Arbuscular mycorrhizal fungi activity in the rhizosphere of tree seedlings subjected to residual herbicides

Trees occurring on the margins of agricultural areas can mitigate damage from residual herbicides. Rhizospheric microbial activity associated with trees is one of the main remedial capacity indicators. The objective of this study was to evaluate the rhizospheric microbiological activity in tree species subjected to the herbicides atrazine and sulfentrazone via the rhizosphere. The experiment was designed in four blocks and a 6 × 3 factorial scheme. The first factor consisted of six tree species from Brazil and the second of atrazine, sulfentrazone, and water solutions. Four herbicide applications were performed via irrigation. The total dry mass of the plants, mycorrhizal colonization, number of spores, basal respiration of the rhizospheric soil, and survival rate of bioindicator plants after phytoremediation were determined. Trichilia hirta had higher biomass when treated with atrazine and sulfentrazone. Herbicides decreased the microbial activity in Triplaris americana and did not affect the microbiological indicators of Myrsine gardneriana, Schizolobium parahyba, and Toona ciliata. Fewer bioindicator plants survived in soil with Triplaris americana and sulfentrazone. Microbiological indicators were influenced in different ways between species by the presence of herbicides in the rhizosphere.

SCRnet: A Spatial Consistency Guided Network Using Contrastive Learning for Point Cloud Registration

Point cloud registration is used to find a rigid transformation from the source point cloud to the target point cloud. The main challenge in the point cloud registration is in finding correct correspondences in complex scenes that may contain many noise and repetitive structures. At present, many existing methods use outlier rejections to help the network obtain more accurate correspondences, but they often ignore the spatial consistency between keypoints. Therefore, to address this issue, we propose a spatial consistency guided network using contrastive learning for point cloud registration (SCRnet), in which its overall stage is symmetrical. SCRnet consists of four blocks, namely feature extraction block, confidence estimation block, contrastive learning block and registration block. Firstly, we use mini-PointNet to extract coarse local and global features. Secondly, we propose confidence estimation block, which formulate outlier rejection as confidence estimation problem of keypoint correspondences. In addition, the local spatial features are encoded into the confidence estimation block, which makes the correspondence possess local spatial consistency. Moreover, we propose contrastive learning block by constructing positive point pairs and hard negative point pairs and using Point-Pair-INfoNCE contrastive loss, which can further remove hard outliers through global spatial consistency. Finally, the proposed registration block selects a set of matching points with high spatial consistency and uses these matching sets to calculate multiple transformations, then the best transformation can be identified by initial alignment and Iterative Closest Point (ICP) algorithm. Extensive experiments are conducted on KITTI and nuScenes dataset, which demonstrate the high accuracy and strong robustness of SCRnet on point cloud registration task.

Eigenvalue inequalities for positive block matrices with the inradius of the numerical range

We prove the operator norm inequality, for a positive matrix partitioned into four blocks in [Formula: see text], [Formula: see text] where [Formula: see text] is the diameter of the largest possible disc in the numerical range of [Formula: see text]. This shows that the inradius [Formula: see text] satisfies [Formula: see text] Several eigenvalue inequalities are derived. In particular, if [Formula: see text] is a normal matrix whose spectrum lies in a disc of radius [Formula: see text], the third eigenvalue of the full matrix is bounded by the second eigenvalue of the sum of the diagonal block, [Formula: see text] We think that [Formula: see text] is optimal and we propose a conjecture related to a norm inequality of Hayashi.

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.

Noise Reduction, Enhancement and Classification for Sonar Images

Ultrasound imaging has some problems with image properties output. These affects the specialist decision. Ultrasound noise type is the speckle noise which has a grainy pattern depending on the signal. There are two parts of this study. The first part is the enhancing of images with adaptive Weiner, Lee, Gamma and Frost filters with 3x3, 5x5, and 7x7 sliding windows. The evaluated process was achieved using signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and maximum difference (MD) criteria. The second part consists of simulating noise in a standard image (Lina image) by adding different percentage of speckle noise from 0.01 to 0.06. The supervised classification based minimum distance method is used to evaluate the results depending on selecting four blocks located at different places on the image. Speckle noise was added with different percentage from 0.01 to 0.06 to calculate the coherent noise within the image. The coherent noise was concluded from the slope of the standard deviation with the mean for each noise. The results showed that the additive noise increased with the slide window size, while multiplicative noise did not change with the sliding window nor with increasing noise ratio. Wiener filter has the best results in enhancing the noise.

Student’s Perception of Higher Education in Technical Universities and Its Connection With Subsequent Professional Development (MSTU, MADI and MIREA - Comparative Analysis) (Ending Part 4)

This article concluded the presentation of the results obtained during a comparative study of students of technical universities: MSTU, MADI and MIREA. These results were received, using a sociological questionnaire with closed-type questions. The questionnaire contained 9 main thematic blocks: 1. Professional choice; 2. Profession; 3. Study and education; 4. Educating; 5. Professional and personal competence; 6. Teachers; 7. Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. The article is primarily devoted to the presentation of the results of a comparative study of technical universities: MSTU, MADI and MIREA. In it we will present data obtained using the last four blocks of the sociological questionnaire: 6. Teachers; 7. Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. At the same time, we also want to supplement them with the results of our previous studies of MSTU students. In particular, we will continue to consider the psychological characteristics found in students studying at the Bauman Moscow State Technical University, at the faculties of Aerospace and Engineering Business and Management.

Multiclassification of license plate based on deep convolution neural networks

In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image data augmentation techniques (rotation, horizontal flip, zoom range) to increase the number of datasets. After that, we feed the augmented images into the convolution layer model, which consists of four blocks of convolution layer. For calculating and optimizing the efficiency of the classification model, a categorical cross-entropy and Adam optimizer used with a learning rate was 0.0001. The model's performance showed 99.17% and 97.50% of the training and validation sets accuracies sequentially, with total accuracy of classification is 96.66%. The time of training is lasting for 12 minutes. An anaconda python 3.7 and Keras Tensor flow backend are used.

Model of adaptive management of formation of digital competence basics of primary school students

The study is devoted to the issues of modeling the processes of adaptive management of the formation of digital competence of students in the process of obtaining primary education. The key aspects of the theoretical foundations of management of socio-pedagogical systems are revealed. Especially, the principles and approaches to management in general and adaptive in particular are described. It is described, that the essence of management of social and pedagogical systems consists in purposeful influences on the managed subsystem for its ordering. It is determined, that the main task of such management is to ensure the purposefulness, consistency of operation and development of the managed subsystem. The essence of adaptive management is specified, which, taking into account the main characteristics of classical management, is based on the processes of dialogic adaptation of managed and managing subsystems. The characteristics of the key definitions of the research are described, and the structure of the model of adaptive management of the process of formation of digital competence of students in the process of obtaining primary education is presented. The model consists of interconnected structural components, which together give an idea of the process of forming digital competence of students to ensure the success of their lives. The model was built on the basis of system and activity approaches using certain stages of the modeling process. The essence of four blocks of the model is described: target, theoretical, content-technological; and final-reflexive. It is noted, that the pedagogy of partnership is a key component of the formula of the New Ukrainian School and a component of the built model of adaptive management of the formation of digital competence of students in the process of obtaining primary education

Labour Process in Informal Sector: A Case Study of Carpet Industry

The Carpet industry is an important informal sector in Kashmir, provides employment opportunities to lakhs of people in the rural and semi-urban areas of Kashmir. The carpet industry has made a significant contribution to production, employment, and export of handicraft products and contributes to economic development. Regardless of generating growth, weavers in carpet industry continue to be locked in the unequal and exploitative labour process. The purpose of the present study is to examine the labour process in the carpet industry with specific focus on organization of production, capital accumulation and wage pattern among carpet weavers in Pulwama district of Kashmir. The present study is both primary and secondary in nature. The primary data collected from four blocks of Pulwama district through interview-schedule, focused group discussion and field observation. The key findings revealed that there is diversity in production relation, weavers are facing with a problem of middlemen/master weaver exploitation, low earning, and long working hours and piece-wage is a mode of surplus extraction for capitalists in the carpet industry.