Search Results (62)

Small and medium-sized enterprises (SMEs) are increasingly targeted by cyber threats but often lack the financial and technical resources to implement advanced security systems. This paper presents HoneyLite, a lightweight and dynamic honeypot-based security solution specifically designed to meet the constraints and cybersecurity needs of SMEs. Unlike traditional honeypots, HoneyLite integrates real-time network traffic analysis with automated malware detection via the VirusTotal API, enabling it to identify a wide range of cyber threats, including TCP scans, FTP/SSH intrusions, ICMP flood attacks, and malicious file uploads. Developed using open-source tools, the system operates with minimal resource overhead and is validated within a simulated virtual environment. It also generates detailed threat reports to support incident analysis and response. By combining affordability, adaptability, and comprehensive threat visibility, HoneyLite offers a practical and scalable solution to help SMEs detect, analyze, and respond to modern cyberattacks in real time. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

To improve the reliability of the shearer output shaft in coal seams with gangue, taking the MG400/951-WD shearer model as the research object, a test system for the physical and mechanical properties of coal seam samples containing gangue was established. Based on the coal breaking theory, the impact load of the spiral drum in a coal seam with gangue was simulated. Combined with rigid-flexible coupling virtual prototype technology, a rigid-flexible coupling virtual prototype model of a shearer with an output shaft as the modal neutral file was established. The output shaft is a typical symmetrical part, and it is of great significance to analyze it by using dynamic theory and mechanical reliability theory. The shearer system modal, the stress distribution of output shaft, and vibration characteristics were obtained by dynamic simulation. Based on resonance failure criterion and combined with a neural network, the output shaft stress reliability, vibration reliability, amplitude reliability, and reliability sensitivity were analyzed under relevant failure modes. The state function of the output shaft reliability optimization design was established, and the structural evolution algorithm obtained the optimal design variables. The results show that the maximum stress of the output shaft is reduced by 14.06%, the natural frequency of the output shaft is increased, the amplitude of the output shaft is reduced by 31.13%, and the reliability of the output shaft is improved. The combination of rigid-flexible coupling virtual prototype technology, reliability sensitivity design theory considering correlated failure modes, and structural evolution algorithm provides a more reliable analysis method for the reliability analysis and design of mechanical equipment transmission mechanisms, which can enhance the reliability of the shearer’s cutting unit and improve safety in fully mechanized coal mining faces. The proposed methodology demonstrates broad applicability in the reliability analysis of critical components for mining machinery, exhibiting universal adaptability across various operational scenarios. Full article

This study explored the integration of technology into teaching practices by examining how faculty members at a newly established university in Saudi Arabia utilized the Blackboard learning system. Specifically, it investigated the use of multimedia e-learning tools by male and female faculty members during regular teaching periods following the COVID-19 pandemic. A survey questionnaire was developed using a 5-point Likert scale. The instrument covered demographic information, content creation, assessment methods, utility tools, and factors influencing Blackboard usage. Upon receiving approval, the survey was distributed via email to all faculty members across nine colleges. A total of 198 responses were collected and analyzed using both descriptive and inferential statistical methods. The findings indicated that gender had little to no statistically significant impact on the use of key Blackboard tools—such as content creation features (e.g., files, folders, items), assessment tools (e.g., tests, assignments), and utilities (e.g., virtual classes, email)—at the college level. However, when analyzed at the university level, some tools’ usage showed statistically significant gender differences at the α = 0.05 level. Furthermore, both male and female faculty members cited convenience, flexibility in uploading materials, access to virtual classes, and remote assessment of students as primary factors influencing their e-learning preferences. Full article

This study examines the impact of different 3D printing technologies on the accuracy of implant positions in printed dental models, a crucial factor in implant-supported prosthetics. A standardized titanium model with three bone-level implants was scanned using an industrial scanner to create a virtual reference model. Ten intraoral scans of the same model were performed, and the generated STL files were used to design physical models printed with three different 3D printers: two utilizing digital light processing (DLP) technology and one employing stereolithography (SLA) (n = 30). The printed models were then rescanned, and deviations from the reference STL file were analyzed. Results showed that the SLA printer exhibited the highest deviations (0.26 ± 0.17 mm), whereas the DLP printers demonstrated greater accuracy, with one DLP system (0.07 ± 0.02 mm) performing slightly better than the other (0.12 ± 0.13 mm). The SLA printer exhibited the most significant errors in the vestibulo-oral and occlusal-apical directions. The findings suggest that DLP printers offer superior precision for implant-supported restorations in digital workflows. Clinically, the choice of 3D printing technology significantly impacts model accuracy, emphasizing the importance of selecting the appropriate printer based on the required precision. 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

G protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane-helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations, together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity in drug design. Here, we describe GPCRVS, an efficient machine learning system for the online assessment of the compound activity against several GPCR targets, including peptide- and protein-binding GPCRs, which are the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding mode they could demonstrate for different types and subtypes of GPCRs. GPCRVS allows for the evaluation of compounds ranging from small molecules to short peptides provided in common chemical file formats. The results of the activity class assignment and the binding affinity prediction are provided in comparison with predictions for known active ligands of each included GPCR. Multiclass classification in GPCRVS, handling incomplete and fuzzy biological data, was validated on ChEMBL and Google Patents-retrieved data sets for class B GPCRs and chemokine CC and CXC receptors. Full article

Precision in diagnosis is essential for achieving optimal outcomes in prosthodontics, orthodontics, and orthognathic treatments. Virtual articulators provide a sophisticated digital alternative to conventional methods, integrating intraoral scans, facial scans, and cone beam computed tomography (CBCT) to enhance treatment predictability. This review examines advancements in virtual articulator technology, including digital workflows, virtual facebow transfer, and occlusal analysis, with a focus on Artificial Intelligence (AI)-driven methodologies such as machine learning and artificial neural networks. The clinical implications, particularly in condylar guidance and sagittal condylar inclination, are investigated. By streamlining the acquisition and articulation of digital dental models, virtual articulators minimize material handling errors and optimize workflow efficiency. Advanced imaging techniques enable precise alignment of digital maxillary models within computer-aided design and computer-aided manufacturing systems (CAD/CAM), facilitating accurate occlusal simulations. However, challenges include potential distortions during digital file integration and the necessity for robust algorithms to enhance data superimposition accuracy. The adoption of virtual articulators represents a transformative advancement in digital dentistry, with promising implications for diagnostic precision and treatment outcomes. Nevertheless, further clinical validation is essential to ensure the reliable transfer of maxillary casts and refine digital algorithms. Future developments should prioritize the integration of AI to enhance predictive modeling, positioning virtual articulators as a standard tool in routine dental practice, thereby revolutionizing treatment planning and interdisciplinary collaboration. This review explores advancements in virtual articulators, focusing on their role in enhancing diagnostic precision, occlusal analysis, and treatment predictability. It examines digital workflows, AI-driven methodologies, and clinical applications while addressing challenges in data integration and algorithm optimization. Full article

Visual programming languages (VPLs) play a significant role in simplifying the process of learning to program and reducing development time. Most VPLs are developed for use in education or specific domains. Recently, some projects have aimed to provide general-purpose VPLs. Among these projects is the Programming Without Coding Technology (PWCT) project, which has been used for several years to develop and maintain the compiler and virtual machine for the Ring programming language. However, PWCT faces several issues related to code generation performance and the operating systems it supports. Additionally, its visual editor lacks many features, such as rich comments, auto-run, and the ability to import textual code, which are highly important in the era of using large language models for generating textual code. In this research, we present the PWCT2 visual programming language, which is distributed on the Steam platform. On Steam, 1772 users have launched the software, and the total usage time recorded exceeds 17,000 h. This generation provides approximately 36 times faster code generation and 20 times lower storage requirements for visual source files. It also allows for the conversion of Ring code into visual code, enabling the creation of a self-hosting VPL. It consists of approximately 92,000 lines of Ring code and comes with 394 visual components. Moreover, using Ring in this project demonstrates the feasibility of utilizing the language for projects of this scale. Ring compiles PWCT2 in less than one second, and the generated bytecode consists of approximately 724,000 instructions. Full article

Background/Objectives: The technical development of implant-supported fixed dental prostheses (iFDP) initially concentrated on the computer-aided manufacturing of prosthetic restorations (CAM). Advances in information technologies have shifted the focus for optimizing digital workflows to AI-based processes for design (CAD). This pre-clinical pilot trial investigated the feasibility of the automatic design of three-unit iFDPs using CAD software (Dental Manger 2021, 3Shape; DentalCAD 3.1 Rijeka, exocad GmbH). Methods: Two clinical scenarios based on a full dentition were created virtually. Physical models were produced and digitized using two intraoral scanners applying quadrant or full-arch scans (Trios3, 3Shape, Copenhagen, Denmark; and Primescan AC, Dentsply Sirona, Bensheim, Germany). For each scenario, iFDP designs were generated automatically using two laboratory software systems (Dental Manger 2021, 3Shape; DentalCAD 3.1 Rijeka, exocad GmbH), resulting in 80 STL datasets (2 scenarios × 2 scan strategies × 2 IOS systems × 5 scan repetitions × 2 software). The files were analyzed clinically for the contact schemes and pontic area. One of the automated designs for each scenario was manually post-processed and one iFDP design for each scenario was manually created by experienced dental technicians (control). The time required for all the design processes was recorded. Results: The automatic design of iFDPs without manual adjustment did not lead to clinically acceptable restorations. The time required for the automatically generated/manually adjusted iFDPs designs was not significantly different to that for the manually designed restorations. Conclusions: Current laboratory software can not automatically generate three-unit iFDPs with clinically acceptable results in terms of the interproximal and occlusal contacts and the pontic design. The automatic iFDP design process currently requires manual adjustment, which means there is no benefit in terms of the working time compared with manually created restorations. 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

Physical Trading Card Games (TCGs) face inherent challenges, including vulnerability to theft, damage, and counterfeiting. Trading systems primarily depend on third-party marketplaces that collect fees from each trade, without benefiting the game developers. Players also deal with problems associated with shipping, such as high prices, long shipping times, and the risk of counterfeit goods. This paper introduces a blockchain-based solution that decouples card ownership from the physical media, which represents ownership with secure and verifiable digital tokens. The system leverages Ethereum Virtual Machine (EVM), the ERC-1155 standard, and InterPlanetary File System (IPFS) storage, ensuring flexibility, scalability, and cost-efficiency. The adoption of the lazy minting technique minimizes upfront costs for game developers by creating tokens only when acquired by end users. Physical representations of such digital goods can be printed on demand as they remain only a game accessory. The system also provides low-cost exchanges, significantly reducing the financial and logistical burdens associated with the trading of physical assets. Finally, the protocol empowers developers to monetize secondary markets through transaction fees. This approach addresses the limitations of physical card systems and also unlocks new opportunities for innovation and revenue in the TCG ecosystem. Full article

Solid-state drives (SSDs) are widely adopted in mobile devices, desktop PCs, and data centers since they offer higher throughput, lower latency, and lower power consumption to modern computing systems and applications compared with hard disk drives (HDDs). However, the performance of the SSDs can be degraded depending on the I/O access pattern due to the unique characteristics of SSDs. For example, random I/O operation degrades the SSD performance since it reduces the spatial locality and induces garbage collection (GC) overhead. In this paper, we present an address reshaping scheme in a virtual file system (VFS) called sVFS for improving performance and easy deployment. To do this, it first sequentializes a random access pattern in the VFS layer which is an abstract layer on top of a more concrete file system. Thus, our scheme is independent and easily deployed on any concrete file systems, block layer configuration (e.g., RAID), and devices. Second, we adopt a mapping table for managing sequentialized addresses, which guarantees correct read operations. Third, we support transaction processing for updating the mapping table to avoid sacrificing the consistency. We implement our scheme at the VFS layer in Linux kernel 5.15.34. The evaluation results show that our scheme improve the random write throughput by up to 27%, 36%, 34%, and 2.35× using the microbenchmark and 25%, 22%, 20%, and 3.51× using the macrobenchmark compared with the existing scheme in the case of EXT4, F2FS, XFS, and BTRFS, respectively. Full article

This research study aimed to evaluate the legibility of Arabic road signage using an eye-tracking approach within a virtual reality (VR) environment. The study was conducted in a controlled setting involving 20 participants who watched two videos using the HP Omnicept Reverb G2. The VR device recorded eye gazing details in addition to other physiological data of the participants, providing an overlay of heart rate, eye movement, and cognitive load, which in combination were used to determine the participants’ focus during the experiment. The data were processed through a schematic design, and the final files were saved in .txt format, which was later used for data extraction and analysis. Through the execution of this study, it became apparent that employing eye-tracking technology within a VR setting offers a promising method for assessing the legibility of road signs. The outcomes of the current research enlightened the vital role of legibility in ensuring road safety and facilitating effective communication with drivers. Clear and easily comprehensible road signs were found to be pivotal in delivering timely information, aiding navigation, and ultimately mitigating accidents or confusion on the road. As a result, this study advocates for the utilization of VR as a valuable platform for enhancing the design and functionality of road signage systems, recognizing its potential to contribute significantly to the improvement of road safety and navigation for drivers. Full article

One of the most important developments in the energy industry is the evolution of smart grids, which record minute details of voltage levels, energy usage, and other critical electrical variables through General Packet Radio Service (GPRS)-enabled meters. This phenomenon creates an extensive dataset for the optimization of the grid system. However, the minute-by-minute energy details recorded by GPRS meters are challenging to store and manage in physical storage resources (old techniques lead to a memory shortage). This study investigates using the distributed file system, replicated Gluster, as a reliable storage option for handling and protecting the enormous volumes of data produced by smart grid components. This study performs two essential tasks. (1) The storage of virtual data received from GPRS meters and load flow analysis of SynerGee Electric 4.0 software from the smart grid (we have extracted electrical data from 16 outgoing feeders, distributed lines, in this manuscript). (2) Tele-trafficking is performed to check the performance of replicated Gluster (RG) for virtual data (electrical data received from the smart grid) storage in terms of User Datagram Protocol (UDP), Transmission Control Protocol (TCP), data flow, and jitter delays. This storage technique provides more opportuni11ty to analyze and perform smart techniques efficiently for future requirement, analysis, and load estimation in smart grids compared to traditional storage methods. Full article