significant competencies; and would also be very attractive to them. The authors of the work focus on the virtual component of the hybrid educational environment of the university's engineering school. The formal description of the technology uses functional diagrams in the IDEF0 notation. The following basic programming environments are used as development tools: Twinmotion, Bigscreen, 3DVista Tour, LMS Moodle.

Development and Launch of the Telegram Bot – Message Aggregator

Describes the process of creating a telegram bot for aggregating messages in order to simplify communication between local government and students of the university dormitory. The bot code is written in the Python programming language in the PyCharm programming environment. To launch the bot, hosting is used, specialized for Python projects: PythonAnywhere.

Automatic Programming: Large Language Models and Beyond

Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance.

Methodology for selecting energy-efficient thermal insulation materials using the Dynamo visual programming environment

Purpose of research: development of a methodology for selecting energy-efficient thermal insulation materials using the Dynamo visual programming environment in order to increase the energy efficiency of building envelopes and structures.Methods. The study was carried out using theoretical and practical methods. The theoretical study was carried out based on an analysis of scientific publications, as well as regulatory requirements in the field of energy efficiency. The practical study was carried out using the implementation of the methodology for selecting thermal insulation materials in the Revit software package using the Dynamo visual programming environment.Results. The capabilities of the visual programming environment Dynamo have been studied, allowing to automate the modeling and development of design documentation in the Revit software package, automate the process of reinforcing structures, designing utility networks and systems, creating automated quality control systems for BIM models, etc. Particular attention in the work is paid to the capabilities of Dynamo for automation performing thermal engineering calculations and selecting energy-efficient thermal insulation materials. A library of thermal insulation materials has been created, which can be supplemented and copied from one project to another using standard Revit functionality. Two Dynamo scripts have been created. The result of the work of the first is the complete automation of thermal engineering calculations in Revit, the result of the work of the second is the selection of thermal insulation material taking into account the requirements for thermal protection of buildings. A methodology has been developed for selecting energy-efficient thermal insulation materials using the Dynamo visual programming environment. The reliability of calculations obtained on the basis of developed scripts is confirmed by the results of manual calculations.Conclusion. The use of modern BIM technologies when choosing energy-efficient thermal insulation materials allows you to optimize the calculation process, and also makes it possible to find the most cost-effective solutions to reduce costs and increases design speed.

Functional Data Science for Secondary-School Students

CODAP is a widely-used programming environment for secondary school data science. Its direct-manipulation–based design offers many advantages to learners, especially younger students. Unfortunately, these same advantages can become a liability when it comes to repeating operations consistently, replaying operations (for reproducibility), and also for learning abstraction. In response, we have extended CODAP with CODAP Transformers, which add a notion of functions to CODAP. These provide a gentle introduction to reuse and abstraction in the data science context. We present a critique of CODAP that justifies our extension, describe the extension, and showcase some novel operations. Our extension has been integrated into the CODAP codebase, and is now part of the standard CODAP tool. It is already in use by the Bootstrap curriculum.