Search results for: soil classification

Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

Procedia PDF Downloads 297

Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Melis Inalpulat, Levent Genc

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In the present study, an attempt was made to state the Land Use Land Cover (LULC) transformation over three decades around the urban regions of Balıkesir, Bursa, and Çanakkale provincial centers (PCs) in Turkey. Landsat imageries acquired in 1984, 1999 and 2014 were used to determine the LULC change. Images were classified using the supervised classification technique and five main LULC classes were considered including forest (F), agricultural land (A), residential area (urban) - bare soil (R-B), water surface (W), and other (O). Change detection analyses were conducted for 1984-1999 and 1999-2014, and the results were evaluated. Conversions of LULC types to R-B class were investigated. In addition, population changes (1985-2014) were assessed depending on census data, the relations between population and the urban areas were stated, and future populations and urban area needs were forecasted for 2030. The results of LULC analysis indicated that urban areas, which are covered under R-B class, were expanded in all PCs. During 1984-1999 R-B class within Balıkesir, Bursa and Çanakkale PCs were found to have increased by 7.1%, 8.4%, and 2.9%, respectively. The trend continued in the 1999-2014 term and the increment percentages reached to 15.7%, 15.5%, and 10.2% at the end of 30-year period (1984-2014). Furthermore, since A class in all provinces was found to be the principal contributor for the R-B class, urban sprawl lead to the loss of agricultural lands. Moreover, the areas of R-B classes were highly correlated with population within all PCs (R²>0.992). Depending on this situation, both future populations and R-B class areas were forecasted. The estimated values of increase in the R-B class areas for Balıkesir, Bursa, and Çanakkale PCs were 1,586 ha, 7,999 ha and 854 ha, respectively. Due to this fact, the forecasted values for 2,030 are 7,838 ha, 27,866, and 2,486 ha for Balıkesir, Bursa, and Çanakkale, and thus, 7.7%, 8.2%, and 9.7% more R-B class areas are expected to locate in PCs in respect to the same order.

Keywords: landsat, LULC change, population, urban sprawl

Procedia PDF Downloads 262

Authors: G. Nampa, M. Ndlovu

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Mutualistic relationships amongst organisms drive diversity in terrestrial ecosystems. Yet, few mutual associations have been documented in the semi-arid savannas of Africa. The levels and benefits of association between Carissa bispinosa, a medium-sized evergreen thorny shrub, and Trinervitermes trinervoides, an ecosystem engineer termite species, were studied at a semi-arid savanna setting in Nylsvley nature reserve, South Africa. It was hypothesized that there would be a close plant-insect association since termite mounds provide nutrients for plant growth and, in return, the thorny shrubs protect mounds from predation and also provide a temperature buffer. Comparative plant and mounds measurements were taken from associated and isolated occurrences seasonally. Soil particle size, macro- and micronutrients were also evaluated from mounds and the adjacent topsoil matrix General Additive Mixed Models were used to assess internal mound temperatures in relation to prevailing ambient and plant shade temperatures. Findings revealed that plants growing on mounds were significantly taller with a wider canopy and remained greener in the dry season with more fruits. On the other hand, termite mounds under plants were less prone to be damaged by aardvarks and pangolins and had a significantly wider diameter than exposed mounds. All soil macronutrients except for calcium and phosphorous were enriched in mounds relative to the matrix. Only Manganese was enriched in mounds while the other micronutrients (Cu, Fe, Zn and B) were not. Termite mounds under plants maintained a better constant and higher mean internal temperature during winter compared to exposed mounds. To our best knowledge, the study has revealed a previously undocumented survival mechanism that termites use to escape extreme temperatures and predation in semi-arid savannas.

Keywords: mound, mutualism, soil nutrients, termites, thermoregulation

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

Procedia PDF Downloads 357

Authors: Alaa El-Din Rezk

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For organic xenobiotics, sorption to Aldrich humic acid is a key process controlling their mobility, bioavailability, toxicity and fate in the soil. Hydrophobic organic compounds possessing either acid or basic groups can be partially ionized (deprotonated or protonated) within the range of natural soil pH. For neutral and ionogenicxenobiotics including (neutral, acids and bases) sorption coefficients normalized to organic carbon content, Koc, have measured at different pH values. To this end, the batch equilibrium technique has been used, employing SPME combined with GC-MSD as an analytical tool. For most ionogenic compounds, sorption has been affected by both pH and pKa and can be explained through Henderson-Hasselbalch equation. The results demonstrate that when assessing the environmental fate of ionogenic compounds, their pKa and speciation under natural conditions should be taken into account. A new model has developed to predict the relationship between log Koc and pH with full statistical evaluation against other existing predictive models. Neutral solutes have displayed a good fit with the classical model using log Kow as log Koc predictor, whereas acidic and basic compounds have displayed a good fit with the LSER approach and the new proposed model. Measurement limitations of the Batch technique and SPME-GC-MSD have been found with ionic compounds.

Keywords: humic acid, log Koc, pH, pKa, SPME-GCMSD

Procedia PDF Downloads 263

Authors: Tauhidur Rahman, Subhrajit Pathak

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In this study, structural response of Bhairabi-Sairang Tunnel due to presence of seismic faults has been thoroughly examined. There may be several active faults located in and around the project. Faults are the key seismic sources from where earthquakes are originated. The magnitude of earthquake will depend on the length of the fault. A long fault more than 200 km can produce earthquake of magnitude (Mw ) more than 8.0 and smaller length less than 10 km will produce small magnitude earthquake. Now-a-days it is very much essential to identify the distance and length of a fault from the project site. Based on this, in the present paper, a case study of the Bhairabi Sairang Tunnel of 1.73 Km length located in the North Eastern Region of India has been selected to calculate the seismic hazard from the surrounding effect of faults. A comparative study of seismic hazard at the tunnel site has been made based on the location of faults with the seismic hazard obtained from the Indian Standards code of Practice. In this paper, a practical problem of a tunnel has been analysed based on the available faults around the project site accounting the soil factor.

Keywords: seismic hazard, effect of fault, soil factor, Bhairabi Sairang tunnel

Procedia PDF Downloads 566

Authors: Rachel Danielson, Brendan Bohannan, S.M. Tsai, Kyle Meyer, Jorge L.M. Rodrigues

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The Amazon Rainforest is a global diversity hotspot and crucial carbon sink, but approximately 20% of its total extent has been deforested- primarily for the establishment of cattle pasture. Understanding the impact of this large-scale disturbance on soil microbial community composition and activity is crucial in understanding potentially consequential shifts in nutrient or greenhouse gas cycling, as well as adding to the body of knowledge concerning how these complex communities respond to human disturbance. In this study, surface soils (0-10cm) were collected from three forests and three 45-year-old pastures in Rondonia, Brazil (the Amazon state with the greatest rate of forest destruction) in order to determine the impact of forest conversion on microbial communities involved in nitrogen fixation. Soil chemical and physical parameters were paired with measurements of microbial activity and genetic profiles to determine how community composition and process rates relate to environmental conditions. Measuring both the natural abundance of 15N in total soil N, as well as incorporation of enriched 15N2 under incubation has revealed that conversion of primary forest to cattle pasture results in a significant increase in the rate of nitrogen fixation by free-living diazotrophs. Quantification of nifH gene copy numbers (an essential subunit encoding the nitrogenase enzyme) correspondingly reveals a significant increase of genes in pasture compared to forest soils. Additionally, genetic sequencing of both nifH genes and transcripts shows a significant increase in the diversity of the present and metabolically active diazotrophs within the soil community. Levels of both organic and inorganic nitrogen tend to be lower in pastures compared to forests, with ammonium rather than nitrate as the dominant inorganic form. However, no significant or consistent differences in total, extractable, permanganate-oxidizable, or loss-on-ignition carbon are present between the two land-use types. Forest conversion is associated with a 0.5- 1.0 unit pH increase, but concentrations of many biologically relevant nutrients such as phosphorus do not increase consistently. Increases in free-living diazotrophic community abundance and activity appear to be related to shifts in carbon to nitrogen pool ratios. Furthermore, there may be an important impact of transient, low molecular weight plant-root-derived organic carbon on free-living diazotroph communities not captured in this study. Preliminary analysis of nitrogenase gene variant composition using NovoSeq metagenomic sequencing indicates that conversion of forest to pasture may significantly enrich vanadium-based nitrogenases. This indication is complemented by a significant decrease in available soil molybdenum. Very little is known about the ecology of diazotrophs utilizing vanadium-based nitrogenases, so further analysis may reveal important environmental conditions favoring their abundance and diversity in soil systems. Taken together, the results of this study indicate a significant change in nitrogen cycling and diazotroph community composition with the conversion of the Amazon Rainforest. This may have important implications for the sustainability of cattle pastures once established since nitrogen is a crucial nutrient for forage grass productivity.

Keywords: free-living diazotrophs, land use change, metagenomic sequencing, nitrogen fixation

Procedia PDF Downloads 194

Authors: Pulcherie Matsodoum Nguemte, Adrien Wanko Ngnien, Guy Valerie Djumyom Wafo, Ives Magloire Kengne Noumsi, Pierre Francois Djocgoue

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The identification of plant species with the capacity to grow on hydrocarbon-polluted soils is an essential step for phytoremediation. In view of developing phytoremediation in Cameroon, floristic surveys have been conducted in 4 cities (Douala, Yaounde, Limbe, and Kribi). In each city, 13 hydrocarbon-polluted, as well as unpolluted sites (control), have been investigated using quadrat method. 106 species belonging to 76 genera and 30 families have been identified on hydrocarbon-polluted sites, unlike the control sites where floristic diversity was much higher (166 species contained in 125 genera and 50 families). Poaceae, Cyperaceae, Asteraceae and Amaranthaceae have higher taxonomic richness on polluted sites (16, 15,10 and 8 taxa, respectively). Shannon diversity index of the hydrocarbon-polluted sites (1.6 to 2.7 bits/ind.) were significantly lower than the control sites (2.7 to 3.2 bits/ind.). Based on a relative frequency > 10% and abundance > 7%, this study highlights more than ten plants predisposed to be effective in the cleaning-up attempts of soils contaminated by hydrocarbons. Based on the floristic indicators, 6 species (Eleusine indica (L.) Gaertn., Cynodon dactylon (L.) Pers., Alternanthera sessilis (L.) R. Br. ex DC †, Commelinpa benghalensis L., Cleome ciliata Schum. & Thonn. and Asystasia gangetica (L.) T. Anderson) were selected for a study to determine their capacity to remediate a soil contaminated with fuel oil (82.5 ml/ kg of soil). The experiments lasting 150 days takes into account three modalities - Tn: uncontaminated soils planted (6) To contaminated soils unplanted (3) and Tp: contaminated soil planted (18) – randomized arranged. 3 on 6 species (Eleusine indica, Cynodon dactylon, and Alternanthera sessilis) survived the climatic and soil conditions. E. indica presents a significantly higher growth rate for density and leaf area while C. dactylon had a significantly higher growth rate for stem size and leaf numbers. A. sessilis showed stunted growth and development throughout the experimental period. The species Eleusine indica (L.) Gaertn. and Cynodon dactylon (L.) Pers. can be qualified as polluo-tolerant plant species; polluo-tolerance being the ability of a species to survive and develop in the midst subject to extreme physical and chemical disturbances.

Keywords: Cameroon, cleaning-up, floristic surveys, phytoremediation

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Authors: Jeron Maheswaran, Sudath C. Siriwardane

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In fatigue analysis, the structural detail of tubular joint has taken great attention among engineers. The DNV-RP-C203 is covering this topic quite well for simple and clear joint cases. For complex joint and geometry, where joint classification isn’t available and limitation on validity range of non-dimensional geometric parameters, the challenges become a fact among engineers. The classification of joint is important to carry out through the fatigue analysis. These joint configurations are identified by the connectivity and the load distribution of tubular joints. To overcome these problems to some extent, this paper compare the fatigue life of tubular joints in offshore jacket according to the stress concentration factors (SCF) in DNV-RP-C203 and finite element method employed Abaqus/CAE. The paper presents the geometric details, material properties and considered load history of the jacket structure. Describe the global structural analysis and identification of critical tubular joints for fatigue life estimation. Hence fatigue life is determined based on the guidelines provided by design codes. Fatigue analysis of tubular joints is conducted using finite element employed Abaqus/CAE [4] as next major step. Finally, obtained SCFs and fatigue lives are compared and their significances are discussed.

Keywords: fatigue life, stress-concentration factor, finite element analysis, offshore jacket structure

Procedia PDF Downloads 453

Authors: Omid Kalatpour, Mohammadreza Ajdari

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This article studied the possible causes of human error-induced incidents in the petrochemical industry maintenance activities by using Human Factors Analysis and Classification System (HFACS). The purpose of the study was anticipating and identifying these causes and proposing corrective and preventive actions. Maintenance department in a petrochemical company was selected for research. A checklist of human error-induced incidents was developed based on four HFACS main levels and nineteen sub-groups. Hierarchical task analysis (HTA) technique was used to identify maintenance activities and tasks. The main causes of possible incidents were identified by checklist and recorded. Corrective and preventive actions were defined depending on priority. Analyzing the worksheets of 444 activities in four levels of HFACS showed 37.6% of the causes were at the level of unsafe actions, 27.5% at the level of unsafe supervision, 20.9% at the level of preconditions for unsafe acts and 14% of the causes were at the level of organizational effects. The HFACS sub-groups showed errors (24.36%) inadequate supervision (14.89%) and violations (13.26%) with the most frequency. According to findings of this study, increasing the training effectiveness of operators and supervision improvement respectively are the most important measures in decreasing the human error-induced incidents in petrochemical industry maintenance.

Keywords: human error, petrochemical industry, maintenance, HFACS

Procedia PDF Downloads 242

Authors: Anas Cherqui, Audrey Pecourt, Manuella Catterou, Candice Mazoyon, Hervé Demailly, Vivien Sarazin, Frédéric Dubois, Jérôme Duclercq

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Aphids are pests that can cause severe yield losses in field crops. Chemical control is currently widely used to control aphids, but this method is increasingly controversial. The pea is able to recruit bacteria that are beneficial to its development, growth and health. However, the effects of this microbial recruitment on plant-insect interactions have generally been underestimated. This study investigated the interactions between Pisum sativum, key bacteria of pea rhizosphere (Rhizobium and Sphingomonas species) and the pea aphid, Acyrthosiphon pisum. We assessed the bottom-up effects of single and combined bacterial inoculations on pea plant health and subsequent aphid performance, as well as the top-down effects of aphid infestation on soil functionality. The presence of S. sediminicola or S. daechungensis limited the fecundity of the pea aphid without strongly affecting its feeding behaviour. Nevertheless, these bacteria limited the effect of A. pisum on the plant phenotype. In addition, the aphid infestation decreased the soil functionality, suggesting a potential strategy to hinder the recruitment of beneficial microorganisms.

Keywords: Acyrthosiphon pisum, Pisum sativum, Sphingomonas, rhizobium, EPG, productivity

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Authors: Weber A. R. Souza, Andre A. N. Dantas, Marcio A. Medeiros, Rafaella F. Costa

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The erosive processes are natural phenomena that cause changes in the soil continuously due to the actions of natural erosive agents and their speed can be intensified or retarded by factors such as climate, inclination, type of matrix rock, vegetation and anthropic activities, the latter being very relevant in occupied areas without planning and urban infrastructure. Inadequate housing sites associated with an inefficient urban drainage network and lack of vegetation cover potentiate the erosive processes that, over time, are gaining alarming proportions, as is the case of the erosion in Planaltina in Federal district, a Brazilian state in the central west. Thus, the aim of this work was to compare the use of Vetiver grass and Alfalfa as vegetation cover to slope protection. For that, a study was carried out in the scientific literature about the improvement of the soil properties provided by them and verification of the safety factor through the simulation of slopes with different heights and inclination using SLOPE / W software. The Vetiver grass presented little more satisfactory results than the Alfalfa, but these obtained results slightly closer to that of the vetiver grass in less time of planting.

Keywords: erosive processes, planting, slope protection, vegetation cover

Procedia PDF Downloads 180

Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

Procedia PDF Downloads 183

Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

Procedia PDF Downloads 125

Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: D. Madhushanka, Y. Liu, H. C. Fernando

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Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

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Authors: Fantazi Walid, Ezzedine Tahar, Bargaoui Zoubeida

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This research presents the main ideas to implement an intelligent system composed by communicating wireless sensors measuring environmental data linked to drought indicators (such as air temperature, soil moisture , etc...). On the other hand, the setting up of a spatio temporal database communicating with a Web mapping application for a monitoring in real time in activity 24:00 /day, 7 days/week is proposed to allow the screening of the drought parameters time evolution and their extraction. Thus this system helps detecting surfaces touched by the phenomenon of drought. Spatio-temporal conceptual models seek to answer the users who need to manage soil water content for irrigating or fertilizing or other activities pursuing crop yield augmentation. Effectively, spatio-temporal conceptual models enable users to obtain a diagram of readable and easy data to apprehend. Based on socio-economic information, it helps identifying people impacted by the phenomena with the corresponding severity especially that this information is accessible by farmers and stakeholders themselves. The study will be applied in Siliana watershed Northern Tunisia.

Keywords: WSN, database spatio-temporal, GIS, web mapping, indicator of drought

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Authors: Tadesse Menjetta, Daniel Dana, Serkadis Debalke

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Schistosomiasis/Bilharziasis is one of the neglected tropical parasitic diseases caused by different species of genus Schistosoma. Among the species, S. mansoni (causative agents of intestinal schistosomiasis) is one of the causes of severe intestinal parasitic infections with high public and medical importance in Ethiopia. There is a scarcity of information about the status of S. mansoni infection among the fisherman in our study area and in the country at large. Therefore, this study was designed to determine the prevalence and risk factors of S.mansoni infection among fishermen at Lake Hawassa, southern Ethiopia. A cross-sectional study was conducted among the fishermen from April to June 2013 in Hawassa, Southern Ethiopia. A total of 243 fishermen were included by systematic sampling from the lists of the fishermen members in the registration book of fishermen associations in the Hawassa Town. Data on socio-demographic features and risk factors were collected by using semi-structured questionnaires. Stool samples were collected and processed using Kato-Katz thick smear techniques and examined between 30- 40 minute for hookworm and after 24 hours for S. mansoni and other soil-transmitted helminths (STHs). The overall prevalence of S.mansoni among the fishermen was 29.21% (71/243), and the mean intensity of infection was 158.88 egg per gram (EPG). The prevalence of intestinal helminths including S. mansoni was 69.54% (169/243). Moreover, the prevalence of soil-transmitted helminths (STHs) was 40.74% (99/243), 35.80% (87/243) and 5.76% (14/243) for A. lumbricoides, T. trichiura and hookworm species, respectively. Almost similar prevalence of S.mansoni, 31.82%, 31.75%, 31.94% were recorded in age groups of 15-19, 20-24 and 25-29 years, respectively. Fishermen who are swimming always were 2.92 times [95% CI: 1.554, 5.502] more likely to acquire S. mansoni infection than other water contacting habit of the study participants. The results of the current investigation indicated the moderate endemicity of S. mansoni among the fishermen at Lake Hawassa, southern Ethiopia. Fishermen could be the potential risk group for S. mansoni infection and might be responsible for the transmission of S. mansoni to other segments of the communities. Since the high prevalence of STH was recorded among the fishermen, integrated prevention and control strategies from different sectors might be important to tackle the problem.

Keywords: S. mansoni, soil transmitted helminths, fishermen, Lake Hawassa, Ethiopia

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Authors: Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac Kuzmanović

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Zhengkun Yi

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Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

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Authors: Víctor Martínez del Rey, Kourosh Nasr Esfahani, Amir Masoud Samani Majd

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In this experimental study, the behaviors of Mixed Oxidant solution components (MOS) and sodium hypochlorite (HYPO) as the most commonly applied surface disinfectant were compared through the effectiveness of chlorine disinfection as a function of the contact time and residual chlorine. In this regard, the variation of pH, free available chlorine (FAC) concentration, and electric conductivity (EC) of disinfection solutions in different concentrations were monitored over 48 h contact time. In parallel, the plant stress activated by chlorine-based disinfectants was assessed by comparing MOS and HYPO. The elements of pH and EC in the plant-soil and their environmental impacts, spread by disinfection solutions were analyzed through several concentrations of FAC including 500 mg/L, 1000 mg/L, and 5000 mg/L in irrigated water. All the experiments were carried out at the service station of Sant Cugat, Spain. The outcomes indicated lower pH and higher durability of MOS than HYPO at the same concentration of FAC which resulted in promising stability of FAC within MOS. Furthermore, the pH and EC value of plant-soil irrigated by NaOCl solution were higher than that of MOS solution at the same FAC concentration. On-site generation of MOS as a safe chlorination option might be considered an imaginary future of smart cities.

Keywords: disinfection, free available chlorine, on-site generation, sodium hypochlorite

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Authors: M. Abdullahi, S. Sadiku, Bashar S. Mohammed, J. I. Aguwa

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The properties of hollow sandcrete blocks produced in Minna, Nigeria are presented. Sandcrete block is made of cement, water and sand bound together in certain mix proportions. For the purpose of this work, fifty (50) commercial sandcrete block industries were visited in Minna, Nigeria to obtain block samples and aggregates used for the manufacture, and to also take inventory of the mix composition and the production process. Sieve analysis tests were conduction on the soil sample from various block industries to ascertain their quality to be used for block making. The mix ratios were also investigated. Five (5) nine inches (9’’ or 225mm) blocks were obtained from each block industry and tested for dimensional compliance and compressive strength. The result of test shows that the grading of the sand falls within the limit required by BS 882: 1990. The sand particles generally satisfy the grading requirement of overall grading and also fall in at least one of the classification of coarse grading, medium grading or fine grading. This clearly indicates that the quality of the aggregates used for the production of sandcrete blocks in Minna, Nigeria are of good quality in terms of grading and workable mix can easily be achieved to obtain high quality product. Physical examinations of the block sizes show slight deviation from the standard requirement in NIS 87:2000. Compressive strength of hollow sandcrete blocks in range of 0.12 N/mm2 to 0.54 N/mm2 was obtained which is below the recommendable value of 3.45 N/mm2 for load bearing hollow sandcrete blocks. This indicates that these blocks are below the standard for load-bearing sandcrete blocks and cannot be used as load bearing walling units. The mix composition also indicated low cement content resulting in low compressive strength. Most of the commercial block industries visited do not take curing very serious. Water were only sprinkled ones or twice before the blocks were stacked and made readily available for sale. It is recommended that a mix ratio of 1:4 to 1:6 should be used for the production of sandcrete blocks and proper curing practice should be adhered to. Blocks should also be cured for 14 days before making them available for consumers.

Keywords: compressive strength, dimensions, mix proportions, sandcrete blocks

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Authors: Gabriela T. Nakashima, Elias R. D. Padilla, Joao L. Barros, Gabriela B. Belini, Hiroyuki Yamamoto, Fabio M. Yamaji

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Biochar can be an alternative to use sugarcane trash. Biochar is a solid material obtained from pyrolysis, that is a biomass thermal degradation with low or no O₂ concentration. Pyrolysis transforms the carbon that is commonly found in other organic structures into a carbon with more stability that can resist microbial decomposition. Biochar has a versatility of uses such as soil fertility, carbon sequestration, energy generation, ecological restoration, and soil remediation. Biochar has a great ability to retain water and nutrients in the soil so that this material can improve the efficiency of irrigation and fertilization. The aim of this study was to characterize biochar produced from sugarcane trash in three different pyrolysis temperatures and determine the lowest temperature with the high yield and carbon content. Physical characterization of this biochar was performed to help the evaluation for the best production conditions. Sugarcane (Saccharum officinarum) trash was collected at Corredeira Farm, located in Ibaté, São Paulo State, Brazil. The farm has 800 hectares of planted area with an average yield of 87 t·ha⁻¹. The sugarcane varieties planted on the farm are: RB 855453, RB 867515, RB 855536, SP 803280, SP 813250. Sugarcane trash was dried and crushed into 50 mm pieces. Crucibles and lids were used to settle the sugarcane trash samples. The higher amount of sugarcane trash was added to the crucible to avoid the O₂ concentration. Biochar production was performed in three different pyrolysis temperatures (200°C, 325°C, 450°C) in 2 hours residence time in the muffle furnace. Gravimetric yield of biochar was obtained. Proximate analysis of biochar was done using ASTM E-872 and ABNT NBR 8112. Volatile matter and ash content were calculated by direct weight loss and fixed carbon content calculated by difference. Porosity measurement was evaluated using an automatic gas adsorption device, Autosorb-1, with CO₂ described by Nakatani. Approximately 0.5 g of biochar in 2 mm particle sizes were used for each measurement. Vacuum outgassing was performed as a pre-treatment in different conditions for each biochar temperature. The pore size distribution of micropores was determined using Horváth-Kawazoe method. Biochar presented different colors for each treatment. Biochar - 200°C presented a higher number of pieces with 10mm or more and did not present the dark black color like other treatments after 2 h residence time in muffle furnace. Also, this treatment had the higher content of volatiles and the lower amount of fixed carbon. In porosity analysis, while the temperature treatments increase, the amount of pores also increase. The increase in temperature resulted in a biochar with a better quality. The pores in biochar can help in the soil aeration, adsorption, water retention. Acknowledgment: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil – PROAP-CAPES, PDSE and CAPES - Finance Code 001.

Keywords: proximate analysis, pyrolysis, soil amendment, sugarcane straw

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Authors: Violina Angelova

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A comparative study has been carried out into the impact of organic meliorants on the uptake of heavy metals, micro and macroelements and the phytoremediation potential of enhanced tobacco BAC F3. The soil used as part of this experiment was sampled from the vicinity of the Non-Ferrous-Metal Works near Plovdiv, Bulgaria. The pot experiment carried out consisted of a randomized, complete block design containing nine treatments and three replications (27 pots). The treatments consisted of a control (with no organic meliorants) and compost and vermicompost meliorants (added at 5%, 10%, 15%, and 30%, and recalculated based on their dry soil weight). Upon reaching commercial ripeness, the tobacco plants were gathered. Heavy metals, micro and macroelement contents in roots, stems, and leaves of tobacco were analyzed by the method of the microwave mineralization. To determine the elements in the samples, inductively coupled emission spectrometry (Jobin Yvon Emission - JY 38 S, France) was used. The distribution of the heavy metals, micro, and macroelements in the organs of the enhanced tobacco has a selective character and depended above all on the parts of the plants and the element that was examined. Pb, Zn, Cu, Fe, Mn, P and Mg distribution in tobacco decreases in the following order: roots > leaves > stems, and for Cd, K, and Ca - leaves > roots > stems. The high concentration of Cd in the leaves and the high translocation factor indicate the possibility of enhanced tobacco to be used in phytoextraction. Tested organic amendments significantly influenced the uptake of heavy metals, micro and macroelements by the roots, stems, and leaves of tobacco. A correlation was found between the quantity of the mobile forms and the uptake of Pb, Zn, and Cd by the enhanced tobacco. The compost and vermicompost treatments significantly reduced heavy metals concentration in leaves and increased uptake of K, Ca and Mg. The 30% compost and 30% vermicompost treatments led to the maximal reduction of heavy metals in enhanced tobacco BAC F3. The addition of compost and vermicompost further reduces the ability to digest the heavy metals in the leaves, and phytoremediation potential of enhanced tobacco BAC F3. Acknowledgment: The financial support by the Bulgarian National Science Fund Project DFNI Н04/9 is greatly appreciated.

Keywords: heavy metals, micro and macroelements, enhanced tobacco BAC F3, phytoremediation, organic meliorants

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Authors: Chien-Kuo Chiu

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In the displacement-based seismic design and evaluation, equivalent linearization method is one of the approximation methods to estimate the maximum inelastic displacement response of a system. In this study, the accuracy of two equivalent linearization methods are investigated. The investigation consists of three soil condition in Taiwan (Taipei Basin 1, 2, and 3) and five different heights of building (H_r= 10, 20, 30, 40, and 50 m). The first method is the Taiwan equivalent linearization method (TELM) which was proposed based on Japanese equivalent linear method considering the modification factor, α_T= 0.85. On the basis of Lin and Miranda study, the second method is proposed with some modification considering Taiwan soil conditions. From this study, it is shown that Taiwanese equivalent linearization method gives better estimation compared to the modified Lin and Miranda method (MLM). The error index for the Taiwanese equivalent linearization method are 16%, 13%, and 12% for Taipei Basin 1, 2, and 3, respectively. Furthermore, a ductility demand spectrum of single-degree-of-freedom (SDOF) system is presented in this study as a guide for engineers to estimate the ductility demand of a structure.

Keywords: displacement-based design, ductility demand spectrum, equivalent linearization method, RC buildings, single-degree-of-freedom

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Authors: Ahmed Altalb

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Fertilizers are natural materials, or industrial contain nutrients, which help to improve soil fertility and is considered (nitrogen, phosphorus, and potassium) is important elements for the growth of crops properly. Fertilization is necessary in order to improve the quality of agricultural products and the recovery in agricultural activities. The use of organic fertilizers and chemical lead to reduce the loss of nutrients in agricultural soils, and this leads to an increase in the production of agricultural crops. Fertilizers are one of the key factors in the increase of agricultural production as well as other factors such as irrigation and improved seeds and Prevention and others; the fertilizers will continue to be a cornerstone of the agriculture in order to produce the food to feed of world population. The use of fertilizers has become commonplace today, especially the chemical fertilizers for the development of agricultural production, due to the provision of nutrients for plants and in high concentrations and easily dissolves in water and ease of use. The choose the right type of fertilizer depends on the soil type and the type of crop. In this subject, find the relationship between the agricultural extension and the optimal use of fertilizers. The extension plays the important role in the advise and educate of farmers in how they optimal use the fertilizers in a scientific way. This article aims to identify the concept the fertilizers. Identify the role of fertilizers in increasing the agricultural production, identify the role of agricultural extension in the optimal use of fertilizers and rural development.

Keywords: agricultural, extension, fertilizers, production

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Authors: Kyomin Lee, Joohee Kim, Sangho Kang

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The commercial nuclear power reactor in South Korea, Kori Unit 1, which was a 587 MWe pressurized water reactor that started operation since 1978, was permanently shut down in June 2017 without an additional operating license extension. The Kori 1 Unit is scheduled to become the nuclear power unit to enter the decommissioning phase. In this study, the preliminary evaluation of the decommissioning wastes for the Kori Unit 1 was performed based on the following series of process: firstly, the plant inventory is investigated based on various documents (i.e., equipment/ component list, construction records, general arrangement drawings). Secondly, the radiological conditions of systems, structures and components (SSCs) are established to estimate the amount of radioactive waste by waste classification. Third, the waste management strategies for Kori Unit 1 including waste packaging are established. Forth, selection of the proper decontamination and dismantling (D&D) technologies is made considering the various factors. Finally, the amount of decommissioning waste by classification for Kori 1 is estimated using the DeCAT program, which was developed by KEPCO-E&C for a decommissioning cost estimation. The preliminary evaluation results have shown that the expected amounts of decommissioning wastes were less than about 2% and 8% of the total wastes generated (i.e., sum of clean wastes and radwastes) before/after waste processing, respectively, and it was found that the majority of contaminated material was carbon or alloy steel and stainless steel. In addition, within the range of availability of information, the results of the evaluation were compared with the results from the various decommissioning experiences data or international/national decommissioning study. The comparison results have shown that the radioactive waste amount from Kori Unit 1 decommissioning were much less than those from the plants decommissioned in U.S. and were comparable to those from the plants in Europe. This result comes from the difference of disposal cost and clearance criteria (i.e., free release level) between U.S. and non-U.S. The preliminary evaluation performed using the methodology established in this study will be useful as a important information in establishing the decommissioning planning for the decommissioning schedule and waste management strategy establishment including the transportation, packaging, handling, and disposal of radioactive wastes.

Keywords: characterization, classification, decommissioning, decontamination and dismantling, Kori 1, radioactive waste

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Authors: Kyi Pyar Zaw, Zin Mar Kyu

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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.

Keywords: chain code frequency, character recognition, feature extraction, features matching, segmentation

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