The rapid advancement of information and communication technology (ICT) solutions has significantly changed the landscape of secure communication systems, especially in scenarios involving unmanned aerial vehicles (UAVs) and real-time data exchange. SecuDroneComm, a hybrid platform created for secure, low-latency communication between drones and command centers, represents an innovative management approach to tackling issues related to data security, latency, and scalability. This paper provides a thorough comparative analysis of SecuDroneComm alongside leading ICT platforms, including ITU-T X.805 frameworks, SmartNet architecture for energy systems, and federated global identity frameworks for mobile and wireless communications. By exploring these systems, we pinpoint essential design principles and innovations that have influenced secure management communication platforms, with a focus on encryption, data integrity, server architecture, and hybrid deployment strategies. The findings highlight the platform's flexibility in managing critical situations like battlefield intelligence, disaster response, and public health monitoring. Additionally, the analysis investigates possible improvements, such as the integration of 5G technologies, blockchain for data validation, and enhanced access control systems. SecuDroneComm’s cutting-edge architecture, which merges security with real-time responsiveness, provides notable benefits compared to both traditional and modern platforms, meeting the changing demands.

The data explosion in enterprises has radically altered organizational demands for middleware solutions, necessitating the development of revolutionary architectural models that go beyond established service-oriented approaches. This article introduces a next-generation middleware architecture that combines real-time data processing with enterprise-wide integration services under an evolutionary hybrid event-driven model. The architected system differentiates itself through adaptive processing mode selection, in which synchronous patterns are used to service latency-critical operations, and asynchronous streaming processes service bulk data operations. At the heart of this contribution lies Kubernetes-native orchestration, allowing automatic scaling of resources based on real-time demand metrics, and it achieves unparalleled resource utilization efficiency while sustaining ultra-low response times geographically distributed throughout deployments. Machine learning-augmented predictive maintenance is a revolutionary concept using ensemble learning models trained on system telemetry to predict possible failures with near-perfect accuracy rates. The architecture uses a new data consistency model that preserves ACID properties between distributed microservices and allows eventual consistency for non-critical processes. Performance validation illustrates superior benefits compared to conventional Service-Oriented Architecture deployments, where the envisioned microservices implementation ensures consistent response rates under heavy load conditions. Real-world applications range from automotive autonomous vehicle systems to retail personalization systems, energy grid management, and smart city infrastructure, evidencing the flexibility of the architecture across a wide range of enterprise environments needing real-time analytics and system ease of integration.

Traceability and visibility of outbound logistics are crucial for companies aiming to enhance customer satisfaction and ensure product quality and reliability. The Internet of Things (IoT) offers promising solutions by enabling real-time tracking and intelligent decision-making in supply chains. However, processing and interpreting heterogeneous IoT data (sensors, actuators) remain challenging, as timely and accurate information dissemination is required. In this paper, we propose EDSOA-OLP-IoT; novel semantic middleware architecture based on the OLP-IoT ontology and designed to optimize outbound logistics operations. Our approach integrates a service-oriented event-driven architecture with a Publish-Subscribe communication model, complex event processing (CEP), and ontology-based reasoning. Unlike traditional IoT frameworks, our system enhances anomaly detection, improves decision-making accuracy, and optimizes resource management by leveraging semantic reasoning. Through experimental simulations, we demonstrate that EDSOA-OLP-IoT effectively reduces response time to critical events and enhances supply chain efficiency. To validate our approach, we conducted simulations based on real-world-inspired scenarios, including temperature monitoring in refrigerated trucks and warehouses. These scenarios showcase the system’s ability to detect anomalies and trigger appropriate responses, highlighting the potential of semantic reasoning and event-driven architectures for real-time logistics optimization.

Abstract. Citizen science, where participants from outside of academia contribute to data collection or analysis, is an important approach in ecological studies that can significantly improve both modelling outcomes and community participation. However, all ecological citizen science platforms developed to date rely on centralised server architecture for data storage and communication with citizen scientists, which can lead to unsustainable server maintenance costs for project managers as well as data sovereignty issues for the concerned communities, thereby endangering project resilience and sustainability after the end of a funded project. Distributed databases, which rely on peer-to-peer technology to store and share data, can address these concerns, but they are complex and conceptually different from centralised systems. As such, their use involves a very steep learning curve that hinders their adoption by citizen science practitioners in ecology, where neither project leaders nor end users are experts in peer-to-peer technologies. In this article, the authors use formal and open-ended feedback from workshops with academics to discuss how well-planned user interface design can be used to facilitate the adoption of peer-to-peer distributed databases in citizen science and provide generalisable key recommendations for the implementation of user interfaces in citizen science applications. In particular, we discuss several key conceptual differences between centralised and distributed applications, such as key-pair authentication and eventual consistency, that must be efficiently and visually communicated to end users. While there is extremely limited literature available on user interface design for distributed systems (and none so far in the ecological field), we find that lessons learned from other fields transfer well to the field of ecological citizen science, that well-designed user interfaces are key to the adoption of new technologies, and that simplicity and efficiency in interface design are more important than showing average users the details of how the underlying technology works. We propose these recommendations as a blueprint for future research and development of citizen science applications based on peer-to-peer distributed database technologies.

The pharmaceutical industry faces increasing challenges in managing its products, ensuring patient safety, and maintaining regulatory compliance. Traditional recall mechanisms depend on reactive approaches that often result in broad product withdrawal and an extended response deadline. Integration of artificial intelligence with serialization infrastructure represents a transformative opportunity to increase pharmaceutical recall management through future analytics and accurate targeting. The verification router service architecture provides a fundamental outline for real-time product authentication in the complex supply chain network. Machine Learning algorithms demonstrate an extraordinary ability to identify initial warning indicators within the serialization dataset, enabling active intervention strategies that prevent potential recall scenarios. Implementation challenges include data quality requirements, regulatory verification protocols, and adequate capital investment. The convergence of an AI-powered future analysis with the pharmaceutical serialization system provides significant benefits, including low recall cost, increased patient safety results, and supply chain transparency.

This paper presents a semantic framework to bridge the gap between IT-OT integration in industrial environments. The proposed solution addresses fundamental challenges of PHM (prognostics and health management) by providing contextualized semantic information from the shop floor to enterprise IT systems. Built upon an OPCUA (Open Platform Communications Unified Architecture) aggregation server architecture, the framework leverages OPCUA Information Models and companion specifications as its foundation for semantic representation. By transforming these models into knowledge graphs stored in RDF format, the system enables sophisticated semantic information retrieval through SPARQL-based semantic queries that can traverse complex relationships between equipment, processes, and operational parameters. The framework further implements GraphQL to automatically generate a Type schema derived from OPCUA types, creating a unified query interface that facilitates IT-like interaction with industrial data. This semantic approach significantly improves fault diagnostics, predictive maintenance, and anomaly detection by preserving contextual relationships that are often lost in traditional data integration methods. Furthermore, the GraphQL schema provides a structured foundation for generative AI applications to formulate contextually appropriate queries, extract relevant maintenance insights, and generate human-interpretable explanations of equipment health patterns, all while maintaining semantic fidelity across the IT-OT boundary. The vertical integration capability ensures that domain-specific models remain coherent across organizational levels such as line, area, floor, etc., enabling PHM practitioners to implement more effective condition-based maintenance strategies with improved visibility into causal factors affecting equipment reliability and performance.

Abstract Background The Welsh NHS dental reform programme seeks to move beyond activity-based performance measures towards prevention, needs-led care, and fuller use of the dental team’s skill-mix. Prior research explored patient experiences and barriers to access, but less is known about public perspectives on what NHS dental services should look like. This study engaged the public in co-identifying priorities and recommending solutions to better align reform with patient needs. Methods A qualitative study was conducted using semi-structured interviews (n = 35) and one focus group (n = 9). Participants were recruited via social media, organisational mailing lists, and research networks, and purposively sampled for diversity. Data collection took place virtually between November 2023 and May 2024. Transcripts were thematically analysed using NVivo 12. To enhance rigour, 30% of transcripts were double-coded, with themes refined collaboratively. Results Three key themes were generated: the architecture of an ideal service, pathways to access and equity, and enabling the patient-provider partnership. Participants prioritised timely, affordable and inclusive care delivered by the right professional, supported by effective communication and education for self-management. There was broad support for utilising team’s skill-mix, risk-based recall and clearer information on urgent and emergency dental services. Many participants valued digital tools, such as online booking and tele-dentistry; however, these findings reflect a relatively young, digitally engaged sample. Participants stressed that digital options should remain optional, with robust non-digital alternatives to ensure equity. Conclusions Participants emphasised the importance of timely, equitable access, inclusive services, clear communication, and support for self-management. They were open to flexible care models, including skill-mix and risk-based recall, provided these were underpinned by patient education and trust. Digital tools were positively viewed but must be complemented by non-digital pathways to avoid widening inequalities. Policymakers should integrate these perspectives into NHS dental reform to strengthen person-centred, equitable care.

A paradigm change in the design of automotive emergency response facilities is the shift towards roadside assistance, where the traditional analog coordination is converted into digital service delivery. This change involves the use of sophisticated service-oriented architectures that handle the emergency incidents as separate events by a standardized processing pipeline. State-of-the-art platforms employing distributed microservices architectures, event-based systems, and machine learning algorithms optimize provider selection and allocation of resources. The digitization generates quantifiable value in various stakeholder groups due to increased operational efficiency, better service transparency, and new capability development. The benefits achieved by customers include vastly decreased response time, the absence of information uncertainties, and access to high-quality service levels. The insurers achieve operational efficiencies in terms of lower call center volumes and automated claims transactions. Service providers enjoy smart job distribution algorithms and resource use. Its deployment entails vigorous privacy shield laws and extensive data governance schemes, balancing operational efficiency and regulation compliance in various jurisdictions. The high level of geofencing and dynamic frozen boundaries qualifies the responsible handling of data without interrupting services.

The financial services industry has undergone a profound architectural transformation as institutions abandon traditional monolithic systems in favor of distributed microservices architectures that better align with modern business demands for agility, scalability, and regulatory compliance. This comprehensive article examines the cutting-edge innovations in microservices architecture specifically tailored for financial environments, exploring how container orchestration technologies, service mesh implementations, and event-driven patterns address the unique challenges of maintaining data consistency, ensuring robust security, and meeting stringent regulatory requirements in distributed systems. The article reveals that successful microservices adoption in financial services requires sophisticated approaches to distributed data management, performance optimization for latency-sensitive operations, and comprehensive observability frameworks that enable effective monitoring and troubleshooting across complex service topologies. Contemporary implementations demonstrate innovative solutions for managing distributed transactions through saga patterns, implementing zero-trust security models, and achieving regulatory compliance through automated audit trails and policy enforcement mechanisms. The article of real-world case studies from major financial institutions and fintech organizations illustrates both the transformative potential and inherent complexities of microservices architectures, highlighting critical success factors including gradual migration strategies, organizational restructuring, and substantial investments in platform automation and team capabilities. Emerging trends toward serverless computing, artificial intelligence integration, and quantum-safe security preparations indicate that the architectural evolution will continue accelerating, requiring financial institutions to develop adaptive technology strategies that balance innovation with the stability and compliance requirements fundamental to financial services operations.

Continuous technological, ecological, and digital transformations reshape urban mobility systems. While sustainable mobility has become a dominant keyword, there are many different approaches and policies to help achieve lasting and properly functioning change. This study applies a comprehensive qualitative policy analysis to influential and leading sustainable mobility approaches (i.a. Mobility Justice, Avoid–Shift–Improve, spatial models like the 15-Minute City and Superblocks, governance frameworks such as SUMPs, and tools ranging from economic incentives to service architectures like MaaS and others). Each was assessed across structural barriers, psychological resistance, governance constraints, and affective dimensions. The results show that, although these approaches provide clear normative direction, measurable impacts, and scalable applicability, their implementation is often undermined by fragmentation, Policy Layering, limited intermodality, weak Future-Readiness, and insufficient participatory engagement. Particularly, the lack of sequencing and pacing mechanisms leads to policy silos and societal resistance. The analysis highlights that the main challenge is not the absence of solutions but the absence of a unifying paradigm. To address this gap, the paper introduces CalmMobility, a conceptual framework that integrates existing strengths while emphasizing comprehensiveness, pacing–sequencing–inclusion, and Future-Readiness. CalmMobility offers adaptive and co-created pathways for mobility transitions, grounded in education, open innovation, and a calm, deliberate approach. Rather than being driven by hasty or disruptive change, it seeks to align technological and spatial innovations with societal expectations, building trust, legitimacy, and long-term resilience of sustainable mobility.

Abstract Operational resilience in modern global supply chains depends on timely and accurate identification of emerging risks. While daily news has become a primary source for such insights, the sheer volume and unstructured nature of these data pose significant analytical challenges, requiring advanced tools to extract relevant and actionable information. This paper introduces an extended evaluation of the LARD-SC framework, a service-oriented architecture for supply chain risk management, by benchmarking five diverse variants of the large language model (LLM) in their capacity to detect, classify, and interpret risks. Drawing on a curated set of 120 real-world news articles on Apple’s Tier 1 suppliers, we adopt a standardized, prompt-based assessment to compare GPT-3.5 turbo, GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, and Claude 3.5 Haiku. Using expert-reviewed metrics, namely the Risk Validation Rate (RVR), Potential Risk Rate (PRR), and False Identification Rate (FIR), we derive a comprehensive Relative Performance Index (RPI) for comparison. Our analysis confirms that advanced GPT-4o variants produce the most consistent accurate risk identifications, achieving higher proportions of validated outcomes while minimizing false positives. Through these results, we highlight the significant promise of LLM-driven analytics for early risk detection in complex supply chains, along with practical considerations such as the influence of prompt engineering, interpretability demands, and the impact of data availability. The findings offer a blueprint for organizations seeking to improve resilience by systematically harnessing the capabilities of LLM within service-oriented risk management ecosystems.

This study originates from the need for a real-time student concentration monitoring system in response to challenges in maintaining learner engagement during the instructional process. The aim of the research is to develop Smart Focus Detection, an early warning system based on deep learning and computer vision designed to automatically detect students’ focus levels. The system was developed using a modern client–server architecture integrating the YOLOv8 behavior detection model, a FastAPI-based backend service, and a Vue.js-based user interface dashboard. Development followed the Agile methodology through the stages of planning, design, implementation, and testing. The YOLOv8 model was trained using public datasets from Kaggle and Roboflow, with data preprocessing and augmentation techniques applied, and evaluated using metrics including Precision, Recall, F1 Score, and Mean Average Precision (mAP). Results show that the model achieved an mAP@0.5 score of 85%, indicating high accuracy in detecting both focused and unfocused student behaviors. Limited trials in classroom settings demonstrated that the system’s interactive dashboard effectively displays data through live monitoring features, statistical visualizations, and automated notifications. The study concludes that Smart Focus Detection holds strong potential as a proactive pedagogical assistant, enabling teachers to conduct timely interventions to enhance student engagement and learning effectiveness. The findings imply promising opportunities for leveraging artificial intelligence in the development of data-driven adaptive learning systems.

In an interview with Evgeny Baboshkin, Head of the Business Development Board at Prime Broker Service (PBS), key aspects of the transformation of the Russian financial and technology sector (hereinafter referred to as fintech) in the face of sanctions pressure are discussed. The conversation touches on practical approaches to import substitution, challenges in restructuring the IT architecture, the impact on customer experience and possible scenarios for the further development of the industry in the context of technological sovereignty. Special attention is paid to the approach to building an independent architecture that is resilient to external shocks and able to adapt to the new regulatory and market environment.

ABSTRACT Accurate and timely vessel traffic information is essential for safe port operations, yet it is often unavailable or delayed. To address this gap, we propose an intelligent traffic organisation service (ITOS) architecture based on e-navigation. ITOS consists of three layers: the data layer for maritime data collection and storage, the business layer for traffic organisation modelling and intelligent service generation and the user layer for vessel interaction and service delivery. This architecture standardises real-time, on-demand exchange of digital traffic information, integrating resources across different channels. A case study at Huanghua Port, Bohai Sea, demonstrated that ITOS effectively provided intelligent traffic services, reducing congestion in space and time. By improving channel utilisation and minimising vessel waiting, ITOS enhances navigational efficiency and port operations. These results highlight ITOS's potential to support vessel traffic services, assist port authorities and strengthen maritime trade competitiveness.

This paper presents MetaTwin, a collaborative platform that enables seamless synchronization between physical and virtual realms for co-existing Extended Reality (XR) experiences. MetaTwin employs a hybrid decentralized server architecture to synchronize user interactions and environments within a shared space, allowing users to collaborate and socialize across physical locations while experiencing the convergence of real and virtual spaces. Integrated IoT devices act as both physical and virtual entities, supporting shared control and enabling resource sharing, such as presentation slides and music. We detail the configuration and deployability of MetaTwin as a solution for XR collaboration. To evaluate performance and feasibility, we compared MetaTwin with an existing XR platform and conducted an ablation study to identify the benefits and limitations of our approach. Additionally, a user study investigates the impact of spatial and temporal synchronization offsets on collaboration quality. Our findings inform the development of operational guidelines for future collaborative XR platforms.

The paper analyzes the experience of development of automation of information provisioning of administrative bodies of territorial government. The analysis made it possible to understand that the information provision of administrative government is fundamentally different from the creation of information systems for industrial enterprises. The example of a specific district administration shows that the creation of a single automated information system in its original understanding in the theory of information systems is impossible. The implementation of the concept of “growing” the system of F. E. Temnikov based on the registration and analysis of incoming requests is presented. The idea was proposed by one of the authors of the article, the head of information and communication department of the Administration of the Kalininsky district of St. Petersburg). After the accumulation of a large volume of unordered decisions and acquired technical means, the concept and model of a multi-level information and control complex were proposed and applied taking into account the features of the administrative government body, providing a holistic view of the accumulated information support. The concept is based on the definition of the system definition article. Stratified model analysis methods have been proposed, helping to development and adjust the structure of the information-control complex. The prospects methods of creating an information system based on the application of service architecture for the field of administrative-government are considered. The relevance of the study is that it shows the usefulness of analyzing the history of information system development of a specific district administration of a city to development a theory of creating administrative management information systems.

BackgroundThis paper presents the application of simulation to assess the functionality of a proposed Digital Twin (DT) architecture for immunisation services in primary healthcare centres. The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard.MethodsThe system modelling is carried out using the Unified Modelling Language (UML) to define the workflows and processes involved, including vaccine storage temperature monitoring and population vaccination status tracking. The proposed architecture is structured into four domains: observable elements/entities, data collection and device control, digital twin platform, and user domain. To validate the system's performance and feasibility, simulations are conducted using SimPy, enabling the evaluation of its response under various operational scenarios.ResultsThe system facilitates the storage, monitoring, and visualisation of data related to the thermal conditions of ice-lined refrigerators (ILR) and thermal boxes. Additionally, it analyses patient vaccination coverage based on the official immunisation schedule. The key benefits include optimising vaccine storage conditions, reducing dose wastage, continuously monitoring immunisation coverage, and supporting strategic vaccination planning.ConclusionThe paper discusses the future impacts of this approach on immunisation management and its scalability for diverse public health contexts. By leveraging advanced technologies and simulation, this digital twin framework aims to improve the performance and overall impact of immunization services.

This article presents a comprehensive analysis of Enterprise Architecture deployment for State-Wide Health Information Exchanges (HIEs) in public healthcare systems. The article examines the technical frameworks, governance models, consent management approaches, and clinical decision support capabilities that underpin successful HIE implementations. Through critical evaluation of architectural patterns, including Service-Oriented Architecture, FHIR-based APIs, and cloud-native platforms, the article identifies effective integration strategies for diverse healthcare environments. The article explores governance structures ranging from centralized to federated models, highlighting the sustainability advantages of hybrid approaches and public-private partnerships. Patient privacy protection is addressed through analysis of consent management frameworks, from opt-out models maximizing data availability to granular opt-in systems providing fine-grained control. The article concludes with an examination of real-time alerting systems and clinical decision support capabilities, demonstrating their impact on care coordination, readmission reduction, and clinical outcomes. Throughout, the article emphasizes the sociotechnical nature of HIE implementation, where success depends on balancing technical architecture with organizational governance and stakeholder alignment.

Ensuring optimal service continuity remains a critical challenge in cloud computing, especially when dealing with high traffic loads and system failure potential that can cause losses. To address this, this research presents the implementation of a high availability (HA) cloud system using the Least Connection load balancing algorithm implemented with Nginx, integrated with early anomaly detection and alert mechanisms. The HA architecture is implemented across two geographically distributed cloud service providers, Alibaba Cloud and Google Cloud, to analyze latency and performance differences under high load conditions. The system's resilience and scalability were evaluated through load testing using K6, simulating workloads ranging from 100 to 1000 Virtual Users (VUs) for single server configurations and 200 to 2000 VUs for HA configurations. The experiment results showed a significant improvement in service availability, reaching 100% uptime with the HA configuration compared to a peak of 98.79% in the single server environment. The Least Connection strategy effectively balanced traffic by monitoring active connections, resulting in a 29.73% increase in processed requests and a 42% reduction in system load at 1000 VUs. Additionally, the alert system successfully sent real-time Telegram notifications for delays or failures, enabling proactive mitigation. These results confirm that combining dynamic load balancing with proactive alerts can significantly improve service reliability, resource efficiency, and resilience to failures in distributed cloud infrastructure providing a viable model for robust and scalable cloud service architectures.

The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users. However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. To address these challenges, this study proposes a privacy-preserving, service-oriented microservice architecture tailored for intelligent Industrial IoT (IIoT) applications. The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. It supports both centralised and distributed deployments, promoting flexible, scalable, and secure analytics. We developed and evaluated differentially private models, including Radial Basis Function Networks (RBFNs), across a range of privacy budgets (varepsilon), using both real-world and synthetic IoT datasets. Experimental evaluations using RBFNs demonstrate that the framework maintains high predictive accuracy (up to 96.72%) with acceptable privacy guarantees for budgets varepsilon ge 0.5. Furthermore, the microservice-based deployment achieves an average latency reduction of 28.4% compared to monolithic baselines. These results confirm the effectiveness and practicality of the proposed architecture in delivering privacy-preserving, efficient, and scalable intelligence for IIoT environments. Additionally, the microservice-based design enhanced computational efficiency and reduced latency through dynamic service orchestration. This research demonstrates the feasibility of deploying robust, privacy-conscious AI services in IIoT environments, paving the way for secure, intelligent, and scalable industrial systems.