Object-oriented Programming Research Articles

Framework for Implementation of Building Automation Control Programs for Industrial Heating and Cooling Systems

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns.

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

PyICLab: An integrated Python-based toolkit for in-silico simulations of ion chromatography

PyICLab is an open-source Python-based package featuring an object-oriented programming (OOP) interface, providing tools for realistic and customized numerical simulations of ion chromatography (IC). In this paper, we showcase PyICLab's use in simulating diverse separation scenarios, including isocratic carbonate elution, gradient hydroxide elution, high-concentration and large-volume injections. The accuracy of the embedded models was validated by demonstrating strong correlations between predicted and experimental results. Additionally, PyICLab's capability to handle complex IC configurations was demonstrated through a simulation of a column-switching system for seawater analysis. PyICLab offers valuable resources for chromatographic optimization, method development, and educational purposes. It is available on PyPI at pypi.org/project/pyIClab. Interested readers can install PyICLab using the pip command in a Python 3.11 or higher environment.

Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms

Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers. Despite the variations among these platforms, the core concepts remain the same. One notable challenge is the absence of a Python-based quantum interpreter to connect these five frameworks, a gap that remains to be fully addressed. In response, our work introduces a tool called Qinterpreter, accessible through a user-friendly web interface, the Quantum Science Gateway QubitHub, which operates alongside Jupyter Notebooks. Built using the Python Object-Oriented Programming System, Qinterpreter unifies the five well-known quantum libraries into a single framework. Designed as an educational tool for students and researchers entering the quantum domain, Qinterpreter enables the straightforward development and execution of quantum circuits across such platforms. This work highlights the quantum programming versatility and accessibility of Qinterpreter and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.

Semantic Enrichment of Non-Graphical Data of a BIM Model of a Public Building from the Perspective of the Facility Manager

Building information modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of detail in terms of geometry. Thanks to object-oriented programming paradigms, BIM models include high-level relationships to ensure interactions between objects, rapid view generation, and documentation. However, these models are not always equally rich in non-graphical data. This is true for parameters at the library object level, with which building object models are saturated, but also at the project, site, building, or floor levels according to the structure of the interoperable industry foundation classes (IFC) format. The current state of knowledge also lacks a clear methodology for inputting such data. For this reason, experimental work was undertaken on semantic enrichment in non-graphical data of a public building (a public kindergarten, Secemin, Poland), which has its BIM model at a high level of geometric detail but is poor in non-graphical data. As a result of the research and development work, all levels of the IFC structure were saturated with non-graphical data and validated, and the possibilities of their use were shown from the perspective of the facility manager. Documentation from the manager was used to achieve this goal, and selected analyses and simulations were performed on the enriched model. This article contributes to the discussion on semantic enrichment from CAD3D to BIM by presenting a detailed process for entering non-graphical data into a BIM model. The presented data entry method can be used by both modelers and facility managers. Thus, this paper fills an important research gap related to semantic enrichment in non-graphical data at different levels of the IFC structure.

Health data science course for clinicians: Time to bridge the skills gap?

Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians data science and the lessons learnt. A 1-day introductory course was conducted within a tertiary hospital in London. It consisted of lectures followed by facilitated pair programming exercises in R, an object-oriented programming language. Feedback was collated and participant responses were graded using a Likert scale. The course was attended by 20 participants. The majority of participants (69%) were in higher speciality cardiology training. While more than half of the participants (56%) received prior training in statistics either through formal taught programmes (e.g., a Master's degree) or online courses, the participants reported several barriers to expanding their skills in data science due to limited programming skills, lack of dedicated time, training opportunities and awareness. After the short course, there was a significant increase in participants' self-rated confidence in using R for data analysis (mean response; before the course: 1.69 ± 1.0, after the course: 3.2 ± 0.9, p = .0005) and awareness of the capabilities of R (mean response; before the course: 2.1 ± 0.9, after the course: 3.6 ± 0.7, p = .0001, on a 5-point Likert scale). This proof-of-concept study demonstrates that a structured short course can effectively introduce data science skills to clinicians and supports future educational initiatives to integrate data science teaching into medical education.

Numerical method for forming an alternative opinion in coalitions of highly qualified specialists using relative usefulness indicator

Subject of research: the formation of an alternative opinion in coalitions of highly qualified specialists. Purpose of research: development of a numerical method for the formation of an alternative opinion in coalitions of highly qualified specialists using a relative utility indicator and its subsequent implementation in the form of a computer program. Methods of research: survey, formalization, system analysis, correlation analysis, Hamming criterion, natural experiment, object-oriented programming. Main results of research: 1. The approach proposed by the author, based on combining methods of mathematical statistics and taking into account the individual competencies of experts, provides new opportunities for the formation of an alternative opinion in coalitions of experts and allows for increased efficiency in decision-making. 2. Verification of the developed numerical method was carried out using the example of a survey of police officers regarding violators of security systems in the field of critical information infrastructure. 3. A computer program has been developed that implements the created method. 4. The study revealed that the relative usefulness indicator of the j-th expert provides an opportunity to form alternative expert positions that have a reliable generalized opinion. 5. The practical significance of the work lies in the development of a computer program that can be used to conduct expert surveys and obtain reliable expert assessments based on them.

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

Object-Oriented Fixpoint Programming with Datalog

Modern usages of Datalog exceed its original design purpose in scale and complexity. In particular, Datalog lacks abstractions for code organization and reuse, making programs hard to maintain. Is it possible to exploit abstractions and design patterns from object-oriented programming (OOP) while retaining a Datalog-like fixpoint semantics? To answer this question, we design a new OOP language called OODL with common OOP features: dynamic object allocation, object identity, dynamic dispatch, and mutation. However, OODL has a Datalog-like fixpoint semantics, such that recursive computations iterate until their result becomes stable. We develop two semantics for OODL: a fixpoint interpreter and a compiler that translates OODL to Datalog. Although the side effects found in OOP (object allocation and mutation) conflict with Datalog's fixpoint semantics, we can mostly resolve these incompatibilities through extensions of OODL. Within fixpoint computations, we employ immutable algebraic data structures (e.g. case classes in Scala), rather than relying on object allocation, and we introduce monotonically mutable data types (mono types) to enable a relaxed form of mutation. Our performance evaluation shows that the interpreter fails to solve fixpoint problems efficiently, whereas the compiled code exploits Datalog's semi-naïve evaluation.

An open-source time-domain wave-based room acoustic software in Python based on the nodal discontinuous Galerkin method

In this work, we introduce an open-source implementation of a time-domain wave-based room acoustic modeling software package, named DG_RoomAcoustics. In this software, the linear acoustic equations are spatially discretized by the nodal discontinuous Galerkin method, and are integrated in time by either the explicit Runge-Kutta or the arbitrary high-order derivatives (ADER) integration schemes. Following the principles of object-oriented programming paradigm, the software is structured to ensure generic applicability and to facilitate future extensions with additional functionalities (e.g., different time integration schemes, boundary conditions). A comprehensive presentation of the physical and numerical aspects of the problem is provided. A detailed exposition of the code structure and components is presented, all of which are released under an open-source license, fostering community feedback and collaborative contributions for ongoing improvements. A brief overview of the current capabilities of the software is introduced. Future work regarding possible functionality extensions and performance optimizing are discussed.

Implementation and application of cell-based S-FEM based on the object-oriented programming framework

Implementation and application of cell-based S-FEM based on the object-oriented programming framework

Demystifying The Object-Oriented Features Of Popular Object-Oriented Programming Languages.

Background: Indeed, Object-Oriented Programming (OOP) has emerged as one of the major fundamental paradigms in today’s world of software development as it contains principles that enhance modularity, reusability, and maintainability of software. This article explores deeper into some of the facets of OOP aspects as exhibited in some of the most frequently used programming languages ranging from Java, Python, C++, and C# with an intent of offering substantive information to aspects like encapsulation, inheritance, polymorphism, and abstraction. The article synthesizes the ones focusing on the relevance and conceptualization of OOP and problems related to it in the context of learners and practitioners. It also describes strengths and weaknesses of using C++, Python, and Java in relation to OOP while stressing peculiarity of these languages. Some of the core principles of OOP are highlighted such as encapsulation, inheritance, polymorphism, and abstraction and how they are complicated and significant in today’s software engineering. Materials and Methods: There was a presentation of a survey on which the research is based to determine the problems that programmers encounter when applying OOP features using a sample of 80 programmers in the computer science fields in some Nigerian Universities. Results: Findings reveal the significance of understanding OOP concepts in popular languages and suggest ways of increasing developers’ efficiency, minimizing errors, and improving the quality of developed software. Conclusion: Suggestions include a necessity for training and education to enhance software developers' understanding of OOP features. The findings can guide the creation of training materials and courses centered on OOP concepts, aiming to improve software development practices and elevate the quality of software products.

SUPPLEMENTARY EDUCATION PORTAL CREATION FOR STUDENTS

The aim of the research described in this article is to develop and implement an educational portal that enables students to acquire additional knowledge and skills by studying the process of creating electronic resources in the educational field. The portal addresses the current demands of digital learning and provides access to supplementary educational opportunities. The article discusses software development methods, the selection of an optimal environment for application creation, and the key stages of the development process. The focus of the research is on creating a supplementary education portal, emphasizing the use of object-oriented programming (OOP) methods, web technologies, web programming, and electronic educational resource creation techniques. As a result of the research, technologies such as PHP, MySQL, and JavaScript were chosen, leading to the successful implementation of the application. An analysis of existing educational portals, such as BilimLand and Daryn Online, was conducted. The outcome of the work was the creation of a functional educational portal that allows students to effectively gain additional knowledge, meeting modern requirements. The portal offers interactive tools available to users and adapts to individual educational needs.

Optimización de la energía libre de Helmholtz a través de un programa en Visual Studio 2005 del método Monte Carlo Annealing

In the present work, we present the design of a program in Visual Studio 2005 implementing the Monte Carlo Annealing Method (MCA), to minimize the functional of the Helmholtz energy to study the Isotropic-Nematic phase transition. The program was made in Visual Studio 2005 using the C# programming language, this because it is an object-oriented language, easy interaction with databases, multiple mathematical functions and facilitates the creation of graphs, with this, it was possible to clearly observe the results produced by the program. In addition, by using C# it allows the application to be mounted on a server or Workstation, so that the program can be downloaded from the internet and, in addition, can be updated in real time. In this same context, Microsoft Access was used as the database manager.

Enhancing topology optimization with colored body-fitted mesh using adaptive filter, dual re-meshing strategy, and OOP programming paradigm

This study introduces a novel topology optimization approach by employing power law-based material interpolation and adaptive filtering in the framework of the unstructured grids. As an extension of the established Solid Isotropic Material with Penalization (SIMP) method that utilizes the fixed structured mesh, the proposed Colored Body-Fitted Optimization (CBFO) method adopts the body-fitted grids to enhance efficiency, accuracy, and adaptability for diverse engineering applications. Notably, incorporating body-fitted meshes with intermediate density profiles enables improved flexibility in the numerical simulations and eliminates the need for re-meshing in each iteration. The dual re-meshing strategy drastically reduces computational costs, with only two re-meshing procedures required throughout the optimization process. This approach facilitates the generation of dense mesh regions around critical boundaries to augment solution accuracy while enabling sparse mesh configurations in the low-sensitivity regions, thereby boosting computational efficiency without compromising performance. The effectiveness, robustness, and efficiency of the CBFO method are validated through testing on multiple standard minimum compliance and compliant mechanism problems. The proposed optimization method can converge in dozens of iterations, obtain better objective function values, and save computational costs by up to 69 % compared to the previous method using the body-fitted mesh. Additionally, a concise MATLAB script implementing the proposed method using an Object-Oriented Programming (OOP) paradigm is provided in the appendix and the supplementary material, complete with annotations.

FLARE: field line analysis and reconstruction for 3D boundary plasma modeling

Abstract The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D boundary plasma codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations (stellarators) and those with symmetry breaking perturbations of an axisymmetric equilibrium (tokamaks) are supported. The code itself is written in Modern Fortran with MPI support for parallel computing, and it incorporates object-oriented programming for the definition of the magnetic field and the material surface geometry. Extended derived types for a number of different magnetohydrodynamic equilibrium and plasma response models are implemented. The core element of FLARE is a field line tracer with adaptive step-size control, and this is integrated into tools for the construction of Poincaré maps and invariant manifolds of X-points. A collection of high-level procedures that generate output files for visualization is build on top of that. The analysis modules are build with Python frontends that facilitate customization of tasks and/or scripting of parameter scans.

Implementation of Project-Based Visual Programming Modules on Problem-Solving Skills Information Technology Education students to Support the SDG’s

Students in the Information Technology Education program need to master programming languages as an essential part of their competencies to support educational goals. One of the courses that require programming proficiency is Visual Programming or desktop-based programming. Problem-solving skills are crucial in the development of desktop-based applications. This study aims to analyze the visual programming problem-solving abilities of PTI students in supporting the SDGs through the implementation of the project-based visual programming module. This research employs a quantitative approach with a pre-experimental design of the One-Group Pretest-Posttest type. The sampling technique used is purposive sampling, involving 15 students currently taking the object-oriented programming course. The data used in this study consists of the test results of problem-solving abilities through the final project. Based on the analysis, the project-based visual programming module has been proven effective in improving students' problem-solving skills, as indicated by the paired sample t-test with a significance value of 0.002 < 0.05. In conclusion, there is a significant difference in students' problem-solving abilities before and after using the project-based module. The improvement in students' problem-solving skills through this module is considered high, with an N-Gain score of 0.839.

Improving Source Code Quality by Minimizing Refactoring Effort

Software maintenance is a time-consuming and costly endeavor. As a part of maintenance, refactoring is aimed at enhancing quality. Due to project deadlines and limited resources, developers need to prioritize refactoring activities. In this paper, we present a livestock management-inspired approach for identifying and prioritizing classes to refactor within an object-oriented program. This approach empowers developers to enhance the time/quality ratio. The novelty of our approach lies in utilizing established metrics for detecting code defects to prioritize each class. To validate its effectiveness, the approach was tested on four distinct Pharo-based open source programs. The results demonstrate the approach's efficacy in improving software quality, reducing development time, and enhancing team productivity

HDLRuby: A Ruby Extension for Hardware Description and Its Translation to Synthesizable Verilog HDL

HDLRuby is a new hardware description language defined as an extension of the Ruby programming language aiming to improve circuit design productivity. HDLRuby allows to model digital circuits at the register transfer level while supporting high-level paradigms comprising object-oriented programming, genericity, metaprogramming, and reflection. By construction, HDLRuby can also execute any code in Ruby and supports all of its libraries. Yet, even if high-level features are beneficial for design productivity, such advantages can be negated if the design tools are not efficient enough for producing in reasonable time quality hardware. This article investigates this issue by presenting the techniques used for compiling HDLRuby descriptions and by evaluating their performance. In detail, it explains how the language has been implemented and how it is translated to synthesizable Verilog HDL. Experiments are then presented for confirming the productivity gain of using HDLRuby and for evaluating the performance of the translation, the size of the resulting code, and the time required by a commercially available synthesis tool to produce an FPGA configuration from it. The HDLRuby descriptions used for the experiments include a set of repetitive designs for single construct evaluations and the implementation of generic convolution neural networks for real-life applications. For these evaluations, the translation time proves to be more than 10 times shorter than the synthesis time.

Improving Student Engagement and Success in Computer Programming Courses through Social Learning in Online Environments

This paper addresses the necessity to enhance the effectiveness of online learning (OL) environments by examining the influence of social interactions on learner motivation, engagement, and success, specifically focusing on online computer science education (CSE). Acknowledging the limitations of peer-to-peer and learner-to-teacher interactions on online platforms, this paper delves into the potential of social learning (SL) organized through learner groups, communities, or networks to significantly enrich the learning experience. To test the hypothesis that SL positively impacts student outcomes, an experiment was conducted with 49 engineering students divided into two groups. The first group undertook an object-oriented programming course in C++ using the Moodle platform, while the second group, in addition to the online course, participated in asynchronous group activities through forums and synchronous interactions via chat. The results reveal a notable positive impact of SL on student outcomes, with participants in the second group reporting higher satisfaction levels and achieving superior results compared to those in the first group. The findings underscore the significance of integrating social interaction into OL environments, with potential implications for enhancing the overall quality of education and student performance in society.