Power Program Research Articles

An IGDT‐stochastic model for low‐carbon economic dispatch of integrated electricity‐natural gas systems considering grid‐enhancing technologies

AbstractUtilizing wind power alongside flexible resources such as power‐to‐gas technology, gas‐fuel generator, demand response (DR) program, grid‐enhancing technologies, and carbon capture and storage can help to low carbon operation of integrated electricity‐gas systems (IEGSs). Accordingly, this paper proposes a low‐carbon economic dispatch model for the IEGS, in which gas‐fuel generators, DR, and gas‐fuel generator are considered to realize the economic and environmentally friendly operation of these systems. Also, the flexible AC transmission system device as one of the grid‐enhancing technologies is innovatively included in IEGSs to guarantee that wind power is deliverable entire the electricity system. Besides, power‐to‐gas equipped with hydrogen storage is used to absorb the excess wind power to produce CH4. On the other hand, to capture the inherent flexibility of the gas network, the gas‐storing characteristic of pipelines is shown by line pack modelling. To manage uncertainties associated with wind power and DR program, the proposed model is formulated as an IGDT‐stochastic problem. For efficient computation purposes, the present work follows the mixed‐integer linear programming framework. Different case studies are performed on an integrated test system. Numerical simulation results show that the proposed model leads to reducing the total cost, carbon emissions, and wind curtailment by 29%, 16.4%, and 100%, respectively. It can be seen that the proposed low‐carbon ED model is environmentally friendly and has economic benefits.

Nuclear Waste Management in India Vis-A-Vis Sustainable Development: Policies and Practices

Energy is everything that comes from the sun to the air we breathe, to water and wind. Now the population is increasing and so is technology level, we are using Energy from every resource that is available on the earth. We now know that there are all forms of energy known to this planet upon which we base our civilization, some renewable, some non-renewable or in the language jargon of today: conventional or non-conventional that have a great deal to do with said improvement in quality of life. Energy is utilized by all sectors such as industry, commerce, agriculture, household, transport and also for telecommunication etc. Thus, in simplest terms Energy is Work done. We can go to the moon or expedition Antarctica just because of harnessing energy fuels. We are moving ahead in the era of modernism and among them, the dependency on energy is rising. Rapidly depleting all other forms of resources, the technology has now turned to nuclear capabilities. Nuclear is not just the largest source of energy, it is also a very clean option for producing power. The management of nuclear wastes in India is a crucial area that at the intersection of larger goals for sustainable development. With India in process of expanding its nuclear power programme by the year 2050 to meet energy needs and to cut down its carbon emissions, responsible management of nuclear waste will become even more vital, for environmental sustainability as well as public safety. Some even confirmed to the query Are we headed towards a nuclear future? & Is nuclear energy indeed be the answer to India's increasing power needs?

C Programming: Structured Programming Language

The c programming language was developed by Dennis Ritchie at AT&T bell laboratories in early 1970s. C is powerful language that provides fast program execution. its power and fast program execution come from its ability to access low level commands, similar to assembly language, but with high level syntax.

The use of cosmetics, personal care and cleaning products during pregnancy: a cross-sectional study

ABSTRACT This study was planned to determine the use of cosmetics, personal care and cleaning products containing chemicals during pregnancy and the affecting factors. The study is quantitative, descriptive and cross-sectional. The sample size was calculated using the G*Power program. 287 puerperant women were reached. Data were obtained with the Introductory Information Form and Cosmetic and Chemical Product Usage Form. In the study, number and percentage values were given and chi-square and regression analysis were performed. In cosmetics and personal care products, shampoo, toothpaste and deodorant/perfume are among the top three most commonly used products, while dishwashing detergent, laundry detergent and bleach are among the cleaning products. The products that are most restricted in use are hair dye and descaling. It has been determined that education level, occupation, income and number of pregnancies are effective in the use of some products. Taking measures to protect pregnant women is important for improving maternal and newborn health.

Within-chain parallelization-Giving Stan Jet Fuel for population modeling in pharmacometrics.

Stan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time-consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within-chain parallelization through both multi-threading using Stan's reduce_sum() function and multi-processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within-chain parallelization.

Effect of Programed Walking Exercise Using Bot Fit in Younger Adults

IntroductionPhysical inactivity and sedentary behavior both increase the risk of chronic disease and mortality. Regular participation in physical activity and reducing sedentary behavior play important roles in maintaining physical health and disease prevention.ObjectiveThe purpose of this study was to investigate the effect of programed walking exercise using a wearable hip exoskeleton, Bot Fit on muscle strength, muscle effort, and the kinematics of the pelvis in younger adults.MethodsWe designed three parallel experimental conditions and randomly assigned participants to one of three groups: those assigned to exercise using an interval program of Bot Fit (interval group), those who used a power program of Bot Fit (power group), and a control group who exercised without Bot Fit. A total of 45 young adults participated in 18 exercise-intervention sessions over six weeks, and all participants were assessed at two time points: before and after the 18 exercise sessions. Each assessment evaluated muscle strength, muscle effort, and the kinematics of the pelvis during walking. In addition, the number of steps, distance, energy expenditure, and heart rate for 30 min during the exercise sessions were recorded.ResultsA significant increase in the maximum voluntary contraction (MVC) of the left biceps femoris (BF) was evident in the interval group, while significant changes in the MVC of the bilateral BF were seen in the power group after Bot Fit exercise. A significant decrease of muscle effort in the right BF in the interval group and right lumbar erector spinae and bilateral BF in the power group were also observed. In addition, the symmetry index of pelvic tilt significantly improved in the interval group, and greater exercise volume and intensity in both the interval and power groups compared with the control group were confirmed as measured by the number of steps, distance, energy expenditure, and heart rate.ConclusionsThe results of this study confirmed the beneficial effect of programed walking exercise using the Bot Fit on muscle strength of trunk and lower extremities, muscle effort, and pelvic movement symmetry in younger adults. Personalized exercise programs can be provided for younger adults using various resistance or assistance modes of robotic device with the Bot Fit.Trial RegistrationClinicalTrials.gov, NCT05862077. Registered 22 March 2022, https://register.clinicaltrials.gov/.

Hypra: A Deductive Program Verifier for Hyper Hoare Logic

Hyperproperties relate multiple executions of a program and are useful to express common correctness properties (such as determinism) and security properties (such as non-interference). While there are a number of powerful program logics for the deductive verification of hyperproperties, their automation falls behind. Most existing deductive verification tools are limited to safety properties, but cannot reason about the existence of executions, for instance, to prove the violation of a safety property. Others support more flexible hyperproperties such as generalized non-interference, but have limitations in terms of the programs and proof structures they support. In this paper, we present the first deductive verification technique for arbitrary hyperproperties over multiple executions of the same program. Our technique automates the generation of verification conditions for Hyper Hoare Logic. Our key insight is that arbitrary hyperproperties and the corresponding proof rules can be encoded into a standard intermediate verification language by representing sets of states of the input program explicitly in the states of the intermediate program. Verification is then automated using an existing SMT-based verifier for the intermediate language. We implement our technique in a tool called Hypra and demonstrate that it can reliably verify complex hyperproperties.

Power distribution system restoration based on soft open points and islanding by distributed generations

Abstract A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced.

Earth’s Interconnected Climate: Understanding Our Changing World

Have you ever thought about what causes the weather conditions that you experience where you live? Maybe you are thinking, “Sure, when there are storm clouds overhead it rains or snows, and when the sun shines strongly in the summer, we have hot weather”. This is true, but the “big” story is more complicated: the conditions you experience in your area can actually be caused by weather and climate events that occur thousands of kilometers away. These long-distance effects are called teleconnections, and they are a natural part of Earth’s climate system. In this article, we provide examples of teleconnections and describe how scientists study them using powerful computer programs. We also explain how climate change might affect these invisible forces that stretch across the Earth, and how resulting changes in teleconnections could cause extreme weather events, like powerful rainstorms or droughts, in certain areas. The more scientists understand about teleconnections, the better we can protect areas that are vulnerable to the dangerous effects of climate change.

Using maple software to reduce problem solving errors in teaching mathematics to future computer science teachers

This article discusses some possible uses of Maple in mathematics. Mathematics is currently in a constant state of development in the application of computer technology in education and research. The use of algebraic systems in education is still relatively small, but growing research and interest in them point to their widespread use. There are complex and abstract ideas that present an insurmountable obstacle for many students in solving basic concepts and proving many mathematical concepts. One of the most powerful computer programs that helps in learning mathematics is Maple. Maple allows you to explain classic mathematical concepts in a practical and effective way. The effectiveness of Maple is due to its ability to visualize concepts and solve complex problems that are difficult to solve manually. Maple's features help students develop new methods and better understand, learn and teach complex concepts, advanced mathematical concepts and more. Ideal for students who are developing problem solving skills and advanced development skills. Allows students to solve mathematical problems step by step. Students can practice using various integration rules.

Investigation of Fractional Order Tumor Cell Concentration Equation Using Finite Difference Method

المعادلة التفاضلية الكسرية توفر إطارًا رياضيًا قويًا لنمذجة وفهم مجموعة واسعة من الظواهر المعقدة وغير المحلية والمعتمدة على الذاكرة في مختلف التطبيقات العلمية والهندسية والعالمية. تعد الاعتلالات الكبدية أورامًا سرطانية ثانوية تتطور في الكبد نتيجة انتشار خلايا السرطان منورام سرطاني أصلي ينشأ في جزء آخر من جسم الإنسان. التركيز الأساسي لهذه الدراسة هو فهم نمو الأورام في الكبد البشري، سواء بوجود أو بدون علاج دوائي. ولتحقيق ذلك، يتم استخدام معادلة تفاضلية جزئية معرفة بالترتيب الزمني والكسري، ويتم تحليلها باستخدام أساليب عددية. يتم استخدام المشتقة كابوتو لاستكشاف تأثير العلاج الدوائي على نمو الورم. لحل النموذج الرياضي عدديًا، تم تطوير طريقة الفروق المحدودة كرانك-نيكولسون. يتم اختيار هذه الطريقة بسبب خصائصها المميزة، بما في ذلك الاستقرار الغير مشروط والدقة من الدرجة الثانية في الأبعاد الزمنية والمكانية. الاستقرار والدقة التي توفرهما هاتان الخصائص أمران حاسمان لضمان موثوقية النتائج المحصلة في هذه البحث. يتم تقديم النتائج والتحققات المستمدة من هذه الدراسة بفعالية من خلال تمثيلات بصرية متنوعة. تعتبر هذه الوسائل البصرية أدوات لا غنى عنها لفهم التأثير العميق للعلاج الدوائي على نمو الأورام. من خلال هذا التمثيل البصري، يمكن للشخص الحصول على فهم أوضح وأكثر تفصيلًا للديناميات المعقدة التي تحدث. تم الوصول إلى الحل العددي لهذه المشكلة المعقدة من خلال تنفيذ خوارزميات تم تطويرها بدقة باستخدام لغة البرمجة بايثون المتعددة الاستخدامات والقوية. مرونة بايثون والمكتبات الشاملة وإمكانيات الحساب العددي القوية تجعلها الخيار المثالي للتعامل مع تعقيدات هذه الدراسة.

Prevention and Management of Risky Behaviors in Adolescents, Focusing on Suicide, Substance Use, and Risky Sexual Behaviors through Empowerment Programs: A Narrative Review.

Empowering adolescents to manage and reduce unhealthy behaviors and promote adaptive behaviors is a promising approach to promoting healthy behaviors. This review was an attempt to evaluate adolescent empowerment programs aimed at preventing and managing risky behaviors. This narrative review was conducted in 2023. The texts were reviewed in two sections: (1) a review of articles and books in databases and (2) a review of international websites. The keywords used to search for studies and programs included substance-related disorders, risky behaviors, empowerment, program, adolescent, suicide, and risky sexual behaviors. These keywords were individually searched in international databases such as "PubMed, Scopus, and Web of Science." In the second section, a search was conducted on the websites of international organizations such as the World Health Organization (WHO). The inclusion criteria included having the full text of the original articles, articles in English, articles related to the development and evaluation of the effectiveness of empowerment programs, and the age range of adolescents. The texts that only had abstracts and were in non-English languages were excluded. The programs designed to empower adolescents were in three areas: (1) community-based and peer-based programs, (2) family-based programs, and (3) comprehensive programs. Most programs focused on peer group participation and skill interventions. A powerful and effective empowering program for adolescents to prevent and control risky behaviors should be designed in the form of comprehensive programs in social, educational, economic, and health dimensions, and with the active engagement and participation of adolescents.

Factors influencing job stress in pediatric nurses during the pandemic period: Focusing on fatigue, pediatric nurse-parent partnership.

The purpose of this study was to identify the factors influencing pediatric nurses'job stress, including their level of fatigue and partnerships with the parents of patients. This investigation aimed to findings of this study may lead to the development of strategies to reduce pediatric nurse's job stress. Participants were recruited from pediatric, pediatric intensive care, and neonatal intensive care units across seven general hospitals. Eligibility requiresa minimum of 6 months of experience in pediatric nursing. The sample size was determined using the G*power program, considering various variables, including age, marital status, presence of children, and work-related characteristics, leading to a final sample size of 135, adjusted for a 10% dropout rate. Data collection was conducted through self-report questionnaires, and analysis involved frequency, percentage, mean, standard deviation, t-tests, ANOVA, and stepwise multiple regression, using SPSS Statistics 27.0. This study confirmed a significant correlation between pediatric nurses'fatigue and job stress, with higher levels of fatigue associated with increased job stress. Stepwise regression analysis showed that fatigue and age were significant predictors of job stress among pediatric nurses, explaining 23% of the variance. However, detailed analysis showed that younger nurses had lower job stress scores compared to older nurses. This result suggests that more experienced nurses may experience higher job stress due to increased responsibilities and emotional burdens. This study identified the need for effective strategies to manage fatigue and reduce job stress among pediatric nurses during the COVID-19 pandemic. Younger nurses, particularly those under 25 and between 26 and 30 years old, experience lower job stress compared to older nurses. Comprehensive support systems should be developed, including workload management, emotional support, and programs to enhance partnerships between nurses and parents. These strategies can improve job satisfaction and the quality of care provided to young patients. Additionally, they ensure a more resilient and effective healthcare workforce during pandemics and similar crises.

Techno-Economic Assessment of Liquefied Petroleum Gas-Powered Alternative Electricity Critical Infrastructure Development in Nigeria’s South-West Geopolitical Zone

The decentralization of electricity authority in Nigeria has made it expedient for the South-West Geopolitcal Zone to develop strategic power generation infrastructure. This study assessed the techno-economic viability of the liquefied petroleum gas-powered alternative electricity infrastructure option in Southwestern Nigeria as a mitigation strategy to endemic inadequate grid power supply under the region's integrated electric power programmes. An energy project planning and foresight analysis methodology was used. The study showed that a 25 MW stand-alone LPG-based CHP power plant would require 8.567 acres of land, consume approximately 15.459 million kg of Liquefied Petroleum Gas (LPG) annually, while generating 210,240 MWh of electricity annually. This power plant initiative was considered to have acceptable risk (Payback Period = 9 years 3 months; Return on Investment = 10.73%) and viability at the minimum tariff of $0.11/kWh. It also had considerable annual CO2 emissions and fuel cost savings (255.75 metric kilotonnes and $137.58 million respectively) relative to diesel-based alternative power generation. The study recommended that the viability of the power plant be improved by reductions in operations costs. The study concluded that the LPG plant initiative was technically feasible, economically viable and environmentally friendly, and suitable for deployment in the study area.

MATHEMATICAL MODELS OF FIBER CONCRETE

The work is devoted to the development of a mathematical model of fiber concrete with additional dispersed steel fiber reinforcement and a review of other most popular mathematical models. When studying the physical and mechanical properties of fiber concrete, two approaches are used, which are usually used when working with composites - phenomenological and structural. In the first case, the material is considered as some kind of isotropic system to which the methods of the mechanics of a deformable solid body are applicable. At the same time, the characteristics of the material are determined on the basis of laboratory studies and tests using methods of the theory of experiment planning and mathematical statistics. In the second case, a structural analysis is used, which assumes the expression of the mechanical characteristics of the material through similar indicators of its components, the coefficient of fiber reinforcement, the type and geometric dimensions of the fiber, etc. The authors propose the construction of a mathematical model of fiber concrete based on the determination of its effective stiffness characteristics. Fiber concrete made on the basis of steel fiber with bent ends is considered as the researched material. When building a mathematical model of fiber concrete, it is important to determine its effective properties, which, in turn, implies the procedure for selecting and modeling a representative volume of the material. Equations of the linear theory of elasticity are used to build the model. But, since the considered medium is heterogeneous, the well-known Eshelby principle is taken into account, which allows you to calculate the deformation energy of the system containing inclusions. The dependencies obtained in the work can be used not only for steel fiber fibers, but also for any other (polymeric, glass, etc.), and the specification of the expressions for the effective elastic characteristics will depend exclusively on the geometric shape of the fiber fiber and the type of its deformation. Structural modeling of fiber concrete, finite element models of I.M. Galieva and V´ıtor M.C.F. Cunha, as well as fiber concrete modeling capabilities in the two most powerful programs - ANSYS and NASTRAN.

Risky Dynamic Typing-related Practices in Python: An Empirical Study

Python’s dynamic typing nature provides developers with powerful programming abstractions. However, many type-related bugs are accumulated in code bases of Python due to the misuse of dynamic typing. The goal of this article is to aid in the understanding of developers’ high-risk practices toward dynamic typing and the early detection of type-related bugs. We first formulate the rules of six types of risky dynamic typing-related practices (type smells for short) in Python. We then develop a rule-based tool named RUPOR, which builds an accurate type base to detect type smells. Our evaluation shows that RUPOR outperforms the existing type smell detection techniques (including the Large Language Models–based approaches, Mypy, and PYDYPE) on a benchmark of 900 Python methods. Based on RUPOR, we conduct an empirical study on 25 real-world projects. We find that type smells are significantly related to the occurrence of post-release faults. The fault-proneness prediction model built with type smell features slightly outperforms the model built without them. We also summarize the common patterns, including inserting type check to fix type smell bugs. These findings provide valuable insights for preventing and fixing type-related bugs in the programs written in dynamic-typed languages.

Revisiting the strategy for marine litter monitoring within the european marine strategy framework directive (MSFD)

Marine litter and non-degradable plastic pollution is of global concern. Regular monitoring programs are being established to assess and understand the scale of this pollution. In Europe, the goal of the European Marine Strategy Framework Directive (MSFD) is to assess trends in Good Environmental Status and support large-scale actions at the regional level. Marine litter monitoring requires tailored sampling strategies, protocols and indicators, that align with specific objectives and are tailored for local or regional needs. In addition, the uneven spatial and temporal distributions of marine litter present a challenge when designing a statistically powerful monitoring program. In this paper, we critically review the existing marine litter monitoring programs in Europe. We discuss the main constraints, including environmental, logistical, scientific, and ethical factors. Additionally, we outline the critical gaps and shortcomings in monitoring MSFD beaches/shorelines, floating litter, seafloor litter, microplastics, and harm. Several priorities must be established to shape the future of monitoring within the MSFD. Recent developments in analytical approaches, including optimizing protocols and sampling strategies, gaining a better understanding of the spatiotemporal heterogeneity of litter and its implications for survey design and replication, and the inclusion of newly validated methodologies that have achieved sufficient technical readiness, must be considered. Although there are well-established methods for assessing beaches, floating and seafloor litter, it will be necessary to implement monitoring schemes for microplastics in sediments and invertebrates as robust analytical methods become available for targeting smaller particle size classes. Furthermore, the inclusion of indicators for entanglement and injury to marine organisms will have to be considered in the near future. Moreover, the following actions will enhance the effectiveness of monitoring efforts: (1) creating an inventory of accumulation areas and sources of specific types of litter (e.g., fishing gear), (2) monitoring riverine inputs of litter, (3) monitoring atmospheric inputs including microplastics, (4) accidental inputs during extreme weather events, and (5) studying how species at risk may be transported by litter. We provide recommendations to support long-term, effective, and well-coordinated marine litter monitoring within the MSFD to achieve a comprehensive and accurate understanding of marine litter in EU waters. This will allow the development of measures to mitigate the impacts of marine pollution and eventually to evaluate the success of the respective measures.

Machine learning approaches for simultaneous spectrophotometric determination of heavy metal ions in water samples

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays. The benefit of MATLAB in the construction of the machine learning model allows the development of a rapid and highly effective analysis of multiple components in the mixtures without separation and enrichment. For individual determinations, the working ranges were discovered as the important information for choosing the initial concentration of each heavy metal in a mixture, r. The results of analysis of Ni2+, Pb2+, and Cd2+ by two methods Partial Least Squares - PLS and Artificial Neural Networks - ANN are sensitive and accurate for simultaneous determination of the concentration of these ions in the synthesis mixture with a high regression coefficient of 0.993, respectively, 0.997, 0.997 for Ni2+, Pb2+ and Cd2+. As for Cu2+ and Co2+, the accuracy is higher when using the ANN method.

Implementation and Evaluation of SIMD Instructions using RISC-V

This thesis introduces and explores the design and verification of a packed Single Instruction, Multiple Data (SIMD) instruction set for a RISC-V processor, known as the RISC-V P extension. This extension enhances the RISC-V instruction set architecture by providing packed SIMD support for 8-bit, 16-bit, and 32-bit integer data types. The significance of this architecture lies in its potential to empower developers in constructing more efficient and powerful data-parallel programs for RISC-V processors. This contribution enhances the overall capabilities of the RISC-V ecosystem, providing a valuable extension to the instruction set architecture for data-parallel applications. Keywords: Data-parallel Programs, instruction set architecture, RISC-V, P extension, Packed SIMD

Using R Programming for Somatoploting

Introduction: Anthropometry is a technique employed to evaluate body dimensions and ratios by examining body length, width, circumference, and skinfold thickness. It is cost-effective, uncomplicated, and easily transportable, and it may be used in diverse industries. Somatotyping is a primary method used to classify the human physique based on three main components: endomorphy, mesomorphy, and ectomorphy. Heath and Carter established and modified the standards of somatotype, which continue to be employed for global measurements. Several software tools have been created for somatoploting, including SAS/GRAPH, Houcine, Orhan, and machine-learning models. Nevertheless, most of these tools are not open-source, resulting in laborious manual enumeration and hindering the accurate representation of differences among groups. A functional, open-source, precise tool is required to categorise somatotypes of extensive sample sizes and illustrate their differences. Method: R programming is a powerful and versatile programme language, particularly popular in statistical computing and graphics. It is widely used in various fields, like biostatistics, bioinformatics, and financial market analysis. R incorporates original programming concepts like object-oriented programming, which users can use transparently. This paper introduces how to use R programming as a tool for somatoploting, introduces the code for somatoploting, inserts x and y data, and executes the program to get a somatochart. It uses anthropometric data of 34 school-going students collected in Shindikurbet, Karnataka, aged between 10 and 12 years, collected through ISAK protocol guidelines to develop somatotypes and further plot them. Result: The paper holistically demonstrates using R programming to plot somatotypes in a 2-D Somatochart. Using this process, the reader can develop high-quality somatocharts in image or PDF formats. Conclusion: This study explores using R programming, an open-source software, for somatoploting and generating somatocharts. This method aids in understanding complex information, fact-explaining, and guiding action in various fields. It offers accessible data processing, analysis, and presentation, making implementing and saving budgets for students, researchers, and institutions easy. Further research could be conducted to make the code easier to use in Excel sheets or mobile applications.