Effective microorganisms input efficiently improves the vegetation and microbial community of degraded alpine grassland

Soil beneficial microorganism deficiency in the degraded grasslands have emerged as the major factors negatively impacting soil quality and vegetation productivity. EM (effective microorganisms) has been regarded as a good ameliorant in improving microbial communities and restoring degraded soil of agricultural systems. However, knowledge was inadequate regarding the effects of adding EM on the degraded alpine grassland. Four levels of EM addition (0, 150, 200, 250 mL m–2) were conducted to investigate the effects of EM addition on soil properties and microorganisms of degraded alpine grassland. The addition of EM increased aboveground biomass, soil organic carbon, total nitrogen, available phosphorus, and microbial biomass, but decreased soil electric conductivity. Meanwhile, the relative biomasses of gram-negative bacteria decreased, while the ectomycorrhizal fungi and arbuscular mycorrhizal fungi increased after EM addition. The relationship between microbial communities and environmental factors has been changed. The restore effect of EM increased with the increase of addition time. These results indicated that EM addition could be a good practice to restore the health of the degraded alpine grassland ecosystem

The Annual Characteristics of Rainwater HCHO in Guiyang City, Southwest of China

HCHO is ubiquitous and important chemical constitutes in the troposphere. The concentrations of the HCHO (aq) in the rainwater were measured in the Guiyang city, southeastern of China from May 2006 to April 2007 and 153 discrete samples were collected. Rainwater (N - 151) HCHO (aq) concentrations ranged from lower than method detection limit (MDL) to 40.2 mu mol/L with a volume weighted mean value of 7.4 +/- 8.8 mu mol/L. The strong correlations between HCHO (aq) and HCOO- (r = 0.69, n = 137), HCHO (aq) and nss-SO42- (r = 0.74, n = 137), HCHO (aq) and NO3- (r = 0.67, n = 137), HCHO (aq) and NH4+ (r = 0.74, n = 133) suggest the significant influence of the anthropogenic input for the HCHO (aq) levels. The concentration levels of rainwater HCHO (aq) was inversely proportional to the amount of rainfall, indicating the below-cloud process is the most important mechanism for rainwater HCHO (aq) scavenging processes. More than 70% of the HCHO (aq) wet deposition took place during the early stage of the rainfall. According to the air mass back-trajectory analysis, the rainwater with industrial back-trajectories coming from the north had the highest levels of HCHO (aq) while the rainwater with the green-covered or marine back-trajectories from the southeast had the lowest concentrations, and this indicate the HCHO (aq) originated from urban or industrial regions served as an important source of the rainwater. The annual HCHO (aq) wet deposition flux was calculated as 6.96 mmol/m(2) per year and the total deposition flux was estimated as 24.35 mmol/m(2) per year, 71.4% of which was dominated by dry deposition.HCHO is ubiquitous and important chemical constitutes in the troposphere. The concentrations of the HCHO (aq) in the rainwater were measured in the Guiyang city, southeastern of China from May 2006 to April 2007 and 153 discrete samples were collected. Rainwater (N - 151) HCHO (aq) concentrations ranged from lower than method detection limit (MDL) to 40.2 mu mol/L with a volume weighted mean value of 7.4 +/- 8.8 mu mol/L. The strong correlations between HCHO (aq) and HCOO(-) (r = 0.69, n = 137), HCHO (aq) and nss-SO(4)(2-) (r = 0.74, n = 137), HCHO (aq) and NO(3)(-) (r = 0.67, n = 137), HCHO (aq) and NH(4)(+) (r = 0.74, n = 133) suggest the significant influence of the anthropogenic input for the HCHO (aq) levels. The concentration levels of rainwater HCHO (aq) was inversely proportional to the amount of rainfall, indicating the below-cloud process is the most important mechanism for rainwater HCHO (aq) scavenging processes. More than 70% of the HCHO (aq) wet deposition took place during the early stage of the rainfall. According to the air mass back-trajectory analysis, the rainwater with industrial back-trajectories coming from the north had the highest levels of HCHO (aq) while the rainwater with the green-covered or marine back-trajectories from the southeast had the lowest concentrations, and this indicate the HCHO (aq) originated from urban or industrial regions served as an important source of the rainwater. The annual HCHO (aq) wet deposition flux was calculated as 6.96 mmol/m(2) per year and the total deposition flux was estimated as 24.35 mmol/m(2) per year, 71.4% of which was dominated by dry deposition

Does Aqueous-Phase Oxidation of HCHO Opens a Pathway to Formic Acids in Atmosphere?

Formic acid is the major contributor to acid rain in some regions but its sources are not fully understood We investigated the aqueous phase reactions of HCHO (aq) and OH radicals at enlarged rainwater pH values (2 49-5 89) in Guiyang China from May 2006 to April 2007 Our results show that there were no significant correlation between the [HCOOH](t)/[HCHO] (aq) and the rainwater pH The ratio did not appear to vary consist ently as a function of rainwater pH as predicted by theoretical model In addition we saw no clear evidence that oxidation of HCHO (aq) would produce significant HCOOH (aq) which indicates this reaction may be only a minor contribution to the budget of HCOOH (g) in atmosphere Further investigation is strongly suggested to be carried out in field cloud water fog water or rainwater because the ratios would be diverged from equilibrium value as a result of other chemical or physical processesFormic acid is the major contributor to acid rain in some regions but its sources are not fully understood We investigated the aqueous phase reactions of HCHO (aq) and OH radicals at enlarged rainwater pH values (2 49-5 89) in Guiyang China from May 2006 to April 2007 Our results show that there were no significant correlation between the [HCOOH](t)/[HCHO] (aq) and the rainwater pH The ratio did not appear to vary consist ently as a function of rainwater pH as predicted by theoretical model In addition we saw no clear evidence that oxidation of HCHO (aq) would produce significant HCOOH (aq) which indicates this reaction may be only a minor contribution to the budget of HCOOH (g) in atmosphere Further investigation is strongly suggested to be carried out in field cloud water fog water or rainwater because the ratios would be diverged from equilibrium value as a result of other chemical or physical processe

Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method

Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has aroused interest. In this work, we address the computing-resource-saving problem by developing a deep model, termed the Gabor convolutional neural network (Gabor CNN), which incorporates highly expression-efficient Gabor kernels into CNNs. In order to effectively imitate the structural characteristics of traditional weight kernels, we improve upon the traditional Gabor filters, having stronger frequency and orientation representations. In addition, we propose a procedure to train Gabor CNNs, termed the fast training method (FTM). In FTM, we design a new training method based on the multipopulation genetic algorithm (MPGA) and evaluation structure to optimize improved Gabor kernels, but train the rest of the Gabor CNN parameters with back-propagation. The training of improved Gabor kernels with MPGA is much more energy-efficient with less samples and iterations. Simple tasks, like character recognition on the Mixed National Institute of Standards and Technology database (MNIST), traffic sign recognition on the German Traffic Sign Recognition Benchmark (GTSRB), and face detection on the Olivetti Research Laboratory database (ORL), are implemented using LeNet architecture. The experimental result of the Gabor CNN and MPGA training method shows a 17–19% reduction in computational energy and time and an 18–21% reduction in storage requirements with a less than 1% accuracy decrease. We eliminated a significant fraction of the computation-hungry components in the training process by incorporating highly expression-efficient Gabor kernels into CNNs

Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt the tracking-by-detection strategy, which relies on compute-intensive sliding windows or anchoring schemes to detect matching targets or candidates in each frame. In this work, we introduce a more efficient and effective spatial–temporal attention scheme to track multiple objects in various scenarios. Using a semantic-feature-based spatial attention mechanism and a novel Motion Model, we address the insertion and location of candidates. Some online-learned target-specific convolutional neural networks (CNNs) were used to estimate target occlusion and classify by adapting the appearance model. A temporal attention mechanism was adopted to update the online module by balancing current and history frames. Extensive experiments were performed on Karlsruhe Institute of Technologyand Toyota Technological Institute (KITTI) benchmarks and an Armored Target Tracking Dataset (ATTD) built for ground-armored targets. Experimental results show that the proposed method achieved outstanding tracking performance and met the actual application requirements

Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a depth feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects

Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information

Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case

Few-Shot Object Detection via Sample Processing

Few-shot object detection (FSOD) eliminates the dependence on tremendous instances with manual annotations in conventional object detection. We deem that the scarcity of positive samples is the main reason that restricts the performance of FSOD detectors. In this paper, a novel FSOD model via sample processing, namely, FSSP, is proposed to detect objects accurately with only a few annotated samples, which is based on the structural design of the Siamese network and uses YOLOv3-SPP as the baseline. Central to FSSP are our designed self-attention (SAM) and positive-sample augmentation (PSA) modules. The former attempts to better extract the representative features of hard samples, and the latter expands the number and enriches the scale distribution of positive samples, inhibiting the growth of negative samples. For the fine-tuning phase, we modify the classification loss function to increase the punishment for hard samples. Experiments conducted on the PASCAL VOC and MS COCO datasets confirm that the proposed FSSP achieves competitive detection performance compared with state-of-the-art detectors

Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands

Cooperation between arbuscular mycorrhizal fungi and plant growth-promoting bacteria and their effects on plant growth and soil quality

The roles of arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) in improving nutrition uptake and soil quality have been well documented. However, few studies have explored their effects on root morphology and soil properties. In this study, we inoculated Elymus nutans Griseb with AMF and/or PGPR in order to explore their effects on plant growth, soil physicochemical properties, and soil enzyme activities. The results showed that AMF and/or PGPR inoculation significantly enhanced aboveground and belowground vegetation biomass. Both single and dual inoculations were beneficial for plant root length, surface area, root branches, stem diameter, height, and the ratio of shoot to root, but decreased root volume and root average diameter. Soil total nitrogen, alkaline phosphatase, and urease activities showed significant growth, and soil electrical conductivity and pH significantly declined under the inoculation treatments. Specific root length showed a negative correlation with belowground biomass, but a positive correlation with root length and root branches. These results indicated that AMF and PGPR had synergetic effects on root morphology, soil nutrient availability, and plant growth