scholarly journals Weather and Stochastic Forecasting Method for Generated Discharge Level Data at Sathanur Dam

2019 ◽  
Vol 8 (10) ◽  
pp. 3797-3802
Keyword(s):  
Trend Analysis ◽  
Error Estimates ◽  
Data Sets ◽  
Forecasting Models ◽  
Level Data ◽  
Forecasting Method ◽  
The Future ◽  
Current Scenario ◽  
Stochastic Forecasting

This article forecasts the future values using stochastic forecasting models for specified fitted values by using downscaling data, which are collected from Sathanoor Dam gauging site. Due to the demand of the water in this current scenario, this study analyzed the perdays Discharge level data collected from Sathanoor Dam where the outcome is predicted in a downscaling data sets in hydrology, extended Thomas –Fiering, ARIMA, MLE models, is used to estimate perdays discharge level data of each month. The error estimates RMSE, MAE of forecasts from above models is compared to identify the most suitable approaches for forecasting trend analysis.

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

New Evidence on Eastern Europe's Pollution Progress

2003 ◽  
Vol 3 (1) ◽  
Author(s):  
Matthew E Kahn
Keyword(s):  
Sulfur Dioxide ◽  
Carbon Dioxide Emissions ◽  
National Level ◽  
Data Sets ◽  
Local Public Good ◽  
Data Set ◽  
The Czech Republic ◽  
Level Data ◽  
Good Improvement ◽  
New Evidence

Abstract Under communism, Eastern Europe's cities were significantly more polluted than their Western European counterparts. An unintended consequence of communism's decline is to improve urban environmental quality. This paper uses several new data sets to measure these gains. National level data are used to document the extent of convergence across nations in sulfur dioxide and carbon dioxide emissions. Based on a panel data set from the Czech Republic, Hungary and Poland, ambient sulfur dioxide levels have fallen both because of composition and technique effects. The incidence of this local public good improvement is analyzed.

Evaluation of Network-Level Data Collection Variability and its Influence on Pavement Evaluation Utilizing Random Forest Method

2021 ◽  
pp. 036119812098043
Author(s):  
Xiaoyang Jia ◽  
Mark Woods ◽  
Hongren Gong ◽  
Di Zhu ◽  
Wei Hu ◽  
...  
Keyword(s):  
Data Collection ◽  
High Variability ◽  
Pavement Management ◽  
Data Sets ◽  
State Highway ◽  
Condition Evaluation ◽  
Level Data ◽  
Pavement Evaluation ◽  
Data Variability ◽  
Parallel Tests

The use of pavement condition data to support maintenance and resurfacing strategies and justify budget needs becomes more crucial as more data-driven approaches are being used by the state highway agencies (SHAs). Therefore, it is important to understand and thus evaluate the influence of data variability on pavement management activities. However, owing to a huge amount of data collected annually, it is a challenge for SHAs to evaluate the influence of data collection variability on network-level pavement evaluation. In this paper, network-level parallel tests were employed to evaluate data collection variability. Based on the data sets from the parallel tests, classification models were constructed to identify the segments that were subject to inconsistent rating resulting from data collection variability. These models were then used to evaluate the influence of data variability on pavement evaluation. The results indicated that the variability of longitudinal cracks was influenced by longitudinal lane joints, lateral wandering, and lane measurement zones. The influence of data variability on condition evaluation for state routes was more significant than that for interstates. However, high variability of individual metrics may not necessarily lead to high variability of combined metrics.

Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time

2011 ◽  
Author(s):  
A. Cavarzere ◽  
M. Venturini
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Current Trend ◽  
Performance Limit ◽  
Industrial Systems ◽  
Forecasting Method ◽  
Double Exponential ◽  
The Future ◽  
Double Exponential Smoothing ◽  
Over Time

The growing need to increase the competitiveness of industrial systems continuously requires a reduction of maintenance costs, without compromising safe plant operation. Therefore, forecasting the future behavior of a system allows planning maintenance actions and saving costs, because unexpected stops can be avoided. In this paper, four different methodologies are applied to predict gas turbine behavior over time: Linear and Non Linear Regression, One Parameter Double Exponential Smoothing, Baesyan Forecasting Method and Kalman Filter. The four methodologies are used to provide a prediction of the time when a performance limit will be exceeded in the future, as a function of the current trend of the considered parameter. The application considers different scenarios which may be representative of the trend over time of some significant parameters for gas turbines. Moreover, the Baesyan Forecasting Method, which allows the detection of discontinuities in time series, is also tested for predicting system behavior after two consecutive trends. The results presented in this paper aim to select the most suitable methodology that allows both trending and forecasting as a function of data trend over time, in order to predict time evolution of gas turbine characteristic parameters and to provide an estimate of the occurrence of a failure.

scholarly journals PERAMALAN TREND KUNJUNGAN WISATAWAN DIKAWASAN KONSERVASI MANGROVE BEKANTAN (KKMB) DI KOTA TARAKAN

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Galih Yogi Rahajeng ◽  
Arni Arni
Keyword(s):  
Trend Analysis ◽  
Proboscis Monkey ◽  
Conservation Area ◽  
Tourist Attractions ◽  
Average Decrease ◽  
Mangrove Ecosystems ◽  
The Future ◽  
Quota Sampling

ABSTRACT Conservation Area for Mangrove and Proboscis in Tarakan City was built to preserve mangrove ecosystems and wildlife in its development and alternative ecotourism destinations. Based on the potential of Conservation Area for Mangrove and Proboscis in Tarakan City, therefore this study aims to predict the trend of tourist visits in Conservation Area for Mangrove and Proboscis in Tarakan City so that it can become the foundation for the development of Conservation Area for Mangrove and Proboscis inTarakan City in the future. In this study using respondents as many as 100 respondents who were Conservation Area for Mangrove and Proboscis in Tarakan City visitors. The sampling used method was accedential sampling and quota sampling. The result shown than visitors of Conservation Area for Mangrove and Proboscis inTarakan from 2018-2022 estimated to reach 164,888,5 visitors. The average decrease in visits per year is 978 people. Because of the facilities in the Conservation Area for Mangrove and Proboscis in Tarakan City are inadequate, such as wooden bridge that begin to be damaged and slippery when it rains, lack of place to sit, cleanliness that has not been maintained, unsanitary toilets, and lack of parking areas, and reduced proboscis monkey. If there is no renovation and repair of facilities, visitors tend not to choose the Conservation Area for Mangrove and Proboscis in Tarakan City as an alternative tourist attractions. Keywords: Conservation of mangrove, Ecotourism, Trend Analysis. ABSTRAK Kawasan Konservasi Mangrove dan Bekantan Kota Tarakan (KKMB) dibangun dan memiliki tujuan sebagai pengembangan satwa liar dan ekowisata alternatif. Berdasarkan potensi yang ada, oleh karena itu maka penelitian ini bertujuan untuk meramalkan trend kunjungan wisatawan di KKMB agar dapat menjadi landasan dalam pengembangan KKMB di masa depan. Dalam penelitian ini menggunakan 100 responden yang merupakan pengunjung KKMB. Metode pengambilan sampel yang digunakan adalah pengambilan sampel akedensial dan kuota. Hasil Penelitian memperlihatkan pengunjung KKMB di Kota Tarakan dari 2018-2022 diperkirakan mencapai 164.888,5 orang. Penurunan rata-rata kunjungan per tahun adalah 978 orang. Karena fasilitas di Kawasan Konservasi Mangrove dan Bekantan Kota Tarakan  tidak memadai, seperti jembatan kayu yang mulai rusak dan licin saat hujan, kurangnya tempat duduk, kebersihan yang belum dijaga, toilet yang tidak bersih, dan kurangnya area parkir, dan  berkurangnya populasi bekantan. Jika tidak ada renovasi dan perbaikan fasilitas, pengunjung cenderung tidak memilih Kawasan Konservasi Mangrove Bekantan di Kota Tarakan sebagai tempat wisata alternatif. Kata kunci: Konservasi mangrove, Ekowisata, Analisis Trend.

scholarly journals Trend Analysis of Cost Components in Select Pharmaceutical Companies

2019 ◽  
Vol 8 (4) ◽  
pp. 2289-2298
Keyword(s):  
Pharmaceutical Industry ◽  
Trend Analysis ◽  
Health Benefits ◽  
Annual Reports ◽  
Pharmaceutical Companies ◽  
Manufacturing Costs ◽  
Operating Profit ◽  
The Future ◽  
The Cost ◽  
Cost Components

The purpose in this paper is to identify the cost components which are vital in consideration towards manufacturing especially in pharmaceutical companies. The manufacturing costs are significant in total expenses in pharmaceutical industry. In this study, a thorough investigation on the cost components and the trend in expenses and operating profit of pharma companies are studied, giving due regard to cost components to have understanding and to find out how they may differ among various types of pharma companies. The data published in the annual reports from 2009 to 2018 of top five pharmaceutical companies based on their annual revenues has been selected for further diagnosis. The analysis reveals that manufacturing costs are different for all the five companies. The study also reveals that there is a considerable indication that the companies are conscious on the much-needed health benefits to the society in the future at an affordable cost

scholarly journals Evaluating the Impact of COVID-19 Pandemic on Dental Academics and the Future of Dental Education Across India

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Rameshwari Singhal ◽  
Anil Chandra ◽  
Shuchi Tripathi ◽  
Pavitra Rastogi ◽  
Richa Khanna
Keyword(s):  
Quantitative Data ◽  
Online Teaching ◽  
Qualitative Data ◽  
Dental Education ◽  
Descriptive Statistics ◽  
The Future ◽  
Current Scenario ◽  
Dental Schools ◽  
The Impact ◽  
New Situation

Background: The outbreak of coronavirus disease 2019 (COVID-19) pandemic has led to the transition of dental education from chair-side clinical teachings to virtual didactic lectures. The future of dental education is not clear in these uncertain times. Objectives: This survey-based study aimed to evaluate the current scenario and preparedness of dental colleges/universities and faculty in adapting to the new situation and understanding the challenges faced during this phase. The survey also explored the opinions, limitations, and possible solutions in dental academics through open-ended qualitative questions. Methods: This survey-based study utilized exploratory mixed methods through both open- and closed-ended questions. The survey was distributed electronically to the majority of dental colleges across India to be answered voluntarily by the dental academicians involved in COVID-19 planning. The survey was inspired by the pre-existing questionnaire proposed by the Association of Dental Education in Europe (ADEE), and it was modified by the committee consisting of the study authors. Validation and piloting of the study were done through in-house dental faculty. Quantitative data were analyzed using descriptive statistics and expressed in percentages. Broad themes for qualitative data were derived by two independent authors and collated by the third author to finalize the results. Results: The questionnaire was answered by 89 dental schools from all parts of the country with varying stages of COVID-19 prevalence. Quantitative data revealed 100% adaptation of dental schools to online teaching, uncertainty regarding online (31.46%) and offline (10.11%) exams, and assessment of clinical competence. Qualitative analysis indicated uncertainty, ambiguity, and lack of direction among study respondents regarding how best to deal with the current situation. Conclusions: According to our results, collaborative effort from governing bodies was urgently required at this point to prevent dental education from being divided into multi-directional, incoherent, and isolated units.

scholarly journals A Review of Crude Oil Prices Forecasting using Hybrid Method

2018 ◽  
Vol 11 (3) ◽  
pp. 1114
Author(s):  
Nurull Qurraisya Nadiyya Md-Khair ◽  
Ruhaidah Samsudin
Keyword(s):  
Crude Oil ◽  
Review Paper ◽  
Oil Prices ◽  
Economic Activities ◽  
Data Decomposition ◽  
Forecasting Models ◽  
Forecasting Methods ◽  
Crude Oil Prices ◽  
Forecasting Method ◽  
Hybrid Forecasting

Crude oil is considered as a crucial energy source in modern days. Consequently, the fluctuation of crude oil prices can cause a significant impact on economic activities. Researchers have proposed many hybrid forecasting models on top of single forecasting methods which are utilized to predict crude oil prices movement more accurately. Nevertheless, many limitations still existed in hybrid forecasting models and models that can predict crude oil prices as accurate as possible is required. The motivations of this review paper are to identify and assess the mostly used crude oil prices forecasting methods and to analyse their current limitations. 12 studies that used “decomposition-and-ensemble” framework was selected for review. Wavelet transform is identified as the mostly used data decomposition method while some limitations have been recognized. Future researches should include more studies to further elucidate the limitations in existing forecasting method so that subsequent forecasting methods can be improved.

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