bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Importance of Data Enhancement Technology in Power Document Understanding
Authors: Gao Ming , Wang Jiayan , Zhang Wenfei , Wang Dehui , Peng Zheng , Lu Hongzhi
Published: March 2021

With the rise and development of the Internet and the artificial intelligence boom, natural language processing has been greatly developed in various fields and industries, including power industry. Intelligent document understanding, as a sub field of natural language understanding, uses artificial intelligence technology to enable machines to have natural language understanding ability. It has always been the focus of researchers and industry, and it is also the core problem of intelligent semantic interaction. As a common method to expand the sample set, data enhancement technology is an important text and image processing technology. Its core purpose is to use limited data and produce value equivalent to that from much more data. This technology has been widely used in various fields of deep learning. This paper reviews the development of data enhancement technology, lists and discusses five kinds of text enhancement technology paths and their corresponding representative technologies, including back translation, random word replacement, non core word replacement, text enhancement based on context information, and text enhancement technology based on generative language model. It also analyzes the effectiveness of text enhancement technology in the aspects of regularization, transfer learning, improving model robustness, manifold and so on, to achieve the goal of improving the accuracy and effectiveness of document understanding in power industry.
Mobile Phone Remote Controlled Wastewater Treatment Equipment Used in Tribe
Authors: Chang Hsiao-Ming , Lin Chun-You , Chiu Mao-Chun
Published: March 2021

This study developed the data acquisition module which involves electronics control hardware and mobile phone APP software, the module could control and monitor wastewater treatment equipment by the APP software running on mobile phone for wastewater treatment in the tribe of eastern Taiwan. The APP user of the tribe can keep abreast of the situation of wastewater treatment and discharge to maintain the water resource with a good water quality.
Development of 2D MXene for Energy Storage
Authors: Fang Mengqi
Published: March 2021

The recently discovered large-scale two-dimensional (2D) early transition metal carbides and carbonitrides have captured people’s attention as hot topics, called MXenes. MXene is a two-dimensional layered solid, linked by strong metals, ions and covalent bonds, e.g. Ti2AlC, Ti3AlC2 and Ta4AlC3, usually by selectively etching the A element from the MAX phase to produce MXene. MXene has a an array of applications in all aspects cause its outstanding electrical conductivity, excellent specific surface area, good hydrophilicity and stability. In this article, the experimental and theoretical developments are reviewed in their synthesis, physicochemical properties and potential applications. MXene is expected to be applied in electrochemical energy storage and also provides current applications in batteries and super capacitor.
Actor-Critic Tracking with Precise Scale Estimation and Advantage Function
Authors: Wang Chuyao , Ling Yuchen
Published: March 2021

In this work, a deep reinforcement learning (DRL) method is proposed to address the problem of real-time object tracking. The adopted framework in this paper is based on the ‘Actor-Critic’ tracker (ACT), since ACT only considers the scale change instead of regression object boundary, which cannot adapt the object size variation. To this end, the ACT method is improved by using a more reasonable action space, which contains a left-top and right-bottom corner coordinates. Precise shape estimation is given by regressing the variation of width and height, respectively. Furthermore, to speed up the whole training and tracking process, the Advantage Function (AF) is adopted, and its performance is compared with ACT, ACT with improved action space (IAS), and ACT with IAS and AF. This method is tested on the OTB100 dataset to validate its effectiveness.
Research on the Application of Financial Robot under the Background of Next Generation Information Technology -- Taking Sinochem International as an example
Authors: Li Xun
Published: March 2021

With the development of the times and the progress of science and technology, the computerized accounting system can no longer meet the problem between the increasing human cost of most companies and the backward high-end talent pool. The transformation of the financial work system is imminent. The emergence of financial robots is undoubtedly an opportunity for the transformation of corporate financial work systems. It has attracted the attention of enterprises due to its unique process automation advantages. To mitigate above issue, this paper analyzes the development, application, and impact of financial robots in enterprises based on a brief introduction to the meaning and development of financial robots, and proposes relevant suggestions for introducing financial robots.
Generation of Handwritten Numbers Using Generative Adversarial Networks
Authors: Qin Zhaoxiang , Shan Yuntao
Published: March 2021

As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.
Long span ranging system based on aircraft vision
Authors: Sun Boyang , Sun Jianmei
Published: March 2021

At present, the transmission line in the field has a large potential safety hazard, and professionals often encounter difficulties in patrol inspection and distance measurement. In view of the current situation of safe distance between equipment in power grid construction, this paper proposes a long-span distance measurement method based on aircraft vision, which can automatically achieve high-precision distance measurement between equipment, and ensure personal safety, avoid equipment failure and other problems. Then the long-span ranging technology based on aircraft vision is analyzed, and the research and application of the given technology are discussed.
Bone Age Estimation with X-ray Images Based on EfficientNet Pre-training Model
Authors: Hao Guoyao , Li Yifei
Published: March 2021

Human bones have different characteristics in different development stages, so the estimation of bone age can reflect the growth and development level of individuals relatively accurately. Bone age estimation aims to predict the biological age of children, which plays an important role on the diagnosis of some pediatric endocrine diseases. Tradition methods are carried out by doctors, and it is not effective in accuracy and speed. To this end, we proposed a deep-learning based method for bone age estimation. Based on the training set of more than 10000 X-ray images of hand bones from Radiological Society of North America (RSNA), this paper studies the processing, segmentation, feature extraction of X-ray hand bone images by using computer image processing and artificial intelligence learning methods, and uses convolution neural network to process samples and analyze them automatically. The main research work and achievements are as follows: (1) Pre-processing of X-ray hand bone image, unifying the size and cutting, reducing the image area without hand bone; (2) The gray-scale image is transformed into a three-channel image, and pre-processing by EfficientNet of ImageNet. Then convolution neural network is used to learn the features of X-ray hand bone image and evaluate it automatically. Finally, the network is evaluated by the minimum mean square error, so that the minimum mean square error is as close as possible to the minimum value. Through the neural network, the bone age from X-ray hand bone image can be quickly judged, and then it can be applied to clinical research.
Foreground detection of human targets with abnormal behavior of mechanical operators
Authors: Luo Hongqi , Liu Zhixin , Liu Xia
Published: March 2021

Complexity of mechanical production environment has brought a certain degree of interference to human target recognition, so it is necessary to preprocess the target video sequence before target detection. The advantages and disadvantages of various detection methods are compared. Research results show that the inter frame difference method has strong adaptability and the target contour is discontinuous. The optical flow method is suitable for the case of lens motion, which is complex in calculation and poor in noise resistance. ViBe algorithm has good effect in obtaining the target contour, clear edge and less holes, and the operation efficiency is not high. The background subtraction method basically retains the features needed by the moving foreground target behavior recognition and detection, and the processing speed is very fast, which can meet the requirements of real-time processing.
Dynamic risk assessment of oil and gas leakage in heating furnace: A DBT-DBN approach
Authors: Wang Mingliang , Zhang Mingguang
Published: March 2021

The heating furnace has dynamic fluctuations of hazard factors in the actual circumstance and is the core equipment of hydrocracking unit, which is the main equipment of petrochemical enterprises. Owing to the limitation of conventional risk analysis approaches, a dynamic risk analysis approach based on dynamic Bow-tie model(short for DBT) and dynamic Bayesian network(short for DBN) was proposed. Taking the failure accident of oil and gas leakage of heating furnace as a case study, the timing sequence of equipment failure of heating furnace is analyzed first, the risk evolution process is obtained, and the dynamic tie model of accident is established. By mapping the dynamic tie model to the dynamic Bayesian network, the risk factors of oil and gas leakage in heating furnace are deduced in two-way reasoning. The dynamic risk trend of heating furnace was predicted eventually, the probability of oil and gas leakage caused by heating furnace is about 1.26E-07 after two year. Through diagnostic reasoning, 6 key factors and 3 accident paths were determined. The results are in accordance with the actual situation, proving that DBT-DBN model can effectively reflect the dynamic characteristics of accident scenarios and provide reference for the risk assessment of petrochemical enterprises.
Research on Service Quality Evaluation Model of Medical Institutions Based on Dempster-Shafer Evidence Theory— Taking Jiangsu Province, China as an Example
Authors: Yu Benhai , He Chuang
Published: March 2021

From the perspective of patients and medical insurance agencies, the paper used the fuzzy rough set method to select the indexes and established the service quality evaluation index system of medical institutions. Using Dempster-Shafer evidence theory to synthesize the subjective weights and the objective weights, the combined weights of the evaluation indexes were obtained. Combining grey relational analysis and Dempster-Shafer evidence theory to establish a regional medical service level evaluation model in an asymmetric environment. The model can accurately identify the quality of medical service in various regions. We can compare and analyze the service quality of medical institutions in various regions from different dimensions, and provide a theoretical basis for each region to formulate policies related to the rational allocation of medical resources.
Research on the Application of System Engineering Theory in Graphic Creative Design
Authors: Zhang Hongqiong
Published: March 2021

“System engineering” is one of the most important academic terms contemporarily. It is to make reasonable use of all resource conditions, optimize the configuration of the structure, and conduct process management to scientifically use resources. Through the practical process of research-based sketching and design transformation, the article uses plant ecological structure as the original experimental sample, and based on system thinking, explains the deconstruction, reorganization and final visual presentation of graphics from multiple dimensions of overall, three-dimensional, structural and dynamic. Explore and put forward a comprehensive hierarchical system model of “objective-deconstruction-reorganization-optimization and integration” to optimize graphic design behavior patterns and improve the effectiveness of design strategies.
Application of Knowledge Service in Integrated Supply Chain of Agricultural Products based on Knowledge Graph
Authors: Yang Bo , Mengying Yang
Published: March 2021

Based on the review of the existing research, this paper summarizes the application of knowledge services in supply chain. Using CiteSpace software for visualization analysis, it can be found that there is a lack of cooperation between research authors and institutions of agricultural products integrated supply chain, but the research content is relatively relevant. “Internet+” and “fresh agricultural products supply chain” will be the frontier hot spots of agricultural products integrated supply chain in the future. Therefore, enterprises need to combine some big data technology to further integrate and optimize the agricultural product supply chain.
Network Risk Propagation Model Based On Boundary-Center-Point-Search Algorithm
Authors: Ping Guo
Published: March 2021

Because the power CPS network with community structure has the unique structural characteristics of internal nodes and boundary nodes, the boundary nodes are more likely to cause cascading failures when attacked, which plays an extremely important role in network risk fault propagation. In this paper, the Boundary node is regarded as an important immune object, and these key node targets are quickly identified to take immune measures. A Boundary center-point-search (BCS) algorithm is proposed. According to the disadvantage of the original risk propagation model which ignores the fact that normal nodes can get immunity through artificial means, a risk propagation model is proposed. Compared with other local immune algorithms, the proposed BCS immune algorithm not only can accurately identify the boundary center points between communities, but also has a faster computational speed, which can provide reference for risk propagation control and prevention in large-scale complex networks with incomplete structural information.
Emotional Response Generation in Multi-Turn Dialogue
Authors: Jinyao Yang , Wu Chunhua
Published: March 2021

The research on emotional response generation is one of the directions in dialog system research. However, most of the researches on emotional dialog generation focus on the expression of expected emotion in single round dialogue. In this paper, we propose a method to model the emotion in multi-turn dialogue. Our model bases on a proposed model: Emotional Chatting Machine, and solves the problem of ECM model that which emotion should be used to the response. We compared the effects of various models and finally chose LSTM to model emotions in multi-turn dialogue. Experiments show that the performance of the dialogue system can be effectively improved by modelling emotion in multi-turn dialogue.
An Overview of Entity Relation Extraction
Authors: Xie Yixuan
Published: March 2021

Plenty of digital text is generated and shared in nowadays life, which constitutes many unstructured text resources. An automatically information extraction method is highly in demand as a large amount of useful information can be stored and output through it. In this paper, several basic concepts about entity relation extraction are introduced, including word embedding, positional embedding and convolutional neutral networks. Several types of relation extraction are also discussed: supervised relation extraction, relation extraction using distant supervision and relation extraction using few-shot approach. In addition, the existing challenges and problems are discussed, like overlapped triples, wrong labeled data and the difficulties that few-shot learning approaches are now facing.
Implementation of Personalized Recommendation Algorithm based on Mahout
Authors: Yang Deguo , Shi Qing
Published: March 2021

In the Information Age, the success of companies and individuals increasingly depends on how quickly and efficiently they can turn massive amounts of data into actionable information. This paper first introduces the definition of recommendation algorithm and the process of collaborative filtering recommendation algorithm, then introduces the classification of algorithms in the Apache Mahout Framework, finally, the principles of user-based personalized recommendation algorithm and project-based personalized recommendation algorithm in Mahout framework are described and implemented respectively.
Lung Nodule Classification in CT Images Using 3D DenseNet
Authors: Zhang Ge , Lin Lan , Wang Jingxuan
Published: March 2021

Lung cancer is the main malignant tumour affecting the health of residents in China. Automatically discriminating benign and malignant pulmonary nodules can facilitate the early detection of lung cancer, which reduces lung cancer mortality. The rising quantity of public available lung CT datasets made it possible to use deep learning approaches for lung nodules malignancy classification. Unlike most of the previous models that focused on 2D convolutional neural nets (CNN), here we explore the use of the DenseNet architecture with 3D filters and pooling kernels. The performance of the proposed nodule classification was evaluated on publicly available LUNA16 dataset, a subset of lung image database consortium and image database resource initiative dataset (LIDC/IDRI). It achieved a 92.4% classification accuracy. The proposed method provides an independent module with encouraging prediction accuracy that can be easily incorporated with a lung cancer computer-aided diagnosis system.
Research on sentiment classification of micro-blog short text based on topic clustering
Authors: Tu Shifen , Yang Bo
Published: March 2021

Aiming at the shortage of research on micro-blog short text fine-grained sentiment classification, a fine-grained sentiment classification method about micro-blog short text based on PLSA model and K-means clustering model was proposed. PLSA is used to calculate the probability matrix between documents and topics, words and topics in the corpus. In terms of the probability distribution of words and topics, K-means algorithm is used to cluster the probability distribution of words on topics and merge the similar topics. Based on the sentiment ontology library, emotion recognition is carried out for the merged topics. Then, according to the merged document and topic probability matrix, the document sentiment category is classified. The experimental results show that the sentiment analysis method integrated with PLSA and K-means can obtain higher classification accuracy than the PLSA model method alone.
Quantum Max-min Ant Colony Algorithm For QoS Routing Problem
Authors: Li Yalong , Wei Wang , Jing Sun , Jie Wan
Published: March 2021

Aiming at the ant colony algorithm is weak to solve the problem of QoS routing, this paper proposes a routing algorithm that combines quantum computing with the max-min ant colony algorithm. The algorithm introduces a quantum selection strategy, uses qubit probability amplitude to encode the path, and dynamically adjusts the quantum rotation gate to update the path quantum coding on the basis of the minimum cost to meet the constraints and other optimal conditions, combined with the maximum and minimum pheromone determination mechanism to find the optimal path, effectively control the evolution speed and direction, and accelerate the algorithm convergence. Experimental results show that the algorithm is effective in controlling the speed and direction of evolution, and has better optimization ability and convergence performance than ant colony algorithm.
An Algorithm for Detecting the Integrity of Outer Frame Protection Net on Construction Site Based on Improved SSD
Authors: Fan Yu , Chen Xianqiao , Xie Jinguang , Fu Zhenjie
Published: March 2021

In recent years, the continuous development of the basic construction industry has promoted the progress of urban social economy, and the safety issues in construction have attracted more and more attention. The integrity of the external protection net is of great significance to the safety of the construction personnel. Most of the traditional methods of detecting damage to the protection net have problems such as poor real-time performance and low detection accuracy. In this paper, we present a novel approach based on SSD for solving the problem of the integrity detection of the external frame protection net in the construction site. The improved network merges adjacent feature layers to enrich the semantics of shallow feature maps, thereby improving the detection accuracy of the network, and simultaneously introduces a shallower feature layer and a more advanced receptive field module to improve the detection ability of small targets. The experimental results show that compared with the SSD algorithm, the improved algorithm has higher detection accuracy in the detection task of the external protective net on the construction site, and can meet the real-time detection requirements.
BiGRU-Multi-Head Self-Attention Network for Chinese sentiment classification
Authors: Wang Shitao , Li Jiangfeng , Hu Defeng
Published: March 2021

The rapid development of Internet and the extensive user base have led to a significant increase in the propagation speed of information and public opinion. Quick and accurate sentiment analysis of public opinion can help analyze and manage public opinion. Since BiGRU can simplify network parameters, and the Multi-Head Self-Attention(MHSA) mechanism can learn long-distance features more accurately and quickly. In this study, we propose a BiGRU-MHSA network to perform Chinese sentiment classification. The innovation of the model lies in the combination of BiGRU and MHSA in order to focus on several semantic centers and comprehend the whole text more precisely and efficiently. At the same time, we use the Chinese comment datasets on Weibo to verify the effectiveness and accuracy of the model. From the experimental results, we find that our proposed model improves the accuracy by about 1% on different comments datasets without consuming too much computing resources.
Intelligent classification of breast cancer based on deep learning
Authors: Liao Yuhang , Peng Yulan , Liu Dongquan , Liu JingYan
Published: March 2021

How to use artificial intelligence to assist physicians in analyzing complex breast cancer medical data has become an urgent problem to be solved. From the perspective of auxiliary diagnosis, we propose a method to classify breast cancer grades by cases. Based on the Chinese electronic medical records of breast cancer provided by the Department of Ultrasound at West China Hospital, we compared neural network methods, including FASTTEXT, TEXTCNN, TEXTRNN. And proposed the N-LM-ATT model. N represents the n-gram feature, LM represents the Long Short-Term Memory (LSTM) network, and ATT represents the Attention Mechanism (AM). This article focuses on the classification of Chinese breast cancer text data based on natural language processing (NLP) method. By comparing the above methods, the N-LM-ATT model achieved the best performance on the breast cancer ultrasound dataset of West China Hospital.
Multi-task Learning Network based on Attention for Aspect-Based Sentiment Analysis
Authors: Dong Yang , Wang Jing , Wang Junwei
Published: March 2021

Aspect-based sentiment analysis is to analyse the sentiment polarity of the aspect in a specific context. The previous methods mostly use the working mode of pipeline model, and internally use Recurrent Neural Network to predict the sentiment polarity of the aspect. This way clearly defines the order of aspect extraction and aspect-level sentiment classification, which will lead to the problem of error transmission. And traditional models have great limitations. In this paper, we propose a multi-task learning network based on attention (MNA), which uses shared word embedding to transmit to downstream tasks, allowing extraction and classification tasks to be processed simultaneously. We design a network architecture based on attention mechanism, and use the improved multi-head attention mechanism to transfer information from extracted tasks to classified tasks. Experimental results on three benchmark datasets show the effectiveness of MNA model.
Performance Comparison of Android Malware Detection Methods
Authors: Wang Yuandi
Published: March 2021

With the growth of Android apps market share, there are more and more kinds of malicious apps for Android system. At the same time, the method for malware detecting is evolving. In order to deal with the growing threat of malware, many detecting methods are combined with machine learning algorithms. In this paper, we compared the ability to detect malware of five algorithms on a dataset about Android apps networking. The result revealed that the networking data could be used as a reference for the classification of Android apps. Moreover, among the five algorithms used in this paper, the ensemble learning algorithm using the decision tree as base leaner performed the best in the comparison. It reached more than 75% accuracy when predicting malware using the test set.
Road Information Detection Method Based on Deep Learning
Authors: Zheng Shangsheng , Zhang Jiangzhou , Che Xiaobo , Li Yanqiang
Published: March 2021

Autonomous driving technology has developed rapidly in recent years, and computer vision has shown significant role in automated vehicles. Researchers use the combination of computer vision and deep learning to improve the speed and accuracy of road element detection technology. This article focuses on the semantic segmentation and road detection elements, especially in lane line segmentation and signal light detection. It summarizes the visual inspection technology and a series of visual perception algorithms based on deep learning. Although semantic segmentation and target detection technologies in general scenarios are already very mature, they are not ideal when applied to autonomous driving environments, especially where segmentation and detection are performed simultaneously. Therefore, this paper builds a fusion network of lane line segmentation and signal light detection suitable for autonomous driving scenarios.
Research on improving LIO-SAM based on Intensity Scan Context
Authors: Wang Chao , Zhang Guobao , Zhang Ming
Published: March 2021

During indoor mobile mapping, localization plays an important role. It is difficult for IMU, odometer, Lidar or SLAM algorithm singly to meet the high efficiency, real-time, accurate and robust performance. So the fusion of the data measured by these different methods is a current research hotspot. However, most of the researches still focus on the loosely coupled fusion based on filtering methods, and the data cannot be fully utilized. In this paper, based on LOM-SAM framework, the Intensity Scan Context is introduced to extract keyframes and detect loop closure which will provide a closed-loop factor, together with IMU pre-integration factor and Lidar odometry factor to construct the factor graph. Then incremental smoothing optimization algorithm is used to get high precision trajectory, realize the tightly-coupled Lidar and IMU positioning. The results show that the number of keyframes is reduced, the elevation error is effectively decreased, and the real-time performance is improved without decreasing the accuracy of LIO-SAM.
Recognition of Multi-Intersecting Features of Boundary Representation CAD Model
Authors: Zhu Jiang , Xie Da , Tanaka Tomohisa
Published: March 2021

This research aims to solve technical problem of automatic feature recognition in a Computer Aided Process Planning system. This feature recognition method was developed to represent a 3D model as classes of manufacturing features based on graph theory. In addition, “accessibility” was proposed to recognize intersecting features with complicated graph structure which cannot be predefined by users. The experimental result shown in case study proofed that this proposed method is effective in multi-intersecting feature recognition.
Port Energy Level-Evaluation Research on the Coordinated Development of Hinterland Economy-Taking Chongqing Port as an Example
Authors: Deng Qiwei , Yu Haihong
Published: March 2021

With economic development, ports assume more responsibilities. However, the level of coordination between ports and hinterlands in my country’s inland river ports needs to be improved. By constructing a comprehensive evaluation system of port energy level-hinterland economic system, entropy method and coupling coordination Degree model for coordination analysis. The article takes Chongqing Port as an example to study the characteristics of coordinated development between port energy level and hinterland economy from 2010 to 2019. The results found that: 1) The coordination evaluation model effectively reflects the development status of Chongqing Port in the past 10 years; 2) With the increase of time, the port energy level and hinterland economic level of Chongqing Port are in a relatively stable state of improvement; 3) Port energy The degree of coordinated development between the economic system of the Hinterland and the hinterland shows a steady upward trend overall, but there is still room for improvement.
Heavy ion collider NICA at JINR
Authors: Kostromin S. , Sidorin A.
Published: March 2021

Main scientific goal of the NICA/MPD project
(Nuclotron-based Ion Collider fAcility/ Multi Purpose Detector)
under construction at JINR (Dubna) is exploration of the phase
diagram of strongly interacting matter in the region of high
compression. The planned experimental program includes studies of
possible signs of the phase transitions and critical phenomena in
heavy ion (up to Au) collisions at the center-of-mass energies up to
11 GeV/u. The collider experiment provides optimum conditions for
efficient energy scan measurements. Attainment of the required
average luminosity of the order of 1027 cm-2·s-1
for such physics experiment faces several accelerator physics and
technology challenges. Contrary to high energy colliders, the
luminosity of NICA case will be mostly limited by the Laslett tune
shift, while the beam-beam tune shift parameter will be negligibly
small. Flexible operational procedures for the beam storage and
short bunch formation have been developed to provide maximum peak
luminosity over wide energy range. Beam cooling is critical to
counteract luminosity decay due to intra-beam scattering. NICA
collider design addresses these issues and satisfies all the
requirements for the colliding beams experiments.
Making farming more sustainable by helping farmers to decide rather than telling them what to do
Authors: Kröbel R , Stephens E C , Gorzelak M A , Thivierge M-N , Akhter F , Nyiraneza J , Singer S D , Geddes C M , Glenn A J , Devillers N , Alemu A W , St. Luce M , Giardetti D
Published: March 2021

In Canada, the agricultural sector has long held a prominent economic, social and cultural position, from substantial evidence of extensive fishing and farming since the times of the first human settlements, to currently accounting for over 100 billion dollars of production and employing 2.3 million people. Steady growth in agricultural production in the country over several decades, supported by strong investment in public agricultural science, has allowed an increasing supply of a wide variety of food and agricultural goods to be available both within the country as well as allowing for substantial exports abroad and deep integration of the Canadian agricultural sector into global markets. Along with securing continued productivity growth in agricultural output for the future, policy makers and public sector agricultural scientists in Canada have become increasingly concerned with managing environmental externalities associated with agricultural production in order to achieve the objective of sustainable intensification of the sector. However, the process of identification of the best tools and practices to improve the sustainability of the agricultural sector in Canada has evolved over time due to shifting research priorities and dynamic changes in the problems facing the sector. In this paper we discuss applied and direct-to-farmer agricultural science research initiatives that are focused on identification and implementation of best environmental management practices at the farm level. We believe that involving farmers directly in scientific research and communication of scientific results provides for a deeper understanding of agro-environmental externalities. It also allows farmers to find greater adoption potential in their specific farm system, thus combining both environmental and economic sustainability. We trace the history of public agricultural science engagement with Canadian farmers to address economic and environmental problems in the sector. We then provide examples of successful public sector projects based in applied agricultural science research that foster effective farmer/scientist collaboration, leading to improved agriculture sustainability in Canada.
Does tall vegetation warm or cool the ground surface? Constraining the ground thermal impacts of upright vegetation in northern environments
Authors: Way R G , Lapalme C M
Published: March 2021

Increased upright vegetation growth (i.e. trees and shrubs) in northern environments can profoundly impact ground surface thermal conditions through winter warming (e.g. enhanced snow trapping) and summer cooling (e.g. increased shading). The debate over these opposite effects emphasizes the need to better constrain net temperature impacts of upright vegetation on soils in northern environments. We generate a series of simulations with a widely-used permafrost model to partition the absolute warming and cooling impacts of upright vegetation on ground surface temperatures for a variety of shading scenarios, climates and surficial materials types (i.e. bedrock, mineral and organic soils). These scenarios simulate annual temperature differences between the air and ground surface caused by upright vegetation to provide likely ranges for the net effects induced by vegetation. These simulations showed that ground surface temperature warming in the winter mostly overwhelmed ground surface cooling in the thawing season even when simulations included extreme shading effects. Constraining the simulations to current best estimates of the possible summer cooling impact of vegetation yielded a dominant winter warming signal for most snow depths and climate types. Differences in the magnitude of air-surface temperature offsets between sites underlain by bedrock, mineral and organic soil highlights the importance of considering differences in unfrozen moisture content in areas where the ground freezes and thaws seasonally. The results of this study suggest that the net ground surface temperature impacts of increased snow trapping by vegetation will far exceed cooling caused by enhanced shading following increases in tall vegetation in most northern environments.
Management model in digital ecosystems
Authors: Burlov V , Grachev M
Published: March 2021

Rapid pace of digitalization of ecosystems in the modern world affect social and economic systems. State-of-the-art web technologies, a huge information system, have an increasing impact on human life. The information environment undergoes adverse effects during its functioning. To maintain functioning of the system information component, a properly constructed system and an adequate model of response to external effects are required. The response model in digital ecosystems will be designed with regard to three processes, namely: the problem formation process, the problem identification process, and the problem neutralization (elimination) process. An indicator of the efficiency of ecosystem functioning will be identification and neutralization of each problem. Simulation results confirmed the main trends in digital ecosystems functioning. The development and subsequent application of methods and techniques to achieve the set goal in a shorter time will always be relevant and in demand. To implement a rational approach to making managerial decisions, the leader responsible for digital ecosystem functioning can use network planning as a plan for the entire cycle of processes for maintaining the information security of the ecosystem as a whole.
Financial ecosystems: analysis and development paths
Authors: Evdokimova Y
Published: March 2021

This research aims to identify the role of ecosystems in the modern financial world. Banks are most actively involved in the formation of ecosystems, concentrating various offers around their traditional areas of activity. The study of ecosystem strategies used by banks revealed the areas that banks consider as the main ones when forming their own ecosystems. The reasons that caused an active interest in the formation of ecosystems by banks are studied. The analysis of the leading financial ecosystems of countries, including the Russian Federation, is made. The main models of banking ecosystems on the market and their features are shown. Factors that have a stimulating effect on the development of ecosystems are identified. Based on this information, General summary conclusions are formulated.
Opportunities for universities to use the German experience in the startup ecosystem development
Authors: Dorofeeva V V
Published: March 2021

The paper analyzes modern theoretical concepts of the startup ecosystem. The subjects of the startup ecosystem are studied, and their main functions are presented. The current state of innovation ecosystems in German universities is analyzed, and the prospects for their development are determined. The essence of the main components of university innovation ecosystems is revealed, and the principles of their formation and implementation are determined. The features of the innovation ecosystem development in German universities are investigated. The directions of the innovation ecosystem formation in Russian universities are shaped.
Energetic and ecological justification of RE-hybrid systems for vulnerable ecosystems
Authors: Elistratov V V , Denisov R S
Published: March 2021

About two-thirds of the territory of Russia, which covers 11 million km2, lies in a region with severe climatic conditions. These northern ecosystems have a unique climate and are supplied with supplies by outdated diesel generation. Therefore, an urgent need has arisen to modernize the power supply systems in question using modern energy-efficient technologies, including power plants based on Arctic renewable energy sources that convert the high natural potential of renewable energy on these territories. For example, the coastal territories of the Russian North are situated in the zone of high wind power potential. In the article a method of modernization through the use of modular hybrid energy complexes are proposed. In total, about 4,700 such stations can be installed in Russia, and the total environmental effect from them will be about $1 million. Moreover, these power plants have a high science intensity. The problem of correct optimization of the parameters and architecture of wind-diesel power plants is also investigated in the article.
Power supply of the center for development of gifted children in Kaliningrad region based on renewable energy sources
Authors: Beley V F , Zadorozhnyy A O , Kiryukhin A A , Yurkov S V
Published: March 2021

One of the modern vectors of world energy development is the transition to an energy self-sufficient principle of functioning of geographically limited local facilities. The paper presents an analysis of the possibility of the transition and the concept of using renewable energy sources in the transition to an autonomous mode of electricity and heat supply of the Center for the Development of Gifted Children in Kaliningrad Region.
Dermacentor reticulatus biorhythms in the northern forest-steppe of the Tyumen region
Authors: Glazunov Yu V , Glazunova L A , Vinogradova Yu A
Published: March 2021

To forecast the number of ticks and take successful measures for decreasing them in population it is necessary to know the features of their phenology in aparticular region. The majority of acarologists pays attention to the features of Ixodes persulcatus activity as they transmitixodic infections. The proved epizootic and epidemiological importance of Dermacentors dictates the necessity of developing and organizing the comprehensive control system against this specie of ticks to prevent diseases. This demand the fullest and comprehensive studying of ecology and phenology features of Dermacentor reticulatus of a particular region. The aim of the research is the detailed studying of bioecology of Dermacentor reticulatus ticks, the most widespread ticks in the Tyumen region. We had monitored over the life cycle features of Dermacentor reticulates in natural biotopes over the 2013 to 2014 period in the subzone of the northern forest-steppe of the Tyumen region. In natural climatic conditions of the specified climatic zone Dermacentor reticulatus are capable to give one generation per a parasitizing season if parasite juvenile forms meet feeders. The whole process of reproduction from egg up to the adult specimen emerge took from 69 to 161 days, and averagely 115±1.99 full days. Metamorphosis of larvae had taken the shortest time period (29.1±2.,0 full days or 25.3% of all cycle time), nymph and imago formations had happened practically for equal periods of 34.5±2.3 and 37.0±5.0 full days respectively. Having studied the regularities of the Dermacentor reticulates life cycles we can prove the emergence and the activity peaks of both juvenile, and adult phases of these ticks activity. That allows to plan actions on controlling these parasites in places of their dwelling.
Main control factors and distribution of carbonate reservoirs in the Hexiwu structural belt
Authors: Zhao Lei
Published: March 2021

The hydrocarbon accumulation conditions of carbonate buried hills in Hexiwu structural belt are superior, and the hydrocarbon accumulation is controlled by multiple factors such as stratum, structure and reservoir, so it is the focus of oil and gas exploration to find high quality reservoirs. Through the analysis of drilling, logging and core data, it is clear that the carbonate reservoir space in Hexiwu structural belt is mainly composed of intergranular pore, tectonic dissolution fracture and tectonic fracture. Sedimentary facies, diagenesis and tectonic movement are the main controlling factors of carbonate buried hill reservoir formation. The favorable sedimentary facies belt is the foundation of reservoir development, the favorable diagenesis is the guarantee of reservoir development, and the favorable tectonic movement is the key to reservoir development. Through the stratified section to carry out comprehensive reservoir prediction, the accuracy of reservoir prediction results is significantly improved, which provides a foundation for oil and gas development.
Short-run and long-run Granger causality analysis of the United Kingdom’s trade patterns in the fossil energy trade
Authors: Hong Yu , Chen Chaojie , Yan Dong , Hu Aijing
Published: March 2021

This study describes the trade pattern of UK fossil energy in terms of imports and exports from 1962 to 2019 and deeply explores the internal causality between the comparative advantage of export and policy interventions including import restriction and export promotion. We document that the UK has been deliberately promoted the import of fossil energy. Granger causality tests show that in the short-run, the policy intervention in import is respectively affected by the policy intervention in export and the comparative advantage of export negatively. Moreover, there are Granger causality running from comparative advantage of exports to import restriction with negative effect in the short-run and with positive effect in the long-run. When it comes to the relationship between policy intervention in export and import, export promotion causes import restriction negatively in the short-run and long-run.
Suitability Evaluation of Seawater Source Heat Pump in a Seashore Refrigeration Station
Authors: Ji Yunzhe , Li Jintian , Wang Xiaojie
Published: March 2021

In this paper, the suitability of using seawater source heat pump in a seaside refrigeration station in Qingdao was evaluated. A systematic evaluation was made on the conventional chiller with cooling tower system, the conventional chiller with titanium plate heat exchanger system and the seawater source heat pump system, including energy saving evaluation, economic evaluation (initial investment cost and operating cost) and environmental evaluation. The evaluation results showed that the initial investment cost was slightly increased, the operating cost was low, the cop value was high and the impact on the marine environment was small when the seawater source heat pump system was directly used.
Multi physical field coupling simulation analysis of power transformer vibration and noise
Authors: Hao Wang , Xianzhe Deng
Published: March 2021

The generation, propagation and distribution of power transformer vibration and noise are studied by modeling and simulation. Based on the theoretical analysis of the mechanism of power transformer vibration and noise, the correlation between the characteristics of power transformer vibration noise and the influencing factors is determined. The solid model of single-phase power transformer is established by using multi physical field finite element analysis software. The multi physical field coupling simulation of electromagnetic field, structural force field and pressure acoustic field is carried out. The spatial distribution and frequency characteristics of vibration and noise signal of single-phase power transformer in each solution domain are displayed. It provides theoretical and technical support for smart grid equipment detection and environmental noise comprehensive control.