scholarly journals Gaussian Transformation Methods for Spatial Data

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 196
Author(s):  
Emmanouil A. Varouchakis
Keyword(s):  
Spatial Data ◽  
Random Number Generator ◽  
Data Transformation ◽  
Rainfall Data ◽  
Public Access ◽  
Negative Sign ◽  
Alternative Methods ◽  
Positive Sign ◽  
Observation Data ◽  
Transformation Methods

Data gaussianity is an important tool in spatial statistical modeling as well as in experimental data analysis. Usually field and experimental observation data deviate significantly from the normal distribution. This work presents alternative methods for data transformation and revisits the applicability of a modified version of the well-known Box-Cox technique. The recently proposed method has the significant advantage of transforming negative sign (fluctuations) data in advance to positive sign ones. Fluctuations derived from data detrending cannot be transformed using common methods. Therefore, the Modified Box-Cox technique provides a reliable solution. The method was tested in average rainfall data and detrended rainfall data (fluctuations), in groundwater level data, in Total Organic Carbon wt% residuals and using random number generator simulating potential experimental results. It was found that the Box-Cox technique competes successfully in data transformation. On the other hand, it improved significantly the normalization of negative sign data or fluctuations. The coding of the method is presented by means of a Graphical User Interface format in MATLAB environment for reproduction of the results and public access.

scholarly journals A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Micromachines ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Junxiu Liu ◽  
Zhewei Liang ◽  
Yuling Luo ◽  
Lvchen Cao ◽  
Shunsheng Zhang ◽  
...  
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Logistic Map ◽  
Random Number Generator ◽  
Alternative Methods ◽  
Random Numbers ◽  
Number Generator ◽  
Stochastic Computing ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

scholarly journals PENGARUH DIMENSI DIMENSI IKLIM ORGANISASI TERHADAP KINERJA PEGAWAI DI BADAN KEPEGAWAIAN DAERAH PROVINSI NUSA TENGGARA BARAT

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Muhammad Amri Fuadi ◽  
Hermanto Hermanto ◽  
Lalu Suparman
Keyword(s):  
Organizational Climate ◽  
Social Environment ◽  
Employee Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Work Behavior ◽  
Positive Sign ◽  
Path Coefficient ◽  
Observation Data ◽  
Pls Analysis

This research is directed to prove the significance of the influence of organizational climate dimensions in the form of physical environment (X1), social environment (X2) and management system (X3) on the dimensions of employee performance in the form of employee work goals (Y1) and employee work behavior (PKP). There are six hypotheses that are proven through a partial least square (PLS) analysis process. The population of this study (observation data) was 82 BKD NTB employees. Data was collected through questionnaires and all questionnaires returned in accordance with data input needs. Through the outer model stage there are two indicators of the social environment that are issued, namely the relationship of superiors with subordinates (LS1) and colleague relations (LS2) as well as two indicators of employee work behavior, namely commitment (PKP3) and leadership (PKP6). Indicators that are classified as valid get a reinforcement of criteria through the parameter AVE values above 0.50 and include reliable indicators through Cronbach's alpha parameters and composite reliability above 0.70. PLS analysis through the inner model stage found that all dimensions of the organization's climate have a positive influence (positive sign of the path coefficient) on the dimensions of employee performance.Keywords : Organizational Climate Dimensions and Employee Performance Dimensions.

scholarly journals Growth and Financing Behaviour of Firms of Textile Industry in Pakistan: A Panel Data Analysis.

2011 ◽  
Vol 50 (4II) ◽  
pp. 699-714 ◽  
Author(s):  
Ijaz Hussain
Keyword(s):  
Economic Growth ◽  
Interest Rate ◽  
Capital Structure ◽  
Textile Industry ◽  
Panel Data Analysis ◽  
Jel Classification ◽  
Negative Sign ◽  
Cost Of Debt ◽  
Positive Sign ◽  
Financial Leverage

High economic growth, extremely low nominal interest rate and negative real interest rate gave a boost to financial leverage (gearing ratio) of the textile sector to its peak in 2005. Firms are now are facing the consequence of high gearing. An explosion in their financing costs along with removal of textile quota from 2005 onwards and later on an acute energy crisis hampered their profitability and ability to repay their debt. This in turn contributed to non-performing loans which is now is likely to pose a big challenge for financial sector and push economy into another crisis. Most of the previous studies including a very few on capital structure of Pakistani firms focus on understanding only the firm specific determinants of financial leverage and completely ignore macroeconomic or institutional factors. Findings of this paper prove that all firm specific determinants including profitability and efficiency, firms‘ growth, risk and collateral excluding size significantly influence corporate financial leverage of textile industry in Pakistan. All macroeconomic variables including overall economic growth, equity market conditions and nominal cost of debt also have significant impact on corporate gearing. Negative sign with the composite measure of profitability and efficiency implies that banks are compelled to fund inefficient and unprofitable firms because demand for loans comes more from inefficient and unprofitable firms. Positive sign with growth and negative sign with risk is indicative of the fact that banks prefer to lend to growing rather than riskier firms. JEL classification: C13, C23, C51, L65, G10, G30 Keywords: Capital Structure Determinants, Corporate Financial Leverage, Corporate Gearing Ratio

scholarly journals Geographic Information and Geo-visualisation in support of Disaster Resilience

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Edward Kurwakumire ◽  
Paul Muchechetere ◽  
Shelter Kuzhazha ◽  
Guy Blachard Ikokou
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Disaster Preparedness ◽  
Geographic Information ◽  
Public Access ◽  
Data Sets ◽  
Disaster Resilience ◽  
Data Systems ◽  
Information Infrastructures ◽  
Timely Access

<p><strong>Abstract.</strong> Society continues to become more spatially enabled as spatial data becomes increasingly available and accessible. This is partly due to democratisation of data achieved through open access of framework data sets. On the other hand, mobile devices such as smartphones have become more accessible, giving the public access to applications that use spatial data. This has tremendously increased the consumption of spatial data at the level of the general public. Spatial data has a history in planning and decision making as detailed in literature on promises and benefits of geographic information. We extend these promises to sustainability and disaster resilience. It is our belief that geographic information (GI) and geographic information infrastructures (GIIs) contribute positively towards the achievement of sustainability in cities and nations and in disaster resilience. This study carries out a review of geo-visualisation and GI applications in order to determine their suitability and impact in disaster resilience. Real-time GI are significant for cities to ensure sustainability and to increase disaster preparedness. Geographic information infrastructures need to be integrated with BIG data systems to ensure that local government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done.</p>

scholarly journals Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 273
Author(s):  
Younghyun Cho
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Performance Test ◽  
Hydrological Modeling ◽  
Rainfall Data ◽  
Storm Event ◽  
Rainfall Runoff ◽  
Hydrologic Simulation ◽  
Great Opportunity ◽  
Spatially Distributed

Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.

scholarly journals Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

2018 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Sanjaya Natadiredja ◽  
I Ketut Sukarasa ◽  
Gusti Ngurah Sutapa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Satellite Data ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Observation Data ◽  
Similar Data ◽  
Strong Positive Correlation ◽  
Cause Analysis ◽  
Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

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