Scalable Software Research Articles

Digital transformation in public sector services: Enhancing productivity and accountability through scalable software solutions

Digital transformation in public sector services is crucial for enhancing productivity and accountability, ensuring efficient service delivery to citizens. This process involves integrating scalable software solutions that streamline operations, improve data management, and facilitate communication among various stakeholders. Key areas of focus include cloud computing, data analytics, and automation technologies, which empower public agencies to optimize workflows and respond promptly to citizen needs. Cloud computing offers the flexibility and scalability necessary for managing large volumes of data and applications, enabling real-time access to information. By leveraging data analytics, public sector organizations can gain insights into service usage patterns, citizen feedback, and operational efficiency, allowing for data-driven decision-making. Furthermore, automation technologies, including robotic process automation (RPA), can significantly reduce manual tasks, freeing up resources for more strategic initiatives. Implementing scalable software solutions not only enhances productivity but also fosters greater transparency and accountability within public sector services. These solutions facilitate tracking and reporting mechanisms, enabling stakeholders to monitor performance metrics and service outcomes. Citizens benefit from improved access to information and services, leading to increased trust in government institutions. Moreover, digital transformation in the public sector encourages collaboration among agencies through integrated platforms that share data and resources. This collaborative approach promotes inter-agency partnerships and enhances the effectiveness of service delivery, ensuring that public sector organizations can address complex societal challenges more effectively. Challenges such as resistance to change, cybersecurity risks, and the need for continuous training must be addressed to maximize the benefits of digital transformation. Developing a culture of innovation and adaptability within public sector organizations is essential for overcoming these barriers and ensuring the successful implementation of scalable software solutions. In conclusion, embracing digital transformation through scalable software solutions positions public sector services to enhance productivity, accountability, and citizen engagement. This strategic shift not only improves operational efficiency but also reinforces the role of government as a responsive and accountable entity in the digital age. Keywords: Digital Transformation, Public Sector Services, Productivity, Accountability, Scalable Software Solutions, Cloud Computing, Data Analytics, Automation Technologies, Citizen Engagement.

Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA Software Technology for Precision Crop Growth Monitoring

In production activities and breeding programs, large-scale investigations of crop high-throughput phenotype information are needed to help improve management and decision-making. The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. To date, there is no automated system that can utilize the canopy images acquired through UAV to conduct a phenotypic character analysis. To address this bottleneck, we developed a new scalable software called CimageA. CimageA uses crop canopy images obtained by UAV as materials. It can combine machine vision technology and machine learning technology to conduct the high-throughput processing and phenotyping of crop remote sensing data. First, zoning tools are applied to draw an area-of-interest (AOI). Then, CimageA can rapidly extract vital remote sensing information such as the color, texture, and spectrum of the crop canopy in the plots. In addition, we developed data analysis modules that estimate and quantify related phenotypes (such as leaf area index, canopy coverage, and plant height) by analyzing the association between measured crop phenotypes and CimageA-derived remote sensing eigenvalues. Through a series of experiments, we confirmed that CimageA performs well in extracting high-throughput remote sensing information regarding crops, and verified the reliability of retrieving LAI (R2 = 0.796) and estimating plant height (R2 = 0.989) and planting area using CimageA. In short, CimageA is an efficient and non-destructive tool for crop phenotype analysis, which is of great value for monitoring crop growth and guiding breeding decisions.

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

Software architecture for implementing a personal budget manager with artificial intelligence

The rapid adoption of artificial intelligence (AI) in consumer markets has led to the need for extensive research into its impact on consumer behavior, particularly in how it exploits cognitive biases. This raises ethical questions and highlights the need to balance such influences with AI systems that help consumers, not manipulate them. The development and implementation of an intelligent financial management system, which not only automates financial processes, but also integrates AI-driven tools to support better financial decision-making, contributes to solving these problems. The work contains a description of the development of the architecture of an intelligent financial management system, which is aimed at solving problems related to financial illiteracy, the complexity of financial markets, and the impact of cognitive biases on financial decision-making. The system provides for the use of modern technologies of artificial intelligence and machine learning to automate financial processes and provide users with personalized recommendations. The development of a system based on microservice architecture requires the use of certain architectural patterns that ensure efficiency, reliability and scalability, so the work was based on a critical analysis of modern architectural paradigms, such as microservice architecture and Event-Driven Architecture. The implementation of AI supports ethical guidelines that prioritize transparency, fairness and consumer autonomy, i.e. ensure that the AI ​​system is free from bias, actions can be easily interpreted by users. The methodology of this research is based on an interdisciplinary approach that combines the analysis of architectural solutions, methods of machine learning and artificial intelligence with methods of ensuring the reliability and scalability of software systems.

Methodologies for developing scalable software frameworks that support growing business needs

Methodologies for developing scalable software frameworks that support growing business needs

HeMiRCA: Fine-Grained Root Cause Analysis for Microservices with Heterogeneous Data Sources

Microservices architecture improves software scalability, resilience, and agility but also poses significant challenges to system reliability due to their complexity and dynamic nature. Identifying and resolving anomalies promptly is crucial because they can quickly propagate to other microservices and cause severe damage to the system. Existing root-cause metric localization approaches rely on metrics or metrics-anomalies correlations but overlook other monitoring data sources ( e.g. , traces). We are the first to identify and leverage the anomaly-aware monotonic correlation between heterogeneous monitoring data, motivated by which we propose a novel framework, HeMiRCA , for hierarchical root cause analysis using Spearman correlation. HeMiRCA is based on the key observation that the microservice responsible for a particular type of fault exhibits a monotonic correlation between the trends of its associated metrics and the trace-based anomaly score of the system. HeMiRCA first calculates time-series anomaly scores using traces and then exploits the correlations between multivariate metrics and the scores to rank the suspicious metrics and microservices. HeMiRCA has been evaluated on two datasets collected from widely used microservice systems. The results show that HeMiRCA outperforms the state-of-the-art approaches by a large margin in identifying root causes at both service level and metric level, achieving a top-1 hit ratio of 82.7% and 74% on average, respectively.

Panacus: fast and exact pangenome growth and core size estimation.

Using a single linear reference genome poses a limitation to exploring the full genomic diversity of a species. The release of a draft human pangenome underscores the increasing relevance of pangenomics to overcome these limitations. Pangenomes are commonly represented as graphs, which can represent billions of base pairs of sequence. Presently, there is a lack of scalable software able to perform key tasks on pangenomes, such as quantifying universally shared sequence across genomes (the core genome) and measuring the extent of genomic variability as a function of sample size (pangenome growth). We introduce Panacus (pangenome-abacus), a tool designed to rapidly perform these tasks and visualize the results in interactive plots. Panacus can process GFA files, the accepted standard for pangenome graphs, and is able to analyze a human pangenome graph with 110 million nodes in less than one hour. Panacus is implemented in Rust and is published as Open Source software under the MIT license. The source code and documentation are available at https://github.com/marschall-lab/panacus. Panacus can be installed via Bioconda at https://bioconda.github.io/recipes/panacus/README.html.

Data Structure Profiler Using MERN Technology

Profiler introduces a cutting-edge approach to efficiently manage and track Data Structures and Algorithms (DSA) in software development projects. In the realm of modern software engineering, where complexity is ubiquitous and scalability is critical, effective management of DSAs is essential for ensuring the robustness, maintainability, and performance of software systems. DSA Profiler seamlessly integrates into the development lifecycle, offering developers a centralized platform to meticulously document, organize, and monitor the implementation of various data structures and algorithms within their codebase. Its intuitive interfaces and powerful functionalities facilitate the process of incorporating, modifying, and optimizing DSAs. By providing detailed insights and analytics, DSA Profiler empowers developers to make informed decisions about the most appropriate data structures and algorithms for their specific needs, ultimately streamlining the development process and significantly enhancing code quality. One of the standout features of DSA Profiler is its ability to benchmark data structures and algorithms, allowing developers to evaluate their performance under different conditions. This benchmarking capability ensures that developers can identify and address performance bottlenecks early in the development cycle. Furthermore, This collaborative environment helps maintain consistency and adherence to standards across projects, which is particularly beneficial in large-scale and distributed development environments. Keywords - data structure benchmarking, data structure optimization, algorithm performance tracking, software scalability, development lifecycle integration, codebase management, performance analytics, collaborative development tools, real-time optimization recommendations.

A Novel Industrial Software Model for Social Manufacturing

Abstract Distinct from the conventional manufacturing paradigm, the functioning of the Social Manufacturing (SocialM) model relies on a vast and dispersed array of social manufacturing resources. SocialM communities/groups emerge autonomously through business interactions, resource self-organization, and production information sharing, all driven by product orders within the SocialM system. This necessitates a more open, distributed, and autonomous software model to facilitate the allocation and operation of social manufacturing resources in support of the SocialM model. Consequently, this paper introduces a novel software development model designed specifically for SocialM. This proposed model employs business interaction software as the system's primary control portal, enabling efficient management of resources and users based on the social manufacturing communities/groups. Additionally, it facilitates the administration and oversight of numerous applications by utilizing an industrial Appstore. Moreover, the paper delves into the technological roadmap for realizing the essential technologies of the software model by integrating multi-agent and Message Queuing Telemetry Transport (MQTT) technologies. A 3D printing task simulation case was employed to demonstrate the model's working principle, which served to confirm the reliability and scalability of the SocialM software, as well as the feasibility of the SocialM theory.

Systematic Review of English/Arabic Machine Translation Postediting: Implications for AI Application in Translation Research and Pedagogy

The twenty-first century has witnessed an extensive evolution in translation practice thanks to the accelerated progress in machine translation tools and software. With the increased scalability and availability of machine translation software empowered by artificial intelligence, translation students and practitioners have continued to show an unwavering reliance on automatic translation systems. Academically, there is little recognition of the need to develop machine translation skillsets amongst translation learners in English/Arabic translation programs. This study provides a systematic review of machine translation postediting with reference to English/Arabic machine translation. Using the Preferred Reporting Items for Systematic Review and Meta-Analysis, the paper reviewed 60 studies conducted since the beginning of the twenty-first century and classified them by different metrics to identify relevant trends and research gaps. The results showed that research on the topic has been primarily prescriptive, concentrating on evaluating and developing machine translation software while neglecting aspects related to translators’ skillsets and competencies. The paper highlights the significance of postediting as an important digital literacy to be developed among Arabic translation students and the need to bridge the existing research and pedagogic gap in MT education.

Determining the minimum required cooling time of profiled assemblies at the Armenian Nuclear Power Plant taking into account the load schedule

Determining the minimum required cooling time of profiled assemblies at the Armenian Nuclear Power Plant, taking into account the load schedule, is an analytical process to establish the shortest required period during which profiled assemblies used in the nuclear power plant energy process must remain in active use. This process considers factors related to changes in the electrical load on the station during different periods. Within the scope of this definition, an analysis of the technical characteristics of profiled assemblies is conducted as well as their impact on the overall performance of the nuclear power plant. Key considerations in the cooling time include operational safety, compliance with regulatory requirements, and efficient distribution of energy resources in accordance with the load schedule. The goal of this process is to ensure optimal and safe operation of profiled assemblies, adapted to changes in the electrical grid and aligned with the load schedule, thereby contributing to the more efficient functioning of the nuclear power plant. This study aims to substantiate the minimum required cooling time for profiled nuclear assemblies characterized by an average uranium enrichment of 3.82% and an average burnup of 45.7 Megawatt-days per kilogram of uranium (MWD/kgU). Calculations are performed using the ORIGEN-ARP program, part of the SCALE software package designed for the analysis and modeling of radioactive and nuclear processes. The study considers parameters such as the composition of nuclear assemblies, fuel burnup, modeling using the ORIGEN-ARP program, and safety analysis. The results obtained aim to make a significant contribution to enhancing the efficiency and safety of nuclear energy installations

ПРОЄКТУВАННЯ АРХІТЕКТУРИ ПРОГРАМНОЇ СИСТЕМИ ДЛЯ СТВОРЕННЯ ЦИФРОВИХ ДВІЙНИКІВ МЕДИКО-БІОЛОГІЧНИХ ОБ'ЄКТІВ

The paper introduces an innovative and universal object-oriented architecture specifically tailored for the creation of digital twins pertaining to medico-biological en-tities, with otolaryngology serving as the primary exemplar. The very essence of this architecture is its profound utilization of polymorphism. By capitalizing on this tech-nique, the architecture offers a substantial reduction in the workload and complexity software developers might face when there's a necessity to adapt or modify the sys-tem for alternative medical specializations. The genesis of this architecture stems from a recognized need to establish a more modular and adaptable framework that can cater to the rapidly evolving land-scape of medical technology and digital twinning. As the medical realm continues to burgeon, having such a flexible and scalable software blueprint becomes paramount, especially when aiming to maintain relevance and efficacy in diverse medical sectors. The paper offers a comprehensive breakdown of the various component groups that constitute the system. Each group is dissected to provide clarity on its function, importance, and integration with the broader system. This hierarchical and systematic representation ensures that software developers, medical researchers, and other stakeholders have a clear roadmap when engaging with the architecture. In summation, this research stands as a pivotal contribution to the intersection of medical technology and software development. By providing a robust and adapt-able architectural blueprint for digital twinning in the medico-biological domain, it paves the way for future innovations and ensures that the medical community can harness the power of digital replication with increased accuracy, efficiency, and ver-satility.

The Role of object-oriented Programming in sustainable and Scalable Software Development

This study empirically evaluates how various practices and principles of object-oriented programming (OOP) impact the sustainability and scalability of software projects. A detailed analysis of real-world projects is carried out, considering aspects such as encapsulation, code reuse, modularity, and the application of inheritance and polymorphism. The goal was to identify patterns and best practices contributing to efficient and adaptable software development. The main results highlight that the adoption of OOP practices, such as encapsulation, code reuse, modularity, inheritance, and polymorphism, can be essential for efficient and adaptable software development over time, effectively addressing scalability and sustainability challenges in real-world projects.

MV-Checker: A software tool for multi-valued model checking intelligent applications with trust and commitment

Intelligent applications are highly susceptible to uncertainty and inconsistency due to the intense and intricate interactions among their autonomous components (or agents), making their verification theoretically and practically challenging. This paper presents the design and implementation of a new open-source and scalable software tool for modeling and verifying intelligent applications with commitment and trust protocols under both uncertainty and inconsistency settings, using reduction-based multi-valued model checking techniques. The proposed tool is equipped with original and novel algorithms that transform our logics of multi-valued commitment (mv-CTLC) and multi-value trust (mv-TCTL) that we recently introduced to their classical two-valued commitment (CTLC) and trust (TCTL) logic versions as well as to Computational Tree Logic (CTL). Moreover, the tool transforms the mv-CTL to CTL, and it is applicable for the classical model checking by transforming the classical logics of trust and commitment to CTL. To demonstrate the practicality and applicability of the proposed tool in real settings, we present and report experimental results over two blockchain-based applications in the healthcare domain. Finally, we provide discussions and comparisons between the proposed approaches regarding scalability and efficiency. Moreover, we provide packages of more than 11 experiments, including the ones we conduct in this paper and enhanced experiments from previous works. Our findings ensure that the proposed approaches and the software tool that implements them are highly efficient and scalable, giving accurate results under varying conditions.

A Comprehensive Review of Interactive System Architecture

This paper presents a detailed review of interactive software architecture (ISA). ISA has emerged as an important aspect in software engineering offering a framework that prioritizes user interaction, real-time responsiveness and adaptability. The paper explores various aspects crucial for the design, development, and maintenance of robust and scalable software systems. The review encompasses architectural patterns, design principles, scalability, maintainability, security, and emerging trends. The paper also examines the significance of ISA and challenges for the same. Objective: The objective of the paper is to provide a comprehensive understanding of the key considerations in interactive system architecture and how they impact the overall success of a software project. The paper also goes on to discuss the challenges and benefits of implementation of ISA Methodology: the study adopted a desktop approach by reviewing existing literature on the topic at hand so as to provide a simplified understanding of the interactive system architecture. Recommendations: at the end of the paper it provides recommendation on future developments in this field.

Design and Development of an IoT Gateway for Smart Building Applications

Due to the depletion of energy resources and increased energy demand, there is an increased focus on the energy consumption and management in buildings. Many building energy management (BEM) software platforms are commercially available to monitor and control energy consumption. These platforms are hosted on the physical hardware within the building, due to which the hardware specifications limit their performance. To address this limitation, cloud technology emerged which facilitates software to be deployed at a remote location that has scalable hardware resources. Conventional BEM software can leverage such a cloud platform to offer scalable and maintenance-free installation. Once hosted on a remote cloud platform, BEM software lacks direct connectivity to building sensors/controllers, hence requires a device to support remote accessibility. Most devices are bound to a local area network and therefore need an additional functional layer on top of its communication stack to perform Network Address Translation (NAT)-Traversal. This functionality is implemented on a scalable software which connects to the devices in the network and acts as a gateway for cloud-based BEM software to access devices in the local area network. Any message sent to this gateway is translated to a respective device protocol. This paper describes the design and implementation of such an Internet of Things (IoT) gateway for a cloud-based BEM system that requires support for BACnet, Modbus, and HTTP RESTful interface devices.

A Review: Attrition's Impact on Small and Medium Scale Software Development Organisation

A Review: Attrition's Impact on Small and Medium Scale Software Development Organisation

Implementation of a scalable platform for real-time monitoring of machine tools

In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher data processing needs than the previous ones.

Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods

In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in mixdistreg, an R software package that allows for the definition of mixtures of many different families, estimation in high-dimensional and large sample size settings and robust optimization based on TensorFlow. Numerical experiments with simulated and real-world data applications show that optimization is as reliable as estimation via classical approaches in many different settings and that results may be obtained for complicated scenarios where classical approaches consistently fail.

The effect of foreign language learning strategies on alleviating cross-cultural social anxiety

BackgroundWith the rapid development of international communication, learners gradually begin to socialize across cultures. However, some introverted students have cross-cultural social anxiety disorder when communicating with foreign friends. In order to alleviate this symptom, this study puts forward the optimization of foreign language learning strategies based on psychological suggestion.Subjects and MethodsIn this study, 400 patients with cross-cultural social anxiety disorder who meet the treatment standards were selected from the school and divided into the experimental group and the control group equally, and the number of male and female students in the two groups was the same. The experimental group and the control group were treated with psychological suggestion and routine psychological care for 12 months, respectively. The study used 3-min confidence assessment scale (3D-CAM) software to compare their social anxiety scores before and after treatment.ResultsBefore and after treatment, the average difference of social anxiety score in the experimental group was 42.5, which showed that the treatment method could effectively alleviate the disease. The value of the control group is only 16.8.ConclusionsThe experimental results show that the optimized diagnosis and treatment methods of foreign language learning strategies proposed in this study can effectively treat learners’ cross-cultural social anxiety disorder, and then improve the social mood of patients and students. This method has almost no sequelae and is suitable for the cure of cross-cultural social anxiety disorder.AcknowledgementThe research is supported by: Jiangxi Social Science Planning Project (Art), English translation study of cultural landscape in Jiangxi Section of Long March National Cultural Park (No. yg2021190).