Search Results (3,155)

The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI. Full article

With deep learning approaches, the fundamental assumption of data availability can be severely compromised when a model trained on a source domain is transposed to a target application domain where data are unlabeled, making supervised fine-tuning mostly impossible. To overcome this limitation, the present work introduces an unsupervised temporal-domain adaptation framework for human action recognition from skeleton-based data that combines Contrastive Prototype Learning (CPL) and Temporal Adaptation Modeling (TAM), with the aim of transferring the knowledge learned from a source domain to an unlabeled target domain. The CPL strategy, inspired by recent success in contrastive learning applied to skeleton data, learns a compact temporal representation from the source domain, from which the TAM strategy leverages the capacity for self-training to adapt the representation to a target application domain using pseudo-labels. The research demonstrates that simultaneously solving CPL and TAM effectively enables the training of a generalizable human action recognition model that is adaptive to both domains and overcomes the requirement of a large volume of labeled skeleton data in the target domain. Experiments are conducted on multiple large-scale human action recognition datasets such as NTU RGB+D, PKU MMD, and Northwestern–UCLA to comprehensively evaluate the effectiveness of the proposed method. Full article

Exploring the intensity and constraint factors of land use conflicts provides essential insights for efficient land use planning. Currently, China’s spatial development is gradually transitioning towards the coordinated development of production, living, and ecological functions (PLEFs). Previous studies have typically focused on land use conflicts from a micro perspective, examining conflicts between production, living, and ecological land uses at a fine scale. There is limited research from a macro perspective that conducts a theoretical analysis based on the production, living, and ecological functions of land use conflicts themselves. In addition, existing studies primarily analyze the influencing factors of land use conflicts, with limited literature directly addressing the constraint factors of land use conflicts. This study focuses on 12 prefecture-level cities in Hubei Province, China, using data from 2010 to 2020. It categorizes land use conflicts at the macro level into production perspective, living perspective, and ecological perspective conflicts. For each of these conflict perspectives, different pressure, state, and response indicators are introduced. This approach leads to the development of a theoretical framework for analyzing land use conflicts at the macro level. On this basis, a spatiotemporal evolution analysis of land use conflicts was conducted. Additionally, using a constraint factor diagnosis model, the study analyzed the constraint factors of land use conflicts at the macro level across cities, leading to the following research conclusions: (1) the land use conflicts from the production and living perspectives in the 12 prefecture-level cities of Hubei showed an upward trend from 2010 to 2020, while the land use conflicts from the ecological perspective exhibited a downward trend; (2) during the study period, Wuhan exhibited the highest intensity of land use conflicts from both the production and living perspectives, while Ezhou experienced the highest intensity of land use conflicts from the ecological perspective for most of the study period; (3) the main constraining factors of land use conflicts from the production perspective in the 12 prefecture-level cities of Hubei are population density, average land GDP, and effective irrigation rate. The primary constraining factors of land use conflicts from the living perspective are population density, urbanization rate, and average land real estate development investment. The main constraining factors of land use conflicts from the ecological perspective are population density, average land fertilizer input, and effective irrigation rate. This study constructs a new theoretical framework for land use conflict assessment at the macro level, providing a novel approach for studying land use conflicts at the macro scale. Full article

Recently, there has been increasing interest in organic carbon (OC) certification of soil as an incentive for farmers to adopt sustainable agricultural practices. In this context, this pilot project combines geochemical and geophysical methods to map the distribution of OC contents in agricultural fields, allowing us to detect variations in time and space. Here we demonstrated a relationship between soil OC contents estimated in the laboratory and the apparent electrical conductivity (ECa) measured in the field. Specifically, geochemical elemental analyses were used to evaluate the OC content and relative isotopic signature in collected soil samples from a hazelnut orchard in the Emilia–Romagna region of Northeastern Italy, while the geophysical Electromagnetic Induction (EMI) method enabled the in situ mapping of the ECa distribution in the same soil field. According to the results, geochemical and geophysical data were found to be reciprocally related, as both the organic matter and soil moisture were mainly incorporated into the fine sediments (i.e., clay) of the soil. Therefore, such a relation was used to create a map of the OC content distribution in the investigated field, which could be used to monitor the soil C sequestration on small-scale farmland and eventually develop precision agricultural services. In the future, this method could be used by farmers and regional and/or national policymakers to periodically certify the farm’s soil conditions and verify the effectiveness of carbon sequestration. These measures would enable farmers to pursue Common Agricultural Policy (CAP) incentives for the reduction of CO₂ emissions. Full article

This study aimed to explore whether early developmental abilities are related to future executive function (EF) in children with motor delays. Fourteen children with motor delays (Mean age = 10.76, SD = 2.55) were included from a larger study. Object interaction and developmental outcomes (Bayley-III) were evaluated at baseline and 3, 6, and 12 months post-baseline. Bayley-III and EF assessments (Minnesota Executive Function Scale) were conducted at 36 months post-baseline. Children with high EF demonstrated advanced early bimanual, visual–bimanual, receptive language, expressive language, and fine motor skills compared to children with low EF. Significant positive correlations between later Bayley-III and EF scores were found for cognitive, expressive language, and fine motor scores. These preliminary results suggest that early developmental skills support the emergence of EF in children with motor delays. Full article

(1) Background: Existing Vehicle travel time prediction applications face challenges in modeling complex road network and handling irregular spatiotemporal traffic state propagation. (2) Methods: To address these issues, we propose a Graph Attention-based Multi-Spatiotemporal Features for Travel Time Prediction (GMTP) model, which integrates an enhanced graph attention network (GATv2) and Bidirectional Encoder Representations from Transformers (BERT) to analyze dynamic correlations across spatial and temporal dimensions. The pre-training process consists of two blocks: the Road Segment Interaction Pattern to Enhance GATv2, which generates road segment representation vectors, and a traffic congestion-aware trajectory encoder by incorporating a shared attention mechanism for high computational efficiency. Additionally, two self-supervised tasks are designed for improved model accuracy and robustness. (3) Results: The fine-tuned model had comparatively optimal performance metrics with significant reductions in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). (4) Conclusions: Ultimately, the integration of this model into travel time prediction, based on two large-scale real-world trajectory datasets, demonstrates enhanced performance and computational efficiency. Full article

Airwave interference presents a major source of noise in seismic exploration, posing significant challenges to the quality control of raw seismic data. With the increasing data volume in 3D seismic exploration, manual identification methods fall short of meeting the demands of high-density 3D seismic surveys. This study employs the YOLOv5 model, a widely used tool in object detection, to achieve rapid identification of airwave noise in seismic profiles. Initially, the model was pre-trained on the COCO dataset—a large-scale dataset designed for object detection—and subsequently fine-tuned using a training set specifically labeled for airwave noise data. The fine-tuned model achieved an accuracy and recall rate of approximately 85% on the test dataset, successfully identifying not only the presence of noise but also its location, confidence levels, and range. To evaluate the model’s effectiveness, we applied the YOLOv5 model trained on 2D data to seismic records from two regions: 2D seismic data from Ningqiang, Shanxi, and 3D seismic data from Xiushui, Sichuan. The overall prediction accuracy in both regions exceeded 90%, with the accuracy and recall rates for airwave noise surpassing 83% and 90%, respectively. The evaluation time for single-shot 3D seismic data (over 8000 traces) was less than 2 s, highlighting the model’s exceptional transferability, generalization ability, and efficiency. These results demonstrate that the YOLOv5 model is highly effective for detecting airwave noise in raw seismic data across different regions, marking the first successful attempt at computer recognition of airwaves in seismic exploration. Full article

Although significant progress has been made in sentiment analysis tasks based on image–text data, existing methods still have limitations in capturing cross-modal correlations and detailed information. To address these issues, we propose a Multi-Granularity Attention Fusion Network for Implicit Sentiment Analysis (MGAFN-ISA). MGAFN-ISA that leverages neural networks and attention mechanisms to effectively reduce noise interference between different modalities and captures distinct, fine-grained visual and textual features. The model includes two key feature extraction modules: a multi-scale attention fusion-based visual feature extractor and a hierarchical attention mechanism-based textual feature extractor, each designed to extract detailed and discriminative visual and textual representations. Additionally, we introduce an image translator engine to produce accurate and detailed image descriptions, further narrowing the semantic gap between the visual and textual modalities. A bidirectional cross-attention mechanism is also incorporated to utilize correlations between fine-grained local regions across modalities, extracting complementary information from heterogeneous visual and textual data. Finally, we designed an adaptive multimodal classification module that dynamically adjusts the contribution of each modality through an adaptive gating mechanism. Extensive experimental results demonstrate that MGAFN-ISA achieves a significant performance improvement over nine state-of-the-art methods across multiple public datasets, validating the effectiveness and advancement of our proposed approach. Full article

To investigate the gravitational effects of massive objects on a typical observer, we studied the dynamics of a test particle following ${\mathrm{BMS}}_3$ geodesics. We constructed the ${\mathrm{BMS}}_3$ framework using the canonical phase space formalism and the corresponding Hamiltonian. We focused on analyzing these effects at fine scales of spacetime, which led us to quantization of the phase space. By deriving and studying the solutions of the quantum equations of motion for the test particle, we obtained its energy spectrum and explored the behavior of its wave function. These findings offer a fresh perspective on gravitational interactions in the context of quantum mechanics, providing an alternative approach to traditional quantum field theory analyses. Full article

Background/Objectives: Vision Transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performances in image classification, especially in the domain of medical imaging analysis. However, ViTs struggle to capture high-frequency components of images, which are critical in identifying fine-grained patterns, while CNNs have difficulties in capturing long-range dependencies due to their local receptive fields, which makes it difficult to fully capture the spatial relationship across lung regions. Methods: In this paper, we proposed a hybrid architecture that integrates ViTs and CNNs within a modular component block(s) to leverage both local feature extraction and global context capture. In each component block, the CNN is used to extract the local features, which are then passed through the ViT to capture the global dependencies. We implemented a gated attention mechanism that combines the channel-, spatial-, and element-wise attention to selectively emphasize the important features, thereby enhancing overall feature representation. Furthermore, we incorporated a multi-scale fusion module (MSFM) in the proposed framework to fuse the features at different scales for more comprehensive feature representation. Results: Our proposed model achieved an accuracy of 99.50% in the classification of four pulmonary conditions. Conclusions: Through extensive experiments and ablation studies, we demonstrated the effectiveness of our approach in improving the medical image classification performance, while achieving good calibration results. This hybrid approach offers a promising framework for reliable and accurate disease diagnosis in medical imaging. Full article

Dialogue systems must understand children’s utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child–robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children’s intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children’s speech to enhance its intention reasoning performance in verbal interactions with children. To this end, we propose a fine-tuning methodology that utilizes the LLM–human judgment discrepancy and interactive response data. The former data represent cases in which the LLM and human judgments of the contextual appropriateness of a child’s answer to a robot’s question diverge. The latter data involve robot responses suitable for children’s utterance intentions, generated by the LLM. We developed a fine-tuned dialogue system using these datasets to achieve human-like interpretations of children’s utterances and to respond adaptively. Our system was evaluated through human assessment using the Robotic Social Attributes Scale (RoSAS) and Sensibleness and Specificity Average (SSA) metrics. Consequently, it supports the effective interpretation of children’s utterance intentions and enables natural verbal interactions, even in cases with syntactic incompleteness and mispronunciations. Full article

Medical image processing has been highlighted as an area where deep-learning-based models have the greatest potential. However, in the medical field, in particular, problems of data availability and privacy are hampering research progress and, thus, rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy but also allows the drawing of new patients with specific characteristics, enabling the development of data-driven models on a much larger scale. This work demonstrates that three-dimensional generative adversarial networks (GANs) can be efficiently trained to generate high-resolution medical volumes with finely detailed voxel-based architectures. In addition, GAN inversion is successfully implemented for the three-dimensional setting and used for extensive research on model interpretability and applications such as image morphing, attribute editing, and style mixing. The results are comprehensively validated on a database of three-dimensional HR-pQCT instances representing the bone micro-architecture of the distal radius. Full article

The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment recognition tasks and the small scale of indoor scene data, a method and model for rapid classification of household environment domain knowledge is proposed, which can achieve high recognition accuracy by using a small-scale indoor scene and tool dataset. This paper uses a knowledge graph to associate data for home service robots. The application requirements of knowledge graphs for home service robots are analyzed to establish a rule base for the system. A domain ontology of the home environment is constructed for use in the knowledge graph system, and the interior functional areas and functional tools are classified. This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. This proposal adopts a combination of MobileNetV3 and transfer learning, integrating large-scale pre-training with fine-tuning for the home environment to address the challenge of limited data for home robots. The results show that the proposed model achieves higher recognition accuracy and recognition speed than other common methods, meeting the work requirements of service robots. With the Scene15 dataset, the proposed scheme has the highest recognition accuracy of 0.8815 and the fastest recognition speed of 63.11 microseconds per sheet. Full article

Traditional gross ecosystem product (GEP) accounting methods often operate at macro scales, failing to reflect the localized and nuanced values of wetland ecosystems. This study addresses these challenges by introducing a fine-grained classification system based on a localized adaptation of international standards. The framework integrates high-precision national land surveys and remote sensing quantitative analysis while incorporating fisheries resource models, climate regulation beneficiary mapping, and visitor interpolation to address data scarcity related to human activities. This approach refines the spatial calculation methods for functional quantity accounting at fine scales. The results demonstrate that the refined classification maintains consistency with traditional methods in total value while adapting to multi-scale accounting, filling gaps at small and medium scales and providing a more accurate representation of localized wetland characteristics. Additionally, the study highlights the dominance of cultural services in GEP, emphasizing the need to balance cultural and regulatory services to ensure fairness in decision-making. Finally, a village-scale decision-support model is proposed, offering actionable guidance for wetland management and sustainable development planning. Full article

In recent years, the design and synthesis of high-performing conjugated materials for the application in organic photovoltaics (OPVs) have achieved lab-scale devices with high power conversion efficiency. However, most of the high-performing materials are still synthesised using complex multistep procedures, resulting in high cost. For the upscaling of OPVs, it is also important to focus on conjugated polymers that can be made via fewer simple synthetic steps. Therefore, an easily synthesised amorphous thiophene−quinoxaline donor polymer, TQ1, has attracted our attention. An analogue, TQ-EH that has the same polymer backbone as TQ1 but with short branched side-chains, was previously reported as a donor polymer with increased crystallinity. We have synthesised copolymers with varied ratios between octyloxy and branched (2-ethylhexyl)oxy-substituted quinoxaline units having the same polymer backbone, with the aim to control the aggregation/crystallisation behaviour of the resulting copolymers. The optical properties, glass transition temperatures and degree of crystallinity of the new copolymers were systematically examined in relation to their copolymer composition, revealing that the composition can be used to fine-tune these properties of conjugated polymers. In addition, multiple sub-T_g transitions were found from some of the polymers, which are not commonly or clearly seen in other conjugated polymers. The new copolymers were tested in photovoltaic devices with a fullerene derivative as the acceptor, achieving slightly higher performances compared to the homopolymers. This work demonstrates that side-chain modification by copolymerisation can fine-tune the properties of conjugated polymers without requiring complex organic synthesis, thereby expanding the number of easily synthesised polymers for future upscaling of OPVs. Full article