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With global warming and rapid urbanization, urban climate is considered to be one important factor impacting urban ecological environment, and the most obvious feature of urban climate is the surface urban heat island(SUHI). The accurate division of rural reference area is critical in evaluating the intensity of SUHI in mountainous cities by urban-rural dichotomy. Therefore, a comprehensive method is proposed based on multi-source satellite remote sensing data to solve this problem, and the spatiotemporal variation of the SUHI in Chongqing metropolitan circle from 2001 to 2020 are analyzed. The results show that the spatial distribution of rural reference area obtained by the comprehensive buffer method is obviously different from that by the traditional buffer method, and the main cause is the limitation of the elevation difference between urban and rural reference area. There are a large number of "false SUHI" pixels in the SUHI product estimated by the traditional buffer method, which makes the surface urban heat island regions obtained by the traditional buffer method significantly larger than that by the comprehensive buffer method. The spatiotemporal distribution results show that the regions affected by the SUHI are concentrated in the core area of Chongqing, Changshou, Fuling and nearby regions, and the cold island are concentrated in the high-altitude areas in the southeast of Chongqing and some mountains in the core of Chongqing. The interannual variation of the SUHI shows that the proportion of areas above strong SUHI level increases with time from 2001 to 2020, and the SUHI is strong in summer and weak in winter. The proportion of strong SUHI area increased from 0.5% in 2001 to 2.95% in 2020, with an average annual growth rate of 0.12%. The proportion of intense heat island area increased from 0.21% in 2001 to 0.78% in 2019, with an average annual growth rate of 0.03%. The change of SUHI is closely related to urban development, and has a significant positive correlation with the economic driving factors such as the total population of the main city and metropolitan area, urban population, GDP, total energy consumption, urban built-up area and civil vehicle ownership. The correlation coefficients are 0.91, 0.94, 0.95, 0.94, 0.90 and 0.96, respectively, indicating that the driving factors of urban economy play an obvious role in promoting the change of SUHI intensity in the main metropolitan area.

Medium Resolution Spectral Imager II (MERSI-II) is one of the core sensors mounted on the FengYun-3D (FY3D) satellite. Two adjacent 250 m long-wave thermal infrared (TIR) channels provide a considerable opportunity for retrieving Land Surface Temperature (LST) with high spatiotemporal resolution. In this paper, Thermodynamic Initial Guess Retrieval (TIGR) dataset and MODTRAN 4.0 model were used to re-fit the parameters of the Split-Window (SW) algorithm suitable for MERSI-II TIR channels, and then the daily 250 m resolution MERSI-II LST product was retrieved. The Radiance-based (R-based) method results showed that the bias value between [Formula: see text] simulated by MODTRAN4.0 and the input [Formula: see text] is 0.16 K, and the MAE value is 0.38 K. Inter-comparison method results showed that the MERSI-II LST and MODIS LST products were consistent in spatial distribution, but there were certain differences between MODIS LST and MERSI-II LST at different land cover types. T-based method results showed that R values between the site-observed LST and MERSI-II LST retrieved by SW algorithm exceeded 0.92, the bias value was between 3.6 K and 4.4 K, and the MAE value was between 2.6 K and 4.5 K. The above results indicating that the SW algorithm proposed in this study has good accuracy and applicability.

Nanospaces within enzymes play a crucial role in chemical reactions in biological systems, garnering significant attention from supramolecular chemists. Inspired by the highly efficient catalysis of enzymes, artificial supramolecular hosts have been developed and widely employed in various reactions, paving the way for innovative and selective catalytic processes and offering new insights into enzymatic catalytic mechanisms. In supramolecular macrocycle systems, weak non-covalent interactions are exploited to enhance substrate solubility, increase local concentration, and stabilize the transition state, ultimately accelerating reaction rates and improving product selectivity. In this review, we will focus on the opportunities and challenges associated with the catalysis of chemical reactions by supramolecular macrocycles in the aqueous phase. Key issues to be discussed include limitations in molecular interaction efficiency in aqueous media, product inhibition, and the incompatibility of catalysts or conditions in "one-pot" reactions. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) has rapidly emerged as the most prevalent chronic liver disease globally, representing a significant and escalating public health challenge. Meconopsis integrifolia (Maxim.) Franch, a traditional Tibetan medicinal herb used for treating hepatitis, remains largely unexplored regarding its therapeutic potential and active components in combating NAFLD. This study first evaluated the in vitro lipid accumulation inhibitory activity of different extraction fractions of M. integrifolia using a HepG2 cell steatosis model. The ethyl acetate fraction was found to significantly reduce triglyceride (TG) and low-density lipoprotein (LDL) levels, inhibit lipid droplet deposition in HepG2 cells, and promote lipid metabolism balance through modulation of the AMPK/SREPB-1c/PPAR-α signaling pathway. Further analysis utilizing chromatographic techniques and nuclear magnetic resonance spectroscopy (NMR) led to the isolation of 13 compounds from the active ethyl acetate fraction. Notably, compounds 6, 9, 10, 11, 12, and 13 were identified for the first time from this Tibetan herb. In vitro activity assays and molecular docking analyses further confirmed that the compounds Luteolin (1), Quercetin 3-O-[2‴, 6‴-O-diacetyl-β-d-glucopyranosyl-(1→6)-β-d-glucopyranoside] (6), and Quercetin 3-O-[2‴-O-acetyl-β-d-glucopyranosyl-(1→6)-β-d-glucopyranoside] (8) are potential key components responsible for the NAFLD-ameliorating effects of M. integrifolia. This study highlights the therapeutic potential of M. integrifolia in treating NAFLD and provides a foundation for its further development and application, underscoring its significance in the advanced utilization of traditional Tibetan medicine. [ABSTRACT FROM AUTHOR]

To promptly detect abnormal events in surveillance videos, this article designs a video anomaly detection method based on multiple instance learning. Generally, abnormal events occur less frequently compared to normal events. Traditional video surveillance relies on manual operation to monitor scenes and detect abnormal events by watching surveillance videos. However, watching surveillance footage is a labor‐intensive task, and prolonged observation can lead to visual fatigue and lack of concentration, which in turn results in missed detections and false positives [1]. Therefore, it is crucial to develop intelligent algorithms for video anomaly detection. The method can detect whether segments of a video contain abnormal events. First, the I3D network is used as a feature extractor to capture spatiotemporal features from the input video. Then, the spatiotemporal information is processed and input into a segment‐level anomaly detector based on multiple instance learning for detection. The authors treat abnormal videos as positive bags and normal videos as negative bags, and automatically learn a deep anomaly ranking model that can predict abnormal segments. Finally, the results of the training were tested and analyzed, demonstrating that the model is capable of detecting abnormal traffic segments. [ABSTRACT FROM AUTHOR]

Missing values in land surface temperature (LST) data are often observed in the cloud-sheltered area, thereby seriously limiting the spatiotemporal continuity of LST. In this work, Remotely Sensed Daily Land Surface Temperature Reconstruction model(RSDAST) is used to gap-fill the pixels sheltered by clouds in FY-3 C/VIRR LST. Result shows that the cloud pixels in VIRR and MODIS original LST (OLST) product can be reconstructed accurately, but the reconstruction accuracy of MODIS LST is better compared to VIRR based on the RSDAST model. In addition, the reconstruction accuracy of VIRR and MODIS LST decreases with the increase in cloud coverage, and the reduction of the reconstruction accuracy of VIRR LST is larger than that of MODIS data. The number of effective dry-wet edge fitted by VIRR RLST/NDVI scatterplot was higher than that of OLST/NDVI, and the number of clear sky pixels in Reconstructed TVDI (RTVDI) images increased significantly, indicating that the RSDAST expands the temporal resolution and spatial coverage of infrared remote sensing data under cloudy conditions. Moreover, in DOY150–DOY243, the correlation between RTVDI and soil moisture is better than that of Original TVDI (OTVDI), indicating that the RSDAST improves the monitoring ability of soil moisture in these conditions.

This article introduces SmoothFlowNet3D, an innovative encoder-decoder architecture specifically designed for bridging the domain gap in scene flow estimation. To achieve this goal, SmoothFlowNet3D divides the scene flow estimation task into two stages: initial scene flow estimation and smoothness refinement. Specifically, SmoothFlowNet3D comprises a hierarchical encoder that extracts multi-scale point cloud features from two consecutive frames, along with a hierarchical decoder responsible for predicting the initial scene flow and further refining it to achieve smoother estimation. To generate the initial scene flow, a cross-frame nearest-neighbor search operation is performed between the features extracted from two consecutive frames, resulting in forward and backward flow embeddings. These embeddings are then combined to form the bidirectional flow embedding, serving as input for predicting the initial scene flow. Additionally, a flow smoothing module based on the self-attention mechanism is proposed to predict the smoothing error and facilitate the refinement of the initial scene flow for more accurate and smoother estimation results. Extensive experiments demonstrate that the proposed SmoothFlowNet3D approach achieves state-of-the-art performance on both synthetic datasets and real LiDAR point clouds, confirming its effectiveness in enhancing scene flow smoothness. [ABSTRACT FROM AUTHOR]

Conventional liquid cooling techniques may provide effective chip cooling but at the expense of high pumping power consumption. Considering that there is dynamic heat load in practice, a self-adaptive cooling technique is desired to reduce operational costs while preserving inherent cooling effectiveness. In this work, a novel self-adaptive cooling strategy is presented to balance the thermal and flow efficiency in accordance with the dynamic thermal load, based on temperature-regulated movement of the metal pillar array in a microfluidic channel. With an illustrative device, the effectiveness of such a strategy is investigated using multiphysics modeling and simulation. As a case study, the device is considered to be initiated with a chip power of 5 W and an inlet coolant velocity of 0.3 m/s. It is shown that the temperature-regulated movement of the metal pillar heat sink will be activated rapidly and equilibrate within 30 s. Parts of the metal pillars immerse into the coolant flow, resulting in significantly improved heat transfer efficiency. The diminished thermal resistance leads to a reduction in chip temperature rise from 225 K (without structural adaptation) to 91.86 K (with structural adaption). Meanwhile, the immersion of metal pillars into the coolant also causes an increased flow resistance in the microfluidic channel (i.e., pressure drop increases from 859.27 Pa to 915.98 Pa). Nevertheless, the flow resistance decreases spontaneously when the working power of the chip decreases. Comprehensive simulation has demonstrated that the temperature-regulated structure works well under various conditions. Therefore, it is believed that the presented self-adaptive cooling strategy offers simple and cost-effective thermal management for modern electronics with dynamic heat fluxes. [ABSTRACT FROM AUTHOR]

As a prerequisite for many 3D visualization tasks, point cloud registration has a wide range of applications in 3D scene reconstruction, pose estimation, navigation, and remote sensing. However, due to the limited overlap of point clouds, the presence of noise and the incompleteness of the data, existing feature-based matching methods tend to produce higher outlier matches, thus reducing the quality of the registration. Therefore, the generation of reliable feature descriptors and the filtering of outliers become the key to solving these problems. To this end, we propose a multi-local-to-global registration (MLGR) method. First, in order to obtain reliable correspondences, we design a simple but effective network module named the local geometric network (LG-Net), which can generate discriminative feature descriptors to reduce the outlier matches by learning the local latent geometric information of the point cloud. In addition, we propose a multi-local-to-global registration strategy to further filter outlier matches. We compute the hypothetical transformation matrix from local patch matches. The point match evaluated as an inlier under multiple hypothetical transformations will receive a higher score, and low-scoring point matches will be rejected. Finally, our method is quite robust under different numbers of samples, as it does not require sampling a large number of correspondences to boost the performance. The numerous experiments on well-known public datasets, including KITTI, 3DMatch, and ModelNet, have proven the effectiveness and robustness of our method. Compared with the state of the art, our method has the lowest relative rotation error and relative translation error on the KITTI, and consistently leads in feature matching recall, inlier ratio, and registration recall on 3DMatch under different numbers of point correspondences, which proves the robustness of our method. In particular, the inlier ratio is significantly improved by 3.62% and 4.36% on 3DMatch and 3DLoMatch, respectively. In general, the performance of our method is more superior and robust than the current state of the art. [ABSTRACT FROM AUTHOR]

With the development of e‐commerce, online shopping has become the main approach to buy clothes, in which accurately classifying clothing through images becomes more and more important. However, most of clothing classification works focus on clothing types. With the increasing volume of online clothing transactions, various platforms have accumulated a large number of unmarked clothing images that cannot be fully utilized. Meanwhile, many consumers pay more attention to the clothing style. In order to solve this problem, this paper proposes a clothing style recognition framework based on Alex Net run on a portable device. The proposed clothing style recognition is evaluated on Deepfashion dataset. The experimental results show that the proposed clothing style recognition is superior to traditional convolutional neural network (CNN), ResNet and Bilinear convolutional neural network (Bilinear‐CNN). [ABSTRACT FROM AUTHOR]