Heavy metals in sediments of the river-lake system in the Dianchi basin, China: Their pollution, sources, and risks.

The accumulation of heavy metals in river and lake sediments in basins seriously threatens ecological safety and human health. To manage the basin effectively, it is crucial to understand pollution levels and identify and quantify the sources and risks of heavy metals in rivers and lakes separately for targeted control. In this study, 34 sediment samples were collected from the Dianchi Basin, China, and the pollution, sources, and risks in the river-lake system sediments were systematically analysed for cadmium (Cd), chromium (Cr), arsenic (As), mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), and nickel (Ni).

Increasing the flexibility of the substrate binding pocket of Streptomyces phospholipase D can enhance its catalytic efficiency in soybean phosphatidylcholine.

The catalytic efficiency of Streptomyces klenkii phospholipase D (SkPLD) in soybean phosphatidylcholine (soy-PC) processing is constrained by its acyl chain specificity. To address this limitation, we engineered the substrate-binding pocket of SkPLD to increase its flexibility. The mutant P343A/Y383L exhibited a 7.

Controlled Crystallization and Enhanced Performance of γ-CsPbI Perovskite Through Methylammonium Iodide-Assisted Coevaporation.

Cesium lead triiodide (CsPbI) perovskites have garnered significant attention owing to their suitable bandgap for tandem silicon substrates and excellent chemical stability. However, γ-CsPbI prepared via low-temperature co-evaporation is limited by a narrow black phase processing window and random crystal orientation, hindering its optoelectronic performance and industrial applications. This study introduced trace amounts of methylammonium iodide (MAI) into the co-evaporation system, enhancing the crystallization process, promoting columnar grain growth, and stabilizing the γ-phase perovskite, resulting in films with improved structural integrity and reduced defect density.

CBG-Net: Cross-modality and cross-scale balance network with global semantics for multi-modal 3D object detection.

Multi-modal 3D object detection is instrumental in identifying and localizing objects within 3D space. It combines RGB images from cameras and point-clouds data from lidar sensors, serving as a fundamental technology for autonomous driving applications. Current methods commonly employ simplistic element-wise additions or multiplications to aggregate multi-modal features extracted from point-clouds and images.

Effect of starch and protein on eating quality of japonica rice in Yangtze River Delta.

This study examined four types of japonica rice from Yangtze River Delta, categorized based on amylose content (AC) and protein content (PC): high AC with high PC, high AC with low PC, low AC with high PC, and low AC with low PC. It systematically explored the effect of starch, protein and their interactions on eating quality of japonica rice. Rheological analysis revealed that increased amylose, long chains amylopectin or protein levels during cooking strengthen starch-protein interactions (hydrogen bonding), forming a firm gel network.

SipMaskv2: Enhanced Fast Image and Video Instance Segmentation.

We propose a fast single-stage method for both image and video instance segmentation, called SipMask, that preserves the instance spatial information by performing multiple sub-region mask predictions. The main module in our method is a light-weight spatial preservation (SP) module that generates a separate set of spatial coefficients for the sub-regions within a bounding-box, enabling a better delineation of spatially adjacent instances. To better correlate mask prediction with object detection, we further propose a mask alignment weighting loss and a feature alignment scheme.

Context and detail interaction network for stereo rain streak and raindrop removal.

Recently stereo image deraining has attracted lots of attention due to its superiority of abundant information from cross views. Exploring interaction information across stereo views is the key to improving the performance of stereo image deraining. In this paper, we design a general coarse-to-fine deraining framework for stereo rain streak and raindrop removal, called CDINet, comprising a stereo rain removal subnet and a stereo detail recovery subnet to restore images progressively.

Non-Destructive Detection of Moldy Walnuts Based on Hyperspectral Imaging Technology.

Walnuts with their shells are a popular agricultural product in China. However, mildew from growth can sometimes be processed into foods. It is difficult to visually determine which walnuts have mildew without breaking the shells.

Continuous Seizure Detection Based on Transformer and Long-Term iEEG.

Automatic seizure detection algorithms are necessary for patients with refractory epilepsy. Many excellent algorithms have achieved good results in seizure detection. Still, most of them are based on discontinuous intracranial electroencephalogram (iEEG) and ignore the impact of different channels on detection.

Non-linear perceptual multi-scale network for single image super-resolution.

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and achieved remarkable progress. However, most of the existing CNN-based SISR networks with a single-stream structure fail to make full use of the multi-scale features of low-resolution (LR) image. While those multi-scale SR models often integrate the information with different receptive fields by means of linear fusion, which leads to the redundant feature extraction and hinders the reconstruction performance of the network.

Hierarchical Regression and Classification for Accurate Object Detection.

Accurate object detection requires correct classification and high-quality localization. Currently, most of the single shot detectors (SSDs) conduct simultaneous classification and regression using a fully convolutional network. Despite high efficiency, this structure has some inappropriate designs for accurate object detection.

From Handcrafted to Deep Features for Pedestrian Detection: A Survey.

Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. Here we present a comprehensive survey on recent advances in pedestrian detection.

Phosphorous doped g-CN supported cobalt phthalocyanine: An efficient photocatalyst for reduction of CO under visible-light irradiation.

The photoreduction of the green-house gas CO into carbon monoxide (CO) is a growing process due to the use of CO for the production of methanol in the Fischer-Tropsch process and the synthesis of many of the bulk chemicals. Here, we have synthesized phosphorous doped graphitic carbon nitride (P-g-CN) sensitized by the cobalt phthalocyanine complex for the molecular reduction of CO into CO under visible-light irradiation-the doping of phosphorous improved the stability as well as the harvesting of the visible region. The CoPc@P-g-CN hybrid photocatalyst exhibited the highest efficiency for the photoreduction of CO with a high yield of 295 μmol-g for CO under the experimental conditions.

Improving Single Shot Object Detection With Feature Scale Unmixing.

Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-shot detectors make scale-aware predictions based on multiple pyramid layers. Typically, small objects are detected on shallow layers while large objects are detected on deep layers.

TJU-DHD: A Diverse High-Resolution Dataset for Object Detection.

Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects (esp. small objects) is far from satisfying the demand of practical systems.

Taking a Look at Small-Scale Pedestrians and Occluded Pedestrians.

Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.

Learning Multilayer Channel Features for Pedestrian Detection.

Pedestrian detection based on the combination of convolutional neural network (CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved great success.

Cascade Learning by Optimally Partitioning.

Cascaded AdaBoost classifier is a well-known efficient object detection algorithm. The cascade structure has many parameters to be determined. Most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically.

Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry.

Most state-of-the-art methods in pedestrian detection are unable to achieve a good trade-off between accuracy and efficiency. For example, ACF has a fast speed but a relatively low detection rate, while checkerboards have a high detection rate but a slow speed. Inspired by some simple inherent attributes of pedestrians (i.

Learning Sampling Distributions for Efficient Object Detection.

Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection.