Language Programming Research Articles

Instantaneous Tiltmeter Triggered by Dynamic Wetting Behavior.

A novel instantaneous tiltmeter with dynamic and static monitoring functions is reported that is based on liquid metal dynamic wetting behavior in a bio-fabricated anisotropic microchannel. The proposed system achieves instantaneous tiltmeter functionality, offering a broad detection range (-90°-90°) with high precision (0.05°), a rapid reaction time (0.11 s), and enhanced durability. Moreover, a seamless integration has enabled water wave detection, language programming, and human limb monitoring. Especially, the integration of tiltmeter and a t3D motion platform results in a surface structure scanning system capable of effectively performing large area (>200 cm2) and height difference scanning functions. This innovative approach holds great potential for transformative changes in the fields of advanced manufacturing, flexible robotics, and the flexible sensing, further facilitating widespread adoption.

CONTENTS OF TRAINING IN CONSTRUCTING CONTROLLED QUANTUM GATES

The article makes a selection and subsequent structuring of the content of training in the construction of controlled quantum gates using twelve decompositions of single-qubit quantum gates (Z-Y, Z-X, X-Y, Y-Z, X-Z, Y-X; Z-Y-X, Z-X-Y, X-Y-Z, X-Z-Y, Y-X-Z, Y-Z-X) used in the process of teaching the section “Quantum Computing and Programming in the QCL Language” of the training course “Applied Programming in High-Level Languages” to students of the Institute of Information Technology and Technological Education (3rd year, Herzen State Pedagogical University of Russia).

Ethical Considerations of Establishing Social Restrictions in the Prevention and Control of the Corona Epidemic from the Point of View of Medical Students of Kermanshah University of Medical Sciences

Background: Covid-19 (coronavirus), with its different dimensions, has created a serious international crisis in the world and has presented mankind with a new challenge. Among these, it can be claimed that the most difficult challenge in the Corona crisis is the moral challenge. Objectives: Considering the significance of ethical considerations and the crucial role students play in conveying these concepts to society, we decided to examine their perspectives. Methods: The present study was a cross-sectional descriptive study, and the statistical population was medical students in the basic sciences of Kermanshah Medical School. The sample size was 205 people based on Cochran's formula (d = 0.05), and the sampling method was random sampling. To collect data, a researcher-made questionnaire was designed based on current issues and approved by several professors (25 questions with a 5-point Likert scale from "very much" to "very little"). The reliability coefficient (0.83 Cronbach's Alpha) was satisfactory for the questionnaire. After completion and collection, the data was entered into SPSS-21 software. Then, it was subjected to statistical analysis with descriptive statistics (prevalence, mean, and standard deviation) and inferential statistics (due to the non-normality of the data distribution, non-parametric tests were used to analyze and compare the answers). Results: The average age was 20.58 ± 2.41 years, 115 people (56.1%) were male, and 198 people (96.6%) were single. Natives accounted for 63.2%. The effective factors in interventions were, in order: Use of different media for information, appropriate, accurate, and timely information, appropriate and sufficient training, preparation and provision of conditions before the above interventions, presentation of decisions and programs in simple and practical language, and fair distribution of protective equipment. Conclusions: According to our results, it was found that the role of using different media for informing, training, preparing, and providing conditions before interventions is very important. Therefore, policymakers and health planners should have the necessary preparation in these matters to control future epidemics.

The Coordination between Arabic Studies Programs and Job Prospects for Undergraduate and Postgraduate Students in Chennai City

In the rapidly evolving landscape of education, it's essential to adapt strategically to changing trends and job opportunities for graduates. The Master's Program in Arabic Language is taking proactive steps to ensure the curriculum remains relevant to the demands of the job market. This study aims to assess the alignment between the curriculum and job requirements for graduates and postgraduates, the time taken to secure employment, the skills utilized in Arabic teaching roles, and the adequacy of the curriculum in preparing graduates for the challenges of the professional world. Utilizing a qualitative descriptive approach, data was gathered from alumni and employers between 2018 and 2022 through questionnaires, interviews, and group discussions. Analysis of the data revealed that 69.9% of graduates found their work aligned with the curriculum, with 39.2% working as Arabic language teachers, 20.3% in Computer, Media and MNC Companies and 28.4% are working outside the education sector. The average time to secure employment was less than six months, with technology-based teaching skills being the most impactful. Recommendations include adapting the curriculum to embrace technology in Arabic teaching, enhancing research methodology, and encouraging publication of articles and books to meet the demands of the digital era and the labour market.

Implementation of Master’s degree programs in Artic indigenous languages in the higher education system (on the example of the Sámi language)

In this article, we discuss the specifics of implementing the Master’s degree program entitled “Technologies of teaching the Sámi language” launched at the Department of Philology, Intercultural Communication, and Journalism of the Murmansk Arctic University during the 2022–23 academic year. The lectures and practical sessions included in the program are briefly described. The methodological aspects of the discipline “Teaching the Sámi language (initial stage, school)” are presented in greater detail. The relevance of introducing a Master’s degree in this profile for reviving the Sámi language, which is on the verge of extinction, is shown. The factors leading to extinction of the indigenous language of the Kola North are outlined; measures for preserving and reviving the Sámi language are proposed.

The RaKJA Application for Designing Android the Based Road Section Performance

Abstract Performance analysis of road sections is usually carried out on road sections that have the potential for high traffic jams, then perform performance calculations and obtain recommendations for problems on these road sections. However, currently it is still difficult to calculate road performance because it has to be done manually in the calculations and looking for the necessary factors. This research aims to create an application to assist in calculating the performance of Android-based roads more easily and flexibly, this application is called RAKJa (Road Performance Analysis Design) which will be published generally on the Google Play Store so that it can be used by anyone who needs it. This RAKJa application will be developed based on the Indonesian Road Capacity Guidelines (PKJI 2014), this application gets results in the form of degree of saturation, level of service, average speed, delays and queue length automatically without needing to look for the required factors in the PKJI 2014 table. Application This RAKJa was created using the Game Maker Language programming language from the Game Maker Studio 2 developer. Calculation of road performance using the RAKJa application produces the same results as calculations carried out manually. Validating the accuracy of the application from previous research urban road performance data based on the PKJI 2014 and primary data obtained accurate results of 90%.

Socioeconomic adversity, maternal nutrition, and the prenatal programming of offspring cognition and language at two years of age through maternal inflammation

Increasing rates of child neurodevelopmental vulnerability are a significant public health challenge. The adverse effect of socioeconomic adversity on offspring cognition may be mediated through elevated prenatal maternal systemic inflammation, but the role of modifiable antecedents such as maternal nutrition has not yet been clarified. This study aimed to examine (1) whether prenatal factors, with an emphasis on maternal nutrition, were associated with prenatal maternal systemic inflammation at 28 weeks’ gestation, including the metabolomic marker glycoprotein acetyls (GlycA); (2) the extent to which the association between prenatal maternal nutrition and child cognition and language at age two years was mediated by elevated maternal inflammation in pregnancy; (3) the extent to which the associations between prenatal socioeconomic adversity and child neurodevelopment were mediated through prenatal maternal nutrition and GlycA levels. We used a prospective population-derived pre-birth longitudinal cohort study, the Barwon Infant Study (Barwon region of Victoria, Australia), where 1074 mother–child pairs were recruited by 28 weeks’ gestation using an unselected sampling frame. Exposures included prenatal factors such as maternal diet measured by a validated food frequency questionnaire at 28 weeks’ gestation and dietary patterns determined by principal component analysis. The main outcome measures were maternal inflammatory biomarkers (GlycA and hsCRP levels) at 28 weeks’ gestation, and offspring Bayley-III cognition and language scores at age two years. Results showed that the ‘modern wholefoods’ and ‘processed’ maternal dietary patterns were independently associated with reduced and elevated maternal inflammation respectively (GlycA or hsCRP p < 0.001), and also with higher and reduced offspring Bayley-III scores respectively (cognition p ≤ 0.004, language p ≤ 0.009). Associations between dietary patterns and offspring cognition and language were partially mediated by higher maternal GlycA (indirect effect: cognition p ≤ 0.036, language p ≤ 0.05), but were less evident for hsCRP. The maternal dietary patterns mediated 22 % of the association between socioeconomic adversity (lower maternal education and/or lower household income vs otherwise) and poorer offspring cognition (indirect effect p = 0.001). Variation in prenatal GlycA levels that were independent of these dietary measures appeared less important. In conclusion, modifiable prenatal maternal dietary patterns were associated with adverse child neurocognitive outcomes through their effect on maternal inflammation (GlycA). Maternal diet may partially explain the association between socioeconomic adversity and child neurocognitive vulnerability. Maternal diet-by-inflammation pathways are an attractive target for future intervention studies.

The Role of Writing Enablers on Writing Proficiency among Hispanic Students

This study examined the contribution of three writing enabling skills (i.e., feedback perception, self-efficacy, and self-regulation) on writing performance among Hispanic students. Participants were 261 students in grades 3-5. Approximately 60% of students were Emerging Bilingual (EB) enrolled in dual language programming. Students completed writing enablers scales and three writing compositions. EB students were classified in proficiency levels based on state-mandated language assessment scores. Factor structure of the writing enablers scales deviated from the original measures. Using Exploratory Structural Equation Modeling, we found that, while feedback perception was not related to writing, self-efficacy and self-regulation significantly predicted writing outcomes, though in opposite directions. Students with different English proficiency showed significant differences in self-efficacy but not feedback perception and self-regulation. Results showed that students with higher writing self-efficacy exhibited better writing performance. Students more proficient in English had stronger self-efficacy, with highly advanced EB students reporting the highest scores. Impact Statement This study examined the role of three writing enablers for successful writing performance among Hispanic students, with a majority of the sample classified as Emerging Bilingual. Findings showed that self-efficacy for writing emerged as a strong predictor of writing proficiency. This is particularly relevant for EB students, as they exhibited large differences in self-efficacy levels across varying levels of linguistic proficiency. Educators can leverage this information to effectively support student beliefs about their writing abilities, which in turn may lead to improved writing outcomes.

Strategies for developing hard skills in higher education students through innovative pedagogical technologies in realistic professional environments

Introduction: the global emphasis on developing hard skills, essential for professional competence in digital optimization, has led to a focus on practical skills like foreign language proficiency, programming, and data analysis. These skills, acquired through education or experience, are crucial across various professions, especially in the digital era, where innovative pedagogical methods are key.Objectives: this study aims to analyze the impact of innovative pedagogical technologies on developing hard skills, particularly foreign language competence, in higher education students, simulating real-world professional environments.Methods: the study employs a range of general scientific methods, including analysis, synthesis, induction, deduction, formalization, and generalization. These methods help identify and describe the development of hard skills and digital competence, focusing on innovative pedagogical strategies.Results: the findings highlight a structural-functional model for professional training in the digital labor market, emphasizing interdisciplinary integration, professional training alignment with market needs, and the use of innovative technologies. Key skills identified include financial management, software development, and foreign language proficiency, essential for making informed managerial decisions in a globalized world.Conclusions: the integration of innovative pedagogical methods, such as incidental learning and argumentation, enhances the development of hard skills, particularly English language competence. The study underscores the importance of creating learning environments that closely mirror professional conditions, thereby improving the quality of education and preparing students for the demands of the modern labor market. Future research should focus on refining digital tools and methodologies to further enhance language skills and overall hard skill development

Understanding 3D seismic data visualization with C++, OpenGL and GLSL

Seismic data visualization in 3D space is a valuable interpretation tool. Several open-source visualization tools are available. However, little explanation is provided about the inner working of visualization process. The current work discusses a “hello world” equivalent source code for 3D seismic data visualization using Graphical Processing Units (GPUs) with OpenGL and the OpenGL Shading Language (GLSL) programming languages. Rendering is the core process generating 2D image that we see on the screen from the 3D data structures being visualized. Texture mapping-based rendering commonly applied to seismic data starts with creating an OpenGL object called texture. The texture is then mapped over a rectangular object to display a seismic line. The work presented here is performed using OpenGL, GLSL, C++ and Qt toolkit. Here Qt provides the application GUI framework, C++ is used for data I/O, filtering, and sorting, and OpenGL and GLSL are used for 3D rendering. This paper describes the data flow through the application, and two implementations of vertex and fragment GLSL shaders. Visualization is critical for seismic data processing and interpretation. The low-level details of the process presented here will hopefully help readers in obtaining better understanding of the visualization concept and inner working principle and facilitate further development.

Exploration of Teaching Reform in C Language Programming Course under the Background of New Engineering

Exploration of Teaching Reform in C Language Programming Course under the Background of New Engineering

Indigenous Latinx students and translanguaging: The possibilities and challenges of using K’iche’ in a kindergarten Spanish-English handwriting classroom

ABSTRACT Many Indigenous Latinx students in Spanish/English dual language programs in the US speak languages that are often not valued or taken advantage of in the process of learning Spanish and English. This participatory critical (collective) autoethnography explores the possibilities of handwriting lessons as a vehicle for the incorporation of K’iche’ (a Mayan language spoken in Guatemala) into the classroom. Findings reveal ways in which the activities helped to elevate the status of K’iche’ in the school and make students/families feel more valued, but also ways to improve the activities so that Indigenous Latinx families have a more central role.

Comparison of Laravel and Symfony - the most popular PHP frame-works, based on a simple CRUD application

The main purpose of the study is to compare the performance of two popular PHP language programming frameworks - Laravel 10 and Symfony 6 based on test CRUD-type applications. The benchmarking also considered the implementation process and support from the development community. The study was conducted using two simple and small test applications with the same sets of functionality. Performance was measured based on the execution times of single CRUD-type operations and handling dozens and hundreds of requests simultaneously. Execution times were measured via the Postman application from when the request was sent to the server until the response was received. The results showed that Symfony 6 is a better solution for applications where performance matters.

НЕЛИНЕЙНАЯ ДЕФОРМАЦИОННАЯ МОДЕЛЬ СП 63.13330 В СТАДИЙНОЙ ПОСТАНОВКЕ

Normative documents in the field of reinforcement of reinforced concrete structures and the design of prefabricated monolithic structures fairly prescribe consideration of the initial stressstrain state of structures before reinforcement. The calculation can be performed using a normalized nonlinear deformed model according to SP 63.13330. At the same time, it does not explicitly provide for a calculation in several stages. The aim of the study is to develop a calculation method based on a normalized nonlinear deformation model in a staged formulation with minimal differences from the provisions of SP 63.13330. To achieve the goal, the following tasks are set: development of a calculation method, implementation of the method in the form of a universal program and verification of the developed program. The article presents a universal nonlinear deformation model in a staged formulation, which allows, within the framework of certain ratios, to calculate the strength and crack resistance of structural sections at an unlimited number of work stages, taking into account reinforcement, prestressing, initial stresses and cyclic loading (unloading and reloading). The method is implemented as a program in the Python programming language and verified on full-scale and numerical (finite element method) experiments. If we assume the initial stresses and deformations to be zero and calculate in one stage, then the proposed model is reduced to the SP 63.13330 model. For this reason, changing the norms according to the proposed method will not affect the current algorithms for calculating structures in one stage, however, it will significantly increase the scope of the model for calculating prefabricated monolithic, steel-reinforced concrete and reinforced structures. In addition, the accuracy of calculations of prestressed structures will increase, and it will be possible to calculate the cyclic loading of the section.

BatFix: Repairing language model-based transpilation

To keep up with changes in requirements, frameworks, and coding practices, software organizations might need to migrate code from one language to another. Source-to-source migration, or transpilation, is often a complex, manual process. Transpilation requires expertise both in the source and target language, making it highly laborious and costly. Languages models for code generation and transpilation are becoming increasingly popular. However, despite capturing code-structure well, code generated by language models is often spurious and contains subtle problems. We propose BatFix , a novel approach that augments language models for transpilation by leveraging program repair and synthesis to fix the code generated by these models. BatFix takes as input both the original program, the target program generated by the machine translation model, and a set of test cases and outputs a repaired program that passes all test cases. Experimental results show that our approach is agnostic to language models and programming languages. BatFix can locate bugs spawning multiple lines and synthesize patches for syntax and semantic bugs for programs migrated from Java to C++ and Python to C++ from multiple language models, including, OpenAI’s Codex .

The Design and Practice of a Multi-solution Teaching Strategy: Taking the C Language Programming Course as an Example

This paper explores one problem with multiple solutions teaching strategy applied to the integration of theory and practice teaching courses in C language programming course. According to the cognitive characteristics of students for the same problem combined with the specific learning situation of higher vocational colleges, the course has adopted the three methods of basic flow control statement, switch statement, and array. Showing the methods and skills to solve the problem from different angles with the gradual implementation of the specific teaching helps students deepen their understanding of C language knowledge. It also guides students to use knowledge to solve practical problems, to achieve the teaching goals.

Leveraging BIM for Enhanced Camera Allocation Planning at Construction Job Sites: A Voxel-Based Site Coverage and Overlapping Analysis

In the construction industry, the imperative for visual surveillance mechanisms is underscored by the need for safety monitoring, resources, and progress tracking, especially with the adoption of vision intelligence technology. Traditional camera installation plans often move toward coverage and cost objectives without considering substantial coverage overlap, inflating processing and storage requirements, and complicating subsequent analyses. To address these issues, this research proposes a voxel-based site coverage and overlapping analysis for camera allocation planning in parametric BIM environments, called the PBA approach. The first step is to collect information from the BIM model, which is the input for the parametric modeling step. After that, the PBA approach simulates the virtual devices and the construction layout by employing visual language programming and then generates a coverage area. Lastly, the performance simulation and evaluation of various placement scenarios against predefined criteria are conducted, including visual coverage and overlapping optimization for eliminating data redundancy purposes. The proposed approach is evaluated through its application to construction projects. The results from these various implementations indicate a marked decrease in data overlap and an overall enhancement in surveillance efficacy. This research contributes a novel, BIM-centric solution to visual information adoption in the construction industry, offering a scalable approach to optimize camera placement while mitigating overlapping areas.

On the effectiveness of hybrid pooling in mixup-based graph learning for language processing

Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers which learn transformations of graph node features, along with graph pooling layers that use graph pooling operators (e.g., Max-pooling) to effectively reduce the number of nodes while preserving the semantic information of the graph. Recently, to enhance GNNs in graph learning tasks, Manifold-Mixup, a data augmentation technique that produces synthetic graph data by linearly mixing a pair of graph data and their labels, has been widely adopted. However, the performance of Manifold-Mixup can be highly affected by graph pooling operators, and there have not been many studies that are dedicated to uncovering such affection. To bridge this gap, we take an early step to explore how graph pooling operators affect the performance of Mixup-based graph learning. To that end, we conduct a comprehensive empirical study by applying Manifold-Mixup to a formal characterization of graph pooling based on 11 graph pooling operations (9 hybrid pooling operators, 2 non-hybrid pooling operators). The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (JAVA250, Python800) demonstrate that hybrid pooling operators are more effective for Manifold-Mixup than the standard Max-pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of producing more accurate and robust GNN models.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Research on The Mixed Teaching Mode of Python Language Programming Course

With the development of the Internet era, big data, artificial intelligence (AI), and other fields of innovation and change, Python has become the most popular programming tool in the digital field because of its simple grammar and convenience for students to learn. It is also one of the important skills that college computer science students need to master in the face of market demand changes. The degree of mastery of the Python language programming course directly affects students’ ability to use computers to analyze and solve problems To improve the teaching effectiveness of the “Python Language Programming” course, this paper mainly explores the integrated and blended teaching mode of said course.

Bayesian analysis suggests independent development of sensitization to different fungal allergens

BackgroundFungi are known for their ability to cause allergies, but data on individual sensitization to them are insufficient. The purpose of the study was to carry out a comprehensive analysis of the fungal allergens’ sensitization profile in the Ukrainian population and to determine both population and individual sensitivity to these allergens. MethodsWe utilized a set of ALEX allergy test data from 20,033 inhabitants of 17 regions of Ukraine from 1 to 89 years conducted in 2020–2022. A complex of programs in the Python language was developed and Bayesian network analysis was applied to determine the sensitivity combinations in individual patients to various fungal components. ResultsSensitivity to Alt a 1 dominated and was observed in 79.39% of patients, and 62.17% of them were sensitive solely to Alt a 1. Exclusive sensitivity to Mala s 6 was second in individual patient profiles with a frequency of 4.06%. Combined sensitivity to Alt a 1 – Asp f 3 was third with a share of 3.28%. Pen ch and Cla h extracts stimulated the production of the lowest median sIgE levels. The highest median sIgE levels were for Alt a 1, Mala s 11 and Asp f 6, respectively. Median sIgE levels increased in adults compared to children for all components of Aspergillus fumigatus, as well as for Mala s 5 and Mala s 11. In the rest of the cases, they decreased in adults compared to children. The sensitization rates to fungi in general and specifically to Alternaria were lower in the western parts of Ukraine, especially in the Carpathian region, situated within the Broad-leaved Forest zone. The results of Bayesian modeling revealed that in the case of Alt a 1, the simultaneous absence of sensitivity to Cla h 8, Mala s 11, Mala s 5 and Mala s 6 molecules could condition the presence of sensitization to the major Alternaria allergen with a probability of 92.42%. In all other cases, there was a high probability of absence of sensitivity to particular allergen against the background of absence of sensitivity to other ones, which may indicate the independent development of sensitization to different fungal allergens. ConclusionsSensitivity to Alt a 1 dominated in the studied population with a lower rate in the western regions. The highest median sIgE levels were induced by Alt a 1, Mala s 11 and Asp f 6. Bayesian Analysis suggest a high probability of the independent development of sensitization to different fungal allergens. The idea that sensitization to one allergen may be protective against sensitization to another one(s) requires further clinical study.