bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-causal Effect of Circulating Vitamin D Levels on the Risk of Rheumatoid Arthritis: A Two-sample Mendelian Randomization Study
Authors: Xiang Kun , Hu Yu-Qian , He Yi-Sheng , Wang Peng , Tao Sha-Sha , Chen Yue , Feng Ya-Ting , Yang Xiao-Ke , Pan Hai-Feng 
Published: May 2021

Abstract
Background: Recently, many studies have indicated the potential roles of vitamin D in rheumatoid arthritis (RA). In the present study, the published available GWAS summary data was used to perform two-sample Mendelian randomization (MR) to infer the causal effect between circulating vitamin D levels and RA risk.Methods: Single nucleotide polymorphisms (SNPs) which significantly associated with exposure were selected as instrumental variables from larger-scale genome-wide association study (GWAS). The robust analytical methods including MR Egger, inverse variance weighted (IVW), weighted median and weighted mode were conducted to infer the causal links. Results: The IVW method suggested that there was no causal relationship of genetically predicted circulating vitamin D on RA (Asian: odds ratio (OR)=0.911, 95% confidence interval (CI), 0.542-1.534, P=0.727; European: OR=0.897, 95% CI, 0.633-1.269, P=0.538). MR Egger (Asian: OR=0.937, 95% CI, 0.373-2.352, P=0.895; European: OR=0.853, 95% CI, 0.469-1.553, P=0.639), weighted median (Asian: OR=0.895, 95% CI, 0.499-1.606, P=0.711; European: OR=0.885, 95% CI, 0.620-1.264, P=0.503) and weighted mode (Asian: OR=0.904, 95% CI, 0.515-1.588, P=0.738; European: OR=0.898, 95% CI, 0.618-1.306, P=0.604) demonstrated that vitamin D was not directly related to RA as well. Conclusions: The MR analysis indicates that there is no evidence of causal effect of genetically predicted circulating vitamin D levels on RA risk.
Osteopathic Manipulative Treatment for long-term postoperative sequelae in children operated for type III esophageal atresia: A series case
Authors: Manzotti Andrea , Alati Alessia , Galli Matteo , Cerritelli Francesco , Leva Chiara , Alberti Adele , Stizzoli Alessandro , Costanzo Sara , Maestri Luciano , Pelizzo Gloria 
Published: May 2021

Abstract
Background and purpose of the study: Esophageal atresia (AE) is a congenital anomaly that affects the normal development of the esophagus in neonates. The timely surgical approach allows its resolution, but long-term morbidities in adolescents show neurological developmental abnormalities and musculoskeletal deformities. The goal in this preliminary study is to investigate the recovery of the range of the right upper limb movement and on the rise of the anthropometric parameters through the osteopathic manipulative treatment, which, by acting on the connective system has a trophotropic effect documented. Materials and Methods: 5 children with type III AE were included in the study. 6 OMTs were performed in 4 months. At each treatment, the following were assessed height, weight, BMI and ROM in elevation of the right upper limb. OMT has been applied to improve scar, larynx, rib cage, and sternum mobility. Results: in the statural growth there was an average change of 2.3 cm in 4 months and in the ROM of the upper limb there was an average increase of 8° in 4 months. There were no significant changes in weight and BMI. Conclusions: This preliminary study suggests that it is a viable option to be further researched. In addition, the study provides potentially valuable evidence that the osteopathic manipulative treatment could benefit the increase in anthropometric parameters and the mobility of the right upper limb of children operated on for AE. In light of the results, new studies to evaluate the effectiveness of OMT in post surgical treatment of congenital malformations of the thorax can be considered in the future.
Study of Safety Investment in Mines Based on Utility Function From a Game Perspective: Experiences From China
Authors: Yu Jian , Chen Zhe , Deng Yan 
Published: May 2021

Abstract
Mining industry, especially coal-mining industry, is faced with many safety problems in China such as the lack of coordination between the mining enterprises and the safety administration, decision-making of production and safety investment and so on. In order to reduce costs and maximize profits, many mining enterprises are gaming in safety investment under the mining safety supervision although relative supervision provisions are strict, which results in frequent occurrence of mine accidents. In order to find out the causes that are behind the economic motivation, based on utility function with two variables, technology and cost, from a game perspective, a safety investment analysis for decision makers in coal-mining enterprises is made in this article by focusing on the technological level and production cost and presenting the gaming process during deciding safety investment among these enterprises. The result is that there is no enough incentive in short term for mining enterprises to make safety investment under the condition of low technological level and high cost in China. Therefore, it is necessary to enhance safety awareness, strengthen supervision and improve safety status during mining, which is also helpful for all mining enterprises and sustainable development.
Dynamic Response of the Vegetation Carbon Storage in the Sanjiang Plain to Changes in Land Use/cover and Climate
Authors: Li Haiyan , Qu Yi , Zeng Xingyu , Zhang Hongqiang , Cui Ling , Luo Chunyu 
Published: May 2021

Abstract
Large-scale human activities especially the destruction of forest land, grassland, and unused land result in a large amount of carbon release into the atmosphere and cause drastic changes in land use/cover in the Sanjiang Plain. As a climate change-sensitive and ecologically vulnerable area, the Sanjiang Plain ecosystem’s carbon cycle is affected by significant climate change. Therefore, studying the impact of the changes in land use/cover and climate on the carbon storage of the vegetation in the Sanjiang Plain is important. The dynamic impact of the changes in land use/cover and climate on the vegetation carbon storage was analyzed using Remote Sensing (RS), Geographic Information System (GIS) technology, and mathematical statistics and taking TM and MODIS Normal Difference Vegetation Index (NDVI) remote sensing data, temperature, and precipitation as main data sources. Research results showed the following observations. From 2001 to 2015, the vegetation carbon storage in the Sanjiang Plain continued to increase (total increase = 97.65 Tg). The carbon storage of the forest land increased by 98.73 Tg, and the carbon storage of cultivated land increased by 0.56 Tg. The carbon storage values of grassland and unused land decreased to 0.86 and 0.78 Tg, respectively. The increase in the carbon storage of cultivated land was caused by the increased area, and that of forest land was attributed to the increased carbon density. The decrease in the carbon storage of grassland and unused land was out of the decrease in its area. Therefore, the transform in land use types and vegetation carbon density changed the vegetation carbon storage. Through the pixel-by-pixel analysis of the invariant region, the carbon storage of various vegetations in the Sanjiang Plain was negatively correlated with the average temperature and positively correlated with the average precipitation. The partial correlation analysis of the changes in climate and land use/cover concluded that the changes in land use/cover and vegetation carbon density in the Sanjiang Plain were the main controlling factors for the changes in the vegetation carbon storage.
Scaffolding Novice L2 Teachers’ Cognitions of Classroom Management: A Sociocultural Perspective
Authors: Karimi Mohammad Nabi , Nazari Mostafa 
Published: May 2021

Abstract
Despite the substantial body of knowledge on classroom management (CM), there is little research addressing the developmental growth of novice teachers’ cognitions of CM. Drawing on mediation and microgenetic analysis components of sociocultural theory, the present study aimed to investigate the contributions of a scaffolding initiative to language teachers’ cognitions of CM. The initiative involved mediating the teachers’ cognitions by prompting a number of central notions in CM to the teachers and investigating their own video-recorded classroom instruction. Data analyses indicated that the teachers navigated the process through the stages of CM-related conceptual change, developmental practice-informed awareness of CM, and heightened expertise in representing CM. The findings of the study imply that novice teachers need more attention from policymakers and teacher educators to facilitate the process of transition to professionalism in the beginning of their career, particularly developing heightened awareness of the importance of CM in their instruction.
An Improved Dynamics Modeling During Milling of the Thin-walled Parts Based on Magnetorheological Damping Fixture
Authors: Jiang Xiaohui , Yang Ning , Zhang Yong , Gao Shan 
Published: May 2021

Abstract
Aerospace thin-walled parts vibrates during the process of milling due to their characteristics of low rigidity, which influences the machining quality of the workpiece. Based on the characteristics of magnetorheological fluid excitation solidification, a magnetorheological damping fixture is designed to semi-actively suppress the vibration generated during the machining process. This paper simplifies the multi-characteristic structure of aerospace thin-walled parts into rectangular thin plates. In accordance with Kirchhoff G.’s small deflection bending theory of elastic thin-plates and Daramberg’s principle, the differential equations for the transverse vibration of the thin-walled parts are established, which aims to obtain the natural frequency as well as vibration mode function of the thin-walled parts. In consideration of damping characteristics and the external dynamic milling force after the magnetorheological fluid excitation solidification, this paper uses the mode superposition method and an improved dynamic response mathematical model of the magnetorheological damping fixture thin-walled parts system of the linear multiple degrees of freedom, which has different volumes of magnetorheological fluids under forced vibration, was established. The maximum error between the predicted and measured values ​​of the fixture-workpiece system dynamic response of displacement and acceleration is 16.2% and 15.5%, respectively. Finally, this paper verifies the feasibility as well as the effectiveness of the model through dynamics experiment and milling machining experiment.
Hall and Ion-Slip Currents Effects on Unsteady MHD Fluid Flow over an Inclined Plate with Inclined Magnetic Field and Variable Temperature
Authors: Islam Mohammad Rafiqul , Nasrin Sonia 
Published: May 2021

Abstract
A numerical study is performed to investigate the effect of Hall and Ion-slip current on unsteady MHD viscous electrically conducting fluid over an electrically non-conducting inclined plate, which is along the plate under the strong inclined magnetic field. The magnetic Reynolds number of flows is taken very small so that the induced magnetic field equation is ignored in our analysis. The governing equations are derived from the Navier-Stokes equation, Energy equation and Concentration equation and boundary layer approximation has been employed. The obtained non-linear, non-dimensional coupled governing equations are solved by using numerical methods of explicit finite difference for velocity, temperature and concentration. The flows are affected by the inclined angle. The effect of necessary important relevant parameters on the velocity, temperature and concentration distributions has been discussed in detail and also the shear stress, Nusselt number and Sherwood number are explained and compare it with its natural behavior, as well as graphically.
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases
Authors: Prajapati Samyak , Swaraj Aman , Lalwani Ronak , Narwal Akhil , Verma Karan , Singh Ghanshyam , Kumar Ashok 
Published: May 2021

Abstract
Background: Time series forecasting methods play a critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred to as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times.Objective: The aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series.Comparison: The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values.Result: The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent methods of time-series prediction (ARIMA).Conclusion: The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt-Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.
Selected ‘Starter Kit’ energy system modelling data for DR Congo (#CCG)
Authors: Cannone Carla , Allington Lucy , Pappis Ioannis , Barron Karla Cervantes , Usher Will , Pye Steve , Brown Edward , Howells Mark , Walker Miriam Zachau , Ahsan Aniq , Charbonnier Flora , Halloran Claire , Hirmer Stephanie , Taliotis Constantinos , Sundin Caroline , Sridha Vignesh , Ramos Eunice , Brinkerink Maarten , Deane Paul , Gritsevskyi Andrii , Moura Gustavo , Rouget Arnaud , Wogan David , Barcelona Edito , Rogner Holger 
Published: May 2021

Abstract
Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for DR Congo, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.
Selected ‘Starter Kit’ energy system modelling data for Gabon (#CCG)
Authors: Allington Lucy , Cannone Carla , Pappis Ioannis , Barron Karla Cervantes , Usher Will , Pye Steve , Brown Edward , Howells Mark , Walker Miriam Zachau , Ahsan Aniq , Charbonnier Flora , Halloran Claire , Hirmer Stephanie , Taliotis Constantinos , Sundin Caroline , Sridha Vignesh , Ramos Eunice , Brinkerink Maarten , Deane Paul , Gritsevskyi Andrii , Moura Gustavo , Rouget Arnaud , Wogan David , Barcelona Edito , Rogner Holger , Cronin Jen 
Published: May 2021

Abstract
Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Gabon, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.
Assessing the Livelihood Strategies of Intensive Vegetable Farmers in the surge of Urbanisation in Kumasi Metropolis, Ghana
Authors: Adusei Frank Yeboah 
Published: May 2021

Abstract
Generally, the objective was to describe the livelihood strategies of intensive vegetable farmers in Kumasi Metropolis. The specific objectives were: to investigate the contributions made by intensive vegetable production, assess the livelihood strategies of the farmers and make new strategies for them to cope with the decreasing land size. The study used the case study technique for data collection. Checklists were used for data collection through semi-structured interviews. Ten participants were selected, eight farmers and two Agricultural Officers. The size of the participants was influenced by difficulty in getting farmers in the Kumasi metropolis. Besides, the intent was to become full with information on the topic at hand but not to maximize numbers. Descriptive and qualitative methods were used to present the primary data collected. The results showed that all the farmers interviewed were food secured. However, the main livelihood strategies employed by them were growing vegetables that are early maturing and investing in non-traditional animal rearing. It was evident that intensive vegetable farming provides fresh vegetables for a large segment of the urban population. It provides employment and income to a host of people including the farmers, marketers and urban poor as farm labourers which helps them to improve their food security status. Although livelihoods were primarily constrained by the loss of lands for residential purposes due to urbanization. It could be inferred that the significant features of farming as a food security strategy have not been fully understood by some stakeholders including policymakers.
Groundwater quality assessment of Dashtestan using WQI index in 2007
Authors: Sorkhabi Omid Memarian 
Published: May 2021

Abstract
Water is a major source of economic development, social security and poverty reduction. The value of this resource on the one hand and its limitation on the other hand has led to increased management measures to maintain the quantity and quality of water by different communities. In this research, the water quality index (WQI) for the Dehroud and Tang Eram regions of Dashtestan in 2007 has been investigated. The average WQI in 2007 is 66,798. WQI was better off in the Northeast in 2007 than in the Southeast. The maximum values ​​of PH, TDS and EC in 2007 are 7.9, 5000 and 7500, respectively.
11 Tera-FLOP/s photonic convolutional accelerator and deep learning optical neural networks
Authors: Moss David 
Published: May 2021

Abstract
Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-FLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
A Novel Approach to Prevent Cloud Infrastructure against Cache Attacks
Authors: Ainapure Bharati Sanjay 
Published: May 2021

Abstract
In cloud computing resources like software and hardware are shared among multiple users by creating multiple instances of virtual machines (VMs). System cache is one of resource which is shared by Virtual machines in cloud environment. Such Virtual machines are targeted for abnormal activity like side channel attacks. Cache based side channel attack is one of the side channel attacks on cloud environments which leaks the private information of the client. The proposed approach includes the detection and prevention of cache based side channel attack in cloud infrastructure. The approach is based on two components: virtual machine status collection in the form of log files and use of fuzzy logic to detect and prevent the cache attack. Fuzzy logic defines set of fuzzy rules to identify the cache attack from the log file. The proposed approach could attain a maximum of 78.15%, 80.04%, and 82.16% of accuracy at 10, 20 and 30 sec. of time intervals.