PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value.

Ways to identify bearings in two-position goniometric airborne radiomonitoring systems.

For aeronautical goniometric systems for radio monitoring of radio emission sources (RES), one of the primary tasks is the identification of bearings. It is especially difficult to solve the problem of identifying bearings if there are several RESs in the observation area in the case when they are located in the same plane with the direction finders. In this case, the problem of identifying bearings in goniometric two-position systems is solved in the process of performing a two-stage procedure. At the first stage, the primary identification of single measurements of bearings is carried out separately at each receiving position (RP) when receiving radio signals from the RES, and at the second stage, the secondary (inter-positional) identification of bearings arriving from both RPs is carried out. In the initial identification, strobe and strobeless identification algorithms are used. In the secondary identification for selection of true and false points of intersection of bearings on the plane, it is proposed to use the kinematic parameters of the relative RES. However, this type of selection does not provide interposition identification with an arbitrary nature of the movement of the RES relative to the RP, and also assumes a constant angular position of the RP base on the plane. More practical are ways of identifying bearings with RES, in which the procedure for constructing a confidence region (CR) in the form of a circle with a certain radius is used. However, a more correct form of CR is an elliptical CR, since the errors in determining the position of the RES are characterized by an error ellipse, a particular case of which is a circle. Therefore, methods for identifying coordinate information have been developed, in which elliptical CRs are used. In this case, not only the bearings of the RES, but also other measured parameters, for example, estimates of the rectangular coordinates of the RES, calculated on the basis of the triangulation method, can be used as coordinate information. The purpose of the article is to systematize and analyze the developed methods for identifying bearings, which allow one to get a fairly general idea of how to solve the problem of identifying bearings and indirect measurements of the coordinates of radio emission sources in aviation goniometric two-position radio monitoring systems. As a result, a classification of identification methods is given. The existing possibilities and limitations of using various identification methods in solving radio monitoring problems are analyzed. The necessary information on the methods and algorithms for interpositional identification of coordinate information about the position of the RES, using ellipsoidal CRs in solving the identification problem, is given. The practical significance of the presented methods is to increase the likelihood of correct identification of coordinate information, as well as the accuracy of the positioning of RES due to the use of elliptical CRs, which more accurately reflect the regularity of the distribution of errors in determining the position of RES.

Cognitive system for analysis behavioral of dynamic objects of video surveillance

The necessary measures to ensure the safety of technical structures, freight/passenger stations and other transport infrastructure facilities include continuous video monitoring with a comprehensive analysis of the scene. In conditions of high density of numerous objects continuously moving through the observation area, one of the main available signs of detecting anomalies in their behavior is their trajectory of the object. In this paper, we propose an approach to building a system for analyzing the behavior of dynamic video surveillance objects based on their tracking, implemented by means of cognitive modeling. The proposed procedures for intelligent analysis of the nature of movement of video surveillance objects are based on a combination of neural network technologies and the logical inference mechanism of the expert system, which expands the basic algorithms for technical equipment of video surveillance systems. The practical significance of the considered solutions is to increase the efficiency of detecting suspicious situations in conditions of high traffic density by conducting a parallel analysis of the movement of numerous objects of the scene, which entails the prevention of possible illegal actions in places of mass presence of people, including transport infrastructure facilities.

Relationship Between Altimetric Quality and Along-Track Spatial Resolution for iGNSS-R Sea Surface Altimetry: Example for the Airborne Experiment

The altimetric quality and the along-track spatial resolution are the critical parameters to characterize the performance of interferometric global navigation satellite systems reflectometry (iGNSS-R) sea surface altimetry, which is closely related to each other through signal processing time. Among them, the quality of sea surface height (SSH) measurement includes precision and accuracy. In order to obtain higher altimetric quality in the observation area, a longer signal processing time is needed, which will lead to the loss of spatial resolution along the track. In contrast, higher along-track spatial resolution requires more intensive sampling, leading to unsatisfactory altimetric quality. In this study, taking the airborne iGNSS-R observation data as an example, the relationship between the altimetric quality and the along-track spatial resolution is analyzed from the perspectives of precision and accuracy. The results indicate that the reduction in the along-track spatial resolution will improve the altimetric quality. The accuracy range is 0.28–0.73 m, and the precision range is 0.24–0.65 m. However, this change is not linear, and the degree of altimetric quality improvement will decrease as the along-track spatial resolution worsens. The research results in this paper can provide a scientific reference for the configuration of parameters for future spaceborne iGNSS-R altimetry missions.

Neural-network fusion processing and inverse mapping to combine multi-sensor satellite data and to analyze significant features

<div>In the last decade, the increase in the number of active and passive earth observation satellites has provided us with more remote sensing data. This fact has led to increased interests in the field of fusion of the different satellite data since some of the satellites have properties complementary to one another. Fusion techniques can improve the estimation in areas of interest (AOIs) by using complementary information and inferring unknown parameters. However, when the observation area is large, extensive human labor and domain expertise are required for processing and analysis. Thus, we propose a neural network which combines and analyzes the data obtained from synthetic aperture radars (SAR) and optical sensors. The neural network employs a modified logarithmic activation function, unlike conventional networks, to realize inverse mapping for significant feature analysis based on dynamics consistent with its forward processing. In this paper, we focus on earthquake damage detection by dealing with the data of the 2018 Sulawesi earthquake in Indonesia. The fusion-based results show increased classification accuracy compared to the results of independent sensors. We further attempt to understand which input features are the significant contributors for which classification outputs by inverse-mapping in the data fusion neural network. We observe that inverse mapping shows reasonable explanations in a consistent manner. It also indicates contributions of features different from straightforward counterparts, namely pre- and post-seismic features, in the detection of particular classes.</div>

Neural-network fusion processing and inverse mapping to combine multi-sensor satellite data and to analyze significant features

<div>In the last decade, the increase in the number of active and passive earth observation satellites has provided us with more remote sensing data. This fact has led to increased interests in the field of fusion of the different satellite data since some of the satellites have properties complementary to one another. Fusion techniques can improve the estimation in areas of interest (AOIs) by using complementary information and inferring unknown parameters. However, when the observation area is large, extensive human labor and domain expertise are required for processing and analysis. Thus, we propose a neural network which combines and analyzes the data obtained from synthetic aperture radars (SAR) and optical sensors. The neural network employs a modified logarithmic activation function, unlike conventional networks, to realize inverse mapping for significant feature analysis based on dynamics consistent with its forward processing. In this paper, we focus on earthquake damage detection by dealing with the data of the 2018 Sulawesi earthquake in Indonesia. The fusion-based results show increased classification accuracy compared to the results of independent sensors. We further attempt to understand which input features are the significant contributors for which classification outputs by inverse-mapping in the data fusion neural network. We observe that inverse mapping shows reasonable explanations in a consistent manner. It also indicates contributions of features different from straightforward counterparts, namely pre- and post-seismic features, in the detection of particular classes.</div>

Analisis Bilangan Richardson (Ri) untuk Stabilitas Atmosfer Iklim Mikro pada Maret 2021 di Konawe Selatan

A microclimate is a complex of environmental variables that affect plants, including temperature, radiation, humidity, and wind. One of the additional atmospheric parameters that can be relevant in microclimate studies is the condition of atmospheric stability. The Richardson number derived from the temperature gradient and wind speed can determine the inversion interval in the atmosphere. The research was conducted at the Konawe Selatan Climatology Station to describe the condition of atmospheric stability and the convection process by calculating dynamic stability based on wind and temperature data at the level of 2 meters, 4 meters, and 7 meters in March 2021 from automatic tools. Based on observations in Condition 1, the atmosphere was seen in the morning dominated by neutral conditions, unstable in the afternoon, and stable in the afternoon. In condition 2 the atmosphere on a not rainy day and a rainy day in the morning was dominated by neutral conditions, free convection during the day, and forced convection at night. Free convection illustrated that the wind in the observation area was still dominated by monsoons and was still entering the rainy season. Also, forced convection illustrated that there was orographic rain; this was supported by the topography of the observation area, which was close to the hills and the Boroboro Mountains.

Population dynamics of the pasture leafhopper complex (Hemiptera: Cercopidae) in brachiaria and mombasa grass

Pasture leafhoppers are considered highly important pests in forage grasses in Brazil due to their widespread occurrence. This insect is one of the most relevant pests in pasture degradation. In order for the control of spittlebugs to be efficient, it is important to know the population behavior of the species of this insect, identify the period of greatest occurrence, as well as the climatic and environmental conditions that favor the development of the pest. Thus, this study aimed to evaluate the population dynamics of spittlebugs, at a quantitative level, in Brachiaria decumbens and Panicum maximum pastures, associating the results with meteorological data from the municipality of Conceição do Araguaia, Southeast Pará. To study the population dynamics of spittlebugs, samples were taken every two weeks, in two types of pastures aged between 5 and 7 years, kept under rotational grazing, with a stocking of 1.5 animal units. The method of monitoring nymphs and adults of leafhoppers was adopted, through walking within the observation area. The level of infestation of spittlebugs in both forage species was evaluated and all results were submitted to analysis of variance by the F test. It was found that the species B. decumbens had a greater number of adults and nymphs when compared to the forage species P. maximum. The population dynamics of spittlebugs occur gradually according to climatic conditions, and the period with not-so-high temperatures (22 ºC to 34 ºC) and good rainfall provide an infestation of this insect pest in the pasture.

Optical telescope with 3.75 ◦ field of view and pre-aperture slewing mirror with observation area of 50 ◦ ×120 ◦ for the system of observation of day time asteroids (the SODA project)

В работе предложена оптическая схема широкоугольного телескопа с полем зрения 3.75 ◦ и апертурой 30 см для космической системы обнаружения декаметровых астероидов (проект СОДА). Основная отличительная особенность телескопа - наличие предапертурного плоского зеркала, обеспечивающего область наведения 50 ◦ × 120 ◦ , время перенаведения между соседними площадками составит не более 3 с. Предложен современный КМОП детектор с мелким пикселем. В работе представлены области обзора телескопов проекта СОДА из точки Лагранжа L 1 при использовании двух, трех и четырех телескопов, кратко обсуждены преимущества и недостатки каждого из вариантов. We propose an optical scheme of a telescope with a field of view of 3.75 ◦ and 30 cm aperture for the space system for observation of decameter size asteroids (the SODA project). The main distinctive feature of this telescope is a pre-aperture flat mirror that provides an observation area of 50 ◦ ×120 ◦ and a repointing time between the adjacent fields of less than 3 s. A modern CMOS detector with a small pixel is proposed. Observable sky area when using 2, 3 and 4 telescopes are described. The advantages and disadvantages of each option are briefly discussed.

Metal-Nano-Ink Coating for Monitoring and Quantification of Cotyledon Epidermal Cell Morphogenesis

During cotyledon growth, the pavement cells, which make up most of the epidermal layer, undergo dynamic morphological changes from simple to jigsaw puzzle-like shapes in most dicotyledonous plants. Morphological analysis of cell shapes generally involves the segmentation of cells from input images followed by the extraction of shape descriptors that can be used to assess cell shape. Traditionally, replica and fluorescent labeling methods have been used for time-lapse observation of cotyledon epidermal cell morphogenesis, but these methods require expensive microscopes and can be technically demanding. Here, we propose a silver-nano-ink coating method for time-lapse imaging and quantification of morphological changes in the epidermal cells of growing Arabidopsis thaliana cotyledons. To obtain high-resolution and wide-area cotyledon surface images, we placed the seedlings on a biaxial goniometer and adjusted the cotyledons, which were coated by dropping silver ink onto them, to be as horizontal to the focal plane as possible. The omnifocal images that had the most epidermal cell shapes in the observation area were taken at multiple points to cover the whole surface area of the cotyledon. The multi-point omnifocal images were automatically stitched, and the epidermal cells were automatically and accurately segmented by machine learning. Quantification of cell morphological features based on the segmented images demonstrated that the proposed method could quantitatively evaluate jigsaw puzzle-shaped cell growth and morphogenesis. The method was successfully applied to phenotyping of the bpp125 triple mutant, which has defective pavement cell morphogenesis. The proposed method will be useful for time-lapse non-destructive phenotyping of plant surface structures and is easier to use than the conversional methods that require fluorescent dye labeling or transformation with marker gene constructs and expensive microscopes such as the confocal laser microscope.