scholarly journals Critical indexes of compositional nutrient diagnosis (CND) and its validation in wheat fields

2021 ◽  
Vol 38 (3) ◽  
pp. 480-504
Author(s):  
Adel Reyhanitabar ◽  
Nosratollah Najafi
Keyword(s):  
Wheat Yield ◽  
Nutrient Deficiency ◽  
Principal Component ◽  
Plant Nutrient ◽  
Varimax Rotation ◽  
Data Set ◽  
Cutoff Value ◽  
Nutrient Imbalance ◽  
Validation Population ◽  
Nutrient Index

Plant nutrient composition of can be used as an evaluation criterion for optimum plant growth. The objectives of present study were to (a) derive critical compositional nutrient (CND) norms for survived wheat fields and sufficiency ranges as CND nutrient index for validation samples, (b) provide a squared CND threshold nutrient imbalance index (CND r2) and compare with DRIS nutrient imbalance indices, (c) determine balanced nutrients concentration with CND indices. The yield cutoff value was 4,232 kg.ha-1. The CND indexes results indicate that Zn is the most deficient nutrient in wheat, followed by Cu, Fe, Mn and B, whereas N is the most excessive nutrient, followed by K, Ca, Mg and P. In the validation trials, the yield cutoff value were reported 5.023 kg.ha-1. The calculated CND r2 in the validation population was lower than that of the survey wheat fields, indicating a more balanced concentration of nutrients due to the application of fertilizer treatments. Significant principal component (PC) loadings were obtained after the varimax rotation. The first three PCs in high- and low-yielding subgroups and whole data set indicated 52.8, 54.6 and 48.8 % total variance, respectively. This study revealed that the decline in the wheat yield was due to the nutrient imbalance associated with multi nutrient deficiency (Zn, Cu, Fe, Mn and B) and multi nutrient excess (N, K, Ca, Mg and P).

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Investigating Occupation Wise Perception of Customers Towards Access to Cooperative Banking Services

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Neetu Andotra ◽  
Tarsem Lal
Keyword(s):  
Secondary Data ◽  
Principal Component ◽  
Himachal Pradesh ◽  
Primary Data ◽  
Varimax Rotation ◽  
Banking Services ◽  
Banking Service ◽  
Total Data ◽  
Cooperative Banks ◽  
Secondary Information

The present paper aims at investigating the occupation-wise perception of customers towards access to cooperative banking services. The study is both expressive and evaluative in nature. In order to investigate the perception of customers towards access to cooperative banking services, both primary and secondary data has been collected. The primary data have been collected from 540 customers of cooperative banks operating in three northern states of India i.e J&K, Himachal Pradesh, and Punjab. The technique of factor analysis has been used through SPSS (version 17.00) with Principal Component Analysis along with varimax rotation for summarisation of the total data into minimum factors. Secondary information was collected from published sources i.e books, journals, files, cooperative bulletins, organizational reports, annual drafts of Planning and Statistical Department (Government of J&K, Himachal Pradesh, and Punjab), RBI reports, magazines, and Internet. ANOVA has been applied for data analysis. The results of the study shows that there exits significant means difference between perception of customers towards access to Cooperative banking service.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

Agreement and reliability of magnetic, angular rate, and gravity (MARG) sensors to assess multiple body segment’s external loads during off-road running

2021 ◽  
pp. 175433712199698
Author(s):  
Daniel Rojas-Valverde ◽  
José Pino-Ortega ◽  
Rafael Timón ◽  
Randall Gutiérrez-Vargas ◽  
Braulio Sánchez-Ureña ◽  
...  
Keyword(s):  
Vastus Lateralis ◽  
Angular Rate ◽  
Wearable Sensors ◽  
Intraclass Correlation ◽  
Principal Component ◽  
Test Reliability ◽  
Data Set ◽  
Body Segments ◽  
External Loads ◽  
Good Agreement

The extensive use of wearable sensors in sport medicine, exercise medicine, and health has increased the interest in their study. That is why it is necessary to test these technologies’ efficiency, effectiveness, agreement, and reliability in different settings. Consequently, the purpose of this article was to analyze the magnetic, angular rate, and gravity (MARG) sensor’s test-retest agreement and reliability when assessing multiple body segments’ external loads during off-road running. A total of 18 off-road runners (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran two laps (1st and 2nd Lap) of a 12 km circuit wearing six MARG sensors. The sensors were attached to six different body segments: left (MPLeft) and right (MPRight) malleolus peroneus, left (VLLeft) and right (VLRight) vastus lateralis, lumbar (L1-L3), and thorax (T2-T4) using a special neoprene suit. After a principal component analysis (PCA) was performed, the total data set variance of all body segments was represented by 44.08%–70.64% for the 1st PCA factor considering two variables, Player LoadRT and Impacts, on L1-L3, respectively. These two variables were chosen among three total accelerometry-based external load indicators (ABELIs) to perform the agreement and reliability tests due to their relevance based on PCAs for each body segment. There were no significant differences between laps in the Player LoadRT or Impacts ( p > 0.05, trivial). The intraclass correlation and lineal correlation showed a substantial to almost perfect over-time test consistency assessed via reliability in both Player LoadRT and Impacts. Bias and t-test assessments showed good agreement between Laps. It can be concluded that MARGs sensors offer significant test re-test reliability and good agreement when assessing off-road kinematics in the six different body segments.

scholarly journals Effect of Speech-to-Noise Ratio and Luminance on a Range of Current and Potential Pupil Response Measures to Assess Listening Effort

2021 ◽  
Vol 25 ◽  
pp. 233121652110093
Author(s):  
Patrycja Książek ◽  
Adriana A. Zekveld ◽  
Dorothea Wendt ◽  
Lorenz Fiedler ◽  
Thomas Lunner ◽  
...  
Keyword(s):  
Time Course ◽  
Principal Component ◽  
Growth Curve Analysis ◽  
Listening Effort ◽  
Data Set ◽  
Holistic View ◽  
Hearing Research ◽  
Speech In Noise ◽  
Noise Test ◽  
Noise Ratio

In hearing research, pupillometry is an established method of studying listening effort. The focus of this study was to evaluate several pupil measures extracted from the Task-Evoked Pupil Responses (TEPRs) in speech-in-noise test. A range of analysis approaches was applied to extract these pupil measures, namely (a) pupil peak dilation (PPD); (b) mean pupil dilation (MPD); (c) index of pupillary activity; (d) growth curve analysis (GCA); and (e) principal component analysis (PCA). The effect of signal-to-noise ratio (SNR; Data Set A: –20 dB, –10 dB, +5 dB SNR) and luminance (Data Set B: 0.1 cd/m2, 360 cd/m2) on the TEPRs were investigated. Data Sets A and B were recorded during a speech-in-noise test and included TEPRs from 33 and 27 normal-hearing native Dutch speakers, respectively. The main results were as follows: (a) A significant effect of SNR was revealed for all pupil measures extracted in the time domain (PPD, MPD, GCA, PCA); (b) Two time series analysis approaches (GCA, PCA) provided modeled temporal profiles of TEPRs (GCA); and time windows spanning subtasks performed in a speech-in-noise test (PCA); and (c) All pupil measures revealed a significant effect of luminance. In conclusion, multiple pupil measures showed similar effects of SNR, suggesting that effort may be reflected in multiple aspects of TEPR. Moreover, a direct analysis of the pupil time course seems to provide a more holistic view of TEPRs, yet further research is needed to understand and interpret its measures. Further research is also required to find pupil measures less sensitive to changes in luminance.

Clinical implication of psoas muscle mass measurement for predicting death within 3 months in patients with incurable uterine cervical or corpus malignancy

2021 ◽  
Author(s):  
Hiroyuki Kurosu ◽  
Yukiharu Todo ◽  
Ryutaro Yamada ◽  
Kaoru Minowa ◽  
Tomohiko Tsuruta ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Muscle Mass ◽  
Cancer Cachexia ◽  
Mass Measurement ◽  
Characteristic Curve ◽  
Lumbar Vertebra ◽  
Psoas Muscle ◽  
Data Set ◽  
Cutoff Value ◽  
Computed Tomography Images

Abstract Objective The aim of this study was to find a clinical marker for identifying refractory cancer cachexia. Methods We analyzed computed tomography imaging data, which included the third lumbar vertebra, from 94 patients who died of uterine cervix or corpus malignancy. The time between the date of examination and date of death was the most important attribute for this study, and the computed tomography images were classified into >3 months before death and ≤ 3 months before death. Psoas muscle mass index was defined as the left–right sum of the psoas muscle areas (cm2) at the level of third lumbar vertebra, divided by height squared (m2). Results A data set of 94 computed tomography images was obtained at baseline hospital visit, and a data set of 603 images was obtained at other times. One hundred (16.6%) of the 603 non-baseline images were scanned ≤3 months before death. Mean psoas muscle mass index change rates at >3 months before death and ≤3 months before death were −1.3 and −20.1%, respectively (P < 0.001). Receiver operating characteristic curve analysis yielded a cutoff value of −13.0%. The area under the curve reached a moderate accuracy level (0.777, 95% confidence interval 0.715–0.838). When we used the cutoff value to predict death within 3 months, sensitivity and specificity were 74.0 and 82.1%, respectively. Conclusions Measuring change in psoas muscle mass index might be useful for predicting cancer mortality within 3 months. It could become a potential tool for identifying refractory cancer cachexia.

Use of the Perceived Physical Competence Scale with Adolescents with Disabilities

1995 ◽  
Vol 80 (2) ◽  
pp. 571-577 ◽  
Author(s):  
Taru Lintunen ◽  
Pilvikki Heikinaro-Johansson ◽  
Claudine Sherrill
Keyword(s):  
Gender Differences ◽  
Factor Analysis ◽  
Principal Component ◽  
Principal Component Factor Analysis ◽  
Validity And Reliability ◽  
Varimax Rotation ◽  
Physical Competence ◽  
Related Literature ◽  
Competence Scale ◽  
Two Factors

The construct validity and reliability of the 1987 Perceived Physical Competence Scale of Lintunen were examined to assess the applicability of the instrument for use with adolescents with disabilities. Subjects were 51 girls and 34 boys ( M age = 15.1 yr.) from several schools in central Finland. Principal component factor analysis with varimax rotation yielded the same two factors for adolescents with disabilities as reported for nondisabled adolescents in the related literature. Cronbach alphas for the two factors were .89 and .56. It was concluded that the scale is an appropriate measure for adolescents with disabilities. Statistical analysis indicated no gender differences for adolescents with disabilities. When compared with nondisabled groups in the related literature, these adolescents had perceived fitness similar to nondisabled peers but significantly lower than that of athletes without disabilities.

Sign in / Sign up

Export Citation Format

Share Document