This paper presents a theoretical model of a system architecture designed to address integration challenges in multimodal hub telematics. Traditional design methods, with their rigid "function-device" sequence, fail to handle heterogeneous data from sources such as AIS and RFID. The proposed model replaces fragmented ("patchwork") solutions by coordinating edge nodes for real-time sensor data preprocessing with scalable cloud resources for batch analytics. The model's core components include dynamic gateway deployment based on NFV and key orchestration elements, such as a load balancer engineered to handle cargo flow spikes. The developed model serves as a foundation for building reference architectures tailored to the specific requirements of multimodal hub telematics. The paper outlines the model's constituents, its scope of application for various hub types, and its implementation stages.

The authors provide a transformative solution to address prevalent challenges in educational assessment in this research work. These challenges include aligning examination papers with syllabi, framing question papers to cover all Bloom’s Levels, and upgrading Bloom’s Levels by re-framing questions. Bloom’s Taxonomy (BT) is a cognitive level-based framework for understanding students’ educational progress through the assessment. The educational assessment method is the foundation for our chatbot, Bloomify. It is designed to revolutionize the assessment and help the keen educationists in three key scenarios. In the first scenario, mostly the educationists are not able to reflect the all the course outcomes of the subject. Bloomify will help them to design the question paper to cover all the course outcomes mapped with Bloom’s levels (BL). Second, often in a hurry, juggling between students’ assessment and institution’s accreditation tasks, the educationists fail to balance the question papers with all BLs covered. Bloomify will overcome the human fatigue and exhaustion associated with the manual creation of balanced question papers. In the third scenario, it is observed that many of the universities use of taxonomies and hence fail to design the assessment for the cognitive development of the students. Bloomify helps the universities that lack taxonomy or use alternative taxonomies, and offers a Bloom’s approach for assessing student development. Further, authors define a Bloom’s Score (BS) to find the average cognitive level of a question paper. BS will help to focus on specific cognitive level of an assessment. For example, the assessment for the graduate students can be defined with BS of 5 or 6 indicating higher Bloom’s levels: evaluate and create. Bloomify automates the calculation of Bloom’s scores ensuring coverage of all the cognitive levels. This will help the educationists in question paper creation process, saving time and effort. Bloomify includes three features: 1. Question classification 2. Suggestion of BL, and 3. Generation of entire question paper based on specific criteria like the syllabus, marking scheme, and desired average Bloom’s score. Bloomify provides a user-friendly interface, ensuring seamless integration with the assessment creation process. Bloomify enhances educational assessment by addressing all cognitive levels, ensuring critical thinking in students. It supports the educationists’ efforts to design an assessment focusing on knowledge application, analysis, evaluation, and creation, aligning with the real-world demands. The effectiveness of Bloomify is gauged by allowing different educationists to use this chatbot and calculating the Mean Opinion Score (MoS). Bloomify received scores of 9, 9.5, and 8 for classification, suggestion, and generation features, respectively from the educators of the authors’ institute.

This paper describes the impact of digitalization on public services in Albania. The specific platform that is studied is e-Albania. E-Albania has transformed the interaction between citizens and government services by automating many processes and reducing bureaucratic inefficiencies. It has promoted transparency in public administration. The platform offers over more than 1500 services in many sectors such as civil registration, taxation, healthcare, and business licensing. It enhances accessibility, and makes the process more efficient. Also, the platform is user-friendly. The study in this paper adopts a mixed-methods approach. It incorporates interviews with government officials and public servants and also surveys of citizens who use the platform. The paper has evaluated the effectiveness of the platform and the improvement of citizen satisfaction, government efficiency, and service accessibility. The paper finds that the platform of e-Albania has contributed to offering a better service to users. However, there are some challenges related to limited digital literacy, infrastructure, and technical problems. The paper has recommendations and future directions for the problems that are addressed. This is important for the sustainability of the digital platform and digital services.

Cloud computing is widely used in government and business sectors. For the business sector, security is one of the most important topics to be considered to protect user and confidential data, especially in a cloud, which cannot manage the infrastructure physically. A private cloud is deployed to utilize only in an organization for all internal users. In addition, a Network Intrusion Detection System (NIDS) is normally used for detecting intruders from outside organizations. However, if an attacker is inside a private cloud, a typical NIDS placement cannot detect this type of attack. Therefore, the objective of this study is to design and analyze the NIDS locations to defend against internal attacks on a private cloud. A private cloud was deployed using OpenStack cloud with 3 physical servers, which were one admin/network node, and two compute nodes. Two types of attacks, DDoS and SSH brute force attacks, were implemented. Three NIDS placement scenarios inside the cloud were proposed and experimented with. The results indicated that NIDS placements on a network node, and compute nodes with a tap port, gave the best performance. In this scenario, NIDS detected all internal attacks, and Application VMs responded to requests up to 79.16% without performance reduction.

This study aims to explore the relationship between parental parenting style and the mental health of college students based on the support vector machine (SVM) algorithm. Collect data on parenting style and mental health status through a questionnaire survey of college students. Using the SVM algorithm to analyze and process data, to reveal the impact mechanism of different parenting styles on the mental health of college students. The research results contribute to a deeper understanding of the role of parenting style in the development of college student's mental health, providing the scientific basis and practical guidance for college students' mental health education, and promoting their comprehensive and healthy growth.

In economics, individuals’ decisions are not always guided by pure rationality; emotional factors often influence choices related to consumption, investment, and saving. Emotions can thus determine market confidence, influence economic cycles, and even affect monetary and fiscal policies. In this regard, the development of social media has increased the formation and dissemination of fake news, whose impact requires particular attention. To detect and contain the spread of fake news in real time, some Artificial Intelligence tools are used. This contributes to promoting more reliable information and protecting society from media manipulation. A focus was placed on the impact of fake news on political elections. Building on this, the capabilities of the Bidirectional Encoder Representations from Transformers (BERT) classifier were tested to distinguish real news from fake news in order to mitigate the aforementioned problem and provide useful tools for the real-time identification of news that could alter not only political impact but also other areas.

The plentiful and low-cost nature of resource-constrained platforms e.g., Raspberry Pi devices has made them a backbone of modern applications particularly in sectors such as IoT, healthcare, and industrial systems. Nevertheless, they still have some constraints in terms of processing power, memory, and available energy that could limit the deployment of some computationally expensive protocols such as Bilinear Pair Cryptography (BPC). Here, we provide a benchmarking of BPC on a Raspberry Pi, exploring the feasibility and performance of the operations at its core: encryption, decryption, signature generation, and key exchange. To better understand the computational requirements of BPC, we analyze various metrics including execution time, memory use, and energy consumption. The research suggests that lightweight operations such as point addition and hash-to-point mapping apply to real-time applications; while over-resource-expensive tasks such as key exchange need optimization and only infrequent utilization. Suggestions to implement encrypted information systems incorporate the usage of algorithmic enhancements, hardware accelerators, and/or hybrid cryptographic conventions for security-efficiency balance. This work highlights the promise of secure hardware-based solutions such as BPC for limited-resource environments while providing an overview of its usage across systems.

Drowning is a severe public health problem that has claimed many lives in Turkey. Our study focuses on a novel strategy to address this problem. We developed a semi-automated technique that uses drone technology and machine learning to prevent drowning in real time. Advanced convolutional neural network (CNN) models, including Xception, ResNet-50, and YOLOv8, were used in our approach. These various models were trained using a special dataset that included simulated drowning situations in the Turkish Aegean Sea and a collection of online images. Though fully automated operation cannot be ensured, this method greatly improves water safety by guiding the drone to the drowning event and alerting the relevant staff. The models performed admirably with relative accuracy rates of 82.1%, 83.40%, and 85.8%. This cutting-edge approach demonstrates how machine learning could fundamentally alter the way major health concerns are handled. It also demonstrates how, when integrated with traditional safety procedures and human supervision, technology can improve and assist human efforts to safeguard the public's health.

Critical infrastructures play a central role in the welfare of contemporary societies and they should properly function 24/7. Since their role is so important, they regularly become targets of malicious parties, terrorists, industrial spies, and even hostile governments. In this paper, the scenario of cyber or physical attacks to CIs from tiny autonomous malicious drones is analyzed. In particular, this work focuses on indoor spaces, protected by mini-drones. The mini-drones are equipped with harmonic radar and run a novel algorithm, which guides them to scan the whole area. Assuming that the malicious drones behave as non-linear systems, the mini-drones transmit signals and analyze the received signals, creating a non-linear system 3D location map for the whole space. In the consecutive scans, any changes on the 3D location map indicate that the malicious drone has changed location. Simulated results and comparisons to state-of-the-art approaches exhibit the cost-effectiveness and time efficiency of the proposed scheme as well as its limitations.

The business area requires the presence of Supply Chain services, to respond to the constantly evolving requirements. Supply chains are dynamic networks that include a continuous flow of processes, starting with the supplier and ending with the customer. For effective supply chain management, a conceptual understanding of the knowledge underlying this field is of utmost importance. On the other hand, ontologies are considered one of the most appropriate ways of representing knowledge and valuable tools for decision-making situations. The purpose of this paper is to present an ontology created to identify the common concepts of supply chain management systems, with the ultimate goal of supporting and promoting Natural Language Processing technologies through the representation of terminological knowledge for this specific field.