Abstract: Digital literacy and technology-enabled education have immense potential to transform rural communities by addressing long-standing challenges such as limited access to quality education, lack of skilled teachers, and infrastructural constraints. Leveraging e-learning and Learning Management Systems (LMS) can provide innovative solutions to these problems, offering tailored learning experiences, access to diverse educational resources, and skill development opportunities. LMS tools, integrated with localized content and multilingual support, can bridge geographical and cultural divides, enabling equitable access to education and vocational training. Despite their transformative potential, implementing e-learning and LMS in rural settings faces numerous obstacles, including inadequate digital infrastructure, low internet penetration, limited affordability of digital devices, and a lack of digital literacy among both learners and educators. Furthermore, community reluctance to adopt modern educational technologies and the need for region-specific customization present additional hurdles. Addressing these challenges requires a multistakeholder approach involving government agencies, non-profit organizations, and technology providers to ensure sustainable adoption and meaningful impact. This review paper examines the role of digital literacy and e-learning in rural education, explores successful case studies of LMS adoption in resource-constrained environments, and analyzes the challenges of implementing these technologies effectively. It also provides actionable recommendations for scaling elearning initiatives, such as improving digital infrastructure, conducting community-focused training programs, and offering online, low-bandwidthcompatible LMS solutions. The findings underscore the critical role of digital literacy and LMS in bridging the rural-urban educational divide, enhancing skill development, and promoting socio-economic progress. The paper concludes with policy recommendations and future directions for research to ensure the scalability and inclusivity of digital learning systems, ultimately contributing to the vision of equitable and effective education for all learners.

Abstract: Retrieval-Augmented Generation (RAG) models have emerged as a powerful paradigm in natural language processing (NLP), combining the strengths of information retrieval and text generation to enhance the quality and accuracy of generated responses. Recent advances in natural language processing have led to the development of Retrieval-Augmented Generation (RAG) models, a hybrid approach that combines the benefits of retrieval-based and generative models. Unlike traditional generative models that rely solely on pre-existing knowledge encoded within the model’s parameters, RAG models leverage external knowledge sources, such as large-scale text corpora, to retrieve contextually relevant information to support the generation process. This ability to incorporate external information enhances the quality, relevance, and factual accuracy of the generated outputs, making RAG models particularly useful for tasks such as open-domain question answering, document summarization, dialogue generation, and specialized domains like legal and medical applications. In this survey, we provide a detailed exploration of RAG models, beginning with a comprehensive review of their underlying architecture. We describe the integration of retrieval mechanisms, such as sparse and dense retrieval, with large-scale pre-trained generative models, highlighting the process through which retrieved knowledge is utilized to guide and enrich the generation process. We also examine the different techniques employed to fuse retrieved information with the generative model, such as attention mechanisms, concatenation methods, and hybrid approaches. The survey further explores the diverse applications of RAG models, demonstrating their effectiveness across various NLP tasks and domains. Despite the success of RAG models, we identify and discuss several critical challenges that must be addressed for further advancement. These challenges include improving the quality and relevance of the retrieved documents, resolving issues of conflicting or ambiguous information between retrieval and generation components, enhancing model scalability to handle large corpora in real-time, and mitigating ethical concerns related to bias, fairness, and the potential generation of misinformation. We also explore the impact of these challenges on the real-world deployment of RAG systems, particularly in sensitive applications such as healthcare, law, and customer service. We provide an in-depth discussion of the current state-of-the-art techniques employed to address these challenges, including hybrid retrieval methods, novel strategies for knowledge integration, and advancements in model efficiency and scalability. Furthermore, we explore ethical considerations, such as the risk of bias in retrieved information and generated content, and propose methods for ensuring fairness and transparency in RAG systems. Additionally, we examine the need for new evaluation metrics that better capture the performance of RAG models in practical settings, where both retrieval quality and generative coherence are critical. Finally, the survey concludes by outlining promising future research directions aimed at advancing RAG models. These directions include the development of more sophisticated retrieval mechanisms, such as context-aware retrieval, the integration of structured knowledge sources like knowledge graphs, and the design of more robust and interpretable generative architectures. We also highlight the importance of addressing ethical concerns, such as improving bias mitigation techniques and enhancing the transparency of generative processes, to ensure that RAG systems can be deployed responsibly in a wide range of applications. This survey aims to serve as a comprehensive guide for researchers and practitioners seeking to understand the current landscape of Retrieval-Augmented Generation models and provides insights into the future evolution of this exciting area in NLP.

Abstract: The volume and diversity of digital information has led to a growing reliance on Machine Learning techniques, such as Natural Language Processing, for interpreting and accessing appropriate data. Among these techniques, vector and graph embeddings are used for representing data points, ranging from individual words to entire documents across multiple corpora. To retrieve this data, we need accurate similarity pipelines, to ensure we get relevant information from a given queried full-text. Current state-of-the-art does not guarantee this, as explainability is not certain. We demonstrate that our pipeline can achieve hybrid explainability, through combining graphs and logic to produce First-Order Logic representations, that are machine and human-readable via Montague Grammar. Preliminary results remark the effectiveness of the proposed approach in accurately capture full-text similarity by comparing our results with the cosine similarity derivable from sentence embedding generated by HuggingFace transformers.

Abstract: This article presents an educational game that would familiarize the user with the methodology and equipment used in the laser alloying process. The aim of the game is to introduce the player to laser welding technology. During the game, the player must prepare the material, select appropriate parameters for the laser alloying process and carry out the process itself on a virtual simulator. This simulation is part of the larger project of the virtual materials engineering laboratory. Therefore, the material obtained in the game can later be used for metallographic tests. The game was developed using the Unity environment, where all three-dimensional machine models, as well as the necessary virtual environment and gameplay scenario, were created. Thanks to the use of virtual reality, students can now learn how to use laser alloying and preparation devices without being physically present in the laboratory. This method is both attractive and safe, as there is no risk of damaging materials or equipment, and there is no danger to the user's health. The use of a 3D game in this study offers an interesting alternative to traditional teaching aids, making it beneficial for not only students but also teachers and others who are interested in learning more about the functioning and methods of operation of laboratory equipment.

Abstract: Topological relations form the backbone of qualitative spatial reasoning, and as such play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in continuous spaces has the concept of organizing these relations into a larger framework (called a conceptual neighborhood graph) been considered. Previous work leveraged matrix differences to derive the anisotropic scaling neighborhood for these relations. In this paper, a simulation protocol is used to derive conceptual neighborhood graphs of qualitative topological relations in Z2 for the operations of translation and isotropic scaling. It is further shown that when aggregating raster relations into their continuous counterparts and collapsing neighborhood connections within these groups that the familiar conceptual neighborhood structures for continuous regions appear.

Abstract: Blockchain technology has evolved beyond financial transactions to revolutionize trust systems. This paper presents a blockchain-based model for decentralized rental agreements and dispute resolution (DRADR). By leveraging smart contracts and implementing two distinct arbitration approaches, our model offers flexible solutions for rental agreement automation, transparency enhancement, and impartial dispute resolution. Our study provides a comprehensive technical analysis of both approaches through theoretical frameworks, smart contract implementation, game-theoretic modeling, and comparative evaluation across multiple legal jurisdictions. We explore the potential of blockchain technology to address long-standing challenges in traditional rental systems, such as power imbalances, inefficiencies, and legal disputes. Key contributions include the integration of decentralized and local justice systems, detailed game-theoretic analysis of strategic behaviors, and comparative insights into gas efficiency, economic viability, and jurisdictional adaptability across both arbitration approaches. This research paves the way for a more equitable and transparent rental market and contributes to the broader acceptance of blockchain-based solutions in everyday transactions.

Abstract: Digitalization is transforming the maritime sector, and the amount and variety of data generated is increasing rapidly. The true potential of data lies in its meaningful use to enable data-driven applications such as for highly-automated maritime systems or an efficient and secure traffic coordination. Data-driven applications usually rely on a heterogeneous data basis. The more context-related information is available, the better results the services can achieve. In practice, this poses an enormous challenge, as the heterogeneous data is not managed centrally by one single party, but is distributed across various actors. Therefore, a solution must be found for how distributed data can be used jointly and securely for the operation of maritime services without violating the sovereignty of the data providers. In this paper, a fully decentralized architecture is proposed to facilitate sovereign and secure data exchange between maritime actors, considering domain-specific challenges such as volatile connectivity, low bandwidth and the consideration of maritime standards. The approach is based on a data space architecture and demonstrates its functionality using a use case from maritime traffic management. It could be shown how the proposed architecture enables the acquisition of heterogeneous data from multiple providers and supports a safer and more efficient coordination of maritime traffic through the operation of a data-driven service.

Abstract: Case Management Model and Notation (CMMN) is a graphical notation used to model less predictable, highly flexible processes that may behave differently in each instance. It uses an event-centred approach and expands on what can be modelled with procedural modelling notations. Nearly a decade since the occurrence of CMMN, its practical use is questionable. We performed this research to identify possible reasons for this and to classify potential advantages and disadvantages of CMMN. With the aforementioned objectives, our research method was a systematic literature review, which provided a broad insight into the state of the investigated object along with techniques for analysing qualitative data, coding and successive approximation. From an initial set of 942 articles, 43 remain relevant. The results of the analysis and synthesis of the obtained data from relevant articles were generalised codes, which were used to explicitly answer the research questions. Results indicate that CMMN has good foundations in the declarative modelling approach and within the Case Management paradigm. Nevertheless, some issues were identified with notation and elements of CMMN and with its complement - Business Process Model and Notation (BPMN).

Abstract:

Obstacles that hinder the mass adoption of additive manufacturing (AM) processes for fabrication and processing of metal parts are discussed. The necessity of integrating an intelligent decision support system (DSS) into the professional activities of AM process engineers is proved. Advantages of applying a two-level ontological approach to the creation of semantic information for developing an ontology-based DSS are pointed out. Its key feature is that ontological models are clearly separated from data & knowledge bases formed on their basis. An ensemble of ontological models is presented, which is the basis for the intelligent DSS being developed. The ensemble includes ontologies for equipment and materials reference databases, a library of laser processing technological operation protocols, knowledge base of settings used for laser processing and for mathematical model database. The ensemble of ontological models is implemented at IACPaaS cloud platform. Ontologies, databases and knowledge base, as well as DSS, are part of the laser-based AM knowledge portal, which was created and is being developed on the platform. Knowledge and experience obtained by various technologists and accumulated in the portal will allow us to lessen a number of trial experiments for finding suitable settings and to reduce requirements to skills of users.

Abstract: Background. Nowadays, bibliometric analyses of data from abstract databases are often used to identify relevant research problems in order to rationalize the use of financial and other resources. The aim of this paper was to demonstrate the importance of pre-processing the text fields of bibliometric records to construct a term co-occurrence network and the feasibility of subsequently using Scimago Graphica to examine different slices of clustering results in detail in order to identify relevant research topics. Materials and Methods. A total of 8051 records exported from Scopus matching a filter (LIMIT-TO (EXACTKEYWORD, ‘Petroleum Reservoir Engineering’)) over the last ten years were used. VOSviewer and Scimago Graphica were applied for bibliometric analysis. The results of the study showed the relevance of using the filter ‘LIMIT-TO EXACTKEYWORD’ in the query to Scopus; the expediency of disclosing abbreviations in the text fields of records and preliminary clarification of texts; the effectiveness of using filters in the Scimago Graphica program to build a network of co-currency of terms in order to identify promising research topics; the proposal of promising research objectives arising from the analysis, which can be described by the following terms: 1. nanopores, shale oil, pore size, molecular; 2. nanoparticles; 2. It is observed that in some cases terms occurring in the same cluster are not the best choice for querying in order to expand the collection of publications on a given topic. Therefore, it is proposed to conduct a separate study using Apriori class algorithms for this purpose.

Abstract: The convergence of the Internet of Physical-Virtual Things (IoPVT) and the Metaverse presents a transformative opportunity for safety and health monitoring in outdoor environments. This concept paper explores how integrating human activity recognition (HAR) with IoPVT within the Metaverse can revolutionize public health and safety, particularly in urban settings with challenging climates and architectures. By seamlessly blending physical sensor networks with immersive virtual environments, the paper highlights a future where real-time data collection, digital twin modeling, advanced analytics, and predictive planning proactively enhance safety and well-being. Specifically, three dimensions of humans, technology, and the environment interact towards measuring safety, bio-health, and the climate. The goal would be to use three cultural scenarios, including urban, rural, and coastal locations. Our envisioned system would deploy smart sensors on external staircases, bio-health climate, and infrastructure sensors. Feeding various cameras, bio-sensors, and IoT sensors facilitates safe human activity recognition, routing, and planning, feeding real-time data into the Metaverse to create dynamic virtual representations of physical spaces. Advanced HAR algorithms and predictive analytics would identify potential hazards, enabling timely interventions and reducing accidents. We discuss the technological innovations enabling this vision, including advancements in sensor technologies, ubiquitous connectivity, and AI-driven HAR techniques. The paper also explores the societal benefits, such as proactive health monitoring, enhanced emergency response, and contributions to smart city initiatives. Additionally, we address the challenges and research directions necessary to realize this future, emphasizing technical scalability, ethical considerations, and the importance of interdisciplinary collaboration for designs and policies. By articulating an AI-driven HAR vision along with required advancements in edge-based sensor data fusion, city responsiveness with fog computing, and social planning through cloud analytics, we aim to inspire the academic community, industry stakeholders, and policymakers to collaborate in shaping a future where technology profoundly improves outdoor health monitoring, enhances public safety, and enriches the quality of urban life.

Abstract: The paper describes the conception and development of a relational database system for facility bookings. The design of the database was based on an Entity-Relationship Diagram (ERD) to outline entities, relationships, and attributes, thus ensuring an optimally scalable system. The practical realization consisted of establishing multiple interrelated tables to hold data about facilities, sites, bookings, clients, and employees. SQL queries were used for constructing the schema, inserting data, and producing business intelligence-related insight reports. The pandemic scenario outlines how the database system might have changed with the introduction of other unexpected global challenges. Findings illustrate how the developed system organizes data better, enhances transaction processing, and provides management analytics for decision-making at facilities. This study demonstrates how important relational databases are for facilitating operations while ensuring data integrity during any business or institutional application.

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The rise of online activities and the increasing prevalence of artificial intelligence (AI) in sociotechnical systems have brought about both significant opportunities and ethical challenges. Among these challenges, one of the most relevant is addressing digital threats such as sexual exploitation leading to sextortion (a form of coercion) that disproportionately affects vulnerable groups, such as minors. This paper advocates the integration of blockchain technology into AI systems to enhance trust, transparency, and ethical governance in combating such threats. The paper argues that by adhering to ethical guidelines through the integration of blockchain operations that bring about strong decentralization, immutability, and auditability, ethical issues in AI are better managed. The paper adopts a mixed research approach of qualitative analyses and conceptual model to develop some set of blockchain-integrated AI operations. Through a literature review of related works on sexual exploitation leading to sextortion, we first identified digital technologies that enable sexual exploitation, the role of AI in mitigating sexual exploitations, ethical issues in these AI applications and blockchain concepts that address them. Then we adopted BPMN modelling to conceptually describe blockchain operations that will limit AI ethical risks. The paper highlights the critical intersection of ethical AI development, social resilience, and digital ethics and addresses the complexities of integrating technologies while emphasizing the need for interdisciplinary collaboration for developing AI applications that address social issues.

Abstract: This paper focuses on a real-time baggage handling monitoring system by proposing a computational ergo-design approach. It presents the optimal system architecture for real-time baggage handling. The proposed architecture, called ARTEMIS (ARchitecture for real-TimE baggage handling and MonitorIng System), is designed for real-time baggage handling and monitoring. The circuit modeling is carried out using a directed graph. Five strategies are simulated to test their effectiveness and evaluate their performance within the system. A simulation that generates key indicators enables preliminary visualization and analysis of AGV behavior through predefined scenarios. These results are presented through an intuitive and ergonomic user interface, designed with a focus on user-computer interaction as a problem-solving process centered on the user’s experience. The results show that if the goal is to balance energy efficiency with effective baggage handling, the Mixed Advance/Delay Strategy appears to be the best overall choice, as it optimizes both energy consumption and baggage handling while maintaining relatively low waiting times. However, if minimizing queue time and maximizing baggage collection are the highest priorities (with less emphasis on energy efficiency), the Turnstile Strategy remains a solid option. In addition, the simulations show that the operator plays a central role in minimizing delays and ensuring the smooth operation of the system. Both local and global system failures depend heavily on the operator’s response time, decision-making, and overall efficiency. Therefore, operator efficiency and a well-designed support system are critical to maintaining a smooth and effective baggage handling process.

Abstract: With the development of large-scale distributed systems, how to efficiently collect and process data to support machine learning will become an increasingly important problem. However, traditional techniques often treat data acquisition and machine-learning tasks as disjoint processes, resulting in sloppy, less performant solutions. In this paper, we propose a new collaborative optimization framework which simultaneously considers the data acquisition and machine learning tasks. The method has innovatively introduced the feedback mechanism between the data acquisition module and machine learning module based on the current distributed learning model, and has achieved adaptive flexible data selection, intelligent resource allocation and dynamic optimization. Through state-of-the-art approaches like distributed reinforcement learning and data-driven scheduling protocols combined with decentralized gradient descent, the model has demonstrated superior scalability, low latency and precision compared to conventional solutions. It has been illustrated by experimental results that the accuracy of the proposed model is 15% higher than the benchmark method.

Abstract: The quality of educational multimedia edition design is determined by a set of characteristics that affect perception, readability and communication efficiency. Predicting the quality of multimedia edition design is based on a comprehensive analysis of characteristics that affect the aesthetics, functionality and adaptability of a multimedia product. Within the framework of this study, the goal is to develop a fuzzy system for predicting the quality of educational multimedia edition design. An approach to determining an integral quality indicator based on fuzzy logic is proposed, which ensures that the influence of various factors that cannot be characterized exclusively by numerical parameters is taken into account. A multilevel model of fuzzy logical inference is constructed, representing the hierarchical dependency between quality factors. Membership functions for linguistic variables are formed and their weight coefficients are determined using pairwise comparison matrices. The developed approach contributes to making informed management decisions in the process of creating multimedia products. The use of fuzzy logic methods allows one to assess the design quality even under conditions where the parameters are subjective or do not have clear numerical characteristics. Thus, quality prediction provides the opportunity to identify the design weaknesses at the stage of its development, optimize the process of creating multimedia editions and increase the efficiency of their use in educational and professional environments. Further research aimed at integrating artificial intelligence for automated updating of the knowledge base and expanding the system by introducing additional assessment criteria is considered to be promising.

Abstract:

Nigeria faces persistent national security threats, including terrorism, insurgency, cybercrime, and communal violence, which have significant socio-economic and governance implications. This study investigates the role of big data analytics in mitigating these security threats by analyzing structured and unstructured data from multiple sources. Using a mixed-methods approach, the study integrates literature review, case studies, and government policy analysis to assess the effectiveness of big data analytics in intelligence gathering, surveillance, cybersecurity, and early threat detection. The findings reveal that while big data enhances predictive capabilities and situational awareness, challenges such as data privacy concerns, infrastructure deficits, and ethical dilemmas must be addressed. The study recommends strengthening legal frameworks, improving technical capacity, and fostering public-private partnerships to maximize the potential of big data in national security strategies. The implications suggest that a data-driven approach can significantly improve Nigeria's ability to respond proactively to emerging security threats while balancing privacy and civil liberties.

Abstract: The quantum-classical interface (QCI) i.e., the boundary where quantum and classical systems interact, introduces unique security challenges and potential vulnerabilities within quantum technologies. This position paper explores the use of entropy as one metric to monitor and secure information transfer across the QCI. We propose an initial QCI security framework, outlining criteria by which entropy-based measures contribute to detecting deviations and potential threats to quantum system integrity. By presenting these theoretical constructs, we aim to catalyze further investigation and discussion, establishing a basis for empirical validations and future development of practical quantum security strategies.

Abstract: Lung cancer is a major health issue and a leading cause of cancer-related mortalities globally. Early diagnosis is essential for improving survival rates, with biopsy as the gold standard for tissue analysis. While digital histopathology enhances image quality and precision, manual analysis is time-consuming for pathologists, creating a need for automated classification methods. This research starts with image preprocessing using an adaptive fuzzy filter and segmentation via a Modified Simple Linear Iterative Clustering (SLIC) algorithm. The Segmented images are input to the Deep Learning architectures like ResNet-50 (RN-50), ResNet-101 (RN-101), and ResNet-152 (RN-152). Features extracted from these ResNet variants are fused using a Deep Weighted Averaging- Based Feature Fusion (DWAFF) technique, resulting in fused features termed ResNet-X (RN-X). To further refine these features, Particle Swarm Optimization (PSO) and Red Deer Optimization (RDO) techniques are employed within the Selective Feature Pooling layer. The optimized features are then passed to a Classification Layer that implements classifiers including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), SoftMax Discriminant Classifier (SDC), Bayesian Linear Discriminant Analysis Classifier (BLDC), and Multilayer Perceptron (MLP). Performance is assessed using K-fold cross-validation with K values of 2, 4, 5, 8, 10, and the results are compared using standard performance metrics. RN-X features obtained from the proposed DWAFF technique, combined with the MLP classifier, achieved a peak accuracy of 98.68% when using segmentation and RDO in the feature selection layer with K=10.

Abstract: The Zero Trust (ZT) model is pivotal in enhancing the security of distributed systems by emphasizing rigorous identity verification, granular access control (AC), and continuous monitoring. To address the complexity and scalability challenges of modern distributed systems, we propose a blockchain-based dynamic access control scheme (DACS) as a practical solution for implementing ZT principles. This framework dynamically manages access control lists (ACLs) and enforces policies through smart contracts. In the DACS framework, each blockchain node maintains an object list specifying access permissions within its ACL and incorporates a minimum trust metric (TM) threshold to evaluate access requests. The TM assigned to each node reflects its trustworthiness. To further enhance security, the framework includes security awareness, enabling the dynamic assessment of the risk factor (RF), which reflects the operational risk level. The TM of access-requesting nodes is updated at runtime based on their behavior, with penalties imposed for malicious actions according to the prevailing RF. Access control policies are dynamically adjusted, mitigating risks posed by potentially untrustworthy users with valid credentials. Implemented and tested on the Ethereum blockchain, the proposed DACS framework demonstrates its efficiency and effectiveness in securing distributed systems.