Prediksi Erosi pada Beberapa Penggunaan Lahan di Desa Labuan Toposo Kecamatan Labuan Kabupaten Donggala

Erosion is one of the factors that causing the decline in soil capacity to support good agriculture production. This often happened in some land use when the land is managed without pay attention to soil conservation and or the soil capacity to support production. Soil erosion prediction can be good information for local government and soil erosion index of several land use types including non-agriculture can be used to manage sustainability of development. The purpose of this study was to predict the soil erosion rate and determine soil erosion erosion index on several land use types at the Labuan Toposo village Donggala District. The soil was analyzed at soil science laboratory of Tadulako University. The study was conducted from October to January 2016. This study was used the method direct survey in the field and continued with the soil sampling for laboratory analysis. Then the analysis result was further processed by using the equation USLE (Universal Soil Loss Equation ). This study showed that the erosion index that occurredat the village of Labuan Toposo was diverse while the erosion hazard index was lowest for forest land, and the highest was found for teak and coconut. This was caused by land use patterns, land management measures, in addition the high erosion hazard index indicate the potential to cause erosion. It is therefore vital to do conservation measures to prevent erosion e.g. by inter cropping and the use of crop residue on land surface such as mulching

Object's shadow removal with removal validation

We introduce in this paper, a shadow detection and removal method for moving objects especially for humans and vehicles. An effective method is presented for detecting and removing shadows from foreground figures. We assume that the foreground figures have been extracted from the input image by some background subtraction method. A figure may contain only one moving object with or without shadow. The homogeneity property of shadows is explored in a novel way for shadow detection and image division technique is used. The process is followed by filtering, removal, boundary removal and removal validation

Association between knowledge, attitude and preventive practices on malaria among pregnant women with and without malaria attending ante-natal care in Zamfara state, Nigeria

Background: Malaria during pregnancy is a major public health problem, with substantial risks for the mother and her foetus. The aim of this study was to assess the knowledge, attitude and preventive practices of malaria among pregnant women diagnosed with and without malaria attending ante-natal care in Zamfara State, Nigeria. Materials and Methods: An unmatched case control study was conducted among pregnant women attending ante-natal care in Zamfara State, Nigeria. Pregnant women both cases and controls were obtained using multistage random sampling. Case and control in this study were defined as pregnant woman attending ante-natal care from the selected general hospitals in Zamfara State, confirmed with and without malaria respectively, using giemsa staining method based on their medical records. Face to face interview and self-administered pretested questionnaire in English and Hausa languages were used to obtain information on knowledge, attitude and preventive practices of the pregnant women using the mean score as a cut-off point. Descriptive analysis, chi-square and multivariate logistic regression were employed to determine the predictors using forward stepwise (likelihood ratio). Data was analysed using SPSS version 21.0. Result: There were 522 pregnant women 261 cases and 261 controls. The overall mean knowledge score of the pregnant women was 46.88 (± 8.322), with 67.8% cases having low knowledge and 75.1% controls having high knowledge on malaria. Overall positive attitude score among cases was only 14.9% as compared to controls with 83.9%. More than half of the cases 73.2% do not practice malaria preventive measures as compared to controls with only 19.9%. The predictors for malaria were having low knowledge (AOR =4.155, 95%CI = 2.344, 7.365, p < 0.001), negative attitude (AOR = 30.634, 95%CI =16.296, 57.589, p <0.001) and low practices (AOR = 15.587, 95%CI = 8.183, 29.689, p < 0.001). Conclusion: This study has identified that pregnant women with malaria in Zamfara State, Nigeria had low level of knowledge, attitude and poor preventive practices towards malaria

Chemical and biological investigations of Delonix regia (Bojer ex Hook.) Raf.

U radu je opisana izolacija pet sastojaka petroleterske i diklormetanske frakcije metanolnog ekstrakta kore biljke Delonix regia: lupeol (1), epilupeol (2), β-sitosterol (3), stigmasterol (4) i p-metoksibenzaldehid (5). Nadalje, testirano je antimikrobno djelovanje različitih ekstrakata difuzijskom metodom na disku (15 μg mm2). Zone inhibicije za sastojke topljive u petroleteru, tetraklormetanu i diklormetanu bile su 914 mm, 1113 mm, odnosno 920 mm, dok je zona inhibicije standarda kanamicina bila 2025 mm. U biološkom pokusu smrtnosti morskih kozica najveću toksičnost pokazali su spojevi topljivi u tetraklormetanu (LC50 = 0,83 μg mL1), dok je topljivost sastojaka topljivih u petroleteru i diklormetanu bila LC50 14,94, odnosno 3,29 μg mL1, a standarda vinkristin sulfata 0,812 μg mL1. Ovo je prvo izvješće o izolaciji sastojaka, antimikrobnom djelovanju i citotoksičnosti biljke D. regia.In this study five compounds, lupeol (1), epilupeol (2), β-sitosterol (3), stigmasterol (4) and p-methoxybenzaldehyde (5) were isolated from the petroleum ether and dichloromethane fractions of a methanolic extract of the stem bark of Delonix regia. Antimicrobial screening of the different extracts (15 μg mm2) was conducted by disc diffusion method. The zones of inhibition demonstrated by the petroleum ether, carbon tetrachloride and dichloromethane fractions ranged from 914 mm, 1113 mm and 920 mm, respectively, compared to kanamycin standard with the zone of inhibition of 2025 mm. In brine shrimp lethality bioassay, the carbon tetrachloride soluble materials demonstrated the highest toxicity with LC50 of 0.83 μg mL1, while petroleum ether and dichloromethane soluble partitionates of the methanolic extract revealed LC50 of 14.94 and 3.29 μg mL1, respectively, in comparison with standard vincristine sulphate with LC50 of 0.812 μg mL1. This is the first report on compounds separation from D. regia, their antimicrobial activity and cytotoxicity

linc00673 (ERRLR01) is a prognostic indicator of overall survival in breast cancer

LncRNAs are novel noncoding RNAs involved in the epigenetic regulation of gene expression by recruiting ribonucleoprotein complexes to specific genomic loci to initiate histone methylation and/or other chromatin modifications. LncRNAs themselves function as tumor suppressors or oncogenes, depending on the gene regulatory networks they govern. We identified lnc00673 (ERRLR01) as a marker of overall survival (OS) in breast cancer patients. Specifically, ERRLR01 levels were elevated in triple-negative breast cancer (TNBC) as compared with Luminal-A, Luminal-B, and HER2 breast cancer subtypes. ERRLR01 levels were also inversely correlated with breast cancer survival across all breast cancer patients. Upon stratification, OS in ERα− tumors correlated with negative overall survival, while in ERα+ tumors, ERRLR01 correlated with positive outcomes. This suggests ERRLR01 is modulated by hormone signaling in breast cancer. Gene-network analysis revealed ERRLR01 correlated with distinct pathways including “epithelial development” and “cellular differentiation.” These data suggest ERRLR01 operates as an oncogene in TNBC, as well as a biomarker in breast cancer patients. © 2017 Taylor & Francis Group, LL

DENDROGRAM CLUSTERING FOR 3D DATA ANALYTICS IN SMART CITY

Smart city is a connection of physical and social infrastructure together with the information technology to leverage the collective intelligence of the city. Cities will build huge data centres. These data are collected from sensors, social media, and legacy data sources. In order to be smart, cities needs data analysis to identify infrastructure that needs to be improved, city planning and predictive analysis for citizen safety and security. However, no matter how much smart city focus on the updated technology, data do not organize themselves in a database. Such tasks require a sophisticated database structure to produce informative data output. Furthermore, increasing number of smart cities and generated data from smart cities contributes to current phenomenon called big data. These large and complex data collections would be difficult to process using regular database management tools or traditional data processing applications. There are multiple challenges for big data, including visualization, mining, analysis, capture, storage, search, and sharing. Efficient data analysis mechanisms are necessary to search and extract valuable patterns and knowledge through the big data of smart cities. In this paper, we present a technique of three-dimensional data analytics using dendrogram clustering approach. Data will be organized using this technique and several output and analyses are carried out to proof the efficiency of the structure for three – dimensional data analytics in smart city

Green and Sustainable Commercial Property Demand in Malaysia and Nigeria

Green building is redefining real estate practices and commercial properties are attracting greater attention of the paradigm shift. Nevertheless, in many countries including Malaysia and Nigeria, green building investment is still beset with uncertainties about the anticipated returns and benefits. The aim of this study is to identify the predictive factors and variables that motivate decisions to demand and invest in green commercial properties, and to apply discriminant analysis technique to assess if there are significant differences in perception between the real estate development team in Malaysia and Nigeria based on the identified variables. The result showed a significant discriminant function separating the two countries based on their perception of the variables. The green building motivation attributes favoured Malaysia. The Wilks’ Lambda’s F test and the standardized discriminant function coefficients, indicated that there are significant differences in perception between the real estate development team  in Malaysia and Nigeria as measured by personal and altruistic environmental motivations, corporate conscience responsibility motivations and economic and financial motivations. However, economic and financial motivation variables were found to have showed the most predictive power in accounting for the differences in perception. Keywords: green building, real estate investment, sustainability, motivations, perceptions

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Socio-demographic and maternal risk factors of malaria among pregnant women attending ante-natal care in Zamfara State, Nigeria

Malaria is a major global health concern. It is one of the world’s most prevalent serious infectious diseases, with approximately 250 million cases and one million deaths per year. The aim of this study is to determine the socio-demographic and maternal risk factors associated with malaria among pregnant women attending ante-natal care in Zamfara State, Nigeria. An unmatched case control study was conducted among pregnant women attending ante-natal care in Zamfara State, Nigeria. A total of 581 pregnant women both cases and controls were obtained using multistage random sampling. Cases and controls were defined as pregnant women attending ante-natal care from the selected general hospitals in Zamfara State, confirmed with and without malaria respectively, using Giemsa staining method based on their medical records. Face to face interview and self-administered pretested questionnaire in English and Hausa languages were used to obtain information on socio-demographic characteristics and maternal history of the respondents from May to August 2014. Data was analysed using SPSS version 21, using simple descriptive and multivariate logistic regression were employed to determine the predictors of malaria. The overall response rate was 89.8%. Pregnant women ≤ 25 years of age (AOR = 1.695, 95% CI = 1.031, 2.789, p = 0.038), informal education (AOR = 9.390, 95% CI = 5.516, 15.985, p < 0.001), unemployed (AOR = 25.948, 95% CI = 14.831, 45.398, p < 0.001) and first trimester (AOR = 1.856, 95% CI = 1.126, 3.060, p 0.015) were the risk factors. This study has identified the risk factors of malaria in Zamfara State, Nigeria. The findings in this study can be used by policy makers in planning how to tackle the factors associated with malaria among pregnant women in Zamfara State