Search Results (1,547)

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

Virtual power plants (VPPs) coordinate distributed energy resources (DERs) to collectively meet grid dispatch instructions. When a dispatch command is issued to a VPP, it must be disaggregated optimally among the individual DERs to minimize overall operational costs. However, existing methods for VPP dispatch instruction disaggregation often require solving complex optimization problems for each instruction, posing challenges for real-time applications. To address this issue, we propose a multi-parametric programming-based method that yields an explicit mapping from any given dispatch instruction to an optimal DER-level deployment strategy. In our approach, a parametric optimization model is formulated to minimize the dispatch cost subject to DER operational constraints. By applying Karush–Kuhn–Tucker (KKT) conditions and recursively partitioning the DERs’ adjustable capacity space into critical regions, we derive analytical expressions that directly map dispatch instructions to their corresponding resource allocation strategies and optimal scheduling costs. This explicit solution eliminates the need to repeatedly solve the optimization problem for each new instruction, enabling fast real-time dispatch decisions. Case study results verify that the proposed method effectively achieves the cost-efficient and computationally efficient disaggregation of dispatch signals in a VPP, thereby improving its operational performance. Full article

Industrial building renovation is a sustainable strategy to preserve urban heritage while meeting modern needs. However, how interior material scenes affect users’ emotions, thermal perception, and functional preferences remains underexplored in adaptive reuse contexts. This study used virtual reality (VR) to examine four common material scenes—wood, concrete, red brick, and white-painted surfaces—within industrial renovation settings. A total of 159 participants experienced four Lumion-rendered VR environments and rated them on thermal perception (visual warmth, thermal sensation, comfort), emotional response (arousal, pleasure, restoration), and functional preference. Data were analyzed using repeated measures ANOVA and Pearson correlation. Wood and red brick scenes were associated with warm visuals; wood scenes received the highest ratings for thermal comfort and pleasure, white-painted scenes for restoration and arousal, and concrete scenes, the lowest scores overall. Functional preferences varied by space: white-painted and concrete scenes were most preferred in study/work settings, wood in social spaces, wood and red brick in rest areas, and concrete in exhibition spaces. By isolating material variables in VR, this study offers a novel empirical approach and practical guidance for material selection in adaptive reuse to enhance user comfort, emotional well-being, and spatial functionality in industrial heritage renovations. Full article

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality. Full article

Successful dental implant therapy relies on accurate planning and placement, e.g., through static, computer-aided implant surgery using CAD/CAM-fabricated surgical guides. This study examined production methods’ influence on surgical guide sleeve housing geometry. A model with two edentulous spaces was digitized using intraoral scanning and CBCT, and two virtually positioned implants were planned. Ten guides per group were produced using milling (MCX5), DLP printing (ASIGA and SHERA), and SLA printing (FORM), printing with 50 µm and 100 µm layers each. Each guide (n = 70) was then digitized using an industrial scanner before and after sterilization. Superimposition of the actual guide data with the reference data allowed for evaluation of deviations at the drill sleeve housing along the x-, y-, z-, and dxyz-axes. Descriptive and statistical evaluation was performed (significance level: p ≤ 0.0125). Significant differences existed among the production methods: Milling and SLA showed higher deviations than the DLP group (p < 0.001). Milled guides post-sterilization showed the highest deviations (0.352 ± 0.08 mm), while one DLP printer at 50 μm layer thickness showed lowest deviations (0.091 ± 0.04 mm). The layer thickness was insignificant, whereas sterilization increased deviation (p < 0.001). DLP produced the most precise implant surgical guides. All 3D printers were suitable for fabricating clinically acceptable surgical guides. Full article

Altitude Test Facility (ATF) compressor systems are widely used in aero-engine tests. These systems achieve the control of gas pressure and transport through complex operation processes. With advancements in the aviation industry, there is a growing demand for higher performance, greater safety, and more energy efficiency in digital ATF systems. Hybrid modeling is a technology that combines many methods and can meet these requirements. The Modular Dynamic System Greitzer (MDSG) compressor model, including mechanistic and data-driven modeling approaches, is combined with a neural network to obtain a BPNN-MDSG hybrid modeling method for the digital turbine system. The digital simulation is linked with the physical sensors of the ATF system to realize real-time simulation and monitoring. The steady and dynamic conditions of the actual system are simulated in virtual space. Compared with the actual results, the average error of steady mass flow is less than 3%, and the error of pressure is less than 1%. The average error of dynamic mass flow is less than 5%, and the error of pressure is less than 3%. The simulation and characteristic predictions are carried out in BPNN-MDSG virtual space. The anti-surge characteristics of the ATF system under start-up conditions are obtained. The full-condition anti-surge operation map of the system is obtained, which provides guidance for the actual operation of the ATF system. Full article

In this work, we introduce IK-AUG, a unified algorithmic framework for kinematics-driven data augmentation tailored to sign language recognition (SLR). Departing from traditional augmentation techniques that operate at the pixel or feature level, our method integrates inverse kinematics (IK) and virtual simulation to synthesize anatomically valid gesture sequences within a structured 3D environment. The proposed system begins with sparse 3D keypoints extracted via a pose estimator and projects them into a virtual coordinate space. A differentiable IK solver based on forward-and-backward constrained optimization is then employed to reconstruct biomechanically plausible joint trajectories. To emulate natural signer variability and enhance data richness, we define a set of parametric perturbation operators spanning spatial displacement, depth modulation, and solver sensitivity control. These operators are embedded into a generative loop that transforms each original gesture sample into a diverse sequence cluster, forming a high-fidelity augmentation corpus. We benchmark our method across five deep sequence models (CNN3D, TCN, Transformer, Informer, and Sparse Transformer) and observe consistent improvements in accuracy and convergence. Notably, Informer achieves 94.1% validation accuracy with IK-AUG enhanced training, underscoring the framework’s efficacy. These results suggest that algorithmic augmentation via kinematic modeling offers a scalable, annotation free pathway for improving SLR systems and lays the foundation for future integration with multi-sensor inputs in hybrid recognition pipelines. Full article

As vital material carriers of human civilization, earthen sites are experiencing continuous surface deterioration under the combined effects of weathering and anthropogenic damage. Traditional surface conservation techniques, due to their poor compatibility and limited reversibility, struggle to address the compound challenges of micro-scale degradation and macro-scale deformation. With the deep integration of digital twin technology, spatial information technologies, intelligent systems, and sustainable concepts, earthen site surface conservation technologies are transitioning from single-point applications to multidimensional integration. However, challenges remain in terms of the insufficient systematization of technology integration and the absence of a comprehensive interdisciplinary theoretical framework. Based on the dual-core databases of Web of Science and Scopus, this study systematically reviews the technological evolution of surface conservation for earthen sites between 2000 and 2025. CiteSpace 6.2 R4 and VOSviewer 1.6 were used for bibliometric visualization analysis, which was innovatively combined with manual close reading of the key literature and GPT-assisted semantic mining (error rate < 5%) to efficiently identify core research themes and infer deeper trends. The results reveal the following: (1) technological evolution follows a three-stage trajectory—from early point-based monitoring technologies, such as remote sensing (RS) and the Global Positioning System (GPS), to spatial modeling technologies, such as light detection and ranging (LiDAR) and geographic information systems (GIS), and, finally, to today’s integrated intelligent monitoring systems based on multi-source fusion; (2) the key surface technology system comprises GIS-based spatial data management, high-precision modeling via LiDAR, 3D reconstruction using oblique photogrammetry, and building information modeling (BIM) for structural protection, while cutting-edge areas focus on digital twin (DT) and the Internet of Things (IoT) for intelligent monitoring, augmented reality (AR) for immersive visualization, and blockchain technologies for digital authentication; (3) future research is expected to integrate big data and cloud computing to enable multidimensional prediction of surface deterioration, while virtual reality (VR) will overcome spatial–temporal limitations and push conservation paradigms toward automation, intelligence, and sustainability. This study, grounded in the technological evolution of surface protection for earthen sites, constructs a triadic framework of “intelligent monitoring–technological integration–collaborative application,” revealing the integration needs between DT and VR for surface technologies. It provides methodological support for addressing current technical bottlenecks and lays the foundation for dynamic surface protection, solution optimization, and interdisciplinary collaboration. Full article

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation. Full article

Creating shared spaces is a key part of making extended reality (XR) and Internet of Things (IoT) technology more interactive and collaborative. Currently, one system which stands out in achieving this end commercially involves spatial anchors. Due to the cloud-based nature of these anchors, they can introduce connectivity and privacy issues for projects which need to be isolated from the internet. This research attempts to explore and create a different approach that does not require internet connectivity. This work involves the creation of an AprilTags-based calibration system as a local solution for creating shared XR spaces and investigates its performance. AprilTags are simple, scannable markers that, through computer vision algorithms, can help XR devices figure out position and rotation in a three-dimensional space. This implies that multiple users can be in the same virtual space and in the real-world space at the same time, easily. Our tests in XR showed that this method is accurate and works well for synchronizing multiple users. This approach could make shared XR experiences faster, more private, and easier to use without depending on cloud-based calibration systems. Full article

This research aims to understand the evolving role of academic libraries, focusing on student perceptions of current services and their vision for the future. Data was collected using a survey at a midwestern research university in the United States. The survey contained both quantitative and qualitative questions. The objective of the survey was to understand the current utilization of library services and students’ future visions for academic libraries. Qualitative and quantitative analysis techniques were utilized as part of the study. Thematic analysis was employed as part of the qualitative analysis, while descriptive and inferential analysis techniques were utilized in the quantitative analysis. The findings reveal that many students use libraries for traditional functions such as studying and accessing resources. There is also an inclination toward digitalization due to convenience, accessibility, and environmental sustainability; however, print materials remain relevant as well. Another finding was a lack of awareness among some students regarding available library services, indicating a need for better marketing and communication strategies. Students envision future libraries as technology-driven spaces integrating artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and innovative collaborative environments. Ethical considerations surrounding AI, including privacy, bias, and transparency, are crucial factors that must be addressed. Some of the actionable recommendations include integrating ethical AI, implementing digital literacy initiatives, conducting ongoing usability and user experience (UX) research within the library, and fostering cross-functional collaboration to enhance library services and student learning. Full article

A Sanskrit manuscript of the Prajñāpāramitā or Perfection of Wisdom in eight thousand verses, now in the Cambridge University Library, Add.1643, is one of the most ambitiously designed South Asian manuscripts from the eleventh century, with the highest number of painted panels known among the dated manuscripts from medieval South Asia until 1400 CE. Thanks to the unique occurrence of a caption written next to each painted panel, it is possible to identify most images in this manuscript as representing those of famous pilgrimage sites or auspicious images of specific locales. The iconographic program transforms Add.1643 into a portable device containing famous pilgrimage sites of the Buddhist world known to the makers and users of the manuscript in eleventh-century Nepal. It is one compact colorful package of a book, which can be opened and experienced in its unfolding three-dimensional space, like a virtual or imagined pilgrimage. Building on the recent research focusing on early medieval Buddhist sites across Monsoon Asia and analyzing the representational potentials and ontological values of painting, this essay demonstrates how this early eleventh-century Nepalese manuscript (Add.1643) and its visual program document and remember the knowledge of maritime travels and the transregional and intraregional activities of people and ideas moving across Monsoon Asia. Despite being made in the Kathmandu Valley with a considerable physical distance from the actual sea routes, the sites remembered in the manuscript open a possibility to connect the dots of human movement beyond the known networks and routes of “world systems”. Full article

The architecture of contemporary museums often emphasizes visual aesthetics, such as large volumes, open-plan layouts, and highly reflective finishes, resulting in acoustic challenges, such as excessive reverberation, poor speech intelligibility, elevated background noise, and reduced privacy. This study quantified the impact of surface—specific absorption treatments on acoustic metrics across eight gallery spaces. Room impulse responses calibrated virtual models, which simulated nine absorption scenarios (low, medium, and high on ceilings, floors, and walls) and evaluated reverberation time (T₂₀), speech transmission index (STI), clarity (C₅₀), distraction distance (r_D), Spatial Decay Rate of Speech (D_2,S), and Speech Level at 4 m (L_p,A,S,4m). The results indicate that going from concrete to a wooden floor yields the most rapid T₂₀ reductions (up to −1.75 s), ceiling treatments deliver the greatest STI and C₅₀ gains (e.g., STI increases of +0.16), and high-absorption walls maximize privacy metrics (D_2,S and L_p,A,S,4m). A linear regression model further predicted the STI from T₂₀, total absorption (Sabins), and room volume, with an 84.9% conditional R², enabling ±0.03 accuracy without specialized testing. These findings provide empirically derived, surface-specific “first-move” guidelines for architects and acousticians, underscoring the necessity of integrating acoustics early in museum design to balance auditory and visual objectives and enhance the visitor experience. Full article

This paper explores the potential and implications arising from the convergence of virtual reality, the metaverse, and digital twins in translating a real-world commemorative event into a virtual environment. It emphasizes how such integration influences digital transformation processes, particularly in reshaping models of social interaction. Virtual reality is conceptualized as an immersive technology, enabling advanced multisensory experiences within persistent virtual spaces, such as the metaverse. Furthermore, this study delves into the concept of digital twins—high-fidelity virtual representations of physical systems, processes, and objects—highlighting their application in simulation, analysis, forecasting, prevention, and operational enhancement. In the context of virtual events, the convergence of these technologies is examined as a means to create interactive, adaptable, and scalable environments capable of accommodating diverse social groups and facilitating global accessibility. As a practical application, a digital twin of the Ferrándiz and Carbonell buildings—the most iconic architectural ensemble on the Alcoi campus—was developed to host a virtual event commemorating the 50th anniversary of the integration of the Alcoi School of Industrial Technical Engineering into the Universitat Politècnica de València in 1972. The virtual environment was subsequently evaluated by a sample of users, including students and faculty, to assess usability and functionality, and to identify areas for improvement. The digital twin achieved a score of 88.39 out of 100 on the System Usability Scale (SUS). The findings underscore the key opportunities and challenges associated with the adoption of these emerging technologies, particularly regarding their adaptability in reconfiguring digital environments for work, social interaction, and education. Using this case study as a foundation, this paper offers insights into the strategic role of the metaverse in extending environmental perception and its transformative potential for the future digital ecosystem through the implementation of digital twins. Full article