bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonlinear Models for the Prediction of Yearly Live Weight of Cattle
Authors: Sultana N. , Khan M.K.I. , Momin M.M. 
Published: March 2022

Background: Growth of animals is important for milk and meat production. In developing countries record keeping is difficult and usually complete recording cannot be obtained from the cattle farming. Mathematical models are used to predict values from incomplete or partially recorded data and reduces the confusion for calculating the yield. Therefore, a study was conducted to know the growth of cattle by comparing three non-linear models and predicts the mature live weight. Mehods: The live weight of cattle at 15 day intervals from 15 to 365 days was recorded and calculated the yearly live weight and weight gains of three genotypes (Red Chittagong cattle, Non-descriptive deshi and their cross) of cattle. Three nonlinear model (Brody, Gompertz and von Bertalanffy model) was fitted to weight-age data of 120 female cattle from 15 to 365 days in three locations. Result: The average live weight and weight gain of three genotype cattle ranging from 198.5 to 207.89 kg and 306 to 361 g/day, respectively. The brody model provides better goodness of fit than other models in all genotypes and Gompertz and the von Bertalanffy model showed better matched between the observed and estimated weights
The Critical Period of Production of the Secondary Metabolite Indican in (Indigofera tinctoria L.) on Light Intensity
Authors: Budiastuti M.T.S. , Setyaningrum D. , Purnomo D. , . Supriyono , Pujiasmanto B. , Manurung I.R. 
Published: March 2022

Background: Indigofera tinctoria plays the role of a natural dye source that produces indigo color and contains the secondary metabolite indican which is highly responsive to light intensity. This study aims to examine the critical period for the formation of the secondary metabolite indican on light intensity. Methods: The method used was a completely randomized block design with a split-plot design. The study consisted of 2 treatment factors, namely: length of shade (main plot) and light intensity (subplot). The length of shade included 5 levels, namely early growth phase (up to 1 month after planting), mid-growth phase (up to 2 months after planting), maximum growth phase (up to 3 months after planting), 1 month before harvest and 2 months before harvest. Light intensity had 3 levels, namely 50% light intensity (38,464.3 lux), 25% (19,232.15 lux) and 10% (7,692.86 lux). Result: The combination of duration of shade and light intensity affected the growth, yield and content of secondary metabolites (indican) in Indigofera tinctoria. The highest number of leaves, plant fresh weight and biomass was found in the combination of shade in the early growth phase (up to 1 month after planting) with 50% light intensity. The highest indican production was found in the mid-growth shading (up to 2 months after planting) with 10% light intensity, which was 843.33 ppm. The critical period of shade to increase indican production along with the number of leaves was the mid-growth phase (up to 2 months after planting).
PELABELAN ODD-GRACEFUL PADA GRAF PRODUK SISIR
Authors: Daniel Juan , Barack Zeveliano Zidane , Ilham Pandu Setya , Sugeng Kiki Ariyanti 
Published: March 2022

Gnanajothi defined a graph with edges to be odd-graceful if there is an injective function such that if every edge is labelled with the resulting edge labels are . She proved that the graph obtained by joining one pendant to every vertex in is odd-graceful if and only if is even. In this paper, we extend her results by proving that is odd-graceful if and only if is even.Keywords: Vertex-labelling, odd-graceful, cycleMSC 2020: 05C78
Effect of different types of available litter materials on the performance and welfare of broiler chicks
Authors: Abougabal Mohammed Sh. , Taboosha Mossad F. , Shehata Abdelrazeq M. 
Published: March 2022

Abstract
This study was conducted to evaluate the quality of some locally available litter materials and their impacts on productivity, health status, carcass traits and welfare as well as the economic efficiency of broiler chickens reared on these types of litter. A total of 450 one-day-old Cobb 500 broiler chicks were randomly distributed into equal five experimental groups, three replicates each. The groups of five different litter materials were as follows; wood shaving (WD), wheat straw (WS), sand (SL), bean straw (BS) and rice husk (RH). Physical and physicochemical characteristics of litter types were measured at 7, 21 and 42 days of age. The results indicated that each type of litter had unique physical and physicochemical characteristics and affected the most studied traits. Compared to the other groups, WD and SL significantly improved growth performance (BWG, ADG, final LBW, FI, FCR and EPEI). Moreover, WD and SL improved (P < 0.05) the economic efficiency, behavior patterns, and birds’ welfare. It is concluded that among the available litter materials tested in this study, sand can be used as an alternative litter of straw- and rice husk-based litter for broiler chickens without adverse implications on birds’ performance and welfare.
CD3+CD4-CD8- double-negative lymphocytes are increased in the aqueous humour of patients affected by retinitis pigmentosa: their possible role in mediating inflammation
Authors: Bacherini Daniela , Maggi Laura , Sodi Andrea , Vannozzi Lorenzo , Mazzoni Alessio , Capone Manuela , Virgili Gianni , Falsini Benedetto , Cosmi Lorenzo , Rizzo Stanislao , Annunziato Francesco , Giansanti Fabrizio , Liotta Francesco 
Published: March 2022

Abstract
Background
Recently, evidence has arisen supporting a significant role for immune and oxidative mediated damage underlying the pathogenesis of different types of retinal diseases, including retinitis pigmentosa (RP). Our study aims to evaluate the possible presence of immune cells and mediators in patients affected by RP using flow cytometric analysis of the aqueous humour (AH).
Methods
We recruited 12 patients affected by RP and 9 controls undergoing cataract surgery. All patients underwent a complete ophthalmological examination, fundus color photography, optical coherence tomography (OCT), Goldmann kinetic perimetry, electroretinographic and genetic tests. Flow cytometric analysis of of peripheral blood (PB) and AH samples provided a membrane staining which targeted surface molecules (CD14, CD16, CD19, CD3 and CD4, CD8, CD161) identifying monocytes, natural killer (NK), B, T cells, and T subpopulations respectively. Moreover, lymphocytes were polyclonally stimulated to evaluate cytokine (CK) production at single cell level.
Results
The circulating immune-cell distribution was comparable between RP patients and controls. Conversely, in the AH of controls we could detect no cells, while in the RP AH samples we found infiltrating leucocytes, consisting of T (CD3+), B (CD19+), NK (CD16 + CD3-) cells and monocytes (CD14+). In the RP patients the frequency of most infiltrating cell populations was substantially comparable between the AH and PB, while among T subpopulations, the frequency of CD3 + CD4 + T cells was significantly reduced in the RP AH compared to PB, and CD3 + CD4-CD8- double-negative (DN) T cells resulted markedly and significantly increased in the RP AH. The analysis of the cytokine production showed a trend towards an increased frequency of CD3 + CD8-CD161 + IFN-ɣ-producing cells and a decrease of CD3 + CD8 + IL-4-producing cells in the RP AH.
Conclusions
The detection of immune cells in the AH of patients with RP is consistent with the hypothesis of an immune-mediated pathogenesis of the disease. The presence of CD3 + CD4-CD8- DN T cells and of a Th1-skewed phenotype in the AH could be related to a potential involvement of immune-mediated and inflammatory mechanisms in the disease. A better understanding of the fundamental features of inflammation pathways will provide significant insights in the pathogenesis of RP, and a possible avenue toward new therapeutic approaches.
Soft Metaclustering for Unsupervised Recognition of Concept Drift
Authors: Ferone Alessio , Maratea Antonio 
Published: March 2022

Abstract
Streaming data analysis presents numerous challenges: the unbounded and sequential nature of the stream requires evolving models in order to capture its intrinsic non-stationarity and related concept drift. Relatively few methods for the detection of the drift are unsupervised and require no labels during training. In this paper, a soft and dynamic metaclustering approach is used for concept drift detection on streaming data. The pro posed method exploits possibilistic memberships for a natural quantifica tion of the drift and is able to effectively recognize both abrupt and recur ring drift. The effectiveness of the proposed method has been assessed on synthetic data from a public benchmark, yielding notable results.
Predictive factor analysis and scoring system for radioiodine-refractory differentiated thyroid cancer: a retrospective study.
Authors: Liu Ye , Wang Yuhua , Zhang Wanchun 
Published: March 2022

Abstract
Background
The incidence of thyroid cancer has been rising worldwide. Differentiated thyroid carcinoma (DTC) originates from abnormal follicular cells and accounts for about 90–95% of thyroid malignancies. The diagnosis of radioiodine-refractory DTC (RR-DTC) is based on clinical evolution and iodine-intake characteristics rather than pathological characteristics, so it takes a long time to become apparent, and the definition of RR-DTC covers multiple aspects. The purpose of this study was to analyze the clinical and molecular imaging characteristics of patients with RR-DTC and identify independent predictors to develop an RR-DTC scoring system. We reviewed data on 404 patients with metastatic DTC who underwent both radioactive iodine therapy scanning and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography. Data on the clinical features and molecular characteristics of RR-DTC and non-RR-DTC were obtained from the medical records. We screened for predictors using univariate analyses, obtained independent predictors through multivariate analyses, then established a scoring system for predicting RR-DTC according to the corresponding odds ratio (OR) values.
Results
Diagnosis at age ≥ 48 years (OR, 1.037; 95% confidence interval [CI], 1.007–1.069), recurrence between the operation and iodine-131 treatment (OR, 7.362; 95% CI, 2.388–22.698), uptake of 18F-FDG (OR, 39.534; 95% CI, 18.590–84.076), and the site of metastasis (OR, 4.365; 95% CI, 1.593–11.965) were highly independently associated with RR-DTC. We established a scoring system for predicting RR-DTC, showing that the area under the receiver operating characteristic curve (AUC) with a cut-off value of 10 points (AUC = 0.898) had a higher discernibility than any other single independent predictor.
Conclusions
This study demonstrated that a predictive model based on four factors has a good ability to predict RR-DTC. An index score ≥ 10 was found to be the optimal index point for predicting RR-DTC. This model may help to establish an active surveillance or appropriate treatment strategy for cases of RR-DTC.
Short-term wind speed prediction based on FEEMD-PE-SSA-BP
Authors: Zhu Ting , wang wenbo , YU MIN 
Published: March 2022

Abstract
As one of the renewable energy power generation methods, wind power generation shows a high growth trend. However, while wind power is connected to the grid,the volatility and instability of wind power make the power system produce a lot of uncertain fluctuations, which greatly affects the power quality and jeopardizes the stable operation of the power system. Therefore, high wind speed forecasting accuracy can provide a solid basis for grid management,, improve the power system's ability to consume wind power, and ensure the safety and stabilization of the power system. In order to solve the problem of inaccurate prediction caused by the non-linearity and unsteadiness of wind speed series, this paper proposes a Fractal Ensemble Empirical Mode Decomposition (FEEMD)-Permutation Entropy (PE)-Sparrow Search Algorithm (SSA)-Error Back Propagation (BP) neural network method for short-term wind speed prediction. This method first uses the fractal dimension-based ensemble empirical modal decomposition (FEEMD) to decompose the original wind speed in order from high to low frequency; then calculates the entropy value of each component, and merges the components with similar entropy values to effectively reduce the computation; finally, the new sub-series are predicted by SSA-BP model, and the predicted value of the merged new sub-sequence is accumulated to obtain the final wind speed prediction result. The simulation study shows that the proposed prediction model is not only fast and accurate, but also suitable for short-term wind speed prediction.
The Probabilistic behavior of AquaCrop parameters: A Monte-Carlo Study
Authors: Etedali Hadi Ramezani , adabi vahid , Gorgin Faraz , azizian asghar 
Published: March 2022

Abstract
Crop growth models are multi-outputs and can be valuable tools for the quantification of crop growth and production. However, these models usually require several input data, which are costly, time-consuming, and sometimes impossible to measure. These input parameters are mostly estimated by calibration and inverse solving. In this study, five output variables of the AquaCrop model, including soil evaporation, crop transpiration, evapotranspiration, crop biomass at maturity, and grain yield, were investigated to study 47 genotypic input parameters on the output time series of the model for wheat in the Qazvin Synoptic Station. The SAFE toolbox in the Matlab environment was used to study the GSA and uncertainty of inputs and their impact on outputs. Applying the Monte-Carlo-based methods on closed-source models is not as easy as open-source models. The uncertainty in the outputs of the validated AquaCrop model in simulating wheat yield in the Qazvin Synoptic Station over 36 years was analyzed using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The uncertainty in the outputs of the validated AquaCrop model in simulating wheat yield in the Qazvin station over 36 years was analyzed using the GLUE method. Using RMSE<0.9 as the threshold in a 95% confidence level, the best parameter sets included all the observations. Results showed that evaporation and yield are the least reliable outputs of the AquaCrop model that have not been calibrated, while others consider them reliable. After that, the new domain of each output was determined based on the two indexes. Then we modified the domain to reduce its size. Finally, the probabilistic behavior of each input on outputs were introduced by the Easy Fit software. The significant result of this study is that the probabilistic nature of the input parameter that is calibrated for a particular output variable can differ from other output variables. Also, when we trust a specific run of the model (calibrated run) as observed data, the uncertainty bounds covering are very high. Finally, we utilized the GLUE to optimize multi-output models by introducing one unique, optimized PDF for each input parameter for all outputs estimated by collecting all accepted output series of all target outputs.
What position does Spain occupy in the EU-28 (2008-2017) from the standpoint of natural and managerial efficiency?
Authors: Yñiguez Rocio , Sanz-Diaz Maria teresa , Ortega-Irizo Francisco Javier , Velasco-Morente Francisco 
Published: March 2022

Abstract
The European Union (EU) has become the group of countries most interested in the fight against climate change and is therefore committed to reducing greenhouse gas emissions. This research analyses the Natural and Managerial efficiency of the EU-28 countries for the period 2008–2017. The Data Envelopment Analysis methodology is used and Natural and Managerial Malmquist indices are obtained by assuming that a shift in the efficiency frontier exists and by considering that frontier crossings are possible across the different periods considered. The input variables used are those of Gross Fixed Capital Formation (GFCF), non-emitting energy consumption, and employment. Gross domestic product (GDP) and greenhouse gas (GHG) emissions are taken as the desirable and undesirable output variables, respectively.The results of the analysis indicate that both Natural and Managerial efficiency have worsened in Spain during the period considered, whereby Spain has dropped in the relevant ranking. With regard to the Malmquist Index (MI), during this entire period, the position of the evolution of Spain in relation to all EU countries was better within the sphere of Managerial efficiency, than in that of Natural efficiency.JEL Classification: Q532
A New Method for Environmental Risk Assessment of Pollutants Based on Multi-dimensional Risk Factors
Authors: Li Le , Dong Yuying , Chen Yuting , Jiao Jian , Zou Xuejun 
Published: March 2022

Abstract
Deterioration of watershed environment and pollutant discharge had seriously threatened human health and ecosystem function. The importance of improving risk warning system is becoming more and more prominent. Traditional chemical risk assessment methods focused on toxicity and exposure of pollutants without considering the impact of persistent pollutants in different environmental media. So there are cases which pollutants that have been assessed as safe using these methods end up being classified as persistent organic pollutants. In this study we proposed a synthetical risk factor (SRF) analysis with multi-dimensional evaluation of pollutants. The new method integrated toxicity endpoint values, environmental exposure level, persistent properties and compartment features from various dimensions. Selected pesticides, perfluorinated compounds, organophosphate and endocrine disruptors were analyzed with both SRF and risk quotient (RQ) methods. The results showed, a higher risk outcome using SRF analysis for PFOS, imazalil, testosterone, androstenedione and bisphenol A, which were different from those obtained with RQ method but were consistent with existing risk management. The study demonstrated that SRF method improved risk assessment of various pollutants in surface water in a more robust fashion, and also provided a more accurate decision basis for ecological environment protection.
PM2.5 and PM10 air pollution peaks are associated with emergency department visits for psychotic and mood disorders: an eleven-year study
Authors: Pignon Baptiste , Borel Cynthia , Lajnef Mohamed , Richard Jean-Romain , Szöke Andrei , Hemery François , Leboyer Marion , Foret Gilles , Schürhoff Franck 
Published: March 2022

Abstract
Particulate matter with a diameter of less than 10 µm (PM 10 ) or less than 2.5 µm (PM 2.5 ) are major air pollutants poorly studies regarding psychiatric disorders. We aimed to explore the association between PM 10 and PM 2.5 air pollution peaks and the daily number of emergency visits for psychotic and mood disorders. Clinical data were collected from the Emergency department of the Centre Hospitalo-Universitaire Henri-Mondor (Créteil, France) from 2008 to 2018. Air pollution data were collected from public databases. Pollution peaks periods were defined as days for which the daily mean level of PM was above nationally predefined warning thresholds (20 µg/m 3 for PM 2.5 , and 50 µg/m 3 for PM 10 ), and the 6 following days. Multivariable analyses compared the number of daily visits for psychotic and mood disorders according to pollution peak period. After adjustment on meteorological variables, the daily number of emergency visits for psychotic disorders was significantly higher during with PM 2.5 and PM 10 air pollution peaks periods; while the number of visits for unipolar depressive disorders was higher only during PM 10 peaks periods (β=0.059, p-value=0.034). There was no significant differences concerning bipolar disorders. Differences in the effects of PM air pollution on psychotic and mood disorders should be analyzed in further studies.
Predictive Effect of Different Blood Lipid Parameters Combined with Carotid Intima-Media Thickness on Coronary Artery Disease
Authors: Hu Xiangming , Yu Bingyan , Li Wei , Zhou Langping , Lin Yan , Wang Weimian , Li Guang , Zhou Yingling , Dong Haojian 
Published: March 2022

Abstract
Background
Blood lipids disorder and atherosclerosis are closely related to coronary artery disease (CAD). This study aims to compare different blood lipid parameters combined with carotid intima-media thickness (cIMT) in predicting CAD.
Methods
This was a retrospective study including patients who underwent coronary angiography for highly suspected CAD. Blood samples were taken for lipid profile analysis and cIMT was evaluated by carotid ultrasound. Logistic analysis was used to establish different models of different lipid parameters in predicting CAD. The area under the receiver operating characteristic curve (AUC) was used to examine the predictive value. The optimal lipid parameter was also used to explore the relationship with multi-vessel CAD.
Results
Patients were classified into two groups based on whether CAD existed. There was no difference in low-density lipoprotein cholesterol (LDL-C) between the two groups. Compared with non-CAD patients, the CAD group had higher lipoprotein (a) (Lp (a)), apolipoprotein B/apolipoprotein A, total cholesterol/high-density lipoprotein cholesterol (HDL-C), triglyceride/HDL-C and LDL-C/HDL-C. According to the AUCs, Lp (a) combined with cIMT (AUC: 0.713, P < 0.001) had the best performance in predicting CAD compared to other lipid parameters. High levels of Lp (a) was also associated with multi-vessel CAD (odds ratio: 1.41, 95% confidence interval: 1.02–1.95, P = 0.036).
Conclusion
For patients with highly suspected CAD, Lp (a) better improved the predictive value of CAD rather than most of blood lipid indices, especially in the absence of high levels of LDL-C. Lp (a) also can be used to predict the multi-vessel CAD.