bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying the mixed and forced convection flow with transverse magnetic field
Authors: Evcin Cansu 
Published: September 2024

AbstractOptimal control of the steady, laminar, magnetohydrodynamics (MHD) mixed convection flow of an electrically conducting fluid is considered under the effect of the transverse magnetic field in a square duct. The viscous and Joule dissipations are included and the flow is driven by a constant pressure gradient. The triple nonlinear set of momentum, induction, and energy equations are solved in dimensionless form by using the mixed finite element method (FEM) with the implementation of Newton's method for nonlinearity with the discretize‐then‐linearize approach. Accordingly, FEM solutions are obtained for various values of the problem parameters to ensure the efficiency of the underlying scheme. This study aims to investigate the problem of controlling the steady flow by using the physically significant parameters of the problem as control variables in the case of a mixed convection flow. In this respect, classification of the type of convection, forced or free, is achieved by controlling the Grashof number (Gr). Besides, single and pairwise controls with Hartmann number (M), Prandtl number (Pr), and magnetic Reynolds number (Rm) are also used to regain the prescribed fluid behaviors and required magnetic field. Control simulations are conducted with the sequential‐least‐squares‐programming (SLSQP) algorithm in the optimization.
The neural networking strategy results in the tri‐hybrid nanofluid flowing over a slippery flat plate to act as an antimicrobial agent through solar radiation
Authors: Ajmal Mohammad Rehan , Gul Taza , Almutairi Fahad M. , Wanees S. Abd El , Alhawiti Aliyah S. , Ayed Hamdi , Dilshad Mohammad 
Published: September 2024

AbstractHybrid nanofluids are employed to enhance the antibacterial effect produced by solar radiation. Antimicrobial properties are present in metal nanoparticles (such as silver, copper, or zinc) or metal oxides (such as titanium dioxide or zinc oxide) when exposed to solar radiation. Nanomaterials that have antimicrobial properties are selected from the available literature. A solar sheet with an inclined plane has been chosen and filled with tri‐hybrid nanofluids (THNFs). Hybrid nanofluids including (CuO), Copper oxide, TiO2 (Titanium oxide), and SiO2 (Silicon dioxide), are selected from metal and metal oxide classes including water as a base fluid. The antimicrobial action caused by solar radiation is enhanced by the slip boundaries and variable porous space. The influence of flow and thermal fields on isotherms, velocities, flow lines, and Nusselt numbers are considered. The transformed system of differential equations is solved by the Control Volume Finite Element Method (CVFEM) and RK‐4 technique. The nanoparticulate volume fraction of CuO, TiO2, and SiO2 is largely responsible for the enhancement in the heat transfer rate (HT), as observed. Improved thermal performance is achieved through the flow of THNFs, which in turn acts as an antimicrobial agent. Increasing values of parameters lead to an improvement in the heat transfer rate, which in turn decreases microbial activity.
Two branches solutions for mixed convection flow of thermally Williamson nanofluid flow with heat transfer due to magnetized shrinking sheet
Authors: Al‐sawalha M. Mossa , Khan Zeeshan , Almheidat Maalee , Shah Rasool , Begum Nasra , Fathima Dowlath 
Published: September 2024

AbstractThe study of magnetohydrodynamics (MHD) and thermal radiation over‐stretching and shrinking sheets has significant applications in a wide range of fields, from chemical manufacturing to transport engine cooling systems, electronic chip cooling, plasma, the nuclear‐powered sector, saltwater, etc. The present study aims to numerically and theoretically analyze the movement of thermally magnetized Williamson nanofluid owing to an extended/contracting sheet. The regulating flow equations are converted to self‐similar equations through similarity transformation, and then numerically solved by bvp4c using MATLAB software. A comparison of the present numerical study with the published study is quite impressive. The investigation demonstrates that the self‐similar equations disclose the two branches for the limited shrinking factor range. For the stretching case, only one solution exists. As a result, the most fundamentally feasible solution has been determined by the linear assessment of temporal stability. For the aim of stability analysis, the lowest eigenvalue sign indicates the stability or instability of a solution. Through stability analysis, it is witnessed that the first solution (first branch) is stable. Due to the presence of the Lorentz force effect, it is perceived that the velocity curves decline in the whole channel. The point to be noted here is that the velocity of the lower branch compared to the upper branch is higher. The reduced Skin Friction is increased in the first branch and declines in the second branch for the two different values of magnetic factor.
AI-Powered Microscopy Platform for Airborne Biothreat Detection
Authors: Pálhalmi János , Mező Anna 
Published: September 2024

AbstractBecause Bacillus anthracis is one of the most lethal bioweapons, it is critical to create rapid, label-free screening and early warning systems to detect and classify anomalies in bacillus form vegetative cell and spore concentrations in the air. Even though significant effort has been invested in the development of various sensor solutions to detect, monitor, and identify airborne biological agents, no standard, interoperable, real-time or near-real-time optical sensor-based biothreat monitoring solution exists. Aside from the numerous advantages of genomic methods in microbe identification, optical sensors and microscopy-based technologies provide advantages in terms of rapid detection and classification capabilities. The AI-powered biothreat detection software platform from DataSenseLabs can perform intermethod comparison to cross-validate the results acquired by various quantitative phase imaging (QPI) measurement methodologies. This platform feature—support for multisensory data input—is not merely the foundation of the R&D level cross-validation approach, but also the key component of interoperable verification of air sample content in the case of airborne biothreat. Depending on the study design, sample type, and light microscopic or QPI measurement method, the platform’s algorithm system can detect and monitor abnormalities in the concentration of bacillus form objects taken from the air with greater than 80–95% accuracy. Another goal of the platform is to serve as a standardized tool for biomedical, environmental, and CBRN scientists to train and validate their concepts in pathogen detection and classification use cases, allowing them to better understand the gaps and challenges associated with artificial intelligence-powered optical sensor systems.
PRAETORIAN: From Protection to Resilience of Critical Infrastructures
Authors: Muñoz-Navarro Eva , Hernández-Montesinos Juan José , Marqués-Moreno Antonio , Papadopoulos Lazaros , Karteris Antonios , Demestichas Konstantinos 
Published: September 2024

AbstractWith the entry into force of the new CER directive, critical entities must focus on resilience. New tools will soon be demanded by Critical Infrastructure operators and innovative technologies will make the difference for the adoption of key measures: carry out risk assessments, take technical and organizational measures to enhance resilience and notify incidents. The H2020 PRAETORIAN project enables the security stakeholders of the CIs in Europe to manage the life cycle of security threats, from forecast, assessment and prevention to detection, response, and mitigation, in a collaborative manner with the security teams from related CIs—being the CIs in the same sector or not. PRAETORIAN supports the decision-making process of CI operators to prevent major damages to the installations, neighboring population, and the environment, while allowing a fast recovery after incidents. This means a significant move toward a Resilience-based framework, going beyond the merely Protection approach required by the former regulation.
Supporting Maintenance Tasks and Upgrading Roadworks Through an Integrated Automated System
Authors: Gkotsis Ilias , Aggelis Aggelos , Perlepes Leonidas , Kostaridis Antonis , Karali Theodora , Bilionis Dimitrios , Camarinopoulos Stefanos , Handanos Yannis , Molcho Solon 
Published: September 2024

AbstractAt a time of zero tolerance (zero accidents, zero operating restrictions, zero ice on our roads, etc.), it is increasingly necessary to control risks and to improve the knowledge of road operators with regard to the condition of structures in order to organize preventive and predictive maintenance that minimizes risks at an acceptable cost. Instrumentation, particularly in the context of preventive monitoring, is a valuable tool, as are risk analysis approaches. It allows, in this case, to better understand the behavior of structures, to know their condition, and thus to provide reliable input data for a robust risk analysis. HERON project aims to develop an integrated automated system to perform maintenance and upgrading roadworks, such as sealing cracks, patching potholes, asphalt rejuvenation, autonomous replacement of CUD elements, and painting markings, but also supporting the pre/post-intervention phase including visual inspections and dispensing and removing traffic cones in an automated and controlled manner. To achieve this, HERON aims at providing (among others) extended situation awareness, granting the users with real-time information stream during road maintenance and inspection operations, and supporting the reduction of fatal accidents, maintenance costs, and traffic disruptions, thus increasing the network capacity and efficiency.
A Semantic Engine for Fighting Cultural Goods Crime
Authors: Daskalakis Emmanouil , Alexakis Theodoros , Peppes Nikolaos , Demestichas Konstantinos , Adamopoulou Evgenia 
Published: September 2024

AbstractLooting and illicit trafficking of cultural objects pose a significant threat to the preservation of cultural heritage. Advanced digital tools for the early detection of such phenomena can play an important role in safeguarding cultural property on a global scale. Fighting illicit trafficking of cultural property in a timely manner is of vital importance, as any loss of time favors smugglers and can seriously undermine the efforts of protecting the cultural heritage. Towards this direction, this paper demonstrates the main functionalities provided by a semantic engine which will apply logical, rule-based reasoning for revealing new relations between the source and the destination points of stolen objects, among the different types of goods and their distribution channels, as well as among diverse structures and activities of traffickers. The semantic engine will display a wide range of alerts alongside a unified graph, thus helping Law Enforcement Agencies to tackle cases of cultural goods crime faster and more effectively.
Assessing AI Technologies for LEA Use: The ALIGNER Methodology
Authors: Casaburo Donatella , Jarlsbo Mathilde , Lückerath Daniel , Normelli Norea 
Published: September 2024

AbstractArtificial intelligence may greatly enhance the capabilities of law enforcement agencies. However, different (emerging) AI applications create different technical, ethical, legal, and organisational risks. To identify the most promising AI technologies for law enforcement use, the H2020 project ALIGNER has developed a sound methodology consisting of a collaborative technology watch process, followed by three cumulative assessments. This book chapter presents ALIGNER’s AI Technology Impact Assessment, Risk Assessment, and Fundamental Rights Impact Assessment.
Implementation and Evaluation of Standardisation Activities in Crisis Management Through a Full-Scale Exercise
Authors: Athanasiadis Spyridon C. , Michalis Panagiotis , Tsougiannis Vangelis , Ouzounoglou Eleftherios , Karagiannidis Lazaros , Amditis Angelos 
Published: September 2024

AbstractSTRATEGY project aims at selecting and implementing existing, evolving and new standards within solutions, tools, procedural guidelines and recommendations. The project will streamline and validate technical and organisational interoperability in a fully transboundary configuration with means of standards through the implementation of use cases involving EU and National Standardisation bodies. The main objective is to enhance the resilience of EU against all types of natural & manmade disasters (multi-hazard approach), through validating the standards of next-generation solutions and procedures ensuring an effective and efficient collaborative response. This article presents the validation activities carried out for two pre-standardisation items entitled ‘Specifications for Digital Scenarios for Crisis Managements’ and ‘Requirements for acquiring digital information from victims during Search and Rescue operations’. The results of the developments and the implementation and evaluation through a Full-Scale Exercise are presented which aimed at collecting feedback from key crisis management stakeholders which included first responders, academia and researchers, industry and public authorities.
AI-Based Framework for Supporting Micro and Small Hosting Service Providers on the Report and Removal of Online Terrorist Content
Authors: Kalpakis George , Paternoster Caterina , Mancuso Marina , Kozhuharova Denitsa , Spathi Theoni , Semertzidis Theodoros , Tsikrika Theodora , Vrochidis Stefanos 
Published: September 2024

AbstractThis work proposes a framework integrating AI technologies and tools to assist micro and small hosting service providers (HSPs) in adhering to the terrorist content online (TCO) Regulation. The framework encompasses: (i) a suite of AI tools for the automated detection and removal of TCO, (ii) a federated learning infrastructure for (re-)training and testing the underlying AI models, (iii) a secure shared hash repository to facilitate the automated prevention of uploading subversive content to HSP platforms, and (iv) a unified reporting mechanism to support the submission of removal orders by competent authorities. The proposed framework is being developed in the context of the ALLIES project in a user-driven manner based on the requirements of HSP and law enforcement agency (LEA) personnel. In addition, its development is guided by a desktop and empirical study on the landscape of online radicalisation, extremism conducted by domain experts. Overall, this framework is envisaged to support micro and small HSPs and protect their platforms from terrorist abuse in an efficient and cost-effective way.
CTC Project: Advancing the Fight against Terrorist Financing with AI Technologies
Authors: Spathi Theoni , Jofre Maria , Djouadi Mateo , Skouras Taxiarchis , Stylianou Nikolaos , Raptis Sotirios , Kosmatopoulos Andreas , Theodosiadou Ourania , Koufakis Alexandros , Semertzidis Theodoros , Sanchez Gisela , Da Bormida Giorgio , Skarlatos Efstathios , Lykousas Nikolaos , Pavué Clément , Kavallieros Dimitris , Tsikrika Theodora , Vrochidis Stefanos 
Published: September 2024

AbstractTerrorist financing (TF) poses a significant threat to the security and stability of the European Union (EU) and its member states. To combat TF in an efficient and timely manner, advanced technologies are required. The EU-funded Cut the Cord project (CTC) seeks to strengthen the EU’s capability to understand and counter TF by exploring innovative technologies and developing artificial intelligence (AI)-based solutions. Moreover, it pioneers the development of a blockchain-based chain-of-custody system that ensures a secure, transparent, and auditable exchange of intelligence among stakeholders, thus providing an immutable record of transactions and contributing to the robustness of evidence that could be presented in a court of law. This paper outlines CTC’s objectives, proposed solutions, and potential impacts, sparking discussion on novel approaches to detect and fight TF.
Learning Domain-Invariant Spatio-Temporal Visual Cues for Video-Based Crowd Panic Detection
Authors: Calle Javier , Unzueta Luis , Leskovsky Peter , García Jorge 
Published: September 2024

AbstractDeveloping a vision-based approach for identifying crowd panic in video surveillance systems is a complex task due to the struggle to gather enough real-world event recordings for training. The use of synthetic data can mitigate this issue, but the domain gap between synthetic and real-world samples needs to be managed to achieve precise results. We present a method to train these systems effectively by combining synthetic and real data to differentiate between normal and panic states. Our method learns domain-invariant spatio-temporal visual cues of the scenes along with supplementary descriptive attributes of crowd directions for the panic state classification. Experimental results show its potential with respect to alternative state-of-the-art methodologies and how it can effectively leverage synthetic data to train this kind of systems with high accuracy.
A Generative Adversarial Network (GAN) Solution for Synthetically Generated Botnet Attack Data Samples
Authors: Peppes Nikolaos , Alexakis Theodoros , Daskalakis Emmanouil , Adamopoulou Evgenia , Demestichas Konstantinos 
Published: September 2024

AbstractThe trend of digitization in almost every aspect of daily human life has raised serious concerns about security in the digital world. With new technologies, solutions, and tools emerging daily, new vulnerabilities also arise. Botnets are among the most widespread cyberthreats in the modern digital landscape, as they can breach and affect entire organizations or domains by infecting just a single device in a network. This study involves the design and implementation of a generative adversarial network (the so-called BotNetGAN - BNGAN) to synthetically generate botnet attack data samples, which are assessed for both quality and quantity using specific data quality indicators. The quality assessment results show that the produced data are very similar to the original ones. Therefore, the significance of GANs in data generation processes is almost undeniable. Furthermore, increasing the volume of annotated data can lead to the improvement and enhancement of AI-based cybersecurity solutions that heavily rely on data availability.
Leveraging Continuous Learning for Fighting Misinformation
Authors: Adamopoulou Evgenia , Alexakis Theodoros , Peppes Nikolaos , Daskalakis Emmanouil , Demestichas Konstantinos 
Published: September 2024

AbstractThe evolution brought by social media as well as the Internet itself has led to new paradigms in journalism and mass communication. New technologies revolutionize the way humans communicate and get informed about what is happening in the world; in parallel, however, individuals and organizations can exploit these new paradigms to pursue their own agenda. In this light, the spread of misinformation and disinformation can have a variety of negative effects on society, for instance, by creating dipoles in the political dialogue and threatening democracy, putting the health and security of citizens at risk through falsified information, or spreading conspiracy theories about climate change and the environment. To help in the fight against misinformation, the present study focuses on an innovative approach that evaluates and combines the results of different content verification services. This approach, entitled “Meta-Detection Toolset (MDT)” for content verification, consists of an algorithmic consensus (voting) mechanism based on weights, which rewards or penalizes verification services on the basis of their prediction results and the ground truth (verification labels) provided by human domain experts (fact checkers or other). Using a dedicated weight recalculation algorithm, as the feedback of the experts is gradually provided, the weights are recalculated and updated constantly, thus forming a continuous learning procedure for content verification.
Best Practices for Creating and Maintaining Clusters in EU-Funded Projects: Insights from the H2020 BES Cluster
Authors: Charalampos Chatzimallis , Danai Kyrkou , Anneta Kampouridou , Christianna Themistocleous , Alkiviadis Astyakopoulos , Artemisia Nikolaidou , Mirela Rosgova , Yildirim Yayilgan Sule , Pantelis Velanas 
Published: September 2024

AbstractEU-funded projects often use clustering or sister projects communities as a dissemination and communication strategy to promote their outputs and exchange knowledge. Maintaining such communities with meaningful impact on projects, during and following their life cycle, may be challenging. This chapter aims to provide answers and best practices to questions related to creating, managing, and maintaining clusters, engaging projects, ensuring sustainability, and supporting efficient joint exploitation activities. Focusing on the latter, it draws insights from the H2020 BES Cluster (H2020 Border External Security Cluster), which consists of security-related H2020 and Horizon Europe projects collaborating to support communication and dissemination activities, exchange good practices and methodologies, and explore possibilities to combine pilot activities. The chapter provides a comprehensive review of the outcomes and impact of the H2020 BES Cluster projects, highlighting their significance and potential for further exploitation. Moreover, it discusses strategies for early exploitation and effective dissemination for projects still in progress. Although the H2020 BES Cluster is border security-related, its proposed methodology and practices can apply to different projects with minor customisation. This chapter aspires to introduce and strengthen innovations and improvements to the security and border control system within the EU by promoting a collaborative environment and drawing attention to the tangible results and benefits of EU-funded projects.
Frugal and Robust AI for Defence Advanced Intelligence
Authors: Papagianni Andromachi , Ioannidis Konstantinos , Tsikrika Theodora , Vrochidis Stefanos , Kompatsiaris Ioannis 
Published: September 2024

AbstractArtificial intelligence (AI) technology has greatly impacted various aspects of modern society and economy. Recent technological advancements have significantly improved decision-making and support systems, as well as autonomous processes, by utilising different types of data, such as text, visual content, and video footage. To provide accurate outcomes, AI models require collection and preparation of a number of representative data that leads to a costly and timely process, in terms of hardware and human resources. Though, their adoption in defence systems is severely affected by the lack of appropriate data either in terms of size or due to their limited classification level. Current AI trends can contribute to overcoming such limitations and improve their overall utilisation in the defence domain hence to modernise current warfare. To this end, the proposed framework aims to comprise an AI package that involves numerous models and addresses the core challenges of a defence system. This research study relies on the development of five, interconnected research pillars and is expected to impact their systems. The ambition of the so-called ‘FaRADAI framework’ is to produce relevant research advances in the development of new technologies which will be successfully applied in the context of AI for defence applications, covering all stages of a military operation supply chain, from planning, to execution, C2, decision-making, and mission adaptation.