bluuebunny/crossref_metadata_2025 huggingface dataset:
Spinal changes after 5-day dry immersion as shown by magnetic resonance imaging (DI-5-CUFFS)
Authors: Robin Adrien , Navasiolava Nastassia , Gauquelin-Koch Guillemette , Gharib Claude , Custaud Marc-Antoine , Treffel Loïc
Published: June 2022

Astronauts frequently report microgravity-induced back pain, which is generally more pronounced in the beginning of a spaceflight. The dry immersion (DI) model reproduces the early effects of microgravity in terms of global support unloading and fluid shift, both of which are involved in back pain pathogenesis. Here, we assessed spinal changes induced by exposure to 5 days of strict DI in 18 healthy men (25–43-yr old) with ( n = 9) or without ( n = 9) thigh cuffs countermeasure. Intervertebral disk (IVD) height, spinal cord position, and apparent diffusion coefficient (ADC; reflecting global water motion) were measured using magnetic resonance imaging before and after DI. After DI, IVD height increased in thoracic (+3.3 ± 0.8 mm; C7–T12) and lumbar (+4.5 ± 0.4 mm; T12–L5) regions but not in the cervical region (C2–C7) of the spine. An increase in ADC after DI was observed at the L1 (∼6% increase, from 3.2 to 3.4 × 10−3 mm2/s; P < 0.001) and L2 (∼3% increase, from 3.4 to 3.5 × 10−3 mm2/s; P = 0.005) levels. There was no effect of thigh cuffs on spinal parameters. This change in IVD after DI follows the same “gradient” pattern of height increase from the cervical to the lumbar region as observed after bed rest and spaceflight. The increase in ADC at L1 level positively correlated with reported back pain. These findings emphasize the utility of the DI model for studying early spinal changes observed in microgravity.
Analysis and Research on Sustainable Development Factors of the Sports Industry Based on Chaos Theory
Authors: Luo Yuankai , Gao Shanshan , Yan Xuanfei , Cao Yecheng
Published: June 2022

The sports industry is an emerging industry with broad development prospects, and it is also full of competition. The sports industry has the characteristics of fluctuation, intermittence, and randomness, which are suitable for the analysis of chaos theory in order to find out the internal development law of the sports industry. In order to solve the above problems, an improved chaos theory method is proposed in this paper and the K‐cluster analysis method is integrated into chaos calculation, in order to reduce the occurrence rate of the “local extreme value” and improve the accuracy of calculation results. The model uses nonlinear and irregular chaos theory to analyze the aggregation degree of sports industry, industrial spatial distribution, and the spatial governance effect and find out the best optimization decision. When selecting the optimization indicators, not only the European distance of each indicator cluster but also the spatial correlation of the indicators are considered to realize the comprehensive analysis of the sports industry and improve the accuracy of optimization. In the simulation analysis of optimization decision‐making, the decision‐making model based on chaos theory is compared with the previous first‐order decision‐making model. The results show that the improved chaos theory can control the data aggregation range of sports industry between (0∼3), the data fusion degree of industrial space between 95 and 99%, and the variation range between 0 and 0.2%, which is significantly better than (0∼9), 90∼95%, and 0∼0.4% of the genetic algorithm. Therefore, the aggregation degree, spatial governance, and decision optimization of the optimization decision‐making model proposed in this paper are better than those of the previous genetic algorithm.
Integration of Artistic Visual Elements in Packaging Design Based upon Pattern Recognition Technology
Authors: Li Weiwei
Published: June 2022

Under the development trend of today’s society, any brand attaches great importance to its outer packaging, and the research and analysis of the design effect of outer packaging has always troubled many brands. The purpose of this paper is to conduct a comprehensive study on the integration of artistic visual elements in the design effect of product packaging through the use of pattern recognition technology. Therefore, this paper proposes an improvement to the feature analysis algorithm of image recognition in pattern recognition technology and understands the specific principle of pattern recognition and improves its specific recognition method. At the same time, in the experiment and analysis part, this paper conducts research experiments on the improved algorithm and explores the performance of the algorithm and the adaptability of the algorithm in the actual packaging design recognition process. The experimental results of this paper show that the improved pattern recognition improves the accuracy of text recognition in packaging by 18% and the accuracy of image noise point recognition by nearly 30%. It can be detected very efficiently in the artistic visual fusion process of packaging design.
Research on the Development and Integration of Inclusive Finance in Agricultural Economy Based on the Artificial Intelligence Machine Learning Algorithm
Authors: Deng Chuyao
Published: June 2022

Inclusive finance can help rural low-income groups, township small and microenterprises, and other groups in China to improve their living and economic standards and can meet the relevant credit needs of relevant rural personnel under the condition of risk prevention. The integration of AI and HP finance has built a more professional information and data platform for customers with different credit needs in the agricultural economy. Based on the development and integration of agricultural economy, this study makes effective analysis and judgment on poor customers with credit needs through the artificial intelligence system platform under the application of inclusive finance, so as to scientifically and effectively identify and screen the big data of different credit data of customers and finally select the most qualified high-quality credit customers, so as to effectively improve the overall economic income of customers with agricultural credit needs. It has laid a foundation for the development of agricultural economy and more effectively extracting high-quality credit customers, so that the development of agricultural economy has been widely used.
Innovative System for Analyzing English Translation Software Based on IoT Big Data
Authors: Niu Rongliang , Yang Wei
Published: June 2022

With the continuous expansion and deepening of international exchanges and cooperation, language differences have become the biggest obstacle to exchanges and cooperation. At present, English translation software is the translation between natural languages made by humans on the basis of computers. As an important technical means to break through language barriers, machine translation is a software that uses computers to process spoken or written language. This paper aims to study the innovative system of English translation software, using the Internet of Things and big data algorithms to analyze some loopholes and deficiencies in the innovative system, and provide some data research for the system update. This paper proposes to analyze the innovative system of translation software based on the big data of the Internet of Things (IoT). Among them, the big data of the Internet of Things chooses the dynamic frame time slot ALOHA algorithm and the lower limit estimation algorithm. Its application in system analysis is very innovative. The system consists of a translation engine and a machine dictionary, which solves the basic problem of insufficient translation database of current translations, and plays a role in helping the English translation software innovation system to improve the accuracy of translations. The experimental results of this paper show that the translation system’s translation software achieves 61.3% of the A-target fluent translations, and 22.7% of the B-targets can translate the original text. It can be seen that English translation software has important research significance.
The Protection Mechanism of Personal Health Information in the Digital Economy Environment
Authors: Liu Nan , Chen Shiyong
Published: June 2022

With the application of big data technology in the medical field, personal health information is experiencing unprecedented challenges while showing great value. Opportunities and risks are like two sides of the same coin, and the rapid development of science and technology has not only brought valuable opportunities, but also made modern society seem to have developed into a risk society. Although countries around the world are committed to promoting the development and application of medical data and pay great attention to the protection of personal health information, the risk attributes of personal health information have rarely been mentioned before. This article first introduces the origin and development of medical big data, paving the way for the special risks faced by personal health information in this context. Then, through the introduction of concepts and characteristics, the content of personal health information is accurately defined. Next, according to a large number of personal health information security incidents, the new semantic scope of personal health information risk in the medical big data environment is analyzed, the necessity of personal health information risk regulation is emphasized, and the value conflicts that may occur in the process of personal health information risk regulation are clarified, laying the groundwork for the establishment of the risk decision‐making procedure in the following article.
Customized Appliance Device for Force Detection in Bruxism Individuals: An Observational Study
Authors: Pollis Matteo , Maoddi Pietro , Letizia Marco , Manfredini Daniele
Published: June 2022

Objective. This study aims to test a customised device for detecting contact-related sleep bruxism in adult patients and to show the efficacy of an established biofeedback method incorporated within the device. Methods. Four volunteers, three of whom suffered from bruxism and one did not, underwent four tests to assess bruxism-related force detection during sleep with concurrent electromyographic recording and to compare SB activity with and without biofeedback stimuli. Results. The device detected sleep bruxism in bruxer individuals, whilst no activity emerged in the control individual. A correlation between EMG and device signals for bruxism-related events emerged. Moreover, bruxism activity showed a significant decrease on the nights when the biofeedback treatment was applied. Conclusion. The force-based device can detect appliance-contacting SB events as reliably as EMG recording. Finally, biofeedback stimuli allowed achieving a reduction in the severity and frequency of SB events.
A Structural Equation Model-Based Study of the Effect of Perceived Risk of Performance on the Consumption Behaviour of Soccer Spectators
Authors: Li Junjie , Su Jiaben
Published: June 2022

The purpose of this research is to examine the intentions of sports consumption of viewers of two Chinese Super League soccer teams and to determine whether changes in behaviour are related to the teams’ league standings. Spectators from both teams participate in the study voluntarily. The study employs descriptive statistics and fieldwork, and the questionnaire used in the study has a reliability coefficient of 0.87. Experts in sports management determine the content validity, while validation factor analysis is used to determine the structural validity. The statistical population is composed of two Chinese Super League teams (one team is relatively successful and one less successful). Six hundred and seventy-eight valid questionnaires are distributed randomly. Following data collection, the link between consumption behaviour and perceived risk is examined. The findings show that performance risk has an effect on game watching intention, t = 0.04, with a standard solution of 1.99, on recommending others watch the game, t = 0.11, with a standard solution of 0.98, on team licenced merchandise consumption, t = −0.05, with a standard solution of −0.41, and on the consumption of media, t = 4.21, with a standard solution of 1.19. Therefore, performance perceived risk significantly affects game watching and media consumption intention has a significant effect but has no significant effect on recommending others to watch the game and licensed merchandise consumption.
Sequential Increase in Complement Factor I, iC3b, and Cells Expressing CD11b or CD14 in Cutaneous Vasculitis
Authors: Rahkola Dina , Lipitsä Tiina , Siiskonen Hanna , Naukkarinen Anita , Harvima Ilkka T.
Published: June 2022

Mast cells contribute to the pathogenesis of cutaneous vasculitis through complement C3 that is cleaved to C3b and then to iC3b by complement factor I. The receptor of iC3b, CD11b, is expressed on neutrophils and monocytes and CD14 on monocytes. Their role in vasculitis is obscure. In this study, frozen skin biopsies from the nonlesional skin, initial petechial lesion, and palpable purpura lesion from 10 patients with immunocomplex-mediated small vessel vasculitis were studied immunohistochemically for complement factor I, iC3b, CD11b, and CD14. Peripheral blood mononuclear cells from 5 healthy subjects were used to study cell migration and cytokine secretion. Already, the nonlesional skin revealed marked immunostaining of complement factor I, iC3b, CD11b, and CD14, and their expression increased sequentially in initial petechial and palpable purpura lesions. Mast cell C3c correlated to iC3b, and both of them correlated to CD11b+ and CD14+ cells, in the nonlesional skin. The stimulation of mononuclear cells with 0.01-0.1 μg/ml iC3b induced cell migration in the transwell assay. C3a stimulated slightly interleukin-8 secretion, whereas 1 μg/ml iC3b inhibited it slightly, in 4/5 subjects. In conclusion, the C3-C3b-iC3b axis is activated already in the early vasculitis lesion leading to progressive accumulation of CD11b+ and CD14+ cells.
The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching
Authors: Xiao Lijun , Luo Yan
Published: June 2022

The rapid growth of artificial intelligence technology has been deployed in art teaching and learning. Radial basis function (RBF) networks have a completely different design compared to most neural network architectures. Most neural networks consist of multiple layers that can introduce nonlinearity by repetitive application of nonlinear activation functions. In this research, people will study the application of the RBF neural network model based on deep learning in flower pattern design in art teaching. The image classification process is finding and labeling groups of pixels or vectors inside an image based on rules. Deep learning is a type of machine learning that uses artificial neural networks to replicate the structure and function of the human brain. The proposed model uses the RBF neural network-based deep learning model in flower pattern design in art teaching and provides efficient results.
Design of Neural Network Model for Cross-Media Audio and Video Score Recognition Based on Convolutional Neural Network Model
Authors: Liu Hongxia
Published: June 2022

In this paper, the residual convolutional neural network is used to extract the note features in the music score image to solve the problem of model degradation; then, multiscale feature fusion is used to fuse the feature information of different levels in the same feature map to enhance the feature representation ability of the model. A network composed of a bidirectional simple loop unit and a chained time series classification function is used to identify notes, parallelizing a large number of calculations, thereby speeding up the convergence speed of training, which also makes the data in the dataset no longer need to be strict with labels. Alignment also reduces the requirements on the dataset. Aiming at the problem that the existing cross-modal retrieval methods based on common subspace are insufficient for mining local consistency within modalities, a cross-modal retrieval method fused with graph convolution is proposed. The K-nearest neighbor algorithm is used to construct modal graphs for samples of different modalities, and the original features of samples from different modalities are encoded through a symmetric graph convolutional coding network and a symmetric multilayer fully connected coding network, and the encoded features are fused and input. We jointly optimize the intramodal semantic constraints and intermodal modality-invariant constraints in the common subspace to learn highly locally consistent and semantically consistent common representations for samples from different modalities. The error value of the experimental results is used to illustrate the effect of parameters such as the number of iterations and the number of neurons on the network. In order to more accurately illustrate that the generated music sequence is very similar to the original music sequence, the generated music sequence is also framed, and finally the music sequence spectrogram and spectrogram are generated. The accuracy of the experiment is illustrated by comparing the spectrogram and the spectrogram, and genre classification predictions are also performed on the generated music to show that the network can generate music of different genres.
Vampire Attack Mitigation and Network Performance Improvement Using Probabilistic Fuzzy Chain Set with Authentication Routing Protocol and Hybrid Clustering-Based Optimization in Wireless Sensor Network
Authors: Alkwai Lulwah M. , Mohammed Aledaily Arwa Naser , Almansour Shahad , Alotaibi Shoayee Dlaim , Yadav Kusum , Lingamuthu Velmurugan
Published: June 2022

The most effective threat for wireless sensor networks (WSN) is Vampire attacks on sensor nodes as they can stretch the network connectivity among them and influence the network’s energy, which can drain the network. Vampire attack has particular malicious nature of sensor nodes in which they can widely exploit features of combined routing protocol. Fuzzy rules and fuzzy sets are highly optimal techniques in mitigating the vampire attacks of the network, which can quantify the uncertain behaviour of sensor nodes. This study aims to propose a novel technique using a probabilistic fuzzy chain set with authentication-based routing protocol and hybrid clustering technique for data optimization of the network. The suggested approach here employs a fuzzy-based chain rule set to combat growing types of vampire assaults using probability formulas. The authentication routing protocol has increased network routing security. The proposed technique (PFCS-ARP_HC) has optimized the energy consumption of network. Simulation for this technique has been carried out using NS2 and experimental results show the performance of the proposed model in terms of throughput of 98%, packet delivery ratio of 89%, energy consumption of 67%, latency of 46% control overhead of 53%, and attack detection ratio of 87.9%.
Identification and Validation of a Novel Immune Infiltration-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma by Machine-Learning Strategies
Authors: Guo Xuli , Xiong Hailin , Dong Shaoting , Wei Xiaobing
Published: June 2022

Objective. To investigate the diagnostic gene biomarkers for hepatocellular carcinoma (HCC) and identify the immune cell infiltration characteristics in this pathology. Methods. Five gene expression datasets were obtained through Gene Expression Omnibus (GEO) portal. After batch effect removal, differentially expressed genes (DEGs) were conducted between 209 HCC and 146 control tissues and functional correlation analyses were performed. Two machine learning algorithms were used to develop diagnostic signatures. The discriminatory ability of the gene signature was measured by AUC. The expression levels and diagnostic value of the identified biomarkers in HCC were further validated in three independent external cohorts. CIBERSORT algorithm was adopted to explore the immune infiltration of HCC. A correlation analysis was carried out between these diagnostic signatures and immune cells. Results. A total of 375 DEGs were identified. GPC3, ACSM3, SPINK1, COL15A1, TP53I3, RRAGD, and CLDN10 were identified as the early diagnostic signatures of HCC and were all validated in external cohorts. The corresponding results of AUC presented excellent discriminatory ability of these feature genes. The immune cell infiltration analysis showed that multiple immune cells associated with these biomarkers may be involved in the development of HCC. Conclusion. This study indicates that GPC3, ACSM3, SPINK1, COL15A1, TP53I3, RRAGD, and CLDN10 are potential biomarkers associated with immune infiltration in HCC. Combining these genes can be used for early detection of HCC and evaluating immune cell infiltration. Further studies are needed to explore their roles underlying the occurrence of HCC.
Level of Clients’ Satisfaction and Associated Factors with the Service of Out-Patient Department in Dilla University Referral Hospital, Southern Ethiopia, 2021
Authors: Melesse Dersolign , Tesfu Mehret , Mantefard Bahiru
Published: June 2022

Background. Client satisfaction with seeking healthcare is generally regarded as one of the core outcomes of the health system. Various efforts are underway to provide hospitals with the necessary manpower, medical equipment, and other services to suit the demands of their patients. The goal of this study was to determine the level of client satisfaction with outpatient department services and the factors that influence it at Dilla Referral Hospital in Ethiopia. Methodology. A cross-sectional investigation was undertaken in a hospital setting. An interviewer-administered quantitative data were collected on socio demographic characteristics of respondents and their satisfaction level with the different components of the outpatient services. SPSS version 20 was used to conduct the analysis. The connection between independent and dependent variables was evaluated using bivariate analysis (

p
<
0.25

). To discover the determinants of client satisfaction and control cofounding, multivariate logistic regression was performed (

p
≤
0.05

). Result. The study enrolled a total of 419 individuals, with a response rate of 98.3%. Overall, 52.2 percent of clients were satisfied with the health services provided by the hospitals’ outpatient departments. Client satisfaction was significantly predicted by the cleanliness of the consultation room (AOR = 2.05, 95% CI: 1.06–3.95), payment status (AOD = 1.68, 95% CI: 1.08–2.63), and telling clients about the etiology of sickness (AOR = 0.55, 95% CI: 0.34–0.87). Conclusion. The general satisfaction of outpatients with Dilla referral hospital’s OPD clinics healthcare services was low. The cleanliness of the consultation room, payment status, and readiness to suggest the service to others were all linked to a positive outcome.
Some Results in Neutrosophic Cubic Graphs with an Application in School’s Management System
Authors: Kou Zheng , Maryam Akhoundi  , Ghassemi M. , Talebi A. A. , Muhiuddin G.
Published: June 2022

Neutrosophic cubic graph (NCG) belonging to FG family has good capabilities when facing
problems that cannot be expressed by FGs. When an element membership is not clear,
neutrality is a good option that can be well supported by a NCG. Hence, in this paper,
some types of edge irregular neutrosophic cubic graphs (EI‐NCGs) such as neighborly edge
totally irregular (NETI), strongly edge irregular (SEI), and strongly edge totally
irregular (SETI) are introduced. A comparative study between NEI‐NCGs and NETI‐NCGs is
done. Finally, an application of neutrosophic cubic digraph to find the most effective
person in a school has been presented.
Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology
Authors: Sun Hongbin , Bai Shizhen
Published: June 2022

This work is conducted to deal with the information security of enterprise management under the background of current global informatization, popularize the modern Internet of Things (IoT) management technology of enterprises, and maintain the information security of enterprises and provide modern upgrading means for enterprise management. In this work, it firstly introduces the application scenarios of current Internet and Artificial Intelligence (AI) technology and expounds the IoT technology. Secondly, the enterprise management platform is designed, the requirements of enterprise modern management are analyzed, and then the design requirements of system functions and the design of the information security architecture of the IoT are proposed. Furthermore, an enterprise information security management platform is designed, which covers four parts: IoT data mining management, equipment management, key management, and database management. In addition, the performance of the security management platform is tested from four parts: concurrency testing, stress testing, large data volume testing, and security testing. The research results show that the IoT-based enterprise information security management platform designed in this work under the background of AI has perfect functions and stable performance of each module. Concurrency testing, stress testing, large data volume testing, and stability testing are performed on it, and the success rate of the platform in each task reaches 100%. The average response time of concurrent testing and stress testing is about 0.13 seconds, and that of the event entry events is 0.25 seconds. The central processing unit (CPU) occupancy rate in each monitoring task is always lower than 20%. Therefore, it is determined that the performance of the IoT-based enterprise information security management platform designed in this work is sufficient to meet the daily management of enterprises. This work can provide a guarantee for enterprise information security management using AI technology, setting an example for future related research.
Quality Data Collection and Quality Monitoring of Smart Electric Energy Meter Based on Acceleration Sensor
Authors: Shen Guang , Ye Shen , Shen Shuming , Xu Yongjin , Shang Huaiying
Published: June 2022

In order to solve the problem of frequent switching and tripping of intelligent power meter and ineffective power failure, a quality data collection and quality monitoring based on acceleration sensor is proposed. Firstly, this paper briefly describes the objects and methods of quality data acquisition and quality monitoring of intelligent watt hour meter. At the same time, based on the analysis of the requirements of quality data acquisition and quality monitoring of intelligent watt hour meter and based on B/S architecture, the quality data acquisition and quality monitoring framework of intelligent watt hour meter is designed. Finally, an intelligent power meter quality monitoring interface with friendly interactive and real-time display acceleration sensor is built using SVG and JavaScript technology. The results show that the collection and monitoring data based on acceleration sensor, three batches of three-phase 0.2 intelligent electric energy meters, meet the operation requirements. Among them, the batch failure rate and classification failure rate in the operation of the third batch are low, and the sampling qualified rate is high, which is an excellent level. The intelligent power meter quality data collection and monitoring system of acceleration sensor can also meet the requirements of full life cycle monitoring of intelligent energy meter quality. It provides a new method for the data acquisition of intelligent electric energy meter and hopes to provide some references for the timely discovery and solution of the quality problems of intelligent electric energy meter in the future.
On Hilfer-Type Fractional Impulsive Differential Equations
Authors: Metpattarahiran Chanisara , Karthikeyan Kulandhaivel , Karthikeyann Panjaiyan , Sitthiwirattham Thanin
Published: June 2022

Using the Schauder fixed point theorem, we prove the existence of impulsive fractional differential equations using Hilfer fractional derivative and nearly sectorial operators in this paper. We’ve gone over the two scenarios where the related semigroup is compact and noncompact for this purpose. We also go over an example to back up the main points.
Experimental Analysis and Improvements of a Visible Spectrophotometer for Detection of Nano Materials
Authors: Rajakumari Pamula , Bojja Polaiah , Ch Smitha Chowdary , Mrudula Sunkara Teena , Putta Krishnam Raju , Gosuadigo Amsalu
Published: June 2022

As the field of nanotechnology advances, there is an increasing need for green nanomaterial identification devices. Recently, a few new studies have reminded us that as nanotechnology gets better and better, so will natural phenomena. As we grow closer to and finally reach the nanoscale, it is feasible that new physical expertise will develop. Developments in the future may allow for new technical advancements. It is the ability of nanotechnology to construct human constructs at the nanoscale that distinguishes it from other fields of science and engineering. Various components, including high-dissociation electron microscopy, centre-ion beam milling tools, and scanner probes, have made this practical. Spectrometers, sometimes known as spectrometers, are used in fabric identification machines. To conclude this inquiry, a nanoparticle scatter spectrometer was devised and built artificially. This study focused on the visible spectrum of spectroscopy because there are a broad number of programmes available for visible optical instruments.
Controlling Effect of Wave-Dominated Delta Sedimentary Facies on Unconventional Reservoirs: A Case Study of Pinghu Tectonic Belt in Xihu Sag, East China Sea Basin
Authors: Chen Qinglong , Pu Renhai , Xue Xiaomin , Han Ming , Wang Yanxin , Cheng Xin , Wu Hanning
Published: June 2022

In order to find the “sweet spots” of unconventional oil and gas from the Pinghu Formation in the Pinghu Tectonic Belt, we are committed to clarifying the development and distribution rules of coal-measure source rocks and tight reservoirs as well as the controlling factors. Using 3D seismic and logging data, combined with logging constraints of target lithology and pyrolysis experiments of source rock, "source and reservoir" research of uncoventional oil and gas was carried out in the Pinghu Tectonic Belt. The results show that two stages of regression and one stage of transgression occurred in the Pinghu Formation, resulting in river-controlled and wave-controlled delta-neritic facies dominated by sedimentary facies, source rocks developed in interdistributary bay and swamp microfacies, and tight sandstones in point bar and distributary channel microfacies are developed. The accumulation of coalbed methane and shale gas is controlled by the sedimentary facies and the degree of thermal evolution under the factors of burial depth. The accumulation of tight sandstone is closely related to the dominantly sedimentary facies and diagenetic modification of physical properties and faults. It is concluded that the structural type, type and distribution of faults, and depositional environment of the Pinghu Tectonic Belt in the Xihu Sag are the key factors controlling the development and accumulation of coal-measure source rocks and tight sandstone reservoirs. This understanding provides a clear direction for the deep exploration of unconventional oil and gas in the sag and provides a reference for finding reservoir “sweet spots.”
A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
Authors: Liu Jinghuai , Zhang Qian , Shen Ang , Gao Ying , Hou Jiaqi , Wang Bin , Yan Tao
Published: June 2022

Different from traditional images, light field images record not only spatial information but also angle information. Due to the large volume of light field data brings great difficulties to storage and compression, light field compression technology has attracted much attention. The epipolar plane image (EPI) contains a lot of low rank information, which is suitable for recovering the complete EPI from a part of EPI. In this paper, a light field image coding framework based on EPI restoration neural network has been proposed. Compared with previous algorithms, the proposed algorithm further takes advantage of the inherent similarity in light field images, and the proposed framework has higher performance and robustness. Experimental results show that the proposed method has superior performance compared to the state‐of‐the‐art both in quantitatively and qualitatively.
Art Design Method of Industrial Products Based on Internet of Things Technology and Virtual VR
Authors: Yu Yu
Published: June 2022

The Internet of Things technology transmits and shares physical information on the Internet, and describes the continuous world through scattered data. From the perspective of internal characteristics, the efficient interconnection of large-scale heterogeneous network elements is composed of the Internet of Things and imported from abroad, so there has been a high degree of accumulation in foreign countries so far. In 1980, the United States proposed a virtual reality concept called VR for short. Embedded virtual VR technology usually uses equipment to close the user’s senses and temporarily isolate the user from the actual environment. This article also discusses the design of industrial products, the continuous development of big data, artificial intelligence, and additive material manufacturing technology to make the design methods and manufacturing processes of industrial products more scientific and systematic, and promote the diversity and precision of industrial product design. Social informatization plays an important role in all areas of digital technology design. Parametric design has changed the design thinking mode of designers and formed new design trends and styles. Taking the search for relationships and rules as the starting point of the design and the algorithm as the core content of the design are very different from traditional design ideas. The computer age has not only cultivated new styles but also cultivated new artistic design techniques for industrial products. Designers should strive to explore the potential functions of algorithm technology and focus on smarter and more logical design and production processes. In this paper, through the research of the Internet of Things technology and virtual VR, it is applied to the research of the artistic design method of industrial products, aiming to promote its better development.
Association Study between Polymorphisms in DNA Methylation–Related Genes and Testicular Germ Cell Tumor Risk
Authors: Grasso Chiara , Popovic Maja , Isaevska Elena , Lazzarato Fulvio , Fiano Valentina , Zugna Daniela , Pluta John , Weathers Benita , D'Andrea Kurt , Almstrup Kristian , Anson-Cartwright Lynn , Bishop D. Timothy , Chanock Stephen J. , Chen Chu , Cortessis Victoria K. , Dalgaard Marlene D. , Daneshmand Siamak , Ferlin Alberto , Foresta Carlo , Frone Megan N. , Gamulin Marija , Gietema Jourik A. , Greene Mark H. , Grotmol Tom , Hamilton Robert J. , Haugen Trine B. , Hauser Russ , Karlsson Robert , Kiemeney Lambertus A. , Lessel Davor , Lista Patrizia , Lothe Ragnhild A. , Loveday Chey , Meijer Coby , Nead Kevin T. , Nsengimana Jérémie , Skotheim Rolf I. , Turnbull Clare , Vaughn David J. , Wiklund Fredrik , Zheng Tongzhang , Zitella Andrea , Schwartz Stephen M. , McGlynn Katherine A. , Kanetsky Peter A. , Nathanson Katherine L. , Richiardi Lorenzo
Published: June 2022

Background:

Testicular germ cell tumors (TGCT), histologically classified as seminomas and nonseminomas, are believed to arise from primordial gonocytes, with the maturation process blocked when they are subjected to DNA methylation reprogramming. SNPs in DNA methylation machinery and folate-dependent one-carbon metabolism genes have been postulated to influence the proper establishment of DNA methylation.

Methods:

In this pathway-focused investigation, we evaluated the association between 273 selected tag SNPs from 28 DNA methylation–related genes and TGCT risk. We carried out association analysis at individual SNP and gene-based level using summary statistics from the Genome Wide Association Study meta-analysis recently conducted by the international Testicular Cancer Consortium on 10,156 TGCT cases and 179,683 controls.

Results:

In individual SNP analyses, seven SNPs, four mapping within MTHFR, were associated with TGCT risk after correction for multiple testing (q ≤ 0.05). Queries of public databases showed that three of these SNPs were associated with MTHFR changes in enzymatic activity (rs1801133) or expression level in testis tissue (rs12121543, rs1476413). Gene-based analyses revealed MTHFR (q = 8.4 × 10–4), methyl-CpG–binding protein 2 (MECP2; q = 2 × 10–3), and ZBTB4 (q = 0.03) as the top TGCT-associated genes. Stratifying by tumor histology, four MTHFR SNPs were associated with seminoma. In gene-based analysis MTHFR was associated with risk of seminoma (q = 2.8 × 10–4), but not with nonseminomatous tumors (q = 0.22).

Conclusions:

Genetic variants within MTHFR, potentially having an impact on the DNA methylation pattern, are associated with TGCT risk.

Impact:

This finding suggests that TGCT pathogenesis could be associated with the folate cycle status, and this relation could be partly due to hereditary factors.
Systematic Review and Meta-Analysis of Cardiovascular Medications in Neonatal Hypotension
Authors: Sarafidis Kosmas , Verykouki Eleni , Nikopoulos Stefanos , Apostolidou-Kiouti Fani , Diakonidis Theodoros , Agakidou Eleni , Kontou Aggeliki , Haidich Anna-Bettina
Published: June 2022

Background: Comparative studies among the various cardiovascular medications used for the treatment of neonatal hypotension are lacking. Methods: This systematic review and pairwise meta-analysis of the anti-hypotensive treatments in preterm and term infants was conducted to evaluate efficacy and impact on outcome. Electronic databases were searched up to February 2021 for relevant articles. As an extension of the current approach for study selection, a machine learning technique was used. Only randomized controlled trials (RCTs) of inotropes, pressors, volume therapy, and corticosteroids were included. Response to treatment was the primary outcome while secondary outcomes included mortality and common morbidities. Results: Nineteen RCTs involving 758 preterm and term neonates were found, and 8 treatments were evaluated. Most studies involved subjects with early hypotension associated with prematurity. Pairwise meta-analysis among treatments showed that dopamine was more effective than dobutamine regarding the response to treatment (restoration of normotension or normalization of blood pressure) (7 trials, 286 neonates, odds ratio, 3.06 [95% CI = 1.06–8.87]; I2 = 49%, very low quality of the evidence per GRADE). Comparisons of other treatments were not significant. No differences were found among regimens regarding survival and other secondary outcomes. Conclusion: In this systematic review and pairwise meta-analysis, only the comparison of dopamine versus dobutamine provided evidence for efficacy of treatment and favored dopamine. No safe conclusions could be reached in regard to other treatments. Data regarding the management of arterial hypotension in conditions other than transition after birth in preterm newborns are sparse both in preterm and term infants.
Association of Statins for Primary Prevention of Cardiovascular Diseases With Hospitalization for COVID‐19: A Nationwide Matched Population‐Based Cohort Study
Authors: Bouillon Kim , Baricault Bérangère , Semenzato Laura , Botton Jérémie , Bertrand Marion , Drouin Jérôme , Dray‐Spira Rosemary , Weill Alain , Zureik Mahmoud
Published: June 2022

Background

There is little evidence on the relationship between statin use and the risk of hospitalization attributable to COVID‐19.

Methods and Results

The French National Healthcare Data System database was used to conduct a matched‐cohort study. For each adult aged ≥40 years receiving statins for the primary prevention of cardiovascular diseases, one nonuser was randomly selected and matched for year of birth, sex, residence area, and comorbidities. The association between statin use and hospitalization for COVID‐19 was examined using conditional Cox proportional hazards models, adjusted for baseline characteristics, comorbidities, and long‐term medications. Its association with in‐hospital death from COVID‐19 was also explored. All participants were followed up from February 15, 2020, to June 15, 2020. The matching procedure generated 2 058 249 adults in the statin group and 2 058 249 in the control group, composed of 46.6% of men with a mean age of 68.7 years. Statin users had a 16% lower risk of hospitalization for COVID‐19 than nonusers (adjusted hazard ratio [HR], 0.84; 95% CI, 0.81–0.88). All types of statins were significantly associated with a lower risk of hospitalization, with the adjusted HR ranging from 0.75 for fluvastatin to 0.89 for atorvastatin. Low‐ and moderate‐intensity statins also showed a lower risk compared with nonusers (HR, 0.78 [95% CI, 0.71–0.86] and HR, 0.84 [95% CI, 0.80–0.89], respectively), whereas high‐intensity statins did not (HR, 1.01; 95% CI, 0.86–1.18). We found similar results with in‐hospital death from COVID‐19.

Conclusions

Our findings support that the use of statins for primary prevention is associated with lower risks of hospitalization for COVID‐19 and of in‐hospital death from COVID‐19.
Discovery, Pursuit, and Justification
Authors: Franklin Allan
Published: June 2022

In this article I suggest a tripartite classification of scientific activity; discovery, pursuit, and justification. I believe that such a classification can give us a more adequate description of scientific practice, help illuminate the various roles that evidence plays in science, and may also help to partially resolve differences between “constructivist” and “epistemologist” views of science. I argue that although factors suggested by the constructivists such as career goals, professional interests, utility for future practice, and agreement with existing commitments do enter into pursuit, it is experimental evidence that is decisive in justification. I illustrate this with two case studies from the history of contemporary science, experiments on atomic parity violation and their relation to the Weinberg-Salam unified theory of electroweak interactions and the fifth force in gravity. I also answer some of the criticisms offered of my earlier account of the episode of atomic parity violation.
Biology, Geology, or Neither, or Both: Vertebrate Paleontology at the University of Chicago, 1892–1950
Authors: Rainger Ronald
Published: June 2022

Vertebrate paleontology was not readily incorporated into interdisciplinary activities at the University of Chicago. During the university’s first forty years serious disputes arose over the subject’s parameters and departmental affiliation. Only after World War II did a cooperative, interdisciplinary program emerge. Changes in biology and geology influenced that development, but even more important were local research and educational initiatives that provided the impetus and resources to create an innovative program.
Towards a unified taxonomic catalogue for the Chilean cacti: assembling molecular systematics and classical taxonomy
Authors: WALTER HELMUT E. , GUERRERO PABLO C.
Published: June 2022

The competition between floristic catalogues and the nomenclatural issues of the treated taxa, is a problem for the botanical knowledge of countries. Consequently, it seems to be necessary to merge former taxonomical proposals into a unified list based on phylogenetic hypotheses, the rules of nomenclature and dichotomous keys to the Chilean subfamilies, tribes and genera. With this approach we here propose an updated catalogue of the Chilean cacti. It would be necessary to merge the various taxonomic proposals into a unified list based on both phylogenetic hypotheses and the rules of nomenclature. With this approach, we here propose to updated the catalogue of Chilean cactus. A neotype was designated for Echinocactus jussieui. In addition, we present a dichotomous taxonomic key to the Chilean subfamilies, tribes, and genera.
THE ROLE OF ECONOMIC GROWTH IN MODULATING MOBILE CONNECTIVITY DYNAMICS FOR FINANCIAL INCLUSION IN DEVELOPING COUNTRIES
Authors: Asongu Simplice A. , Odhiambo Nicholas M.
Published: June 2022

This study establishes economic growth needed for supply-side mobile money drivers in developing countries to be positively related to mobile money innovations in the perspectives of mobile money accounts, the mobile phone used to send money, and the mobile phone used to receive money. The empirical evidence is based on Tobit regressions. For the negative net relationships that are computed, minimum economic growth thresholds are established above which the net negative relationships become net positive relationships. The following minimum economic growth rates are required for nexuses between supply-side mobile money drivers and mobile money innovations to be positive: (i) 6.109 percent (6.193%) of Gross Domestic Product (GDP) growth for mobile connectivity performance to be positively associated with the mobile phone used to send (receive) money and (ii) 4.590 percent (4.259%) of GDP growth for mobile connectivity coverage to be positively associated with the mobile phone used to send (receive) money.
Human Echolocators Have Better Localization Off Axis
Authors: Thaler Lore , Norman L. J. , De Vos H. P. J. C. , Kish D. , Antoniou M. , Baker C. J. , Hornikx M. C. J.
Published: June 2022

Here, we report novel empirical results from a psychophysical experiment in which we tested the echolocation abilities of nine blind adult human experts in click-based echolocation. We found that they had better acuity in localizing a target and used lower intensity emissions (i.e., mouth clicks) when a target was placed 45° off to the side compared with when it was placed at 0° (straight ahead). We provide a possible explanation of the behavioral result in terms of binaural-intensity signals, which appear to change more rapidly around 45°. The finding that echolocators have better echo-localization off axis is surprising, because for human source localization (i.e., regular spatial hearing), it is well known that performance is best when targets are straight ahead (0°) and decreases as targets move farther to the side. This may suggest that human echolocation and source hearing rely on different acoustic cues and that human spatial hearing has more facets than previously thought.