Deploying post-quantum cryptography on highly constrained devices remains challenging due to the large key sizes and substantial storage and memory-traffic demands of leading lattice-based schemes. Although constructions such as Kyber, Dilithium, and NTRU offer strong resistance against quantum adversaries, their multi-kilobyte public keys and intensive memory access patterns limit practical adoption in microcontrollers, smart cards, and low-power edge environments. This work proposes a hybrid key-encapsulation mechanism that integrates a compact, seed-generated Module-LWE structure with a quantum-secure hash-based authentication layer. The design employs a small public seed to instantiate lattice matrices on demand via a lightweight pseudorandom generator and incorporates a Merkle-tree commitment to represent compressed auxiliary error information. Additional design considerations—including sparsity-aware secret keys, SIMD-friendly polynomial operations, and cache-efficient decryption paths—are intended to reduce runtime memory usage and computational overhead. The security of the proposed construction is analysed under both Module-LWE and hash-based one-way assumptions, with further consideration of constant-time execution and cache-line alignment to mitigate side-channel risks. This hybrid approach outlines a design pathway toward post-quantum key-encapsulation mechanisms suitable for deployment on memory-limited and energy-constrained platforms.

In the context of business systems, efficient data analysis through various Online Analytical Processing (OLAP) models represents a key challenge for performance optimization and timely decision-making. This study examines tabular and multidimensional OLAP models within the SQL Server (MSSQL) and Visual Studio environments to determine which model facilitates more effective data processing in a specific business context. An experimental analysis was conducted using the Stats dataset, where the same business question was addressed through both models, comparing their response times and query execution efficiency. Particular attention was paid to execution speed, data processing methodologies, and resource optimization strategies. Results indicated that the tabular model, which relies on in-memory technology and the Data Analysis Expressions (DAX) language, reduces data processing time by approximately 38 % and offers simpler modeling capabilities, making it suitable for analyses where rapid result retrieval is critical. In contrast, the multidimensional model, utilizing Multidimensional Expressions (MDX), provides advanced analytical features and greater scalability, rendering it more appropriate for complex analyses involving large datasets and predefined aggregations. Based on this comparison, it was concluded that the choice between tabular and multidimensional OLAP models depends on specific analytical requirements. If speed and flexibility are prioritized, the tabular model enables faster execution, whereas the multidimensional model offers enhanced control over analytical processes.

Online Analytical Processing (OLAP) technology facilitates efficient multidimensional data analysis, providing users with valuable insights for decision-making processes. Previous studies have explored the implementation of OLAP technology across various domains; however, a limited number of investigations have compared the Multidimensional Analysis Project and Pentaho on the same database or within a single study. This research contributes to existing literature by evaluating the performance and flexibility of these two tools using Microsoft SQL Server as a benchmark dataset, which represents the database of a specific blog application or an application based on user interactions with diverse posts. The manuscript details the modeling and implementation processes for OLAP cubes in both systems, emphasizing fundamental aspects of OLAP technology, key functionalities, performance metrics, and adaptability characteristics. Furthermore, a comparative analysis between Microsoft's solution and Pentaho was conducted, highlighting their respective advantages and limitations within the context of data analytics.

.

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems.

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education.

Software developers often need guides navigating them in the process of choosing the most suitable frameworks and programming languages for their needs. In this study, the impact of the programming languages on the performance of four popular backend frameworks: Spring Boot, ASP.NET Core, Express.js, and Django is examined using tools such as Apache JMeter and Docker under uniform conditions. With metrics like latency, throughput, docker build time, and deployment time the experiments revealed that ASP.NET Core exhibited the lowest latency (1ms for HTTP POST and GET), while Django achieved the shortest deployment time (0.31 seconds). Spring Boot and Express.js occupied the middle ground, balancing flexibility and performance. Besides valuable insights into the efficiency of each framework in real-world applications, this paper also includes a review of similar studies while complementing them by providing additional perspectives through concrete measurements and analyses under realistic conditions. This study contributes to a better understanding of architectural decisions and their relationship to performance while making the way for further research, such as analyzing more complex applications and energy efficiency.

This work looks into the utilization of blockchain technology within the telecommunications sector, emphasizing enhancements in security, privacy, and efficiency of data management. The “TelecomSecurity” smart contract, demonstrates blockchain’s features of decentralization and immutability, enabling robust user data protection, transparent identity management, and process automation. The paper focuses on protection mechanisms and resource optimization, showcasing detailed metrics of performance and gas consumption compared to traditional environments like Python and Flask. Additionally, it includes an analysis of the study “Blockchain technology empowers telecom network operation” by Dinh C. Nguyen, Pubudu N. Pathirana, and Ming Ding, published in IEEE Communications Magazine 2020, to discuss the blockchain’s potential to enhance operations within telecom networks, especially when integrating 5G technologies. The research establishes parallels between theoretical insights and practical findings, underscoring the blockchain’s relevance and use cases in real-world telecom scenarios. It also discusses potential applications in 5G networks and IoT devices, positioning blockchain as a transformative technology for the digital age, enhancing security, lowering costs, and improving operational efficiency. More specifically, this study explores how blockchain-based decentralized user management and smart contract automation can enhance telecom service agreements, reducing reliance on centralized authorities while improving transparency and operational efficiency.

Embedded systems, particularly when integrated into the Internet of Things (IoT) landscape, are critical for projects requiring robust, energy-efficient interfaces to collect real-time data from the environment. As these systems become complex, the need for dynamic reconfiguration, improved availability, and stability becomes increasingly important. This paper presents the design of a framework architecture that supports dynamic reconfiguration and “on-the-fly” code execution in IoT-enabled embedded systems, including a virtual machine capable of hot reloads, ensuring system availability even during configuration updates. A “hardware-in-the-loop” workflow manages communication between the embedded components, while low-level coding constraints are accessible through an additional abstraction layer, with examples such as MicroPython or Lua. The study results demonstrate the VM’s ability to handle serialization and deserialization with minimal impact on system performance, even under high workloads, with serialization having a median time of 160 microseconds and deserialization having a median of 964 microseconds. Both processes were fast and resource-efficient under normal conditions, supporting real-time updates with occasional outliers, suggesting room for optimization and also highlighting the advantages of VM-based firmware update methods, which outperform traditional approaches like Serial and OTA (Over-the-Air, the ability to update or configure firmware, software, or devices via wireless connection) updates by achieving lower latency and greater consistency. With these promising results, however, challenges like occasional deserialization time outliers and the need for optimization in memory management and network protocols remain for future work. This study also provides a comparative analysis of currently available commercial solutions, highlighting their strengths and weaknesses.

In the era of exponentially expanding data, particularly driven by social media development, effective data management and query processing have become critical challenges in application development. Graph databases, such as Neo4j, JanusGraph, ArangoDB, and OrientDB, offer significant advantages for applications requiring intensive processing of interconnected data, including social networks and recommendation systems. In this work, we focus on Neo4j as a representative of graph databases and MySQL as a representative of relational SQL databases for clarity and precision in data representation. We begin by introducing fundamental optimization techniques specific to each type of database. Subsequently, we concentrate on an experimental and investigative analysis of query performance on Neo4j and MySQL databases using original datasets and structures under consideration. The findings reveal that SQL databases outperform simpler queries, whereas graph databases excel in handling complex structures with multiple relationships. Moreover, the complexity of composing queries becomes apparent when addressing territories requiring table mergers (or node and relationship manipulation in graph databases). We also evaluate related research in this area, which further demonstrates that integrating graph and relational databases effectively can lead to optimal data management solutions, while utilizing both types of databases may offer combined advantages depending on the application requirements.