Search results for: spatial rainfall prediction

Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Peifang Hsieh

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The prevention of child abuse has aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported child abuse cases that doubled over the past decade and the scarcity of social workers. New Taipei city, with the most population in Taiwan and over 70% of its 4 million citizens are migrant families in which the needs of children can be easily neglected due to insufficient support from relatives and communities, sees urgency for a social support system, by preemptively identifying and outreaching high-risk families of child abuse, so as to offer timely assistance and preventive measure to safeguard the welfare of the children. Big data analysis is the inspiration. As it was clear that high-risk families of child abuse have certain characteristics in common, New Taipei city decides to consolidate detailed background information data from departments of social affairs, education, labor, and health (for example considering status of parents’ employment, health, and if they are imprisoned, fugitives or under substance abuse), to cross-reference for accurate and prompt identification of the high-risk families in need. 'The Service Center for High-Risk Families' (SCHF) was established to integrate data cross-departmentally. By utilizing the machine learning 'random forest method' to build a risk prediction model which can early detect families that may very likely to have child abuse occurrence, the SCHF marks high-risk families red, yellow, or green to indicate the urgency for intervention, so as to those families concerned can be provided timely services. The accuracy and recall rates of the above model were 80% and 65%. This prediction model can not only improve the child abuse prevention process by helping social workers differentiate the risk level of newly reported cases, which may further reduce their major workload significantly but also can be referenced for future policy-making.

Keywords: child abuse, high-risk families, big data analysis, risk prediction model

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Authors: R. Sumalatha, M. V. Subramanyam

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In medical image processing the quality of the image is degraded in the presence of noise. Especially in ultra sound imaging and Magnetic resonance imaging the data was corrupted by signal dependent noise known as salt and pepper noise. Removal of noise from the medical images is a critical issue for researchers. In this paper, a new type of technique Adaptive Spatial Mask Filter (ASMF) has been proposed. The proposed filter is used to increase the quality of MRI and ultra sound images. Experimental results show that the proposed filter outperforms the implementation of mean, median, adaptive median filters in terms of MSE and PSNR.

Keywords: salt and pepper noise, ASMF, PSNR, MSE

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Fangjing Zhou, Wen Chen, Brian J. Hall, Yu Wang, Carl Latkin, Li Ling, Joseph D. Tucker

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Background: Economic and social reforms designed to open China to the world has been successful, but also appear to have rapidly laid the foundation for the reemergence of STIs since 1980s. Changes in sexual behaviors, relationships, and norms among Chinese contributed to the STIs epidemic. As the massive population moved during the last 30 years, early coital debut, multiple sexual partnerships, and unprotected sex have increased within the general population. Our objectives were to assess associations between residences location, sexual risk behaviors and sexually transmitted infections (STIs) among adults living in Guangzhou, China. Methods: Stratified cluster sampling followed a two-step process was used to select populations aged 18-59 years in Guangzhou, China. Spatial methods including Geographic Information Systems (GIS) were utilized to identify 1400 coordinates with latitude and longitude. Face-to-face household interviews were conducted to collect self-report data on sexual risk behaviors and diagnosed STIs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of sexual risk behaviors and STIs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results: In this study, 1215 of 1400 households attempted surveys, with 368 refusals, resulting in a sample of 751 completed surveys. The prevalence of self-reported sexual risk behaviors was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STIs was 7.06%. Anal intercourse clustered in an area located along the border within the rural-urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou less than one year (p=0.007) overlapped this cluster. Excess cases for sex without a condom (p=0.031) overlapped the cluster for college students (p<0.001). Conclusions: Short-term migrants and college students reported greater sexual risk behaviors. Programs to increase safer sex within these communities to reduce the risk of STIs are warranted in Guangzhou. Spatial analysis identified geographical clusters of sexual risk behaviors, which is critical for optimizing surveillance and targeting control measures for these locations in the future.

Keywords: cluster analysis, migrant, sexual risk behaviors, spatial distribution

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Authors: Qais H. Alsafasfeh

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The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model. The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model.

Keywords: load forecast, peak demand, spatial load, electrical distribution

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Dorota Witkowska, Paweł Gosek, Lukasz Swiecicki, Wojciech Jernajczyk, Bruce J. West, Miroslaw Latka

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In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4.

Keywords: alpha waves, antidepressant, treatment outcome, wavelet

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Authors: Bola M. Tunde-Awe

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The study investigated the influence of teacher’s non-verbal communication on secondary school students’ learning outcomes in English language. The study was a survey research. Participants were three hundred Senior Secondary School II students randomly selected from ten schools in Akoko South West Local Government Area of Ondo State, Nigeria. The instrument used for data collection was a questionnaire containing twenty items on a four-point Likert scale which measured teacher’s use of three types of non-verbal communication modes: body movement, eye contact and spatial distance. The data collected was analysed using simple percentage. Findings revealed that teacher’s use of these non-verbal communication modes enhanced learners’ learning outcomes in English language: a total of 271 (90.33%) participants affirmed that teacher’s body language influenced their learning of English; 224 (74.66%) maintained the same stand for eye contact; while 202 (67.33%) affirmed that teacher’s spatial distance had positive influence. Consequent upon these findings, it was recommended that teachers of English language should constantly utilize non-verbal communication in their instructional delivery. Also, non-verbal communication modes should be included in teacher education programme to equip prospective pre-service teachers with the art of non-verbal communication.

Keywords: non-verbal communication, body language, eye contact, spatial distance, learning outcomes

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Authors: Hanlin Liu, Hua Li, Yintan AI

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Network performance prediction (NPP) is essential for the management and optimization of software-defined networking (SDN) and contributes to improving the quality of service (QoS) in SDN to meet the requirements of users. Although current deep learning-based methods can achieve high effectiveness, they still suffer from some problems, such as difficulty in capturing global information of the network, inefficiency in modeling end-to-end network performance, and inadequate graph feature extraction. To cope with these issues, our proposed Graphormer-based architecture for NPP leverages the powerful graph representation ability of Graphormer to effectively model the graph structure data, and a node-edge transformation algorithm is designed to transfer the feature extraction object from nodes to edges, thereby effectively extracting the end-to-end performance characteristics of the network. Moreover, routing oriented centrality measure coefficient for nodes and edges is proposed respectively to assess their importance and influence within the graph. Based on this coefficient, an enhanced feature extraction method and an advanced centrality encoding strategy are derived to fully extract the structural information of the graph. Experimental results on three public datasets demonstrate that the proposed GraphNPP architecture can achieve state-of-the-art results compared to current NPP methods.

Keywords: software-defined networking, network performance prediction, Graphormer, graph neural network

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Authors: Majid Galoie, Khodadad Safavi, Abdolreza Karami Nejad, Reza Roshan

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In this study, a comprehensive scouring model has been developed in order to evaluate the accuracy of various empirical relationships which were suggested for prediction of scour hole depth in plunge pools by Martins, Mason, Chian and Veronese. For this reason, scour hole depths caused by free falling jets from a flip bucket to a plunge pool were investigated. In this study various discharges, angles, scouring times, etc. have been considered. The final results demonstrated that the all mentioned empirical formulas, except Mason formula, were reasonably agreement with the experimental data.

Keywords: scour hole depth, plunge pool, physical model, reliability assessment

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Authors: Danladi Ali

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In this paper, we measured GSM signal strength in the Dnepropetrovsk city in order to predict path loss in study area using nonlinear autoregressive neural network prediction and we also, used neural network clustering to determine average GSM signal strength receive at the study area. The nonlinear auto-regressive neural network predicted that the GSM signal is attenuated with the mean square error (MSE) of 2.6748dB, this attenuation value is used to modify the COST 231 Hata and the Okumura-Hata models. The neural network clustering revealed that -75dB to -95dB is received more frequently. This means that the signal strength received at the study is mostly weak signal

Keywords: one-dimensional multilevel wavelets, path loss, GSM signal strength, propagation, urban environment and model

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Inês Amorim Leitão, Loes van Shaick, António Dinis Ferreira, Violette Geissen

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Plastic pollution is a growing concern worldwide: plastics are commercialized in large quantities and it takes a long time for them to degrade. When in the environment, plastic is fragmented into microplastics (<5mm), which have been found in all environmental compartments at different locations. Microplastics contribute to the environmental pollution in water, air and soil and are linked to human health problems. The progressive increase of population living in cities led to the aggravation of the pollution problem worldwide, especially in urban environments. Urban areas represent a strong source of pollution, through the roads, industrial production, wastewater, landfills, etc. It is expected that pollutants such as microplastics are transported diffusely from the sources through different pathways such as wind and rain. Therefore, it is very complex to quantify, control and treat these pollutants, designated current problematic issues by the European Commission. Green areas are pointed out by experts as natural filters for contaminants in cities, through their capacity of retention by vegetation. These spaces have thus the capacity to control the load of pollutants transported. This study investigates the spatial distribution of microplastics in urban soils of different land uses, their transport through atmospheric deposition, wind erosion, runoff and streams, as well as their deposition in vegetation like grass and tree leaves in urban environment. Coimbra, a medium large city located in the central Portugal, is the case-study. All the soil, sediments, water and vegetation samples were collected in Coimbra and were later analyzed in the Wageningen University & Research laboratory. Microplastics were extracted through the density separation using Sodium Phosphate as solution (~1.4 g cm−3) and filtration methods, visualized under a stereo microscope and identified using the u-FTIR method. Microplastic particles were found in all the different samples. In terms of soils, higher concentrations of microplastics were found in green parks, followed by landfills and industrial places, and the lowest concentrations in forests and pasture land-uses. Atmospheric deposition and streams after rainfall events seems to represent the strongest pathways of microplastics. Tree leaves can retain microplastics on their surfaces. Small leaves such as needle leaves seem to present higher amounts of microplastics per leaf area than bigger leaves. Rainfall episodes seem to reduce the concentration of microplastics on leaves surface, which suggests the wash of microplastics down to lower levels of the tree or to the soil. When in soil, different types of microplastics could be transported to the atmosphere through wind erosion. Grass seems to present high concentrations of microplastics, and the enlargement of the grass cover leads to a reduction of the amount of microplastics in soil, but also of the microplastics moved from the ground to the atmosphere by wind erosion. This study proof that vegetation can help to control the transport and dispersion of microplastics. In order to control the entry and the concentration of microplastics in the environment, especially in cities, it is essential to defining and evaluating nature-based land-use scenarios, considering the role of green urban areas in filtering small particles.

Keywords: microplastics, cities, sources, pathways, vegetation

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Authors: Afroditi Zamanidou, Dionysios Giannakopoulos, Konstantinos Manolitsis

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A notable amount of electrical energy demand in many countries worldwide is used to cover public energy demand for road, square and other public spaces’ lighting. Renewable energy can contribute in a significant way to the electrical energy demand coverage for public lighting. This paper focuses on the sizing and design of a hybrid energy system (HES) exploiting the solar-wind energy potential to meet the electrical energy needs of lighting roads, squares and other public spaces. Moreover, the proposed HES provides coverage of the electrical energy demand for a Wi-Fi hotspot and a charging hotspot for the end-users. Alongside the sizing of the energy production system of the proposed HES, in order to ensure a reliable supply without interruptions, a storage system is added and sized. Multiple scenarios of energy consumption are assumed and applied in order to optimize the sizing of the energy production system and the energy storage system. A database with meteorological prediction data for 51 areas in Greece is developed in order to assess the possible deployment of the proposed HES. Since there are detailed meteorological prediction data for all 51 areas under investigation, the use of these data is evaluated, comparing them to real meteorological data. The meteorological prediction data are exploited to form three hourly production profiles for each area for every month of the year; minimum, average and maximum energy production. The energy production profiles are combined with the energy consumption scenarios and the sizing results of the energy production system and the energy storage system are extracted and presented for every area. Finally, the economic performance of the proposed HES in terms of Levelized cost of energy is estimated by calculating and assessing construction, operation and maintenance costs.

Keywords: energy production system sizing, Greece’s deployment potential, meteorological prediction data, wind-solar hybrid energy system, levelized cost of energy

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Authors: Amidu Owolabi Ayeni

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Studies have shown that spatio-temporal distribution and variability of climatic variables, urban land use, and population have had substantial impact on water supply. It is based on these facts that the impacts of climate, urbanization, and population on water supply in Lagos State Nigeria remain the focus of this study. Population and water production data on Lagos State between 1963 and 2006 were collected, and used for time series and projection analyses. Multi-temporal land-sat images of 1975, 1995 and NigeriaSat-1 imagery of 2007 were used for land use change analysis. The population of Lagos State increased by about 557.1% between 1963 and 2006, correspondingly, safe water supply increased by 554%. Currently, 60% of domestic water use in urban areas of Lagos State is from groundwater while 75% of rural water is from unsafe surface water. Between 1975 and 2007, urban land use increased by about 235.9%. The 46years climatic records revealed that temperature and evaporation decreased slightly while rainfall and Relatively Humidity (RH) decreased consistently. Based on these trends, the Lagos State population and required water are expected to increase to about 19.8millions and 2418.9ML/D respectively by the year 2026. Rainfall is likely to decrease by -6.68mm while temperature will increase by 0.950C by 2026. Urban land use is expected to increase by 20% with expectation of serious congestion in the suburb areas. With these results, over 50% of the urban inhabitants will be highly water poor in future if the trends continue unabated.

Keywords: challenges, climate change, urbanization, water supply

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Authors: Babawale Akin Adeyemi, Esan Temitayo, Adeyemi Olabisi Omowumi

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The paper analyzed waste disposal system in Akure, Ondo State using GIS techniques. Specifically, the study identified the spatial distribution of collection points and existing dumpsite; evaluated the accessibility of waste collection points and their proximity to each other with the view of enhancing better performance of the waste disposal system. Data for the study were obtained from both primary and secondary sources. Primary data were obtained through the administration of questionnaire. From field survey, 35 collection points were identified in the study area. 10 questionnaires were administered around each collection point making a total of 350 questionnaires for the study. Also, co-ordinates of each collection point were captured using a hand-held Global Positioning System (GPS) receiver which was used to analyze the spatial distribution of collection points. Secondary data used include administrative map collected from Akure South Local Government Secretariat. Data collected was analyzed using the GIS analytical tools which is neighborhood function. The result revealed that collection points were found in all parts of Akure with the highest concentration around the central business district. The study also showed that 80% of the collection points enjoyed efficient waste service while the remaining 20% does not. The study further revealed that most collection points in the core of the city were in close proximity to each other. In conclusion, the paper revealed the capability of Geographic Information System (GIS) as a technique in management of waste collection and disposal technique. The application of Geographic Information System (GIS) in the evaluation of the solid waste management in Akure is highly invaluable for the state waste management board which could also be beneficial to other states in developing a modern day solid waste management system. Further study on solid waste management is also recommended especially for updating of information on both spatial and non-spatial data.

Keywords: assessment, geospatial, system, waste disposal

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Authors: Thabo M. Bafitlhile, Adewole Oladele

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Botswana has been affected by floods since long ago and is still experiencing this tragic event. Flooding occurs mostly in the North-West, North-East, and parts of Central district due to heavy rainfalls experienced in these areas. The torrential rains destroyed homes, roads, flooded dams, fields and destroyed livestock and livelihoods. Palapye is one area in the central district that has been experiencing floods ever since 1995 when its greatest flood on record occurred. Heavy storms result in floods and inundation; this has been exacerbated by poor and absence of drainage structures. Since floods are a part of nature, they have existed and will to continue to exist, hence more destruction. Furthermore floods and highway plays major role in erosion and destruction of roads structures. Already today, many culverts, trenches, and other drainage facilities lack the capacity to deal with current frequency for extreme flows. Future changes in the pattern of hydro climatic events will have implications for the design and maintenance costs of roads. Increase in rainfall and severe weather events can affect the demand for emergent responses. Therefore flood forecasting and warning is a prerequisite for successful mitigation of flood damage. In flood prone areas like Palapye, preventive measures should be taken to reduce possible adverse effects of floods on the environment including road structures. Therefore this paper attempts to estimate return periods associated with huge storms of different magnitude from recorded historical rainfall depth using statistical method. The method of annual maxima was used to select data sets for the rainfall analysis. In the statistical method, the Type 1 extreme value (Gumbel), Log Normal, Log Pearson 3 distributions were all applied to the annual maximum series for Palapye area to produce IDF curves. The Kolmogorov-Smirnov test and Chi Squared were used to confirm the appropriateness of fitted distributions for the location and the data do fit the distributions used to predict expected frequencies. This will be a beneficial tool for urgent flood forecasting and water resource administration as proper drainage design will be design based on the estimated flood events and will help to reclaim and protect the road structures from adverse impacts of flood.

Keywords: drainage, estimate, evaluation, floods, flood forecasting

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Authors: Olalekan Sunday Alabi, Titus Adeyemi Alonge, Olumuyiwa Idowu Ojo

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Agricultural land suitability analysis and mapping play an imperative role for sustainable utilization of scarce physical land resources. The objective of this study was to prepare spatial database of physical land resources for irrigated agriculture and to assess land suitability for irrigation and developing suitable area map of the study area. The study was conducted at Kampe-Omi irrigation scheme located at Yagba West Local Government Area of Kogi State, Nigeria. Temperature and rainfall data of the study area were collected for 10 consecutive years (2005-2014). Geographic Information System (GIS) techniques were used to develop irrigation land suitability map of the study area. Attribute parameters such as the slope, soil properties, topography of the study area were used for the analysis. The available data were arranged, proximity analysis of Arc-GIS was made, and this resulted into five mapping units. The final agricultural land suitability map of the study area was derived after overlay analysis. Based on soil composition, slope, soil properties and topography, it was concluded that; Kampe-Omi has rich sandy loam soil, which is viable for agricultural purpose, the soil composition is made up of 60% sand and 40% loam. The land-use pattern map of Kampe-Omi has vegetal area and water-bodies covering 55.6% and 19.3% of the total assessed area respectively. The landform of Kampe-Omi is made up of 41.2% lowlands, 37.5% normal lands and 21.3% highlands. Kampe-Omi is adequately suitable for agricultural purpose while an extra of 20.2% of the area is highly suitable for agricultural purpose making 72.6% while 18.7% of the area is slightly suitable.

Keywords: remote sensing, GIS, Kampe–Omi, land suitability, mapping

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Authors: Endeshaw Assefa Derso, Maria Gabriella Campolo, Angela Alibrandi

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Objectives:Early childhood malnutrition can have long-term and irreversible effects on a child's health and development. This study uses the Bayesian method with spatial variation to investigate the flexible trends of metrical covariates and to identify communities at high risk of injury. Methods: Cross-sectional data on underweight are collected from the 2016 Ethiopian Demographic and Health Survey (EDHS). The Bayesian geo-additive model is performed. Appropriate prior distributions were provided for scall parameters in the models, and the inference is entirely Bayesian, using Monte Carlo Markov chain (MCMC) stimulation. Results: The results show that metrical covariates like child age, maternal body mass index (BMI), and maternal age affect a child's underweight non-linearly. Lower and higher maternal BMI seem to have a significant impact on the child’s high underweight. There was also a significant spatial heterogeneity, and based on IDW interpolation of predictive values, the western, central, and eastern parts of the country are hotspot areas. Conclusion: Socio-demographic and community- based programs development should be considered compressively in Ethiopian policy to combat childhood underweight malnutrition.

Keywords: bayesX, Ethiopia, malnutrition, MCMC, semi-parametric bayesian analysis, spatial distribution, P- splines

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Authors: Zulfajri Basri Hasanuddin

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There is an increasing demand for broadband services in Indonesia. Therefore, the answer is the use of Ka-Band which has some advantages such as wider bandwidth, the higher transmission speeds, and smaller size of antenna in the ground. However, rain attenuation is the primary factor in the degradation of signal at the Kaband. In this paper, the author will determine whether the Ka-band frequency can be implemented in Indonesia which has high intensity of rainfall.

Keywords: Ka-band, link budget, link availability, BER, Eb/No, C/N

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Authors: Serap Kaymakci, Dogan Gundogdu, M. Bugra Yagcioglu

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Erosion (soil erosion) is a phenomenon in which the soil on the slope surface is exposed to natural influences such as wind, rainfall, etc. in open areas. The most natural solution to prevent erosion is to plant surfaces exposed to erosion. However, proper ground and natural conditions must be provided in order for planting to occur. Erosion is prevented in a fast and natural way and the loss of soil is reduced mostly. Lead to allowing plants to hold onto the soil with its three-dimensional and hollow structure are as follows: The types of geomat called MacMat that is used in a case study in Turkey in order to prevent water carry over due to rainfall. The geosynthetic combined with double twisted steel wire mesh. That consists of 95% Zn–5% Al alloy coated double twisted steel wire based that is a reinforced MacMat (geosynthetic three-dimensional erosion control mat) obtained by a polypropylene consisted (mesh type 8x10-Wire diam. 2.70 mm–95% Zn–5% Al alloy coated). That is developed by the progress of the technology. When using reinforced MacMat on top clay liners, fixing pins should not be used as they will rupture the mats. Mats are simply anchored (J Type) in the top trench and, if necessary, in intermediate berm trenches. If the slope angle greater than 20°, it is necessary to use additional rebar depending soil properties also. These applications may have specific technical and installation requirements. In that project, the main purpose is erosion control after that is greening. There is a slope area around the factory which is located in Gebze, İstanbul.

Keywords: erosion, GeoMat, geosynthetic, slope

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Authors: Chaiyaporn Yuksen

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Backgroud: Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). Method: The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. Result: 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times Conclusion: The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: clinical prediction score, SVT, recurrence, emergency department

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Authors: Chhordaneath Hen, Ty Sok, Ilan Ich, Ratboren Chan, Chantha Oeurng

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The damages caused by hydrological extremes such as flooding have been severe globally, and several research studies indicated extreme precipitations play a crucial role. Cambodia is one of the most vulnerable countries exposed to floods and drought as consequences of climate impact. Prek Thnot River Basin in the southwest part of Cambodia, which is in the plate and plateau region and a part of the Mekong Delta, was selected to investigate the changes in extreme precipitation and hydrological extreme. Furthermore, to develop a statistical relationship between these phenomena in this basin from 1995 to 2020 using Multiple Linear Regression. The precipitation and hydrological extreme were assessed via the attributes and trends of rainfall patterns during the study periods. The extreme flow was defined as a dependent variable, while the independent variables are various extreme precipitation indices. The study showed that all extreme precipitations indices (R10, R20, R35, CWD, R95p, R99p, and PRCPTOT) had increasing decency. However, the number of rain days per year had a decreasing tendency, which can conclude that extreme rainfall was more intense in a shorter period of the year. The study showed a similar relationship between extreme precipitation and hydrological extreme and land use change association with hydrological extreme. The direct combination of land use and precipitation equals 37% of the flood causes in this river. This study provided information on these two causes of flood events and an understanding of expectations of climate change consequences for flood and water resources management.

Keywords: extreme precipitation, hydrological extreme, land use, land cover, Prek Thnot river basin

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Authors: J. S. Bedi, J. P. S. Gill, R. S. Aulakh, Prabhjit Kaur

Abstract:

The present study was undertaken to monitor organochlorine, organophosphate and synthetic pyrethroid pesticide residues in butter and ghee samples collected from six different districts of Punjab. The estimation of pesticide residues was done by multiple residue analytical technique using gas chromatography equipped with GC-ECD and GC-FTD. The confirmation of residues was done on gas chromatography mass spectrometry in both SIM and Scan mode. Results indicated the presence of HCH and pp DDE as predominant contaminant in both butter and ghee even after their ban/restriction on usage in India. Residues of HCH were detected in 25.5 and 23.2 % samples of butter and ghee, respectively, while residues of pp DDE were recorded in 29.3 and 25.0 % butter and ghee samples, respectively. More importantly, the presence of endosulfan, cypermethrin, fenvalerate, deltamethrin and chlorpyrifos was observed in few butter and ghee samples indicating the serious concerns. The spatial variation of pesticide residues occurrence indicated the cotton belt of Punjab as most affected.

Keywords: butter, ghee, pesticides residues, Punjab

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Authors: S. Girish, N. Ajay

Abstract:

Permeation properties have been widely used to quantify durability characteristics of concrete for assessing long term performance and sustainability. The processes of deterioration in concrete are mediated largely by water. There is a strong interest in finding a better way of assessing the material properties of concrete in terms of durability. Water sorptivity is a useful single material property which can be one of the measures of durability useful in service life planning and prediction, especially in severe environmental conditions. This paper presents the results of the comparative study of sorptivity of Self-Compacting Concrete (SCC) with conventionally vibrated concrete. SCC is a new, special type of concrete mixture, characterized by high resistance to segregation that can flow through intricate geometrical configuration in the presence of reinforcement, under its own mass, without vibration and compaction. SCC mixes were developed for the paste contents of 0.38, 0.41 and 0.43 with fly ash as the filler for different cement contents ranging from 300 to 450 kg/m3. The study shows better performance by SCC in terms of capillary absorption. The sorptivity value decreased as the volume of paste increased. The use of higher paste content in SCC can make the concrete robust with better densification of the micro-structure, improving the durability and making the concrete more sustainable with improved long term performance. The sorptivity based on secondary absorption can be effectively used as a durability index to predict the time duration required for the ingress of water to penetrate the concrete, which has practical significance.

Keywords: self-compacting concrete, service life prediction, sorptivity, volume of paste

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

Abstract:

The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Andra Costache, Rădiţa Alexe

Abstract:

The analysis of the spatial disparities of social marginalization is a requirement in the present-day socio-economic and political context of Romania, an East-European state, member of the European Union since 2007, at present faced with the imperatives of the growth of its territorial cohesion. The main objective of this article is to develop a methodology for the assessment of social marginalization, in order to understand the intensity of the marginalization phenomenon at different spatial scales. The article proposes a social marginalization index (SMI), calculated through the integration of ten indicators relevant for the two components of social marginalization: the material component and the symbolical component. The results highlighted a strong connection between the total degree of social marginalization and the dependence on social benefits, unemployment rate, non-inclusion in the compulsory education, criminality rate, and the type of pension insurance.

Keywords: Romania, social marginalization index, territorial disparities, EU

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