Programming Analysis Research Articles

Using the Rstudio Environment in Researching Problems in Mathematics, Physics and Economics

The purpose of the article is to study the possibilities of using the RStudio environment to solve problems in mathematics, physics and economics. In particular, the emphasis is on the integration of statistical analysis tools, the use of a set of mathematical functions and operations that ensure efficiency and accuracy in calculations. Research methods. An analysis of scientific papers, methodological recommendations and examples of RStudio application in various fields of knowledge related to mathematics, physics and economics was conducted, the functionality of the RStudio environment was tested on practical tasks, and the results obtained using RStudio were compared with the results obtained using other methods to assess the effectiveness of the environment. The scientific novelty lies in the use of R functionality, which allows developing and implementing new methods and approaches to data analysis and solving scientific problems. Conclusions. The RStudio environment demonstrates its effectiveness for solving problems in mathematics, physics and economics due to a wide range of functions for numerical modeling, statistical analysis and data visualization. Using RStudio allows you to significantly simplify the process of solving complex mathematical problems, in particular, it contributes to the integration of mathematical methods in the study of physical phenomena and economic processes, which allows you to form new approaches to data analysis and modeling. RStudio can be used as an effective tool in education, contributing to the formation of analytical thinking and programming skills in students engaged in mathematical, physical and economic problems. In particular, the use of the Tukey criterion and the functions of the RStudio environment for comparing data in large arrays, such as the results of experiments or measurements. The results of the study confirm the feasibility of implementing RStudio in the practice of teaching and scientific research, and also indicate the need for further research into the capabilities of the environment in solving problems in scientific fields.

Experimental analysis and gene expression programming optimization of sustainable concrete containing mineral fillers.

Rapid urbanization has led to a high demand for concrete, causing a significant depletion of vital natural resources, notably river sand, which is crucial in the manufacturing process of concrete. As a result, there is a growing need for environmentally sustainable alternatives to fine aggregate in concrete. Quarry dust (QD) has evolved as a viable and ecologically friendly substitute in response to this demand. In the past, limited experimental investigations and only conventional modeling techniques were used to promote sustainable mineral fillers in concrete. This study proposed a robust soft computing technique using gene-expression programming (GEP) to enhance the usability of sustainable alternatives in concrete. Initially, an experimental study was carried out to examine the feasibility and mechanical characteristics of concrete made from materials including quarry dust and superplasticizer as a partial replacement for fine aggregate. Ten mixed proportions with various proportions (0%, 20%, 40%, and 60%) of quarry dust were used to make M15 and M20 grades of concrete. A series of experimental tests, such as workability, compressive strength (CS), and tensile strength (TS), were conducted to examine the fresh and hardened properties of modified concrete. The established database from the experimental investigations was then used to develop machine learning (ML) models using GEP. The outcomes of the GEP models were validated by comparing them with multi-linear regression (MLR) models and using various statistical metrics such as root mean squared error (RMSE), performance index (PI), correlation coefficient (R), and external validation methods. Finally, sensitivity analysis was performed to investigate the influence of ingredients such as mineral fillers, superplasticizers, and others on the mechanical properties of concrete. To enhance the practical usage of the study, a graphical user interface (GUI) was also created. The study revealed that 40% of the replacement of fine aggregates with mineral filler and superplasticizer shows the optimum properties. GEP models outperformed MLR, achieving R² values of 0.96 in CS and 0.92 in TS, compared to MLR's lower values of 0.85 in CS and 0.81 in TS. The proposed GEP equations and user-friendly GUI can be used to develop the pre-mix design of concrete using quarry dust and superplasticizers.

Rancang Bangun Lengan Robot Berbasis Arduino Nano

Technological advances in the Industrial Revolution 4.0 era have had a big impact, especially in the industrial sector with the emergence of automatic robots which help increase efficiency and productivity. This research discusses the design of an Arduino Nano-based robot arm that is controlled using a joystick system. The objective of this research is to establish a robotic arm mechanical system that is responsive to the movement of the joystick and sevo motor as a driver of the closed feedback system and to identify the parameters for the success of the hard ware and software system in moving objects. The research stages used involve design analysis, device assembly, and system programming. The research results show that the developed robot arm is able to effectively move objects with success parameters including the type of shape and physical structure of the object being lifted. This research found that objects with simple shapes such as blocks have a higher level of ease of lifting, while soft physical structures also influence ease of lifting.

Metal-organic frameworks for atmospheric water extraction: Kinetic analysis and stochastic programming under climate variability

Increasing demands for sustainable and distributed freshwater sources drive the exploration of water extraction from ambient air. This study presents a comprehensive computational approach for optimizing unit harvesting cost of the adsorption-based atmospheric water extraction (AWE) systems. There are three objectives (i) assessing the impact of climate variability: utilizing k-means clustering, utilizing climate data in different regions to explore the effects of ambient conditions in dry-hot (California), humid-hot (Florida), and dry-cold (Wyoming) regions, resulting in a preference for harvesting under humid-hot conditions. (ii) performing kinetic analysis: The derived kinetic model connects climate variability to operational time and regeneration temperature, critical process design variables. (iii) assessing adsorption materials: three materials (MIL-100 (Fe), MOF-303, and ZJNU-30) were assessed revealing the impact of variations in maximum capacity and isotherm shape on performance and cost. The optimization algorithm uses a two stage stochastic programming approach to account for climate variability and enables an optimization that balances the capital and operating costs across a range of temperature and humidity conditions.

Maximizing the number of allowable approximation errors when building a linear regression model

Subject of research: errors in approximation of a linear regression model within the framework of a logical-algebraic approach to data analysis. Purpose of the study: to develop an algorithmic method for solving the problem of maximizing the number of permissible approximation errors using a linear-Boolean programming computer. Methods and objects of research: the object of research is a linear regression model, the methods are linear regression analysis and mathematical programming apparatus. Main results of the study: an algorithmic method is proposed for maximizing the number of permissible absolute and relative errors in approximation of a linear regression equation, which reduces to solving linear-Boolean programming problems of a dimension acceptable for practical situations. Solving generated problems of this type should not cause computational problems due to a significant number of effective software tools, for example, the LPsolve program, which is freely available on the Internet.

AI-Powered Eye Tracking for Bias Detection in Online Course Reviews: A Udemy Case Study

The rapid growth of e-learning increased the use of digital reviews to influence consumer purchases. In a pioneering approach, we employed AI-powered eye tracking to evaluate the accuracy of predictions in forecasting purchasing patterns. This study examined customer perceptions of negative, positive, and neutral reviews by analysing emotional valence, review content, and perceived credibility. We measured ‘Attention’, ‘Engagement’, ‘Clarity’, ‘Cognitive Demand’, ‘Time Spent’, ‘Percentage Seen’, and ‘Focus’, focusing on differences across review categories to understand their effects on customers and the correlation between these metrics and navigation to other screen areas, indicating purchasing intent. Our goal was to assess the predictive power of online reviews on future buying behaviour. We selected Udemy courses, a platform with over 70 million learners. Predict (version 1.0.), developed by Stanford University, was used with the algorithm on the consumer neuroscience database (n = 180,000) from Tobii eye tracking (Tobii X2-30, Tobii Pro AB, Danderyd, Sweden). We utilised R programming, ANOVA, and t-tests for analysis. The study concludes that AI neuromarketing techniques in digital feedback analysis offer valuable insights for educators to tailor strategies based on review susceptibility, thereby sparking interest in the innovative possibilities of using AI technology in neuromarketing.

Approximation Algorithms for Dynamic Inventory Management on Networks

We provide the first approximation algorithm for dynamic inventory management on a network with stochastic demand and backlogging. Specifically, under a mild cost condition, we prove the cost of a specially designed base-stock policy is less than 1.618 times the cost of an optimal policy. We develop a novel stochastic programming analysis to prove this result: We carefully calibrate two stochastic programs (providing upper and lower bounds on the optimal policy), and compare their objectives. The upper bound arises from a new class of base-stock policies we define to address the currently unresolved issue of how to assign and fulfill backlogs in a system with fulfillment flexibility. We show the optimal policy in this class takes a simple and intuitive form: backlogs are assigned to their lowest cost activities for replenishment, and the ordered resources for those activities are committed to the backlogs for fulfillment. Next, the lower bound stochastic program is derived through a novel cost accounting scheme that captures the tradeoff between current inventory decisions and future backlog decisions. We then exploit this tradeoff to bound the ratio of the two stochastic program objectives and prove our main result. We also demonstrate our policy’s practicality with numerical simulations that show it performs within 1% of optimal on average across a wide range of problem instances. Finally, we show that our techniques and results extend to more general settings, demonstrating their potential for broader applicability. Importantly, our approach extends to problems with lead times, where we prove an approximation guarantee for base-stock policies that is independent of both the lead time and network structure. Thus, our work provides the new managerial insight that properly designed base-stock policies can be effective in network settings. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02965 .

Approach for multi-valued integer programming in multi-material topology optimization: Random discrete steepest descent (RDSD) algorithm

The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex programming to discrete point-set functions, the discrete convex analysis and discrete steepest descent (DSD) algorithm are introduced. To overcome combinatorial complexity of the DSD algorithm, we employ the sequential approximate integer programming (SAIP) to explicitly and linearly approximate the implicit objective and constraint functions. Considering the multiple potential changed directions for multi-valued design variables, the random discrete steepest descent (RDSD) algorithm is proposed, where a random strategy is implemented to select a definitive direction of change. To analytically calculate multi-material discrete variable sensitivities, topological derivatives with material contrast is applied. In all, the MVIP is finally transferred as the linear 0–1 programming that can be efficiently solved by the canonical relaxation algorithm (CRA). Explicit nonlinear examples demonstrate that the RDSD algorithm owns nearly three orders of magnitude improvement compared with the commercial software (GUROBI). The proposed approach, without using any continuous variable relaxation and interpolation penalization schemes, successfully solves the minimum compliance problem, strength-related problem, and frequency-related optimization problems. Given the algorithm efficiency, mathematical generality and merits over other algorithms, the proposed RDSD algorithm is meaningful for other structural and topology optimization problems involving multi-valued discrete design variables.

Unique Tachycardia Recorded on a Cardiac Implantable Electronic Device: What is the Mechanism?

In a 55-year-old man with nonischemic cardiomyopathy, a unique tachycardia was recorded on a dual-chamber implantable cardioverter defibrillator (St. Jude Medical [Abbott]) as an automatic mode switch episode. This case report discusses the mechanism of tachycardia, how it can be differentiated from similar rhythms, and its clinical implications. The report explores device limitations in managing such tachycardias and presents reprograming strategies to prevent recurrence. This case emphasizes the importance of careful analysis of device-mediated arrhythmias and appropriate programming.

TORCHLITE: New, Open Analytical Tools and Infrastructure for a Mega‐Scale Digital Library

ABSTRACTThis paper introduces TORCHLITE, an innovative HathiTrust Research Center (HTRC) open analytical and computational framework designed to offer efficient, open, and approachable access to the HTRC Extracted Features (EF) dataset via a well‐documented web‐based API. This poster will summarize project goals and progress, and discuss community engagement, which has played a pivotal role in this project. During a hackathon event held in spring 2024, TORCHLITE fostered collaboration among digital humanities and information science scholars to develop widgets and notebooks utilizing the EF API. Through the hackathon, participants explored the API's capabilities, leading to the creation of over a dozen analytical widgets and interactive programming notebooks (e.g., Jupyter).

Grey models for data analysis and decision-making in uncertainty during pandemics

We analyse in this study quite a few uncertain decision-making situations concomitant to a pandemic spread and some of the models using grey theory to deal with such situations. We present four stylised models to tackle different decision support situations at the time of any pandemic crisis, such as COVID-19. Eight diverse problems or situations of risk mitigation and decision-making under uncertainties are proposed in this research and the methodologies employing grey incidence analysis, grey clustering, grey prediction, and grey programming models are offered. Numerical illustrations of the applications of these methodologies are also typified in this paper for future development. A practical application of the widely used grey prediction model is also demonstrated in this study to predict the number of COVID-19 infections and fatalities of five Indian states during a considered period of study. The implications of the study are for data scientists, decision-makers, and practitioners to use the benefits of grey theory in dealing with various situations of uncertainty, such as a spread of a pandemic.

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

Analysis of the Structural Organization of the Human Finger Phalanges Using Anatomical Network Models

Despite the fact that the morphogenetic mechanisms of human finger transformation during ontogenesis are currently known, the issues of how they are organized into a complex integrated structure of the distal hand remains open. This question remains unanswered for several reasons, including the lack of consensus on conceptual definitions and approaches, as well as tools for assessing and comparing variations in several anatomical parts of the hand. The aim of the study was to investigate the structural organization of the human finger phalanges using anatomical network analysis (AnNA). Material and methods. In this study, the authors applied the IGRAPH package functions in the R data analysis programming environment for AnNA. Network modeling and layout were performed using the Fruchterman-Reingold algorithm. Analysis of the structure, as well as modularity and integration in the networks, was performed using the spin-glass algorithm. X-ray osteometric indices of the I–V fingers phalanx length were used to assess AnNA in 100 men and 100 women of the middle age without traumatic changes, deformations, and developmental deviations. Results. AnNA demonstrates a two-level organization of the distal hand in the form of a proximal module including the proximal phalanges and a distal module combining the middle and distal phalanges. When comparing the features of the network models of the distal hand, it was found that in women, the organization of the finger phalanges is characterised by higher morphological integration and modularity (modularity 0.43) than in men (modularity 0.38). orphological modularity and integration are organizing factors in the structure of the finger phalanges of the human distal hand. Conclusion. The study results demonstrate that the structural organization of the finger phalanges of the human hand is a system of individual anatomical modules of the phalanges.

Pendampingan Perancangan dan Deteail Engineering Design (DED) Gedung Serbaguna PCM Sawit Boyolali

The Community Service Program (PkM) titled "Design of the Multi-Purpose Building at Pimpinan Cabang Muhammadiyah (PCM) Sawit Boyolali" was initiated to address the need for infrastructure in the form of a multi-purpose building. The scope of the activity includes the planning and design of the building so that it can adequately accommodate various activities according to the required capacity and comfort, while also meeting technical building standards. The method used consists of four main stages. First, gathering data on the site, user needs, and the necessary building technical standards. Second, conducting an analysis of space planning and programming. Third, formulating the design concept. Fourth, developing the design into technical drawings or Detailed Engineering Drawings (DED) and preparing a cost estimate (RAB). The final goal of this PkM is to produce a Design Document for the Multi-Purpose Building for PCM Sawit Boyolali.

Programming analysis of the lexical unit context

Modern natural language processing tools make it possible to operate with big textual data, but the linguists have not yet fully used these tools to solve their (often highly specialized) issues. This applied research is devoted to the development of the original method of (automatic) analysis of the contextual environment of a given lexical unit in a coherent text based on the corpus approach. The relevance of the study is due to the need to create software for linguistic purposes in Russian Federation, as well as the significant interest shown in the topic of natural language processing and corpus research in our country and abroad. The research is based on the original text of the novel “The Castle” by F. Kafka as well as texts from the online version of the “Spiegel” magazine, collected in July, 2024. Methods of modeling, experiment, corpus analysis, as well as the method of professionally oriented programming are used. As a result, two functions were written for the software complex “Balanced Linguistic Corpus Generator and Corpus Manager”, created at the Laboratory for Fundamental and Applied Issues of Virtual Education at Moscow State Linguistic University, providing the operator with data for evaluating given lexical units, which build a frequency list of the connected tokens. The nature of the connection in the first case is determined by being within the same sentence, and in the second case – by taking into account the multi-level hierarchy “main word – subordinate word” in the sentence. The author comes to the conclusion that the proposed method provides more informative data when analyzing media texts. The algorithms did not reveal errors in the programming code, but in the future they can be improved in terms of the efficiency of selecting the tokens that are significant for the analysis.

Variation in Accelerometer-Derived Instantaneous Acceleration Distribution Curves of Elite Male Soccer Players According to Playing Position: A Pilot Study.

The incorporation of triaxial accelerometers into Global Positioning Systems (GPS) has significantly advanced our understanding of accelerations in sports. However, inter-positional differences are unknown. This study aimed to explore the variability of acceleration and deceleration (Acc) distribution curves according to players' positions during soccer matches. Thirty-seven male players from a national-level Portuguese club were monitored using 10 Hz GPS with an embedded accelerometer during the 2021/2022 season. Resultant Acc was obtained from the x (lateral), y (frontal/back), and z (vertical) axes and expressed in gravitational units (g). Statistical Parametric Mapping was employed to compare playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM), and strikers (ST). All positions exhibited a decreasing Acc distribution curve, very similar in shape, with a high frequency of events in the lower ranges (i.e., 0 to 1 g) and a lower frequency of events in the higher values (2 to 10 g). Post hoc comparisons revealed significant differences between all positions, except between FB and WM. Out of 1000 points in the curve, CD had 540, 535, 414, and 264 different points compared to FB, CM, WM, and ST, respectively. These findings indicate that players in different positions face distinct demands during matches, emphasizing the need for position-specific Acc analysis and training programming. By analyzing Acc as a continuous variable, this study highlights the importance of individualized monitoring to ensure the comprehensive and precise tracking of all player activities, without overlooking or omitting critical information.

Indonesian Islamic Banks’ Post-Pandemic Profitability Alignment with Sustainability Goals

Purpose — The Islamic banking sector in Indonesia has recovered from the COVID-19 pandemic. Despite the sector’s resilience to the crisis, Islamic banks have lower levels of profitability than their conventional counterparts. They therefore need to optimise profitability as well as implement sustainability practices due to their high reliance on income from intermediary functions and their core principle of protecting life and preventing harm. This study aims to optimise the post-pandemic profitability of Indonesian Islamic banks while seeking to align profitability with sustainable development goals (SDGs). Design/Methodology/Approach — The model is developed based on the combination of linear programming and financial analysis, with constraints considering banking regulations and practices. The model highlights financing product types, financing fields of business, and types of deposit products that contribute to higher profits and support sustainability practices. Findings — The recommended financing product types, financing fields of business, and deposit product types lead to profitability growth of up to 85 per cent, even better than the pre-pandemic performance of Islamic banking and the overall Indonesian banking industry. This research also finds that Islamic banks’ strategy for profit optimisation is consistent with the increasing support for SDGs. Originality/Value — There is a lack of literature discussing the alignment of Islamic banks’ profit optimisation with the SDGs. This study contributes to the literature by aligning the Islamic banks’ profitability strategy with sustainable financing. Research Limitations/Implications — The study is expected to promote the implementation of sustainability practices as well as increase the profitability of Islamic banks, drawing from evidence in Indonesia.

Reforming military organisational culture: an empirical study of the Army in Taiwan

ABSTRACT This study investigates the behaviour patterns of four key roles within Army units – policy initiators, policymakers, implementers (or supervisors), and policy beneficiaries – during the development of new policies, defined by the Army as those significantly diverging from previous practices. The research finds that when policymakers and implementers align with initiators and adopt a policy beneficiary-centred approach throughout planning, execution, and evaluation, there is a positive correlation with the policy’s long-term sustainability. Conversely, when policymakers operate within an authoritarian or hierarchical culture, focusing on the demands of initiators rather than addressing the needs of the policy beneficiaries, the policy outcomes often fall short. Through in-depth interviews with senior officers and surveys of Army personnel regarding policies that promote voluntary activities for soldiers, the study identifies these roles and analyzes their dynamics. Statistical methods, including T-tests, R programming (Central Limit Theorem, Monte Carlo Method), and decision tree analysis, validate the hypotheses. The study’s findings contribute to existing literature by offering a “gear model” centered on policy beneficiaries, providing a practical framework for future policy development. This model underscores the importance of considering all roles in policy formulation to ensure effective and sustainable outcomes.

Koopmans, Dantzig, and the Wartime Origins of Activity Analysis

Abstract In the introduction to Activity Analysis of Production and Allocation (Cowles Monograph No. 13), Tjalling C. Koopmans recalled that he developed the model of his “Optimal Utilization of the Transportation System” (in the proceedings of 1947 International Statistical Congress, which were reissued as an Econometrica supplement, 1949) “under the stimulation of statistical work for the Combined Shipping Adjustment Board, the British-American board dealing with merchant shipping problems during the second world war.” Similarly, the contributions of George B. Dantzig and Marshall K. Wood to Cowles Monograph No. 13 (two revised journal articles and five new chapters) emerged from wartime work for the US Army Air Force and postwar work for the Department of the Air Force. This article examines the context and consequences of the wartime roots of these foundational contributions to activity analysis and linear programming, with particular attention to Koopmans's 1942 memorandum for the Combined Shipping Adjustment Board on “Exchange Ratios between Cargoes on Various Routes” (first published in his Scientific Papers, 1970).

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