Development Of System Architecture Research Articles

Method of Collaboration between Humans and Generative Artificial Intelligence in the Development of Information Systems

We propose a method for information system (IS) design with a focus on the applicability of intelligent chatbots. An analysis of existing chatbots shows that current solutions do not have the ability to formulate IS requirements during designing without human intervention. The study proposes to integrate ChatGPT (GPT4 model) as a chatbot to solve the problem of writing requirements elicitation for IS. The proposed method for solving the problem is based on a gradual reduction in entropy of chatbot response, which can be achieved by controlling such basic query parameters as the form, depth and breadth of the prompt. Based on the proposed method, an algorithm for controling prompts during a dialogue with a chatbot was developed, regulating the form, depth and breadth of the question to obtain the required level of content in the answer. The following results were obtained during the study: a method for obtaining new knowledge based on entropy of chatbot response reduction; algorithm for prompts control; the second phase of IS development is automated (including identification of business requirements and system objectives; analysis of existing solutions and technologies; determination of functional requirements and system capabilities; development of system architecture, including service definition and interaction; creation of interfaces for service interaction; establishment of a data model and database and formulation of business process logic). The method was tested to design an IS to support strategic decision making in the forestry industry. The design results received a positive assessment from experts. Analysis of the obtained results revealed the advantages and limitations of using chatbots as co-pilots in the design of IS and outlined directions for future research.

Development of a Web-Based Boarding House Information System with GPS Integration for Students

Introduction: This study develops an information system aimed at helping students locate appropriate boarding houses, tackling the shortcomings of conventional approaches that depend on recommendations from local residents or peers. Frequent challenges, such as restricted access, erroneous data, and unfulfilled anticipations, underscore the necessity for an efficient resolution. The system is designed to deliver thorough and current information regarding available accommodations, thereby improving accessibility and facilitating informed decision-making for students.Methods: The study utilizes the waterfall model, which includes four distinct phases: analysis, design, coding, and testing. In the analysis phase, user requirements were discerned through surveys and interviews, yielding valuable insights into the particular needs of students. The design phase encompassed the development of system architecture and prototypes for user-friendly interfaces, with a strong emphasis on accessibility and functionality. The coding phase executed the system, converting designs into a working web-based platform. The testing process included thorough validation conducted by media professionals and users to evaluate usability and effectiveness.Results: The validation from media experts resulted in a score of 0.88, indicating that the system is classified as "meeting standards" in terms of technical design and functionality. The evaluations conducted by users resulted in a score of 90.1, which is classified as "highly feasible," indicating a strong level of satisfaction and usability. The findings validate the system's capability to streamline the boarding houses search process.Conclusions: Essential elements encompass comprehensive room descriptions, current availability, pricing details, and location mapping, providing students with a dependable and effective resource to obtain accommodations that meet their requirements. By minimizing the time and effort needed to locate boarding houses, the system improves user satisfaction and fosters a more fluid academic experience.

System of Monitoring the Financial Activity of the Enterprise

Monitoring of financial activity is one of the key functions of enterprise management, because it, in particular, ensures control of current costs and income; contributes to the analysis of the financial state of the enterprise; supports planning of future financial flows. The purpose of the article is the research of information systems for monitoring the financial activity of enterprises, the identification of problems of financial activity, the analysis of the ways of using the appropriate software and the development of the system for monitoring the financial activity of enterprises. The research methods are the main methodological approaches and technological tools for the development of financial monitoring systems. Such methods, in particular, are: systematic and comparative analyzes – to identify the peculiarities of the creation of systems for monitoring the financial activity of enterprises; the method of expert assessments, which involves the analysis of literary sources and information resources, conducting interviews and surveys of experts, as well as the processes of development and testing of scalable and high-performance financial monitoring systems. The novelty of the research is the creation of a complex system for monitoring financial activity, which includes an intuitive interface, a flexible database and the ability to generate various reports, giving users the opportunity to effectively manage their financial data, simplifying the process of entering, editing and analyzing information through the use of a stack of modern technologies. The conclusion of the research carried out in the article is that the work was: identified the main problems of monitoring the financial activity of enterprises, which are solved with the help of modern financial monitoring systems; an analysis of existing software solutions was carried out; the advantages and disadvantages of the existing software for monitoring the financial activities of enterprises were analyzed; the main so-called «bottlenecks» of the automation of financial monitoring, which need to be solved, have been identified. On the basis of the conducted analysis, an author’s system for monitoring the company’s financial activity was developed, focused on ease of use and efficiency of financial data management. The development of the software of the author’s system included: the selection of the appropriate technological stack; system architecture development; implementation of basic functional modules (for example, such as transaction management, categories and reports); system database development; system interface development. The results of the conducted research open up new opportunities for improving financial management and making informed decisions based on the received data.

From Stars to Dogs: A Data Analytic Approach to Identifying “Out-Of-Favor” Products on E-Commerce Platforms

Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go “out of favor.” Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as “out-of-favor” or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics, 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising “star” product to turn into a liability (“dog”). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.

Building a Cloud-Based Data Engineering Pipeline for AI-Powered Customer Analytics

This article comprehensively examines implementing a cloud-based data engineering pipeline for AI-powered customer analytics in the retail sector. It explores the architecture of advanced analytics solutions, their implementation strategies, and their impact on modern retail environments. The article details the development of a sophisticated system architecture encompassing data integration, processing pipelines, and machine learning capabilities, along with best practices for data quality, security, and scalability. It demonstrates how cloud-based analytics platforms can significantly improve customer engagement, reduce churn, enhance marketing effectiveness, and drive operational efficiency through real-world implementation examples and performance metrics. The article provides valuable insights into the transformative potential of AI-powered analytics in retail operations, highlighting key considerations for organizations undertaking similar digital transformation initiatives.

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

Integration of the Functional Hazard Assessment Within a Model-Based Systems Engineering Framework

A major challenge in developing novel aircraft concepts is demonstrating the safety of increasingly complex and multifunctional aircraft systems. Aircraft manufacturers are adopting model-based systems engineering approaches to develop these new aircraft. The safety assessment process follows suit with model-based safety assessment. However, system and safety engineers still transfer information that is mainly document-based during the system architecting process. This paper aims to improve this process. First, a framework for developing system architecture specification models is introduced using the Architecture Analysis and Design Integrated Approach (ARCADIA)/Capella methodology and tool, illustrated with an aircraft landing gear braking system. Secondly, the paper proposes enhancements to the system specification model to enable functional hazard assessment and to capture the results within the system architecture specification model, i.e., using color-coding of system functions according to the severity of their associated failures as a visual aid to the system architect. In addition, the proposed features in the system specification model can help the safety engineer analyze failure relationships better. In summary, the proposed method improves consistency between the system architect and the safety expert in making safety-informed architecting decisions early in the development process, improving its effectiveness.

Cyber-Physical Distributed Intelligent Motor Fault Detection.

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber-physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber-physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates.

SEMA3: A free smartphone platform for daily life surveys

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 (http://www.SEMA3.com), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3’s development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform’s limitations and propose directions for future development of SEMA3.

Effects of Protein Hydrolysate Derived from Anchovy By-Product on Plant Growth of Primrose and Root System Architecture Analysis with Machine Learning

Protein hydrolysates (PHs) derived from waste materials are promising for sustainable practices in agricultural production. This study evaluated the effects of PH enzymatically derived from anchovy by-products on the root system architecture (RSA) and aboveground development of potted primrose. The plants were treated with 0.5, 1.0, and 1.5 g/L concentrations of PH by drenching with 100 mL/pot at two-week intervals and irrigated once a week with 100 mL/pot during winter and twice weekly during spring. The results revealed that the 1.5 g/L treatment statistically significantly improved dry weight and leaf area, while the highest leaf chlorophyll content was observed with the 1.0 g/L treatment. The treatments did not influence leaf and flower numbers. Treatment with 1.0 g/L produced the most substantial improvement in root surface area, projected area, volume, length, tips, and forks. Additionally, the study employed machine learning (ML) algorithms, including GP, RF, XGBoost, and an ANN-based MLP. The input variables (root surface area, projected area, volume, length, tips, and forks) were assessed to model and predict the root traits. The ML and ANN algorithms’ R-squared rates were noted in the following order: MLP > GP > RF > XGBoost. These outcomes hold significant implications for enhancing primrose growth.

АРХІТЕКТУРА ПРОГРАМНОЇ СИСТЕМИ ДЛЯ ВИРІШЕННЯ ЗАДАЧІ КЛАСИФІКАЦІЇ НА ОСНОВІ ПРИВАТНИХ ДАНИХ

Data analysis and artificial intelligence systems are becoming widely used in various spheres of human life. This is confirmed by more typical cases of their use, in particular, the selection of recommendations for the user in e-commerce, the detection of spam in e-mail services, and the moderation of user comments; as well as cases of personal use of such tools (for example, chatbots ChatGPT, Google Bard, Microsoft Copilot have appeared and gained significant popularity in the last two years). One of the key elements of such systems is data, which is necessary for training and testing software systems of intelligent data analysis. A significant amount of diverse data contributes to the construction of a software system with high accuracy. Considering this, the task of choosing and preparation of datasets that can be used in the construction of such systems is important. One of the difficulties in this task is the presence of private information in the datasets, which limits their use for systems of intelligent data analysis. The paper is devoted to the development of the software system architecture for solving the classification problem based on private data. The existing methods and architectural approaches for privacy-preserving in machine learning were considered. The architecture of the software system was proposed, the characteristic feature of which is the protection of private datasets by using of functional encryption, which allows to increase the number of datasets for training publicly available data analysis and artificial intelligence systems. The proposed architecture of the software system is based on the client-server architecture and functional encryption. The components are a classifier, a generator of encryption keys, and modules of functional encryption and decryption. Prospects for further research were discussed.

Methodology of creation and development of information systems for technological safety of mining facilities

Purpose. To develop methodological approaches to the process of informatization of technological security (TS) and to propose a project of step-by-step development of the architecture of information system (IS) of TS, which will contribute to effective integration of digital and mining technologies. To develop a mathematical model for finding the number of sensors required for probabilistic assessment of the concentration gradients of hazardous gases. Methodology. General and special methods of knowledge are used: structural analysis – to establish the structure and tasks of IS of TS; logical generalization – for formation of methodological system of directions of TS information measures; system analysis – to establish synergistic effect of complex implementation of the indicated directions; scientific abstraction – for development of a project for IS architecture derivation; mathematical formalization – for development of mathematical model for estimating gradients of concentration gases and the number of sensors. Findings. A project for the phased development of IS architecture of mining facilities based on the implementation of diffusion principle is developed, which will allow solving a complex of issues: integrated monitoring of danger of gas environment of underground structure for detection of gas traps and permanent determination of concentration gradients of hazardous gases, implementation of intelligent safety loops using analytical resources of technological mining process. A methodological system of directions for diffusion implementation of TS information measures is formed. The use of the method of system analysis made it possible to point out the synergistic effect of the complex implementation of the directions of implementation of TS information measures. Originality. A project for the phased development of the information system architecture of the technological safety of mining enterprises has been developed. A mathematical model has been developed to estimate the concentration gradients of hazardous gases and the number of sensors to ensure the appropriate level of probability of finding a sensor in each cell of the underground mine. Practical value. The methodological approach makes it possible to form an IS that implements a comprehensive approach to ensuring the appropriate level of technological safety of mining facilities.

Architecture design of a vehicle–road-cloud collaborative automated driving system

Transportation systems serve as a crucial foundation for maintaining the normal operation of cities and satisfying the requirements of public life. With the development of next-generation information technologies, automated driving technologies have brought new opportunities to improve the performance of traffic systems and the intelligence level of cities. Currently, significant research efforts have been conducted to develop automated driving systems in three major industries, i.e., automobile, roadway, and telecommunication. However, the collaboration and integration of automated driving systems among automobile, roadway, and telecommunications are still lacking, especially for collaborative development of system architecture and objectives. To address the need, this study first proposes a system architecture of vehicle–road-cloud collaborative automated driving system (VRC-CADS). Three levels of collaborative development, i.e., collaborative sensing, collaborative decision-making, and collaborative control, are designed for the VRC-CADS. Based on that, the typical scenarios of automated driving for each level of the system are further defined and interpreted. Moreover, feasible and systematic suggestions for the collaborative development of the VRC-CADS are provided, considering the cross-cutting collaboration among government agencies, academia, and industry. The proposed system architecture of the VRC-CADS will facilitate the optimization of urban lifelines and the evolution of intelligent cities.

Implementing text-messaging to support and enhance delivery of health behavior change interventions in low- to middle-income countries: case study of the Lifestyle Africa intervention

The prevalence of non-communicable diseases, such as diabetes and cardiovascular disease, is rising in low- and middle-income countries (LMICs). Health behavior change (HBC) interventions such as the widely used Diabetes Prevention Program (DPP) are effective at reducing chronic disease risk, but have not been adapted for LMICs. Leveraging mobile health (mHealth) technology such as text messaging (SMS) to enhance reach and participant engagement with these interventions has great promise, yet we lack evidence-informed approaches to guide the integration of SMS specifically to support HBC interventions in LMIC contexts. To address this gap, we integrated guidance from the mHealth literature with expertise and first-hand experience to establish specific development steps for building and implementing SMS systems to support HBC programming in LMICs. Specifically, we provide real-world examples of each development step by describing our experience in designing and delivering an SMS system to support a culturally-adapted DPP designed for delivery in South Africa. We outline eight key SMS development steps, including: 1) determining if SMS is appropriate; 2) developing system architecture and programming; 3) developing theory-based messages; 4) developing SMS technology; 5) addressing international SMS delivery; 6) testing; 7) system training and technical support; and 8) cost considerations. We discuss lessons learned and extractable principles that may be of use to other mHealth and HBC researchers working in similar LMIC contexts.Trial registration Clinicaltrials.gov, NCT03342274. Registered 10 November 2017.

Research on ICD design method of airborne system

Interface control file (ICD) is the intermediate link to undertake system architecture and software development during airborne system design process. Aiming at the design of the ICD of complex airborne systems, a set of design process based on systems engineering is proposed according to the engineering practice experience. The ICD management process is divided into two stages (data integration and change) according to NASA Systems Engineering Manual and practical engineering experience. In the face of a great number of ICD design changes and diversified design scenarios, an ICD design method based on a single data source is proposed, using the object-oriented idea to abstract the airborne system and bus data, and establish a business object data model, which greatly improves the productivity of the interface design and management process.

Phloem anatomy restricts root system architecture development: theoretical clues from in silico experiments

Abstract Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models (CPlantBox and PiafMunch) to help understand experimental findings about how local phloem anatomical features influence the root system architecture. Our hypothesis was that strong local phloem resistance would restrict local carbon flow and locally modify root growth patterns. To test this hypothesis, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico experiments highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open a door to a better representation of growth processes in a plant computational model.

A very long chain fatty acid responsive transcription factor, MYB93, regulates lateral root development in Arabidopsis.

Lateral roots (LRs) are critical to root system architecture development in plants. Although the molecular mechanisms by which auxin regulates LR development have been extensively studied, several additional regulatory systems are hypothesized to be involved. Recently, the regulatory role of very long chain fatty acids (VLCFAs) has been shown in LR development. Our analysis showed that LTPG1 and LTPG2, transporters of VLCFAs, are specifically expressed in the developing LR primordium (LRP), while the number of LRs is reduced in the ltpg1/ltpg2 double mutant. Moreover, late LRP development was hindered when the VLCFA levels were reduced by the VLCFA synthesis enzyme mutant, kcs1-5. However, the details of the regulatory mechanisms of LR development controlled by VLCFAs remain unknown. In this study, we propose a novel method to analyze the LRP development stages with high temporal resolution using a deep neural network and identify a VLCFA-responsive transcription factor, MYB93, via transcriptome analysis of kcs1-5. MYB93 showed a carbon chain length-specific expression response following treatment of VLCFAs. Furthermore, myb93 transcriptome analysis suggested that MYB93 regulated the expression of cell wall organization genes. In addition, we also found that LTPG1 and LTPG2 are involved in LR development through the formation of root cap cuticle, which is different from transcriptional regulation by VLCFAs. Our results suggest that VLCFA is a regulator of LRP development through transcription factor-mediated regulation of gene expression and the transportation of VLCFAs is also involved in LR development through root cap cuticle formation.

Development and Evaluation of a Unified Integrated Platoon Control System Architecture

Vehicle platooning has become a topic of substantial interest for development of safer and more efficient means of transportation. It is described as a string of connected autonomous vehicles traveling closely, maintaining certain inter-vehicular distances at a set speed boosting the road capacity, improving safety and lowering adverse environmental significance. In this study, several platooning concepts addressed in previous literature are revealed. A unified integrated platoon control architecture is constructed, employing the aforementioned concepts. This architecture contemplates a generic decoupled longitudinal and lateral engine-based vehicle model incorporating the powertrain dynamics. Several control algorithms are investigated, optimized and compared to manage the longitudinal inter-vehicular spacing distances of a formed platoon, as well as the lateral platoon motion tracking. The superior longitudinal controller in terms of spacing error convergence, velocity tracking and acceptable control effort, in addition to the outperformed lateral controller for its accurate lane change tracking and lower computational cost are promoted to be tested in the integrated architecture. Simulations are visualized using the Multi-Robot Systems Intelligent Transportation Systems (MRS-ITS) visualization tool for further realization of the results.

Design a Crime Detection System based Fog Computing and IoT

The Internet of Things (IoT) is a cutting-edge innovation that facilitates the cost-effective development of smart system architectures. Although current regulations necessitate installing an analog fire alarm system, such a system lacks the intelligence to instantly notify the appropriate parties in a timely fashion. In addition, since people are not always present, an analog fire alarm will not be able to prevent immediate danger or damage in the event of a fire. Therefore, the incident must be reported as soon as possible to the appropriate party in order to lessen the impact of a fire. In this study, we suggest a smart fire-alarm system made of a fire sensor and a sound sensor that can both detect fire and noise as well as the status of the analog fire alarm system to ascertain whether the analog fire alarm system is operational. We first tested our proposed smart fire alarm system to determine its effectiveness before putting it into use. From there, we ran experiments to determine how well it worked. The outcomes show that the system is trustworthy in a range of scenarios.

An architecture decomposition method of pneumatic catapult system based on OPM and DSM

The architecture strategy of the Unmanned Aerial Vehicle (UAV) pneumatic launch system should continue to evolve to adapt to complex and variable operating environments. Architecture representation, decomposition perspective, and cluster analysis play a vital role in the early phase of system architecture development. In order for the system to emerge anticipated and desirable intrinsic functional properties, an architecture decomposition method based on the Object-Process Methodology (OPM) and Design Structure Matrix (DSM) is put forward in this paper. The OPM is proposed to model the UAV launch process formally, and the matrix representation of the architecture of the pneumatic launch system is established. After the extension of the definition and operations of DSM, with the Idicula-Gutierrez-Thebeau Algorithm plus (IGTA + ) clustering algorithm, the transformation of the pneumatic launch system architecture from process decomposition to function decomposition is demonstrated in this paper. The analysis shows that the architecture decomposition of the pneumatic launch system meets the functional requirements of stakeholders.