Search results for: volatile detection

Authors: Ali A. Aldosari

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Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region.

Keywords: spatial analysis, geographical information system, change detection

Procedia PDF Downloads 377

Authors: Duc V. Nguyen

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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.

Keywords: fault detection, FFT, induction motor, predictive maintenance

Procedia PDF Downloads 136

Authors: Temesgen Geremew

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The excessive emission of heavy metal ions from industrial processes poses a serious threat to both the environment and human health. This study presents a distinct approach utilizing (PTh-MnS/CoS NPs) for the highly selective and sensitive detection of Hg²⁺ ions in water. Such detection is crucial for safeguarding human health, protecting the environment, and accurately assessing toxicity. The fabrication method employs a simple and efficient chemical precipitation technique, harmoniously combining polythiophene, MnS, and CoS NPs to create highly active substrates for SERS. The MnS@Hg²⁺ exhibits a distinct Raman shift at 1666 cm⁻¹, enabling specific identification and demonstrating the highest responsiveness among the studied semiconductor substrates with a detection limit of only 1 nM. This investigation demonstrates reliable and practical SERS detection for Hg²⁺ ions. Relative standard deviation (RSD) ranged from 0.49% to 9.8%, and recovery rates varied from 96% to 102%, indicating selective adsorption of Hg²⁺ ions on the synthesized substrate. Furthermore, this research led to the development of a remarkable set of substrates, including (MnS, CoS, MnS/CoS, and PTh-MnS/CoS) nanoparticles were created right there on SiO₂/Si substrate, all exhibiting sensitive, robust, and selective SERS for Hg²⁺ ion detection. These platforms effectively monitor Hg²⁺ concentrations in real environmental samples.

Keywords: surface-enhanced raman spectroscopy (SERS), sensor, mercury ions, nanoparticles, and polythiophene.

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Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

Procedia PDF Downloads 102

Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

Procedia PDF Downloads 134

Authors: Izuru Nomura, Tatsuya Takino, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of crater detection from the image of the lunar surface captured by the small space probe. We use the principal component analysis (PCA) to detect craters. Nevertheless, considering　severe environment of the space, it is impossible to use generic computer in practice. Accordingly, we have to implement the method in FPGA. This paper compares FPGA and generic computer by the processing time of a method of crater detection using principal component analysis.

Keywords: crater, PCA, eigenvector, strength value, FPGA, processing time

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Authors: Dinesh Gundavaram

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This paper presents an investigation on bridge damage detection based on the dynamic responses estimated from a passing vehicle. A numerical simulation of steel truss bridge for railway was used in this investigation. The bridge response at different locations is measured using CSI-Bridge software. Several damage scenarios are considered including different locations and severities. The possibilities of dynamic properties of global modes in the identification of structural changes in truss bridges were discussed based on the results of measurement.

Keywords: bridge, damage, dynamic responses, detection

Procedia PDF Downloads 244

Authors: Nuur Ezaini Akmar Ismail, Norbazilah Rahim, Norul Huda Md Rasdi, Maslina Daud

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A web application is the most targeted by the attacker because the web application is accessible by the end users. It has become more advantageous to the attacker since not all the end users aware of what kind of sensitive data already leaked by them through the Internet especially via social network in shake on ‘sharing’. The attacker can use this information such as personal details, a favourite of artists, a favourite of actors or actress, music, politics, and medical records to customize phishing attack thus trick the user to click on malware-laced attachments. The Phishing attack is one of the most popular attacks for social engineering technique against web applications. There are several methods to detect phishing websites such as Blacklist/Whitelist based detection, heuristic-based, and visual similarity-based detection. This paper illustrated a comparison between the heuristic-based technique using features of a uniform resource locator (URL) and visual similarity-based detection techniques that compares the content of a suspected phishing page with the legitimate one in order to detect new phishing sites based on the paper reviewed from the past few years. The comparison focuses on three indicators which are false positive and negative, accuracy of the method, and time consumed to detect phishing website.

Keywords: heuristic-based technique, phishing detection, social engineering and visual similarity-based technique

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Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: Raeesa Moolla, Ryan S. Johnson

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Air travel, and the use of airports, has experienced proliferative growth in the past few decades, resulting in the concomitant release of air pollutants. Air pollution needs to be monitored because of the known relationship between exposure to air pollutants and increased adverse effects on human health. This study monitored a group of volatile organic compounds (VOCs); specifically BTEX (viz. benzene, toluene, ethyl-benzene and xylenes), as many are detrimental to human health. Through the use of passive sampling methods, the spatial variability of BTEX within an international airport was investigated, in order to determine ‘hotspots’ where occupational exposure to BTEX may be intensified. The passive sampling campaign revealed BTEX_total concentrations ranged between 12.95–124.04 µg m^-3. Furthermore, BTEX concentrations were dispersed heterogeneously within the airport. Due to the slow wind speeds recorded (1.13 m.s^-1); the hotspots were located close to their main BTEX sources. The main hotspot was located over the main apron of the airport. Employees working in this area may be chronically exposed to these emissions, which could be potentially detrimental to their health.

Keywords: air pollution, air quality, hotspot monitoring, volatile organic compounds

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Authors: Pajaree Donkhampa, Fuangfa Unob

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Ceftazidime is an antibiotic drug commonly used to treat several human and veterinary infections. However, the presence of ceftazidime residues in the environment may induce microbial resistance and cause side effects to humans. Therefore, monitoring the level of ceftazidime in environmental resources is important. In this work, a melamine foam platform was proposed for simultaneous extraction and colorimetric detection of ceftazidime based on the azo dye formation on the surface. The melamine foam was chemically modified with polyethyleneimine (PEI) and characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Ceftazidime is a sample that was extracted on the PEI-modified melamine foam and further reacted with nitrite in an acidic medium to form an intermediate diazonium ion. The diazotized molecule underwent an azo coupling reaction with chromotropic acid to generate a red-colored compound. The material color changed from pale yellow to pink depending on the ceftazidime concentration. The photo of the obtained material was taken by a smartphone camera and the color intensity was determined by Image J software. The material fabrication and ceftazidime extraction and detection procedures were optimized. The detection of a sub-ppm level of ceftazidime was achieved without using a complex analytical instrument.

Keywords: colorimetric detection, ceftazidime, melamine foam, extraction, azo dye

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Authors: Luz Stella Cadavid-Rodriguez, Viviana E. Castro-Lopez

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Fish waste (FW) has a high content of potentially biodegradable components, so it is amenable to be digested anaerobically. In this line, anaerobic digestion (AD) of FW has been studied for biogas production. Nevertheless, intermediate products such as volatile fatty acids (VFA), generated during the acidogenic stage, have been scarce investigated, even though they have a high potential as a renewable source of carbon. In the literature, there are few studies about the Inoculum-Substrate (I/S) ratio on acidogenesis. On the other hand, it is well known that pH is a critical factor in the production of VFA. The optimum pH for the production of VFA seems to change depending on the substrate and can vary in a range between 5.25 and 11. Nonetheless, the literature about VFA production from protein-rich waste, such as FW, is scarce. In this context, it is necessary to deepen on the determination of the optimal operating conditions of acidogenic fermentation for VFA production from protein-rich waste. Therefore, the aim of this research was to optimize the volatile fatty acid production from artisanal fishing waste, studying the effect of pH and the I/S ratio on the acidogenic process. For this research, the inoculum used was a methanogenic sludge (MS) obtained from a UASB reactor treating wastewater of a slaughterhouse plant, and the FW was collected in the port of Tumaco (Colombia) from the local artisanal fishers. The acidogenic fermentation experiments were conducted in batch mode, in 500 mL glass bottles as anaerobic reactors, equipped with rubber stoppers provided with a valve to release biogas. The effective volume used was 300 mL. The experiments were carried out for 15 days at a mesophilic temperature of 37± 2 °C and constant agitation of 200 rpm. The effect of 3 pH levels: 5, 7, 9, coupled with five I/S ratios, corresponding to 0.20, 0.15, 0.10, 0.05, 0.00 was evaluated taking as a response variable the production of VFA. A complete randomized block design was selected for the experiments in a 5x3 factorial arrangement, with two repetitions per treatment. At the beginning and during the process, pH in the experimental reactors was adjusted to the corresponding values of 5, 7, and 9 using 1M NaOH or 1M H2SO4, as was appropriated. In addition, once the optimum I/S ratio was determined, the process was evaluated at this condition without pH control. The results indicated that pH is the main factor in the production of VFA, obtaining the highest concentration with neutral pH. By reducing the I/S ratio, as low as 0.05, it was possible to maximize VFA production. Thus, the optimum conditions found were natural pH (6.6-7.7) and I/S ratio of 0.05, with which it was possible to reach a maximum total VFA concentration of 70.3 g Ac/L, whose major components were acetic acid (35%) and butyric acid (32%). The findings showed that the acidogenic fermentation of FW is an efficient way of producing VFA and that the operating conditions can be simple and economical.

Keywords: acidogenesis, artisanal fishing waste, inoculum to substrate ratio, volatile fatty acids

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Authors: Saeedeh Hajihosseini, Zahra Aghili, Navid Nasirizadeh

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An innovative method of a DNA electrochemical biosensor based on Oracet Blue (OB) as an electroactive label and gold electrode (AuE) for detection of Helicobacter pylori, was offered. A single–stranded DNA probe with a thiol modification was covalently immobilized on the surface of the AuE by forming an Au–S bond. Differential pulse voltammetry (DPV) was used to monitor DNA hybridization by measuring the electrochemical signals of reduction of the OB binding to double– stranded DNA (ds–DNA). Our results showed that OB–based DNA biosensor has a decent potential for detection of single–base mismatch in target DNA. Selectivity of the proposed DNA biosensor was further confirmed in the presence of non–complementary and complementary DNA strands. Under optimum conditions, the electrochemical signal had a linear relationship with the concentration of the target DNA ranging from 0.3 nmol L-1 to 240.0 nmol L-1, and the detection limit was 0.17 nmol L-1, whit a promising reproducibility and repeatability.

Keywords: DNA biosensor, oracet blue, Helicobacter pylori, electrode (AuE)

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.

Keywords: antenna array, signal detection, ToA, AoA estimation

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

Procedia PDF Downloads 354

Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration

Procedia PDF Downloads 182

Authors: Noor-ul-Ain, Muhammad Tahir Khan, Kashif Khan, Adeela Ajmal, Hamid Mustafa

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The objective of this study was to evaluate the preservative and upgrading potential of urea (70g/kg DM) added to high moisture oat silage at laboratory scale trial and urea was hydrolysed 95%. Microbial activity measured by pH and volatile fatty acids (VFA) and lactate production was reduced (p<0.001) by the urea addition. The pH of oat silage (without treated) was measured 5.7 and increased up to 8.00 on average while; volatile fatty acids (VFA) concentration was decreased. Relative proportions of fermentation acids changed after urea addition, increasing the acetate and butyrate and decreasing the propionate and lactate proportions. The addition of urea to oat silages increased (P<0.001) water soluble and ammonium nitrogen of the forage. These nitrogen fractions represented more than 40% of total nitrogen. After urea addition, total nitrogen content of oat silages increased from 21.0 g/kg DM to 28 g/kg DM. Application of urea at a rate of 70 g/kg DM significantly increased (P<0.001) the in situ degradation of neutral-detergent fibre after 48h of rumen incubation (NDF-situ). The NDF-situ was 200 g/kg NDF higher on oat forages ensiled with urea than on oat forages ensiled without urea. Oat silages can be effectively preserved and upgraded by ensiling with 70 g urea/kg dry matter. Further studies are required to evaluate voluntary intake of this forage.

Keywords: oat, silage, urea, pH, forage

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Authors: R. Nagarani

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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

Procedia PDF Downloads 394

Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

Procedia PDF Downloads 87

Authors: Goreti Botelho, Filomena Gomes, Fernanda M. Ferreira, Ilda Caldeira

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The strawberry tree (Arbutus unedo L.) is a small tree or shrub from botanical Ericaceae family that grows spontaneously nearby the Mediterranean basin and produce edible red fruits. A traditional processed fruit application, in Mediterranean countries, is the production of a spirit (known as aguardente de medronho, in Portugal) obtained from the fermented fruit. The main objective of our study was to contribute to the knowledge about the influence of the degree of maturation of fruits in the volatile composition and quality of arbutus spirit. The major volatiles in the three distillates fractions (head, heart and tail) obtained from fermentation of two different fruit maturation levels were quantified by GC-FID analysis and ANOVA one-way was performed. Additionally, the total antioxidant capacity and total phenolic compounds of both arbutus fruit spirits were determined, by ABTS and Folin-Ciocalteau method, respectively. The methanol concentration is superior (1022.39 g/hL a.a.) in the spirit made from fruits with highest total soluble solids, which is a value above the legal limit (1000 g/hL a.a.). Overall, our study emphasizes, for the first time, the influence of maturation degree of arbutus fruits in the spirit volatile composition and quality.

Keywords: arbutus fruit, maturation, quality, spirit

Procedia PDF Downloads 359

Authors: Simon B. N. Thompson

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Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.

Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning

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Authors: Aboul-Nasr Amal, Sabry Soraya, Sabra Mayada

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The effects of phosphorus amendments and arbuscular mycorrhizal (AM) fungi Glomus intraradices on the sweet basil (Ocimum basilicum L.), chemical composition and percent of volatile oil, and metal accumulation in plants and its availability in soil were investigated in field experiment at two seasons 2012 and 2013 under contaminated soil with Pb and Cu. The content of essential oil and shoot and root dry weights of sweet basil was increased by the application of mineral phosphorus as compared to control. Inoculation with AM fungi reduced the metal concentration in shoot, recording a lowest value of (33.24, 18.60 mg/kg) compared to the control (46.49, 23.46 mg/kg) for Pb and Cu, respectively. Availability of Pb and Cu in soil were decreased after cultivation in all treatments compared to control. However, metal root concentration increased with the inoculation, with highest values of (30.15, 39.25 mg/kg)compared to control (22.01, 33.57mg/kg) for Pb and Cu, respectively. The content of linalool and methyl chavicol in basil oil was significantly increased in all treatments compared to control. We can thus conclude that the AM-sweet basil symbiosis could be employed as an approach to bioremediate polluted soils and enhance the yield and maintain the quality of volatile oil of sweet basil plants.

Keywords: arbuscular mycorrhizal fungus, heavy metals, sweet basil, oil composition

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Sadia Ameen, M. Nazim, Hyumg-Kee Seo, Hyung-Shik Shin

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TiO2 nanotube (NT) arrays were grown on titanium (Ti) foil substrate by electrochemical anodic oxidation and utilized as working electrode to fabricate a highly sensitive and reproducible chemical sensor for the detection of harmful phenyl hydrazine chemical. The fabricated chemical sensor based on TiO2 NT arrays electrode exhibited high sensitivity of ~40.9 µA.mM-1.cm-2 and detection limit of ~0.22 µM with short response time (10s).

Keywords: TiO2 NT, phenyl hydrazine, chemical sensor, sensitivity, electrocatalytic properties

Procedia PDF Downloads 468

Authors: Chanho Park, Juneseok You, Kuewhan Jang, Sungsoo Na

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Detection technique of nanotoxic materials is strongly imperative, because nano-toxic materials can harmfully influence human health and environment as their engineering applications are growing rapidly in recent years. In present work, we report the DNA immobilized quartz crystal microbalance (QCM) based sensor for detection of nano-toxic materials such as silver ions, Hg2+ etc. by using functionalization of quartz crystal with a target-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz crystal is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated fast and in situ detection of nanotoxic materials using quartz crystal microbalance. We report the label-free and highly sensitive detection of silver ion for present case, which is a typical nano-toxic material by using QCM and silver-specific DNA. The detection is based on the measurement of frequency shift of Quartz crystal from constitution of the cytosine-Ag+-cytosine binding. It is shown that the silver-specific DNA measured frequency shift by QCM enables the capturing of silver ions below 100pM. The results suggest that DNA-based detection opens a new avenue for the development of a practical water-testing sensor.

Keywords: nano-toxic ions, quartz crystal microbalance, frequency shift, target-specific DNA

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Belhaj Mohamed, M. Saidi, N. Boucherab, N. Ouertani, I. Bouazizi, M. Ben Jrad

Abstract:

Natural hydrocarbon seepage has helped petroleum exploration as a direct indicator of gas and/or oil subsurface accumulations. Surface macro-seeps are generally an indication of a fault in an active Petroleum Seepage System belonging to a Total Petroleum System. This paper describes a case study in which multiple analytical techniques were used to identify and characterize trace petroleum-related hydrocarbons and other volatile organic compounds in groundwater samples collected from Sousse aquifer (Central Tunisia). The analytical techniques used for analyses of water samples included gas chromatography-mass spectrometry (GC-MS), capillary GC with flame-ionization detection, Compund Specific Isotope Analysis, Rock Eval Pyrolysis. The objective of the study was to confirm the presence of gasoline and other petroleum products or other volatile organic pollutants in those samples in order to assess the respective implication of each of the potentially responsible parties to the contamination of the aquifer. In addition, the degree of contamination at different depths in the aquifer was also of interest. The oil and gas seeps have been investigated using biomarker and stable carbon isotope analyses to perform oil-oil and oil-source rock correlations. The seepage gases are characterized by high CH4 content, very low δ13CCH4 values (-71,9 ‰) and high C1/C1–5 ratios (0.95–1.0), light deuterium–hydrogen isotope ratios (-198 ‰) and light δ13CC2 and δ13CCO2 values (-23,8‰ and-23,8‰ respectively) indicating a thermogenic origin with the contribution of the biogenic gas. An organic geochemistry study was carried out on the more ten oil seep samples. This study includes light hydrocarbon and biomarkers analyses (hopanes, steranes, n-alkanes, acyclic isoprenoids, and aromatic steroids) using GC and GC-MS. The studied samples show at least two distinct families, suggesting two different types of crude oil origins: the first oil seeps appears to be highly mature, showing evidence of chemical and/or biological degradation and was derived from a clay-rich source rock deposited in suboxic conditions. It has been sourced mainly by the lower Fahdene (Albian) source rocks. The second oil seeps was derived from a carbonate-rich source rock deposited in anoxic conditions, well correlated with the Bahloul (Cenomanian-Turonian) source rock.

Keywords: biomarkers, oil and gas seeps, organic geochemistry, source rock

Procedia PDF Downloads 419

Authors: N. Chaitawittanun, M. Munlin

Abstract:

Due to rapid advancement of powerful image processing software, digital images are easy to manipulate and modify by ordinary people. Lots of digital images are edited for a specific purpose and more difficult to distinguish form their original ones. We propose a clustering method to detect a copy-move image forgery of JPEG, BMP, TIFF, and PNG. The process starts with reducing the color of the photos. Then, we use the clustering technique to divide information of measuring data by Hausdorff Distance. The result shows that the purposed methods is capable of inspecting the image file and correctly identify the forgery.

Keywords: image detection, forgery image, copy-paste, attack detection

Procedia PDF Downloads 309