Search Results (112)

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

Light non-aqueous phase liquids (LNAPLs), which include various petroleum products, are a significant source of groundwater contamination globally. Once introduced into the subsurface, these contaminants tend to accumulate in the vadose zone, causing chronic soil and water pollution. The vadose zone often contains lens-shaped bodies with diverse properties that can significantly influence the migration and distribution of LNAPLs. Understanding the interaction between LNAPLs and these lens-shaped bodies is crucial for developing effective environmental management and remediation strategies. Prior research has primarily focused on LNAPL behavior in homogeneous media, with less emphasis on the impact of heterogeneous conditions introduced by lens-shaped bodies. To investigate the impact of lens-shaped structures on the migration of LNAPLs and to assess the specific effects of different types of lens-shaped structures on the distribution characteristics of LNAPL migration, this study simulates the LNAPL leakage process using an indoor two-dimensional sandbox. Three distinct test groups were conducted: one with no lens-shaped aquifer, one with a low-permeability lens, and one with a high-permeability lens. This study employs a combination of oil front curve mapping and high-density resistivity imaging techniques to systematically evaluate how the presence of lens-shaped structures affects the migration behavior, distribution patterns, and corresponding resistivity anomalies of LNAPLs. The results indicate that the migration rate and distribution characteristics of LNAPLs are influenced by the presence of a lens in the gas band of the envelope. The maximum vertical migration distances of the LNAPL are as follows: high-permeability lens (45 cm), no lens-shaped aquifer (40 cm), and low-permeability lens (35 cm). Horizontally, the maximum migration distances of the LNAPL to the upper part of the lens body decreases in the order of low-permeability lens, high-permeability lens, and no lens-shaped aquifer. The low-permeability lens impedes the vertical migration of the LNAPL, significantly affecting its migration path. It creates a flow around effect, hindering the downward migration of the LNAPL. In contrast, the high-permeability lens has a weaker retention effect and creates preferential flow paths, promoting the downward migration of the LNAPL. Under conditions with no lens-shaped aquifer and a high-permeability lens, the region of positive resistivity change rate is symmetrical around the axis where the injection point is located. Future research should explore the impact of various LNAPL types, lens geometries, and water table fluctuations on migration patterns. Incorporating numerical simulations could provide deeper insights into the mechanisms controlling LNAPL migration in heterogeneous subsurface environments. Full article

Automated sandbox-based analysis systems are dominantly focused on sequences of API calls, which are widely acknowledged as discriminative and easily extracted features. In this paper, we argue that an extension of the feature set beyond API calls may improve the malware detection performance. For this purpose, we apply the Cuckoo open-source sandbox system, carefully configured for the production of a novel dataset for dynamic malware analysis containing 22,200 annotated samples (11,735 benign and 10,465 malware). Each sample represents a full-featured report generated by the Cuckoo sandbox when a corresponding binary file is submitted for analysis. To support our position that the discriminative power of the full-featured sandbox reports is greater than the discriminative power of just API call sequences, we consider samples obtained from binary files whose execution induced API calls. In addition, we derive an additional dataset from samples in the full-featured dataset, whose samples contain only information on API calls. In a three-way factorial design experiment (considering the feature set, the feature representation technique, and the random forest model hyperparameter settings), we trained and tested a set of random forest models in a two-class classification task. The obtained results demonstrate that resorting to full-featured sandbox reports improves malware detection performance. The accuracy of 95.56 percent obtained for API call sequences was increased to 99.74 percent when full-featured sandbox reports were considered. Full article

This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency. Full article

Nowadays, some of the most expensive real estate is not “real” at all. Several investors are purchasing land in the virtual world of the Metaverse. To be more accurate in the wording, they are buying “meta-estates”. This work is dedicated to opening a debate about this emerging research field within the real estate discipline. It begins by discussing market segmentation, ownership, and pricing by comparing the traditional real estate market with the virtual estate market. Furthermore, this study involved interviews with six seasoned Metaverse land investors who participated in two Analytic Hierarchy Processes (AHPs). The first AHP ranked 14 investment typologies, while the second focused on ranking and discussing the most important characteristics of meta-estates that influence the formation of prices. As a result, the most appealing investments identified were day-trading, virtual land trading (buying to resell), and virtual land development (transforming and reselling). The primary characteristics of meta-estates considered by investors include the platform (e.g., Earth 2, Sandbox), the location within the platform (proximity to famous neighbours), and the architectural design of the buildings (designed by renowned architects). It is evident that the Metaverse represents a new frontier for land investors, and the primary aim of this study was to encourage other researchers to explore and investigate this evolving field. Full article

The aim of this paper is to discuss how young Latinx men living in Australia negotiate, embody, and complicate existing dominant and racialized masculinities. Queer and feminist theories are used to explore how Latinx men negotiate and embody masculinities, sexualities, and being ‘other’ in a White dominant cultural context. These tensions were explored through semi-structured in-depth interviews and a creative visual method known as sandboxing with twenty-one Latinx men. Sandboxing aims to elicit conversation and allows for the reflection and sharing of a visual and symbolic representation of participants’ lives. The findings suggest masculinities are lived and embodied alongside negotiating racialization and sexualities. The fluidity of masculinities surfaces in participants’ reflexive engagement with masculinities and the nuances in negotiating and simultaneously reproducing gender binary norms. Participants’ careful negotiation in engaging with feminine culture led to developing the concept ‘feminine threshold’, a theoretical contribution offered in this article, in understanding how Latinx men negotiate masculinities. Full article

Currently, digital situational awareness systems are popular in complex infrastructure construction projects. These systems monitor and assess environmental events, progress, resource availability, risks, and other project decision-making variables to support decision-making. However, little is known about how infrastructure construction (IC) professionals who utilize situational awareness systems perceive how they support or hinder situational management. The purpose of this exploratory research is to study, in depth, the relevance, challenges, and adoption of situational management in IC projects using digital systems. The data were collected via semistructured interviews with 21 IC managers and situational awareness management experts from 11 companies involved in railway projects. The main findings indicate that problem-solving improved with situational management in general, especially with digital situational awareness systems. Seizing the possibilities for transparency that accompany digital situational awareness systems helped in discussing emerging problems and making project choices. Expectations about the realism of such expectations were easier to align with historical event data. On the other hand, the informants reported difficulty in motivating contractors to collect situational data in digital form, possibly because of a lack of understanding about the purpose of data collection, the manual nature of data collection, the perceived excessiveness of data collection, or the manual transfer of collected data into digital form. For these reasons, the informants reported limited faith in these systems. A perceived drawback of situational management, whether supported by digital situational awareness systems or not, was its lack of applicability to the realities of a construction site. Systems were designed for project management needs but not tailored to the needs of construction projects. The interviewees’ statements indicate that maintaining situational awareness requires active interaction and constant checking of the provided information, even requiring pressure on the contractors providing the information. This study highlights the need for practical human approaches to effectively use digital situational awareness technologies and situational management in IC. Full article

The problems of information isolation, inefficiency, and paper-based data archiving in traditional highway survey and design methods are investigated in this paper. A novel digital sandbox platform framework was developed to promote the efficiency of route design, model data integration, and information sharing. Under the presented framework, an integrated application method for both the Building Information Modeling (BIM) and Geographic Information System (GIS) technologies was designed by using Unreal Engine technology. Firstly, a digital base model was established by integrating multi-disciplinary BIM model data and GIS three-dimensional (3D) multi-scale scene model data. On this basis, using Unreal Engine technology for visualization development, a digital sandbox platform with the data visualization, traffic organization simulation analysis, 3D spatial analysis, component information query, and scene switching functions was developed, which satisfies the 3D visualization and digitalization needs in the current highway planning and design. Additionally, the Analytic Hierarchy Process (AHP) was employed to analyze the impact of digital base model on the development and application of platform modules, including five crucial factors: data accuracy, data representation, multi-source data fusion, data management capability, and scene semantic representation. Finally, the research results indicate that the proposed digital sandbox platform framework provides users with a platform for integrated data management, information sharing, and 3D data visualization, while reducing design time by 30%, total design cost by 12%, and land occupancy rate by 10%. Full article

Today, malware is arguably one of the biggest challenges organisations face from a cybersecurity standpoint, regardless of the types of devices used in the organisation. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper presents research on the functionalities and performance of different malicious Android application package analysis tools, including one that uses machine learning techniques. In addition, it investigates how these tools streamline the detection, classification, and analysis of malicious Android Application Packages (APKs) for Android operating system devices. As a result of the research included in this article, it can be highlighted that the AndroPytool, a tool that uses machine learning (ML) techniques, obtained the best results with an accuracy of 0.986, so it can be affirmed that the tools that use artificial intelligence techniques used in this study are more efficient in terms of detection capacity. On the other hand, of the online tools analysed, Virustotal and Pithus obtained the best results. Based on the above, new approaches can be suggested in the specification, design, and development of new tools that help to analyse, from a cybersecurity point of view, the code of applications developed for this environment. Full article