bluuebunny/crossref_metadata_2025 huggingface dataset:
Formation of beam-produced plasma by a forevacuum plasma-cathode source of a pulsed large-radius electron beam
Authors: Kazakov A V , Medovnik A V , Oks E M , Panchenko N A
Published: August 2021

The research of formation of beam-produced plasma by a pulsed low-energy (up to 7 keV) large-radius electron beam, generated by a forevacuum plasma-cathode electron source based on a cathodic arc, is presented. Density of the beam-produced plasma depends nonmonotonically on accelerating voltage. A certain threshold voltage, which depends on the gas pressure and a distance from the extractor of the electron beam source, provides maximal plasma density. At a voltage higher than the threshold voltage, a further increase in the accelerating voltage leads to a decrease in the density of the beam-produced plasma, and the plasma density increases as the distance from the extractor of the electron source increases. These dependencies of plasma density are caused by change in the electron-impact ionization cross sections of gas. Plasma density increases with increasing electron beam current and increasing gas pressure. For the used forevacuum plasma-cathode source of the large-radius electron beam and experimental conditions, varying parameters of the electron beam provides formation of beam-produced plasma with cross-section radius up to 4–5 cm and plasma density up to 1017 – 1018 m-3 at distance from the extractor up to 19 cm.
Influence of vacancy on helium interaction with α-Zirconium
Authors: Svyatkin L A , Terenteva D V , Laptev R S
Published: August 2021

The first-principle calculations of the interaction between helium and zirconium have been carried out. The main feature of studying such systems is the localization of a He atom in a region near the vacancy in Zr. It has shown that the location of a helium atom in a vacancy vicinity leads to lower formation energy. The calculated density of electron states curves revealed shifts of He 1s state by ~ 0.5 eV towards higher binding energies while located the vacancy vicinity against He-in-vacancy position. Moreover, He 2s states are observed in a region of Zr 4d states from –1.2 to –0.1 eV suggest the hybridization between these states. The crystal orbital Hamilton populations curves have been analyzed to reveal the features of the Zr-He chemical interaction due to the hybridization of He 2s and Zr 4d states.
Onboard device for UAS remote identification
Authors: Bragin D S , Urazayev D R , Konev A A , Cherepanova I V
Published: August 2021

The unsupervised use of UAS creates threats to the security of general aviation and ground objects, including objects of critical information infrastructure. Equipping UAS with a remote identification device can be the solution to this problem. It should increase the sense of responsibility among UAS pilots and allow the public, aviators and law enforcement to identify and track UAS and their operators during flight. The goal of the article is the technical requirements specifications of a device for UAS remote identification. The existing technologies of UAS remote identification have been reviewed during this research, and the criteria of their choice have been defined. The result of the work takes into account the territorial features of Russia and the concept of the RUTM system. The authors propose the network model for UAS remote identification using the ADS-B direct broadcast technology to form the aircraft pilots’ situational awareness of the air situation near the vessel. ADS-B 1090 ES, cellular and satellite communications have been chosen as data transmission channels for the onboard UAS remote identification device. Based on this, the technical requirements have been developed for the onboard device for UAS remote identification.
Decision-making in the recommendation system of personnel security of the company
Authors: Shelupanov A , Nemirovich-Danchenko M , Glukhareva S
Published: August 2021

The paper provides an assessment of personal’s reliability in the personnel security system of a company. The analysis of existing solutions in various industries is carried out. The stages of assessment and levels of reliability are shown.
Power Energy Supply and Demand Scheduling Method Based on Blockchain Technology
Authors: Li Wei , Wang Lixia , Wang Dawei
Published: August 2021

In order to solve the problem of a large difference between the dispatching operation power and the actual required operation power existing in the traditional power energy dispatching, which affects the dispatching efficiency and causes serious energy waste, the design of power energy supply and demand scheduling method based on blockchain technology is carried out. Based on the power energy supply and demand scheduling hierarchy, based on the supply and demand scheduling model of the blockchain technology architecture, the distributed consensus mechanism for the supply and demand scheduling of power energy is established, and a new power supply and demand scheduling method is proposed. Through the experiment, it is proved that compared with the traditional dispatching method, the dispatching power obtained by the new dispatching method can be close to the actual dispatching power on the basis of meeting the normal operation needs of each equipment, realizing the energy saving of electric power, and providing technical support for the construction of green power grid.
English billboard text recognition using deep learning
Authors: Yu Enbo , Zhang Zitong
Published: August 2021

English billboards are common in our daily work and life, and how to effectively recognize them is a problem worthy of study. This paper mainly uses Progressive Scale Expansion Network (PSENet) and Convolutional Recurrent Neural Network (CRNN) to conduct text recognition experiments on English billboards. The English billboards are divided into four categories: text distortion, larger background, special text font, and normal format. PSENet is used for text detection, and CRNN is used for text recognition. The identification results of PSENet and CRNN revealed differences among the four groups of English billboards: normal format text recognition is the best, and large background text recognition is the worst, and the recognition effect of text distortion and special text font is less accurate. Finally, the work of this paper is analyzed and summarized, and the future work is prospected.
Feature-Enhanced Data Classification Using B-center Function
Authors: Li Enyu , Lyu Leyu , Mao Wenfei
Published: August 2021

Linear-kernel Support Vector Machine (SVM) classification algorithm needs to constantly calculate the similarity between the data to be tested and the sample during classification. If samples in the dataset are linearly indivisible, the accuracy of the algorithm will get worse. The feature weight calculation based on category information in Multi-layer Perceptron (MLP) algorithm is not accurate enough to express the relationship between features and categories, i.e. it cannot distinguish categories precisely when the features with the same category frequency. In order to improve the performance of MLP and linear-kernel SVM algorithms in classification, an improved function: the Base function, which is based on clustering, is proposed in this paper. Before running the classification algorithm, a series of possible centers is predicted, after which the Base function is used for data clustering to increase the dimension of data, and then samples with more features are passed to classification models. In the high-position Gaussian data space, the Base function can obviously improve the spatial distribution of samples. Experimental comparison in the thesis also proved that the Base function can improve the performance of MLP or SVM in classification effectively. The proposed feature-enhanced MLP is superior to the baseline with 32.6% accuracy promotion on the Gaussian dataset.
An End-to-End Deep Learning Approach for Diagnosis of Parkinson’s Disease Based on Hand Drawing
Authors: Wang Yifan , Zhang Yifan
Published: August 2021

At present, Parkinson’s disease is mainly subject to the doctor’s subjective assessment of the patient’s symptoms through the unified Parkinson’s disease score scale, which easily leads to a high rate of misdiagnosis. Some wearable devices have been developed to assist in providing accurate diagnostic results. However, this process requires the cooperation of patients and doctors, which is very inconvenient for Parkinson’s patients who have limited mobility. According to reports, Parkinson’s patients will show some writing disabilities in the early stages. Therefore, to provide a non-invasive and convenient diagnosis program, this research is devoted to developing a Parkinson’s disease diagnosis program based on hand-drawn data. This search provides diagnostic solutions for Convolutional Neural Network (CNN) and Residual Neural Network (ResNet). Unlike the traditional hand drawing preprocessing scheme that only retains the trajectory, the color information of the image for the hand drawing was reserved, which allows the subsequent model to learn the stroke pressure, speed, stroke time, and other based on the color information. Compared with CNN, the ResNet can better solve the semantic gap problem, achieve faster convergence speed, and get higher diagnostic accuracy (accuracy = 88.9%). In general, the two diagnostic schemes proposed in this study only require the patient to provide a hand drawing to confirm whether the patient has Parkinson’s disease, which is of great significance for the convenient diagnosis of Parkinson’s disease.
Unsupervised feature learning for data classification
Authors: An Shixuan , Lu Ruicheng , Zhang Tianyi
Published: August 2021

Data classification is a critical task for data analysis. However, recent methods aim to model this task as a classification or regression task, which needs ground truth for discriminative representation learning. This paper aims to learn a more efficient and effective feature for data analysis in an unsupervised learning manner. Our method consists of two main components: a customized autoencoder network (C-AENet) and a customized squeeze-and-excitation network (C-SENet). The C-AENet aims to reconstruct the feature using the fewer dimension, and C-SENet is to improve the representation by providing channel attention. Experiment on the Avila dataset shows that both modules are effective in data classification with fewer feature dimension.
Using Machine Learning Algorithms to Predict Efficiency of US’s States Mask Policy During COVID-19
Authors: Cui Jin
Published: August 2021

Over the past year, the COVID-19 outbreak deeply and thoroughly changed the way the world is. However, the control policy’s efficiency is still in dispute. Through the way of machine learning, now we are able to find and to probe into the data about corona-virus spreading patterns in a short period of time, suiting the remedy to the case, to launch targeted prevention policies, and minimize the economic loss under the premise of control the spread of the virus on a large scale. We directly use the LES algorithm and K-means clustering to make a comparison about the data feature. Therefore, the results are much more convincing than using any other recursive analyzing method alone. It is precise because of the ID3 algorithm, which we use for further analysis, to find the reason why those policies work.
Research on hand key point recognition and localization method based on SSD network
Authors: Cheng Xiaoyu , Jiang Tao , Tan Xuewen , Huang Lidong
Published: August 2021

With the development of the times and the progress of society, multimedia presentation has become a necessary skill for people. In the field of education, the application of multimedia presentations for teaching activities has become a necessary and common way. In this paper, we propose to use camera capture for image analysis and wireless data transmission technology for information interaction with computers. Among them, SwiftShow performs image analysis by capturing the gestures of the presenter using the camera. The device provides personalization, and users can choose the mode that suits them through the interaction interface, and even customize the gestures. The device can be controlled by the simple hand gesture recognition overhead, which not only can realize the basic functions such as ppt play, pause, page turn, and focus, but also includes many practical and unique presentation functions, the research aims to completely free the presenter from the computer.
Machine Learning Algorithm for Classification
Authors: Tan Haoyuan
Published: August 2021

Recently, machine learning methods have a good performance in the field of classification tasks. Summarizing and comparing the performances of different classifiers in the application of their specific classification tasks has a reference significance. In this paper, five classical machine learning classifiers, including GMM, Random Forest, SVM, XGBoost, and Naive Bayes, are compared to show their computing characteristics. The advantages and disadvantages are analysed in this paper. Based on the different datasets, namely different specific classification tasks, the different classifiers perform similarly. However, the SVM-based classifier has the lowest accuracy while processing the text data to apply the text classification task. This result shows that if the classification task is difficult, the accuracy would not be high. This research summarizes the performances of different machine learning methods in the application of specific classification tasks. And this research has a reference significance for the machine learning-based classifiers.
A method for forecasting alpine area load based on artificial neural network model
Authors: Wu Guoliang , Zu Guangxin , Zheng Jun
Published: August 2021

In order to realize the modernization of power grid management, mid-term power load forecasting has become an important research in power dispatching. Accurate mid-term load forecasting is of great significance to future power system dispatching and safe operation, and is a prerequisite for safe and economical operation of power systems. Based on error back propagation (BP) neural network has the advantages of generalization ability, self-learning ability and self-adapting ability, and simple local calculation, this paper proposes to use the BP neural network model to forecast the power load in the alpine region. Firstly, the historical load data and meteorological data of a certain area in Harbin are studied, analyzed and screened, and they are used as the input data of the network, a mid-term load forecasting model based on BP neural network is established, and then the model is simulated and improved through simulation to predict changes in the future development of electric load demand. The prediction data results and images show that the medium-term load prediction accuracy and training speed based on the BP neural network can reach the target, and the performance is good.
Mobile edge computing scheduling algorithm based on minimum total delay-time
Authors: Zhao Yuancheng
Published: August 2021

As an emerging architecture, mobile edge computing (MEC) extends cloud computing services to network edges close to users through MEC servers, satisfying the application needs of real-time control and real-time data analysis. However, due to the limited computing power of MEC server, the task has a long delay-time. In order to improve the current situation, this paper proposes a scheduling algorithm of minimum total delay-time (MTD), in which the server determines the optimal order of task computation to MTD. In addition, this paper proposes an incentive mechanism for users to submit tasks with reasonable computation and expected completion time, while reducing the number and computation of submitted tasks when server computing resources are insufficient. The results show that the performance of the algorithm is close to that of the traditional scheduling algorithm, and total delay-time and average delay-time have improved by 17% to 200%.
SPECT bone imaging thyroid lesion segmentation
Authors: Mingyang-Luo  , Qiang-Lin  , Tongtong-Li
Published: August 2021

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.
Ant Colony Algorithm for Container-based Microservice Scheduling in Hybrid Cloud
Authors: Lin Miao , Xi Jianqing , Bai Weihua , Wu Jiayin
Published: August 2021

The microservice model divides the application into a group of loosely coupled and collaborative fine-grained services, which results in the workflow scheduling problem, especially in the complex environment of resource diversity such as hybrid cloud. To solve this problem, this paper establishes a workflow scheduling model of microservice based on multiple container instances and proposes an ant colony optimization (ACO) algorithm to optimize finish time and resource cost. The algorithm proposed optimizes update strategies of multi-objective heuristic information to improve the selection probability of the dominant path. The comparative experiments show that the proposed optimization algorithm achieves better results in scheduling objectives in the hybrid cloud.
A Deep Neural Network for Stock Price Prediction
Authors: Dai Hengwei , Wang Wenfei , Cao Jiashang , Wu Haotian
Published: August 2021

The stock market is a large, complex system that consists of many human factors. Due to the human action’s randomness to the market environment, the market behavior is usually erratic. In this case, many previous works were trying to find out an automatic prediction model that can capture relationships between stock market prices and the surroundings of the stock markets. Recently, a simple LSTM-attention mechanism was proposed to advance the prediction accuracy of previous works. However, the simple LSTM with attention mechanism only demonstrates how the past data influence the next. This forward-only relationship does not reflect a backward relationship, which is how the future data is related to the past data. To enforce the backward relationship on the attention-LSTM model, a multilayer bidirectional LSTM model with an attention mechanism is proposed. A bidirectional LSTM layer that can encode the data relationship in both directions is leveraged. Then attention mechanism is applied to introduce the model with the ability to focus on critical content and ignore the disturbance. Experimental result shows that the attention-BiLSTM model has a coefficient of the determinant of 0.9940, which is better than the baselines.
Research on the state detection of the secondary panel of the switchgear based on the YOLOv5 network model
Authors: Chen Wang , Liqiang Zhang , Tianpeng Yan , Tao Jiang , Yijing Jia , Zhihao Lai
Published: August 2021

In recent years, with the development of artificial intelligence algorithms, deep learning algorithms are widely used in target detection. The switchgear in the power system plays a key role in the safe operation of the system and there are a large number of them. In order to improve the work efficiency of testers and reduce manual misjudgment, it is proposed to apply the deep learning YOLOv5 algorithm to detect the status of the switchgear secondary panel in real time. The algorithm uses the configuration environment, The data set training and target test obtain the light state information, and the experimental results prove the effectiveness of the algorithm, which provides technical support for the field of switchgear electrical test robots.
Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm
Authors: Li Shurui , Ou Jiechen
Published: August 2021

With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.
Experimental Investigation of Lensless Fourier Transform Hologram Multiple Images
Authors: Gao Zhiqiang , Fan Qi
Published: August 2021

In order to solve the problem of multiple reconstructed images in digital hologram, the lensless fourier transform hologram is taken as an example for comparative experiment. When the same experimental light path and recording conditions are used and the only variable is the sensitive element, the reconstructed image of CCD does not appear multiple images, while the reconstructed image of CMOS appears multiple images. In order to determine the reliability of the experiment, two correlated beams were imaged with the same optical path. The experimental phenomenon is still that there are multiple images in CMOS, but not in CCD. The working principle of CCD and CMOS in digital holography experiment is different.
Research on image segmentation and edge detection technology based on computer vision
Authors: Qi Ji , Yang HaiTao
Published: August 2021

With the continuous development of computer vision technology, image segmentation technology and edge detection technology are becoming more and more mature. Firstly, this paper simulates the fixed threshold method and adaptive threshold method in image segmentation, and concludes that the fixed threshold method is applicable to images with obvious targets and backgrounds, and the adaptive threshold method is applicable to images with uneven distribution of light and dark, and secondly, the comparative experiment and result evaluation of edge detection of images by Sobel operator and Canny operator are used to conclude that the Sobel algorithm is more accurate in positioning edges, while Canny algorithm image edge information is more continuous and single edge response is better. Its edge detection is also better.
Piecewise Linear map enabled Harris Hawk optimization algorithm
Authors: Zhao Juan , Gao Zheng-Ming , Zhang Yu-Jun
Published: August 2021

Chaotic maps were usually introduced to improve the original swarm-based nature-inspired algorithms. Due to their chaotic characteristics, the chaotic maps were introduced to replace the pseudo random numbers in computer engineering and consequently better performance would be achieved. In this paper, we introduce another chaotic improvement to the Harris hawk optimization (HHO) algorithm with Piecewise Linear map. Nevertheless, the chaos would be introduced to improve the randomness of the controlling parameter which was used to balance the ratio of exploration and exploitation. Monte Carlo simulation experiments were carried out and results confirmed this kind of improvements would significantly raise the capability in optimization.;
A Dynamic Scheduling Workflow Algorithm Based On Critical Path
Authors: Tongyue Zhu , Wenhui Yang
Published: August 2021

With the explosion in the size of scientific workflows, local workflows can no longer satisfy existing computing needs, and cloud computing platforms have become the first choice for scientific workflows. Compared with traditional local computing, cloud computing platform not only needs to consider the loss of scheduling time and transmission time, but also involves the cost of cloud resources. Therefore, whether the scheduling scheme is reasonable becomes the decisive factor of workflow efficiency. Aiming at how to achieve efficient scheduling under cost constraints, a dynamic scheduling algorithm based on critical path is proposed. The algorithm uses the optimized Dijkstra algorithm to classify the task nodes, guarantees the completion time of key nodes in the scheduling process, adjusts the priority of nodes dynamically, and selects the best resources according to the loss weight. Experiments show that the optimization rate increases with the increase of the number of task nodes. The algorithm proposed in this paper is suitable for large-scale workflow operation and can effectively reduce the scheduling time.
Predicting and Evaluating the Online News Popularity based on Random Forest
Authors: Zhang Yuchao , Lin Kun
Published: August 2021

With the rapid development of science and technology, the internet has become a large media of information spread. There is a large quantity message on this platform. And online articles are the main form of information propagation. If the press can know what kind of articles will be more popular, they can construct an article that can help them spread the information they want to spread. Therefore, it’s very important to predict the popularity of these articles. Some models in machine learning could be applied to this problem. In this paper, it will introduce an approach based on Random Forest. To avoid too much calculation, the experiment first uses PCA to make dimension reduction. Then the model evaluation uses the ROC area values to assess the accuracy of the model. Its performance is better than CART and C4.5.
An improved hybrid recommendation algorithm based on feature preference
Authors: XU Jiwen
Published: August 2021

In order to solve the problem of data matrix sparsity and cold start in the era of big data. This paper designs a collaborative filtering algorithm, which is an improved hybrid recommendation algorithm based on feature preference analysis. It combines the analysis of user feature preference and item feature, and then uses the traditional collaborative filtering idea to recommend the best scoring object to users. The experimental results show that the accuracy of the proposed algorithm is improved obviously.
Ultrafine particles, blood pressure and adult hypertension: a population-based survey in Northeast China
Authors: Lin Li-Zi , Gao Meng , Xiao Xiang , Knibbs Luke D , Morawska Lidia , Dharmage Shyamali C , Heinrich Joachim , Jalaludin Bin , Lin Shao , Guo Yuming , Xu Shu-Li , Wu Qi-Zhen , Chen Gongbo , Yang Bo-Yi , Zeng Xiao-Wen , Yu Yunjiang , Hu Li-Wen , Dong Guang-Hui
Published: August 2021

The toxicity of ultrafine particles (UFPs) on blood pressure (BP) has not been well studied. We aimed to evaluate the associations of long-term UFP exposure with different components of BP and the risk of prehypertension/hypertension. We included a total of 24 845 Chinese adults (18–74 years old) in a cross-sectional survey (Liaoning province, China). The 4 year (2006–2009) average concentrations of UFP was estimated using a chemical transport model. We measured systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP), prehypertension and hypertension. We used a generalized linear mixed model to examine the associations while controlling for important individual covariates. One unit (1 μg m−3) increase in UFP was associated with an increase in SBP of 1.52 mm Hg [95% confidence interval (CI): 0.48–2.55], DBP of 0.55 mm Hg (95% CI: 0.01–1.08) in DBP, MAP of 0.92 mm Hg (95% CI: 0.23–1.61) and PP of 0.62 mm Hg (95% CI: 0.05–1.20). The UFP-BP associations were stronger in women than in men. One unit increase in UFP was associated with an increased risk of hypertension and prehypertension (OR = 1.23, 95% CI: 1.09–1.38; OR = 1.12, 95% CI: 1.04–1.21) compared to normotension group, and these associations were stronger in overweight/obese participants. Our study showed that long-term exposure to UFP was associated with elevated BP and higher odds of hypertension. These findings suggest that strategies to monitor and reduce UFPs, which are not a regulated pollutant, could have beneficial cardiovascular effects.
Surface coatings analysis and their effects on reduction of tribological properties of coated aluminum under motion with ML approach
Authors: Chowdhury Mohammad Asaduzzaman , Hossain Nayem , Masum Abdullah Al , Islam Md Sakibul , Shahin Mohammad , Hossain Md Imran , Shuvho Md Bengir Ahmed , Ali Md Ramjan , Ahmed A K M Foysal , Nandee Mr Rajib
Published: August 2021

The popularity of coated aluminum is gaining significant attention in numerous sectors in the industry due to its specific strength, corrosion resistance, and recyclability. However, because of friction, its lifetime reduces which causes a billion-dollar loss every year to our property. Many types of research are going around the world on how friction and wear loss can be reduced. This research focuses on the tribological study of coated aluminum in different conditions in the experiments, lubricant is used to find its efficiency, and coating materials have also its self-lubricating properties. Both reciprocating motion of pin and simultaneous motion of pin and disc applied. The combined effects of lubrication and motions are correlated with the reduction of tribological properties to a certain extent. The velocity of both pin and disc is also varied. Applied loads are changed in different experiments as well. Roughness analysis has also been done to observe the effect of lubricant, motion, and applied load on the surface of the specimens. SEM, EDX, XRD, and FTIR tests are also performed to check the morphology of the specimens. The experiments show that comparatively less friction and wear are in at lubricated, reciprocating, and less velocity of pin and disc conditions. Less coefficient of friction is observed at higher applied load but less wear is produced at lower applied load. The Machine Learning (ML) approach is used to detect patterns automatically in datasets and create models to predict future data or other outcomes.
Stacking fault energy and fcc→hcp transformation driving force in Fe-Mn-C-Cr-Si high manganese steels and experimental investigation
Authors: Lang Dong , Li Qiangguo , Huang Xuefei , Huang Weigang
Published: August 2021

In order to improve the strength and work hardening capacity of high manganese steel, the effects of alloying elements on the stacking fault energy (SFE) and driving force of fcc→hcp transformation in Fe-Mn-C-Cr-Si high manganese steels were explored in detail. Based on the thermodynamic calculations, the Fe-0.6C-15Mn-(4,6)Cr-(0,3)Si (wt.%) steels were prepared to investigate the microstructure and mechanical properties. The calculated results show that the Cr reduces the SFE of Fe-C-Mn-Cr high Mn steels linearly and the reduction rate of SFE is greater as the C content increasing. With increasing the Si concentration, the SFE of Fe-C-Mn-Si steels decreases when the Mn content is higher than 18 wt.%. However, when carbon content is less than 1 wt.% and Mn content less than 18 wt.%, the SFE reaches to a maximum value and then declines with the increase of Si content. The combined effect of Cr and Si on the SFE of Fe-C-Mn-Si-Cr steels appears the similar behaviors to that of Si. Generally, the value of driving force,

Δ

G

γ
→
ε

,

of fcc→hcp transformation in Fe-Mn-C-2Cr-Si (wt.%) steels increases with the increase of C and Mn and decreases with the Si content increasing. However, for 0.6 wt.% C and 7 wt.% Mn steels, the value of

Δ

G

γ
→
ε

first increases and then decreases with the increase of Si content. The results of tensile test indicated that the Fe-15Mn-0.6C-6Cr-3Si (wt.%) high manganese steel demonstrates a better combination of the ultimate strength of 987 MPa, yield strength of 470 MPa, fracture elongation of 41.5% and high work hardening capacity because of the ε-martensitic transformation caused by the great

Δ

G

γ
→
ε

(large negative value) and low SFE under stress.
High dose rate 192Ir versus high dose rate 60Co brachytherapy: an overview of systematic reviews of clinical responses of gynecological cancers from 1984 to 2020
Authors: Abtahi M , Gholami S , Jassim H H
Published: August 2021

The Purpose. Radioisotope of 192Iradium (192Ir) has a half-life (74 days) and is not easily accessible in developing countries. As a result, by the time source shipment clearance and the customs paperwork are completed, a large proportion of useful activity had already been decayed away. In fact, 60Cobalt (60Co) remote afterloading systems are commercially available by many venders. As a result, it may well become an alternative source to 192Ir and conform many of these challenges. The aim of this study is that to report clinical responses of different types of gynaecological cancers treated with high dose rate (HDR) 192Ir and HDR60Co brachytherapy in order to check whether HDR 60Co could be used as an alternative brachytherapy, source to HDR 192Ir. Materials and Methods. A retrospective study of clinical responses of different types of gynaecological cancers, staged from I to IV according to recommendations of International Federation of Gynaecology and Obstetrics (FIGO), treated by brachytherapy alone, radiotherapy alone (combined brachytherapy and radiotherapy) and combined radiotherapy and chemotherapy (brachytherapy, radiotherapy and chemotherapy) between 1984 and December 2020 was conducted. The patients were treated with external beam radiotherapy 45–51 Gy boosted with HDR 192Ir and HDR 60Co afterloading brachytherapy of 18–30 Gy to point A. The results. The study scrutinized the data of 11086 patients with different types of gynaecological cancers. Most of the patients, 70 percent of them, were diagnosed with gynaecological cancers in stages II and III. For patients treated with 192Ir brachytherapy source 5-years overall survival rate (OS), local control, 2-years, 5-years and 10-years disease free survival (DFS), complications of gastro-intestine (GI) and complications of genito-urinary (GU) were 63.5%, 92%, 72.6%, 64.07%, 43.75%, 3.9% and 5.92%, but for those treated with 60Co they were 57.7%, 86.63%, 82.5%, 53.35%, 43.75%, 4.8% and 3.7%, respectively. Conclusions. The use of HDR 60Co brachytherapy has the capacity to produce overall survival rate and disease control in patients with carcinoma of the gynaecology comparable to that reported for HDR 192Ir brachytherapy. Currently, the toxicity and damage of the normal tissues and radiation-related second cancers are of a similar incidence to that of standard HDR 192Ir brachytherapy. Source exchange frequency is not a serious concern because it requires less frequency of replacement, and commissioning can be accomplished within years; hence, replacing HDR 192Ir with HDR 60Co brachytherapy achieves significant cost saving. Therefore, we recommend that 60Co source ought to be the first choice for low resource radiotherapy setting as it offers economic advantages over 192Ir and have comparable clinical outcomes to that of 192Ir source.
Net zero adaptation—a review of built environment sustainability assessment tools
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Authors: Shuttleworth Amie , MacAskill Kristen
Published: August 2021

In the context of the built environment there has been a greater focus of research and policy on climate mitigation efforts, compared with adaptation. Buildings designed today will likely need upgrades within their lifetime if adaptation is not adequately considered. There is currently no widely adopted system for assessing building adaptation in design and clearer guidance is needed. By way of example, a review of three established, geographically diverse building sustainability assessment tools (BREEAM, Green Star and LEED) demonstrates the extent that adaptation to climate change and mitigation of greenhouse gas emissions have been integrated. Despite recent assessment updates, emphasis on action pertaining to climate issues is heavily weighted towards mitigation; the integration of adaptation considerations is limited and in most cases not mandatory. This state of play in assessment tools is reflective of wider shortfalls in meaningfully addressing climate change risk in asset design. To ensure that asset investment considers climate-related impacts in a holistic manner, better mechanisms for achieving an integrated perspective of mitigation and adaptation are required.
Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source
Authors: Noguchi Yoshihiro , Tachi Tomoya , Teramachi Hitomi
Published: August 2021

Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug–drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.
Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Tracking
Authors: Chen Hui , Li Zhao , Feng Sheng , Richard-Greenblatt Melissa , Hutson Emily , Andrianus Stefen , Glaser Laurel J , Rodino Kyle G , Qian Jianing , Jayaraman Dinesh , Collman Ronald G , Glascock Abigail , Bushman Frederic D , Lee Jae Seung , Cherry Sara , Fausto Alejandra , Weiss Susan R , Koo Hyun , Corby Patricia M , Oceguera Alfonso , O’Doherty Una , Garfall Alfred L , Vogl Dan T , Stadtmauer Edward A , Wang Ping
Published: August 2021

Background

High-sensitivity severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen assays are desirable to mitigate false negative results. Limited data are available to quantify and track SARS-CoV-2 antigen burden in respiratory samples from different populations.

Methods

We developed the Microbubbling SARS-CoV-2 Antigen Assay (MSAA) with smartphone readout, with a limit of detection of 0.5 pg/mL (10.6 fmol/L) nucleocapsid antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs. We developed a computer vision and machine learning–based automatic microbubble image classifier to accurately identify positives and negatives and quantified and tracked antigen dynamics in intensive care unit coronavirus disease 2019 (COVID-19) inpatients and immunocompromised COVID-19 patients.

Results

Compared to qualitative reverse transcription−polymerase chain reaction methods, the MSAA demonstrated a positive percentage agreement of 97% (95% CI 92%–99%) and a negative percentage agreement of 97% (95% CI 94%–100%) in a clinical validation study with 372 residual clinical NP swabs. In immunocompetent individuals, the antigen positivity rate in swabs decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity. Antigen was detected for longer and variable periods of time in immunocompromised patients with hematologic malignancies. Total microbubble volume, a quantitative marker of antigen burden, correlated inversely with cycle threshold values and days-after-symptom-onset. Viral sequence variations were detected in patients with long duration of high antigen burden.

Conclusions

The MSAA enables sensitive and specific detection of acute infections and quantification and tracking of antigen burden and may serve as a screening method in longitudinal studies to identify patients who are likely experiencing active rounds of ongoing replication and warrant close viral sequence monitoring.
Segmental assessment of blood flow efficiency in the total cavopulmonary connection using four-dimensional flow magnetic resonance imaging: vortical flow is associated with increased viscous energy loss rate
Authors: Rijnberg Friso M , Juffermans Joe F , Hazekamp Mark G , Helbing Willem A , Lamb Hildo J , Roest Arno A W , Westenberg Jos J M , van Assen Hans C
Published: August 2021

Aims

To study flow-related energetics in multiple anatomical segments of the total cavopulmonary connection (TCPC) in Fontan patients from four-dimensional (4D) flow magnetic resonance imaging (MRI), and to study the relationship between adverse flow patterns and segment-specific energetics.

Methods and results

Twenty-six extracardiac Fontan patients underwent 4D flow MRI of the TCPC. A segmentation of the TCPC was automatically divided into five anatomical segments [conduit, superior vena cava (SVC), right/left pulmonary artery (LPA), and the Fontan confluence]. The presence of vortical flow in the pulmonary arteries or Fontan confluence was qualitatively scored. Kinetic energy (KE), viscous energy loss rate, and vorticity were calculated from the 4D flow MRI velocity field and normalized for segment length and/or inflow. Energetics were compared between segments and the relationship between vortical flow and segment cross-sectional area (CSA) with segment-specific energetics was determined. Vortical flow in the LPA (n = 6) and Fontan confluence (n = 12) were associated with significantly higher vorticity (P = 0.001 and P = 0.015, respectively) and viscous energy loss rate (P = 0.046 and P = 0.04, respectively) compared to patients without vortical flow. The LPA and conduit segments showed the highest KE and viscous energy loss rate, while most favourable energetics were observed in the SVC. Conduit CSA inversely correlated with KE (r = −0.614, P = 0.019) and viscous energy loss rate (r = −0.652, P = 0.011).

Conclusions

Vortical flow in the Fontan confluence and LPA associated with significantly increased viscous energy loss rate. Four-dimensional flow MRI-derived energetics may be used as a screening tool for direct, MRI-based assessment of flow efficiency in the TCPC.
Quality indicators in the management of elderly Chinese patients with atrial fibrillation: a report from the Optimal Thromboprophylaxis in Elderly Chinese Patients with Atrial Fibrillation (ChiOTEAF) registry
Authors: Guo Yutao , Kotalczyk Agnieszka , Imberti Jacopo F , Wang Yutang , Lip Gregory Y H ,   ChiOTEAF Registry Investigators
Published: August 2021

Aims

To evaluate the quality measures and clinical performance indicators among elderly Chinese patients with atrial fibrillation (AF). The management of patients with AF requires a holistic, multidisciplinary approach. Quality indicators have been proposed to assess the quality of care in ‘real-world’ clinical practice when managing patients with AF.

Methods and results

The Optimal Thromboprophylaxis in Elderly Chinese Patients with Atrial Fibrillation (ChiOTEAF) registry is a prospective, observational, large-scale multicentre registry conducted between October 2014 and December 2018 in China. Data were collected at the enrolment and during the follow-up visits by the local investigators. In the ChiOTEAF registry, 14 primary and 8 secondary indicators from six domains of care were assessed. Six thousand four hundred twenty patients who completed the 1-year follow-up were included in the analysis. Median age was 76 years, and the majority of patients was male (60.8%). Overall, 3246 patients (54.8%) were not treated with oral anticoagulants (OACs) appropriate to their risk of stroke; specifically, among those at highest risk of stroke, OACs were prescribed in only 43.3% patients (1258/2905). Among patients with permanent AF, 32 (3.6%) were prescribed antiarrhythmic drugs, and among those with paroxysmal AF, catheter ablation was performed in 20.7%. Patients were overburdened with multi-morbidities, including hypertension, diabetes mellitus, obesity, and sleep apnoea. During 1-year follow-up, 435 deaths (6.8%) and 89 thromboembolic events (1.4%) occurred. Patient-reported outcomes showed that 55% of patients had indicators of reduced quality of life.

Conclusion

Assessment of quality indicators revealed the gaps in AF care among Chinese patients, highlighting the need for a more integrated or holistic approach to AF management.
Engaging in pedagogical translanguaging in a Shanghai EFL high school class
Authors: Zhou Xiaozhou (Emily)
Published: August 2021

Translanguaging and codeswitching are frequently employed in second/foreign language classrooms. Though the differences between these two practices have been examined from a conceptual perspective, the potential of translanguaging as a pedagogical resource has not been empirically addressed. This article opens with a concise comparison of translanguaging and codeswitching, arguing that they offer different analytical and practical advantages in second/foreign language education. Using extracts from a practitioner study conducted in a high school EFL classroom in Shanghai, this paper illustrates how different linguistic and semiotic resources are deployed in a teacher’s and students’ translanguaging practices in order to achieve pedagogical goals, thus demonstrating the possibilities that pedagogical translanguaging encompasses. The paper argues that practitioners should be encouraged to make full use of these resources so as to create arenas of opportunity for the students.
How will Brexit affect the patterns of European agricultural and food exports?
Authors: Cheptea Angela , Huchet Marilyne , Henry Lucile
Published: August 2021

We analyse how Brexit will affect European agrifood trade using a structural gravity model and by comparing the present deal to four alternative scenarios on the UK’s trade policy. European Union (EU) countries will suffer marginal drops in imports, exports and real income, with stronger effects for Ireland. EU exports to the UK will drop by 10 per cent. European products will be redirected to both intra-EU and extra-EU markets and sold at slightly lower prices. Meat products will suffer the largest losses, Ireland being particularly vulnerable due to strong interconnections with British production chains and increased costs for accessing EU and extra-EU markets.