Search Results (74,155)

UAV-assisted mobile edge computing combines the flexibility of UAVs with the computing power of MEC to provide low-latency, high-performance computing solutions for a wide range of application scenarios. However, due to the highly dynamic and heterogeneous nature of the UAV environment, the optimal allocation of resources and system reliability still face significant challenges. This paper proposes a two-stage optimization (DSO) algorithm for UAV-assisted MEC, combining Stackelberg game theory and auction mechanisms to optimize resource allocation among servers, UAVs, and users. The first stage uses a Stackelberg game to allocate resources between servers and UAVs, while the second stage employs an auction algorithm for UAV-user resource pricing. Blockchain smart contracts automate task management, ensuring transparency and reliability. The experimental results show that compared with the traditional single-stage optimization algorithm (SSO), the equal allocation algorithm (EAA) and the dynamic resource pricing algorithm (DRP), the DSO algorithm proposed in this paper has significant advantages by improving resource utilization by 7–10%, reducing task latency by 3–5%, and lowering energy consumption by 4–8%, making it highly effective for dynamic UAV environments. Full article

Given that rotating machinery is one of the most commonly used types of mechanical equipment in industrial applications, the identification of its health status is crucial for the safe operation of the entire system. Traditional equipment health status identification mainly relies on conventional single-modal data, such as vibration or acoustic modalities, which often have limitations and false alarm issues when dealing with real-world operating conditions and complex environments. However, with the increasing automation of coal mining equipment, the monitoring of multimodal data related to equipment operation has become more prevalent. Existing multimodal health status identification methods are still imperfect in extracting features, with poor complementarity and consistency among modalities. To address these issues, this paper proposes a multimodal joint representation learning and residual neural network-based method for rotating machinery health status identification. First, vibration, acoustic, and image modal information is comprehensively utilized, which is extracted using a Gramian Angular Field (GAF), Mel-Frequency Cepstral Coefficients (MFCCs), and a Faster Region-based Convolutional Neural Network (RCNN), respectively, to construct a feature set. Second, an orthogonal projection combined with a Transformer is used to enhance the target modality, while a modality attention mechanism is introduced to take into consideration the interaction between different modalities, enabling multimodal fusion. Finally, the fused features are input into a residual neural network (ResNet) for health status identification. Experiments conducted on a gearbox test platform validate the proposed method, and the results demonstrate that it significantly improves the accuracy and reliability of rotating machinery health state identification. Full article

Since the Montreal Protocol (dated 1987), the reduction of the environmental impact has been one of the main goals in the HVAC sector, which has led to the replacement of widely used fluids with new environmentally friendly ones. Nevertheless, only new fluids with suitable heat transfer features can be used. The refrigerant mixture R449a, one of the fourth-generation refrigerants, was tested during flow boiling inside a horizontal smooth tube. The experiments were carried out at six different mass fluxes G ∈ [175;400] kg·m⁻²·s⁻¹ and four different bubble temperatures T_b ∈ [2.5;10] °C, while the nominal values for inlet and outlet quality were selected as x_Ti = 0.1 and x_To = 0.9, respectively. The results highlighted that, as the bubble temperature increases, it has an opposite effect on the pressure drop per unit length and the heat transfer coefficient: the former decreases while the latter grows. The comparison between experimental results and the correlations showed that the Zhang and Webb formula provides the best prediction of pressure drop, while the models provided by Bertsch yield the most reliable predictions for the heat transfer coefficient. Nevertheless, for both quantities, other correlations with similar performances are available. Full article

Solving the Euler equations often requires expensive computations of complex, high-order time derivatives. Although Taylor Discontinuous Galerkin (TDG) schemes are renowned for their accuracy and stability, directly evaluating third-order tensor derivatives can significantly reduce computational efficiency, particularly for large-scale, intricate flow problems. To overcome this difficulty, this paper presents an optimized numerical procedure that combines Taylor series time integration with the Discontinuous Galerkin (DG) approach. By replacing cumbersome tensor derivatives with simpler time derivatives of the Jacobian matrix and finite difference method inside the element to calculate the high-order time derivative terms, the proposed method substantially decreases the computational cost while maintaining accuracy and stability. After verifying its fundamental feasibility in one-dimensional tests, the optimized TDG method is applied to a two-dimensional forward-facing step problem. In all numerical tests, the optimized TDG method clearly exhibits a computational efficiency advantage over the conventional TDG method, therefore saving a great amount of time, nearly 70%. This concept can be naturally extended to higher-dimensional scenarios, offering a promising and efficient tool for large-scale computational fluid dynamics simulations. Full article

In multicriteria decision-making (MCDM), methods such as TOPSIS are essential for evaluating and comparing alternatives across multiple criteria. However, traditional normalization techniques often struggle with datasets containing outliers, large variances, or heterogeneous measurement units, which can lead to skewed or biased rankings. To address these challenges, this paper proposes an adaptive, cluster-based normalization approach, demonstrated through a real-world logistics case study involving the selection of a host city for an international event. The method groups alternatives into clusters based on similarities in criterion values and applies logarithmic normalization within each cluster. This localized strategy reduces the influence of outliers and ensures that scaling adjustments reflect the specific characteristics of each group. In the case study—where cities were evaluated based on cost, infrastructure, safety, and accessibility—the cluster-based normalization method yielded more stable and balanced rankings, even in the presence of significant data variability. By reducing the influence of outliers through logarithmic normalization and allowing predefined cluster profiles to reflect expert judgment, the method improves fairness and adaptability. These features strengthen TOPSIS’s ability to deliver accurate, balanced, and context-aware decisions in complex, real-world scenarios. Full article

Climate change is an urgent issue, and the current mindset of private ownership, particularly of private vehicles, needs to shift. Shared mobility is rapidly emerging as a key part of the solution to contemporary transportation challenges, driven by technological advancements and the growing demand for more sustainable travel options. This paper provides a comprehensive analysis of shared mobility operational models in Europe, focusing on carsharing and its current research on fleet optimization, bikesharing, and scooter sharing. The study draws on three scientific literature databases, with searches centered on keywords relevant to Shared Mobility. This study contributes to the literature by defining each Shared Mobility modality and examining the different operational models. Full article

In our previous study, we proposed a vision-based ranging algorithm (LRA) that utilized a monocular camera with four lasers (MC4L) for indoor positioning in dark environments. The LRA achieved a positioning error within 2.4 cm using a logarithmic regression algorithm to establish a linear relationship between the illuminated area and real distance. However, it cannot distinguish between obstacles and walls. Hence, it results in severe errors in complex environments. To address this limitation, we developed an LBP-CNNs model that combines local binary patterns (LBPs) and self-attention mechanisms. The model effectively identifies obstacles based on the laser reflectivity of different material surfaces. It reduces positioning errors to 1.27 cm and achieves an obstacle recognition accuracy of 92.3%. In this paper, we further enhance LBP-CNNs by combining it with fast Fourier transform (FFT) to create an LBP-FFT-CNNs model that significantly improves the recognition accuracy of obstacles with similar textures to 96.3% and reduces positioning errors to 0.91 cm. In addition, an inertial measurement unit (IMU) is integrated into the MC4L device (MC4L-IMU) to design an inertial-based indoor positioning algorithm. Experimental results show that the LBP-FFT-CNNs model achieves the highest determination coefficient (R² = 0.9949), outperforming LRA (R² = 0.9867) and LBP-CNN (R² = 0.9934). In addition, all models show strong stability, and the prediction standard index (PSI) values are always below 0.02. To evaluate model robustness and MC4L-IMU work reliably under different conditions, the experiments were conducted in a controlled indoor environment with different obstacle materials and lighting conditions. Full article

Additive Manufacturing (AM) processes, particularly PBF-LB/M, are considered advantageous due to their flexibility, which allows process engineers to design and fabricate intricate structures both in the prototyping and component manufacturing phases. It is well known that the behavior of the process directly impacts the quality of the materials and thereby induces inhomogeneities on the powder bed on the building platform. Several parameters can be tuned to keep the process under control, getting rid of process uncertainty and distinguishing aspects of a specific machine model. Such behavior requires an extended analysis of the powder bed inhomogeneities and the definition of limits in the printing process. In this work, carried out on Alloy 718 specimens printed using an EOS M290 machine, the inhomogeneities of the melt pool stability, density, and material properties were investigated based on three main factors: the amount of area melted or fused, the gas flow speed setpoint, and the location on the building platform. The test results for Track Stability, melt-pool shape, and porosity analysis show that criticality occurs when more than 50% of the building platform is exposed. This can be partly fixed by raising the differential pressure value. Full article

This research paper investigates the integration of sustainable performance metrics into digital maturity models specifically tailored for small and medium-sized manufacturing enterprises (SMMEs), which represent a significant pillar of the Canadian economy. Despite their economic importance, SMMEs face increasing challenges in adopting digital technologies while ensuring sustainable performance. However, traditional digital maturity models often fail to capture the economic, social, and environmental impacts of digital transformation, creating a gap in assessing sustainability within this transition. This study aims to bridge this gap by proposing a framework that integrates sustainable performance indicators into existing digital maturity models. Through a systematic literature review, this study categorises indicators into three main dimensions—economic, social, and environmental—addressing aspects such as resource efficiency, employee well-being, and ecological impact. The proposed framework enables SMMEs to evaluate both their digital maturity and its impact on sustainable performance dimensions. By aligning these metrics with digital maturity assessment, this framework enhances decision-making for SMEs aiming to balance technological adoption with sustainability goals. Furthermore, the study consolidates key performance indicators relevant to SMMEs, providing a structured approach to assess the intersection of digital maturity and sustainability. The results emphasise the importance of incorporating sustainability dimensions into digital transformation strategies, offering SMMEs a structured framework to better access the relationship between digital maturity and sustainable performance while maintaining competitiveness in the digital economy. Full article

This paper reviews the evolution of flow control methods for bladed linear cascades, focusing on passive techniques like riblets, grooves, vortex generators (VGs), and blade slots, which have proven effective in reducing drag, suppressing flow separation, and enhancing stability. The review outlines key historical developments that have improved flow efficiency and reduced losses in cascades. Bio-inspired designs, including riblets and grooves, help stabilize the boundary layer, reduce loss coefficients, and improve flow turning, which is vital for controlling drag and secondary flow effects. Vortex generators, fences, and slotted wingtips enhance stall margins and suppress corner separation, improving performance under off-design conditions. These methods are optimized based on aerodynamic parameters such as Reynolds number and boundary layer characteristics, offering substantial efficiency gains in high-performance compressors. Advancements in computational tools, like high-fidelity simulations and optimization techniques, have provided deeper insights into complex flow phenomena, including turbulence and vortex dynamics. Despite these advancements, challenges remain in fully optimizing these methods for diverse operating conditions and ensuring their practical application. This review highlights promising strategies for improving flow control efficiency and robustness, contributing to the design of next-generation turbomachinery. Full article

The educational assessment is an essential task within the educational process. The generation of right and correct assessment content is a determinant process within the assessment. The creation of an automated method of generation similar to a human experienced operator (teacher) deals with a complex series of issues. This paper presents a compiled set of methods and tools used to generate educational assessment content in the form of assessment tests. The methods include the usage of various structures (e.g., trees, chromosomes and genes, and genetic operators) and algorithms (graph-based, evolutionary, and genetic) in the automated generation of educational assessment tests. This main purpose of the research is developed in the context of the existence of several requirements (e.g., degree of difficulty, item topic), which gives a higher degree of complexity to the issue. The paper presents a short literature review related to the issue. Next, the description of the models generated in the authors’ previous research is presented. In the final part of the paper, the results related to the implementations of the models are presented, as well as results and performance. Several conclusions were drawn based on this compilation, the most important of them being that tree and genetic-based approaches to the issue have promising results related to performance and assessment content generation. Full article

Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies—collectively known as Extended Reality (XR)—have ushered in a new era of immersive and interactive instruction in applied sciences education. This systematic literature review aims to examine the application of XR technologies across various scientific and educational domains, evaluate their impact on learning outcomes, and identify the challenges hindering their broader integration. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted using Web of Science, ScienceDirect, and IEEE Xplore, focusing on empirical studies published between 1 January 2010 and 1 November 2024, resulting in the inclusion of 56 studies. Among these, 32 studies (53%) employed VR, 25 studies (42%) utilized AR, and 3 studies (5%) adopted MR, with 4 studies exploring the combined application of VR and AR. The findings indicate that VR is primarily applied in higher education settings, such as universities and graduate programs, whereas AR is more prevalent in primary and secondary education; although MR is less frequently used, it exhibits distinct advantages in disciplines requiring high interactivity and realism. Overall, each XR modality can enhance learning motivation, efficiency, and immediate knowledge acquisition in short-term interventions, while long-term implementation may contribute to improved memory retention, increased learner confidence, and sustained engagement. Despite persistent challenges—including high equipment costs, spatial and temporal constraints, small sample sizes, and insufficient longitudinal evidence—these findings underscore the transformative potential of XR technologies in applied sciences education. Full article

The increasing adoption of blockchain technology has presented significant challenges in maintaining the security and reliability of smart contracts. This study addresses the problem of identifying security flaws in smart contracts, which may result in monetary damages and diminished confidence in blockchain systems. A Hybrid Quantum–Classical Deep Neural Network (HQCDNN) approach was proposed, combining quantum computing principles with classical deep learning methods to identify various vulnerability types, including access control, arithmetic, front-running, reentrancy, time manipulation, denial of service, and unchecked low calls. The SmartBugs Wild Dataset was used for training, with TF-IDF employed as a preprocessing technique optimized for hybrid architectures. Experiments were conducted using hybrid architectures with 2-qubit and 4-qubit quantum layers, alongside a classical deep neural network (DNN) model for comparative analysis. The HQCDNN model attained accuracy levels ranging from 96.4% to 78.2% and F1-scores between 96.6% and 80.2%, showcasing enhanced performance compared to the classical and deep learning models referenced in the literature. These results highlight the capability of HQCDNNs to improve the identification of security flaws in smart contracts. Future work could focus on evaluating the model on actual quantum devices and expanding its application to larger datasets for further validation. Full article

This paper introduces a method that can enhance the accuracy and efficiency of point cloud data registration. This method selects the centroid of the point cloud as the feature point and uses the projected distance of this feature point within the dynamic neighborhood to other points as the feature information. Through this feature information, it accomplishes the registration of two sets of point cloud data. This method increases the density and integrity of point cloud data, improves the accuracy and robustness of point cloud registration, and the selection of feature points reduces the computational load thereby enhancing processing efficiency. The introduction of the dynamic neighborhood enables the method to flexibly handle point cloud data of different scales and densities. Experimental results show that the proposed method has good performance in terms of accuracy and efficiency for achieving point cloud data registration and dealing with data under various complex conditions and can effectively improve the effect of point cloud data registration and fusion. Full article

Conventional design methodologies for Frequency Selective Surfaces (FSSs) are often plagued by challenges such as difficulties in determining unit cell structures, a plethora of optimization parameters, and substantial computational demands. In response, researchers have developed deep learning-based approaches for FSS design, highlighting their advantages in terms of high efficiency and low resource consumption. However, these methods are typically confined to designing FSSs within the spectral ranges defined by their datasets, significantly limiting their applicability. This paper systematically analyzes the impact of material and geometric parameters of FSSs on their spectral characteristics, thereby establishing a theoretical foundation for the cross-band transfer learning capability of neural networks. Building on this foundation, we utilized COMSOL (Version 6.0) and MATLAB (Version R2021b) co-simulations to recollect 6000 sets of FSS data in the millimeter-wave band. Using only 23.1% of the data volume, we achieved training results comparable to those obtained with the full dataset in a significantly shorter time frame, with a mean absolute error of 0.07 on the test set. This demonstrates the feasibility of transfer learning and successfully implements cross-band transfer learning of convolutional neural networks from the terahertz band to the millimeter-wave band. The findings of this study provide valuable insights for the integration of deep learning with FSSs, enhancing data utilization efficiency, and further advancing the development of efficient, concise, and universal FSS design methodologies. This advancement extends the scope from solving specific problems to addressing a broader class of issues. Full article