Search Results (118)

This article presents a simulation of a long-term retardation performance Mock-up test of the multi-field coupling of buffer materials, with the aim to study the thermo-hydro-mechanical (THM) processes occurring in the engineered barrier system of a high-level waste (HLW) repository. In view of the theory of mixtures and mechanics of continuous media, the coupled THM mathematical model of unsaturated buffer materials is established, considering heat transport and multiphase fluid flow. Using the buffer material Mock-up test of multi-field coupling as a model, the partial differential equation (PDE) module in the general finite element software COMSOL Multiphysics was developed by a second development stage. The dynamic response process of buffer material under the condition of THM coupling was numerically simulated, and the spatial distribution and variation law of suction, porosity, horizontal displacement, temperature and swelling pressure in the engineered barrier were investigated. The porosity of the buffer material under THM coupling was influenced by the swelling pressure and the suction. The welling pressure evolution of the buffer material may be influenced by the thermal expansion induced by high temperature and the swelling pressure generated by buffer material saturation. The evolution of the horizontal displacement of the heater used to simulate a container with radioactive waste was validated. This paper provides technical reference for the design and safety evaluation of underground laboratory barrier engineering in China. Full article

In recent years, it has become a hot topic to combine graph neural networks with contrastive learning, a method which has been applied not only in recommendation tasks but has also achieved impressive results in many fields, such as text processing and spatio-temporal modeling. However, existing methods are still constrained by several issues: (1) Most graph learning methods do not explore the imbalance of node and edge type distribution caused by different user–item interactions. (2) The randomness of data expansion and sampling strategies in contrastive learning may lead to confusion about the importance of key items for users. To overcome the problems, in this paper, we propose an explanation-guided contrastive recommendation model based on interactive message propagation and dual-hypergraph convolution (ECR-ID). Specifically, we designed two different interactive propagation mechanisms for the user–item dual-hypergraph sets to promote comprehensive dynamic information propagation and exchange, which further mitigates the imbalance problem of hyperedges and nodes in the hypergraph convolution, as well as the propagation loss of synergistic information between nodes. In addition, we developed an explanation-guided contrastive learning framework, which highlights the important items in user–item interactions through an explanation-based approach and guided the training of the contrastive learning framework based on the differences in the importance scores of the items, thus generating accurate positive and negative views and improving the contrastive learning performance. Finally, we integrated the contrastive learning framework with the dual-hypergraph networks based on joint training to further improve the recommendation performance. Extensive experimental evaluations on real datasets show that ECR-ID outperforms state-of-the-art recommendation algorithms. In the future, we will conduct in-depth tests based on a wider range of real-world datasets to alleviate the limitation that the existing experimental datasets all comprise data from single business services like Alibaba and Amazon, thus validating the effectiveness of our model more comprehensively. Full article

In recent years, Generation Z has had a pivotal influence across global industries, with tourism being significantly impacted. As the first generation fully immersed in digital technology, Generation Z members have travel preferences, behaviours, and values which differ markedly from previous generations, presenting both challenges and opportunities for the tourism sector. This study explores studies on Generation Z in the context of the tourism industry through key research questions, including the primary trends and thematic clusters in the existing literature, the most influential journals and authors, and the temporal evolution of research in this area. To address these questions, this article presents a comprehensive bibliometric analysis of 217 publications on Generation Z and tourism, spanning from 2018 to 2024, sourced from the Web of Science. Using VOSviewer, the analysis maps the main trends, the most influential publications, and the emerging themes, providing insights into how Generation Z is reshaping the tourism landscape. The bibliometric analysis and VOSviewer mapping add a rigorous methodological approach, offering a structured overview of existing research while identifying gaps for further exploration, especially in this emerging field. The study highlights the most published and cited journals, providing a broad contextual view, followed by an in-depth exploration of thematic clusters within Generation Z tourism research. The results indicate that although research on this topic gained attention in 2018, it is still in its early stages, with significant room for expansion. Current studies primarily focus on social media marketing communications for Generation Z, as well as Generation Z tourism experiences and their attitudes and preferences towards tourism, suggesting areas for future exploration. Full article

In view of the deep burial depth, high formation pressure, and presence of top and bottom water in offshore extra-heavy-oil reservoirs, this paper conducts a study on the production performance and flow field variation law of steam huff-and-puff to steam flooding conversion in thick heavy-oil reservoirs based on physical simulation, and analyzes the development effect of the conversion from steam huff-and-puff to steam flooding. On this basis, by comprehensively considering the advantages of gravity-assisted steam flooding and a three-dimensional HHSD well pattern obtained from physical simulation experiments, this paper proposes a well pattern development mode of steam huff-and-puff to composite displacement and drainage, and analyzes the development effect of this well pattern mode using the reservoir numerical simulation method. The research results show that, compared with the planar well pattern of steam huff-and-puff to steam flooding conversion, the adoption of the three-dimensional well pattern can significantly improve the degree of reservoir production and the expansion dynamics of the steam chamber, and mitigate adverse effects such as the increase in water cut caused by top and bottom water on thermal recovery. The composite development of steam huff-and-puff to composite displacement and drainage can be divided into three stages: thermal communication, gravity drainage-assisted steam flooding, and thermal breakthrough erosion and oil washing. The steam chamber presents a development mode of “single-point development–rapid longitudinal expansion–rapid transverse expansion upon reaching the top–polymerization into a sheet”, and simultaneously possesses the oil displacement mechanisms of both steam displacement and gravity drainage. The proposed composite mode of steam huff-and-puff to composite displacement and drainage has guided the implementation of adjustment wells in the Bohai L Oilfield, and the recovery factor has been increased by about 20% compared with the steam huff-and-puff development of the basic well pattern. This study has reference and guiding significance for the efficient thermal recovery development of this oilfield. Full article

As online shopping has increased, the business models of online stores have diversified. When consumers cannot experience an actual product, merchants will promote products through a display to attract customers. Virtual reality (VR) provides an immersive platform for consumers to interact with virtual scenarios. Unfortunately, cybersickness remains a problem in VR. The uncomfortable effects of VR hinder its commercial expansion and the broader adoption of 3D virtual stores. Cybersickness has many causes, including personal characteristics, hardware interfaces, and operation behavior. This study develops a fuzzy-control anti-cybersickness intelligent system (FCACIS) with these factors dynamically and actively. The system retrieves the operation value and inferences the cybersickness symptom value (CSSV). When the CSSV exceeds the alarm value, a dialog mode is introduced to remind users to be aware of possible cybersickness. If the CSSV continues to increase, a cybersickness defense mechanism is activated, such as decreasing the field of view and freezing the screen. The experimental results revealed a significant difference in SSQ scores between subjects who navigated a 3D virtual store with and without the FCACIS. The SSQ scores of subjects with the FCACIS (SSQ = 20.570) were significantly lower than those of subjects without the FCACIS (SSQ = 32.880). The FCACIS effectively alleviated cybersickness for subjects over 40 years old. Additionally, the FCACIS effectively slowed the onset of cybersickness in men and women. The anti-cybersickness effect of the FCACIS on flat-panel displays was greater than that on HMDs. The symptoms of cybersickness for a 3DOF controller were also reduced. Full article

Background: Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. Objective: In order to solve the problem of limited field of view associated with computer detection of the corn ear shape index against a complex background, this paper proposes a panoramic splicing method for corn ears against a complex background, which can splice 10 corn ear panoramic images at the same time, to improve information collection efficiency, display comprehensive information, and support data analysis, so as to realize automatic corn seed examination. Methods: A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. Results: The experimental results showed that the proposed corn ear panoramic stitching method could effectively solve the problems of virtual stitching, obvious stitching seams, and too-high similarity between multiple images. The success rate of stitching was as high as 100%, and the speed of single-corn-ear panoramic stitching was about 9.4 s, indicating important reference value for corn breeding and disease and insect detection. Discussions: Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model’s segmentation recognition rate and precision. Recently, our panoramic splicing algorithm has been deployed in practical applications with satisfactory results. We plan to continue optimizing the algorithm and more broadly promote its use in fields such as corn breeding and pest and disease detection in an effort to advance the development of agricultural automation technology. Full article

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker’s center point relative to the MAV’s body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs. Full article

In the process of tunnel construction, problems such as high-stress rockburst, large deformation of soft rock, water inrush and mud gushing, secondary cracking of linings, blasting interference, man-made damage, and mechanical damage are often encountered. These pose a great challenge to the installation of monitoring equipment and line protection. In order to solve these problems, the 2# inclined shaft of Muzhailing Tunnel in the Gansu Province of China, which exists under high stress, water bearing, and bias conditions, was taken as the research object in this paper. By assembling a string, drilling grouting and sealing, and introducing multiple modes of protection, new fiber grating sensor group installation and line protection methods were proposed. The automatic continuous monitoring of the deep deformation of surrounding rock and the automatic continuous monitoring of steel arch stress were realized. The field monitoring results showed that: (1) the fiber grating displacement sensor group could be used to verify the authenticity of the surface displacement results monitored by the total station; (2) the NPR anchor cable coupling support effectively limited the large deformation of soft rock and the expansion of surrounding rock in a loose circle, and the range of the loose circle was stable at about 1 m; and (3) the main influence range of blasting was at a depth of 0~5 m in surrounding rock, and about 25 m away from the working face. In addition, to secure weak links in the steel arch due to the hardening phenomenon, a locking tube was set at the arch foot. In the support design, the fatigue life of the steel was found to be useful as the selection index for the steel arch frame to ensure the stability of the surrounding rock and the long-term safety of the tunnel. The present research adopted a robust method and integrates a variety of sensor technologies to provide a multifaceted view of the stresses and deformations encountered during the tunneling process, and the effective application of the above results could have certain research and reference value for the design and monitoring of high stress, water-bearing, and surrounding rock supports in tunnels. Full article

This study presents the design and development of a high-resolution convex grating dispersion hyperspectral imaging system tailored for unmanned aerial vehicle (UAV) remote sensing applications. The system operates within a spectral range of 400 to 1000 nm, encompassing over 150 channels, and achieves an average spectral resolution of less than 4 nm. It features a field of view of 30°, a focal length of 20 mm, a compact volume of only 200 mm × 167 mm × 78 mm, and a total weight of less than 1.5 kg. Based on the design specifications, the system was meticulously adjusted, calibrated, and tested. Additionally, custom software for the hyperspectral system was independently developed to facilitate functions such as control parameter adjustments, real-time display, and data preprocessing of the hyperspectral camera. Subsequently, the prototype was integrated onto a drone for remote sensing observations of Spartina alterniflora at Yangkou Beach in Shouguang City, Shandong Province. Various algorithms were employed for data classification and comparison, with support vector machine (SVM) and neural network algorithms demonstrating superior classification accuracy. The experimental results indicate that the UAV-based hyperspectral imaging system exhibits high imaging quality, minimal distortion, excellent resolution, an expansive camera field of view, a broad detection range, high experimental efficiency, and remarkable capabilities for remote sensing detection. Full article

In view of the complexity and diversity of hyperspectral images (HSIs), the classification task has been a major challenge in the field of remote sensing image processing. Hyperspectral classification (HSIC) methods based on neural architecture search (NAS) is a current attractive frontier that not only automatically searches for neural network architectures best suited to the characteristics of HSI data, but also avoids the possible limitations of manual design of neural networks when dealing with new classification tasks. However, the existing NAS-based HSIC methods have the following limitations: (1) the search space lacks efficient convolution operators that can fully extract discriminative spatial–spectral features, and (2) NAS based on traditional differentiable architecture search (DARTS) has performance collapse caused by unfair competition. To overcome these limitations, we proposed a neural architecture search method with receptive field spatial–spectral attention (RFSS-NAS), which is specifically designed to automatically search the optimal architecture for HSIC. Considering the core needs of the model in extracting more discriminative spatial–spectral features, we designed a novel and efficient attention search space. The core component of this innovative space is the receptive field spatial–spectral attention convolution operator, which is capable of precisely focusing on the critical information in the image, thus greatly enhancing the quality of feature extraction. Meanwhile, for the purpose of solving the unfair competition issue in the traditional differentiable architecture search (DARTS) strategy, we skillfully introduce the Noisy-DARTS strategy. The strategy ensures the fairness and efficiency of the search process and effectively avoids the risk of performance crash. In addition, to further improve the robustness of the model and ability to recognize difficult-to-classify samples, we proposed a fusion loss function by combining the advantages of the label smoothing loss and the polynomial expansion perspective loss function, which not only smooths the label distribution and reduces the risk of overfitting, but also effectively handles those difficult-to-classify samples, thus improving the overall classification accuracy. Experiments on three public datasets fully validate the superior performance of RFSS-NAS. Full article

The phonon-related properties of crystalline polymers are highly relevant for various applications. Their simulation is, however, particularly challenging, as the systems that need to be modeled are often too extended to be treated by ab initio methods, while classical force fields are too inaccurate. Machine-learned potentials parametrized against material-specific ab initio data hold the promise of being extremely accurate and also highly efficient. Still, for their successful application, protocols for their parametrization need to be established to ensure an optimal performance, and the resulting potentials need to be thoroughly benchmarked. These tasks are tackled in the current manuscript, where we devise a protocol for parametrizing moment tensor potentials (MTPs) to describe the structural properties, phonon band structures, elastic constants, and forces in molecular dynamics simulations for three prototypical crystalline polymers: polyethylene (PE), polythiophene (PT), and poly-3-hexylthiophene (P3HT). For PE, the thermal conductivity and thermal expansion are also simulated and compared to experiments. A central element of the approach is to choose training data in view of the considered use case of the MTPs. This not only yields a massive speedup for complex calculations while essentially maintaining DFT accuracy, but also enables the reliable simulation of properties that, so far, have been entirely out of reach. Full article

Grating-type spectral imaging systems are frequently employed in scenes for high-resolution remote-sensing observations of the Earth. However, the entrance of the grating-type spectral imaging system is a slit or a pinhole. This structure relies on the push broom method, which presents a challenge in capturing spectral information of transiently changing targets. To address this issue, the IFU is used to slice the focal plane of the telescope system, thereby expanding the instantaneous field of view (IFOV) of the grating-type spectral imaging system. The aberrations introduced by the expansion of the single-slice field of view (FOV) of the IFU are corrected, and the conversion of the IFU’s FOV from arcseconds to degrees is achieved. The design of a spectral imaging system based on an image-slicer IFU for remote sensing is finally completed. The system has a wavelength range of 1400 nm to 2000 nm, and a spectral resolution of better than 3 nm. Compared with the traditional grating-type spectral imaging system, its IFOV is expanded by a factor of four. And it allows for the capture of complete spectral information of transiently changing targets through a single exposure. The simulation results demonstrate that the system has good performance at each sub-slit, thereby validating the effectiveness and advantages of the proposed system for dynamic target capture in remote sensing. Full article

Augmented reality (AR) technology has been widely applied across a variety of fields, with head-up displays (HUDs) being one of its prominent uses, offering immersive three-dimensional (3D) experiences and interaction with digital content and the real world. AR-HUDs face challenges such as limited field of view (FOV), small eye-box, bulky form factor, and absence of accommodation cue, often compromising trade-offs between these factors. Recently, optical waveguide based on pupil replication process has attracted increasing attention as an optical element for its compact form factor and exit-pupil expansion. Despite these advantages, current waveguide displays struggle to integrate visual information with real scenes because they do not produce accommodation-capable virtual content. In this paper, we introduce a lensless accommodation-capable holographic system based on a waveguide. Our system aims to expand the eye-box at the optimal viewing distance that provides the maximum FOV. We devised a formalized CGH algorithm based on bold assumption and two constraints and successfully performed numerical observation simulation. In optical experiments, accommodation-capable images with a maximum horizontal FOV of 7.0 degrees were successfully observed within an expanded eye-box of 9.18 mm at an optimal observation distance of 112 mm. Full article

Fisheye cameras play a crucial role in various fields by offering a wide field of view, enabling the capture of expansive areas within a single frame. Nonetheless, the radial distortion characteristics of fisheye lenses lead to notable shape deformation, particularly at the edges of the image, posing a significant challenge for accurate object detection. In this paper, we introduce a novel method, ‘VP-aided fine-tuning’, which harnesses the strengths of the pretraining–fine-tuning paradigm augmented by visual prompting (VP) to bridge the domain gap between undistorted standard datasets and distorted fisheye image datasets. Our approach involves two key elements: the use of VPs to effectively adapt a pretrained model to the fisheye domain, and a detailed 24-point regression of objects to fit the unique distortions of fisheye images. This 24-point regression accurately defines the object boundaries and substantially reduces the impact of environmental noise. The proposed method was evaluated against existing object detection frameworks on fisheye images, demonstrating superior performance and robustness. Experimental results also showed performance improvements with the application of VP, regardless of the variety of fine-tuning method applied. Full article

This paper aims to examine the views of managers, accountants, and auditors on the impact of innovation and information technology on financial resilience, and answers the question of whether in today’s businesses, which are rapidly changing and evolving and where events are unpredicted, organizations can increase their economic resilience through innovation and information technology. The research population was managers, accountants, and auditors of small and medium-sized companies in Razavi Khorasan in 2024, and the study was conducted with a questionnaire in both paper and electronic forms through in-person visits to the companies under research, where 357 auditors and 371 accountants and managers completed the questionnaire. The findings show that the innovation of products and services and the expansion of information technology increase the financial resilience of organizations. It is suggested that organizations increase the innovation of products and services and use information technology to eliminate and take effective action in dealing with possible risks. The findings suggest exciting facts about the effect of advanced digital space on financial resilience in organizations active in Iran’s economy, as well as possible damages in this field that cause delays in digitalization and, as a result, the economic resilience of organizations. Full article