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The rapid advancement of technology and innovation is also impacting education across different levels. The rise of Artificial Intelligence (AI) is beginning to transform education in various areas, from course materials to assessment systems. This requires educators to reconsider how they evaluate students’ knowledge. It is crucial to understand if and to what extent assignments can be completed using AI tools. This study explores two hypotheses about the risks of using code-based 3D modeling software in education and the potential for students to delegate their work to AI when completing assignments. We selected two tasks that students were able to successfully complete independently and provided the same amount of information (both textual and image) to AI in order to generate the necessary code. We tested the widely used ChatGPT and Gemini AI bots to assess their current performance in generating code based on text prompts or image-based information for the two models. Our findings indicate that students are not yet able to entirely delegate their work to these AI tools. Full article

Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm. Full article

An influence network of events is a view of the universe based on events that may be related to one another via influence. The network of events forms a partially ordered set which, when quantified consistently via a technique called chain projection, results in the emergence of spacetime and the Minkowski metric as well as the Lorentz transformation through changing an observer from one frame to another. Interestingly, using this approach, the motion of a free electron as well as the Dirac equation can be described. Indeed, the same approach can be employed to show how a discrete version of some of the features of Euclidean geometry including directions, dimensions, subspaces, Pythagorean theorem, and geometric shapes can emerge. In this paper, after reviewing the essentials of the influence network formalism, we build on some of our previous works to further develop aspects of Euclidean geometry. Specifically, we present the emergence of geometric shapes, a discrete version of the parallel postulate, the dot product, and the outer (wedge product) in $2+1$ dimensions. Finally, we show that the scalar quantification of two concatenated orthogonal intervals exhibits features that are similar to those of the well-known concept of a geometric product in geometric Clifford algebras. Full article

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome. Full article

It is necessary to extract and recognize the cloud regions presented in imagery to generate satellite imagery as analysis-ready data (ARD). In this manuscript, we proposed a new deep learning model to detect cloud areas in very-high-resolution (VHR) satellite imagery by fusing two deep learning architectures. The proposed UNet3+ model with a hybrid Swin Transformer and EfficientNet (UNet3+STE) was based on the structure of UNet3+, with the encoder sequentially combining EfficientNet based on mobile inverted bottleneck convolution (MBConv) and the Swin Transformer. By sequentially utilizing convolutional neural networks (CNNs) and transformer layers, the proposed algorithm aimed to extract the local and global information of cloud regions effectively. In addition, the decoder used MBConv to restore the spatial information of the feature map extracted by the encoder and adopted the deep supervision strategy of UNet3+ to enhance the model’s performance. The proposed model was trained using the open dataset derived from KOMPSAT-3 and 3A satellite imagery and conducted a comparative evaluation with the state-of-the-art (SOTA) methods on fourteen test datasets at the product level. The experimental results confirmed that the proposed UNet3+STE model outperformed the SOTA methods and demonstrated the most stable precision, recall, and F1 score values with fewer parameters and lower complexity. Full article

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security. Full article

For years, gold nanoparticles (AuNPs) have been widely used in medicine and industry. Although various experimental procedures have been reported for their preparation and manipulation, none of them is optimal for all purposes. In this work, we engineered the N-terminus of the pIII minor coat protein of bacteriophage (phage) M13 to expose a novel HLYLNTASTHLG peptide that effectively and specifically binds gold. In addition to binding gold, this engineered phage could synthesize spherical AuNPs of 20 nm and other sizes depending on the reaction conditions, aggregate them, and precipitate gold from a colloid, as revealed by transmission electron microscopy (TEM), atomic force microscopy (AFM), and scanning electron microscopy (SEM), as well as ultraviolet–visible (UV–vis) and Fourier-transform infrared (FTIR) spectroscopic methods. We demonstrated that the engineered phage exposing a foreign peptide selected from a phage-displayed library may serve as a sustainable molecular factory for both the synthesis of the peptide and the subsequent overnight preparation of AuNPs from gold ions at room temperature and neutral pH in the absence of strong reducing agents, such as commonly used NaBH₄. Taken together, the results suggest the potential applicability of the engineered phage and the new, in vitro-identified gold-binding peptide in diverse biomimetic manipulations. Full article

In this study, silver nanoparticles (AgNPs) were synthesized using a green method from an extract of the edible insect Oxya chinensis sinuosa (O_extract). The formation of AgNPs (O_AgNPs) was confirmed via UV–vis spectroscopy, and their stability was assessed using Turbiscan analysis. The size and morphology of the synthesized particles were characterized using transmission electron microscopy and field-emission scanning electron microscopy. Dynamic light scattering and zeta potential analyses further confirmed the size distribution and dispersion stability of the particles. The average particle size was 111.8 ± 1.5 nm, indicating relatively high stability. The synthesized O_AgNPs were further characterized using X-ray photoelectron spectroscopy (XPS), high-resolution X-ray diffraction (HR-XRD), and Fourier transform infrared (FTIR) spectroscopy. XPS analysis confirmed the chemical composition of the O_AgNP surface, whereas HR-XRD confirmed its crystallinity. FTIR analysis suggested that the O_extract plays a crucial role in the synthesis process. The antibacterial activity of the O_AgNPs was demonstrated using a disk diffusion assay, which revealed effective activity against common foodborne pathogens, including Salmonella Typhimurium, Escherichia coli, Staphylococcus aureus, and Bacillus cereus. O_AgNPs exhibited clear antibacterial activity, with inhibition zones of 15.08 ± 0.45 mm for S. Typhimurium, 15.03 ± 0.15 mm for E. coli, 15.24 ± 0.66 mm for S. aureus, and 13.30 ± 0.16 mm for B. cereus. These findings suggest that the O_AgNPs synthesized from the O_extract have potential for use as antibacterial agents against foodborne bacteria. Full article

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China’s “Dual-Carbon Goals” and “Clean Heating” policy call for the creation of a green and comfortable living environment for rural residential buildings. This paper considers the impact of the improvement of the thermal performance of envelope structures on the initial investment of the transformation program and the rated power of the ASHP and proposes an energy-saving transformation method to replace traditional coal-fired boilers with the ASHP on the basis of the improvement of the thermal performance of envelope structures. By establishing a typical rural residential building model in Northeast China, this energy-saving method is simulated based on TRNSYS. The results show that the payback period of investment of the transformation method of “envelope structure + heating system” is not superior to that of the transformation method of only improving the thermal performance of the envelope structure, but it has advantages in the comprehensive life-cycle benefits and it has great advantages in improving the satisfaction of rural residents in the use of heating systems. Full article

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field. Full article

Urban–rural integration, which aims to balance economic growth with sustainable land use, is becoming an increasingly critical strategy for regional development. This study provides crucial insights into the relationship between land use changes and ecosystem service values (ESVs) in rapidly urbanizing areas by analyzing the urban–rural integration process in Henan Province, a typical agricultural province in China. This research investigated the relationship between land use transformation and ESVs in Henan Province, China, from 1990 to 2020. Utilizing land use data and employing the equivalent factor method and elasticity model, we analyzed shifts in land use and their impacts on ecosystem services across 17 prefecture-level cities. Results indicated a gradual improvement in the urban–rural integration development index of Henan Province, particularly after 2000, but with notable disparities among cities. Zhengzhou, the provincial capital, consistently demonstrated high urban–rural integration development index (URII) values, influencing the integration efforts of neighboring cities. Conversely, peripheral cities exhibited lower integration indices. Notable shifts in land use patterns characterized by diverse transfer dynamics distinctively influenced ESVs across regions. Urban sprawl initially exerted substantial impacts on ecosystem services and stabilized over time. Suburbanization impacts peaked in the early and middle stages, while agricultural intensification initially affected ecosystem services, but their effects diminished with increased efficiency. Ecological restoration efforts consistently enhanced ESVs. The findings contribute to a more comprehensive understanding of the dynamic interactions between land use transitions and ecosystem services in the context of urban–rural integration. Full article

Gas boilers exhibit thermal inefficiency and unsatisfying pollutant emissions. In this study, numerical simulations were conducted to examine the effect of humidified oxygen-enriched air on methane combustion in a furnace and the effects of different premixed ratios of air on the temperature field inside the furnace, intermediate product OH groups, component concentration distribution, and pollutants. Although humidification of ambient air effectively reduced the flame center temperature and mass concentration of the NOx generated during combustion in the furnace, the highest growth rate of CO concentration at the furnace outlet was 18.6%. Humidification of oxygen-enriched air increased the center temperature and outlet NO concentration of the furnace compared with those during no oxygen enrichment, but the outlet CO concentration showed a decreasing trend, with the highest decrease rate of 34.6%. This study determined an optimal CO–air premix ratio with a moisture concentration of 50 g/kg dry air and an oxygen concentration of 23%. The air humidification and oxygen enrichment technology proposed in this article provides a technical reference for low nitrogen transformation of existing gas boilers. Full article

This study explores the applications and implications of blockchain technology in the Australian higher education system, focusing on its integration with artificial intelligence (AI). By addressing critical challenges in credential verification, administrative efficiency, and academic integrity, this integration aims to enhance the global competitiveness of Australian higher education institutions. A comprehensive review of 25 recent research papers quantifies the benefits, challenges, and prospects of blockchain adoption in educational settings. Our findings reveal that 52% of the reviewed papers focus on systematic reviews, 28% focus on application-based studies, and 20% combine both approaches. The keyword analysis identified 287 total words, with “blockchain” and “education” as the most prominent themes. This study highlights blockchain’s potential to improve credential management, academic integrity, administrative efficiency, and funding mechanisms in education. However, challenges such as technical implementation (24%), regulatory compliance (32%), environmental concerns (28%), and data security risks (40%) must be addressed to achieve widespread adoption. This study also discusses critical prerequisites for successful blockchain integration, including infrastructure development, staff training, regulatory harmonisation, and the incorporation of AI for personalised learning. Our research concludes that blockchain, when strategically implemented and combined with AI, has the potential to transform the Australian higher education system, significantly enhancing its integrity, efficiency, and global competitiveness. Full article

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions. Full article

The compressive strength of concrete is a crucial parameter in structural design, yet its determination in a laboratory setting is both time-consuming and expensive. The prediction of compressive strength in fly ash-based concrete can be accelerated through the use of machine learning algorithms with artificial intelligence, which can effectively address the problems associated with this process. This paper presents the most innovative model algorithms established based on artificial intelligence technology. These include three single models—a fully connected neural network model (FCNN), a convolutional neural network model (CNN), and a transformer model (TF)—and three hybrid models—FCNN + CNN, TF + FCNN, and TF + CNN. A total of 471 datasets were employed in the experiments, comprising 7 input features: cement (C), fly ash (FA), water (W), superplasticizer (SP), coarse aggregate (CA), fine aggregate (S), and age (D). Six models were subsequently applied to predict the compressive strength (CS) of fly ash-based concrete. Furthermore, the loss function curves, assessment indexes, linear correlation coefficient, and the related literature indexes of each model were employed for comparison. This analysis revealed that the FCNN + CNN model exhibited the highest prediction accuracy, with the following metrics: R² = 0.95, MSE = 14.18, MAE = 2.32, SMAPE = 0.1, and R = 0.973. Additionally, SHAP was utilized to elucidate the significance of the model parameter features. The findings revealed that C and D exerted the most substantial influence on the model prediction outcomes, followed by W and FA. Nevertheless, CA, S, and SP demonstrated comparatively minimal influence. Finally, a GUI interface for predicting compressive strength was developed based on six models and nonlinear functional relationships, and a criterion for minimum strength was derived by comparison and used to optimize a reasonable mixing ratio, thus achieving a fast data-driven interaction that was concise and reliable. Full article