Scalability Of System Research Articles

Blockchain-Enabled Pension System Innovations: A Hungarian Case Study on Business Process Management Integration

This paper explores the integration of Business Process Management (BPM) with blockchain technology to enhance pension systems, using Hungary as a case study. Specifically, it addresses scientific challenges related to data access management, regulatory compliance, and system scalability within blockchain-based pension frameworks. This study investigates how BPM can improve the transparency, efficiency, and security of blockchain applications in pension administration by optimizing workflows and automating compliance with regulations such as GDPR. By analyzing operational flow diagrams and implementing architectural models, this paper presents an innovative approach to pension management, demonstrating significant improvements in service quality and operational efficiency. Findings from this research provide empirical evidence of the benefits of BPM-enhanced blockchain systems, offering insights applicable to pension systems beyond the Hungarian context, including examples from other countries.

Some Aspects of Interactive Simulation of Software Development with Integration of Ontological Models

Modern simulation technologies are of great importance, especially in those areas where errors in real processes can lead to significant costs and risks caused by the consequences of errors. The purpose of the article is to research of the interactive simulators in the field of software development with the integration of ontological models. The research methods are the main methodological approaches and technological tools for the development of interactive simulators in the field of software development with the integration of ontological models. Such methods, in particular, are: system and comparative analyzes – to identify the features of the creation and further use of interactive simulators in the field of software development with the integration of ontological models; the method of expert assessments, which involves the analysis of literary sources and information resources, conducting interviews and surveys of experts, the processes of development and testing of scalable and high-performance interactive simulators in the field of software development with the integration of ontological models. The novelty of the conducted research is the research of the possibility of integrating ontological models directly into the process of developing software simulated in interactive environments. The conclusion of the research carried out in the article is that the work was: the research was conducted on the use of simulation technologies in various subject areas; the role of interactive simulators is determined, which help the participants practice various scenarios in a virtual environment; development of critical thinking; improving decision-making skills; it is determined that interactive simulators are an important learning tool in the field of software development, where the integration of ontological models allows automating part of the processes and ensuring the standardization of procedures. Difficulties in implementing simulation training were identified: high initial costs and technical difficulties. For the successful integration of simulations, it is necessary to: provide a clear definition of educational goals (in particular, for the training of IT specialists); adapt simulations to the specific needs of the organization; integrate simulations into the general training program of IT specialists. It was determined that: ontological models play a key role in the improvement of interactive simulators, helping to structure and formalize knowledge in various subject areas; the use of ontologies contributes to the process of integration of various software components, thereby ensuring flexibility and scalability of systems.

A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring

Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance.

The Role of Blockchain in Secure and Scalable Distributed Learning Systems

Blockchain technology has emerged as a transformative tool for enhancing security, transparency, and scalability in distributed learning systems. This paper explores how blockchain can address critical challenges in these systems, such as data integrity, trust among participants, and scalability of learning models. By leveraging blockchain's decentralized ledger and smart contract capabilities, it becomes possible to establish secure data sharing, mitigate privacy concerns, and promote collaborative learning without reliance on central authorities. Furthermore, we discuss novel consensus mechanisms optimized for distributed learning, ensuring efficiency and scalability. Real-world applications in sectors like healthcare, finance, and education are also reviewed to highlight the practical benefits of integrating blockchain into distributed learning systems. Finally, the paper identifies open research challenges and future directions to advance this interdisciplinary field.

Comprehensive Smart Mess Management Web Platform

The Mess Management System is an innovative software solution designed to automate the manual process of managing mess-related activities such, inventory management, and billing. The system is developed using PHP as the programming language and MySQL as the database management system. The primary objective of this system is to reduce the workload on mess staff, increase efficiency, and reduce errors in the mess management process. This research paper focuses on the design and implementation of the Mess Management System and its impact on college hostel mess management. This is a cost- effective, efficient, and user-friendly software solution that automates the manual mess management process. The system can be customized to cater to the specific needs of different institutions and can significantly reduce the workload on mess staff. The system's scalability, flexibility, and ease of use make it a promising solution for college hostel mess management. However, further research can be carried out to address the system's limitations, such as the need for regular maintenance and the requirement of technical expertise.

Comparative analysis of traditional and automated financial management methods

The article examines the problems of using traditional and automated methods of financial management, the relevance of which is due to the rapid digital transformation, which creates the need to rethink the methodological foundations. In science, there is a contradiction between established classical management practices and new capabilities of automated systems, which creates uncertainty when choosing optimal approaches to the organization of financial processes. The purpose of the study is to conduct a comprehensive comparative analysis of the management methods under consideration based on a system of criteria (methodology for processing financial information, time characteristics of accounting processes, structure of information flows, mechanisms for controlling operations, principles of reporting, organization of analytical accounting, decision–making procedures, data integration, system scalability, documentation support). The author’s vision of the advantages, disadvantages and limitations of classical options and automation is formulated. It was possible to conclude that the fundamental differences between the characterized methods are determined not only by technological potential, but also by fundamentally different approaches to the organization of information processes. At the same time, the identified differences do not exclude the possibility of methodological integration, provided that the basic principles of financial management are preserved. The presented materials are of practical value to the heads of financial departments, accounting process methodologists, developers of automated management systems.

Targeted Drug Delivery in Cancer Therapy

Targeted medication delivery in cancer therapy is a promising method for increasing efficacy while minimizing side effects. This technique uses NANO materials, antibodies, or LIGAND-conjugated medicines to deliver medications directly to cancer cells while limiting damage to healthy organs. Advances in nanotechnology, such as liposomes, DENDRIMERS, and NANO particles, have enabled precise tumor targeting based on specific molecular markers expressed on cancer cells. Furthermore, the emergence of stimuli-responsive drug delivery systems, which release their payload in reaction to environmental changes like pH, temperature, or certain enzymes, provides new opportunities for site-specific treatment. Clinical trials have shown improved therapeutic outcomes, such as increased medication stability, decreased systemic toxicity, and improved tumor targeting. However, issues like immune system evasion, scalability, and tumor heterogeneity remain to be solved. Further research and innovation in tailored drug delivery platforms show significant promise for transforming cancer treatment, enhancing patient quality of life, and potentially overcoming resistance to standard medicines[1].

Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism

Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised data accuracy. These inefficiencies are crucial to address, as they can degrade the user experience by causing delays, compromising data integrity, and limiting system scalability. High CRT frustrates users, while increased IL reduces data accuracy, undermining trust and system reliability. The primary issue addressed in this study is the need for an advanced privacy-preserving mechanism that can provide multilevel security while maintaining optimal system performance. To overcome these limitations, the Layered Secure Online Collaborative Verification (LSOCV) algorithm is proposed, designed to offer a scalable solution with tiered privacy controls based on user requirements. LSOCV enhances Privacy Retrieval Accuracy (PRA), significantly reduces CRT, and minimizes IL. The experimental results show that LSOCV achieved a PRA of 91.97%, reduced CRT to 7ms, and decreased IL by up to 8% for 500KB files, outperforming existing approaches. This method provides robust privacy protection and efficient data handling on social networks, with the potential for future application in big data environments, such as Hadoop, to ensure scalable, secure, and efficient privacy-preserving solutions.

Air moisture harvesting in soil for indoor agriculture: A comparative study of moisture dynamics in shallow porous beds

Indoor farming can mitigate water scarcity, declining crop yields, and excessive chemical use in agriculture. However, it demands innovative solutions to reach its full potential. This paper presents a novel indoor plant cultivation technique that leverages atmospheric moisture. Shallow soil bed cooling from below can induce condensation within the soil pores, providing a sustainable water source for plant growth. We tested this method on wheat seed cultivation, observing a 40% growth increase in seedlings with cooled soil beds. We conducted a detailed study of moisture dynamics in porous sand beds to understand the underlying mechanisms of this technique. Choosing sand as a medium isolated the effects of porosity, temperature, and capillary action on moisture condensation. Sand's inertness allows a concentrated analysis of moisture dynamics without interference from chemical reactions or microbial activity. Experiments with cooled sand of varying particle sizes showed moisture condensation levels of 0.025, 0.042, and 0.092 kg/kg for coarse, fine, and superfine sand over 11 days. In soil, moisture reached 0.124 kg/kg over 22 days, highlighting the impact of porosity, temperature, and capillary forces. Our findings reveal exponential moisture increase over time and a linear relationship between bed water content and specific heat. The method is practical and adaptable, especially for remote locations and arid regions, as renewable energy sources can power it. This approach could revolutionize indoor agriculture, particularly in controlled environment systems. Controlling soil temperature can optimize growth conditions, increase yields, and minimize environmental impact. It offers versatility and scalability for various crops and systems.

IOT Based Parking Lot for Smart City

Abstract: This project presents an intelligent parking lot system using infrared sensors to detect available parking spaces in realtime. The system improves user convenience and reduces traffic congestion by providing accurate parking availability data. Infrared sensors, chosen for their reliability and low power consumption, are installed in each parking slot. The sensor data is processed centrally and shared via thingspeak website and digital displays. The system will be piloted in a section of the parking lot, with calibration ensuring accuracy. Results show reduced time searching for parking and decreased congestion. The system's scalability, energy efficiency, and real-time capabilities enhance user experience and optimize parking operations. Future developments include predictive analytics and expansion to other hightraffic areas.

Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques

The financial industry faces an increasingly complex landscape for portfolio management, where data-driven insights are crucial for optimizing asset allocation and managing risk. This paper explores the integration of predictive analytics and scalable data modeling techniques in enhancing financial portfolio management. By leveraging machine learning algorithms, big data architectures, and real-time data processing, predictive analytics can forecast asset trends, detect market anomalies, and assess portfolio risk with high precision. We evaluate several predictive models, including time-series forecasting, neural networks, and ensemble methods, for their efficacy in financial prediction. The study also discusses the role of scalable data modeling frameworks, such as Apache Spark and cloud-based data lakes, in handling vast volumes of unstructured data across different markets and asset classes. Findings indicate that predictive analytics, when paired with robust data models, can deliver real-time, actionable insights, enhancing decision-making for fund managers and institutional investors. Furthermore, this paper highlights best practices for implementing scalable models in financial institutions, addressing challenges like data latency, model interpretability, and system scalability. By adopting these advanced analytics frameworks, portfolio managers can achieve improved risk-adjusted returns, better asset diversification, and enhanced adaptability to market volatility.

A Survey on Modern Online Ticket Booking Systems

This study examines developments in the design and operation of online movie ticket booking systems and provides a thorough overview of the integration of secure QR code technologies, with a focus on visual cryptography. Although they make data access and exchange more efficient, QR codes are susceptible to risks such data tampering, counterfeiting, and unauthorized access. The use of visual cryptography to safeguard QR code data by splitting the code into discrete, unintelligible "shares" that must be combined in order to reconstruct the original data is the main topic of this survey. This study also examines advancements in ticketing platforms, particularly the move to frameworks like Node.js and MongoDB, which improve system scalability and performance. The findings show that data security, dependability, and user experience are much enhanced by contemporary encryption and backend developments in reservation systems.

The Integration of IoT, AI, and Machine Learning in Urban Systems

The integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) into urban systems represents a transformative approach to addressing the challenges of modern cities. By enabling real-time data collection, predictive analytics, and intelligent decision-making, these technologies enhance the efficiency, sustainability, and livability of urban environments. IoT sensors collect vast amounts of data from interconnected systems, including transportation, energy, waste management, and public safety. AI and ML algorithms analyze this data, offering actionable insights and optimizing resource allocation. This research explores the synergistic impact of IoT, AI, and ML on urban systems, emphasizing applications such as smart traffic management, energy-efficient buildings, and predictive maintenance of infrastructure. Additionally, the study addresses the ethical and technical challenges of implementing these technologies, including data privacy, cybersecurity, and system scalability. By examining real-world case studies and innovative frameworks, this paper highlights the potential of these integrated technologies to redefine urban planning and management, paving the way for intelligent and sustainable cities of the future.

RID-Cloud: Spectral Recurrent Neural Network-Based Intrusion Detection in Cloud Environment

Cloud computing (CC) is one of the most promising technologies for effectively storing data and offering internet services. There are several benefits to using this quickly evolving technology instead of more conventional defenses to shield computer-based systems from cyberattacks. Nevertheless, there are a number of drawbacks to the current approaches, including limited precision, which may affect system performance, scalability, security, and efficiency. To overcome these issues, a novel spectral recurrent neural network (RNN)-based intrusion detection in a cloud environment has been proposed for improve the security in CC. Initially, data preprocessing uses IoT-23 dataset values to reduce null or inappropriate feature values. Then the fuzzified entity is used for feature selection to analyze the features based on its threshold values. Using support index based on the behavioral rate, these threshold values select the relational features based on their maximum threshold range. Soft-max deep spectral RNN is used for identifying intrusion or non-intrusion. The intruded data is classified using recursive multi-perception neural classifier for categorized the risk level. The proposed model is superior based on accuracy, precision, recall, specificity, and F measure, according to the experimental data. The suggested tactics provide outcomes with an accuracy rate of 99.14%, which is a notable enhancement over previous studies and substantiates the effectiveness of the proposed methodology.

Evaluating Learning Engagement in MyPerjuangan Game-Based Learning Using E-Game Flow: A Case Study of History Education in Malaysia

Passing the History subject in Malaysia is compulsory for students who want to receive the Sijil Pelajaran Malaysia (SPM) certificate. Traditional teaching methods, often focused on rote memorization, have led to disengagement and low enjoyment among students. This research addresses the need for improved learning engagement and information management by developing MyPerjuangan, a game-based learning platform focusing on Malaya's Japanese occupation. The aim is to evaluate the effectiveness of game-based learning in managing educational content and enhancing student engagement in history education. The study developed the platform using the Game Development Life Cycle (GDLC) methodology. It employed the E-Game Flow model to evaluate user experience across several dimensions: concentration, feedback, goal clarity, and knowledge improvement. Data were collected through a survey of 10 respondents, consisting of teachers and students, with an overall satisfaction rate of 94.3%. The mean values for key factors such as concentration (92%) and knowledge improvement (100%) indicate high levels of engagement and effective information management within the platform. These findings suggest that MyPerjuangan successfully manages and delivers historical information engagingly, significantly improving students’ interaction with the material. However, limitations such as the small sample size and the platform’s exclusivity to desktop use were identified. Future research should expand the study to a larger, more diverse sample and explore multi-platform accessibility to enhance the system's scalability. In conclusion, game-based learning is valuable for improving educational content management and student engagement in history education.

Scalable order dispatching through Federated Multi-Agent Deep Reinforcement Learning

Efficient order dispatching is crucial for online ride-hailing systems, directly influencing user experience and platform revenue. Traditional methods often focus on maximizing immediate revenue through local observations of individual vehicles, ignoring the long-term potential benefits, the dynamic nature of dispatching systems, and the importance of collaboration among distributed vehicles. This typically results in suboptimal performance. To address these issues, we propose FedMARL4OD, a novel Federated Multi-Agent Deep Reinforcement Learning framework designed to optimize order dispatching. This framework integrates local learning via Multi-Agent Reinforcement Learning (MARL) for individual vehicles and global learning via Federated Multi-Agent Reinforcement Learning (FedMARL) across all vehicles. Specifically, we introduce an innovative reward mechanism in local learning that considers both the current revenue of each order and the supply–demand dynamics of the system related to potential future revenue, thereby improving dispatching performance. Moreover, we introduce a scalable model aggregation method in global learning that explicitly models interactions among distributed vehicles to facilitate collaborative learning. By progressively integrating local and global insights through average parameter aggregation, this method not only reduces communication overhead and enhances the learning efficiency of agents, but also ensures system scalability and maintains data privacy. Extensive real-world simulations demonstrate that FedMARL4OD outperforms baseline methods, achieving a 9.17% increase in Accumulated Driver Income (ADI) and a 7.75% improvement in Order Response Rate (ORR). The ADI improvements demonstrate the framework’s effectiveness in boosting revenue, while the enhanced ORR indicates a quicker fulfillment of users’ requests, improving user experience.

Collaboration Across Disciplines to Integrate Clinical Expertise into Medical Software Development: The Approach of the Dedalus Medical Office.

Medical informatics is a multidisciplinary field combining clinical and technical expertise. Addressing the challenge of aligning software design with clinicians' real-world needs, Dedalus established the Medical Office, a dedicated department designed to integrate clinical expertise directly into the software development process, in 2022. This paper details the approach and impact of the Medical Office. An international team of 15 healthcare professionals with experience in medical informatics was assembled. The team employed a multifaceted approach, incorporating global communication sessions and a ticketing system to track and analyze service requests. Over two years, 398 tickets were received, categorized into nine areas: clinical content curation, medical terminologies, clinical safety, clinical evaluation, design support, clinical UX, research & publication, real-world medical cases, and pre-sales support. The average duration of ticket resolution decreased over time, attributed to process fine-tuning and the formation of a relevant expert group. A preliminary satisfaction survey indicated positive feedback from technical teams. The collaborative model improved software design, usability, and clinical safety, demonstrating the value of clinician involvement. While preliminary results are promising, ongoing evaluation and adaptation are essential. The study emphasizes the importance of interdisciplinary collaboration in medical informatics and the benefits of clinician involvement in healthcare technology development. Future studies should explore this model's long-term impacts and scalability in other organizations and healthcare systems.

Movie recommendation and classification system using block chain

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Development of a Smart Lock System using QR Code Technology

A smart lock system refers to a modern security solution that enables users to remotely control access to their homes or businesses using a smart device such as smartphone, tablet, or computer. This paper presents the development of a smart lock system using Quick Response (QR) code technology to address the problem of insecurity, inconvenience, and inflexibility in access control. The proposed system eliminates the need for physical keys or complex authentication and verification methods. The system integrates an ESP8266 microcontroller, barcode scanner, 16x2 liquid-crystal display (LCD), solenoid lock, relay, and a 12V power supply. The system allows authorised users to generate unique QR codes representing their access credentials through a user-friendly interface. These QR codes are scanned by the system, which processes the codes, verifies access permissions via a central server and provides real-time feedback through the LCD. Upon successful verification, the system triggers a relay to control the solenoid lock, to grant or deny access. The implementation and testing of the smart lock system demonstrated successful operation. The performance indices include authentication speed, system reliability, accuracy and response time, which were evaluated and found to be satisfactory. The obtained results showed that the proposed system outperformed traditional methods such as keys, and fingerprint authentication, with an access speed of 2.00 seconds, which is significantly faster than the alternatives. Comparative analysis with existing access control techniques demonstrated the developed system's enhanced security features and user-friendly operation, making it applicable for residential, commercial, and industrial use. Additionally, the system's scalability and adaptability allow it to be customised for various environments, offering a versatile solution to diverse security challenges. This work contributes to the field of security technology by providing an efficient and scalable smart lock system with novel algorithms, which enhances the convenience, robustness of access control and seamless user experience.