Search results for: abnormal activity detection

Authors: Navpreet Kaur, Himkusha Thakur, Nirmal Prabhakar

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One of the most publicized and controversial issue in crop production is the use of agrichemicals- also known as pesticides. This is evident in many reports that Organophosphate (OP) insecticides, among the broad range of pesticides are mainly involved in acute and chronic poisoning cases. Therefore, detection of OPs is very necessary for health protection, food and environmental safety. In our study, a nanocomposite of poly (3,4 ethylenedioxythiophene) (PEDOT) and multi-walled carbon nanotubes (MWCNTs) has been deposited electrochemically onto the surface of fluorine doped tin oxide sheets (FTO) for the analysis of malathion OP. The -COOH functionalization of MWCNTs has been done for the covalent binding with amino groups of AChE enzyme. The use of PEDOT-MWCNT films exhibited an excellent conductivity, enables fast transfer kinetics and provided a favourable biocompatible microenvironment for AChE, for the significant malathion OP detection. The prepared PEDOT-MWCNT/FTO and AChE/PEDOT-MWCNT/FTO nano-biosensors were characterized by Fourier transform infrared spectrometry (FTIR), Field emission-scanning electron microscopy (FE-SEM) and electrochemical studies. Electrochemical studies were done using Cyclic Voltammetry (CV) or Differential Pulse Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS). Various optimization studies were done for different parameters including pH (7.5), AChE concentration (50 mU), substrate concentration (0.3 mM) and inhibition time (10 min). The detection limit for malathion OP was calculated to be 1 fM within the linear range 1 fM to 1 µM. The activity of inhibited AChE enzyme was restored to 98% of its original value by 2-pyridine aldoxime methiodide (2-PAM) (5 mM) treatment for 11 min. The oxime 2-PAM is able to remove malathion from the active site of AChE by means of trans-esterification reaction. The storage stability and reusability of the prepared nano-biosensor is observed to be 30 days and seven times, respectively. The application of the developed nano-biosensor has also been evaluated for spiked lettuce sample. Recoveries of malathion from the spiked lettuce sample ranged between 96-98%. The low detection limit obtained by the developed nano-biosensor made them reliable, sensitive and a low cost process.

Keywords: PEDOT-MWCNT, malathion, organophosphates, acetylcholinesterase, nano-biosensor, oxime (2-PAM)

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Authors: Cheyma Bensaci, Mokhtar Saidi, Zineb Ghiaba

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The first objective of this study is to determine the phenolic contents and antioxidant capacities of three different varieties of date palm (Phoenix dactylifera L.) fruit (DPF) from Algeria were using three different solvents. As for the second objective is to find the correlation of phenolic contents with the both DPPH radical scavenging and reducing power activity. These results showed that date had strongly scavenging activity on DPPH .The IC50 value for DPPH radical scavenging activity was 0.15 mg/ml in acetone/H2O extract from Gh. And also, acetone/H2O extract from Gh showed the best AEAC value for reducing power was 8,48 mM. The results also showed that there are a positive correlation, so confined values between 0.153 and 0.972.

Keywords: phoenix dactylifera, antioxidant activity, correlation, reducing power

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Authors: A. Degale Desta, Cheng Jian

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Background: Vehicle detection accuracy is critical to intelligent transportation systems and autonomous driving. The state-of-the-art object identification technology YOLOv8 has shown significant gains in efficiency and detection accuracy. This study uses the BDD100K dataset, which is renowned for its extensive and varied annotations, to assess how well YOLOv8 performs in vehicle detection. Objectives: The primary objective of this research is to assess YOLOv8's performance in intelligent transportation system vehicle identification and its ability to accurately identify cars in urban environments for safety prioritization. Methods: The primary objective of this research is to assess YOLOv8's performance in intelligent transportation system vehicle identification and its ability to accurately identify cars in urban environments for safety prioritization. Results: The results show that YOLOv8 achieves high mAP, recall, precision, and F1-score values, indicating state-of-the-art performance. This suggests that YOLOv8 can identify cars in complex urban environments with a high degree of accuracy and reliable results in a variety of traffic scenarios. Conclusion: The results indicate that YOLOv8 is a useful tool for enhancing vehicle detection accuracy in intelligent transportation systems, hence advancing urban public safety and security. The model's demonstrated performance shows how well it may be incorporated into autonomous driving applications to improve situational awareness and responsiveness.

Keywords: vehicle detection, YOLOv8, BDD100K, object detection, deep learning

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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

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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

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Authors: Namita Mittal, Himakshi Shekhawat, Ankit Vidyarthi

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In medical research study, doctors and radiologists face lot of complexities in analysing the brain tumors in Magnetic Resonance (MR) images. Brain tumor detection is difficult due to amorphous tumor shape and overlapping of similar tissues in nearby region. So, radiologists require one such clinically viable solution which helps in automatic segmentation of tumor inside brain MR image. Initially, segmentation methods were used to detect tumor, by dividing the image into segments but causes loss of information. In this paper, a hybrid method is proposed which detect Region of Interest (ROI) on the basis of difference in intensity values and texture values of tumor region using nearby tissues with Gradient Vector Flow (GVF) technique in the identification of ROI. Proposed approach uses both intensity and texture values for identification of abnormal section of the brain MR images. Experimental results show that proposed method outperforms GVF method without any loss of information.

Keywords: brain tumor, GVF, intensity, MR images, segmentation, texture

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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: 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: Shanshan Zhu, Mohammad Nasim

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Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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Authors: J. M. Hung, J. S. Chan, M. I. Kuo, D. S. Chan, C. P. Lu

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Wheat germ is a by-product obtained from wheat milling and it contains highly concentrated nutrients. Due to highly lipase and lipoxygenase activities, wheat germ products can easily turn into rancid flavor and cause a short life. The objective of this study is to control moisture content and retard lipid hydrolysis by fluidized-bed drying. The raw wheat germ of 2 kg was dried with a vertical batch fluidized bed with the following varying conditions, inlet air temperature of 50, 80 and 120°C, inlet air velocity of 3.62 m/s. The experiment was designed to obtain a final product at around 40°C with water activity of 0.3 ± 0.1. Changes in the moisture content, water activity, enzyme activity of dried wheat germ during storage were measured. Results showed the fluidized-bed drying was found to reduce moisture content, water activity and lipase activity of raw wheat germ. After drying wheat germ, moisture content and water activity were between 5.8% to 7.2% and 0.28 to 0.40 respectively during 12 weeks of storage. The variation range of water activity indicated to retard lipid oxidation. All drying treatments displayed inactivation of lipase, except for drying condition of 50°C which showed relative high enzyme activity. During storage, lipase activity increased slowly during the first 6 weeks of storage and reached a plateau for another 6 weeks. As a result, using a fluidized-bed dryer was found to be effective drying technique in improving storage stability of wheat germ.

Keywords: wheat germ, fluidized-bed dryer, storage, lipase, stability

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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

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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: Supatra Karnjanapratum, Soottawat Benjakul

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Gelatin hydrolysate, especially from marine resource, has been known to possess antioxidative activity. Nevertheless, the activity is still lower in comparison with the commercially available antioxidant. Maillard reactions can be use to increase antioxidative activity of gelatin hydrolysate, in which the numerous amino group could be involved in glycation. In the present study, gelatin hydrolysate (GH) from unicorn leatherjacket skin prepared using glycyl endopeptidase with prior autolysis assisted process was used for preparation of Maillard reaction products (MRPs) under dry condition. The impacts of different factors including, types of saccharides, GH to saccharide ratio, incubation temperatures, relative humidity (RH) and times on antioxidative activity of MRPs were investigated. MRPs prepared using the mixture of GH and galactose showed the highest antioxidative activity as determined by both ABTS radical scavenging activity and ferric reducing antioxidant power during heating (0-48 h) at 60 °C with 65% RH, compared with those derived from other saccharide tested. GH to galactose ratio at 2:1 (w/w) yielded the MRPs with the highest antioxidative activity, followed by the ratios of 1:1 and 1:2, respectively. When the effects of incubation temperatures (50, 60, 70 °C) and RH (55, 65, 75%) were examined, the highest browning index and the absorbance at 280 nm were found at 70 °C, regardless of RH. The pH and free amino group content of MRPs were decreased with the concomitant increase in antioxidative activity as the reaction time increased. Antioxidative activity of MRPs generally increased with increasing temperature and the highest antioxidative activity was found when RH of 55% was used. Based on electrophoresis of MRP, the polymerization along with the formation of high molecular weight material was observed. The optimal condition for preparing antioxidative MRPs was heating the mixture of GH and galactose (2:1) at 70 °C and 55% RH for 36 h. Therefore, antioxidative activity of GH was improved by Maillard reaction and the resulting MRP could be used as natural antioxidant in food products.

Keywords: antioxidative activity, gelatin hydrolysate, maillard reaction, unicorn leatherjacket

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Authors: Abhijit Das, Runu Chakraborty

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Tea is the most widely consumed beverage aside from water; it is grown in about 30 countries with a per capita worldwide consumption of approximately 0.12 liter per year. Green tea is of growing importance with its antioxidant contents associated with its health benefits. The various extraction methods can influence the polyphenol concentrations of green tea. The purpose of the study was to quantify the polyphenols, flavonoid and antioxidant activity of both caffeinated and decaffeinated form of tea manufactured commercially in Assam, North Eastern part of India. The results display that phenolic/flavonoid content well correlated with antioxidant activity which was performed by DPPH (2,2-diphenyl-1-picrylhydrazyl) and FRAP (Ferric reducing ability of plasma) assay. After decaffeination there is a decrease in the polyphenols concentration which also affects the antioxidant activity of green tea. For the enrichment of antioxidant activity of decaffeinated tea a herbal plant extract is used which shows a synergistic effect between green tea and herbal plant phenolic compounds.

Keywords: antioxidant activity, decaffeination, green tea, flavonoid content, phenolic content, plant extract

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Authors: N. Chaitawittanun, M. Munlin

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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

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Authors: Rahul Agarwal, Ashutosh Singh

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Colorectal cancer (CRC) also refers as bowel cancer or colon cancer. It involves the development of abnormal growth of cells in colon or rectum part of the body. This work leads to the development of a miRNA database in colorectal cancer. We named this database- miCoRe. This database comprises of all validated colon-rectal cancer miRNAs information from various published literature with an effectual knowledge based information retrieval system. miRNAs have been collected from various published literature reports. MySQL is used for main-framework of miCoRe while the front-end was developed in PHP script. The aim of developing miCoRe is to create a comprehensive central repository of colorectal carcinoma miRNAs with all germane information of miRNAs and their target genes. The current version of miCoRe consists of 238 miRNAs which are known to be implicated in malignancy of CRC. Alongside with miRNA information, miCoRe also contains the information related to the target genes of these miRNA. miCoRe furnishes the information about the mechanism of incidence and progression of the disease, which would further help the researchers to look for colorectal specific miRNAs therapies and CRC specific targeted drug designing. Moreover, it will also help in development of biomarkers for the better and early detection of CRC and will help in better clinical management of the disease.

Keywords: colorectal cancer, database, miCoRe, miRNAs

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Authors: Nasibeh Azizi Khereshki

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Olive leaf (OL) extract as a green reductant agent was utilized for the biogenic synthesis of silver nanoparticles (Ag NPs) for the first time in this study, and then its performance was evaluated for colorimetric detection of Fe3+ in different media. Some analytical methods were used to characterize the nanosensor. The effective sensing parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) application. Then, the prepared material's applicability in antibacterial and optical chemical sensing for naked-eye detection of Fe3+ ions in aqueous solutions were evaluated. Furthermore, OL-Ag NPs-loaded paper strips were successfully applied to the colorimetric visualization of Fe3+. The colorimetric probe based on OL-AgNPs illustrated excellent selectivity and sensitivity towards Fe3+ ions, with LOD and LOQ of 0.81 μM and 2.7 μM, respectively. In addition, the developed method was applied to detect Fe3+ ions in real water samples and validated with a 95% confidence level against a reference spectroscopic method.

Keywords: Ag NPs, colorimetric detection, Fe(III) ions, green synthesis, olive leaves

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Authors: Maria Medrano, Cristina Cadenas-Sanchez, Maddi Oses, Lide Arenaza, Maria Amasene, Idoia Labayen

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The COVID-19 outbreak began in December 2019 in China and was rapidly expanded globally. Emergency measures, such as social distance or home confinement, were adopted by many country governments to prevent its transmission. In Spain, one of the most affected countries, the schools were closed, and one of the most severe mandatory home confinement was established for children from 14th March to 26th April 2020. The hypothesis of this study was that the measures adopted during the COVID-19 pandemic may have negatively affected physical activity and screen time of children. However, few studies have examined the effects of COVID-19 pandemic on lifestyle behaviours. Thus, the aim of the current work was to analyse the effects of the COVID-19 confinement on physical activity and screen time in Spanish children. For the current purpose, a total of 113 children and adolescents (12.0 ± 2.6 yr., 51.3% boys, 24.0% with overweight/obesity according to the World Obesity Federation) of the MUGI project were included in the analyses. Physical activity and screen time were longitudinally assessed by 'The Youth Activity Profile' questionnaire (YAP). Differences in physical activity and screen time before and during the confinement were assessed by dependent t-test. Before the confinement, 60% did not meet physical activity recommendations ( ≥ 60/min/day of moderate to vigorous physical activity), and 61% used screens ≥ 2 h/day. During the COVID-19 confinement, children decreased their physical activity levels (-91 ± 55 min/day, p < 0.001) and increased screen time ( ± 2.6 h/day, p < 0.001). The prevalence of children that worsened physical activity and screen time during the COVID-19 confinement were 95.2% and 69.8%, respectively. The current study evidence the negative effects of the COVID-19 confinement on physical activity and screen time in Spanish children. These findings should be taken into account to develop and implement future public health strategies for preserving children's lifestyle behaviours and health during and after the COVID-19 pandemic.

Keywords: COVID-19, lifestyle changes, paediatric, physical activity, screen time

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Authors: Sehreen Moorat, Mussarat Lakho

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A method of artificial intelligence using digital mammograms data has been proposed in this paper for detection of breast cancer. Many researchers have developed techniques for the early detection of breast cancer; the early diagnosis helps to save many lives. The detection of breast cancer through mammography is effective method which detects the cancer before it is felt and increases the survival rate. In this paper, we have purposed image processing technique for enhancing the image to detect the graphical table data and markings. Texture features based on Gray-Level Co-Occurrence Matrix and intensity based features are extracted from the selected region. For classification purpose, neural network based supervised classifier system has been used which can discriminate between benign and malignant. Hence, 68 digital mammograms have been used to train the classifier. The obtained result proved that automated detection of breast cancer is beneficial for early diagnosis and increases the survival rates of breast cancer patients. The proposed system will help radiologist in the better interpretation of breast cancer.

Keywords: medical imaging, cancer, processing, neural network

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Authors: Harshada Vaidya-Kannur, Dattatraya Naik

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Propolis, one of the beehive products also referred as bee-glue is sticky dark coloured complex mixture of compounds. The volatile oil can be isolated from the propolis by hydrodistillation. The mark that is left behind after the removal of volatile oil is referred as the de-oiled propolis. Antioxidant as well as anti-inflammatory properties of total ethanolic extract of de-oiled propolis (TEEDP) was investigated. Another lot of deoiled propolis was successively exacted with hexane, ethyl acetate and ethanol. Activities of these fractions were also determined. Antioxidant activity was determined by studying ABTS, DPPH and NO radical scavenging. Determination of anti-inflammatory activity was carried out by topical TPA induced mouse ear oedema model. It is noteworthy that ethyl acetate fraction of deoiled propolis (EAFDP) exhibited 49.45 % TEAC activity at the concentration 0.2 mg/ml which is equivalent to the activity of trolox at the concentration 0.2 mg/ml. Its DPPH scavenging activity (72.56%) was closely comparable to that of trolox (75%). However its NO scavenging activity was comparatively low. From IC50 values it could be concluded that the efficiency of scavenging ABTS radicals by the de-oiled propolis was more pronounced as compared to scavenging of other radicals. Studies by TPA induced mouse ear inflammation model indicated that the de-oiled propolis of Indian origin had significant topical anti-inflammatory activity. The EAFDP was found to be the most active fraction for this activity also. The purification of EAFP yielded six pure crystalline compounds. These compounds were identified by their physical data and spectral data.

Keywords: anti-inflammatory activity, anti-oxidant activity, column chromatography, de-oiled propolis

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Authors: Maryam Kiani

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Non-precious electrocatalysts play a crucial role in the oxygen reduction reaction (ORR) for regenerative fuel cells and rechargeable metal-air batteries. To enhance ORR activity, La (a less active element) is added to modify the activity of Ni. This addition increases the surface contents of Ni2+, N, and S species in LaNi/N-S-C, while still maintaining a substantial specific surface area and hierarchical porosity. Therefore, the additional La is essential for the successful ORR process.In addition, the presence of extra La in the LaNi/N-S-C electrocatalyst enhances the efficiency of charge transfer and improves the surface acid-base characteristics, facilitating the adsorption of oxygen molecules during the ORR process. As a result, this superior and desirable electrocatalyst exhibits significantly enhanced ORR bifunctional activity. In fact, its ORR activity is comparable to that of the 20 wt% Pt/C.

Keywords: fuel cells, batteries, dual-doped carbon black, ORR

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Authors: Khadidja Bouzid, Fouzia Benali Toumi, Mohamed Bouzouina

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We made a comparison between the total phenolic content, concentrations of flavonoids and antioxidant activity of four different extracts (butanol, ethyl acetate, chloroform, water) of Arbutus unedo L. fruit (Ericacea) of El Marsa and Terni area. The total phenolic content in the extracts was determined using the Folin-Ciocalteu reagent and it ranged between 26.57 and 48.23 gallic acid equivalents mg/g of dry weight of extract. The concentrations of flavonoids in plant extracts varied from 17.98 to 56.84 catechin equivalents mg/g. The antioxidant activity was analyzed in vitro using the DPPH reagent; among all extracts, ethyl acetate fraction from El Marsa area showed the highest antioxidant activity.

Keywords: antioxidant activity, Arbutus unedo L., fruit flavonoids, phenols, Western Algeria

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Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

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Authors: Md. Aftabuzzaman, Md. Kamruzzaman

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Road-related expenditure and building construction activity are two significant drivers of the Victorian economy. This paper investigates the relationship between building construction activity and road-related expenditure. Data for construction activities were collected from Victorian Building Authority, and road-related expenditure data were explored by the Bureau of Infrastructure and Transport Research Economics. The trend between these two sectors was compared. The analysis found a strong relationship between road-related expenditure and the volume of construction activity, i.e., the more construction activities, the greater the requirement of road-related expenditure, or vice-versa. The road-related expenditure has a two-year lag period, suggesting that the road sector requires two years to respond to the growth in the building sector.

Keywords: building construction activity, infrastructure, road expenditure, Victorian Building Authority

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Authors: Sonali Banrjee, Swarup Kumar Mitra, Aritra Acharyya

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A piano note recognition method has been proposed by the authors in this paper. The authors have used a comprehensive method for onset detection of each note present in a piano piece followed by segmented short-time Fourier transform (STFT) for the identification of piano notes. The performance evaluation of the proposed method has been carried out in different harsh noisy environments by adding different levels of additive white Gaussian noise (AWGN) having different signal-to-noise ratio (SNR) in the original signal and evaluating the note detection error rate (NDER) of different piano pieces consisting of different number of notes at different SNR levels. The NDER is found to be remained within 15% for all piano pieces under consideration when the SNR is kept above 8 dB.

Keywords: AWGN, onset detection, piano note, STFT

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Authors: S. Jagadeesh Kumar

Abstract:

Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation.

Keywords: curvature analysis, ring correction in polar coordinate method, face detection, face recognition, human computer interaction

Procedia PDF Downloads 287

Authors: Zohreh Bayat, Dariush Minai-Tehrani

Abstract:

Lipases constitute one of the most important groups of industrial enzymes that catalyze the hydrolysis of triacylglycerol to glycerol and fatty acids. Muscarinic antagonist relieves smooth muscle spasm of the gastrointestinal tract and effect on the cardiovascular system. In this research, the effect of a muscarinic antagonist on the lipase activity of Pseudomonas aeruginosa was studied. Lineweaver–Burk plot showed that the drug inhibited the enzyme by competitive inhibition. The IC50 value (60 uM) and Ki (30 uM) of the drug revealed the drug bound to the enzyme with high affinity. Determination of enzyme activity in various pH and temperature showed that the maximum activity of lipase was at pH 8 and 60°C both in presence and absence of the drug.

Keywords: bacteria, inhibition, kinetics, lipase

Procedia PDF Downloads 453

Authors: Cheng-Yen Lu, Chin-Yuan Hsu

Abstract:

Ambient temperature reduction (ATR) can extend the lifespan of an annual fish (Nothobranchius rachovii), but the underlying mechanism is unknown. In this study, the expression, concentration, and activity of cellular-degraded molecules were evaluated in the muscle of N. rachovii reared under high (30 °C), moderate (25 °C), and low (20 °C) ambient temperatures by biochemical techniques. The results showed that (i) the activity of the 20S proteasome, the expression of microtubule-associated protein 1 light chain 3-II (LC3-II), the expression of lysosome-associated membrane protein type 2a (Lamp 2a), and lysosome activity increased with ATR; (ii) the expression of the 70 kD heat shock cognate protein (Hsc 70) decreased with ATR; (iii) the expression of the 20S proteasome, the expression of lysosome-associated membrane protein type 1 (Lamp 1), the expression of molecular target of rapamycin (mTOR), the expression of phosphorylated mTOR (p-mTOR), and the p-mTOR/mTOR ratio did not change with ATR. These findings indicated that ATR activated the activity of proteasome, macroautophagy, and chaperone-mediated autophagy. Taken together these data reveal that ATR likely activates cellular degradation activity to extend the lifespan of N. rachovii.

Keywords: ambient temperature reduction, autophagy, degradation activity, lifespan, proteasome

Procedia PDF Downloads 462