Molecular dynamic simulation of low-energy FIB irradiation induced damage in diamond

In this article, a large scale multi-particle molecular dynamics (MD) simulation model was developed to study the dynamic structural changes in single crystal diamond under 5 keV Ga+ irradiation in conjunction with a transmission electron microscopy (TEM) experiment. The results show that the thickness of ion-induced damaged layer (∼9.0 nm) obtained from experiments and simulations has good accordance, which demonstrates the high accuracy achieved by the developed MD model. Using this model, the evolution of atomic defects, the spatial distributions of implanted Ga particles and the thermal spike at the very core collision area were analysed. The local thermal recrystallizations observed during each single ion collision process and the increase of the density of the non-diamond phase (mostly sp2 bonded) at irradiation area are fund to be the underling mechanisms responsible for ion fluence dependent amorphization of diamond observed in previous experiments

HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness

RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins

RGB-Event Fusion for Moving Object Detection in Autonomous Driving

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approaches are only frame-based and may fail to reach reasonable performance when dealing with dynamic traffic participants. Recent advances in sensor technologies, especially the Event camera, can naturally complement the conventional camera approach to better model moving objects. However, event-based works often adopt a pre-defined time window for event representation, and simply integrate it to estimate image intensities from events, neglecting much of the rich temporal information from the available asynchronous events. Therefore, from a new perspective, we propose RENet, a novel RGB-Event fusion Network, that jointly exploits the two complementary modalities to achieve more robust MOD under challenging scenarios for autonomous driving. Specifically, we first design a temporal multi-scale aggregation module to fully leverage event frames from both the RGB exposure time and larger intervals. Then we introduce a bi-directional fusion module to attentively calibrate and fuse multi-modal features. To evaluate the performance of our network, we carefully select and annotate a sub-MOD dataset from the commonly used DSEC dataset. Extensive experiments demonstrate that our proposed method performs significantly better than the state-of-the-art RGB-Event fusion alternatives

Alignment-free HDR Deghosting with Semantics Consistent Transformer

High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual information, including both dynamic and static semantics, for better image generation. Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion. However, there is no research on jointly leveraging the dynamic and static context in a simultaneous manner. To delve into this problem, we propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules in the network. The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames. Aside from this, we introduce a novel realistic HDR dataset with more variations in foreground objects, environmental factors, and larger motions. Extensive comparisons on both conventional datasets and ours validate the effectiveness of our method, achieving the best trade-off on the performance and the computational cost

Quantization and diagnosis of Shanghuo (Heatiness) in Chinese medicine using a diagnostic scoring scheme and salivary biochemical parameters

Background: This study aims to establish a diagnostic scoring scheme for Shanghuo (Heatiness) and to evaluate whether Shanghuo is associated with biochemical parameters of salivary lysozyme (LYZ), salivary secreted immunoglobulin (S-IgA), salivary amylase (AMS), and saliva flow rate (SFR). Methods: We collected 121 Shanghuo patients at the Affiliated Hospitals of Guangzhou University of Traditional Chinese Medicine in Guangdong Province, 60 cases as a Shanghuo recovered group, and 60 healthy cases as a healthy control group. The diagnostic scoring scheme was established by probability theory and maximum likelihood discriminatory analysis on the basis of epidemiology with the design of self-controlled clinical trial. Subsequently, we used the same methods to collect 120 Shanghuo patients, 60 Shanghuo recovered cases, and 60 healthy cases in both Hunan Province and Henan Province. The levels of LYZ, S-IgA, AMS, and SFR were tested when the patients suffered from Shanghuo or recovered, respectively. Results: The diagnostic score table for Shanghuo syndrome was established first. In the retrospective tests, the sensitivity, specificity, accuracy, and positive likelihood ratio of the diagnostic score table were 98.9%, 93.5%, 97.5%, and 14.34%, respectively. In the prospective tests, the corresponding values were 94.9%, 85.7%, 91.7%, and 6.64%, respectively. Shanghuo was classified into three degrees based on the diagnostic scores, common Shanghuo: 63–120; serious Shanghuo: 121–150; very serious Shanghuo: >150. A negative correlation was found between Shanghuo and S-IgA (R = -0.428; P = 0.000). The level of S-IgA was also affected by seasonal and regional factors. No significant correlations were found between Shanghuo and the levels of LYZ, AMS, and SFR. Conclusions: In this study, Shanghuo could be diagnosed by the combination of the diagnostic score table and S-lgA level

Biogeographic Distribution Patterns of Bacteria in Typical Chinese Forest Soils

Microbes are widely distributed in soils and play a very important role in nutrient cycling and ecosystem services. To understand the biogeographic distribution of forest soil bacteria, we collected 115 soil samples in typical forest ecosystems across eastern China to investigate their bacterial community compositions using Illumina MiSeq high throughput sequencing based on 16S rRNA. We obtained 4,667,656 sequences totally and more than 70% of these sequences were classified into five dominant groups, i.e. Actinobacteria, Acidobacteria, Alphaproteobacteria, Verrucomicrobia and Planctomycetes (relative abundance > 5%). The bacterial diversity showed a parabola shape along latitude and the maximum diversity appeared at latitudes between 33.50°N and 40°N, an area characterized by warm-temperate zones and moderate temperature, neutral soil pH and high substrate availability (soil C and N) from dominant deciduous broad-leaved forests. Pairwise dissimilarity matrix in bacterial community composition showed that bacterial community structure had regional similarity and the latitude of 30°N could be used as the dividing line between southern and northern forest soils. Soil properties and climate conditions (MAT and MAP) greatly accounted for the differences in the soil bacterial structure. Among all soil parameters determined, soil pH predominantly affected the diversity and composition of the bacterial community, and soil pH = 5 probably could be used as a threshold below which soil bacterial diversity might decline and soil bacterial community structure might change significantly. Moreover, soil exchangeable cations, especially Ca2+ (ECa2+) and some other soil variables were also closely related to bacterial community structure. The selected environmental variables (21.11%) explained more of the bacterial community variation than geographic distance (15.88%), indicating that the edaphic properties and environmental factors played a more important role than geographic dispersal limitation in determining the bacterial community structure in Chinese forest soils

DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over conventional compression methods. However, this method suffers from non-trivial system overheads incurred by delta maintenance and read penalty, which prevents its applicability on mobile devices. To this end, this paper proposes DeltaFS, a metadata-enabled Delta compression on log-structured File System for mobile devices, to achieve utmost compressing efficiency and zero hardware costs. DeltaFS smartly exploits the out-of-place updating ability of Log-structured File System (LFS) to alleviate the problems of write amplification, which is the key bottleneck for delta compression implementation. Specifically, DeltaFS utilizes the inline area in file inodes for delta maintenance with zero hardware cost, and integrates an inline area manage strategy to improve the utilization of constrained inline area. Moreover, a complimentary delta maintenance strategy is incorporated, which selectively maintains delta chunks in the main data area to break through the limitation of constrained inline area. Experimental results show that DeltaFS substantially reduces write traffics by up to 64.8\%, and improves the I/O performance by up to 37.3\%

Environmental filtering, spatial processes and biotic interactions jointly shape different traits communities of stream macroinvertebrates

The metacommunity concept has been widely used to explain the biodiversity patterns at various scales. It considers the influences of both local (e.g., environmental filtering and biotic interactions) and regional processes (e.g., dispersal limitation) in shaping community structures. Compared to environmental filtering and spatial processes, the influence of biotic interactions on biodiversity patterns in streams has received limited attention. We investigated the relative importance of three ecological processes, namely environmental filtering (including local environmental and geo-climatic factors), spatial processes and biotic interactions (represented by interactions of macroinvertebrates and diatom), in shaping different traits of macroinvertebrate communities in subtropical streams, Eastern China. We applied variance partitioning to uncover the pure and shared effects of different ecological processes in explaining community variation. The results showed that environmental filtering, spatial processes, and biotic interactions jointly determined taxonomic and trait compositions of stream macroinvertebrates. Spatial processes showed a stronger influence in shaping stream macroinvertebrate communities than environmental filtering. The contribution of biotic interactions to explain variables was, albeit significant, rather small, which was likely a result of insufficient representation (by diatom traits) of trophic interactions associated with macroinvertebrates. Moreover, the impact of three ecological processes on macroinvertebrate communities depends on different traits, especially in terms of environmental filtering and spatial processes. For example, spatial processes and environmental filtering have the strongest effect on strong dispersal ability groups; spatial processes have a greater effect on scrapers than other functional feeding groups. Overall, our results showed that the integration of metacommunity theory and functional traits provides a valuable framework for understanding the drivers of community structuring in streams, which will facilitate the development of effective bioassessment and management strategies.Peer Reviewe

MD simulation study on defect evolution and doping efficiency of p-type doping of 3C-SiC by Al ion implantation with subsequent annealing

We use molecular dynamics (MD) simulation with numerical characterisation and statistical analysis to study the mechanisms of damage evolution and p-type doping efficiency by aluminum (Al) ion implantation into 3C silicon carbide (SiC) with subsequent annealing. By incorporating the electronic stopping power for implantation, a more accurate description of the atomic-scale mechanisms of damage evolution and distribution in SiC can be obtained. The simulation results show a novel observation that the recrystallization process occurs in the region below the subsurface layer, and develops from amorphous-crystalline interface to the damage center region, which is a new insight into previously published studies. During surface recrystallization, significant compressive stress concentration occurs, and more structural phase transition atoms and dislocations formed at the damage-rich-crystalline interface. Another point of interest is that for low-dose implantation, more implantation-induced defects hamper the doping efficiency. Correspondingly, the correlation between lattice damage and doping efficiency becomes weaker as the implant dose increases under the same annealing conditions. Our simulation also predicts that annealing after high temperature (HT) implantation is more likely to lead to the formation of carbon vacancies (V-C).Peer reviewe