Exploring the spatiotemporal relationship between green infrastructure and urban heat island under multi‐source remote sensing imagery:A case study of Fuzhou City

Green Infrastructure(GI)has garnered increasing attention from various regions due to its potential to mitigate urban heat island(UHI),which has been exacerbated by global climate change.This study focuses on the central area of Fuzhou city,one of the“furnace”cities,and aims to explore the correlation between the GI pattern and land surface temperature(LST)in the spring and autumn seasons.The research adopts a multiscale approach,starting from the urban scale and using urban geographic spatial characteristics,multispectral remote sensing data,and morphological spatial pattern analysis(MSPA).Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression(GWR)experiment.The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of“high temperature in the middle and low temperature around,”which is coupled with a“central scattered and peripheral concentrated”distribution of GI.This suggests that remote sensing data can effectively be utilised for UHI inversion.Additionally,the study finds that the complexity of GI,whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area,has an impact on the alleviation of UHI in both seasons.In conclusion,this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.

Model of Surface Texture for Honed Gear Considering Motion Path and Geometrical Shape of Abrasive Particle

Gear power-honing is mainly applied to finish small and medium-sized automotive gears,especially in new energy vehicles.The distinctive curved surface texture greatly improves the noise emission and service life of honed gears.The surface texture for honed gear considering the motion path and geometrical shape of abrasive particles has not been investigated.In this paper,the kinematics of the gear honing process is analyzed,and the machining marks produced by the abrasive particles of honing wheel scratching abrasive particles against the workpiece gear are calculated.The tooth surface roughness is modeled considering abrasive particle shapes and material plastic pile-ups.This results in a mathematical model that characterizes the structure of the tooth surface and the orientation of the machining marks.Experiments were used to verify the model,with a maximum relative error of less than 10%when abrasive particles are spherical.Based on this model,the effects of process parameters on the speeds of discrete points on the tooth flank,orientations of machining marks and roughness are discussed.The results show that the shaft angle between the workpiece gear and the honing wheel and the speed of the honing wheel is the main process parameters affecting the surface texture.This research proposes a surface texture model for honed gear,which can provide a theoretical basis for optimizing process parameters for gear power-honing.

The Current Situation and Development Prospect of Mechanized Foxtail Millet Seeding in China

Based on the analysis of the present application of millet mechanization planting machines, we summarized the basic situation of millet mechanization planting machines at present stage to improve of planting efficiency and develop mechanization, and made suggestions on millet sowing mechanization.

Insights into spinal muscular atrophy from molecular biomarkers

Spinal muscular atrophy is a devastating motor neuron disease characterized by severe cases of fatal muscle weakness.It is one of the most common genetic causes of mortality among infants aged less than 2 years.Biomarker research is currently receiving more attention,and new candidate biomarkers are constantly being discovered.This review initially discusses the evaluation methods commonly used in clinical practice while briefly outlining their respective pros and cons.We also describe recent advancements in research and the clinical significance of molecular biomarkers for spinal muscular atrophy,which are classified as either specific or non-specific biomarkers.This review provides new insights into the pathogenesis of spinal muscular atrophy,the mechanism of biomarkers in response to drug-modified therapies,the selection of biomarker candidates,and would promote the development of future research.Furthermore,the successful utilization of biomarkers may facilitate the implementation of gene-targeting treatments for patients with spinal muscular atrophy.

Enhancing both selectivity and coking-resistance of a single-atom Pd1/C3N4 catalyst for acetylene hydrogenation

Selective hydrogenation is an important industrial catalytic process in chemical upgrading, where Pd-based catalysts are widely used because of their high hydrogenation activities. However, poor selectivity and short catalyst lifetime because of heavy coke formation have been major concerns. In this work, atomically dispersed Pd atoms were successfully synthesized on graphitic carbon nitride （g-C3N4） using atomic layer deposition. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy （HAADF-STEM） confirmed the dominant presence of isolated Pd atoms without Pd nanoparticle （NP） formation. During selective hydrogenation of acetylene in excess ethylene, the g-C3N4-supported Pd NP catalysts had strikingly higher ethylene selectivities than the conventional Pd/Al2O3 and Pd/SiO2 catalysts. In-situ X-ray photoemission spectroscopy revealed that the considerable charge transfer from the Pd NPs to g-C3N4 likely plays an important role in the catalytic performance enhancement. More impressively, the single-atom Pd1/C3N4 catalyst exhibited both higher ethylene selectivity and higher coking resistance. Our work demonstrates that the single-atom Pd catalyst is a promising candidate for improving both selectivity and coking-resistance in hydrogenation reactions.

TLR4 is essential for dendritic cell activation and anti-tumor T-cell response enhancement by DAMPs released from chemically stressed cancer cells

The combination of immunotherapy and chemotherapy is regarded as a promising approach for the treatment of certain types of cancer. However, the underlying mechanisms need to be fully investigated to guide the design of more efficient protocols for cancer chemoimmunotherapy. It is well known that danger-associated molecular patterns （DAMPs） can activate immune cells, including dendritic cells （DCs）, via Toll-like receptors （TLRs）; however, the role of DAMPs released from chemical drug-treated tumor cells in the activation of the immune response needs to be further elucidated. Here, we found that colorectal cancer （CRC） cells treated with oxaliplatin （OXA） and/or 5-fluorouracil （5-Fu） released high levels of high-mobility group box 1 （HMGB1） and heat shock protein 70 （HSP70）. After OXA/5-Fu therapy, the sera of CRC patients also exhibited increased levels of HMGB1 and HSP70, both of which are well-known DAMPs. The supernatants of dying CRC cells treated with OXA/5-Fu promoted mouse and human DC maturation, with upregulation of HLA-DR, CD80 and CD86 expression and enhancement of IL-lp, TNF-a, MIP-la, MIP-lp, RANTES and IP-IO production. Vaccines composed of DCs pulsed with the supernatants of chemically stressed CRC cells induced a more significant IFN-y-producing Thl response both in vitroand in vivo. However, the supernatants of chemically stressed CRC cells failed to induce phenotypic maturation and cytokine production in TLR4-deficient DCs, indicating an essential role of TLR4 in DAMP-induced DC maturation and activation. Furthermore, pulsing with the supernatants of chemically stressed CRC cells did not efficiently induce an IFN-y-producing Thl response in TLR4-deficient DCs. Collectively, these results demonstrate that DAMPs released from chemically stressed cancer cells can activate DCs viaTLR4 and enhance the induction of an anti-tumor T-cell immune response, delineating a clinically relevant immuno-adjuvant pathway triggered by DAMPs.

10 Gbps wavelength division multiplexing using UV-A, UV-B, and UV-C micro-LEDs

Deep ultraviolet(DUV) optical wireless communications have seen increased interest in recent years due to the unique properties of light in this spectral region. However, the reported DUV data rates remain significantly lower than comparable demonstrations at visible wavelengths due to lower modulation bandwidths and/or output power of the sources. Here, we present a wavelength division multiplexing demonstration using three UV microlight-emitting diodes emitting at nominal peak wavelengths of 285, 317, and 375 nm, respectively, each with an emitting area of approximately 1369 μm^(2)(equivalent to circular device pixels of diameter;0 μm). Using orthogonal frequency division multiplexing, data rates of 4.17, 3.02, and 3.13 Gbps were achieved from the 285, 317, and 375 nm devices, respectively, for a combined data rate of 10.32 Gbps transmitted over a distance of 0.5 m.

Heterogeneous-attributes enhancement deep framework for network embedding

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.