Search Results (115)

Augmented Reality (AR) is an innovative tool in education, enhancing learning experiences across multiple domains. This literature review explores the application of AR in education, with a particular focus on geographical learning. The study begins by tracing the historical development of AR, distinguishing it from Virtual Reality (VR) and highlighting its advantages in an educational context. The integration of AR into learning environments has been shown to improve engagement, comprehension of abstract concepts, and collaboration among students. The use of AR in geographical education through interactive applications, such as GeoAR and AR Sandbox, improves the exploration of spatial relationships, topographic maps, and environmental changes. Studies demonstrate that AR enhances students’ ability to recall information and understand geographical processes more effectively than with traditional methods. Furthermore, AR Sandbox implementations, including Illuminating Clay, SandScape, and AR Sandbox, are analyzed and compared. The paper also discusses future developments in AR for geography education for AR Sandbox, such as the integration of a mobile application for extended learning and improving computing solutions through Raspberry Pi. These advancements aim to make AR systems more accessible and to increase the benefits to both students and professors. Full article

Regulatory sandboxes have emerged as policy instruments designed to support FinTech innovation while maintaining supervisory oversight. By allowing firms to test financial products in controlled environments, sandboxes aim to reduce regulatory uncertainty and facilitate market entry. Despite their growing adoption, empirical evidence of their effectiveness remains limited, particularly in emerging markets. This study explores the impact of regulatory sandboxes on innovation and market access through a qualitative comparative case study of the United Kingdom, Singapore, and Hungary. Drawing on document analysis and thematic coding, the research evaluates sandbox design, regulatory support, and innovation outcomes across the three jurisdictions. Findings show that sandboxes enhance access to funding, accelerate product development, and foster regulator–firm collaboration. While the UK and Singapore benefit from mature ecosystems and structured frameworks, Hungary illustrates sandbox potential in developing markets. The paper contributes to FinTech regulation literature and provides policy recommendations for optimizing sandbox design across varied institutional contexts. Full article

The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation. Full article

Malware continues to evolve rapidly, posing significant challenges to network security. Traditional signature-based detection methods often struggle to cope with advanced evasion techniques such as polymorphism, metamorphism, encryption, and stealth, which are commonly employed by cybercriminals. As a result, these conventional approaches frequently fail to detect newly emerging malware variants in a timely manner. To address this limitation, Zero-Shot Learning (ZSL) has emerged as a promising alternative, offering improved classification capabilities for previously unseen malware samples. ZSL models leverage auxiliary semantic information and binary feature representations to enhance the recognition of novel threats. This study proposes a Transductive Zero-Shot Learning (TZSL) model based on the Vector Quantized Variational Autoencoder (VQ-VAE) architecture, integrated with a malware knowledge graph constructed from sandbox behavioral analysis of ransomware families. The model is further optimized through hyperparameter tuning to maximize classification performance. Evaluation metrics include per-family classification accuracy, precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curves to ensure robust and reliable detection outcomes. In particular, the harmonic mean (H-mean) metric from the Generalized Zero-Shot Learning (GZSL) framework is introduced to jointly evaluate the model’s performance on both seen and unseen classes, offering a more holistic view of its generalization ability. The experimental results demonstrate that the proposed VQ-VAE model achieves an F1-score of 93.5% in ransomware classification, significantly outperforming other baseline models such as LeNet-5 (65.6%), ResNet-50 (71.8%), VGG-16 (74.3%), and AlexNet (65.3%). These findings highlight the superior capability of the VQ-VAE-based TZSL approach in detecting novel malware variants, improving detection accuracy while reducing false positives. Full article

Using the PRISMA-ScR methodology, this scoping review systematically analyzes how EU laws and regulations influence the development, adoption, and deployment of AI-driven digital solutions in energy generation, transmission, distribution, consumption, and markets. It identifies key regulatory barriers such as stringent risk assessments, cybersecurity obligations, and data access restrictions, along with enablers like regulatory sandboxes and harmonized compliance frameworks. Legal uncertainties, including AI liability and market manipulation risks, are also examined. To provide a comparative perspective, the EU regulatory approach is contrasted with AI governance models in the United States and China, highlighting global best practices and alignment challenges. The findings indicate that while the EU’s risk-based approach to AI governance provides a robust legal foundation, cross-regulatory complexity and sector-specific ambiguities necessitate further refinement. This paper proposes key recommendations, including the integration of AI-specific energy sector guidelines, acceleration of standardization efforts, promotion of privacy-preserving AI methods, and enhancement of international cooperation on AI safety and cybersecurity. These measures will help strike a balance between fostering trustworthy AI innovation and ensuring regulatory clarity, enabling AI to accelerate the clean energy transition while maintaining security, transparency, and fairness in digital energy systems. Full article

Saltwater intrusion (SWI) in coastal aquifers poses a significant threat to freshwater resources, exacerbated by climate change and rising sea levels. This study investigates SWI dynamics using laboratory experiments, geophysical monitoring with the self-potential (SP) method, and numerical simulations to assess the impact of varying salt concentrations (7 g/L and 35 g/L) on intrusion rates and electrochemical responses. Laboratory experiments were conducted in a custom-designed sandbox model, with SP data collected in real time using a 192-electrode system. Numerical simulations were performed to replicate experimental conditions and validate the model’s predictions. Results show that salt concentration significantly influences intrusion rates and SP responses. In low-salinity systems (7 g/L), SP values increased gradually from 0 mV to 20 mV, with a slow intrusion rate of 0.034 m/h. In contrast, moderate-salinity systems (35 g/L) exhibited rapid SP changes (0 mV to 5 mV) and a faster intrusion rate of 0.1 m/h. Sharp SP anomalies near the intrusion source, with values dropping from 10 mV to −40 mV, were observed in low-salinity systems, highlighting localized charge imbalances. The model’s performance was evaluated using relative RMSE, showing a good fit in Experiment (1) (RMSE = 5.00%) and acceptable results for Experiment (2) (RMSE = 23.45%). These findings demonstrate the potential of the SP method for real-time monitoring of SWI and provide insights for improving management strategies in coastal aquifers. Full article

This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages Quest Pro’s body-tracking capabilities to enable ergonomic, immersive interactions with planned eye-tracking integration for improved interactivity and accuracy. The Niryo One robot aids users in hands-on disassembly while generating synthetic data to refine robot motion planning models. A Robot Operating System (ROS) bridge enables the seamless simulation and control of various robotic platforms using Unified Robotics Description Format (URDF) files, bridging virtual and physical training environments. A Long Short-Term Memory (LSTM) model predicts user interactions and robotic motions, optimizing trajectory planning and minimizing errors. Monte Carlo dropout-based uncertainty estimation enhances prediction reliability, ensuring adaptability to dynamic user behavior. Initial technical validation demonstrates the platform’s potential, with preliminary testing showing promising results in task execution efficiency and human–robot motion alignment, though comprehensive user studies remain for future work. Limitations include the lack of multi-user scenarios, potential tracking inaccuracies, and the need for further real-world validation. This system establishes a sandbox training framework for HRC in disassembly, leveraging VR and AI-driven feedback to improve skill acquisition, task efficiency, and training scalability across industrial applications. Full article

The rapid proliferation of ransomware variants necessitates more effective detection mechanisms, as traditional signature-based methods are increasingly inadequate. These conventional methods rely on manual feature extraction and matching, which are time-consuming and limited to known threats. This study addresses the escalating challenge of ransomware threats in cybersecurity by proposing a novel deep learning model, LSTM-EDadver, which leverages Generative Adversarial Networks (GANs) and Carlini and Wagner (CW) attacks to enhance malware detection capabilities. LSTM-EDadver innovatively generates adversarial examples (AEs) using sequential features derived from ransomware behaviors, thus training deep learning models to improve their robustness and accuracy. The methodology combines Cuckoo sandbox analysis with conceptual lattice ontology to capture a wide range of ransomware families and their variants. This approach not only addresses the shortcomings of existing models but also simulates real-world adversarial conditions during the validation phase by subjecting the models to CW attacks. The experimental results demonstrate that LSTM-EDadver achieves a classification accuracy of 96.59%. This performance was achieved using a dataset of 1328 ransomware samples (across 32 ransomware families) and 519 normal instances, outperforming traditional RNN, LSTM, and GCU models, which recorded accuracies of 90.01%, 93.95%, and 94.53%, respectively. The proposed model also shows significant improvements in F1-score, ranging from 2.49% to 6.64% compared to existing models without adversarial training. This advancement underscores the effectiveness of integrating GAN-generated attack command sequences into model training. Full article

This paper presents three 3D reconstructions of different analogue models used to reproduce, interpret, and describe the geological setting of a seismogenic area in Southern Italy—the Messina Strait. Three-dimensional analysis is a technique that allows for less sparse and more congruent and coherent information about a study zone whose complete understanding reduces uncertainties and risks. A thorough structural and geodynamic description of the effects of low-angle normal faulting in the same region through analogue models has been widely investigated in the scientific literature. Sandbox models for fault behaviour during deformation and the effects of a Low Angle Normal Fault (LANF) on the seismotectonic setting are also studied. The deformational patterns associated with seismogenic faults, rotational behaviour of faults, and other related problems have not yet been thoroughly analysed. Most problems, like the evolution of normal faults, fault geometry, and others, have been cited and briefly outlined in earlier published works, but a three-dimensional approach is still significant. Here, we carried out a three-dimensional digital model for a complete and continuous structural model of a debated, studied area. The aim of this study is to highlight the importance of fully representing faults in complex and/or non-cylindrical structures, mainly when the shape and dimensions of the fault(s) are key parameters, like in seismogenic contexts. Full article

This study presents a predictive maintenance system designed for industrial Internet of Things (IoT) environments, focusing on resource efficiency and adaptability. The system utilizes Nicla Sense ME sensors, a Raspberry Pi-based concentrator for real-time monitoring, and a Long Short-Term Memory (LSTM) machine-learning model for predictive analysis. Notably, the LSTM algorithm is an example of how the system’s sandbox environment can be used, allowing external users to easily integrate custom models without altering the core platform. In the laboratory, the system achieved a Root Mean Squared Error (RMSE) of 0.0156, with high accuracy across all sensors, detecting intentional anomalies with a 99.81% accuracy rate. In the real-world phase, the system maintained robust performance, with sensors recording a maximum Mean Absolute Error (MAE) of 0.1821, an R-squared value of 0.8898, and a Mean Absolute Percentage Error (MAPE) of 0.72%, demonstrating precision even in the presence of environmental interferences. Additionally, the architecture supports scalability, accommodating up to 64 sensor nodes without compromising performance. The sandbox environment enhances the platform’s versatility, enabling customization for diverse industrial applications. The results highlight the significant benefits of predictive maintenance in industrial contexts, including reduced downtime, optimized resource use, and improved operational efficiency. These findings underscore the potential of integrating Artificial Intelligence (AI) driven predictive maintenance into constrained environments, offering a reliable solution for dynamic, real-time industrial operations. Full article

Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios. Four types of segments replicate actual driving situations by directing the car using strategically positioned waypoints. A training dataset thus generated was used to train a behavioral driving model that employs a convolutional neural network to detect the lane and ensure that the car steers autonomously to remain within lane boundaries. The model was evaluated on real-world driving footage from Comma.ai, exhibiting an autonomy of 77% in low challenge road conditions and of 66% on roads with sharper turns. Full article

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods. The predominant static analysis methodologies employ algorithms to convert the files. The analytic process is contingent upon the tool’s functionality; if the tool malfunctions, the entire process is obstructed. Most dynamic analysis methods necessitate the execution of a binary file within a sandboxed environment to examine its behavior. When executed within a virtual environment, the detrimental actions of the file might be easily concealed. This research examined a novel method for depicting malware as images. Subsequently, we trained a classifier to categorize new malware files into their respective classifications utilizing established neural network methodologies for detecting malware images. Through the process of transforming the file into an image representation, we have made our analytical procedure independent of any software, and it has also become more effective. To counter such adversaries, we employ a recognized technique called involution to extract location-specific and channel-agnostic features of malware data, utilizing a deep residual block. The proposed approach achieved remarkable accuracy of 99.5%, representing an absolute improvement of 95.65% over the equal probability benchmark. Full article

The current cybersecurity ecosystem is proving insufficient in today’s increasingly sophisticated cyber attacks. Malware authors and intruders have pursued innovative avenues to circumvent emulated monitoring systems (EMSs) such as honeypots, virtual machines, sandboxes and debuggers to continue with their malicious activities while remaining inconspicuous. Cybercriminals are improving their ability to detect EMS, by finding indicators of deception (IoDs) to expose their presence and avoid detection. It is proving a challenge for security analysts to deploy and manage EMS to evaluate their deceptive capability. In this paper, we introduce the Hydrakon framework, which is composed of an EMS controller and several Linux and Windows 10 clients. The EMS controller automates the deployment and management of the clients and EMS for the purpose of measuring EMS deceptive capabilities. Experiments were conducted by applying custom detection vectors to client real machines, virtual machines and sandboxes, where various artifacts were extracted and stored as csv files on the EMS controller. The experiment leverages the cosine similarity metric to compare and identify similar artifacts between a real system and a virtual machine or sandbox. Our results show that Hydrakon offers a valid approach to assess the deceptive capabilities of EMS without the need to target specific IoD within the target system, thereby fostering more robust and effective emulated monitoring systems. Full article

In the context of global warming, rising sea levels are intensifying seawater intrusion in coastal areas. Due to the complex hydrodynamic conditions and increasing groundwater over-extraction in these regions, understanding the patterns of seawater intrusion is crucial for effective prevention and control. This study employed a sandbox model to investigate both vertical and horizontal seawater intrusion into a coastal unconfined aquifer with an impermeable dam under varying conditions of sea level rise, coastal slope, and groundwater pumping rate. Additionally, a two-dimensional SEAWAT model was developed to simulate seawater intrusion under these experimental conditions. The results indicate that sea level rise significantly increases the extent and intensity of seawater intrusion. When sea level rises by 3.5 cm, 4.5 cm, and 5.5 cm, the areas of the saline wedge reached 362 cm², 852 cm², and 1240 cm², respectively, with both horizontal and vertical intrusion ranges expanding considerably. When groundwater extraction is superimposed, vertical seawater intrusion is notably intensified. At an extraction rate of 225 cm³/min, the vertical intrusion areas corresponding to sea level rises of 3.5 cm, 4.5 cm, and 5.5 cm were 495 cm², 1035 cm², and 1748 cm², respectively, showing significant expansion, and this expansion becomes more pronounced as sea levels rise. In contrast, slope variations had a significant impact only on vertical seawater intrusion. As the slope decreased from tanα = 1/5 to tanα = 1/9, the upper saline wedge area expanded from 525 cm² to 846 cm², considerably increasing the vertical intrusion range. Finally, the combined effects of groundwater extraction and sea level rise exacerbate seawater intrusion more severely than either factor alone, presenting greater challenges for coastal water resource management. Full article

Monitoring crude oil spills in coastal areas is challenging due to limitations in traditional in situ methods. Electrical resistivity imaging (ERI) offers a high-resolution approach to monitoring the subsurface spatial distribution of crude oil, but its effectiveness in highly-resistive, unsaturated coastal sands with varying salinity remains unexplored. This study assessed the effectiveness of ERI for monitoring crude oil spills in sandy soil using a 200 × 60 × 60 cm 3D sandbox filled with medium-fine-grained sand under unsaturated conditions. Two liters of crude oil were spilled under controlled conditions and monitored for 48 h using two surface ERI transects with 98 electrodes spaced every 2 cm and a dipole–dipole electrode array. The influence of varying salinity was simulated by varying the pore-fluid conductivities at four levels (0.6, 20, 50, and 85 mS/cm). After 48 h, the results show a percentage resistivity increase of 980%, 280%, 142%, and 70% for 0.6, 20, 50, and 85 mS/cm, respectively. The crude oil migration patterns varied with porewater salinity as higher salinity enhanced the crude oil retention at shallow depth. High salinity produces a smaller resistivity contrast, thus limiting the sensitivity of ERI in detecting the crude oil contaminant. These findings underscore the need to account for salinity variations when designing remediation strategies, as elevated salinity may restrict crude oil migration, resulting in localized contaminations. Full article