bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating EFL Learners’ Ability to Analyse Poetic Language: A Pedagogical Corpus Stylistic Approach
Authors: Jaafar Eman Adil , Ganapathy Malini 
Published: May 2022

Pedagogical stylistics refers to the application of the tools of stylistics in the teaching of the English language as a foreign or a second language. Teaching and learning poetry is challenging. Thus, pedagogical corpus stylistics (Henceforth, PCS) approach has been introduced to Iraqi undergraduate foreign language learners (EFL) to guide them to analyze poetic language. The study aims to make students interact with authentic examples of poetic language and answer questions about it. The main objective of the study is to examine whether PCS tools enable the learners to provide linguistic evidence from the poetic texts they are exposed to. This in turn ensures objective poetic analyses. Moreover, it aims to enable EFL Iraqi students to discover salient norms related to the Standard English language. The study reveals that the pedagogical corpus stylistic approach helps the students to discover language patterns using corpus tools with the help of the teacher’s guidance at the beginning but this eventually leads to independent learning and discoveries about poetic language.
​​Forage Yield Potential and Adaptation Performance of Alfalfa Genotypes in Acidic Soil Condition of Southwestern Ethiopia
Authors: Mohammed Seid , Wato Tamirat , Ali Mohammed 
Published: May 2022

Background: The adaptability and forage yields of some alfalfa varieties are very low compared to the other varieties. Thus, this experiment was conducted to evaluate the adaptability, forage yield potential and farmers’ preference of seven alfalfa genotypes across two agro-ecological conditions of Chena and Gimbo districts, Southwestern Ethiopia in the year 2019 and 2020. Methods: Seven varieties of alfalfa forage including Magna 788, Magna 801, FL-L77, Hunter river, FG9-09, FG10-09 and Pioneer 1995 were used as experimental treatments. The genotype was arranged using an RCBD with three replications. All the relevant data were collected and then subjected to SAS software. To assess the adaptability and productivity potential of the selected varieties, all necessary data were recorded. Result: The effect of varieties on adaptation performance and yield potential showed significant differences (P≤0.05) for all parameters except the date of emergence at both locations. Dry matter yield, biomass yield, leaf weight and stem weight were significantly (P≤0.01) affected among all varieties of alfalfa. Therefore, all varieties of Alfalfa were found to be adaptable and performed well for most growth parameters and yield components except seed production under the conditions of Gimbo and Chena agroecology.
Evaluation of Different Machine Learning Frameworks to Estimate CO2 Solubility in NaCl Brines: Implications for CO2 Injection into Low-Salinity Formations
Authors: Mohammadian Erfan , Liu Bo , Riazi Amin 
Published: May 2022

Abstract
An accurate estimation of carbon dioxide (CO2) solubility in brine is of great significance for industrial applications such as quantifying CO2 sequestration in subsurface formations, CO2 surface mixing, and different CO2-based enhanced recovery methods (EOR). In this research, four different data-driven/machine learning techniques, extreme gradient boosting (XGB), multilayer perceptron (MLP), K-nearest neighbor (KNN), and in-house genetic algorithm (GA), were used to estimate solubility in terms of pressure, temperature, and salinity. Pressure, temperature, and salinity were used as model inputs, while CO2 solubility was the output. The experimental database used in this study was collected by dissolving CO2 into NaCl brines at salinity ranging from 0 to 15000 ppm, temperature ranging from 298 to 373 K, and pressures up to 200 atm. All data-driven models accurately estimated solubility through a coefficient of correlation (R2) ranging from 0.95 to 0.99, and a precise simple-to-use empirical solubility equation was developed using GA. The performance of the models was analyzed using proper model metrics (such as mean absolute error and relative error). A detailed feature importance analysis was conducted using feature importance, permutation, and Shapley values to clarify the correlation between the input and output parameters. The pressure was found to be the most impactful feature, followed by temperature and salinity. The model’s accuracy was compared to a well-established solubility model from the literature, and a good agreement between the two models’ results was observed. Lastly, conducting sensitivity analysis on the model revealed that the model’s estimations were still accurate when pressure and salinity were beyond the scopes of the original dataset.
Development of an equation free surrogate model using deep learning algorithm for heat transfer simulation
Authors: afzali Somayeh , Moayyedi Mohammad Kazem , Fotouhi Faranak 
Published: May 2022

Abstract
The Partial differential equations are one of the main tools in modeling many phenomena in real life. Since the formation and solving of the governing equations requires high processing time and computational costs. This study seeks to provide a method based on deep learning algorithms that can solve the equations independently of direct and numerical solution methods only by applying the boundary conditions of the problem on the neural network. This work explores the application of this paradigm on two-dimensional steady-state heat transfer equation. Due to the challenges of preparation large data with large dimensions, a novel method has been proposed to reduce the necessity of gathering data in large dimensions and size. In this method, first the steady-state heat transfer pattern encrypted in a kernel using thermal data in small size and dimensions. It is then used to train the steady-state predictor network without observing any steady-state heat transfer data simply by imposing restrictions on its outputs. This model was compared with a supervised model that trained by the large size of labeled data in the original dimensions. The result signifies that the proposed model has more accuracy, learning capability, and higher speed during the training stage.
Physical and Biological dosimetric margin according to prescription method for Stereotactic Body Radiation Therapy
Authors: Kawahara Daisuke , Saito Akito , Nagata Yasushi 
Published: May 2022

Abstract
ObjectiveTo expand the BCF model which covert physical dosimetric margin (PDM) to biological dosimetric margin (BDM) for point prescription with 3DCRT and the marginal prescription method with volumetric-modulated radiotherapy (VMAT).MaterialsCoplanar VMAT of marginal prescription and non-coplanar 3D-CRT of point prescription with lung stereotactic body radiation therapy (SBRT) were planned in RayStation. The biological equivalent dose (BED) for the dose per fraction (DPF) of 3–20 Gy/fr was calculated from these plans. The dose was perturbed with the calculation by 1-mm steps isocenter shift. The dose covering 95% of the target was greater than or equal to 90% of the prescribed physical dose and BED were defined as PDM and BDM, respectively. The BCF was created as a function of the DPF.ResultsThe PDM and BDM for all DPFs were larger with the point prescription method than the marginal prescription method. The marginal prescription method, that with 60% isodose line, had a larger PDM and BDM. The BCF with the point prescription was smaller than for the marginal prescription in left-right, anterior-posterior, and cranio-caudal direction. For the marginal prescription method, that with 60% isodose line had a larger BCF. Conclusion: The improved BCF method could convert to the BDM for point prescription with 3DCRT and the marginal prescription method with VMAT.Advances in knowledgeWe improved the biological conversion factor that converts the physical dosimetric margin to the biological dosimetric margin, which is needed to be used for stereotactic radiation therapy in radio-biological based treatment planning.
Resource Allocation for UAV-assisted 5G mMTC Slicing Networks using Deep Reinforcement Learning
Authors: Gupta Rohit Kumar , Kumar Saubhik , Misra Rajiv 
Published: May 2022

Abstract
The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G) network slicing technologies and hence IoT becoming significantly important beyond fifth-generation (B5G) networks. However, communication with IoT devices is more sensitive in disasters because the network depends on the main power supply and devices are fragile. In this paper, we consider Unmanned Aerial Vehicles (UAV) as a flying base station (BS) for the emergency communication system with 5G mMTC Network Slicing to improve the quality of service. The UAV-assisted mMTC creates a base station selection method with the aim of maximizing the system energy efficiency. Then, the system model is reduced into the stochastic optimization based problem using Markov Decision Process (MDP) theory. We propose a Dueling-Deep-Q-Networks (DDQN) based approach based on Reinforcement Learning (RL) technique for maximization of energy efficiency to solve the resource allocation problem. We compare the proposed model with DQN and Q-Learning models and found that the proposed DDQN based model performs better for resource allocation in terms of low transmission power and maximum energy efficiency.
Does green financial reform pilot policy promote green technology innovation？—— Empirical evidence from China
Authors: Wang Xueyang , Sun Xiumei , Zhang Haotian , Xue Chaokai 
Published: May 2022

Abstract
As a new financial model that balances economic and ecological benefits, green finance (GF) plays an important role in promoting green economic development and ecological environmental protection. Based on the panel data set of 30 provinces in China from 2010 to 2020, this paper uses the synthetic control method (SCM) to explore the impact of the green financial reform pilot policy (GFRP) on the green technology innovation (GTI) capabilities of pilot areas and evaluate the policy effects. The specific research conclusions are as follows: (1) On the whole, the GFRP has a positive role in promoting the GTI capability of the pilot areas, but this role is different due to the different resources, environment, and economic development levels of each region. The areas with economic development levels in the middle and head are obviously affected by the policy, and the less developed areas are less affected by the policy or even have a restraining effect. (2) Although the pilot policy has improved the GTI capability of the pilot area, the promotion effect is unstable, that is, the implementation effect of the policy is unstable. In the early stage of policy implementation, the promotion effect of the policy on the regional green innovation capacity is the most obvious, and this promotion effect begins to show a downward or stable trend in the 2–3 years after the policy is implemented. Based on the above conclusions, it can provide some reference for the revision and improvement of GFRP.
Functional identification of bHLH transcription factor MdSAT1 in the ammonium response in apple
Authors: Li Tong , Feng Zi-Quan , Zhu Bai-Hui , Li Ming-Li , Li Guo-Dong , Gao Wen-Sheng , You Chun-Xiang , Wang Xiao‐Fei 
Published: May 2022

Abstract
Plants mainly uptake inorganic nitrogen from soil as ammonium and nitrate. Less energy is required to assimilate ammonium compared to nitrates, and plants prefer to take up ammonium when the external nitrogen concentration is low. Investigating the patterns and mechanisms of ammonium absorption can help improve crop nitrogen utilization. In this study of apple, we isolated MdSAT1, a gene encoding an ammonium-responsive bHLH transcription factor. MdSAT1 promoted the growth and development of lateral roots and root hairs. Overexpression of MdSAT1 increased the transcript levels of genes related to ammonium uptake and assimilation and promoted the activities of ammonium assimilation-related enzymes, indicating that MdSAT1 can enhance ammonium uptake and utilization. MdSAT1 also can modulate ROS accumulation to ultimately regulate plant growth. Taken together, these findings provide insight into the mechanisms by which MdSAT1 controls ammoniun utilization as well as plant growth and development in apple.
Antagonist activation exercises elicit higher post-activation performance enhancement than agonist activities on throwing performance
Authors: Pisz Anna , Blazek Dusan , Jebavy Radim , Kolinger Dominik , Wilk MIchal , Krzysztofik Michal , Stastny Petr 
Published: May 2022

Abstract
Background: This study aimed to determine the acute effect of agonist and antagonist conditioning activities (CA) on medicine ball throw performance among female softball players. Methods: Thirteen national level female softball players (age 22.2 ± 3.1 years; body mass 68.3 ± 11.3 kg; softball experience 7.3 ± 2.4 years) performed 3 medicine ball chest throws before CA and after CA respectively in 3rd, 6th, and 9th minute. CA was the bench press (BP) and bent-over barbell row with 2 sets of 4 repetitions at 60% and 80% of one-repetition maximum, and 2 sets 4 repetition bodyweight push up.Results: Two-way ANOVA revealed an increase in throwing distance (p < 0.001) after bent over barbell row exercise and in throwing speed (m/s) (p < 0.001) after BP and bent over barbell row. The throwing distance was highest after bent over barbell row (Cohen’s d 0.41). Although the BP had the highest effect (Cohen’s d 0.41) to increase the throwing speed, its effect on throwing distance was not significant. Conclusions: We conclude that upper body throwing performance is higher after antagonist exercise than agonist CA and both agonist and antagonist CA increases power. In the resistance training practice, we recommend the interchange of agonist and antagonist PAPE stimulation at push-up bodyweight or BP and bent over raw submaximal (80% of 1RM) intensity to succeed PAPE on upper limbs.
Attitudes and adoption related to technology and active and healthy ageing - A national survey with a generational perspective
Authors: Offerman Jens , Fristedt Sofi , Schmidt Steven , Löfqvist Charlotte , Iwarsson Susanne 
Published: May 2022

Abstract
Research is needed to understand attitudes and adoption of the broad range of technologies currently available to support active and healthy ageing, in different generations. Previous and current research tends to neglect the diversity of products and services, as well as typically focuses on current older people rather than those representing future generations. Research-based knowledge is warranted to support development of future technologies that successfully promote active and healthy ageing. The present study aims to give an overview of the (Blinded) survey and sample, followed by a description of attitudes and acceptance related to technology in general and as a means to support active and healthy ageing. A national cross-sectional survey was conducted with a randomised sample (N = 2,121) including men and women from three age cohorts (30–39, 50–59 and 70–79 year-old persons) in Sweden, including questions on attitudes and acceptance related to a broad perspective of technology. The results show that perspectives were shared across generations rather than just within, especially in the younger and middle-aged generations. However, there were significant generational differences regarding what kind of technologies are preferred to support active and healthy ageing as well as reasons for using certain technologies. Our study described generational perspectives rarely displayed in research and shows that attitudes toward and acceptance of technologies for active and healthy ageing are shared across generations. Notable differences were displayed concerning choosing and adopting new technology, which deserve attention when developing new products and services. The study shows that research applying a comparative generational perspective has potential to nurture the understanding of acceptance and adoption throughout the ageing process, and guide development and implementation of technologies for active and healthy ageing.
Cardiovascular risk reduction with integrated care: results of 8 years follow up
Authors: Smits Geert H.J.M. , Doorn Sander van , Bots Michiel L. , Hollander Monika 
Published: May 2022

Abstract
BackgroundCare groups organize integrated cardiovascular risk management programs in primary care for high risk patients. Results of risk reduction with long-term participation are scarce. The aim was to evaluate the effects of participation in integrated CVRM care on LDL cholesterol, systolic blood pressure and smoking between 2011 and 2018.MethodsA protocol was developed for delegated practice nurse activities. A multidisciplinary data registry was used for uniform registration. The care group organized annual education, feedback meetings and intervision for general practitioners and practice nurses. From 2015 onwards, the care group has started with practice visitations to discuss performance and support practices with improving integrated CVRM care. Results Participants grew from 34,628 patients (mean age 67 years, 53% women, 29% with a previous CVD) in 2011 to 48,397 patients (mean age 70 years, 51% women, 44% with a previous CVD) in 2018. In patients eligible for primary prevention as well as for secondary prevention similar trends were observed: lipid modifying and blood pressure lowering medication increased, mean LDL-cholesterol and mean systolic blood pressure decreased, patients on target for LDL cholesterol and systolic blood pressure increased and the proportion of non-smokers with both LDL-cholesterol and SBP on target increased.Conclusion In patients participating in an integrated CVRM care program, we saw considerable improvements in 3 important cardiovascular risk factors between 2011 and 2018.
Sensitivity Analysis of River Chlorophyll-a to Environmental Factors Based on Support Vector Machine Regression Algorithm in Northern China
Authors: Xu Li , Hao Guizhen , Li Simin , Guo Peiran , Song Fengzhi , Zhao Yong 
Published: May 2022

Abstract
The outbreak of planktonic algae seriously affects the water environmental quality of the artificially reformed rivers in the north of China, and it is difficult to control. In this study, taking a typical small river dam area in the north of China—Qingshui River dam area in Zhangjiakou City as an example, the spatiotemporal variation characteristics of environmental factors were analyzed firstly. On this basis, the Chlorophyll-a (Chl-a) prediction model was established by using the Support Vector Regression (SVR). Further, the sensitivity of Chl-a was analyzed, moreover, we confirmed the main limiting factors of Chl-a. The results showed that in 2018, the average content of Chl-a was 126.25ug/L, while the content of DO and pH was also totally high. The maximum content of TN was 16.68mg/L and high in the whole year; the maximum value of CODmn was 50.00mg/L, which was slightly higher in summer; the average value of NH4+-N and TP was only 0.78mg/L and 0.18mg/L. The eutrophication evaluation showed that the proportion of areas with hyper eutrophication is 73%, and the most severe eutrophication was in summer. We used the RBF kernel function SVR model and 10-fold cross-validation method to optimize the parameters. The penalty parameter c is 1.4142, the kernel function parameter g is 1, and the training and verification errors were only 0.032 and 0.067. The application effect was good. Based on the sensitivity analysis of SVR prediction model, the sensitivity coefficients of Chl-a were TP, WT, DO, pH, TN, NH+ 4-N in turn. According to the analysis of the present water pollution situation, TP is the limiting factor of Chl-a in Qingshui River with 0.571 sensitive coefficient and 33% contribution rate, and it is also the main prevention and control factor of planktonic algae outbreak.
Back and forth: day-night alternation between cover types reveals complementary use of habitats in a large herbivore
Authors: De Groeve Johannes , Cagnacci Francesca , de Weghe Nico Van , Ranc Nathan , Morellet Nicolas , Bonnot Nadège C. , Gehr Benedikt , Heurich Marco , Hewison Mark A.J. , Kröschel Max , Linnell John D.C. , Mysterud Atle , Sandfort Robin 
Published: May 2022

Abstract
Context
The Complementary Habitat Hypothesis posits that animals access resources for different needs by moving between complementary habitats that can be seen as ‘resource composites’. These movements can occur on a range of temporal scales, from diurnal to seasonal, responding to multiple drivers, such as access to food, weather constraints, risk avoidance, human disturbance. Within this framework, we hypothesised that large herbivores may cope with human-altered landscapes through the alternate use of complementary habitats at both daily and seasonal scales.
Objectives
We tested the Complementary Habitat Hypothesis in European roe deer (Capreolus capreolus) by classifying 3900 habitat-annotated movement trajectories of 154 GPS-monitored individuals across contrasting landscapes.
Methods
We considered day-night alternation between food-rich open and closed refuge habitats as a proxy for complementary habitat use. We first identified day-night alternation by using IM-SAM, then we modelled the proportion of day-night alternation over the year in relation to population and individual characteristics.
Results
We found that day-night alternation is a widespread behaviour in roe deer, even across markedly different landscapes. Day-night alternation followed seasonal trends in all populations, partly linked to vegetation phenology. Within populations, seasonal patterns of open/closed habitat alternation differed between male and female adults, but not in juveniles.
Conclusion
Our results support the Complementary Habitat Hypothesis by showing that roe deer adjust the alternate use of habitats in response to changes in resource availability and individual needs. By repeatedly crossing habitat boundaries large herbivores may “engineer” their landscape by transporting seeds, parasites and nutrients between those habitats.
The Transcription Factor Zfp503 Promotes the D1 MSN Identity and Represses the D2 MSN Identity
Authors: Zhang Zhuangzhi , Shang Zicong , Yang Lin , Wang Ziwu , Tian Yu , Gao Yanjing , Su Zihao , Guo Rongliang , Li Weiwei , Liu Guoping , Li Xiaosu , Yang Zhengang , Li Zhenmeiyu 
Published: May 2022

Abstract
The striatum is primarily composed of two types of medium spiny neurons (MSNs) expressing either D1- or D2-type dopamine receptors. However, the fate determination of these two types of neurons is not fully understood. Here, we found that D1 MSNs undergo fate switching to D2 MSNs in the absence of Zfp503. Furthermore, scRNA-seq revealed that the transcription factor Zfp503 affects the differentiation of these progenitor cells in the lateral ganglionic eminence (LGE). More importantly, we found that the transcription factors Sp8/9, which are required for the differentiation of D2 MSNs, are repressed by Zfp503. Finally, sustained Zfp503 expression in LGE progenitor cells promoted the D1 MSN identity and repressed the D2 MSN identity. Overall, Zfp503 promotes the D1 MSN identity and represses the D2 MSN identity by regulating Sp8/9 expression during striatal MSN development.