bluuebunny/crossref_metadata_2025 huggingface dataset:
Cystic Artery Pseudoaneurysm: Current Review of Aetiology, Presentation, and Management
Authors: Taghavi Seyed Mohammad Javad , Jaya kumar Mahendra , Damodaran Prabha Ramesh , Puhalla Harald , Sommerville Craig
Published: November 2021

Background. Cystic artery pseudoaneurysms are rare. Most commonly, they occur secondary to acute cholecystitis or after a cholecystectomy. Complications include haemobilia, biliary obstruction, and haemorrhage. Given the rarity and associated morbidity, a high index of suspicion is required. This article reviews the current literature on cystic artery pseudoaneurysms to investigate its aetiology, clinical presentation, and management options. Methods. A broad search of the Medline and PubMed databases was carried through. All peer reviewed literatures published in the English language between 1991 and 2020 with keywords “cystic” and “artery” and “pseudoaneurysm” in the title were selected for review. No further exclusion criteria; all studies yielded from the search were included in the results of this review. Additionally, we present a case of cystic artery pseudoaneurysm treated at our centre and included this in our analysis. Results. Sixty-seven case reports were found between 1991 and 2020. Aetiologies: Aetiology of cystic artery pseudoaneurysm was found to be cholecystitis in 41 instances (61.2%), cholecystectomy in 18 instances (26.8%), idiopathic in 6 instances (8.9%) cholelithiasis in 1 instance (1.5%), and pancreatitis in 1 instance (1.5%). Complications: Fifty-two cases were complicated by haemobilia (77.6%), 36 by anaemia (53.7%), 25 by biliary obstruction (37.3%), 13 by haemodynamic shock (19.4%), 9 by haemoperitoneum (13.4%), and 6 by contained rupture (8.9%). Most commonly, patients had two or more of these complications. Management: Forty-four patients were managed with endovascular embolisation (65.7%), 21 with endoscopic intervention (31.3%), 18 with open cholecystectomy (26.9%), 13 with laparoscopic cholecystectomy (19.4%), and 6 with pseudoaneurysm ligation (9%). Delayed presentation postcholecystectomy ranged from 8 days to 3 years. Conclusions. Cystic artery pseudoaneurysms are rare complications of a common operation. The most common clinical presentation is haemobilia, which can be difficult to diagnose clinically. A high index of suspicion and prompt investigation with targeted imaging and intervention is required. This is especially pertinent in gastrointestinal bleeding postlaparoscopic cholecystectomy as a missed diagnosis could cause significant morbidity.
Body Mass Index and Gestational Weight Gain Are Associated with Maternal and Neonatal Outcomes Based on Chinese Women
Authors: Wang Tingzhao , Li Lichun , Wu Chunchun , Cao Rong , Li Qingli , Yu Liji , Chen Youguo
Published: November 2021

The objective of the study is to analyze the association between early pregnancy body mass index (BMI), gestational weight gain (GWG), and maternal and neonatal outcomes. The retrospective cohort study was conducted at Quanzhou First Hospital Affiliated to Fujian Medical University from January 2018 to May 2021, with 552 women enrolled. Women were divided into the underweight group, normal weight group, overweight group, and obese group according to early pregnancy BMI. Univariate and multivariate logistic regression analyses were performed. The absolute risk of adverse maternal and neonatal outcomes in the early pregnancy BMI group was calculated to further analyze the association between GWG and adverse maternal and neonatal outcomes. Of the 552 women, 390 (70.65%) women had adverse maternal and neonatal outcomes. The result revealed that overweight was associated with increased risk of adverse maternal and neonatal outcomes (odds ratio (OR): 1.643, 95% confidence interval (CI): 1.006-2.684), maternal complications (OR: 1.937, 95% CI: 1.188-3.159), and large for gestational age (LGA) (OR: 1.905, 95% CI: 1.061-3.422). In the obese group, the risk of adverse maternal and neonatal outcomes (OR: 5.760, 95% CI: 1.997-16.786), maternal complications (OR: 3.112, 95% CI: 1.645-5.887), gestational diabetes mellitus (GDM) (OR: 2.943, 95% CI: 1.509-5.741), cesarean section (OR: 1.899, 95% CI: 1.002-3.599), and preterm delivery (OR: 4.752, 95% CI: 1.395-16.185) increased. Besides, there was an association between insufficient GWG and decreased risk of LGA (OR: 0.392, 95% CI: 0.187-0.826) and higher risk of preterm delivery (OR: 2.818, 95% CI: 1.171-6.784). This study demonstrates that BMI and GWG are related to maternal and neonatal outcomes. It is necessary to regularly monitor the weight of pregnant women during pregnancy. And regional guidelines for GWG also need to be explored.
Design and Application of English Grammar Error Correction System Based on Deep Learning
Authors: Hongli Chen
Published: November 2021

In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.
A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction
Authors: Balogun Abdullateef O. , Basri Shuib , Mahamad Saipunidzam , Capretz Luiz Fernando , Imam Abdullahi Abubakar , Almomani Malek A. , Adeyemo Victor E. , Kumar Ganesh
Published: November 2021

The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation‐based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation‐based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non‐disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F‐measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state‐of‐the‐arts HFS methods.
Internet of Things in the Bathroom: Smart Health-Monitoring Bidet System
Authors: Heo Sung-Phil , Jeong Suyong
Published: November 2021

The Internet of things (IoT) helps our everyday lives such as by monitoring objects and tracking behaviors in various settings, but studies on enhancing the bathroom experience are rare. This article describes full details about development and implementation of a smart health-monitoring bidet based on our study published previously in the conference. A smart bidet system is designed to monitor the users’ health through several contact-type sensors, such as pressure, oxygen, and thermometer. The system is equipped with a built-in artificial intelligence software platform and is designed to detect anal and spinal diseases. The attached sensors normally operate under waterproof conditions: we tested their performances under X6 international protection marking conditions. These devices were designed to operate properly even in extremely waterproof conditions. The temperature, pressure, and oxygen sensors of the bidet system had error rates of about 4.1, 0.6, and 1.1 percent, respectively.
Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion
Authors: Zhang Qinghui , Hou Lei , Lv Pengtao , Zhang Mengya , Yang Hongwei
Published: November 2021

The medical information carried in electronic medical records has high clinical research value, and medical named entity recognition is the key to extracting valuable information from large-scale medical texts. At present, most of the studies on Chinese medical named entity recognition are based on character vector model or word vector model. Owing to the complexity and specificity of Chinese text, the existing methods may fail to achieve good performance. In this study, we propose a Chinese medical named entity recognition method that fuses character and word vectors. The method expresses Chinese texts as character vectors and word vectors separately and fuses them in the model for features. The proposed model can effectively avoid the problems of missing character vector information and inaccurate word vector partitioning. On the CCKS 2019 dataset for the named entity recognition task of Chinese electronic medical records, the proposed model achieves good performance and can effectively improve the accuracy of Chinese medical named entity recognition compared with other baseline models.
An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems
Authors: Riaz Saleem , Lin Hui , Waqas Muhammad , Afzal Farkhanda , Wang Kai , Saeed Nasir
Published: November 2021

Traditional and typical iterative learning control algorithm shows that the convergence rate of error is very low for a class of regular linear systems. A fast iterative learning control algorithm is designed to deal with this problem in this paper. The algorithm is based on the traditional P-type iterative learning control law, which increases the composition of adjacent two overlapping quantities, the tracking error of previous cycle difference signals, and the current error difference. Using convolution to promote Young inequalities proved strictly that, in terms of Lebesgue-p norm, when the number of iterations tends to infinity, the tracking error converges to zero in the system and presents the convergence condition of the algorithm. Compared with the traditional P-type iterative learning control algorithm, the proposed algorithm improves convergence speed and evades the defect using the norm metric’s tracking error. Finally, the validation of the effectiveness of the proposed algorithm is further proved by simulation results.
A Management Model of Small- and Medium-Sized Enterprises Based on Deep Learning Algorithm
Authors: Wang Junshu
Published: November 2021

The traditional management method of small- and medium-sized enterprises (SMEs) has the problem of poor turnover prediction effect. Therefore, this paper proposes a management model of SMEs based on the deep learning algorithm. Firstly, the proposed system analyzes the characteristics of SMEs, and based on the deep learning algorithm, the gating structure of the management of SMEs is designed. The internal structure diagram of the long- and short-term neural network is given, and the circulating neural network model of the management of SMEs is constructed. Finally, the experimental indexes of the management model of SMEs are designed, and the comparative experiments are carried out. The experimental results show that the proposed method is more accurate in predicting the turnover of SMEs and has better management effect on them.
Function of N6-Methyladenosine Modification in Tumors
Authors: Zhang Nan , Zuo Yuxin , Peng Yu , Zuo Lielian
Published: November 2021

N6-Methyladenosine (m6A) modification is a dynamic and reversible methylation modification at the N6-position of adenosine. As one of the most prevalent posttranscriptional methylation modifications of RNA, m6A modification participates in several mRNA processes, including nuclear export, splicing, translation, and degradation. Some proteins, such as METTL3, METTL14, WTAP, ALKBH5, FTO, and YTHDF1/2/3, are involved in methylation. These proteins are subdivided into writers (METTL3, METTL14, WTAP), erasers (ALKBH5, FTO), and readers (YTHDF1/2/3) according to their functions in m6A modification. Several studies have shown that abnormal m6A modification occurs in tumors, including colorectal cancer, liver cancer, breast cancer, nasopharyngeal carcinoma, and gastric cancer. The proteins for m6A modification are involved in tumor proliferation, angiogenesis, metastasis, immunity, and other processes. Herein, the roles of m6A modification in cancer are discussed, which will improve the understanding of tumorigenesis, as well as the diagnosis, treatment, and prognosis of tumors.
Secured Insurance Framework Using Blockchain and Smart Contract
Authors: Hassan Abid , Ali Md. Iftekhar , Ahammed Rifat , Khan Mohammad Monirujjaman , Alsufyani Nawal , Alsufyani Abdulmajeed
Published: November 2021

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy.
Analysis on Influence of Business Intelligence Information Quality over User Information Adoption Based on Multiple Mediating Effects
Authors: Guo Xiaogang , Wang Lifang , Gao Yanni , Guo Lixiang
Published: November 2021

From the perspective of information, this paper constructs a theoretical model based on information system (IS) success model and information adoption model (IAM), aiming to further disclose the action mechanism of business intelligence (BI) on user information adoption (UIA). Firstly, the BI factors affecting UIA were modeled in reference to the existing literature. Next, a questionnaire survey was carried out to collect valid samples from 423 Chinese enterprises. After that, an empirical analysis on structural equations was conducted on Amos 24.0 and SPSS 26.0. The results show that BI information content quality (ICQ), expected performance (EP), expected ease-of-use (EEOU), and perceived risk (PR) have a direct and significant influence on UIA, and ICQ further significantly influences UIA via mediators such as EP, EEOU, and PR; BI information access quality (IAQ) has a direct yet insignificant influence on UIA, but exerts a significant positive effect on UIA of BI via EP and EEOU. The research provides a new perspective into BI user behaviors in the context of big data, and a good reference for the successfully implementation and use of BI in practice.
IoT-Assisted Hybrid Intelligent Learning Architecture Based on Digital Education in a Diverse Society
Authors: Ma Zongpei
Published: November 2021

The existing research paradigm has caused the research of educational theory to be criticized for lack of logic and scientificity. With the gradual deepening and refinement of the development of smart education platforms, the educational process can be quantified, and the research paradigm of educational science has moved from a sampling model to a full-sample model, and it has become a real empirical science. This paper combines the Internet of Things technology to construct a hybrid intelligent learning system and applies it to digital education in a diverse society to construct a functional structure of the extraction system. Moreover, starting from the actual situation of intelligent education, this paper combines a simulation system to implement an IoT-assisted hybrid intelligent learning architecture and evaluates the teaching effect of the system. Through experimental research, it can be known that the IoT-assisted hybrid intelligent learning framework based on social diversified digital education has certain effects and has a certain reference effect for the development of subsequent intelligent education.
Fusion Analysis of Economic Data of the Medical and Health Industry Based on Blockchain Technology and Two-Way Spectral Cluster Analysis
Authors: Liu Runhua , Zhang Chengcheng , Feng Tenglong
Published: November 2021

Due to the huge potential in gene expression analysis, which is helpful for disease diagnosis, new drug development, and life science research, the two-way clustering algorithm was proposed and it was widely used in gene expression data research. In order to understand the economic data of medical and health industry, this paper analyzes the economic data of the medical and health industry in different regions of China based on blockchain technology and two-way spectral cluster analysis and makes statistics on the economic data of the medical and health industry in eastern, central, and western regions of China. This paper studies the development status of China’s medical and health industry and the factors affecting the agglomeration of medical and health service industry and analyzes them under the blockchain technology and two-way spectral cluster analysis method. The results show that the overall development trend of China’s medicine and health is from government-led to government, society, and individual sharing. After the transformation of blockchain technology and two-way spectral cluster analysis, the output value of the pharmaceutical industry increased by about 10%.
Algorithm of Ecocompensation in Sloping Land Conversion Program Based on Heckman’s Two-Step Model
Authors: Wang Haotian , Ke Chen , Yang Xiaojun
Published: November 2021

In this paper, we broadly generalize the assignment auction algorithm to solve linear minimum cost network flow problems. It is significant to establish a market-based compensation mechanism by way of conservation auctions based on peasant households’ willingness, which can promote the innovation of ecocompensation policies, green development, and balanced growth. Using the survey data collected from 453 households within 3 national pilot counties in ecologically fragile regions in northwest Liaoning for the Sloping Land Conversion Programme, measuring peasant households’ willingness to accept ecocompensation through sealed auctions, we built a database through cloud computing to realize information collation and query and applied the Heckman’s Two-Step Model to study the impact of risk preference, social capital, cognitive preference, land parcel characteristics, and family endowments on farmers’ willingness to participate in protection auctions and their bid prices. The results reveal that the average bid price of peasant households in the ecologically fragile region in northwest Liaoning for the Sloping Land Conversion Programme is annually 274.5 yuan per mu and that risk preference and social capital have positive impacts on peasant households’ willingness to participate in conservation auctions and on their bid prices, cognitive preference has a positive impact on peasant households’ bid prices in conservation auctions, and land plot characteristics have a negative impact on peasant households’ bid prices in conservation auctions. It is suggested that ecocompensation policies should be optimized with such methods as lowering peasant households’ perception of high risks, setting role models for them to follow, and strengthening their perception of the environment, income, and property rights.
A Novel Dual-Band “C+O” Structure Antenna
Authors: Xie Tangyao , Yu Jianguo , Lin Ziheng , Li Yao , Zhang Guodong , Yu Zhen
Published: November 2021

This article proposes a novel multiband antenna with “C + O” structure, which uses two classic circular letters and combines them. The antenna is suitable for wireless applications such as second generation (2G), third generation (3G), fourth generation (4G), WLAN, and Bluetooth. The antenna is based on the structural characteristics of the classic monopole antenna. It is a vertical quarter-wavelength antenna. The radiator of the antenna is mainly composed of letters, and the radiator is symmetrical along the feeder line. The antenna radiator is composed of “C + O” structure. The antenna uses a coplanar waveguide feeding method. After actual testing, the antenna covers two frequency bands: 1.82–2.66 GHz and 3.46–3.72 GHz. The center frequency points are 2.06 GHz and 3.68 GHz. The antenna uses FR-4 dielectric material, the relative dielectric constant of the dielectric plate is 4.4, and the actual size of the antenna is 15 × 15 × 1.6 mm³. The test and simulation have good consistency, which verifies that the proposed antenna meets the requirements of various wireless applications.
Impact of the COVID-19 Lockdown on Ophthalmological Assistance in the Emergency Department at a Spanish Primary Level Hospital
Authors: González-Martín-Moro Julio , Guzmán-Almagro Elena , Izquierdo Rodríguez Carlos , Fernández Hortelano Ana , Lozano Escobar Inmaculada , Gómez Sanz Fernando , Contreras Inés
Published: November 2021

Purpose. To analyze the changes in ophthalmological emergencies during the COVID-19 pandemic lockdown at a Spanish primary level hospital. Methods. The number and type of emergencies attended in the emergency department of Hospital Universitario del Henares between March 10 and August 31, 2020 (COVID-19 cohort) were compared with the emergencies attended during the same period of 2019 (pre-COVID-19 cohort). Data on the diagnosis, patient age, and gender was retrospectively collected from the electronic medical records of the hospital. The different diagnoses were organized into “clusters,” which include those conditions that affect the same ocular tissue and that have similar clinical expression. Results. The number of ophthalmological emergencies during the study period was 841, compared to 1343 during the same month of 2019, which represents a reduction of 37.4%. The percentage reduction in each cluster was as follows: conjunctiva (−65.4%), cornea (−35.8%), uveitis (−3.6%), eyelid and orbital and lacrimal (−35.5%), strabismus (−60%), neuro-ophthalmology (−11.8%), retina (−10.6%), cataract (+16.4%), glaucoma (−37%), and miscellaneous (−45.1%). The number of people seen with viral conjunctivitis decreased by −87.1% compared to 2019. Patients with complications due to conjunctivitis also decreased: patients with pseudomembranes dropped from 16 to 4 cases and patients with corneal subepithelial infiltrates from 9 to 3 cases. Conclusions. Most diagnostic clusters showed a similar decrease. Clusters that included vision-threating conditions (retina, neuro-ophthalmology, and uveitis) remained mostly stable. During the COVID-19 lockdown, the diagnosis of adenoviral conjunctivitis decreased nearly 10 times. This fact may represent a decrease in the transmission of these infections.
A New Secure Model for Data Protection over Cloud Computing
Authors: Sauber Amr M. , El-Kafrawy Passent M. , Shawish Amr F. , Amin Mohamed A. , Hagag Ismail M.
Published: November 2021

The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one‐time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG‐Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.
Mechanism of Chronic Stress-Induced Glutamatergic Neuronal Damage in the Basolateral Amygdaloid Nucleus
Authors: Wang Songjun , Liu Xia , Shi Weibo , Qi Qian , Zhang Guozhong , Li Yingmin , Cong Bin , Zuo Min
Published: November 2021

Stress is a ubiquitous part of our life, while appropriate stress levels can help improve the body’s adaptability to the environment. However, sustained and excessive levels of stress can lead to the occurrence of multiple devastating diseases. As an emotional center, the amygdala plays a key role in the regulation of stress-induced psycho-behavioral disorders. The structural changes in the amygdala have been shown to affect its functional characteristics. The amygdala-related neurotransmitter imbalance is closely related to psychobehavioral abnormalities. However, the mechanism of structural and functional changes of glutamatergic neurons in the amygdala induced by stress has not been fully elucidated. Here, we identified that chronic stress could lead to the degeneration and death of glutamatergic neurons in the lateral amygdaloid nucleus, resulting in neuroendocrine and psychobehavioral disorders. Therefore, our studies further suggest that the Protein Kinase R-like ER Kinase (PERK) pathway may be therapeutically targeted as one of the key mechanisms of stress-induced glutamatergic neuronal degeneration and death in the amygdala.
Experimental Study and Prediction Model of the Flexural Strength of concrete Containing Fly Ash and Ground Granulated Blast‐Furnace Slag
Authors: Zhang Hua , Li Qing-Fu , Zhou Hua-De , Song Zong-Ming
Published: November 2021

Orthogonal experiments were performed to study the flexural strength of an eco‐friendly concrete containing fly ash (FA) and ground granulated blast‐furnace slag (GGBFS). The effects of different test parameters, such as water‐binder ratio (W/B), FA content, GGBFS content, sand ratio, gravel gradation, and curing time, on the flexural strength of the concrete were analyzed. The significance level of each influencing factor and the optimal mixing proportion of the concrete were determined by range analysis and hierarchy analysis. It was found that the W/B ratio had the greatest influence on the flexural strength of the concrete. The flexural strength of the concrete decreased gradually with the increase of W/B. The GGBFS content and the sand ratio had a greater influence in the early stage of concrete curing. The middle and later stages of concrete curing were mainly affected by gravel gradation and the FA content. A flexural strength prediction model of the concrete was developed based on a backpropagation neural network (BPNN) and a support vector machine (SVM) model. It was noticed that the BPNN and SVM models both had higher accuracy than the empirical equation, and the BPNN model was more accurate than the SVM model.
Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection
Authors: Liu Can , Li Zongping , Li Yueyang
Published: November 2021

In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.
Production Management of Professional Farmers under the New Rural Construction Based on Big Data Technology
Authors: Zhang Wei , Yang Chaodan , Cheng Ying , Chen Haiying
Published: November 2021

By collecting theoretical research and practical cases on the application of big data in agricultural production and management at home and abroad, this article is based on the actual production and management of rural farmers, deeply analyzes the current situation of big data construction, and finds a model suitable for the development of local agricultural big data. We establish an agricultural big data system suitable for rural characteristics and provide suggestions and suggestions. First of all, this article is based on actual research. Through interviews and questionnaires on the occupational differentiation and agricultural production management of local farmers, the relevant theoretical connections are made, through descriptive statistical analysis of data, and Stata is used for intersectionality. This study uses modular construction methods to build a complete industrialized farm house system and, on this basis, combines the nature of family industry and family intergenerational relationship to divide the unit types and combinations of industrialized modular farm houses. In corresponding analysis and discussion, the test shows that the professional differentiation of farmers is related to the willingness of agricultural production management and agricultural production management behavior. Secondly, this paper uses logistic model to carry out empirical analysis of influencing factors and conducts research on the influencing factors of agricultural production management willingness and agricultural production management behaviors of farmers with different occupational differentiation degrees and models agricultural production management willingness and agricultural production management behaviors, respectively, for empirical testing. Farmers’ professional differentiation has related influence factors on agricultural production management willingness and land transfer behavior. After analyzing the results of the model, this article concludes that the occupational differentiation of farmers has a significant impact on the willingness of agricultural production management, and the specific manifestations are significant in many aspects such as farmers’ age, education level, whether they have nonagricultural employment skills, and the number of family agricultural labors. The professional differentiation of farmers also has a significant impact on agricultural production management behavior, which is specifically manifested in many aspects such as farmers’ age, education level, nonagricultural employment skills, and geographical location of land.
Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training
Authors: Li Dongnan , Zhang Jianpeng
Published: November 2021

As the world's largest sport, football has affected a wide area and a large number of participants and had a great impact on political economy and culture, which has become the best embodiment of the social function of football. Throughout the experience of football in developed countries in the world, the stable development of youth football is the best way to improve the level of football in a country, and the Chinese Football Association has invested more energy in professional leagues and national teams. The development of youth campus football is basically in a state of no management. Therefore, people gradually realize the concept that “football should serve education.” In order to solve the problem of football players' lack of exercise in multiple subjects, it is particularly important to design systems and make plans for their respective physical characteristics. After the failure of various important competitions, the Chinese national football team reflected on the specific factors of backwardness. Under today's system, no one manages the specific development of youth campus football. Especially for young people, training programs that adapt to their individual characteristics should be formulated according to their growth stage and physical characteristics. It can effectively improve the efficiency of football teaching and training (FTT) by managing football players' training information and coaches' teaching information in an intelligent and informatized way. The different sports in which athletes participate in training are identified through the motion recognition layer. The data generated during the entire exercise process, including exercise time, number of exercises, score settlement, and other data, are stored, and the data are finally uploaded to the server, to carry out scientific analysis and management and generate sports training prescriptions in line with their own characteristics. This paper proposes research methods based on the intelligent integrated system of FTT, including literature retrieval, questionnaire survey, training empirical method, comparative analysis method, interview method, and support vector machine model for action recognition, which are used in football teaching and the design experiment of the intelligent integrated system for training; the overall architecture design of the football teaching intelligent integration system and the specific design of the football teaching intelligent integration system are proposed. The experimental results of this article show that 90.70% of players like the teaching mode of intelligent FTT, and the intelligent FTT system can help improve the enthusiasm of players in training and learning.
Influence of Steel Slag‐Superfine Blast Furnace Slag Composite Mineral Admixture on the Properties of Mortar and Concrete
Authors: Liu Zhenguo , Huang Zongxian
Published: November 2021

A composite mineral admixture was prepared by steel slag and superfine blast furnace slag. The influence of superfine blast furnace slag content of the composite mixture on the mortar and concrete was investigated. The results show that the composite mineral admixture may decrease the strength of concrete at the early age but improve the strength development over time. Increasing the content of superfine blast furnace slag can reduce the degradation of the early strength. The reduction of the autogenous shrinkage and adiabatic temperature rise is significant when the composite mineral admixture is added. The reduction is more obvious when the water‐to‐solid ratio (w/s) is low. The results show that with steel slag and superfine blast furnace slag playing as complementary parts in the composite mineral admixture, it can be used as an effective substitute of cement.
Live-Cell Imaging Shows Uneven Segregation of Extrachromosomal DNA Elements and Transcriptionally Active Extrachromosomal DNA Hubs in Cancer
Authors: Yi Eunhee , Gujar Amit D. , Guthrie Molly , Kim Hoon , Zhao Dacheng , Johnson Kevin C. , Amin Samirkumar B. , Costa Megan L. , Yu Qianru , Das Sunit , Jillette Nathaniel , Clow Patricia A. , Cheng Albert W. , Verhaak Roel G.W.
Published: November 2021

Oncogenic extrachromosomal DNA elements (ecDNA) play an important role in tumor evolution, but our understanding of ecDNA biology is limited. We determined the distribution of single-cell ecDNA copy number across patient tissues and cell line models and observed how cell-to-cell ecDNA frequency varies greatly. The exceptional intratumoral heterogeneity of ecDNA suggested ecDNA-specific replication and propagation mechanisms. To evaluate the transfer of ecDNA genetic material from parental to offspring cells during mitosis, we established the CRISPR-based ecTag method. ecTag leverages ecDNA-specific breakpoint sequences to tag ecDNA with fluorescent markers in living cells. Applying ecTag during mitosis revealed disjointed ecDNA inheritance patterns, enabling rapid ecDNA accumulation in individual cells. After mitosis, ecDNAs clustered into ecDNA hubs, and ecDNA hubs colocalized with RNA polymerase II, promoting transcription of cargo oncogenes. Our observations provide direct evidence for uneven segregation of ecDNA and shed new light on mechanisms through which ecDNAs contribute to oncogenesis.

Significance:

ecDNAs are vehicles for oncogene amplification. The circular nature of ecDNA affords unique properties, such as mobility and ecDNA-specific replication and segregation behavior. We uncovered fundamental ecDNA properties by tracking ecDNAs in live cells, highlighting uneven and random segregation and ecDNA hubs that drive cargo gene transcription.

See related commentary by Henssen, p. 293.

This article is highlighted in the In This Issue feature, p. 275
Indolent T-Lymphoblastic Proliferation in Idiopathic Multicentric Castleman Disease
Authors: Karki Nabin Raj , Arfa Ahmed Samire , Savage Natasha , Kutlar Abdullah
Published: November 2021

Benign and polyclonal proliferation of immature T cells in a lymph node with preserved morphological architecture is called indolent T-lymphoblastic proliferation (iT-LBP). Although overall rare, they have been described in association with both benign and malignant disorders including Castleman disease. We report the first case of idiopathic multicentric Castleman disease associated with iT-LBP, all previous reports of iT-LBP in Castleman disease were unicentric. A 37-year-old-male presented with 3 months of fevers and B-symptoms and was found to have enlargement of multiple bilateral lymph node sites on both sides of diaphragm along with splenomegaly. Anemia, elevated C-reactive protein, hypoalbuminemia, and elevated interleukin-6 levels were present. Biopsy of a lymph node showed features suggestive of idiopathic multicentric Castleman disease and iT-LBP. Bone marrow biopsy was unremarkable. Siltuximab and steroids were used to treat the condition.
Fluid evolution from quartz veins in micaschists from the thermal aureole around the Acari batholith, Northeast Brazil
Authors: Souza Laécio Cunha de , Delgado Regina Celia de Oliveira Brasil , Maia Heitor Neves
Published: November 2021

Micaschists that host the Acari batholith (Ediacaran age, 572 to 577 My) are characterized by a large number of quartz veins. The veins are more abundant in higher-temperature metamorphic zones and, together with lower metamorphic zones, form an aureole centered in the batholith. Most of the fluid inclusions are two-phase (H2O-CO2 and liquid/vapor), but three-phase varieties (liquid/vapor/salt cubes; liquid/liquid/vapor) occur locally. The analyzed veins come from the biotite + chlorite + muscovite, biotite + garnet, cordierite + andalusite, and cordierite + sillimanite metamorphic zones. CO2 melting temperatures (TmCO2) vary from -62.6 to -56.7°C, suggesting CH4 and/or N2. Eutectic temperatures (Te) in quartz veins show average values of -30.8°C in the biotite + chlorite + muscovite and biotite + garnet zones, and -38.6°C in the cordierite + andalusite and cordierite + sillimanite zones. Ice-melting temperatures (Tmice) are lower in the higher-temperature metamorphic zones. The mode values are -3.8, -5.5, -5.6, and -7.3°C, corresponding respectively to the biotite + chlorite + muscovite, biotite + garnet, cordierite + andalusite, and cordierite + sillimanite zones. A fluid characterized by the H2O-Na-Cl (KCl)-MgCl2-FeCl2-CaCl2 system is defined by: Tmice from near -1.9 to -32°C, the presence of salt cubes mainly in the cordierite + andalusite and cordierite + sillimanite zones, and recorded eutectic temperatures (Te) from -16.5 to -59.1°C. In addition, total homogenization temperatures (Tht) ranging from 117 to 388°C were obtained for primary aqueous fluid inclusions. This indicates a long period of fluid circulation under conditions of falling temperatures. Our results are consistent with an increase in the salinity of the aqueous fluid across the thermal aureole toward the granitic batholith.
Fazer ver e fazer falar: notas sobre o estilo filosófico de Merleau-Ponty
Authors: Cardim Leandro Neves
Published: November 2021

Através da investigação destas duas figuras da retórica clássica na obra de Merleau-Ponty pretende-se verificar como ele as incorpora a sua filosofia. Para realizar esta tarefa o artigo compara passagens de todos os períodos de sua obra. Quando o filósofo usa estas figuras ele nos faz pensar nas relações da pintura, do cinema e da literatura com a filosofia. Ao trabalhar estas questões o artigo pretende não só conduzir uma interrogação de fundo sobre o modo como Merleau-Ponty trabalha com questões tradicionais, mas também sobre o alcance da ontologia.
Reduction in Right Atrial Pressures Is Associated With Hemodynamic Improvements After Transcatheter Edge-to-Edge Repair of the Tricuspid Valve
Authors: Mahowald Madeline K. , Nishimura Rick A. , Pislaru Sorin V. , Mankad Sunil V. , Nkomo Vuyisile T. , Padang Ratnasari , Thaden Jeremy J. , Alkhouli Mohamad , Guerrero Mayra , Rihal Charanjit S. , Eleid Mackram F.
Published: November 2021

Background:

Investigational transcatheter edge-to-edge repair (TEER) for severe tricuspid regurgitation (TR) has shown promise as an alternative to surgery, but factors influencing outcomes, optimal patient selection, and procedural timing remain incompletely defined. Given the limitations of determining TR severity by conventional echocardiography, our objectives were to determine whether invasive right atrial (RA) pressures performed during the procedure are related to patient outcomes.

Methods:

This study was a retrospective review of patients who underwent off-label tricuspid TEER using MitraClip (Abbott Vascular, Menlo Park, CA) for significant TR at a single institution. Intraprocedural mean RA pressure, RA peak V-wave, RA pressure nadir, and systolic increase in RA pressure (XV height) were recorded.

Results:

Thirty-eight patients underwent tricuspid TEER; 33 underwent concomitant mitral TEER for mitral regurgitation. The study cohort was 39% female with a mean age of 78.6±14.3 years. Median follow-up was 339 days (interquartile range, 100–601). Any reduction in mean RA pressure, RA peak V-wave, RA nadir, and XV height occurred in 74%, 82%, 45%, and 87% of patients, respectively. At 1 year, event-free survival was 47%. Postprocedure XV height correlated with TR severity as determined by echocardiography (
P
<0.0001). The highest quartile of postprocedure XV height (>8 mm Hg) had worse event-free survival compared with those who had concluding XV height ≤8 mm Hg (
P
=0.02). Attainment of a concluding XV height less than or equal to median value was associated with a lower creatinine the next day (1.27±0.47 versus 1.64±0.47 mg/dL,
P
=0.04).

Conclusions:

Intraprocedural XV height correlates with TR severity after tricuspid TEER, and lower concluding pressures are associated with improved outcomes. Analysis of RA pressures may serve as a complementary tool for the evaluation of disease severity and procedural guidance.