Search Results (469)

Cropland constitutes a foundational resource for global food security and agricultural sustainability, and its accurate extraction from high-resolution remote sensing imagery is essential for agricultural monitoring and land management. However, existing deep learning-based segmentation methods often struggle to balance global contextual modeling and fine-grained boundary representation, leading to boundary blurring and omission of small cropland parcels. To address these challenges, this study proposes a novel CNN–Transformer dual-branch fusion network, named CAFM-Net, which integrates a convolution and attention fusion module (CAFM) and an edge-assisted supervision head (EH) to jointly enhance global–local feature interaction and boundary delineation capability. Experiments were conducted on a self-built PlanetScope cropland dataset from Suihua City, China, and the GID public dataset to evaluate the effectiveness and generalization ability of the proposed model. On the self-built dataset, CAFM-Net achieved an overall accuracy (OA) of 96.75%, an F1-score of 96.80%, and an Intersection over Union (IoU) of 93.79%, outperforming mainstream models such as UNet, DeepLabV3+, TransUNet, and Swin Transformer by a clear margin. On the GID public dataset, CAFM-Net obtained an OA of 94.58%, an F1-score of 94.19%, and an IoU of 89.02%, demonstrating strong robustness across different data sources. Ablation experiments further confirm that the CAFM contributes most significantly to performance improvement, while the EH module effectively enhances boundary accuracy. Overall, the proposed CAFM-Net provides a quantitatively validated and robust solution for fine-grained cropland segmentation from high-resolution remote sensing imagery, with clear advantages in boundary precision and small-parcel detection. Full article

As an important ecological obstacle prone to climatic changes, the Tibetan Plateau has been transformed by retreating glaciers, degrading permafrost, and deteriorating grasslands. Recent ecological remote sensing evaluations typically use medium-resolution and single-source optical imagery, highlight natural factors while ignoring human impacts, and encounter difficulties with time-focused interpretability and continuity within complex terrains. This research proposes a theory combining large language models with satellite embedding to holistically examine the ecology of the Tibetan Plateau between 2000 and 2024. We created an ecological satellite embedding (ESE) model applying self-supervised learning to integrate 12 ecological variables into combined space and time representations as of 2024, according to the Prithvi-Earth Observation (Prithvi-EO) foundational model involving low-rank adaptation (LoRA). GeoChat reasoning was applied to turn the embedded variables into a comprehensive representation feature (CRF). Field research demonstrated strong accuracy for the fraction of absorbed photosynthetically active radiation (FAPAR, R² = 0.9923) and aboveground biomass (AGB, R² = 0.8690). Space and temporal analyses demonstrated a general ecology-dependent enhancement accompanied by significant space-based clustering (Moran’s I = 0.50–0.80), hotspots in humid southeastern areas, major upward trends in vegetation indices and productivity metrics (p < 0.05), and higher shifts in transition regions. Despite the marginal degradation risk, the grassland carrying capacity has expanded extensively in the main farming regions. The comprehensible CRF schema identified three management areas: potential risk, enhancement potential, and stable conservation management. This transferable modular approach connects expert reasoning with data-driven modeling, presenting adaptable methods for assessing ecosystems in high-altitude, data-sparse environments, and practical ways to promote ecological management. Full article

Truly impactful innovations are developed by outsiders out of a sense of need; those that rise to mainstream recognition and acceptance, however, are colonized by dominant hegemonies. This paper traces cycles of innovation and colonization in literature, publishing, and computing as ancestral domains to electronic literature, which has been subject to the same gendered and othered frontier-colonization cycles that dominated its forebears. Elit was a new frontier for writing and publishing, a strong site of marginalized creativity, until it was codified and colonized into publishing and academia by the dominant class: women could create, but men had the actual and cultural capital to create and develop the structures to platform their work into the dominant discourse. This paper analyzes how feminist and marginalized digital writers resist colonization of their innovations and erasure of their innovations by hacking platforms, subverting narrative conventions, and amplifying hidden voices. The paper examines elements of innovation-colonization cycles in elit and adjacent practices (indie games, fanfic), showcases Lillian-Yvonne Bertram’s algorithmically-generated epoetry as a site of subversion, and presents fanfic community Archive of Our own as a preliminary model of value-sensitive and inclusive community design. It argues for the development of feminist-first platforms—digital spaces that actively resist the structural colonization of marginalized storytelling. Full article

Satellite remote sensing has revolutionized the monitoring of atmospheric carbon dioxide (CO₂) concentrations, yet its integration into studies of air–sea CO₂ flux dynamics remains limited. Leveraging high-resolution observations from the Orbiting Carbon Observatory 2 (OCO-2) and Copernicus Marine Environment Monitoring Service (CMEMS), this study investigated the spatiotemporal heterogeneity of atmospheric column-averaged CO₂ (XCO₂) and sea surface partial pressure of CO₂ (pCO₂) between 2015 and 2020. Our analysis reveals pronounced latitudinal gradients, with the Northern Hemisphere exhibiting stronger seasonal XCO₂ variability (5.67 ± 0.42 ppm annual amplitude) compared to the Southern Hemisphere (1.2 ± 0.18 ppm). Notably, the XCO₂ growth rate was marginally higher in the Southern Hemisphere (2.48 ppm yr⁻¹) than the Northern Hemisphere (2.39 ppm yr⁻¹), while coastal regions showed elevated atmospheric CO₂ concentrations, but slower pCO₂ increases relative to the open ocean, suggesting a buffering capacity of marginal seas. Furthermore, we identified distinct seasonal phasing between land and ocean XCO₂, with oceanic signals lagging terrestrial ones by approximately one month. These findings highlight the utility of satellite data in resolving fine-scale air–sea carbon flux dynamics and provide critical insights into how heterogeneous atmospheric CO₂ changes propagate across marine systems. Full article

Vegetation cover characteristics underpin the understanding of regional ecosystem status and guide sustainable development. While extensive research has documented long-term vegetation dynamics in Qinghai Province, critical gaps remain in identifying driving factors, quantifying their thresholds, and uncovering nonlinear relationships governing vegetation cover. In view of this, based on the MOD13Q1V6 dataset from the Google Earth Engine (GEE) platform, this study constructed a kernel normalized difference vegetation index (kNDVI) dataset for Qinghai Province spanning the period 2001–2023. Furthermore, the spatiotemporal characteristics and future evolution trends of vegetation cover were revealed by employing methods including the Theil–Sen–Mann–Kendall (Theil–Sen–MK) trend test, Hurst exponent, and centroid migration model. At a grid scale of 5 km × 5 km, based on the combined model of Extreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP), this study integrated 10 multi-source remote sensing variables related to natural conditions, socioeconomic factors, and geographical accessibility to reveal the nonlinear effects between driving factors and kNDVI and identify the key threshold inflection points. The results showed the following: (1) From 2001 to 2023, the kNDVI of Qinghai Province exhibited a fluctuating growth trend with an annual growth rate of 0.0016 per year, presenting a spatial pattern of being higher in the southeast and lower in the northwest. Specifically, the kNDVI of unused land achieved the highest growth rate (65.96%), which was significantly higher than that of other land use types. (2) The kNDVI in Qinghai Province was dominated by stable areas, accounting for 52.75%. Future trend analysis indicated that the region was primarily characterized by sustainable improvement zones (39.91%), while areas with uncertain future trends accounted for 39.70%. (3) The XGBoost-SHAP model revealed that the annual mean precipitation (AMP) (47.26%) and Digital Elevation Model (DEM) (20.40%) exerted substantial impacts on the kNDVI. Marginal effect curves identified distinct threshold inflection points for the major characteristic factors: AMP = 363.2 mm (95%CI: 361.2–365.2 mm), DEM = 4463.9 m (95%CI: 4446.0–4481.1 m), grazing intensity = 1.8 SU (Stocking Unit)·ha⁻¹ (95%CI: 1.8–1.9 SU·ha⁻¹), and slope = 2.8° (95%CI: 2.7–3.0°) and 19.0° (95%CI: 18.8–19.3°). The interaction combinations of AMP × DEM and DEM × distance to construction land exerted a strong positive effect on the kNDVI in the study area, which was conducive to enhancing vegetation cover. These findings verified the effectiveness of ecological projects implemented in Qinghai Province to a certain extent and provided data support for subsequent differentiated restoration and management. Full article

Phytoplankton blooms represent a typical ecological process in marine systems. Climate change drives shifts in its phenology, both directly via impacts on physiology and indirectly by modifying stratification intensity, nutrients, light availability, and grazing pressure. Using satellite remote sensing and reanalysis data from 2000 to 2022, this study partitions the Yellow Sea based on interannual variability in the Yellow Sea Cold Water Mass (YSCWM). Clear spatial differences in autumn bloom phenology are observed within the YSCWM. Earlier initiation dominates the Southern YSCWM (SYSCWM), while delayed later initiation concentrates in the Northern YSCWM (NYSCWM) and along the SYSCWM’s eastern margins. This pattern can be explained by the differences in regional hydrodynamics, i.e., the Yellow Sea Warm Current (YSWC) enhances upwelling and convergence in some YSCWM areas, boosting nutrient supply and earlier blooms, whereas weaker circulation-driven nutrient supply causes the bloom delay. Interannual variation analysis further reveals that the bloom timing is regulated by seasonal YSCWM dissipation since intensified autumn northerly winds accelerate dissipation and nutrient supply, thereby advancing blooms, while weaker northerly winds and stable circulation delay bloom progress by maintaining strong thermocline stability. These findings provide further insights into the underlying mechanisms driving autumn bloom dynamics and support ecosystem monitoring efforts in shelf seas. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article

Airplanes are the most popular investigation objects as a dynamic and critical component in remote sensing images. Accurately identifying and monitoring airplane behaviors is crucial for effective air traffic management. However, existing methods for interpreting fine-grained airplanes in remote sensing data depend heavily on large annotated datasets, which are both time-consuming and prone to errors due to the detailed nature of labeling individual points. In this paper, we introduce Text2AIRS, a novel method that generates fine-grained and realistic Airplane Images in Remote Sensing from textual descriptions. Text2AIRS significantly simplifies the process of generating diverse aircraft types, requiring limited texts and allowing for extensive variability in the generated images. Specifically, Text2AIRS is the first to incorporate ground sample distance into the text-to-image stable diffusion model, both at the data and feature levels. Extensive experiments demonstrate our Text2AIRS surpasses the state-of-the-art by a large margin on the Fair1M benchmark dataset. Furthermore, utilizing the fine-grained airplane images generated by Text2AIRS, the existing SOTA object detector achieves 6.12% performance improvement, showing the practical impact of our approach. Full article

Accurate intraoperative differentiation between malignant and benign breast tissues, particularly the assessment of lymph node status and tumor margins, is critical for surgical decision-making and prognosis. Traditional histopathological methods, such as frozen section analysis, are time-consuming and labor-intensive. Magnetic Particle Imaging (MPI) is a novel, radiation-free modality that senses the microenvironmental properties of tissues through the dynamic response of magnetic tracers. In this study, we propose a diagnostic method utilizing the higher-order harmonic response of magnetic nanoparticles. Various ex vivo breast tissue samples were immersed in Synomag-50 nanoparticles. Using a custom-built MPI spectrometer (5 kHz excitation, 9 mT amplitude) operating in spectroscopic mode, we implemented a rapid acquisition protocol in which each sample was measured 10 times, with 0.1 s per cycle. We analyzed the magnetic response spectrum and calculated the ratio of the third to the fifth harmonic ($H_3/H_5$). Histological analysis confirmed the effective infiltration of MNPs into the interstitial spaces. The repeated measurement data demonstrated high stability. A distinct stepwise increase in harmonic ratios was observed from normal tissue to tumor-adjacent tissue and finally to malignant tumors. Specifically, malignant samples showed ratios that generally exceeded 2.2, whereas benign samples remained below 2.0. These preliminary findings suggest that the harmonic ratio could serve as a sensitive biomarker reflecting the microenvironmental constraints associated with malignancy. This study validates the feasibility of utilizing MPI signal harmonics as a quantitative metric with rapid signal acquisition capabilities for differentiating benign and malignant lymph nodes. Full article

This study examines how structured interventions influence team creativity on a metaverse-based collaboration platform. Using B.sket, a custom virtual workspace, we tested two interventions during an online brainstorming task: (1) real-time participation feedback delivered as a communication barcode showing each member’s speaking time and sequence (an informational cue), and (2) a group norm communication encouraging equal participation (a social-normative cue). Eighty-one university students in South Korea, recruited through online advertisements using a convenience sampling method, participated in a 2 (group norm prompt: provided vs. not) × 2 (participation feedback: provided vs. not) between-subject factorial design. Team creativity was evaluated by fluency, flexibility, and originality. Results revealed that, contrary to expectations, participation feedback significantly reduced idea fluency and showed marginally negative effects on flexibility and originality. The group norm prompt produced no significant improvements in creativity. We speculate that these findings can be explained by self-determination theory and ego depletion theory, such that real-time participation feedback may undermine individuals’ sense of autonomy and induce cognitive distraction, thereby reducing creative performance. We discuss practical implications that team process interventions for promoting equal participation should be designed carefully to avoid these unexpected consequences. Full article

Background: Catheter ablation is a validated treatment for ventricular arrhythmias (VA), but conventional radiofrequency (RF) energy may cause collateral injury due to non-selective thermal damage. Pulsed Field Ablation (PFA), a non-thermal modality based on irreversible electroporation, offers myocardial tissue selectivity and enhanced safety. While PFA is widely adopted for atrial arrhythmias’ ablation, its application in the ventricles remains an evolving frontier. Methods: We report a single-center experience using the Centauri PFA system integrated with a focal, contact-force sensing irrigated catheter (Tacticath™ SE, Abbott Laboratories, St. Paul, MN, USA) in four consecutive patients with drug-refractory VA. Two patients presented with frequent premature ventricular complexes (PVC) arising from the right and left ventricular outflow tract, respectively, while two had ischemic cardiomyopathy with recurrent scar-related ventricular tachycardia (VT). All procedures were guided by high-density mapping using the EnSite X system (Abbott Laboratories, St. Paul, MN, USA). Procedural safety, acute efficacy, and early follow-up outcomes were assessed. Results: All ablations achieved acute procedural success without complications. In both PVC cases, PFA led to immediate and complete suppression of ectopy, with a ≥95% reduction in arrhythmic burden at 12- and 9-months follow-up, respectively. In the VT cases, the arrhythmogenic substrate was effectively modified, rendering the clinical VT non-inducible. ICD interrogation during a 9-month follow-up showed complete absence of recurrent sustained VT. No coronary spasm, atrioventricular block, pericardial effusion, or other adverse events occurred. Conclusions: In this initial experience, focal PFA using a contact-force sensing catheter appeared feasible and effective for both focal and scar-related VA. This system provides an intuitive workflow similar to RF ablation. While our data suggest a favourable safety profile, larger studies are required to definitively confirm safety margins near critical structures. Full article

In the process of in situ stress fracturing drilling in coal mines, obtaining downhole vibration data not only improves drilling efficiency but also plays a key role in ensuring operational safety. Nevertheless, the energy supply techniques used in current vibration detectors reduce operational performance and escalate excavation expenses. This research proposes a self-powered vibration sensor based on the triboelectric nanogenerator, designed for the operational environment of coal mine in situ stress fracturing drilling. It can simultaneously detect axial and lateral vibration frequencies, and the inclusion of redundant sensing units provides the sensor with high reliability. Experimental outcomes demonstrate that the device functions across a frequency span of 0 to 11 Hz, maintaining error margins for frequency and amplitude under 4%. Furthermore, it functions reliably in environments where temperatures are under 150 °C and humidity is under 90%, proving its strong resilience to environmental factors. In addition, the device possesses self-generating potential, achieving a maximum voltage of 68 V alongside an output current of 51 nA. When connected to a 6 × 10⁷ Ω load, the maximum output power can reach 3.8 × 10⁻⁷ W. Unlike traditional subsurface oscillation detectors, the proposed unit combines self-generation capabilities with highly reliable measurement characteristics, making it more suitable for practical drilling needs. Full article

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this paper presents a novel intelligent electric drive wheel (i-EDW) with an integrated transmission system and a load-sensing unit (LSU). The i-EDW adopts an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), while the integrated LSU ensures high-precision measurement of six-dimensional wheel forces and moments. According to this multi-axis force information, a real-time estimation and stability control method based on the tire–road friction circle concept is proposed. Instead of the complex decoupling and multi-objective optimization with the multi-actuator systems, this paper focuses on minimizing the tire load rate of i-EDWs, which significantly advances the state of the art in terms of calculation efficiency and respond speed. To validate this theoretical framework, a full-vehicle model equipped with four i-EDWs is developed. In the MATLAB R2022A/Simulink co-simulation environment, a virtual prototype is tested under typical driving scenarios, including the straight-line acceleration and double-moving-lane (DML) steering. The simulation results prove a reliable safety margin from the friction circle boundaries, laying a solid foundation for precise motion control and improved system robustness in future intelligent vehicles. Full article

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures. Full article

Understanding ocean temperature structure and its spatiotemporal variability is essential for studying ocean circulation, climate, and marine ecosystems. While previous approaches using observations and numerical models have advanced our understanding, they face limitations such as sparse data coverage and computational bias. To address these issues, we developed an ensemble of data-driven neural network models trained with in situ vertical profiles and daily remote sensing inputs. Unlike previous studies that were limited to open-ocean regions, our model explicitly included coastal areas with complex bathymetry. The model was applied to the East/Japan Sea and reconstructed 31 years (1993–2023) of daily three-dimensional ocean temperature fields at 13 standard depths. The predictions were validated against observations, showing RMSE < 1.33 °C and bias < 0.10 °C. Comparisons with previous studies confirmed the model’s ability to capture short- to mid-term temperature variations. This data-driven approach demonstrates a robust alternative to traditional methods and offers an applicable and reliable tool for understanding long-term ocean variability in marginal seas. Full article