scholarly journals Southern Hemisphere Pressure Relationships during the 20th Century—Implications for Climate Reconstructions and Model Evaluation

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 413 ◽  
Author(s):  
Logan Clark ◽  
Ryan Fogt

The relationship between Southern Hemisphere middle and high-latitude regions has made it possible to generate observationally-based Antarctic pressure reconstructions throughout the 20th century, even though routinely collected observations for this continent only began around 1957. While nearly all reconstructions inherently assume stability in these relationships through time and in the absence of direct observations, this stationarity constraint can be fully tested in a model setting. Seasonal pressure reconstructions based on the principal component regression (PCR) method spanning 1905–2013 are done entirely within the framework of the Community Atmospheric version 5 (CAM5) model in this study in order to evaluate this assumption, test the robustness of the PCR procedure for Antarctic pressure reconstructions and to evaluate the CAM5 model. Notably, the CAM5 reconstructions outperformed the observationally-based reconstruction in every season except the austral summer. Other tests indicate that relationships between Antarctic pressure and pressure across the Southern Hemisphere remain stable throughout the 20th century in CAM5. In contrast, 20th century reanalyses all display marked changes in mid-to-high latitude pressure relationships in the early 20th century. Overall, comparisons indicate both the CAM5 model and the pressure reconstructions evaluated here are reliable estimates of Antarctic pressure throughout the 20th century, with the largest differences between the two resulting from differences in the underlying reconstruction predictor networks and not from changes in the model experiments.

2016 ◽  
Vol 20 (1) ◽  
pp. 311-331
Author(s):  
Elena Menichelli ◽  
Richard Ling

There is little research examining the confluence of what communication channel is used for which purpose with which person. This study examines the “setting” for communication that includes what is communicated (e.g. positive or negative messages), the nature of the relationship (close versus distant), and the information channel. The respondents to a web-based questionnaire ( n = 627) were Norwegian smartphone users aged 16–35 years. Respondents evaluated mobile communication services that they used in specific social settings by “checking off” all that apply. Two methods of analysis are used to examine the material. First, a Principal Component Regression validated the main method, namely a mixed model for the Analysis of Variance. Results show the probability of using a mobile communication service is based on the effects of social group, communication purpose, communication channel, and their interaction. The relationship to the interlocutor was found to have the strongest effect on channel choice.


Author(s):  
Ati Atul Quddus

Abstrak Penelitian ini bertujuan untuk menduga kandungan energi bruto tepung ikan untuk bahan pakan ternak menggunakan teknologi Near Infrared (NIR). Tepung ikan yang digunakan dalam penelitian ini diperoleh dari poultry shop yang ada di beberapa daerah di Indonesia dan industri pakan ternak. Penelitian ini menggunakan 50 tepung ikan. Tiga puluh lima sampel digunakan untuk kalibrasi, sedangkan 15 sampel digunakan untuk validasi. Pengukuran NIR reflektan menggunakan sistem NIR. Energi bruto diukur menggunakan bomb calorimeter. Data dianalisis dengan menggunakan regresi linier berganda (RLB) dan Principal Component Regression (PCR). Persamaan kalibrasi dari reflektan dianalisis menggunakan 29 panjang gelombang untuk memprediksi energi bruto. Hasil dari validasi menunjukkan akurasi yang tinggi dengan standar eror dan koefisien variasi untuk energi bruto yaitu 6,6 Kkal/Kg dan 0,2%. Persamaan kalibrasi dari metode PCR menggunakan data absorban. Hasil dari validasinya menunjukkan kurang akurasi dengan nilai standar eror dan koefisien variasi yaitu 119,2 Kkal/kg dan 4,16%. Kata kunci : energi bruto, NIR, RLB, PCR Abstract This experiment was aimed to predict gross energy (GE) content of fishmeal by using Near Infrared (NIR) technology. Fishmeal that was used in this experiment was obtained from the poultry shop in several regions in Indonesia and from animal feed industries. This experiment was conducted by using 50 fishmeals. Thirty five samples out of 50 samples fishmeal was used to develop the NIR of calibration and the rest 15 samples was used to test the accuracy of the calibration. NIR reflectant was measured by NIR system. Gross energy was measured by bomb calorimeter. Collected data were analyzed by using multivariate linier regression (MLR) and principal component regression (PCR). Calibration equation of reflectant was analyzed by using 29 wavelengths for predicting GE. The results of the validation indicated high accuracy with standard error and coefficient of variation for GE: SEp = 6.6 Kkal/Kg, CV = 0.2 % . Calibration equation was obtained from PCR method by using absorbent data. The result of the validation indicated less accuracy with standard error and coefficient of variation for GE: SEp = 119.92 Kkal/Kg, CV = 4.16% . Keywords : Gross Energy, Near infrared Reflectant (NIR), fishmeal, Multivariate Linier Regression (MLR), Principal Component Regression (PCR)


2021 ◽  
Author(s):  
Mathew Wheto ◽  
Nkiruka Goodness Chima ◽  
Henry T Ojoawo ◽  
Matthew A Adeleke ◽  
Sunday O Peters ◽  
...  

Abstract This study aimed to assess the relationship among carcass traits of meat line FUNAAB Alpha chicken genotype, to identify the components that defined bled weight in them using multivariate principal component regression. A total of 14 different carcass traits from sixty-eight birds were recorded and subjected to one-way analysis of variance to vet for sex effect. Phenotypic relationships among the carcass traits were also established to pave way for the principal component analysis. The results reveal significant effects between the traits measured. The male significantly (P<0.05) had greater mean values for the traits measured. Correlations among the considered carcass traits were found to be positive and significant ranging from r = 0.406 (LrWt) - 0.981 (EdWt) for the female chicken; r = 0.330 (Head Wt) - 0.978 (BdWt) for the male chicken. The extracted components PC1 to PC7 contributed 95.66% with PC1 accounting for 68.68% of the variability in the original parameters. Communality estimates varied from 0.466 (thigh weight) to 0.983 (liver weight). In the principal component regression models, Eviscerated weight accounted for 95% of the variation observed in bled weight. The use of PC1 as a single predictor, explained 96.4% of the variability, whilst combining PC1 and PC4 showed improvements in the variance explained (R2 = 96.7%) with a lower Mallow's cp (5.31). Using the principal components scores from the chicken morphometric traits was more appropriate than using the original traits in bled weight prediction.


2021 ◽  
Vol 2 (2) ◽  
pp. 11-20
Author(s):  
Soul Washaya ◽  
Wesley Bvirwa ◽  
Godfrey Nyamushamba

Body measurements are important criteria in the selection of elite animals for breeding. The objective of this study was to determine the relationship, accuracy of prediction of body weight from body measurements, and identifying multicollinearity from three beef breeds.  Four classes of stock (bull, cows, steers, and heifers) were considered. Correlation, simple, and multiple linear regression models were fitted with body weight (BW) as the dependent variable and body length (BL), heart girth (HG), height at wither (HW), muzzle circumference (MC), and shank circumference (SC) as the independent variables. The BW of the animals ranged from 218 to 630 kg, the least being heifers and bulls were the heaviest. The pairwise phenotypic correlations showed a high and significant positive relationship between BW and body dimensions (r = 0.751- 0.96; P<0.01). However, negative correlations were observed between BW with BL and MC of r = -0.733 and -0.703 and -0.660, -0.650, for cows and heifers, respectively. Regressing BW on BL, HG, and HW measurements gave statistically significant (P<0.01) equations with R2 ranging from 0.60 to 0.79. Collinearity, as portrayed by high variance inflation factors (VIFs), tolerance values, and low eigenvalues, was evident in four of the variables. It was concluded that the regression model was useful in BW prediction for smallholder farms and the relationship between BW and other body measurements was influenced by breed and class of stock. It is recommended that ridge regression or principal component regression be used in cases where multicollinearity exisists.


Author(s):  
Anderson G. Costa ◽  
Eudócio R. O. da Silva ◽  
Murilo M. de Barros ◽  
Jonatthan A. Fagundes

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.


1992 ◽  
Vol 46 (9) ◽  
pp. 1420-1425 ◽  
Author(s):  
D. Bertrand ◽  
C. N. G. Scotter

This paper describes an approach for studying collections of near-infrared spectra by using multivariate analyses. The method is illustrated with the use of two sets of spectra of gelatinized starch, recorded in the transmission mode between 650 and 1235 nm. The first set consisted of 99 spectra of partly gelatinized samples (from 24.5 to 100% gelatinization). Application of principal component analysis (PCA) made it possible to identify an outlying sample and to identify the importance of spectral variations due to the effect of scattering. Hence, it was possible to eliminate the scatter variations. From principal component regression (PCR), it was shown that the relationship between corrected spectra and gelatinization was not linear. Discriminant analysis was applied to seven classes of starch gelatinization. Only five samples out of 98 were incorrectly identified. The second set of samples was designed for studying the effect of temperature variation on the spectra of fully gelatinized starch samples. It was possible to show from PCR that the relationship between the spectra and temperature was linear. The “spectral patterns” assessed from discriminant analysis of starch gelatinization and from the PCR of temperature were compared.


2009 ◽  
Vol 22 (20) ◽  
pp. 5319-5345 ◽  
Author(s):  
Julie M. Jones ◽  
Ryan L. Fogt ◽  
Martin Widmann ◽  
Gareth J. Marshall ◽  
Phil D. Jones ◽  
...  

Abstract Seasonal reconstructions of the Southern Hemisphere annular mode (SAM) index are derived to extend the record before the reanalysis period, using station sea level pressure (SLP) data as predictors. Two reconstructions using different predictands are obtained: one [Jones and Widmann (JW)] based on the first principal component (PC) of extratropical SLP and the other (Fogt) on the index of Marshall. A regional-based SAM index (Visbeck) is also considered. These predictands agree well post-1979; correlations decline in all seasons except austral summer for the full series starting in 1958. Predictand agreement is strongest in spring and summer; hence agreement between the reconstructions is highest in these seasons. The less zonally symmetric SAM structure in winter and spring influences the strength of the SAM signal over land areas, hence the number of stations included in the reconstructions. Reconstructions from 1865 were, therefore, derived in summer and autumn and from 1905 in winter and spring. This paper examines the skill of each reconstruction by comparison with observations and reanalysis data. Some of the individual peaks in the reconstructions, such as the most recent in austral summer, represent a full hemispheric SAM pattern, while others are caused by regional SLP anomalies over the locations of the predictors. The JW and Fogt reconstructions are of similar quality in summer and autumn, while in winter and spring the Marshall index is better reconstructed by Fogt than the PC index is by JW. In spring and autumn the SAM shows considerable variability prior to recent decades.


Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 201
Author(s):  
Yang Li ◽  
Hexuan Shi ◽  
Shijun Ji ◽  
Fusheng Liang

In order to investigate the thermal effect of a servo axis’ positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation.


2018 ◽  
Vol 31 (17) ◽  
pp. 6669-6685 ◽  
Author(s):  
James Doss-Gollin ◽  
Ángel G. Muñoz ◽  
Simon J. Mason ◽  
Max Pastén

During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions.


2020 ◽  
Vol 4 (4) ◽  
pp. 502-511
Author(s):  
Mardiantono Mardiantono ◽  
Fachruddin Fachruddin ◽  
Zulfahrizal Zulfahrizal

Abtrak. Kadar Air merupakan salah satu komponen penting dalam beras ketan putih yang dapat mempengaruhi kualitas dari beras ketan putih. Penelitian ini bertujuan menguji dan mengevaluasi teknologi NIRS sebagai metode cepat dan tepat dalam memprediksi kadar air beras ketan dengan metode Principal Component Regression (PCR) serta menentukan metode koreksi spektrum yang terbaik dan akurat untuk memprediksi kadar air beras ketan dengan menggunakan pretreatment De- Trending, Derivative-2, dan Standart Normal Variate (SNV). Penelitian ini menggunakan beras ketan putih yang didapat dari pasar Rukoh Banda Aceh, yang berjumlah 35 sampel. Perlakuan yang diberikan adalah tanpa perendaman, dibasahi, dan perendaman selama 5, 10, 15, 20, dan 25 menit. Prediksi kadar air beras ketan dengan NIRS menggunakan alat self developed FT-IR IPTEK T-1516 dan metode referensi yang digunakan adalah metode gravimetri yang berdasarkan pada Association of Official Analytical Chemists (AOAC). Pengolahan data menggunakan Unsclambers sofware® X version 10.5. Hasil penelitian menunjukkan bahwa NIRS dengan metode PCR mampu menghasilkan model yang baik untuk pendugaan beras ketan. Penelitian ini menghasilkan empat model pendugaan kadar air beras ketan dimana satu model tergolong very good performance (RPD3) dan tiga model tergolong good model performance (RPD2) sehingga dapat dikatakan bahwa semua model yang dihasilkan layak dan baik untuk pendugaan kadar air beras ketan. Pretreatment terbaik pada penelitian ini adalah Standart Normal Variate (SNV) dengan nilai RPD 3,12, r sebesar 0,95, R2 sebesar 0,89, dan RMSEC sebesar 2,34.Estimation of White Gluttony Rice Rate With NIRS Technology Using Principal Component Regression Method (Pretreatment De-Trending, Derivative-2, dan Standart Normal Variate)Abstract. Water content is one important component in white glutinous rice which can affect the quality of white glutinous rice. This study aims to test and evaluate NIRS technology as a fast and precise method for predicting glutinous rice water content with the Principal Component Regression (PCR) method and determine the best and accurate spectrum correction method for predicting glutinous rice water content using the De-Trending, Derivative pretreatment -2, and Standard Normal Variate (SNV). This study uses white sticky rice obtained from the Rukoh market in Banda Aceh, which amounted to 35 samples. The treatment given is without soaking, soaking, and soaking for 5, 10, 15, 20, and 25 minutes. The prediction of glutinous rice moisture content with NIRS uses a self-developed FT-IR IPTEK T-1516 tool and the reference method used is the gravimetric method based on the Association of Official Analytical Chemists (AOAC). Data processing using Unsclambers software X version 10.5. The results showed that NIRS with the PCR method was able to produce a good model for estimating glutinous rice. This study produced four models of estimation of glutinous rice water content where one model was classified as very good performance (RPD 3) and three models were classified as good model performance (RPD 2) so that it could be said that all the models produced were suitable and good for estimating rice water content sticky rice. The best pretreatment in this study is the Standard Normal Variate (SNV) with an RPD value of 3.12, r of 0.95, R2 of 0.89, and RMSEC of 2.34. 


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