Developed Model Research Articles

СИГНАЛЬНА МАКРОМОДЕЛЬ ОПТОПАРИ НА СТРУКТУРАХ ОРГАНІЧНОЇ ЕЛЕКТРОНІКИ

The paper is devoted to the basic approaches to the realization of the macromodel of optocouplers on organic structures for sensor devices. It is shown that the use of such structures is largely limited due to the time drift of parameters and temperature instability. In the course of the research, the possibility of compensating for these parameters directly during the operation of organic optocouples by measuring and analyzing the parameter drift was established. For this purpose, specialized built-in in-situ diagnostic systems and controlled signal transducers are used. In order to verify the functioning and optimize the parameters of signal transducers of built-in in- situ diagnostic systems, the paper describes the main approaches and the result of the implementation of the optocouple macromodel. The macromodel provides the ability to specify the components of sensor electronics optocouplers in accordance with the approaches and syntax of SPICE modeling and reflects parametric modulation with changes in ambient temperature, time instability, and flicker noise. The macromodel consists of five modules that describe the processes and specify the characteristics of the optocoupler: the light emitter (LED), self-heating of the optocoupler structure by the supply current, the optical medium or active layer of the optosensor, time drift and flicker noise, and the light receiver (photodiode or photoresistor). The modules are represented by substitution schemes of electrical components in accordance with the principle of functional analogy. The main approaches to specifying the parameters of macromodel components and examples of parametric studies based on it are considered. The use of the developed model makes it possible to obtain the values of compensation parameters for operational adjustment, in accordance with the operating conditions and properties of organic structures.

PYTHON-МОДЕЛЬ ПРОТОКОЛУ УЗГОДЖЕННЯ СЕКРЕТНОГО КЛЮЧА У ГРУПІ З ДОВІЛЬНОЇ КІЛЬКОСТІ УЧАСНИКІВ

The article is devoted to the problem of common secret key agreement in a group of an arbitrary number of participants. Data is exchanged between participants through open data channels. The problem of sharing a secret key over open data channels arose due to the need for a secure exchange of information between two or more parties that may be remote from each other and do not have a common confidential communication channel. Reliable methods of secret key exchange, such as transferring the key in person or using a secure channel, are not practical in remote or scalable scenarios. In the process of developing and modelling cryptographic systems, in which there is a need of cryptographic keys agreement in a group of two or more participants, it is very convenient to have a model that implements these algorithms. The agreement protocol is based on the Diffie-Hellman protocol on elliptic curves (ECDH). The paper contains theoretical justifications, a flow chart of the algorithm, and a Python implementation of the algorithm that performs the secret key agreement in a group of an arbitrary number of participants. To implement cryptographic operations on elliptic curves, the Python library Cryptography is used, in particular, the X25519 algorithms that use the elliptic curve Curve25519. The results of the work are shown on an example for a group of four participants, which demonstrate the correct operation of the model and the same secret key obtained as a result of agreement process. The paper also contains the link to a GitHub repository with the full program. The multi_participant_ecdh.py file contains a secret key agreement program for N participants written in Python. The file two_participant_ecdh.py demonstrates a typical two-participant Diffie-Hellman protocol. Both programs use the X25519 algorithm implemented in the Cryptography Python library. Thus, this work makes it possible to better understand the principles of secret key exchange algorithms between two and an arbitrary number of participants, to compare the results with other implementations, to apply the developed model for educational and demonstration purposes, and may be useful for a number of other scientific and engineering tasks.

Augmenting Academic Efficiency: The Integration of Assessments Within the Addie Model for Pedagogical Development

This paper explores the integration of assessments within the ADDIE model for pedagogical development, emphasizing its crucial role in enhancing learning experiences. Through a meticulous literature review, theoretical frameworks, practical implementations, and outcomes of assessment integration are examined. Assessments, spanning formative, summative, and authentic types, are shown to be integral to each phase of the ADDIE model, promoting customized interventions, continuous improvement, and data-driven decision-making. While challenges such as resource constraints and expertise gaps exist, strategies including professional development and technology integration offer pathways to overcome them. Furthermore, actively involving learners in the assessment process, providing timely feedback, and aligning assessments with learner interests can enhance motivation and engagement, fostering intrinsic motivation and self-directed learning. This study underscores the importance of integrating assessments into the ADDIE Model, advocating for learner-centered approaches and instructional design practices.

Depth Control of an Underwater Sensor Platform: Comparison between Variable Buoyancy and Propeller Actuated Devices

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications.

Normal Contact Model of Fractal Surfaces Considering Friction and Asperity Interactions

Friction as well as asperity interactions have significant influence on the contact area and force of rough surfaces. In this work, a single-asperity, elastoplastic contact model, considering friction, is established by means of continuum mechanics and power-exponential functions. Then, the deformation of the asperity interactions is modeled by the displacement of the average line for asperity height. Using fractal theory, the actual deformation and contact stiffness of an asperity are derived, which take the friction and asperity interactions into account. Furthermore, the actual contact area and normal stiffness models for fractal surfaces are developed, accounting for friction and asperity interactions. The accuracy of the developed models is confirmed by using published experimental results. The developed models are closer to experimental results than the reported fractal contact models. Finally, the effects of the friction coefficient and surface morphology parameters on contact characteristics are discussed.

Novel intrusion detection system based on a downsized kernel method for cybersecurity in smart agriculture

Smart farming aims to ameliorate the quantity and quality of farms products using modern information and communication technologies. Agriculture has always used cutting-edge technology to improve profitability, efficiency, and safety as well as creating the opportunity for more environmentally friendly operations. In smart agriculture network communication between smart devices is necessary to managing the agriculture process. Consequently, the possibility of information being exposed to hacking is very likely, which makes the information security system extremely important and cannot be neglected. Even though many solutions have been developed to overcome cyberattacks in smart networks, some limitations are observed, such as solving multi-class problems, high feature dimensions, and overhead computation. To address the aforementioned issues, an intelligent Intrusion Detection System (IDS) for identifying cyberattacks in the Internet of Agriculture Things (IoAT) is proposed. The developed model uses a proposed reduced kernel method, the Downsized Kernel Partial Least Square (DKPLS) to extract and reduce data feature dimension to improve detection performance. The DRKPLS approach is used to reduce the dimension of a kernel matrix generated using the Kernel Partial Least Square (KPLS) technique by selecting the most important features. In classification phase, the Kernel Extreme Learning Machine (KELM) is used to classify data for binary and multi-class classification. The proposed IDS is evaluated on a new Industrial IoT Dataset, X-IIoTID. The developed approach achieved higher performances for binary classification and for multi–class classification compared to others machine learning and deep learning approaches. The proposed approach achieved an accuracy rate of 99.92% for binary classification and 99.99% for multi–class classification. Furthermore, the higher percentage in multi-class classification is obtained for the two types of attacks Ransom Denial of Service (RDoS) and Command_Control respectively.

The Effectiveness of Direct Instruction Model in Teaching Solfeggio for Beginners

The background of this study was rooting down from the students music learning circumstances, of which they have low skill and knowledge of solfeggio, low ability of transferring solfeggio automatically into a new context although they have learned it, unavailability of learning material, syllabus, and lesson plan, standardized method and media of learning. Based on the problems stated above, it is very important to design a research dealing with learning solfeggio which lasting and producing a complete, practical, reliable and valid curricula of solfeggio. The basis model of development is using ADDIE theory. This research belongs to R & D research. The subject of the research is the students of art and music department, FBS Padang State University. The instrumentation of data gathering was using interview, questionnaire, tryout, performance-test. To analyze the raw data to see the level of reliability and practicality the researcher used the formula of Rho Spearman Brown, and the result is that r-obtain 1.2 exceeds r-table within significance level of 95% (0.05) it is 0.349 or by (0,01) it is 0.449. To prove the effectiveness of the product of this research ( the model of learning solfeggio ), the writer distinguish the data of pre-test to post-test by accumulating the scores the students obtained by using match t-test, and the result is that t-obtain 9.311 exceeds t-table 1.697. It means that, the model of learning solfeggio as the product of this research is much more effective significantly since the result of the match t-test is so high exceeding the number of the t-tab. It can be concluded that this model is very useful for the lecturers and the students as well in learning solfeggio at the music department of FBS Padang State University.

KRUSKAL WALLIS ANALYSIS TO FIND OUT THE IMPROVEMENT OF DESIGN THINKING SKILLS IN OBJECT-ORIENTED PROGRAMMING MATERIALS THROUGH EDUCATIONAL CONTENT

Education legislation in Indonesia currently requires Informatics subjects, which is closely related to programming-based material. However, many students in Indonesia still have difficulty learning about programming material. One of them is due to the background of students about the ability to think in the process of solving a program code. Seeing the phenomenon of today's students who are active on social media, it is a good step if social media is used as a learning media platform. This study aims to analyze the improvement of design thinking skills by using educational content-based learning media. The research method used was R&D with the ADDIE media development model and One Group Pretest-Posttest as the research design. The results showed an increase after students were given treatment in the form of educational content based learning media. The Kruskal Wallis test results showed a significant difference between the upper class, middle class, and lower class. The N-Gain obtained in the upper class is 0.560, in the middle class is 0.505, and in the lower class is 0.303. The average value of the results of student responses to learning media is 96.59 percent with the category "Very Good".

Development of Spiritually Charged Learning Media to Improve Learning Outcomes

Learning media development is an activity to create, improve, or improve the quality of learning information sources so that the desired learning objectives can be achieved. The development of learning media for Automation of Financial Management includes a processing process that involves all activities related to finance, making financial reports and achieving goals for the common good. The development of learning media with spirituality content is related to the profile of Pancasila students in the first element dimension, namely the dimension of faith, fear of God, and noble character, which teaches students to have good morals in relation to God. The problem in this study is how to develop learning media with spirituality to improve learning outcomes. The purpose is to find out the development of learning media with spirituality to improve learning outcomes at SMKS Ngunut Tulungagung. A research approach that conducts research, development, and testing of a product is called a research and development approach which produces certain products that are in accordance with the needs analysis. This research and development can test the effectiveness of the product so that the product can be useful for students. This research uses the Borg and Gall development model, which uses several stages for its development. The research uses the ASSURE approach with the steps needed to design and create an effective, efficient, and attractive teaching material. The results showed that (1) The first material expert validation received a score of 80, meaning good assessment, and the second material expert validation received a score of 93, meaning very good. (2) The first learning media expert validation received a score of 72, which means it is quite qualified, and the second learning media expert validation received a score of 83, which means it is good. (3) So the development of spiritually charged learning media with the One Day One Letter system can be used to improve learning outcomes. Keywords: Financial Governance Automation Learning Media, Spirituality, Learning Outcomes

ELEVATING LEARNING: DEVELOPING HOTS CHALLENGES FOR SENIOR STUDENTS AT SMK PUI Cikijing

ELEVATING LEARNING: DEVELOPING HOTS CHALLENGES FOR SENIOR STUDENTS AT SMK PUI Cikijing

DESIGN AND DEVELOPMENT OF CAMPUS ROOM USE MONITORING APPLICATION BASED ON ANDORID WITH MAGNETIC DOOR LOCK IN THE DEPARTMENT OF INFORMATICS AND COMPUTER ENGINEERING UNM

The condition of campus room doors that are often open has the potential for misuse of campus rooms that do not comply with the schedule set in the room and affect the security of the room facilities. This research aims to produce an Android-based campus room usage monitoring application using magnetic door locks at JTIK FT UNM as well as knowing the results of functional testing and performance testing or system performance. This research uses the Research and Development method with the Rapid Application Development Development model with stages: needs analysis, system design, system building, validation, testing, and final product. The final product of this research is a system developed in an Android-based room usage monitoring application that is integrated with a magnetic door lock using the QR Code feature. In this case, the system utilizes the ESP32 microcontroller as a chip that receives and provides signals according to the program in the system. The test results obtained validation of the Android-based campus room usage monitoring application using magnetic door locks can be said to be valid and suitable for use.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models’ performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Cyclical issues of pricing of regulated services

The article reflects current issues of the formation and regulation of tariffs that have developed in the modern economy. A brief analysis of the historical development of tariff regulation with the assessment of the used methods and ways is conducted. The authors identified expediency of tariff formation and regulation by a single body determining functions and set tasks. The study uses descriptive, analytical, and comparative methods. The article describes a positive example of applying the practice of deregulation with subsequent possible extension to other areas. The reasons of inefficiency of using the marginal indices mechanism are determined and justified, which is directly related to underfunding of the industry and lack of investment components. The question is raised about the fulfillment of the development, or social function by tariff regulation. It is proved that the current tariff system is not able to change the situation of increasing the investment base necessary to stabilise the deterioration of infrastructure. The main components of the current tariff regulation system are identified, and the directions of a new development model are proposed with consideration to the problems found by the study. According to the research results, conclusions are given that reflect the essence of the designated topic for possible solutions to issues existing in the functioning development model.

Stress Concentration Factors of Concrete-Filled Double-Skin Tubular K-Joints

Tubular joints are important connecting parts of a welded steel tube structure. The S-N curves based on the hot spot stress (HSS) method are often used to evaluate the fatigue life of tubular joints in practical engineering. The stress concentration factor (SCF) is a key parameter to calculate HSS. In this paper, stress concentration tests of hollow-section and concrete-filled double-skin tubular (CFDST) K-joints were carried out, respectively, and then finite element models of K-joints considering the weld were established. The developed models were validated with the experimental results. The influence of key geometrical parameters, such as the diameter ratio of brace to chord β, the diameter to thickness ratio of chord γ, the wall thickness ratio of brace to chord τ, brace angle θ, and hollow section ratio ζ on the distribution and key position of SCFs along the weld toe, was discussed. Parametric studies were conducted to obtain the calculating equations for the SCF values of CFDST K-joints. The results demonstrate that infill concrete can effectively reduce SCFs along the weld on the chord. When the hollow section ratio was reduced to 0.317, the SCF was reduced by 77.2%. Notably, the SCF reduction rate was sensitive to γ and θ, with a decrease observed as γ increased. The hollow section ratio ζ had a less pronounced effect on SCF distribution patterns, but as ζ decreased, the chord’s stiffness improved, suggesting a potential approach to enhance joint performance. The distribution of SCFs is similar for joints of the same type but different geometric configurations. The innovatively integrated hollow section ratio in the CFDST design equation significantly simplifies and enhances the precision of SCF calculations for CFDST K-joints.

Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study.

The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer. The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism. The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% (P = 0.027); stage II, 72% (P = 0.014); stage III, 50% (P = 0.032); and stage IV, 45% (P = 0.041). The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.

Application of hybrid modeling to predict California bearing ratio of soil

California Bearing Ratio (CBR) is used to assess bearing capacity, deformation characteristics of roadbed soil, and base layer material in pavement structure. In general, CBR is often determined by laboratory or in-situ tests. However, it is time- and cost-consuming to conduct this experiment because this test requires cumbersome equipment such as a compressor. In this study, two Artificial Intelligence models are developed, including a simple model (Decision Tree Regression, DT) and a hybrid model (AdaBoost - Decision Tree, AB-DT). Using 214 data samples from Van Don - Mong Cai expressway, Vietnam, 10 input variables of the model were considered namely particle composition (content of gravel (X1), coarse sand (X2), fine sand (X3), silt clay (X4), organic (X5)), Atterberg limits (Liquid limit (X6), Plastic limit (X7), Plastic index (X8)), and compaction curve (optimum water content (X9) and maximum dry density (X10)). The developed models were evaluated by using a variety of statistical indicators, including coefficient of determination (R2­­), Root mean square error (RMSE), and Mean absolute error (MAE). The results show that AB-DT model has higher accuracy than the DT model. Moreover, the SHAP value analysis shows that the variable X10 influences the CBR value the most. Thus, it implies that applying the AB-DT model to effectively predict the CBR of the roadbed soil saves time and money for experiments.

Lesson Study as a Professional Development Model for Teaching Spatial Ability in Primary STEM

This study explores the efficacy of a professional development (PD) model that employs lesson study to teach spatial ability skills in primary STEM education. The structure of the PD supported the ‘Insights’ mechanism by focusing on visualisation, mental rotation, construction and deconstruction, and spatial orientation, which are vital for nurturing students’ spatial abilities. The ‘Motivation’ mechanism was addressed through goal setting in lesson planning, motivating teachers to integrate spatial tasks into their curricula. Continuous feedback and practical support facilitated the ‘Technique’ mechanism, embedding learned skills into everyday teaching practices. Last, the ‘Embed in Practice’ mechanisms, including action planning and prompts, were effectively translated into classroom practices, evidencing the model’s operational efficacy.

The Influence of the Soil Profile on the Formation of the Elemental Image of Grapes and Wine of the Cabernet Sauvignon Variety

The features for assessing the authenticity of wines by region of origin are studied, based on the relationship between the mineral composition of the wine, the grapes, and the soil profile (0 to 160 cm) from the place of growth of Cabernet Sauvignon grapes. Soil, grape, and wine samples were taken from the territories of six vineyards in the Anapa district of Krasnodar Territory, Russia. Using the methods of ICP-OES, thermal, and X-ray phase analysis, the soils were differentiated into three groups, differing in mineralogical and mineral compositions. The soil samples of the first group contained up to 31% quartz, the second group up to 25% quartz and 19% mixed calcite, and the third group up to 32% calcite and 15% quartz. The formation of the elemental image of the grapes was studied, taking into account the total content and mobile forms of metals in the soil. The territorial proximity of the vineyards did not affect the extraction of elements from the soil into the grape berry, and the migration of metals for each territory was selective. According to the values of the biological absorption coefficient, the degree of transition of metals from the soil to a berry was estimated. For K, Ti, Zn, Rb, Cu, and Fe in all berries, the coefficient was higher than 1.00, which means that the berry extracts contained not only mobile-form, but also difficult-to-dissolve metal compounds. The migration of macro-components from the soil to the berry was low, and amounted to 6–7% for Ca, 0.8–3.0% for Na, and 25–70% for Mg of the concentration of their mobile forms. For all territories, the maximum correlation between metal concentrations in grapes and soil was observed for samples from a depth of 0–40 cm. The discriminant model based on concentrations of Rb, Al, K, Sr, Co, Na, Pb, Ca, and Ni showed the formation of clusters in the territories of vineyard cultivation. The developed model allow the problems of identifying wines by region to be solved with high accuracy, using their elemental image.

How does technological progress affect low carbon economic growth? Evidence from regional heterogeneity in China.

The rapid economic development leads to excessive consumption of fossil energy, resulting in a large number of carbon emissions, which makes the sustainable development of China's economy and society face great challenges. Under the background of global warming and environmental deterioration, developing low-carbon economy has become an inevitable choice for China to change its development model and improve its international competitiveness. Technological progress is the first driving force to promote economic growth. However, the influence degree and mechanism of technological progress on the development of low-carbon economy are still unclear. Based on this, the paper proposes the concept of low-carbon GDP, which is used to measure the low-carbon economic development of 30 Chinese provinces. Then, the panel fixed effect model is used to study the effect degree and mechanism of technological progress on low-carbon GDP and the moderating effect of other factors on the relationship between them. The results show that, first, economically developed provinces and regions have higher per capita low-carbon GDP and low-carbon GDP index, but the speed and quality of low-carbon economic development are not necessarily higher. Second, technological progress can promote the growth of low-carbon GDP and make greater contributions to resources-poor regions. Third, improving local education can make technological progress more effective in low-carbon economic development. China needs to upgrade technological development by developing new and high-tech technologies, promoting new urbanization and strengthening education in order to promote low-carbon economic development.

МОДЕЛЬ ОБРАЩЕНИЯ ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ НА БАЗЕ ОТЕЧЕСТВЕННОЙ АНАЛИТИЧЕСКОЙ ПЛАТФОРМЫ LOGINOM

Striving to improve the economic efficiency of pharmaceutical business in an unstable market situation and high competition requires the subjects of drug circulation to implement modern approaches to the organization of their activities. In these conditions, an important problem that the management has to solve in the process of circulation of medicines is the problem of quality of source information accompanying the business processes of pharmaceutical activities. The source information is not intended for the purposes of analysis, is presented, as a rule, rather raw material that requires preliminary processing by special methods, including their sampling, purification and transformation. The article discusses the advantages of practical application of the capabilities of Loginom analytical platform, which implements the above mentioned methods of pre-processing of raw material and allows solving the tasks of modeling the characteristics of drug circulation. Based on the concept of low-code architecture that provides visual design of complex data analysis scenarios, without involving IT-specialists and realize programming tasks by the business user. The object of the research was the current data obtained from the accounting system of material assets of a wholesale pharmaceutical organization. When organizing a request to the accounting system, based on the considerations of commercial confidentiality, it was decided to limit the 12-month time period and the assortment group, including only antibiotics. During the research, a data processing scenario was developed in the Loginom environment, representing the sequence of actions that need to be carried out to analyze the data. A processing script is a combination of data processing nodes configured by the developer to solve a specific problem. Each script node performs a separate operation on specific data. The drug circulation model scenario developed during the study, consisting of five nodes, includes two nodes with initial data “Receipt Data” and “Consumption Data”, as well as nodes “Data Combination”, “Sorting” and “Grouping”. The developed drug circulation model is an alternative to modeling based on the popular Excel spreadsheet processor, the capabilities of which are limited by the volume of processed data, low productivity on large volumes, the difficulty of automating the execution of sequences of actions, and the inconvenience of reusing processing scripts.