scholarly journals A method for forecasting visibility at Hindon

MAUSAM ◽  
2021 ◽  
Vol 51 (1) ◽  
pp. 47-56
Author(s):  
O. P. MADAN ◽  
N. RAVI ◽  
U. C. MOHANTY
Keyword(s):  
Multiple Regression ◽  
Personal Experience ◽  
Time Frame ◽  
Data Sets ◽  
Suitable Model ◽  
Independent Data ◽  
Regression Methods ◽  
Skill Scores ◽  
Quantitative Manner ◽  
Auto Regression

At present the approach to forecasting visibility is synoptic and personal experience of the weather forecaster. The month of December typically a winter month, is associated with poor visibility. Aviators require visibility forecast in terms of a definite quantitative value at a specific place in specific time frame. Therefore, in this study an attempt is made to develop a suitable model for forecasting visibility in December at a place Hindon near Delhi in a quantitative manner.   In the development process of forecasting visibility, different approaches such as auto-regression, multiple regression, climatology and persistence have been attempted. The models are developed using seven years (1984-90) data of December. The model is evaluated with the independent data sets from the recent years 1994-95. It is found that climatology-persistence method provides better results as compared to the multiple regression and auto-regression methods. The developed model provided positive skill scores as high as 70% on development as well as independent data sets.

Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods

1996 ◽  
Vol 26 (4) ◽  
pp. 590-600 ◽  
Author(s):  
Katherine L. Bolster ◽  
Mary E. Martin ◽  
John D. Aber
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Data Sets ◽  
Independent Data ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Regression Methods

Further evaluation of near infrared reflectance spectroscopy as a method for the determination of nitrogen, lignin, and cellulose concentrations in dry, ground, temperate forest woody foliage is presented. A comparison is made between two regression methods, stepwise multiple linear regression and partial least squares regression. The partial least squares method showed consistently lower standard error of calibration and higher R2 values with first and second difference equations. The first difference partial least squares regression equation resulted in standard errors of calibration of 0.106%, with an R2 of 0.97 for nitrogen, 1.613% with an R2 of 0.88 for lignin, and 2.103% with an R2 of 0.89 for cellulose. The four most highly correlated wavelengths in the near infrared region, and the chemical bonds represented, are shown for each constituent and both regression methods. Generalizability of both methods for prediction of protein, lignin, and cellulose concentrations on independent data sets is discussed. Prediction accuracy for independent data sets and species from other sites was increased using partial least squares regression, but was poor for sample sets containing tissue types or laboratory-measured concentration ranges beyond those of the calibration set.

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

scholarly journals A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 484
Author(s):  
Claudiu Vințe ◽  
Marcel Ausloos ◽  
Titus Felix Furtună
Keyword(s):  
Stock Market ◽  
Time Frame ◽  
Data Sets ◽  
Entropy Model ◽  
Daily Data ◽  
Volatility Models ◽  
Derivative Instruments ◽  
Securities Industry ◽  
Time Frames ◽  
Financial Securities

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

scholarly journals Glia Not Neurons: Uncovering Brain Dysmaturation in a Rat Model of Alzheimer’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 823
Author(s):  
Ekaterina A. Rudnitskaya ◽  
Tatiana A. Kozlova ◽  
Alena O. Burnyasheva ◽  
Natalia A. Stefanova ◽  
Nataliya G. Kolosova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Brain Structures ◽  
Time Frame ◽  
Oxys Rats ◽  
Unknown Etiology ◽  
Suitable Model ◽  
Model Of Alzheimer’S Disease ◽  
The Brain

Sporadic Alzheimer’s disease (AD) is a severe disorder of unknown etiology with no definite time frame of onset. Recent studies suggest that middle age is a critical period for the relevant pathological processes of AD. Nonetheless, sufficient data have accumulated supporting the hypothesis of “neurodevelopmental origin of neurodegenerative disorders”: prerequisites for neurodegeneration may occur during early brain development. Therefore, we investigated the development of the most AD-affected brain structures (hippocampus and prefrontal cortex) using an immunohistochemical approach in senescence-accelerated OXYS rats, which are considered a suitable model of the most common—sporadic—type of AD. We noticed an additional peak of neurogenesis, which coincides in time with the peak of apoptosis in the hippocampus of OXYS rats on postnatal day three. Besides, we showed signs of delayed migration of neurons to the prefrontal cortex as well as disturbances in astrocytic and microglial support of the hippocampus and prefrontal cortex during the first postnatal week. Altogether, our results point to dysmaturation during early development of the brain—especially insufficient glial support—as a possible “first hit” leading to neurodegenerative processes and AD pathology manifestation later in life.

scholarly journals Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

BMC Cancer ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
James E Korkola ◽  
Ekaterina Blaveri ◽  
Sandy DeVries ◽  
Dan H Moore ◽  
E Shelley Hwang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Signature ◽  
Data Sets ◽  
Independent Data ◽  
Breast Cancer Outcome ◽  
Cancer Outcome

scholarly journals Attitudes of medical students towards psychiatry and mental illnesses: a cross-sectional study from Pakistan

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S303-S304
Author(s):  
Syed Muhammad Jawad Zaidi ◽  
Muhammad Hamza ◽  
Raja Adnan Ahmed ◽  
Mishal Fatima ◽  
Hassan Nadeem ◽  
...  
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
Multiple Regression ◽  
Personal Experience ◽  
Positive Attitude ◽  
Cross Sectional Study ◽  
Mental Illnesses ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Crisis

AimsThe increasing burden of mental disorders coupled with the social stigmatization in Pakistan is an immense barrier in combating the emerging mental health crisis. The low number of qualified psychiatrists and poor intake in post-graduate psychiatry training programs in the region further complicates the problem. Thus, our study aims to assess the attitudes of Pakistani medical students towards psychiatry. Furthermore, we also aim to evaluate how experience and different levels of exposure to psychiatry among students affect their attitudes towards psychiatry as a career choice.MethodThis cross-sectional study was conducted via an online survey made on Google Forms. A total of 831 medical students studying across various private and public medical institutions of Pakistan responded to the survey. The questionnaire comprised of demographical details (gender, age, institution, and academic year) exposure to psychiatry, duration of psychiatry rotation, and personal experience with mental illness. The attitudes of medical students towards psychiatry were evaluated using the English version of the 30-item Attitudes Towards Psychiatry (ATP-30) scale. Chi-square test and multiple regression with backward method were used to analyze the data.ResultThe Cronbach's alpha value of the ATP-30 scale was 0.830. The participants in our study had a mean score of 107.6 ± 12 on ATP-30. Overall, most participants had a positive attitude towards psychiatry. Multiple regression analysis revealed a significant model pertaining to predictors of attitude toward psychiatry (F (df) = 11.28 (830), P < 0.001). However, the predictors included in the model accounted for only 5.8% of the variation in ATP-30 scores. According to it, those students had a more positive attitude toward psychiatry who identified as female, older and having any sort of exposure toward psychiatric specialty, direct involvement in psychiatric patient care, and reporting personal experience of mental illnesses.ConclusionOur study showed that medical students had a positive attitude towards psychiatry but female medical students, students with previous exposure to psychiatry, and students with longer psychiatry rotations tend to view psychiatry more positively. The generally positive trend towards psychiatry in Pakistan indicates the need to sustain improvements through proactive measures. We recommend longer placements for medical students in mental health settings for at least 4 weeks or longer. Medical schools should also promote research, discussions, and seminars on different psychiatric illnesses in order to enhance awareness among the students.

scholarly journals Empirical estimation of present-day Antarctic glacial isostatic adjustment and ice mass change

2013 ◽  
Vol 7 (4) ◽  
pp. 3497-3541 ◽  
Author(s):  
B. C. Gunter ◽  
O. Didova ◽  
R. E. M. Riva ◽  
S. R. M. Ligtenberg ◽  
J. T. M. Lenaerts ◽  
...  
Keyword(s):  
Time Frame ◽  
Gravity Models ◽  
Mass Change ◽  
Data Sets ◽  
Glacial Isostatic Adjustment ◽  
Empirical Estimation ◽  
Satellite Gravity ◽  
Amundsen Sea ◽  
Average Value ◽  
Isostatic Adjustment

Abstract. This study explores an approach that simultaneously estimates Antarctic mass balance and glacial isostatic adjustment (GIA) through the combination of satellite gravity and altimetry data sets. The results improve upon previous efforts by incorporating reprocessed data sets over a longer period of time, and now include a firn densification model to account for firn compaction and surface processes. A range of different GRACE gravity models were evaluated, as well as a new ICESat surface height trend map computed using an overlapping footprint approach. When the GIA models created from the combination approach were compared to in-situ GPS ground station displacements, the vertical rates estimated showed consistently better agreement than existing GIA models. In addition, the new empirically derived GIA rates suggest the presence of strong uplift in the Amundsen Sea and Philippi/Denman sectors, as well as subsidence in large parts of East Antarctica. The total GIA mass change estimates for the entire Antarctic ice sheet ranged from 53 to 100 Gt yr−1, depending on the GRACE solution used, and with an estimated uncertainty of ±40 Gt yr−1. Over the time frame February 2003–October 2009, the corresponding ice mass change showed an average value of −100 ± 44 Gt yr−1 (EA: 5 ± 38, WA: −105 ± 22), consistent with other recent estimates in the literature, with the mass loss mostly concentrated in West Antarctica. The refined approach presented in this study shows the contribution that such data combinations can make towards improving estimates of present day GIA and ice mass change, particularly with respect to determining more reliable uncertainties.

FEM-ANN Sequential Modelling of Laser Transmission Welding for Prediction of Weld Pool Dimensions

2019 ◽  
pp. 249-261
Author(s):  
Bappa Acherjee
Keyword(s):  
Neural Network ◽  
Weld Pool ◽  
Welding Process ◽  
Time Frame ◽  
Data Sets ◽  
Fe Model ◽  
Ann Model ◽  
Laser Transmission Welding ◽  
Scripting Language ◽  
Artificial Neural Network Ann

In this chapter, a sequential modeling approach has been applied for modeling of laser transmission welding process using finite element method (FEM) and artificial neural network (ANN) technique to predict the weld pool dimensions in a shorter time frame. The scripting language, APDL (ANSYS® Parametric Design Language), is used to develop the three-dimensional FE model. During preprocessing, all the major physical phenomena of laser transmission welding process are incorporated into the model physics. Based on the temperature field predicted by the model, the weld pool dimensions (i.e., weld width and weld penetration depth) are calculated. The weld dimensions predicted by the developed FE model are further used for training a neural network model. It is found from the results of test data sets that the developed ANN model can predict the outputs with significant accuracy and takes less prediction time, which in turn saves time, cost, and the efforts for performing experiments.

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