scholarly journals Evaluation of determinants of Xiphopenaeus kroyeri (Heller, 1862) catch abundance along a Southwest Atlantic subtropical shelf

2014 ◽  
Vol 71 (7) ◽  
pp. 1793-1804 ◽  
Author(s):  
Juliana Almeida Kolling ◽  
Antônio Olinto Ávila-da-Silva
Keyword(s):  
Linear Models ◽  
Time Series Data ◽  
Series Data ◽  
Catch Per Unit Effort ◽  
Southwest Atlantic ◽  
Extreme Caution ◽  
Factors Affecting ◽  
Increasing Trend ◽  
Xiphopenaeus Kroyeri ◽  
Period Duration

Abstract Generalized linear models were applied to identify factors affecting the capture rates (catch per unit effort, cpue) of Xiphopenaeus kroyeri and to estimate time-series data with standardized abundance indices. The adjusted models revealed that the most powerful vessels (with greater HP) were up to 3.5 times more efficient than vessels with less powerful engines. The seasonal variation of the resource changed from year to year, most likely due to variations in the recruitment season and the timing of temporary fishing bans. The variation of cpue was similar between the northern and southern sectors of the study area. In these, X. kroyeri abundances increased in the years 1996, 1997, 2001 and 2002, while in the central sector, the cpue fluctuated with a period duration of approximately two years. During the period, the relative abundance of the species displayed neither a decreasing nor an increasing trend, indicating that this has been harvested at stable levels. However, X. kroyeri stock was reduced by overfishing during the 1980s and exhibits variations in abundance that may occur in response to environmental fluctuations; thus, the harvesting of this species should be managed with extreme caution.

Temporal trends of the association between extreme temperatures and hospitalisations for schizophrenia in Hefei, China from 2005 to 2014

2021 ◽  
Vol 78 (5) ◽  
pp. 364-370
Author(s):  
Rubing Pan ◽  
Qizhi Wang ◽  
Weizhuo Yi ◽  
Qiannan Wei ◽  
Jian Cheng ◽  
...  
Keyword(s):  
Time Series Data ◽  
Temporal Trends ◽  
Extreme Temperature ◽  
Series Data ◽  
Extreme Temperatures ◽  
Distributed Lag ◽  
Increasing Trend ◽  
Related Hospitalisation ◽  
Extreme Cold ◽  
And Gender

ObjectiveWe aimed to examine the temporal trends of the association between extreme temperature and schizophrenia (SCZ) hospitalisations in Hefei, China.MethodsWe collected time-series data on SCZ hospitalisations for 10 years (2005–2014), with a total of 36 607 cases registered. We used quasi-Poisson regression and distributed lag non-linear model (DLNM) to assess the association between extreme temperature (cold and heat) and SCZ hospitalisations. A time-varying DLNM was then used to explore the temporal trends of the association between extreme temperature and SCZ hospitalisations in different periods. Subgroup analyses were conducted by age (0–39 and 40+ years) and gender, respectively.ResultsWe found that extreme cold and heat significantly increased the risk of SCZ hospitalisations (cold: 1st percentile of temperature 1.19 (95% CI 1.04 to 1.37) and 2.5th percentile of temperature 1.16 (95% CI 1.03 to 1.31); heat: 97.5th percentile of temperature 1.37 (95% CI 1.13 to 1.66) and 99th percentile of temperature 1.38 (95% CI 1.13 to 1.69)). We found a slightly decreasing trend in heat-related SCZ hospitalisations and a sharp increasing trend in cold effects from 2005 to 2014. However, the risk of heat-related hospitalisation has been rising since 2008. Stratified analyses showed that age and gender had different modification effects on temporal trends.ConclusionsThe findings highlight that as temperatures rise the body’s adaptability to high temperatures may be accompanied by more threats from extreme cold. The burden of cold-related SCZ hospitalisations may increase in the future.

scholarly journals Pemodelan Kasus DBD di Provinsi Jawa Timur dengan Metode Data Panel

2019 ◽  
Vol 8 (2) ◽  
pp. 101
Author(s):  
Annisa Dwinda Shafira
Keyword(s):  
Panel Data ◽  
Population Density ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Panel Regression ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Panel Data Regression ◽  
Data Regression

The combination of panel data regression consist of time series data, it was collected based on a characteristic at a certain time (cross section). This research aimed to analyze the affecting factors and dominant factors of Dengue Hemoragic Fever (DHF) cases in East Java using panel data regression. This research uses secondary data published by the East Java Provincial Health Office, namely the Health Profile and the East Java Provincial Statistics Agency such as documents of each Districts/City in Numbers of East Java on 2014––2017 using total research population that were collected in all districts/cities in East Java Province. The data of new cases of DHF and factors affecting the incidence of DHF including clean and healthy living behavior in the household, poverty, population density, rainfall in East Java on 2014––2017. Panel regression analysis is used to determine the best model of the CEM, FEM and REM using Chow test, Hausman test and Langrange Multiplier test. Based on the results, the best model of panel regression is FEM with affecting variables such as poverty, population density, and rainfall.

scholarly journals Faktor-Faktor yang Mempengaruhi Alih Fungsi Lahan Sawah di Kabupaten Aceh Besar

2018 ◽  
Vol 3 (4) ◽  
pp. 525-533
Author(s):  
Raudhatul Husna ◽  
Azhar Azhar ◽  
Edy Marsudi
Keyword(s):  
Time Series ◽  
Population Density ◽  
Rice Field ◽  
Time Series Data ◽  
Statistical Tests ◽  
Secondary Data ◽  
Series Data ◽  
Factors Affecting ◽  
Rice Productivity ◽  
Negative Impacts

Abstrak. Alih fungsi lahan atau lazimnya disebut sebagai konversi lahan adalah  perubahan fungsi sebagian atau seluruh kawasan lahan dari fungsinya semula (seperti yang direncanakan) menjadi fungsi lain yang membawa dampak negatif terhadap lingkungan dan potensi lahan itu sendiri. Penelitian ini bertujuan untuk mengetahui apakah harga lahan, kepadatan penduduk, produktivitas padi dan jumlah PDRB dapat mempengaruhi alih fungsi lahan sawah di Kabupaten Aceh Besar. Data yang digunakan dalam penelitian ini adalah data sekunder. Data yang dikumpulkan adalah data time series dengan range tahun 2002 sampai 2016. Penelitian ini menggunakan metode analisis  regresi linier berganda. hasil penelitian dan pembahasan serta pengujian SPSS menunjukkan bahwa harga lahan, kepadatan penduduk, dan produktivitas padi berpengaruh nyata terhadap alih fungsi lahan sawah di Kabupaten Aceh Besar. sedangkan jumlah PDRB tidak berpengaruh terhadap alih fungsi lahan sawah. Hal ini ditunjukkan oleh koefisien regresi untuk variabel jumlah PDRB sebesar 0,00015. Hasil pengujian statistik menunjukkan nilai t hitung untuk jumlah PDRB sebesar 1,315 dengan nilai signifikan sebesar 0,218. Sedangkan nilai t tabel sebesar 1,782 yang berarti nilai t hitung t tabel (1,315 1,782).  Factors Affecting The Conversion Of Paddy Fields In Kabupaten Aceh Besar Abstract. Land use change or commonly referred to as land conversion is a change in the function of part or all of the land area from its original function (as planned) into other functions that bring negative impacts to the environment and the potential of the land itself. This study aims to find out whether the price of land, population density, rice productivity and the amount of GRDP can affect the conversion of rice field functions in Aceh Besar District. The data used in this research is secondary data. The data collected is time series data with range of year 2002 until 2016. This research use multiple linier regression analysis method. the results of research and discussion and testing of SPSS showed that land price, population density, and rice productivity significantly affected the conversion of wetland in Aceh Besar district. while the number of GDP does not affect the conversion of wetland. This is indicated by the regression coefficient for the GRDP variable of 0.00015. The results of statistical tests show the value of t arithmetic for the amount of GRDP by 1.315 with a significant value of 0.218. While the value of t table of 1.782 which means the value of t arithmetic t table (1,315 1.782).

scholarly journals Market model analysis and forecasting behavior of watermelon production in Bangladesh

2014 ◽  
Vol 26 (1-2) ◽  
pp. 47-56
Author(s):  
Murshida Khanam ◽  
Umme Hafsa
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Linear Models ◽  
Time Series Data ◽  
Arima Model ◽  
Model Analysis ◽  
Market Model ◽  
Series Data ◽  
Log Linear

An attempt has been made to study various models regarding watermelon production in Bangladesh and to identify the best model that may be used for forecasting purposes. Here, supply, log linear, ARIMA, MARMA models have been used to do a statistical analysis and forecasting behavior of production of watermelon in Bangladesh by using time series data covering whole Bangladesh. It has been found that, between the supply and log linear models; log linear is the best model. Comparing ARIMA and MARMA models it has been concluded that ARIMA model is the best for forecasting purposes. DOI: http://dx.doi.org/10.3329/bjsr.v26i1-2.20230 Bangladesh J. Sci. Res. 26(1-2): 47-56, December-2013

Use of ordered logit model with time series data for determining the factors affecting the milk yield of Holstein Friesians

2019 ◽  
Author(s):  
Özge Akkuş ◽  
Volkan Sevinç
Keyword(s):  
Time Series ◽  
Milk Yield ◽  
Logit Model ◽  
Time Series Data ◽  
Series Data ◽  
Ordered Logit ◽  
Factors Affecting ◽  
Ordered Logit Model ◽  
Lactation Length ◽  
Cattle Breeders

This article aims to introduce the use of ordered logit model with time series data in milk productivity studies and determine the important factor levels affecting the milk yield of Holstein Friesians. The data consists of 2002 records collected for the years 2009-2015 from the reports of the Cattle Breeders’ Association of Turkey (CBAT) in Muðla province in Turkey. The direct and marginal effects of the variables: parity, lactation length and year of calving on milk yield are investigated and the probabilities regarding the milk yield production for a given specific parity, lactation length and calving year are calculated. The results show that milk yield slightly increases on the 4th parity of cows. As far as the years concerned, although there had mostly been a steady amount of milk production between 2009 and 2015 years, there was a significant decrease in 2011 and increase in 2014.

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Competitiveness Analysis and Factors Affecting Trade of Main Commodities between Indonesia and Turkey

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Fauziyah Adzimatinur
Keyword(s):  
Wheat Flour ◽  
Time Series Data ◽  
Negative Impact ◽  
Positive Impact ◽  
Woven Fabrics ◽  
Least Square ◽  
Series Data ◽  
Trade Integration ◽  
Tariff Barrier ◽  
Factors Affecting

This study aims to analyze the competitiveness, trade integration, trade complementarity, and factors affecting the export and import of main commodities between Indonesia and Turkey. Data used in this study is time series data in 1996-2018 and the methods used are Revealed Comparative Advantage (RCA), Intra-Industry Trade (IIT), Trade Complementarity Index (TCI), and Ordinary Least Square (OLS). Results of RCA showed Indonesia's main export commodities to Turkey are woven fabrics, stearic acid, palm oil and natural rubber. While IIT showed that there is only one way trade from Indonesia. Import commodities from Turkey are carpets, borax, wheat flour, and tobacco. TCI showed low complementarity between Indonesia�s export and Turkey�s import. GDP per capita has positive impact on exports and imports. The exchange rate has positive impact on exports and negative on imports. Price and tariff rate have negative impact on both exports and imports. Dummy Non-tariff barrier has negative impact on exports while in import side, it only affects the wheat flour negatively. The Government of Indonesia should pursue a strategy in trade cooperation as efforts to reduce trade barriers such as tariffs and non-tariffs for some commodities that have competitiveness in the Turkish market.

scholarly journals Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model

2018 ◽  
Vol 5 (9) ◽  
pp. 3962
Author(s):  
Manikandan M. ◽  
Vishnu Prasad R. ◽  
Amit Kumar Mishra ◽  
Rajesh Kumar Konduru ◽  
Newtonraj A.
Keyword(s):  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Series Data ◽  
Road Traffic Accidents ◽  
Increasing Trend ◽  
Health Organization

Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.

scholarly journals Predicting Stock Market Prices using Fine-Tuned IndRNN

2020 ◽  
Vol 9 (7) ◽  
pp. 309-315
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Linear Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Activation Function ◽  
Series Data ◽  
Current Time

Prediction and analysis of stock market data have a vital role in current time’s economy. The various methods used for the prediction can be classified into 1) Linear Algorithms like Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA). 2) Non-Linear Models like Artificial Neural Networks and Deep Learning. In this work, we are using the results of previous research papers to demonstrate the potential of some models like ARIMA, Multi-Layer Perception (MLP) ), Convolutional Neural Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) for forecasting the stock price of an organization based on its available historical data. Then, implementing some of these methods to check and compare their efficiency within the same issue. We used Independently RNN (IndRNN) to explore a better efficiency for stock prediction and we found that it gives better accuracy prevailing methods in the current time. We also proposed an enhancement to IndRNN by replacing its default activation function with a more effective function called Parametric Rectified Linear Unit (PreLU). Our proposed approach can be used as an alternative method for predicting time series data efficiently other than the typical approaches today

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