Search Results (476)

To address the challenges of cross-modal feature misalignment and ineffective information fusion caused by the inherent differences in imaging mechanisms, noise statistics, and semantic representations between visible and synthetic aperture radar (SAR) imagery, this paper proposes a multimodal remote sensing object detection method, namely YOLO-CMFM. Built upon the Ultralytics YOLOv11 framework, the proposed approach designs a Cross-Modal Fusion Module (CMFM) that systematically enhances detection accuracy and robustness from the perspectives of modality alignment, feature interaction, and adaptive fusion. Specifically, (1) a Learnable Edge-Guided Attention (LEGA) module is constructed, which leverages a learnable Gaussian saliency prior to achieve edge-oriented cross-modal alignment, effectively mitigating edge-structure mismatches across modalities; (2) a Bidirectional Cross-Attention (BCA) module is developed to enable deep semantic interaction and global contextual aggregation; (3) a Context-Guided Gating (CGG) module is designed to dynamically generate complementary weights based on multimodal source features and global contextual information, thereby achieving adaptive fusion across modalities. Extensive experiments conducted on the OGSOD 1.0 dataset demonstrate that the proposed YOLO-CMFM achieves an $mAP@50$ of 96.2% and an $mAP@50$:95 of 75.1%. While maintaining competitive performance comparable to mainstream approaches at lower IoU thresholds, the proposed method significantly outperforms existing counterparts at high IoU thresholds, highlighting its superior capability in precise object localization. Also, the experimental results on the OSPRC dataset demonstrate that the proposed method can consistently achieve stable gains under different kinds of imaging conditions, including diverse SAR polarizations, spatial resolutions, and cloud occlusion conditions. Moreover, the CMFM can be flexibly integrated into different detection frameworks, which further validates its strong generalization and transferability in multimodal remote sensing object detection tasks. Full article

Ancient walnut trees (Juglans regia L.), revered as “cultural heritage in motion,” have coexisted harmoniously with dense clusters of Tibetan traditional villages for centuries. However, accelerating climate change and expanding human activities along the middle reaches of the Yarlung Tsangpo River have increasingly threatened their survival. To quantitatively evaluate the health of these ancient trees and identify the underlying driving mechanisms, this study developed a remote sensing-enhanced Structural Equation Model (SEM) that integrated satellite-derived ecological indices, land-use intensity, and field-measured morphological and physiological indicators. A total of 135 ancient walnut trees from villages such as Gamai in Jiacha County, Tibet, were examined. Key findings: (1) The SEM demonstrated an excellent model–data fit (Minimum Discrepancy Divided by Degrees of Freedom (CMIN/DF) = 1.372, Root Mean Square Error of Approximation (RMSEA) = 0.053, Tucker–Lewis Index (TLI) = 0.956, and Comparative Fit Index (CFI) = 0.962), confirming its robustness. (2) Among the latent variables, overall condition exerted the strongest influence (weight = 0.360), whereas foliage condition contributed least (0.289). (3) Approximately 35.56% of trees were healthy or sub-healthy, while 61.48% showed varying levels of decline. (4) Tree health was jointly shaped by intrinsic and extrinsic factors, with intrinsic drivers exhibiting stronger explanatory power. Externally, human disturbance negatively affected health, whereas ecological quality was positively associated. These results highlight the effectiveness of integrating remote sensing and SEM for ancient tree assessment and underscore the urgent need for long-term monitoring and adaptive conservation strategies to enhance ecological resilience. Full article

Remote sensing semantic segmentation holds significant application value in urban planning, environmental monitoring, and related fields. In recent years, multimodal approaches that fuse optical imagery with normalized Digital Surface Models (nDSM) have attracted widespread attention due to their superior performance. However, existing methods typically treat nDSM merely as an additional input channel, failing to effectively exploit its inherent 3D geometric priors, which limits segmentation accuracy in complex urban scenes. To address this issue, we propose a Geometry-aware Interactive Multi-Modal Network (GIMMNet), which explicitly models the geometric structure embedded in nDSM to guide the spatial distribution of semantic categories. Specifically, we first design a Geometric Position Prior Module (GPPM) to construct 3D coordinates for each pixel based on nDSM and extract intrinsic geometric priors. Next, a Geometry-Guided Disentangled Fusion Module (GDFM) dynamically adjusts fusion weights according to the differential responses of each modality to the geometric priors, enabling adaptive multimodal feature integration. Finally, during decoding, a Geometry-Attentive Context Module (GACM) explicitly captures the dependencies between land-cover categories and geometric structures, enhancing the model’s spatial awareness and semantic recovery capability. Experimental results on two public remote sensing datasets—Vaihingen and Potsdam—show that the proposed GIMMNet outperforms existing mainstream methods in segmentation performance, demonstrating that enhancing the model’s geometric perception capability effectively improves semantic segmentation accuracy. Notably, our method achieves an mIoU of 85.2% on the Potsdam dataset, surpassing the second-best multimodal approach, PACSCNet, by 2.3%. Full article

This article re-interprets V. S. Naipaul’s “Tell Me Who to Kill” from In a Free State (1971) through a Heideggerian lens, focusing on the ‘groundlessness’ of existence and the dialectics of ‘danger’ that structure the unnamed narrator’s life within colonial ‘modernity’. Using Hiedegger’s phenomenology as a rhetorical hermeneutic, it traces how ordinary existential structures—fear, anxiety, boredom, curiosity, idle talk, and ambiguity—surface in the narrator’s and other characters’ comportments and speech. In Heidegger’s sense, these moods do not simply describe psychological states but reveal the conditions of Dasein’s being-in-the-world and the ontological disclosures of a being unhomed by empire. By situating Heidegger’s concepts of Dasein, thrownness, and fallenness within Naipaul’s world of migration, labour, and racial precarity, the paper reveals how metaphysical homelessness becomes historically tangible. The narrator’s obsessive drive for success, his failed fraternal duty, and his descent into estrangement dramatize a colonial subjectivity torn between aspiration and abjection. In reframing Heidegger through the postcolonial experience, the article both deprovincializes European existentialism and reclaims phenomenology as a site for interrogating the psychic economies of empire. Ultimately, the novella becomes a poetics of nihilism—where the search for authenticity collapses under the weight of displacement. Full article

This paper discusses the application of on-chip terahertz (THz) filters attached to waveguides that can act as sensor elements, including for scanned imaging applications. Our work presents a comparative numerical study of several different geometries (comprising five split-ring resonator geometries and a quarter-wavelength stub resonator, the latter being well established as a sensor at THz frequencies and therefore able to act as a benchmark). We designed each structure to have a resonant frequency of 500 GHz, allowing the impact of resonator geometry on sensing performance to be isolated; the performance was quantified by assessing each design using four figures of merit: resonance quality factor, sensitivity (relative frequency shift under dielectric loading), responsivity (sensitivity weighted by resonance sharpness), and the electric field confinement area. Simulations were conducted using Ansys HFSS using the properties of a commercially available photoresist (Shipley 1813) as a dielectric load to assess performance under conditions comparable to previous experimental studies. The analysis showed that while sensitivity remained broadly similar across geometries, responsivity and quality factor differed substantially between resonators. Furthermore, the spatial distribution of the electric field and current density, particularly in rotated configurations, was found to significantly impact coupling efficiency between the resonator and transmission line. Our findings provide guidance for the general design of systems employing THz sensors while establishing a framework with which to benchmark future sensor geometries. Full article

Accurate semantic segmentation of complex landforms in remote sensing imagery is hindered by pronounced intra-class heterogeneity, blurred boundaries, and irregular geomorphic structures. To overcome these challenges, this study presents LENet (Landforms Expert Segmentation Net), a novel segmentation network that combines axial semantic modeling with deformation-aware compensation. LENet follows an encoder–decoder framework, where the decoder integrates three key modules: the Expert Enhancement Block (EEBlock) for capturing long-range dependencies along axial directions; the Feature Expert Compensator (FEC) employing deformable convolutions with channel–spatial decoupled weights to emphasize ambiguous intra-class regions; and the Cross-Sparse Attention (CSA) mechanism that suppresses background noise via multi-rate sparsity masks and enhances intra-class consistency through cosine-similarity weighting. Experiments conducted on the PKLD plateau karst and GVLM landslide datasets demonstrate that LENet achieves IoU scores of 70.39% and 80.95% and Recall values of 83.33% and 91.38%, surpassing eight state-of-the-art methods. These results confirm that LENet effectively balances global contextual understanding and local detail refinement, providing a robust and accurate solution for complex landform segmentation in remote sensing imagery. Full article

Anomaly detection using multivariate time-series data remains a significant challenge for complex industrial systems, such as Cyber–Physical Systems (CPSs), Industrial Control Systems (ICSs), Intrusion Detection Systems (IDSs), the Internet of Things (IoT), and Remote Sensing Monitoring Platforms, including satellite Earth observation systems and Mars Rovers. In these systems, sensors are highly interconnected, and local anomalies frequently affect multiple components. Because these interconnections are often implicit and involve complex interactions, systematic characterization is required. To address this, our study employs graph neural networks with a structure-entropy-based attention mechanism, which models multi-element relationships and formally represents implicit relationships within complex industrial systems using a network-based structural model. Specifically, our method distinguishes the weights of different high-order neighbor nodes based on their locations, rather than treating all nodes equally. Through this formalization, we identify and represent key adjacent elements by analyzing system entropy. We validate our method on SMAT, MSL, SWaT, and WADI datasets, and experimental results demonstrate improved detection performance compared to baseline approaches. Full article

Wildfires are among the most severe disturbances in Mediterranean ecosystems, altering vegetation structure, soil properties, and hydrological functioning. Understanding post-fire hydrological dynamics is crucial for predicting flood and erosion risks and vegetation restoration in fire-prone regions. This study investigates the hydrological responses of Mediterranean watersheds following a wildfire event by integrating WiMMed (Watershed Integrated Management in Mediterranean Environments), a distributed, physically based hydrological model, with high-resolution vegetation data derived from LiDAR and Landsat imagery. A Priority Post-Fire Restoration Index (PPRI) was calculated as the weighted sum of the six parameters runoff (mm), flow accumulation (mm), distance to drainage network (m), slope (%), erodibility (K), lithology, and LiDAR index under a sediment reduction and runoff peak reduction scenario. The post-fire hydrological processes modeled with WiMMed described the dynamics of surface runoff and soil moisture redistribution across the upper soil layers after fire, and their gradual attenuation with vegetation regrowth. The spatial distribution of the PPRI identified specific zones within the burned watershed that require urgent restoration measures (10% and 4.55% under sediment reduction and peak reduction scenarios, respectively). The combined use of process-based modeling and remote sensing offers valuable insights into watershed-scale hydrological resilience and supports the design of post-fire restoration strategies in Mediterranean landscapes. Full article

An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep learning models, limitations remain in handling significant variations in building scales and complex architectural forms, which may lead to inaccurate boundaries or difficulties in extracting small or irregular structures. Therefore, the present study proposes MSA-UNet, a reliable semantic segmentation framework that leverages multiscale feature aggregation and attentive skip connections for an accurate extraction of building footprints. This framework is constructed based on the U-Net architecture, incorporating VGG16 as a replacement for the original encoder structure, which enhances its ability to capture low-discriminative features. To further improve the representation of image buildings with different scales and shapes, a serial coarse-to-fine feature aggregation mechanism was used. Additionally, a novel skip connection was built between the encoder and decoder layers to enable adaptive weights. Furthermore, a dual-attention mechanism, implemented through the convolutional block attention module, was integrated to enhance the focus of the network on building regions. Extensive experiments conducted on the WHU and Inria building datasets validated the effectiveness of MSA-UNet. On the WHU dataset, the model demonstrated a state-of-the-art performance with a mean Intersection over Union (mIoU) of 94.26%, accuracy of 98.32%, F1-score of 96.57%, and mean Pixel accuracy (mPA) of 96.85%, corresponding to gains of 1.41% in mIoU over the baseline U-Net. On the more challenging Inria dataset, MSA-UNet achieved an mIoU of 85.92%, indicating a consistent improvement of up to 1.9% over the baseline U-Net. These results confirmed that MSA-UNet markedly improved the accuracy and boundary integrity of building extraction from HR data, outperforming existing classic models in terms of segmentation quality and robustness. Full article

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

With the continuous development of cities and the increasing utilization of underground space, ambient noise seismic imaging has become an essential approach for exploring and monitoring the urban subsurface. The integration of Distributed Acoustic Sensing (DAS) with ambient noise imaging offers a more convenient and effective solution for investigating shallow subsurface structures in urban environments. To overcome the limitations of conventional ambient noise seismic nodes, which are costly and incapable of achieving high-density data acquisition, this work makes use of existing urban telecommunication fibers to record ambient noise and perform sliding-window cross-correlation on it. Then the Phase-Weighted Stack (PWS) technique is applied to enhance the quality and stability of the cross-correlation signals, and fundamental-mode Rayleigh wave dispersion curves are extracted from the cross-correlation functions through the High-Resolution Linear Radon Transform (HRLRT). In the inversion stage, an adaptive regularization strategy based on automatic L-curve corner detection is introduced, which, in combination with the Preconditioned Steepest Descent (PSD) method, enables efficient and automated dispersion inversion, resulting in a well-resolved near-surface S-wave velocity structure. The results indicate that the proposed workflow can extract useful surface-wave dispersion information under typical urban noise conditions, achieving a feasible level of subsurface velocity imaging and providing a practical technical means for urban underground space exploration and utilization. Full article

Obesity remains a major global health challenge, with glucagon-like peptide-1 receptor agonists (GLP-1RAs) providing substantial yet sensitive benefits in weight reduction, glycemic control, and cardiovascular protection. Despite robust trial data, real-world persistence is limited by cost, tolerability, and hedonic adaptation. Intermittent fasting and time-restricted eating offer physiologically complementary, low-cost strategies that enhance fat oxidation, insulin sensitivity, and metabolic flexibility while engaging behavioral mechanisms of self-control and dietary regularity. This narrative review synthesizes current evidence and proposes a pragmatic, phased framework integrating GLP-1RA therapy with structured intermittent fasting and protein-optimized nutrition. The model emphasizes sequential initiation, transition, and maintenance phases designed to align pharmacologic appetite suppression with lifestyle-driven metabolic remodeling. Mechanistically, GLP-1RAs target vascular and neuroendocrine pathways, whereas fasting activates nutrient-sensing networks (AMPK, mTOR, sirtuins) associated with autophagy and longevity. Combined application may preserve lean mass, improve psychological autonomy, and reduce healthcare costs. Future research should validate this hybrid strategy in randomized trials assessing long-term weight durability, functional outcomes, and cost-effectiveness. By uniting pharmacologic potency with behavioral sustainability, phased GLP-1–fasting integration may represent an effective, affordable, and longevity-oriented paradigm for metabolic health. Full article

Affordable, sensor-driven lower-limb prostheses remain scarce in middle-income health systems. We report the design, numerical justification, and bench validation of a semi-active transfemoral prosthesis featuring surface electromyography (EMG) control and inertial sensing for low-resource deployment. The mechanical architecture combines a titanium–aluminum–carbon composite frame (total mass 0.87 kg; parts cost < USD 400) with topology optimization (SIMP) to minimize weight while preserving stiffness. Finite-element analyses (critical load 2.94 kN) confirmed structural safety (yield safety factor ≥ 1.6) and favorable fatigue margins. A dual-channel sensing scheme—surface EMG from the rectus femoris and an IMU—drives a five-state gait finite state machine implemented on a low-power STM32H platform. The end-to-end EMG→PWM latency remained <200 ms (mean 185 ms). Bench tests reproduced commanded flexion within ±2.2%, with average electrical power of ~4.6 W and battery autonomy of ~5.7 h using a 1650 mAh Li-Po pack. Results demonstrate a pragmatic trade-off between functionality and cost: semi-active damping with EMG-triggered control and open, modular hardware suitable for small-lab fabrication. Meeting target metrics (mass ≤ 1 kg, latency ≤ 200 ms, autonomy ≥ 6 h, cost ≤ USD 500), the prototype indicates a viable pathway to broaden access to intelligent prostheses and provides a platform for future upgrades (e.g., neural network control and higher-efficiency actuators). Full article

Existing Generative Adversarial Networks (GANs) frequently yield remote sensing images with blurred fine details, distorted textures, and compromised spatial structures when applied to super-resolution (SR) tasks, so this study proposes a Multi-Attention Fusion Generative Adversarial Network (MAF-GAN) to address these limitations: the generator of MAF-GAN is built on a U-Net backbone, which incorporates Oriented Convolutions (OrientedConv) to enhance the extraction of directional features and textures, while a novel co-calibration mechanism—incorporating channel, spatial, gating, and spectral attention—is embedded in the encoding path and skip connections, supplemented by an adaptive weighting strategy to enable effective multi-scale feature fusion, and a composite loss function is further designed to integrate adversarial loss, perceptual loss, hybrid pixel loss, total variation loss, and feature consistency loss for optimizing model performance; extensive experiments on the GF7-SR4×-MSD dataset demonstrate that MAF-GAN achieves state-of-the-art performance, delivering a Peak Signal-to-Noise Ratio (PSNR) of 27.14 dB, Structural Similarity Index (SSIM) of 0.7206, Learned Perceptual Image Patch Similarity (LPIPS) of 0.1017, and Spectral Angle Mapper (SAM) of 1.0871, which significantly outperforms mainstream models including SRGAN, ESRGAN, SwinIR, HAT, and ESatSR as well as exceeds traditional interpolation methods (e.g., Bicubic) by a substantial margin, and notably, MAF-GAN maintains an excellent balance between reconstruction quality and inference efficiency to further reinforce its advantages over competing methods; additionally, ablation studies validate the individual contribution of each proposed component to the model’s overall performance, and this method generates super-resolution remote sensing images with more natural visual perception, clearer spatial structures, and superior spectral fidelity, thus offering a reliable technical solution for high-precision remote sensing applications. Full article

Relative humidity (RH) and temperature are crucial parameters in environmental monitoring and have attracted significant attention. However, traditional commercial sensors typically suffer from inherent limitations such as structural complexity, bulkiness, and high manufacturing costs. To address these issues, this study proposes a novel tilted fiber Bragg grating (TFBG)-based optical fiber humidity sensor, coated with a composite film of polyvinyl alcohol (PVA) and graphene oxide (GO). First, the sensing mechanisms of the TFBG functionalized with nanofiber films were theoretically analyzed, and the transmission spectra of TFBG under varied structural parameters were simulated. These theoretical investigations laid a solid foundation for subsequent experimental validation. Subsequently, PVA/GO composite nanofiber films tailored for humidity sensing were fabricated by electrospinning technology, and the proposed TFBG sensor was experimentally implemented in accordance with the theoretical design. The experimental results indicate that the developed sensor exhibits a humidity sensitivity of −0.24 pm/%RH within the RH range of 35–85%. Furthermore, we calculated temperature and RH changes while discounting cross-sensitivity, thereby enabling simultaneous decoupling of temperature and RH measurements. Owing to its distinctive advantages of compact size, light weight, and cost-effectiveness, the proposed TFBG sensor holds great promise for practical applications in environmental monitoring. Full article