bluuebunny/crossref_metadata_2025 huggingface dataset:
Sparsity Augmented Restoration of Underwater Images using Compressive Sensing
Authors: Mathias Ajisha , Dhanalakshmi Samiappan , Kumar R
Published: September 2021

The study of underwater environments is a difficult task because both laws of reflection and refraction are influenced in the visualization of underwater medium. Other variables, such as aquatic flora and fauna, also have a major effect on the lighting conditions. While optical processing of such images is regarded, degradation can take place due to various environmental and capturing device defects. Also, due to over hitting of samples, the algorithm based on traditional Nyquist algorithm can prove computationally sensitive and produce lower accuracy. Compressive Sensing (CS) provides an alternative approach to overcome the over-hitting problem. The efficiency of underwater algorithms can be improved by incoherent sampling combined with sparsity. The proposed model is applied to the underwater blurred image and the algorithm is evaluated for restoration using directional gradient priors. The contribution of the proposed algorithm can be characterized as: a) Incoherent sampling reduces the problem of hitting (b) Enhancement of sparsity coefficients for restoration algorithms. The proposed contribution to the application of underwater imaging is mainly because there are less random samples in the compressive sensing that produce better sparsity prior to the initial stage, which makes the solution to the ill-posed problem highly convergent with high accuracy. Therefore, we propose a new Temporal Sparse Bayesian Learning (TSBL) using compressive sensing that leads to higher resolution image enhancement in underwater conditions.
Analytical Modeling of Triple-Material Trigate Junctionless Tunnel Field Effect Transistor
Authors: Maria Jossy A , Vigneswaran T , Malarvizhi S , Nagarajan K K
Published: September 2021

An analytical two-dimensional (2D) model is developed to obtain expressions for various parameters of triple-material trigate junctionless tunnel field-effect transistors (TMTG-JTFETs). The 2D Poisson’s equation uses the superposition approximation method to calculate the sum of potentials arising from different point charges. The electric field distribution in the channel is obtained from the gradient of the electric potential. The drain current of the device is calculated using the Kane’s model in the presence of an electric field and a band structure. The TMTG-JTFET, with a channel length of 40nm, exhibits significant electrical characteristics with very low off-current (Ioff) and high on-current compared to a dual-material double-gate TFET. To validate the model, analytical results are confirmed via comparison with technology computer-aided design simulation results.
A Coarse-to-Fine Object Tracking Based on Attention Mechanism
Authors: Wen Haixi , Chen Xiaoming , Zhou Li
Published: September 2021

In the visual object tracking task, although the convolutional neural network has excellent performance, it is difficult to learn the global and long-range semantic information interaction due to the inherent locality of convolution. Simultaneously, the existing template updating methods focus on the target position of the next frame, which will integrate more irrelevant background and cause tracking instability. Therefore, to address this issue, inspired by Transformer, in this paper, we propose a coarse-to-fine tracking method based on the attention mechanism (CTFT). First, employing swin transformer block instead of the original convolutional network backbone to achieve self-attention from local to global. Second, using attention to effectively combine template and search region features in the part of feature fusion, and explore the possibility of using attention mechanism as the backbone in object tracking for the first time. Finally, a tracking strategy from coarse to fine is proposed. In the offline coarse tracking stage, the initial estimation of the target object is generated to obtain the coarse regress bounding box of the target. In the online fine tracking stage, we make use of the coarse regress bounding box to expand the corresponding object frame, and use the segmentation to get the fine regress bounding box of the object. Experiments on challenging benchmarks including GOT-10k, LaSOT, VOT2018, VOT2019, UAV123 and OTB-100 demonstrate that the proposed CTFT outperforms many state-of-the-art trackers and achieves leading performance.
Evaluate the viscosity of small aquaculture water by computer vision technology
Authors: Ma Yinchi , Wei Wei
Published: September 2021

The royal goldfish has high ornamental value and is usually cultured in small aquaculture water, which requires high water quality. In the industry, water viscosity is usually used to measure the quality of water quality. The fresh and suitable water is conducive to the survival and reproduction of the royal goldfish, while the water with excessive viscosity is easy to cause the royal goldfish to get sick or even die. In this paper, the computer vision technology is used to track and detect the bubble group on the surface of the royal goldfish small aquaculture water in real-time, and the time change fitting model of the bubble group covering the water surface area is established, which can directly reflect the change of the bubble burst rate of the small aquaculture water. Based on this, the viscosity level of the small aquaculture water is quantitatively evaluated. The experimental results show that the coefficient a in the fitting model is highly correlated with the viscosity level. This method can evaluate the viscosity of small aquaculture water in real-time and automatically, and send early warning information to aquaculture technicians quickly, which is of great significance to ensure the health and safety of royal goldfish culture.
Cross-Domain Sequential Recommendation Based on Self-Attention and Transfer Learning
Authors: Liu Dantong , Zhu Cui
Published: September 2021

Cross-domain recommendation provides effective solutions to the problems of cold start and data sparsity by transferring information from one or more source domains to another target domain. However, people’s interactive behaviour usually occurs in a sequence, but most cross-domain recommendation models often ignore the sequential information. At the same time, most sequential recommendation models only consider the case of single-domain. In this paper, we propose a new cross-domain sequential recommendation model (SATLR) based on self-attention and transfer learning, which make recommendations based on user preferences and sequential dependencies learn from cross-domain. The experiment results demonstrate that on the three groups of datasets from amazon, our model is better than other recommendation models.
An improved algorithm for disparity estimation of SGM stereo matching based on edge detection
Authors: Zhang Jian , Huang Jing
Published: September 2021

In the fields of depth measurement and computer vision, generating sufficiently accurate disparity maps through various stereo matching algorithms is an important foundation. The SGM semi-global matching algorithm is one of the best stereo matching algorithms, But its filling accuracy is poor in the invalid parallax area. Therefore, on the original basis, an improved algorithm of SGM stereo matching disparity filling based on edge detection is proposed. The experiment uses a standard data set to verify the algorithm. The results show that compared with the original SGM stereo matching algorithm, the paper algorithm can achieve better disparity estimation results in the invalid disparity area.
An Optimized Positioning Algorithm Based on Improved Gaussian Filtering
Authors: Chen Min , Zhang Dongmei , Zhao Yue , Wu Taojiang
Published: September 2021

WiFi offers simple, convenient, ubiquitous, and economic solutions for indoor positioning services, by matching a pre-established WiFi’s RSSI fingerprint database to a mobile terminal’s received RSSI values. A setback of this fingerprint matching method is its low precision, only miserably on order of meters, due to signal impairment by indoor complicated environment. To circumvent this, we revise the traditional weighted K-neighborhood algorithm by incorporating a Bayesian probability optimization. The proposed combination of Bayesian with weighted K-neighborhood algorithm improves the accuracy and reduces the average running time. Computer simulation shows that proposed Bayesian probabilistic optimization algorithm improves the positioning accuracy from 34% to 46%, with an average of about 14.86%, and the computation stability is also enhanced.
Speech biohashing authentication based on power spectrum
Authors: Huang Yibo , Chen Tengfei , Pu Xiangrong , Zhang Qiuyu
Published: September 2021

In order to solve hashing sequences leakage in the existing speech authentication algorithms, this paper proposed a speech biohashing authentication based on power spectrum. Fristly, the algorithm pre-processed the original speech, then used the improved covariance method to extract the power spectrum feature of the speech, and generated a one-dimensional feature vector from the feature matrix. A pseudo-random matrix was generated by using Sinc function transform, the feature vector and pseudo-random matrix are projected and mapped to construct the biometric template. Finally, binarization process was used to generate hash index, and upload it to the cloud. The experimental results show that the algorithm has good discrimination, robustness and authentication performance. At the same time, the biometric template has good security and ensures the irreversibility of the hashing sequence.
Computational simulation in Molecular Recognition and Spectroscopic Characteristics of a Probe for Zn2+, Cu2+, Hg2+, Fe3+
Authors: Wang Xiaofeng , Lan Wenbo , He Liping , Tan Bin , Meng Yanbin , Lu Guowen , He Junshan , Li Manhua , Chen Aoling , Shi Yurong
Published: September 2021

Using computer technology and density functional theory methods, the binding and recognition ability of quinoline derivative probes to Zn2+, Cu2+, Hg2+ and Fe3+ ions were studied. And the UV-Vis absorption and fluorescence spectra before and after the complexes formed by the probe and Zn2+, Cu2+, Hg2+ and Fe3+ were compared and analyzed. Finally, the energy gap of the frontier molecular orbital energy of each complex was explored and compared. The results of the study show that the probe has obvious differences in coordination recognition ability with Zn2+, Cu2+, Hg2+ and Fe3+. The binding energy of the probe to Fe3+ is much greater than that of other ions. Moreover, the complexes formed by the probe and the metal ion have significant differences in the ultraviolet-visible absorption spectrum and the fluorescence spectrum. These will provide an important reference for the probe molecule’s identification of different metal ions and the qualitative and quantitative detection and analysis of metal ions.
Design and implementation of a general SQL parser
Authors: Wu Kehe , Di Dan , Li Wei , Cui Wenchao
Published: September 2021

With the rapid development of computer network, the information security of database becomes more and more important. In database security protection, SQL statements need to be parsed and reconstructed. However, the database audit products on the market lack accurate analysis of SQL statements. Therefore, this paper constructs an SQL parser from four aspects: lexical analysis, syntax analysis, SQL spanning tree optimization and error detection.
Complex-valued U-Net for PolSAR Image Semantic Segmentation
Authors: Liu Ao , Yu Lingjuan , Zeng Zhaoxin , Xie Xiaochun , Guo Yuting , Shao Qiqi
Published: September 2021

As an image semantic segmentation network, U-Net has the advantage of simple structure which is suitable for semantic segmentation of PolSAR images with small datasets. However, the original U-Net is a real-valued (RV) network, whose input must be RV. If it is directly used in the segmentation of PolSAR image, the complex-valued (CV) input must be converted into RV, which results in the loss of information. In this paper, a CV U-Net, which is mathematically strict, is proposed for semantic segmentation of PolSAR images. Considering that the PolSAR dataset is small, the structure and parameters of CV U-Net are furtherly simplified based on the original U-Net to prevent overfitting. Experimental results on the Flevoland dataset show that the proposed CV U-Net has better segmentation performance than the original RV U-Net and some other semantic segmentation networks.
Research Progress of Speech Emotion Recognition Based on Discrete Emotion Model
Authors: Yang Chunfeng , Lu Jiajia , Wu Qiang , Chen Huiyu
Published: September 2021

Speech emotion recognition is one of the important technologies of human-computer interaction, and neural networks have made great contributions in it. In this survey, the commonly used discrete emotion databases and speech emotion feature parameters are firstly introduced. Then the feature extraction methods and emotion recognition models in recent three years’ research in China are summarized, and the former one can be divided into two directions, selecting the feature subset from the existing features and using neural network to extract new features. Finally, the challenges encountered in speech emotion recognition are described, and the future research and development of speech emotion recognition based on discrete emotion models are prospected.
Signal acquisition system for broken wire of elevator wire rope based on TMR sensor
Authors: Dai Liyong , Zhao Mingfu , Tang Bin , Yang Ninghui , Xiao Yu , Wu Taojiang
Published: September 2021

Aiming at the problems of low detection accuracy, large size and quality of the detection equipment, and difficulty in online monitoring in the magnetic flux leakage detection of elevator wire ropes, a broken wire signal acquisition system was built based on the tunnel magnetoresistance sensor TMR2703, and a dual-loop excitation device was designed and simulated to verify. The dipole model determines the detection direction of the sensor, acquires the magnetic flux leakage signal with the signal preprocessing circuit and the differential detection ring, develops the host computer in LabVIEW, displays the broken wire signal waveform in real time, and stores the collected data in the form of TDMS files, completed the feature extraction of broken wire signals. Experiments show that the system can collect the magnetic flux leakage signal of the wire rope in real time, and can accurately extract the characteristic value of the magnetic flux leakage signal with different numbers of broken wires. The peak value, valley value, peak-to-peak value and signal wave width of the signal have an increase with the number of broken wires. The law of increase can be used to judge the damage degree of the wire rope according to the detection waveform.
Action recognition based on element-level fine-grained multi-modal fusion
Authors: Peng Guozheng , Han Lixin , Yang Jiaxue
Published: September 2021

Traditional action recognition algorithms often only pay attention to video RGB features or optical flow features. These methods do not make good use of the audio information in the video. Based on RGB and optical flow characteristics, this paper introduces the processing of audio information, and classifies videos based on element-level fine-grained multi-modal fusion. Through experimental comparison, the accuracy of the multi-modal fusion algorithm proposed in this paper is improved by 7.38% on the HMDB51 dataset and 3.18% on the UCF101 dataset compared to the simple modal splicing. At the same time, it is proved that the introduction of audio modes can effectively improve the performance of the model.
Feature Fusion Network for Low-Light Image Enhancement
Authors: Yu Yabin
Published: September 2021

With the popularity of electronic cameras, we can get pictures under various lighting conditions in our daily life. The images which obtained in low illumination environment will lose contrast, colour, and content details which will bring obstacles for understanding the image. To tackle this problem, a method is proposed in this paper to tackle low-light image enhancement. This method fuses the high level and the low level feature, uses an attention module to model the context information and selects the critical feature to supplemented the high level feature to reconstruct the enhanced image. A number of experiments demonstrate that our method can get images with improved and reduced noise.
An unsupervised prediction method for radar transmitter degradation fault based on isolation forest
Authors: Zhai Yuting , Shao Xiaofang , Li Jiasen , Fang Shaojun
Published: September 2021

The transmitter is prone to premature degradation and failure for the frequent work and the harsh working environment of the marine radars. The existing method of degradation fault based on data-driven require a large number of negative samples or need to set failure threshold. To solve the above problems, an unsupervised prediction method for radar transmitter degradation fault based on isolation forest is proposed. The proposed unsupervised prediction method can predict the degradation fault of radar transmitter when there are no fault samples and the fault threshold is not reached.
A novel visible-infrared image fusion method based on visual enhancement and multiscale decomposition
Authors: Li Lingxiao , Feng Yong , Ma Zezhong
Published: September 2021

Aiming at the problems of low contrast, low signal-to-noise ratio and scattered energy in the field of optical detection and imaging, a visual enhancement and multi-scale decomposition method for fusion of visible and infrared images is proposed based on the visual characteristics of different bands’ image. Firstly, the infrared image with less texture information and low contrast is preprocessed to weaken the background noise and improve the visual contrast. On this basis, the multi-scale image is decomposed by detail preserving filter, and the saliency map of each scale image is obtained by saliency extraction method. Then, the multi-source images of each scale are fused with saliency map, and binary filtering fusion rules are adopted for the regions with salient details while weighted fusion rules are adopted for other regions. Finally, the images of all scale are reconstructed to get better fusion results. The experimental results show that this method can significantly improve the visual contrast of the fused object, and the objective evaluation indexes are superior to other comparison methods.
IGBT lifetime prediction based on EMD-LSTM
Authors: Wang Ziyang , Du Xianjun
Published: September 2021

Aiming at the problem of fatigue failure caused by the cyclic impact of thermal stress and electrical stress during IGBT operation, a long short-term memory (LSTM) network life prediction method based on EMD (Empirical Mode Decomposition) decomposition is proposed. This experiment uses the accelerated aging data set provided by the NASA PCoE laboratory, analyse and select the collector-emitter transient spike voltage as the failure characteristic parameter, and use the EMD algorithm to decompose the original time series into multiple relatively stable and with different characteristic scales the eigenmode function and trend term reduce the complexity of the time series. Through the LSTM network prediction model, the decomposed eigenmode function components and trend items are respectively predicted, and then the prediction results are superimposed to obtain the result. The results show that the prediction accuracy of the EMD-LSTM model is higher, and it can better realize the life prediction of IGBT, and it also has certain reference value for the life prediction of other power electronic devices.
Fault detection observer design in finite-frequency domain for networked interconnected systems
Authors: Guo Haixiao , Chen Xiaoming , Wang Yanping
Published: September 2021

With the development of network technology, information can flow flexibly, and the research of interconnected systems are booming. The article proposes an H
∞ / H
- fault detection observer design means for a networked interconnected system. First, convert a linear networked interconnection systems to linear network systems. Next, the H
− index is accustomed to measuring the worst-case sensitivity to failure and H
∞ norm from ungiven inputs to residuals is accustomed to measure disturbance robustness performance, so that the residuals generated by the designed fault detection observer are sensitive to failures and robust to disturbance in finite-frequency domain. Besides, sufficient conditions of the observer design are derived and transformed by a set of LMIs. Lastly, numerical simulations are provided to demonstrate the correctness and effectiveness of the designed means.
A Test Method for Instructional Software of Evaluation and Exercise Based on Mobile Platform
Authors: Yu Jiujiu , Zhang Jishan , Chen Yun , Wu Ning , Mei Yingying , Zhang Deqing , Zhu Lili , Zhu Canglu , Sheng Yuliang
Published: September 2021

The research is based on the characteristics of instructional software of evaluation and exercise, taking a small examination system for driving license as an example, this study is devoted to exploring a feasible test method for instructional software of evaluation and exercise based on mobile platform. The test method consists of four testing phases of unit and integration testing, functionality testing, comprehensive testing and teaching evaluation which existed research of functionality testing for modern Android mobile Apps is fully used for reference. Furthermore, further work is put forward. Firstly, a special defect management database for bug definition and analysis needs to be established. Secondly, major test points in comprehensive testing phase should be converted to user stories in modern agile testing environment for software exploratory test methods positively.
Malicious Code Detection Method Based on Static Features and Ensemble Learning
Authors: Li Wei , Zhang Chenyi , Zhou Jieying , Wang Dan , Li Nuannuan
Published: September 2021

In recent years, malicious code emerges in endlessly. New types of malicious code evade the traditional malicious code detection technology through polymorphism, shelling, confusion and other ways. In order to solve the challenges brought by various technologies to the research of malicious code detection, in this paper, we propose a malicious code detection method based on static features and ensemble learning. This method extracts the information in the PE header file of malicious code samples as static features, and on this basis, builds a malicious code detection model by using stacking ensemble learning. In order to verify the effectiveness of the model, experiments are carried out on a dataset containing 5000 malicious codes and 4943 benign codes. Experiments show that the classification model based on stacking ensemble learning is the best, with 97.22% precision rate and 96.45% stable F1 score.
Vehicle detection base on Road Segmentation for surveillance satellites video
Authors: Meng Yun , Wang Xiaoyong , Yuan Shuai
Published: September 2021

With the development of data acquisition ability, the LEO satellites can work with a surveillance camera on board by focusing a particular area for dozens of seconds, so it drives us to develop some applications based on this data. In this paper, a data preparation method named DERS (Density Estimation based Road Segmentation) is proposed to divide the road from background to reduce the worse impact from relative-motion and then improve the precession and recall for vehicles for surveillance satellite video sequence. DERS has three main steps: frame difference, morphology, pyramid iteration, segmentation. Our experiments with the satellite videos from SkySat-1, and compared between Gaussian Mixture Model (GMM) and GMM with Ders shows that the precision rate and recall has improved of the traditional method with Ders, the precision of GMM with Ders has been improved from 64.42% to 88.71, the recall of GMM with Ders is from 76.21% to 74.16%, and the F-Score has been improved from 69.82% to 80.79%.
Automatic Diagnosis of COVID-19 Using a tailored Transformer-Like Network
Authors: Liu Chengeng , Yin Qingshan
Published: September 2021

The emergence of the novel coronavirus(COVID-19) has left disastrous effect on global health and individuals. Even though in most areas, the RT-PCR test used as the dominant approach for diagnosis of COVID-19 has shown good accuracy, the test requires equipment, personnel and it is time-consuming. Researches have shown the effectiveness of X-ray images for predicting COVID-19. In this study, we applied a transformer-like deep-learning model on this problem with transfer learning technique, to diagnose X-ray images as COVID-19 or normal. The model outperformed the CNN SOTA. The model achieved a classification accuracy of 99.7% on the targeting dataset.
The Research and Implementation of Electronic Device Detection System Based on STM32H7
Authors: Li Chao , Zhang ShaoYang
Published: September 2021

In engineering practice, it is often necessary to test the performance of electronic devices. In order to provide a convenient and fast solution for the job of electronic device detect, this paper has designed and implemented an electronic device test system based on STM32H7 platform. This system can output frequency and amplitude controllable sinusoidal wave, collect and monitor the output voltage of electronic device in real time, analyze and judge the detection results automatically, and display the detection results in real time through the combination of sound and light. In this paper, through the experimental verification, the system can achieve the detect function of electronic device, it has the advantages of simple operation, high accuracy, great reliability and strong scalability.
Industrial Control System Defense Decision-Making Method Based on Dynamic Attack-Defense Game
Authors: Zhang Shenyi
Published: September 2021

Industrial control systems are different from traditional information systems and require extremely high system availability. In the face of potential attack incidents, blind defense deployment may affect system performance and efficiency, which is counterproductive. For this reason, this paper proposes a defense decision-making method for industrial control systems based on dynamic attack-defense games. This method first analyzes the actual attack-defense confrontation scenarios in the industrial control system, and constructs a dynamic attack-defense game model based on this. After that, an industrial control system attack-defense game tree defined on this model is constructed, and the game tree is used to describe the specific attack-defense confrontation process. Then the backward induction method is used in the game tree to find the equilibrium path. The information on this path is of great reference value for defenders to choose a reasonable and efficient defense plan, maintain system stability and reduce defense costs. At the end of this paper, this method is applied to a real limestone powder preparation system for experiments. The experimental results verify the effectiveness of the method in actual scenarios.
Infrared moving dim point target detection based on spatial-temporal local contrast
Authors: Zhao Bendong , Lu Fengshun , Hu Xingzhi , Liu Duoneng , Wang Wenkai
Published: September 2021

Aiming at the detection of dim and small moving point targets in space-based infrared (IR) imaging systems, a detection method based on spatial-temporal local contrast (STLC) is presented. Firstly, use the spatial and temporal information to estimate the background gray value of each pixel, and obtain two prediction results respectively. Then, the spatial local contrast (SLC) and the temporal local contrast (TLC) are defined according to the original image and two estimated backgrounds. Next, the STLC is defined as the product of SLC and TLC. Finally, a threshold value is set in the STLC for target detection. Four groups of experiments are conducted, and the simulation results indicate that the proposed method has a great advantage in terms of background suppression factor (BSF) and gain of signal-to-noise (GSNR).
Emergency valve fault location based on improved optimal binary tree support vector machine
Authors: Cui Yupeng , Liu Ze
Published: September 2021

This paper presents a new algorithm IOBT SVM (improved optimal binary tree support vector machine) to locate the fault of the passenger train emergency valve and improve the test efficiency of the brake system. To reduce the classification classes and identify different faults, every two carriage faults are merged into one class and multiple sets of air pressure curve characteristics are extracted in sections. Furthermore, the structure of the classification tree is constructed from the leaf nodes to the root node combining with the class separability between every two classes. Every two most similar classes are selected in turn for classification and will be merged into one class after classification until the last class is left. Experimental results show that the structure of the classification tree generated by this algorithm is reasonable, which improves the efficiency and accuracy of emergency valve fault location.
Design of Water Quality Monitoring Visualization System Based on Cloud Platform
Authors: Huang Ziheng , Tang Bin , Wang Renjie , Zhao Mingfu , Chen Qing , Miu Junfeng , Liu Keyan
Published: September 2021

In response to the problem of domestic and severe water pollution control, in order to help the water environment monitoring department, this paper proposes a structure of B/S/D as architecture, Spring Boot + MyBatis + MySQL + CXF for the programming framework Water quality visual cloud monitoring system. The system abandoned the traditional method using Spring/Spring MVC as a program framework, using the Spring Boot new frame structure, which greatly enhances the readability of programming simple and program, and changed the lengthy, low running rate, and front end of the web page. Slow disadvantages. The system is actually developed, and the deployment results show that the system operates stable, and can perform long-term continuous, real-time monitoring, and can send real-time data and historical data to the information terminal to provide reliable cases and solutions to strengthen water quality supervision.
Live weight and digestibility of feed nutrients when using amino acids and silicon in the diet of broilers
Authors: Mustafina A S , Sizova E A , Kholodilina T N , Mustafin R Z , Klimova T A
Published: September 2021

The article presents the results of a study to determine the effect of ultra-large silicon dioxide and amino acids (arginine + lysine+methionine) both separately and in a complex on the live weight of broiler chickens. The average daily gains, as well as the coefficients of digestibility of the main components of feed were determined. It was found that the combined use of UFP SiO2 and amino acids increased the live weight of the experimental bird for 21 days by 6.1% (p≤0.05), and by the end of the experiment for 12.3 % (p≤0.05). The average daily increase 83.18 g in live weight was noted by the end of the experiment, which is 12.77 grams higher than in the control group. There was also an increase in the digestibility coefficients of dry matter of mixed feed by 4.2% (p≤0.01), organic matter – by 3.7% (p≤0.05), crude fat and crude protein - by 3.4% (p≤0.05). Thus, the data obtained in the experiment are consistent with the results of other researchers describing the effect of preparations of various ultra-large metals and amino acids on the productivity of poultry.
Early ripening variety of spring soft wheat Kanskaya
Authors: Sidorov A V , Neshumaeva N A , Plekhanova L V , Fedosenko D F , Bogdanov V V
Published: September 2021

The forest-steppe zone, where the main sown areas of spring wheat are located, is characterized by a short frost-free period and frequent repetition of the spring-summer type of drought. The purpose of this research was to create an early ripening variety of spring wheat that combines high productivity and drought resistance. The article presents biological, agronomic, genetic, technological and morphological characteristics of the new variety. The experimental part of the work was carried out on the experimental fields of the Krasnoyarsk Scientific Research Institute of Agriculture. The predecessor is fallow, the seeding rate is 5 million germinating grains per hectare, the soil is leached chernozem. Standard varieties - early ripening Novosibirskaya 15 and mid-early Altayskaya 70. Variety Kanskaya was created by the method of intraspecific hybridization and subsequent individual selection from a hybrid population (Altai 98 x G-17-1) x Altai 98. Since 2015, the variety is included in the State Register of Breeding Achievements of the East Siberian region. Kanskaya belongs to lutescens type. The variety is medium early. In terms of the growing season, it occupies an intermediate position between Novosibirskaya 15 and Altayskaya 70. For four years of competitive variety testing (2011-2014), the average grain yield of the new variety was 31.8 c / ha (from 16.8 to 38.2 c / ha). Kanskaya surpassed Novosibirskaya 15 by 6.6 c / ha, Altai 70 by 1.1 c / ha. In the conditions of the Krasnoyarsk Scientific Research Institute of Agriculture, the variety showed resistance to brown rust and head smut. Has an individual allelic composition of gliadins and a high component composition in the electrophoretic spectrum. In terms of grain quality, the variety is classified as a valuable wheat.
Improving commodity lending to the agro-industrial complex as an element of the state support system
Authors: Zhichkin K , Nosov V , Zhichkina L , Eryushev M , Sleptsova L , Udovik E
Published: September 2021

The study relevance is due to the fact that commodity lending is a fairly new procedure for agricultural producers in the state support. The study purpose is to diagnose the mechanisms of commodity lending in the livestock industries in the Samara region AIC and to develop methods for their optimization. Today, the current state of commodity lending is largely a prerequisite for the development of the economy and an integral element of the country’s economic growth. At the same time, we propose to introduce a system of control over the use of funds aimed at commodity lending, grants, subsidies, state support, to optimize the purposefulness of the received commodity lending funds, in order to prevent their further use for other purposes. The recommendations will contribute to improving the process of commodity lending to the AIC, both in the medium and long term. Competent organization of the commodity lending process will ensure the uninterrupted and effective work of the SUE SR “Veles”, the Ministry of Agriculture, agricultural producers in the field of commodity lending and, as a result, will strengthen the economy of the region as a whole.
A way to increase crop science productivity
Authors: Grishin A P , Grishin A A , Grishin V A , Semenova N A
Published: September 2021

It is shown that in earlier studies, the results of which were reported in the article "Digital wireless mini-sensor of plant thermoregulation", the presence of three phases of the thermoregulation process was experimentally revealed, and the task of further research aimed at theoretical and experimental confirmation of the hypothesis was set that the maximum productivity of the plant is ensured at the optimum air temperature and the maximum consumption of the nutrient solution. A number of statements, which do not require proof, are presented about the relationship between photosynthesis and air temperature and their effect on productivity. The article sets the task of developing a universal algorithm for maintaining the air temperature. Thermoregulation processes of plants were chosen as the object of research. These included the temperature of the leaf surface, the temperature difference between the leaf surface and the ambient air, and the flow rate of water for cooling evaporation. Two plant samples were chosen as the subject of research: salad variety "Red Dubolistny" and tomato variety "Fighter" (Buyan). The change in the mass of water in the container was measured using an ML-A01 balance with a measurement accuracy of 0.01 g, and the temperature was measured with an infrared IR thermometer Kelvin IKS 4-20 / 5. Data were recorded at 5-minute intervals and repeated three times over a short period of time. The plants were placed in containers with an airtight lid, which contains an oil seal for the plant stem. Moreover, the roots were placed under a lid with an aqueous nutrient solution. At the boundary of phase 2, there is a zone of maximum consumption of nutrient solution through plants, while the temperature is optimal, which corresponds to the maximum rate of photosynthesis. Further, the object of research was the process of forming a productive mass in a plant, depending on the air temperature. The weight of the plant was measured separately, on more accurate scales of the ML-A01 brand with a measurement limit of 100 g and a graduation of 0.01 g, twice a day: in the morning and in the evening. Next, weight gain dependence graph on the air temperature was built and the trend was found in the form of a second-order polynomial, which had a maximum. Its coordinates are 29.29 Co and 0.75 g of green weight gain. The maximum increase is provided at an air temperature corresponding to the boundary of phases 2 and 3. From previous studies, we found this value, it was 29.36 Co, that is it differs by 0.2%.
Anderson localization induced by complex potential
Authors: Wang R , Zhang K L , Song Z
Published: September 2021

In the Hermitian regime, uncorrelated disorder potential in one-dimensional lattice induces Anderson localization, whereas quasiperiodic potential can lead to both localized and extended phases, depending on the potential strength. In this study, we investigate the non-Hermitian regime. We analytically and numerically study Anderson localization in a one-dimensional lattice with the non-Hermitian complex disorder and quasiperiodic potential. We present a non-Hermitian Su-Schrieffer-Heeger (SSH) chain and demonstrate that the Hermitian counterpart with full real spectrum is a standard Anderson chain, which indicates that a nonzero imaginary disorder on-site potential can induce standard Anderson localization. We further demonstrate that the non-Hermitian Aubry-André -Harper (AAH) model exhibits a transition in parameter space, which separates the localization and delocalization phases and is determined by the self-duality of the model. This indicates that a pure imaginary quasiperiodic potential plays the same role as a real quasiperiodic potential in the transition point between localization and delocalization. Notably, a system with complex quasiperiodic potential exhibits an interference-like pattern on the transition points, which arises from the interplay between the real and imaginary components.
OptM: estimating the optimal number of migration edges on population trees using Treemix
Authors: Fitak Robert R
Published: September 2021

The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called “OptM” and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix.
Is positron emission tomography enough to rule out cardiac sarcoidosis? A case report
Authors: Huang Siyi , Kunchakarra Siri , Rathod Ankit
Published: September 2021

Background

Cardiac sarcoidosis (CS) is associated with poor prognosis, yet the clinical diagnosis is often challenging. Advanced cardiac imaging including cardiac magnetic resonance (CMR) and positron emission tomographic (PET) have emerged as useful modalities to diagnose CS.

Case summary

A 66-year-old woman presented with palpitations. A 24-h Holter monitor detected a high premature ventricular contraction burden of 25.6%. She underwent two transthoracic echocardiograms; both showed normal results. Stress perfusion CMR did not show any evidence of ischaemic aetiology; however, myocardial lesions detected by late gadolinium enhancement (LGE) imaging raised suspicion for CS. While there was no myocardial uptake of fluorodeoxyglucose (FDG) in subsequent cardiac PET, high FDG uptake was seen in hilar lymph nodes. Lymph node biopsy confirmed the diagnosis of sarcoidosis.

Discussion

Cardiac magnetic resonance and PET imaging are designed to evaluate different aspects CS pathophysiology. The characteristic LGE in the absence of increased FDG uptake suggested inactive CS with residual myocardial scarring.
Maternal hypercholesterolaemia during pregnancy affects severity of myocardial infarction in young adults
Authors: Cacciatore Francesco , Bruzzese Giuseppe , Abete Pasquale , Russo Giuseppe , Palinski Wulf , Napoli Claudio
Published: September 2021

Aims

Elevated maternal cholesterol during pregnancy (MCP) enhances atherogenesis in childhood, but its possible impact on acute myocardial infarction (AMI) in adults is unknown.

Methods and results

We retrospectively evaluated 310 patients who were admitted to hospital and whose MCP data were retrievable. Eighty-nine AMI patients with typical chest pain, transmural infarction Q-waves, elevated creatinine kinase, and 221 controls hospitalized for other reasons were identified. The AMI cohort was classified by MI severity (severe = involving three arteries, left ventricle ejection fraction ≤35, CK-peak >1200 mg/dL, or CK-MB >200 mg/dL). The association of MCP with AMI severity was tested by linear and multiple regression analysis that included conventional cardiovascular risk factors, gender, age, and treatment. Associations of MCP with body mass index (BMI) in patients were assessed by linear correlation. In the AMI cohort, MCP correlated with four measures of AMI severity: number of vessels (β = 0.382, P = 0.001), ejection fraction (β = −0.315, P = 0.003), CK (β = 0.260, P = 0.014), and CK-MB (β = 0.334, P = 0.001), as well as survival time (β = −0.252, P = 0.031). In multivariate analysis of patients stratified by AMI severity, MCP predicted AMI severity independently of age, gender, BMI, and CHD risk factors (odds ratio = 1.382, 95% confidence interval 1.046–1.825; P = 0.023). Survival was affected mainly by AMI severity.

Conclusions

Maternal cholesterol during pregnancy is associated with adult BMI, atherosclerosis-related risk, and severity of AMI.
Integrated analyses of SNP-genotype and environmental data in a continuously distributed snapper species (Lutjanus johnii, Bloch, 1792) reveals a mosaic of populations and a challenge for sustainable management
Authors: Taillebois Laura , Davenport Danielle , Barton Diane P , Crook David A , Saunders Thor , Hearnden Mark , Saunders Richard J , Newman Stephen J , Travers Michael J , Dudgeon Christine L , Maher Safia L , Ovenden Jennifer R
Published: September 2021

Understanding population structure is important for predicting the outcomes of anthropogenic development, harvest, and environmental change on the abundance of fisheries species. Logically, the use of several data sources is best for delineating population structure, but combining across data types adds extra challenges as results can be incongruent. Here, genetic and environmental markers were used to test hypotheses about population structure in a collection of 523 tropical inshore finfish (Lutjanus johnii) across northern Australia from 20 locations. Redundancy analyses (RDA) were used to statistically test for correlations among data sources providing a basis for reconciliation and insights into ecological and evolutionary processes. Genomic data (10349 SNP loci) revealed low, but significant genetic divergence (max FST 0.0402) between 16 locations. A total of 12 putative contiguous biological stocks were proposed whose distributions were correlated with IMCRA bioregions but not to existing regions used for management of the fishery. RDA showed a significant correlation between environmental markers (otolith chemistry and parasite abundances) and SNP genotype in two of three region-wide analyses supporting the proposed stock structure, and suggesting that L. johnii may be locally adapted to the heterogeneous environment. These findings are significant for the conservation of the commercially and recreationally important L. johnii and can be used to guide future fisheries management actions.
Comparison of MICs in Escherichia coli isolates from human health surveillance with MICs obtained for the same isolates by broth microdilution
Authors: Suwono Beneditta , Hammerl Jens André , Eckmanns Tim , Merle Roswitha , Eigner Ulrich , Lümen Michaela , Lauter Sven , Stock Rüdiger , Fenner Ines , Boemke Eva , Tenhagen Bernd-Alois
Published: September 2021

Objectives

Human health surveillance and food safety monitoring systems use different antimicrobial susceptibility testing (AST) methods. In this study, we compared the MICs of Escherichia coli isolates provided by these methods.

Methods

E. coli isolates (n = 120) from human urine samples and their MICs were collected from six medical laboratories that used automated AST methods based on bacterial growth kinetic analyses. These isolates were retested using broth microdilution, which is used by the food safety monitoring system. The essential and categorical agreements (EA and CA), very major errors (VME), major errors (ME) and minor errors (mE) for these two methods were calculated for 11 antibiotics using broth microdilution as a reference. For statistical analysis, clinical breakpoints provided by EUCAST were used.

Results

Five study laboratories used VITEK®2 and one MicroScan (Walkaway Combo Panel). Out of 120 isolates, 118 isolates (98.3%) were confirmed as E. coli. The 99 E. coli isolates from five study laboratories that used VITEK®2 showed high proportions of EA and CA with full agreements for gentamicin, meropenem, imipenem and ertapenem. Additionally, 100% CA was also observed in cefepime. Few VME (0.5%), ME (1.9%) and mE (1.5%) were observed across all antibiotics. One VME for ceftazidime (7.1%) and 12 MEs for ampicillin (29.4%), cefotaxime (2.4%), ciprofloxacin (3.2%), tigecycline (1.5%) and trimethoprim (22.2%) were detected.

Conclusions

MICs from E. coli isolates produced by VITEK®2 were similar to those determined by broth microdilution. These results will be valuable for comparative analyses of resistance data from human health surveillance and food safety monitoring systems.