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"Li, Xinghua"
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A remote sensing assessment index for urban ecological livability and its application
Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators - greenness, temperature, dryness, water-wetness, and atmospheric turbidity - which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction.
Journal Article
Amorphous organic-hybrid vanadium oxide for near-barrier-free ultrafast-charging aqueous zinc-ion battery
Fast-charging metal-ion batteries are essential for advancing energy storage technologies, but their performance is often limited by the high activation energy (
E
a
) required for ion diffusion in solids. Addressing this challenge has been particularly difficult for multivalent ions like Zn
2+
. Here, we present an amorphous organic-hybrid vanadium oxide (AOH-VO), featuring one-dimensional chains arranged in a disordered structure with atomic/molecular-level pores for promoting hierarchical ion diffusion pathways and reducing Zn
2+
interactions with the solid skeleton. AOH-VO cathode demonstrates an exceptionally low
E
a
of 7.8 kJ·mol
−1
for Zn
2+
diffusion in solids and 6.3 kJ·mol
−1
across the cathode-electrolyte interface, both significantly lower than that of electrolyte (13.2 kJ·mol
−1
) in zinc ion battery. This enables ultrafast charge-discharge performance, with an Ah-level pouch cell achieving 81.3% of its capacity in just 9.5 minutes and retaining 90.7% capacity over 5000 cycles. These findings provide a promising pathway toward stable, ultrafast-charging battery technologies with near-barrier-free ion dynamics.
Promoting solid ion-diffusion is essential for fast-charging battery. Here, authors present near-barrier-free ion dynamics in an amorphous organic-hybrid vanadium oxide-based zinc ion battery and developed Ah-level fast-charging pouch cell.
Journal Article
cMyc-mediated activation of serine biosynthesis pathway is critical for cancer progression under nutrient deprivation conditions
Cancer cells are known to undergo metabolic reprogramming to sustain survival and rapid proliferation, however, it remains to be fully elucidated how oncogenic lesions coordinate the metabolic switch under various stressed condi- tions. Here we show that deprivation of glucose or glutamine, two major nutrition sources for cancer cells, dramat- ically activated serine biosynthesis pathway （SSP） that was accompanied by elevated cMyc expression. We further identified that cMyc stimulated SSP activation by transcriptionally upregulating expression of multiple SSP enzymes. Moreover, we demonstrated that SSP activation facilitated by cMye led to elevated glutathione （GSH） production, cell cycle progression and nucleic acid synthesis, which are essential for cell survival and proliferation especially un- der nutrient-deprived conditions. We further uncovered that phosphoserine phosphatase （PSPH）, the final rate-lim- iting enzyme of the SSP pathway, is critical for cMyc-driven cancer progression both in vitro and in vivo, and impor- tantly, aberrant expression of PSPH is highly correlated with mortality in hepatocellular carcinoma （HCC） patients, suggesting a potential causal relation between this cMyc-regulated enzyme, or SSP activation in general, and cancer development. Taken together, our results reveal that aberrant expression of cMyc leads to the enhanced SSP activa- tion, an essential part of metabolic switch, to facilitate cancer progression under nutrient-deprived conditions.
Journal Article
Eye-Gaze Controlled Wheelchair Based on Deep Learning
In this paper, we design a technologically intelligent wheelchair with eye-movement control for patients with ALS in a natural environment. The system consists of an electric wheelchair, a vision system, a two-dimensional robotic arm, and a main control system. The smart wheelchair obtains the eye image of the controller through a monocular camera and uses deep learning and an attention mechanism to calculate the eye-movement direction. In addition, starting from the relationship between the trajectory of the joystick and the wheelchair speed, we establish a motion acceleration model of the smart wheelchair, which reduces the sudden acceleration of the smart wheelchair during rapid motion and improves the smoothness of the motion of the smart wheelchair. The lightweight eye-movement recognition model is transplanted into an embedded AI controller. The test results show that the accuracy of eye-movement direction recognition is 98.49%, the wheelchair movement speed is up to 1 m/s, and the movement trajectory is smooth, without sudden changes.
Journal Article
Enhanced Hourly Precipitation Estimation Using a Geographically Constrained Multi‐Source Fusion Network With Cross Attention
Precipitation plays a crucial role in the global hydrological cycle, and its irregular distribution contributes directly to natural hazards such as floods, waterlogging, and droughts. Satellite remote sensing has emerged as an effective tool for global precipitation monitoring. However, accurately estimating hourly precipitation from satellite observations remains a major challenge due to its high spatiotemporal variability. To address this challenge, we propose a novel framework—Geographically constrained multi‐source Fusion Network with cross Attention (GeoFNA)—designed to enhance the accuracy of hourly satellite precipitation estimates. GeoFNA integrates a spatiotemporal convolutional network with cross‐attention mechanisms to effectively capture complex spatiotemporal patterns and nonlinear relationships across multi‐source precipitation data sets and auxiliary variables. To further improve model robustness, geographically associated input constraints and weight constraints are incorporated to account for the skewed distribution and rapid variability of hourly precipitation. Results demonstrated that GeoFNA outperformed three baseline models, achieving significantly higher agreement with in situ measurements. Specifically, GeoFNA increased the Pearson Correlation Coefficient from 0.38 to 0.89 and reduced the Mean Squared Error from 2.39 to 0.50 (mm/h)2 compared to the original satellite precipitation data. Additionally, GeoFNA exhibited strong spatial robustness, underscoring its potential for accurate and reliable quantitative precipitation estimation. These advancements pave the way for improved hydrological modeling and meteorological research.
Journal Article
Analysis and Simulation Verification of the Verticality Measurement Model for Single Offshore Pile Based on Binocular Vision
Accurately measuring the verticality of a single pile is of crucial importance for ensuring the safe operation of offshore wind power projects. However, mainstream methods have disadvantages such as high dependence on manual labor, low real-time performance, and susceptibility to construction site conditions and marine environmental impacts. The method of measuring the verticality of a single offshore pile based on binocular vision is one of the emerging measurement methods, but there is currently a lack of research on measurement models. In order to clarify the principle of the method for measuring the verticality of a single pile at sea based on binocular vision, this paper starts from the imaging principle of the camera and studies and derives the measurement model of the verticality of a single pile in the global coordinate system and the error model of the measurement system. To verify the correctness of the model and method, a testing experimental platform was built to simulate the measurement of the ship under static and dynamic conditions, and the measurement results were compared with those of the total station. The experimental results show that in the static simulation experiment, the maximum absolute error of the verticality of a single pile is 0.2°, the maximum absolute error of the roll angle is 0.3°, and the maximum absolute error of the pitch angle is 0.3°. In the dynamic simulation experiment, the maximum absolute error of the verticality of a single pile is 0.4°, the maximum absolute error of the roll angle is 0.3°, and the maximum absolute error of the pitch angle is 0.3°. This paper verified the correctness of the model and provided model support for measuring the verticality of single piles at sea.
Journal Article
On demand synthesis of hollow fullerene nanostructures
Hollow nanostructures are widely used in chemistry, materials, bioscience, and medicine, but their fabrication remains a great challenge. In particular, there is no effective strategy for their assembly and interconnection. We bring pottery, the oldest and simplest method of fabricating hollow containers, into the nanoscale. By exploiting the liquid nature of the xylene template, fullerene hollow nanostructures of tailored shapes, such as bowls, bottles, and cucurbits, are readily synthesized. The liquid templates permit stepwise and versatile manipulation and hence, modular assembly of nodes and junctions leads to interconnected hollow systems. As a proof-of-concept, we create multi-compartment nano-containers, with different nanoparticles isolated in the separate pockets. This methodology expands the synthetic freedom for hollow nanostructures, building a bridge from isolated hollow units to interconnected hollow systems.
At nanoscale, it is synthetically very difficult to increase the structural complexity of hollow structures. Here, the authors use a stepwise liquid templating strategy to build, assemble, and interconnect fullerene hollow nanostructures, just like the synthetic freedom one could have with pottery.
Journal Article
Modeling biogenic and anthropogenic secondary organic aerosol in China
A revised Community Multi-scale Air Quality (CMAQ) model with updated secondary organic aerosol (SOA) yields and a more detailed description of SOA formation from isoprene oxidation was applied to study the spatial and temporal distribution of SOA in China in the entire year of 2013. Predicted organic carbon (OC), elemental carbon and volatile organic compounds agreed favorably with observations at several urban areas, although the high OC concentrations in wintertime in Beijing were under-predicted. Predicted summer SOA was generally higher (10–15 µg m−3) due to large contributions of isoprene (country average, 61 %), although the relative importance varies in different regions. Winter SOA was slightly lower and was mostly due to emissions of alkane and aromatic compounds (51 %). Contributions of monoterpene SOA was relatively constant (8–10 %). Overall, biogenic SOA accounted for approximately 75 % of total SOA in summer, 50–60 % in autumn and spring, and 24 % in winter. The Sichuan Basin had the highest predicted SOA concentrations in the country in all seasons, with hourly concentrations up to 50 µg m−3. Approximately half of the SOA in all seasons was due to the traditional equilibrium partitioning of semivolatile components followed by oligomerization, while the remaining SOA was mainly due to reactive surface uptake of isoprene epoxide (5–14 %), glyoxal (14–25 %) and methylglyoxal (23–28 %). Sensitivity analyses showed that formation of SOA from biogenic emissions was significantly enhanced due to anthropogenic emissions. Removing all anthropogenic emissions while keeping the biogenic emissions unchanged led to total SOA concentrations of less than 1 µg m−3, which suggests that manmade emissions facilitated biogenic SOA formation and controlling anthropogenic emissions would result in reduction of both anthropogenic and biogenic SOA.
Journal Article
SEMA-YOLO: Lightweight Small Object Detection in Remote Sensing Image via Shallow-Layer Enhancement and Multi-Scale Adaptation
Small object detection remains a challenge in the remote sensing field due to feature loss during downsampling and interference from complex backgrounds. A novel network, termed SEMA-YOLO, is proposed in this paper as an enhanced YOLOv11-based framework incorporating three technical advancements. By fundamentally reducing information loss and incorporating a cross-scale feature fusion mechanism, the proposed framework significantly enhances small object detection performance. First, the Shallow Layer Enhancement (SLE) strategy reduces backbone depth and introduces small-object detection heads, thereby increasing feature map size and improving small object detection performance. Then, the Global Context Pooling-enhanced Adaptively Spatial Feature Fusion (GCP-ASFF) architecture is designed to optimize cross-scale feature interaction across four detection heads. Finally, the RFA-C3k2 module, which integrates Receptive Field Adaptation (RFA) with the C3k2 structure, is introduced to achieve more refined feature extraction. SEMA-YOLO demonstrates significant advantages in complex urban environments and dense target areas, while its generalization capability meets the detection requirements across diverse scenarios. The experimental results show that SEMA-YOLO achieves mAP50 scores of 72.5% on the RS-STOD dataset and 61.5% on the AI-TOD dataset, surpassing state-of-the-art models.
Journal Article