Search

Background: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. Methods: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. Results: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. Conclusion: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity. [ABSTRACT FROM AUTHOR]

The graphical representation of the correlation matrix by means of different multivariate statistical methods is reviewed, a comparison of the different procedures is presented with the use of an example data set, and an improved representation with better fit is proposed. Principal component analysis is widely used for making pictures of correlation structure, though as shown a weighted alternating least squares approach that avoids the fitting of the diagonal of the correlation matrix outperforms both principal component analysis and principal factor analysis in approximating a correlation matrix. Weighted alternating least squares is a very strong competitor for principal component analysis, in particular if the correlation matrix is the focus of the study, because it improves the representation of the correlation matrix, often at the expense of only a minor percentage of explained variance for the original data matrix, if the latter is mapped onto the correlation biplot by regression. In this article, we propose to combine weighted alternating least squares with an additive adjustment of the correlation matrix, and this is seen to lead to further improved approximation of the correlation matrix.

Soybean is an important oilseed crop worldwide; however, it has a high sensitivity to temperature variation, particularly at the vegetative stage to the pod-filling stage. Temperature change affects physiochemical and genetic traits regulating the soybean agronomic yield. In this regard, the current study aimed to comparatively evaluate the effects of varying regimes of day and night temperatures (T1 = 20°C/12°C, T2 = 25°C/17°C, T3 = 30°C/22°C, T4 = 35°C/27°C, and T5 = 40°C/32°C) on physiological (chlorophyll, photosynthesis, stomatal conductance, transpiration, and membrane damage) biochemical (proline and antioxidant enzymes), genetic (GmDNJ1, GmDREB1G;1, GmHSF-34, GmPYL21, GmPIF4b, GmPIP1;6, GmGBP1, GmHsp90A2, GmTIP2;6, and GmEF8), and agronomic traits (pods per plant, seeds per plant, pod weight per plant, and seed yield per plant) of soybean cultivars (Swat-84 and NARC-1). The experiment was performed in soil plant atmosphere research (SPAR) units using two factorial arrangements with cultivars as one factor and temperature treatments as another factor. A significant increase in physiological, biochemical, and agronomic traits with increased gene expression was observed in both soybean cultivars at T4 (35° C/27°C) as compared to below and above regimes of temperatures. Additionally, it was established by correlation, principal component analysis (PCA), and heatmap analysis that the nature of soybean cultivars and the type of temperature treatments have a significant impact on the paired association of agronomic and biochemical traits, which in turn affects agronomic productivity. Furthermore, at corresponding temperature regimes, the expression of the genes matched the expression of physiochemical traits. The current study has demonstrated through extensive physiochemical, genetic, and biochemical analyses that the ideal day and night temperature for soybeans is T4 (35°C/27°C), with a small variation having a significant impact on productivity from the vegetative stage to the grain-filling stage. [ABSTRACT FROM AUTHOR]

People must be well-informed on market swings in today's difficult economic times in order to cut excessive spending. Rising expenditures in a variety of sectors, including business, education, and healthcare can be burdensome for consumers, and accurate forecasting of household is necessary given the current technological innovation. The Consumer Price Index (CPI) is one of the statistical indicators used to estimate the changes in prices for commodities. Forecasting CPI can assist individuals in developing a plan for making decisions on their daily consumption. This study seeks to develop a SARIMA model for forecasting consumer price indices (CPI) in Tanzania by using data collected from International Monetary Fund (IMF) website from January 2010 to December 2022. Data were evaluated using time series methods such as time plots and stationarity tests. It was discovered that there is seasonality in the CPI index. However, a serial correlogram test was performed using a residual correlogram after which the variable was estimated using the SARIMA model and SARIMA (0, 1, 0) (1, 1, 1)12 was fitted to the time series variable. The residual analysis was explored and because almost all correlations are zero, the SARIMA (1,1,1) (0,1,2)12 model was appropriate for forecasting CPI index in Tanzania. Consumer price index was predicted for the next eighteen months and it has been observed that the trend of CPI is likely to increase in the next eighteen months.

People must be well-informed on market swings in today's difficult economic times in order to cut excessive spending. Rising expenditures in a variety of sectors, including business, education, and healthcare can be burdensome for consumers, and accurate forecasting of household is necessary given the current technological innovation. The Consumer Price Index (CPI) is one of the statistical indicators used to estimate the changes in prices for commodities. Forecasting CPI can assist individuals in developing a plan for making decisions on their daily consumption. This study seeks to develop a SARIMA model for forecasting consumer price indices (CPI) in Tanzania by using data collected from International Monetary Fund (IMF) website from January 2010 to December 2022. Data were evaluated using time series methods such as time plots and stationarity tests. It was discovered that there is seasonality in the CPI index. However, a serial correlogram test was performed using a residual correlogram after which the variable was estimated using the SARIMA model and SARIMA (0, 1, 0) (1, 1, 1)12 was fitted to the time series variable. The residual analysis was explored and because almost all correlations are zero, the SARIMA (1,1,1) (0,1,2)12 model was appropriate for forecasting CPI index in Tanzania. Consumer price index was predicted for the next eighteen months and it has been observed that the trend of CPI is likely to increase in the next eighteen months. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual review of the kriging metamodel that is introduced for the design and analysis of computer experiments (DACE). Kriging is a statistical interpolation method to build an approximation model from a set of evaluations of the function at a finite set of points. The method originally developed for geostatistics, and it is now widely used in the domains of spatial data analysis and computer experiments analysis. The main difference between these domains the dimensionality of the problems. Geostatistics and spatial data are mainly deal with the coordinates. Computer experiments, simulation outputs and other engineering problems have multidimensional input variables. With this study, it is aimed to examine the limitations of the prediction performance of the DACE-kriging metamodel. The result of the study shows that the regression part of the DACE-kriging metamodel is the most important part to develop an approximation, and if there is a spatial relationship of the residuals, kriging part will also contribute to the improvement of the prediction performance. Otherwise, kriging will have no contribution to the DACE-kriging metamodel, and even worsen the prediction performance. If the regression part perfectly fit to the observations, the residual will have poor spatial relationship and the kriging part will be meaningless anymore.

Lentil (Lens culinaris Medik.), an important rabi pulse crop grown in the plains of West Bengal, is vulnerable to Stemphylium blight, caused by the fungal pathogen Stemphylium botryosum. Considering the severity of the disease in the existing cropping system, an experiment was undertaken at the instructional farm of Uttar Banga Krishi Viswavidyalaya during Rabi 2021 with the objective to observe the correlation between the quantitative traits and the Area Under Disease Progress Curve (AUDPC) among the 50 lentil genotypes under the incidence of Stemphylium blight. The experiment was planned in Alpha Lattice design with two replications. The disease parameter AUDPC was derived from Hashemi’s disease score. A total of twenty quantitative traits viz., days to 50% flowering, plant height, primary branches/ plant, secondary branches/ plant, nodes/ plant, leaflet number, leaflet length, leaf length, tendril length, peduncle length, 100 - seed weight, seed width, seed thickness, seeds/ pod, pods/ cluster, pods/ plant, days to maturity, pod yield, harvest index and seed yield were recorded. Significant positive correlation was recorded for AUDPC with days to 50% flowering and primary branches/ plant as against significant negative correlation for AUDPC with 100- seed weight and harvest index. The study on Principal Component Analysis (PCA) revealed that the traits viz., seed yield, pod yield, leaf length, leaflet length, pods/ plant, secondary branches, seeds/ pod, days to maturity, AUDPC, nodes/ plant, days to 50% flowering, pods/ cluster, seed thickness and harvest index showed the maximum contribution towards variation (19.49%) among the PC1 variables. The PCA biplot revealed that seed yield and pod yield strongly influenced PC1. Thus, the findings of the current study might be helpful to schedule the crop improvement program as far as the response of the crop to the disease is concerned.

This paper presents a conceptual review of the kriging metamodel that is introduced for the design and analysis of computer experiments (DACE). Kriging is a statistical interpolation method to build an approximation model from a set of evaluations of the function at a finite set of points. The method originally developed for geostatistics, and it is now widely used in the domains of spatial data analysis and computer experiments analysis. The main difference between these domains the dimensionality of the problems. Geostatistics and spatial data are mainly deal with the coordinates. Computer experiments, simulation outputs and other engineering problems have multidimensional input variables. With this study, it is aimed to examine the limitations of the prediction performance of the DACE-kriging metamodel. The result of the study shows that the regression part of the DACE-kriging metamodel is the most important part to develop an approximation, and if there is a spatial relationship of the residuals, kriging part will also contribute to the improvement of the prediction performance. Otherwise, kriging will have no contribution to the DACE-kriging metamodel, and even worsen the prediction performance. If the regression part perfectly fit to the observations, the residual will have poor spatial relationship and the kriging part will be meaningless anymore. [ABSTRACT FROM AUTHOR]

Rainfall has both positive and negative effects on human activities hence, correct prediction of the period of occurrence is very essential. However, the traditional method of predicting months of heavy rainfall is gradually fading out as irregular rainfall pattern is been experienced in most regions of the world. The rainfall pattern in Calabar city in Southern Nigeria has been reported in past literatures of being irregular. Hence, this research applied the Auto-Regressive Integrated Moving Average (ARIMA) also known as Box-Jenkins method to model the rainfall pattern of Calabar. This was achieved by subjecting 50years monthly rainfall (1971 - 2020) to gretl software version 2021b. The analysed data showed that the rainfall of the study area required just one-time differencing to attain stationarity at 95% confidence, while the order of the Auto-Regressive AR(p) and Moving Average MA(q) models were either 1 or 2 in both cases. Hence ARIMA(1,1,1), ARIMA(1,1,2), ARIMA(2,1,1) and ARIMA(2,1,2) were identified and further analyses revealed that ARIMA(2,1,2) best suited the rainfall of the study area. A diagnostic check was carried out on the selected ARIMA (2,1,2) model and it was observed to be reliable (minimal white noise) thereafter, it was used to forecast the rainfall of the study area for some months. [ABSTRACT FROM AUTHOR]

A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, ρ DCCA , we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series. [ABSTRACT FROM AUTHOR]

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann-Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung-Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3)12 model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3)12, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas. [ABSTRACT FROM AUTHOR]

The relevance of the study of identity formation stages of a university student is determined by changes in modern Russian higher education primarily related to digitalization processes. The article presents the description of students’ identity formation features under conditions of higher education digitalization. The expediency of using an ecopsychological (ontological) model of identity formation is shown, according to which the process of formation of subject qualities is viewed in the ontological continuum, i.e. “subject of spontaneous activity – subject of arbitrary action”. With this model used as a theoretical basis, the aim of the study was to empirically identify correlation relationships (correlograms) between the stages of identity formation in university students. We have put forward a hypothesis that the relationships between stages of identity formation are peculiar and describe the individual style of learning actions of a student in the temporal aspect. The necessity of using specifically developed methods of process diagnostics and nonparametric statistical methods has been shown. The results of a longitudinal study of the identity formation stages in the course of education at a technical university are presented (n = 150 students; first and second year students; M = 18,86 years; Sd = 0,79; 46% male), longitude duration is 8–10 weeks. There is an example of a correlogram, which demonstrates individual structure of correlation interrelations between the stages of student’s identity. Interpretations of this structure of interrelations are suggested taking into account the style of subjectivity formation. Recommendations on application of obtained data regarding the stages of identity formation in the university educational process are given.

Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further. [ABSTRACT FROM AUTHOR]

Almost all state enterprises and private sector companies try to foresee future expectations. From the viewpoint of economic, productive, and efficient business management, this is highly important. By making rational decisions, all enterprises aim to rich maximum profitability by taking sales, cost, human resource needs, profits into account. For this reason, enterprises have to make reliable and reasonable forecasts to take the right decisions. Such forecasts might be used in budgeting, cost, and profit analysis. Forecasted scenarios might come true in the future with a great likelihood. The researcher utilizing time series analysis assumes that all findings that come out will be almost the same happened in the past. Analyzing the time series consist of four aims such as defining, modeling forecasting, and controlling. To define a series, it is needed to compute definitional statistics and to draw its graphic. The second purpose of analyzing the time series is to find the appropriate model of the time series. With that work called "Time series and application to sale data", it is tried to make a suitable guess model by analyzing the data of personal loans of a bank 2004-2010 sale data based on unit. During the stagnation stage of the sequence correlogram and root, analyses are performed. The sequence is analyzed with the help of the Eviews 5,1 program. At the end of the survey, it is seen that natural logarithmic personal loan sale sequences are at their level and in the first gap it is not constant and it is also seen that when the second gap is taken, the constant is obtained. The sequence of which the second gap is taken is shown based on time-way graphs and correlogram. When the constant is provided, the guessed model is formed by taking the second gap. The suitability of the model is observed by the correlogram, Akaike information criteria (AIC), and Schwarz information criteria (SIC) merits. [ABSTRACT FROM AUTHOR]

Soybean is an important oilseed crop worldwide; however, it has a high sensitivity to temperature variation, particularly at the vegetative stage to the pod-filling stage. Temperature change affects physiochemical and genetic traits regulating the soybean agronomic yield. In this regard, the current study aimed to comparatively evaluate the effects of varying regimes of day and night temperatures (T1 = 20°C/12°C, T2 = 25°C/17°C, T3 = 30°C/22°C, T4 = 35°C/27°C, and T5 = 40°C/32°C) on physiological (chlorophyll, photosynthesis, stomatal conductance, transpiration, and membrane damage) biochemical (proline and antioxidant enzymes), genetic ( GmDNJ1 , GmDREB1G;1 , GmHSF-34 , GmPYL21 , GmPIF4b , GmPIP1;6 , GmGBP1 , GmHsp90A2 , GmTIP2;6 , and GmEF8 ), and agronomic traits (pods per plant, seeds per plant, pod weight per plant, and seed yield per plant) of soybean cultivars (Swat-84 and NARC-1). The experiment was performed in soil plant atmosphere research (SPAR) units using two factorial arrangements with cultivars as one factor and temperature treatments as another factor. A significant increase in physiological, biochemical, and agronomic traits with increased gene expression was observed in both soybean cultivars at T4 (35°C/27°C) as compared to below and above regimes of temperatures. Additionally, it was established by correlation, principal component analysis (PCA), and heatmap analysis that the nature of soybean cultivars and the type of temperature treatments have a significant impact on the paired association of agronomic and biochemical traits, which in turn affects agronomic productivity. Furthermore, at corresponding temperature regimes, the expression of the genes matched the expression of physiochemical traits. The current study has demonstrated through extensive physiochemical, genetic, and biochemical analyses that the ideal day and night temperature for soybeans is T4 (35°C/27°C), with a small variation having a significant impact on productivity from the vegetative stage to the grain-filling stage., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ding, Alghabari, Rauf, Zhao, Javed, Alshamrani, Ghazy, Al-Doss, Khalid, Yang and Shah.)

The Trans-African Hydro-Meteorological Observatory (TAHMO) is a promising initiative aiming to install 20,000 stations in sub-Saharan Africa counteracting the decreasing trend of available measuring stations. To achieve this goal, it is particularly important that the installed weather stations are cost-efficient, appropriate for African conditions, and reliably measure the most important variables for hydro-meteorological applications. Since there exist no performance studies of TAHMO stations while operating in Africa, it is necessary to investigate their performance under different climate conditions. This study provides a first analysis of the performance of 10 selected TAHMO stations across Burkina Faso (BF). More specifically, the analysis consists of missing value statistics, plausibility tests of temperature (minimum, maximum) and precipitation, spatial dependencies (correlograms) by comparison with daily observations from synoptical stations of the BF meteorological service as well as cross-comparison between the TAHMO stations. Based on the results of this study for BF for the period from May 2017 to December 2020, it is concluded that TAHMO potentially offers a reliable and cost-efficient solution for applications in hydro-meteorology. The usage of wind speed measurements cannot be recommended without reservation, at least not without bias correcting of the data. The limited measurement period of TAHMO still prevents its usability in climate (impact) research. It is also stressed that TAHMO cannot replace existing observation networks operated by the local meteorological services, but it can be a complement and has great potential for detailed spatial analyses. Since restricted to BF in this analysis, more evaluation studies of TAHMO are needed considering different environmental and climate conditions across SSA.

The article is focused on microbiological and silvicultural properties of bacterial wetwood of silver birch (Betula pendula), also known as European white birch. During the active phase of the disease, bacterial wetwood (i.e. bacterial dropsy, vascular parenchymatous bacteriosis or flux slime) is characterised by crust and periderm bloating, necrotic wet stains and abundance of exudate. The disease is more likely to occur in older (r = 0.56, p < 0.01) and less-dense (r = −0.29, p < 0.01) stands. The statistical model showed that the chance of bacterial wetwood increases with birch age by 0.36% per year. The stands with birch proportion of over 70% demonstrated 15.3% lower infection rate compared to the stands with lower birch presence. The stands with lower stocking demonstrated a higher proportion of infected tree distribution by 7.5% compared to the stands with higher birch representation. The most vulnerable were larger, older B. pendula trees with longitudinally fissured bark that grow on poorer soils and experience frequent water stress. Birch associations with Pteridium aquilinum and Vaccinium myrtillus were more susceptible to infection (31.6% and 44.3%, respectively), whereas associations with Brachypodium sylvaticum, Sphagnum palustre and Calluna vulgaris were at lower risk. Strong ecological and trophic association of bacterial wetwood was present between silver birch and Tremex spp., particularly Tremex fuscicornis. Mycobiota was represented by Rhizopus microsporus, Mucor mucedo, Penicillium aurantiogriseum, Penicillium purpurogenum and Acremonium strictum. Enterobacter, Xanthomonas, Pantoea and Bacillus spp. associated with bacterial wetwood of silver birch were isolated. Enterobacter nimipressuralis was found to be the primary causative agent through means of artificial infection, while other bacteria were found to be either weak pathogens or concomitant. E. nimipressuralis formed the largest number of colony-forming units (CFU) for bark and cambium (164 and 127 CFU, respectively) and was also found in a small amount as a vital obligate in the automicrobiota in healthy birch trees. [ABSTRACT FROM AUTHOR]

We analyse the directional predictability of a daily dataset of aggregate and regional (10 major metropolitan cities) housing markets of the United States using the quantilogram – a model-free procedure. We overwhelmingly reject the weak-form of the efficient market hypothesis (EMH), which has been derived thus far by the extant literature based on unit root tests and long-memory models. [ABSTRACT FROM AUTHOR]

A method for calculating the frequency sweep law of a vibration source is proposed and substantiated, which yields a flat spectrum of the correlogram of the target wave, regardless of natural amplitude–frequency distortions introduced by the medium and source. Such a spectrum serves as the main prerequisite for achieving maximum resolution of closely spaced wave pulses in the resulting seismogram. The methodology includes test sounding of a typical seismic geological object with any linear sweep signal in the desired frequency range, separation and spectral analysis of the vibrogram of the target wave, splitting of the resulting spectrum into many narrow frequency intervals, and calculating for each of the intervals its own partial linear sweep. Construction of a continuous sequence of these partial sweeps is the final operation in synthesizing an adaptive nonlinear (piecewise linear) sweep signal. In this study, the necessary conditions are found and formulated that ensure correctness of the coupling procedure for partial sweeps. [ABSTRACT FROM AUTHOR]

Rapid growth in the oil industry has been accompanied concomitant increases in risks of spills or leaks triggered by natural or anthropogenic causes that cause soil changes and plant damage. Bio-scavenging and phytoremediation plants are important tools for identifying pollutants and mitigating environmental damage. The objective of this study was to evaluate the phytoremediation potential of Ricinus communis cultivated in soils contaminated with mineral oil, and to determine the possible visual, anatomical and physiological effects. R. communis seeds were pre-germinated in individual pots containing Red Latosol contaminated with Lubrax Essential SL (15W-40) mineral oil at concentrations of 0 (control), 5, 10, and 15 g kg−1. After exposure to treatments, emergency evaluations were performed, and after 45 days of cultivation, visual, morphoanatomical, physiological and oil removal effects were evaluated. There was no difference in emergence showed between treatments. Visual effects were characterized by necrosis and chlorosis formation in R. communis, evidenced on the 45th day of cultivation in all treatments tested, followed by parenchymal tissue alterations with collapsed cell formation and damage to photosynthesis with increasing doses. We found that R. communis removed up to 81% of hydrocarbons in soils, classifying it as potential phytoremediator of contaminated soils. The strong correlation between the variables suggests that R. communis can be used as an indicator of pollutant action. [ABSTRACT FROM AUTHOR]

The study examined the incidence of the rate of Neonatal Mortality in Nigeria. The incidence tested with use of Time Series Analysis (ARIMA). The trend plot of the incidence shows that there has been steady decrease in the incidence rate over the years. The series was stationarity using the Augmented Dickey-Fuller (ADF) Test, the result found to be stationary. The Correlogram of the incidence also supports the stationarity of the series at 1% level of significance. The model that best describes the incidence was tested using the Box-Jenkins four step procedures, which involve identification, explanation, diagnostic and forecasting. ARMA (1, 1), and ARIMA (1, 1, 1) models were tested, and ARIMA (1, 1, 1) model happens to be the best model that best fits the series. The time series analysis shows that neonatal mortality rate has reduced by 17.8% from 51. 7% in the year 1990 to 33. 9% in the year 2017. Keywords: Auto regression, Moving average, Mortality, Stationarity, Correlogram

Dam inflow forecasting information is essential for planning and management of the dam system. Time series analysis is the most commonly employed technique to forecast the future values based on historical information. In this study, Palar-Porandalar dam in Tamil Nadu inflow series were forecasted in R software package using ARIMA model with seasonal factors. The monthly inflow series of the dam from 2003 January to 2017 December were used as an input source for modeling and forecasting process. Mann-Kendall's trend test and various Stationarity test were performed to verify the Stationary nature of the data set. From the Correlogram plot, different models were identified; their parameters were optimized and residuals were diagnostically tested using Autocorrelation plot and Ljung Box test. Finally, the best model was selected based on minimum Akaike Information Criteria (AIC), BIC, RMSE and Theil's U statistic values. From various models, SARIMA (0, 0, 1) (1, 0, 2)12 model was selected as the best one for forecasting the inflow series. [ABSTRACT FROM AUTHOR]

The study examined the incidence of the rate of Neonatal Mortality in Nigeria. The incidence tested with use of Time Series Analysis (ARIMA). The trend plot of the incidence shows that there has been steady decrease in the incidence rate over the years. The series was stationarity using the Augmented Dickey-Fuller (ADF) Test, the result found to be stationary. The Correlogram of the incidence also supports the stationarity of the series at 1% level of significance. The model that best describes the incidence was tested using the Box-Jenkins four step procedures, which involve identification, explanation, diagnostic and forecasting. ARMA (1, 1), and ARIMA (1, 1, 1) models were tested, and ARIMA (1, 1, 1) model happens to be the best model that best fits the series. The time series analysis shows that neonatal mortality rate has reduced by 17.8% from 51. 7% in the year 1990 to 33. 9% in the year 2017. [ABSTRACT FROM AUTHOR]

Spatial dependence can obscure relationships between response and explanatory variables because of structuring within the residuals reducing variance and biasing coefficient estimates. Here, we highlight the influence of the spatial component, in the presence of spatial dependence, on abundance trends. This is illustrated using abundance data for a Critically Endangered reef fish, dageraad Chrysoblephus cristiceps, which were obtained from a long-term monitoring programme in the Tsitsikamma National Park marine protected area, South Africa. Correlograms illustrate distinct spatial structuring in the abundance data, and spatial variables were determined as more important than temporal variables when ranked according to predictive power using a random forest analysis. A generalised additive model (GAM) that did not account for spatial dependencies was compared to a generalised additive mixed model (GAMM) that incorporated a spatial residual correlation structure. Results derived from the spatially explicit GAMM differed considerably from the GAM lacking a spatial component, with the latter deemed to produce over-precise and partially biased abundance trends. The study emphasises the importance of space in accurately modelling abundance estimates, particularly temporal trends, and provides an introduction to the minimal statistical requirements necessary to address the violations associated with spatial autocorrelation. [ABSTRACT FROM AUTHOR]

Spam mail classification has been playing a vital role in recent days due to the uncontrollable growth happening in the electronic media. Literature presents several algorithms for email spam classification based on classification methods. In this paper, we propose a spam classification framework using S-Cuckoo and hybrid kernel based support vector machine (HKSVM). At first, the features are extracted from the e-mails based on the text as well as the image. For the textual features, TF-term frequency is used. For the image dependent features, correrlogram and wavelet moment are taken. The hybrid features have then high dimension so the optimum features are identified with the help of hybrid algorithm, called S-Cuckoo search. Then, the classification is done using proposed classifier HKSVM model which is designed based on the hybrid kernel by blending three different kernel functions and then it is used in the SVM classifier. The additional features provided based on image and the modification of SVM classifier provides significant improvement as compared with existing algorithms. The spam classification performance is measured by db1 (combining bare-ling spam and Spam Archive corpus) and db2 (combining lemm-ling spam and Spam Archive corpus). Experimental results show that the proposed spam classification framework has outperformed by having better accuracy of 97.235% when compared with existing approach which is able to achieve only 94.117%. [ABSTRACT FROM AUTHOR]

Vibration analysis is one of the most popular methods for state monitoring of rotating machines, and feature extraction is of much importance in the design of the monitoring system. In this paper, a new high-level feature extraction method based on correlograms for vibration signal analysis is proposed, and it includes two phases. Firstly, in the learning process, a codebook is created from training data using the k-means algorithm. Next, in the testing process, for a given data stream collected from a monitoring rotating machine, the correlogram in each cycle is obtained by comparing every data point with all codewords in the codebook at first; the entropy is then computed to form final high-level features to measure the state of the machine. A change decision can be made finally based on features extracted from null hypothesis testing. Based on an experimental setup used in our previous work, the proposed method is evaluated with application to the speed change monitoring of a rotating machine. Experimental results demonstrate the excellent performance and the priority of the method compared with ten typical features. [ABSTRACT FROM AUTHOR]

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.

Fourier-transform near infrared spectroscopy (FT-NIR) is a technique used in the compositional and sensory analysis of foodstuffs. In this work, we have measured the main maturity parameters for grape (sugars and acids) using hundreds of intact berry samples to build models for the prediction of these parameters from berries of two very different varieties: “Victoria” and “Autumn Royal”. Together with the chemical composition in terms of sugar and acidic content, we have carried out a sensory analysis on single berries. Employing the models built for sugars and acids it was possible to learn the sweetness and acidity of each berry before the destructive sensory analysis. The direct correlation of sensory data with FT-NIR spectra is difficult; therefore, spectral data were exported from the spectrometer built-in software and analyzed with R software using a statistical analysis technique (Spearman correlation) which allowed the correlation of berry appreciation data with specific wavelengths that were then related to sugar and acidic content. In this article, we show how it is possible to carry out the analysis of single berries to obtain data on chemical composition parameters and consumer appreciation with a fast, simple, and non-destructive technique with a clear advantage for producers and consumers.

We consider a measurable stationary Gaussian stochastic process. A criterion for testing hypotheses about the covariance function of such a process using estimates for its norm in the space $L_{p}(\mathbb{T})$, $p\ge 1$, is constructed.

Abstract: Less than 0.2% of all spider species live in close associations with conspecifics. Among these, subsocial spiders show characteristics of both solitary spiders (e.g., individuals disperse for breeding) and social spiders (e.g., prolonged cooperative behaviours at least prior to independent reproduction). Dispersing individuals build small webs, usually with one inhabitant, whereas colonies are large webs with plant debris and harbouring multiple females. We studied the spatiotemporal dynamics of dispersal in the subsocial spider Anelosimus baeza. We followed the occupancy of all colonies and dispersal webs over the breeding season by mapping the number and sex of spiders with respect to their location in three dimensions. We studied the settlement patterns of new webs and fluctuation in web occupancy through movement between occupied and abandoned webs of colonies and dispersal webs. The occupancy of webs was highly dynamic with changes occurring at small time scales. The similarity in the patterns of web occupancy by females among dispersal webs was partially explained by their spatial and their temporal proximity. Our results suggest that dispersal webs may be used by spiders as a temporary refuge by both sexes during the breeding season. Patterns described here suggest new approaches to dispersal studies in group living spiders. [ABSTRACT FROM AUTHOR]

This research studies the relationship between the climate index and the groundwater level of the lower Chao Phraya basin, in order to forecast the groundwater level in the studied area by using Autoregressive Integrated Moving Average with Explanatory (ARIMAX). The combination of 6 climate indices--Dipole Mode Index, Indian Summer Monsoon Index, Multivariate ENSO Index, Sea Surface Temperature NINO4, Southern Oscillation Index and the Western North Pacific Monsoon Index--were used, along with the groundwater level data from 14 stations during the period 1980-2011 to develop the forecast model and verify it with the data of 2012. The first step was correlation of the ARIMA model with Autocorrelation Function and Partial Autocorrelation Function. The possible model was then selected using BIC statistics. Diagnostic Checking was done to consider the white noise characteristic of estimated residuals by using the statistics of Box and Ljung (Q-statistic). If the selected models were found to be proper, then the Granger Causality Test of the leading parameters or the climate index would be performed as the next step. The results show that there is a relationship between the groundwater level and the climate index. The model could be used to forecast effectively the average RMSE value at 0.6. The last step was to develop the MODFLOW for a conceptual model and synthesize groundwater levels in the study area, which covers around 43,000 km² and has 8 layers of groundwater, with Bangkok clay on the top. All other boundary values were set to be steady. The calibration was done using the data of 325 observed wells. The normalized RMS value was 9.705%. The results were verified by the data using ARIMAX over the same time periods. To conclude, the simulated results of the monthly groundwater level in 2012 of the wells have a confidence interval of around 95%, which is near the result from the ARIMAX model. The advantages of the ARIMAX model include high accuracy, no requirement for a large amount of data and inexpensive implementation. It is one of the effective tools for the groundwater prediction. [ABSTRACT FROM AUTHOR]

This paper has established a link between the spatial structure of radar rainfall, which more robustly describes the spatial structure, and gauge rainfall for improved daily rainfield simulation conditioned on the limited gauged data for regions with or without radar records. A two-dimensional anisotropic exponential function that has parameters of major and minor axes lengths, and direction, is used to describe the correlogram (spatial structure) of daily rainfall in the Gaussian domain. The link is a copula-based joint distribution of the radar-derived correlogram parameters that uses the gauge-derived correlogram parameters and maximum daily temperature as covariates of the Box-Cox power exponential margins and Gumbel copula. While the gauge-derived, radar-derived and the copula-derived correlogram parameters reproduced the mean estimates similarly using leave-one-out cross-validation of ordinary kriging, the gauge-derived parameters yielded higher standard deviation (SD) of the Gaussian quantile which reflects uncertainty in over 90% of cases. However, the distribution of the SD generated by the radar-derived and the copula-derived parameters could not be distinguished. For the validation case, the percentage of cases of higher SD by the gauge-derived parameter sets decreased to 81.2% and 86.6% for the non-calibration and the calibration periods, respectively. It has been observed that 1% reduction in the Gaussian quantile SD can cause over 39% reduction in the SD of the median rainfall estimate, actual reduction being dependent on the distribution of rainfall of the day. Hence the main advantage of using the most correct radar correlogram parameters is to reduce the uncertainty associated with conditional simulations that rely on SD through kriging. [ABSTRACT FROM AUTHOR]

Intra-prediction modes (IPMs) of H.264/AVC, as visual feature components, are easily extracted from the compressed domain. However, in spite of their efficiency and effectiveness, they can be sensitive to encoder settings such as encoder-type and encoding-profile. In content-based video retrieval using different encoders between a video and its copy can affect performance. Similarly, applying different encoding-profiles can also have a negative impact. Experiments with different IPM-based descriptors, on a subset of the TRECVID/CCD 2011 collection with content-preserving visual distortions, compare the performance of four H.264/AVC encoding profiles ( High10 , High , Baseline and Main ) and also investigate the impact of three encoders ( FFmpeg , MediaCoder and JM ). The research shows that ordinal pattern distribution of unified IPMs can improve their tolerance to changes in encoder settings. Furthermore, it is shown that a Correlogram rather than a Histogram of the IPMs is more robust with respect to the impact of encoding-profiles and encoder-types. [ABSTRACT FROM AUTHOR]

This paper gives the finite-length analysis of a spectrum estimation method for the case that the samples are obtained at a rate lower than the Nyquist rate. The method is referred to as the averaged correlogram for undersampled data. It is based on partitioning the spectrum into a number of segments and estimating the average power within each spectral segment. This method is able to estimate the power spectrum density of a signal from undersampled data without essentially requiring the signal to be sparse. We derive the bias and the variance of the spectrum estimator, and show that there is a tradeoff between the accuracy of the estimation, the frequency resolution, and the complexity of the estimator. A closed-form approximation of the estimation variance is derived, which clearly shows how the variance is related to different parameters. The asymptotic behavior of the estimator is also investigated, and it is proved that in the case of a white Gaussian process, this spectrum estimator is consistent. Moreover, the estimation made for different spectral segments becomes uncorrelated as the signal length tends to infinity. Finally, numerical examples and simulation results are provided, which approve the theoretical conclusions. [ABSTRACT FROM AUTHOR]