Search results for: driver drowsiness detection

Authors: Darko Babic, Maja Modric, Dario Babic, Mario Fiolic

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For road markings as a part of traffic control plan, it is considered to have a positive impact on road safety. Their importance is particularly evident in low visibility conditions when the field of vision and the driver's visual acuity are significantly reduced. The aim of this article is to analyze how road marking retroreflectivity affects the frequency of traffic accidents in low visibility conditions. For this purpose, 10,417.4 km single carriageway roads were analysed across Croatia in the period from 2012 to 2016. The research included accidents that may be significantly affected by marking retroreflectivity: head-on collisions, running off the road, hitting a stationary object on the road and hitting a stationary roadside object. The results have shown that the retroreflectivity level is negatively correlated to the total number of accidents and the number of casualties and injuries, which ultimately means that the risk of traffic accidents and deaths and/or injuries of participants will be lower with the increase of road markings retroreflectivity. These results may assist in defining minimum values of retroreflectivity that the markings must meet at any time as well as the suitable technologies and materials for their implementation.

Keywords: retroreflectivity, road markings, traffic accidents, traffic safety

Procedia PDF Downloads 136

Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

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Authors: Myungsu Lim, William Xiu Shun Wong, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Namgyu Kim

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The amount of real-time data generated through various mass media has been increasing rapidly. In this study, we had performed topic analysis by using the unstructured text data that is distributed through news article. As one of the most prevalent applications of topic analysis, the issue tracking technique investigates the changes of the social issues that identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has limitation that it cannot discover dynamic mutation process of complex social issues. The purpose of this study is to overcome the limitations of the existing issue tracking method. We first derived core issues of each period, and then discover the dynamic mutation process of various issues. In this study, we further analyze the mutation process from the perspective of the issues categories, in order to figure out the pattern of issue flow, including the frequency and reliability of the pattern. In other words, this study allows us to understand the components of the complex issues by tracking the dynamic history of issues. This methodology can facilitate a clearer understanding of complex social phenomena by providing mutation history and related category information of the phenomena.

Keywords: Data Mining, Issue Tracking, Text Mining, topic Analysis, topic Detection, Trend Detection

Procedia PDF Downloads 386

Authors: Soukaina Motia, Nadia El Alami El Hassani, Alassane Diouf, Benachir Bouchikhi, Nezha El Bari

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Parabens are the antimicrobial molecules largely used in cosmetic products as a preservative agent. Among them, the methylparaben (MP) is the most frequently used ingredient in cosmetic preparations. Nevertheless, their potential dangers led to the development of sensible and reliable methods for their determination in environmental samples. Firstly, a sensitive and selective molecular imprinted polymer (MIP) based on screen-printed gold electrode (Au-SPE), assembled on a polymeric layer of carboxylated poly(vinyl-chloride) (PVC-COOH), was developed. After the template removal, the obtained material was able to rebind MP and discriminate it among other interfering species such as glucose, sucrose, and citric acid. The behavior of molecular imprinted sensor was characterized by Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS) techniques. Then, the biosensor was found to have a linear detection range from 0.1 pg.mL^-1 to 1 ng.mL^-1 and a low limit of detection of 0.12 fg.mL^-1 and 5.18 pg.mL^-1 by DPV and EIS, respectively. For applications, this biosensor was employed to determine MP content in four wastewaters in Meknes city and two cosmetic products (shower gel and shampoo). The operational reproducibility and stability of this biosensor were also studied. Secondly, another MIP biosensor based on tungsten trioxide (WO₃) functionalized by gold nanoparticles (Au-NPs) assembled on a polymeric layer of PVC-COOH was developed. The main goal was to increase the sensitivity of the biosensor. The developed MIP biosensor was successfully applied for the MP determination in wastewater samples and cosmetic products.

Keywords: cosmetic products, methylparaben, molecularly imprinted polymer, wastewater

Procedia PDF Downloads 299

Authors: N. Varsamis, M. Gerasimou, P. Christodoulou, S. Mantzoukis, G. Kolliopoulou, N. Zotos

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Purpose: Body fluids (pleural, peritoneal, synovial, pericardial, cerebrospinal) are an important element in the detection of microorganisms. For this reason, it is important to examine them in the Intensive Care Unit (ICU) patients. Material and Method: Body fluids are transported through sterile containers and enriched as soon as possible with Tryptic Soy Broth (TSB). After one day of incubation, the broth is poured into selective media: Blood, Mac Conkey No. 2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. The above selective media are incubated directly for 2 days. After this period, if any number of microbial colonies are detected, gram staining is performed. After that, the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) and followed by a sensitivity test on the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by Kirby Bauer-based plate test. Results: In 2017 the Laboratory of Microbiology received 60 samples of body fluids from the ICU. More specifically the Microbiology Department received 6 peritoneal fluid specimens, 18 pleural fluid specimens and 36 cerebrospinal fluid specimens. 36 positive cultures were tested. S. epidermidis was identified in 18 specimens, S. haemolyticus in 6, and E. faecium in 12. Conclusions: The results show low detection of microorganisms in body fluid cultures.

Keywords: body fluids, culture, intensive care unit, microorganisms

Procedia PDF Downloads 185

Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Giuseppe Spampinato, Salvatore Curti, Ivana Guarneri, Arcangelo Bruna

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This document describes an advanced system and methodology for Cross Traffic Alert (CTA), able to detect vehicles that move into the vehicle driving path from the left or right side. The camera is supposed to be not only on a vehicle still, e.g. at a traffic light or at an intersection, but also moving slowly, e.g. in a car park. In all of the aforementioned conditions, a driver’s short loss of concentration or distraction can easily lead to a serious accident. A valid support to avoid these kinds of car crashes is represented by the proposed system. It is an extension of our previous work, related to a clustering system, which only works on fixed cameras. Just a vanish point calculation and simple optical flow filtering, to eliminate motion vectors due to the car relative movement, is performed to let the system achieve high performances with different scenarios, cameras and resolutions. The proposed system just uses as input the optical flow, which is hardware implemented in the proposed platform and since the elaboration of the whole system is really speed and power consumption, it is inserted directly in the camera framework, allowing to execute all the processing in real-time.

Keywords: clustering, cross traffic alert, optical flow, real time, vanishing point

Procedia PDF Downloads 183

Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: Praveena Nair Sivasankaran, Thomas Arthur Ward, Rubentheren Viyapuri

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Paper describes a methodology to fabricate a simplified dragonfly wing structure using balsa wood and red prepreg fibre glass. These simplified wing structures were created for use in Biomimetic Micro Air Vehicles (BMAV). Dragonfly wings are highly corrugated and possess complex vein structures. In order to mimic the wings function and retain its properties, a simplified version of the wing was designed. The simplified dragonfly wing structure was created using a method called spatial network analysis which utilizes Canny edge detection method. The vein structure of the wings were carved out in balsa wood and red prepreg fibre glass. Balsa wood and red prepreg fibre glass was chosen due to its ultra- lightweight property and hence, highly suitable to be used in our application. The fabricated structure was then immersed in a nanocomposite solution containing chitosan as a film matrix, reinforced with chitin nanowhiskers and tannic acid as a crosslinking agent. These materials closely mimic the membrane of a dragonfly wing. Finally, the wings were subjected to a bending test and comparisons were made with previous research for verification. The results had a margin of difference of about 3% and thus the structure was validated.

Keywords: dragonfly wings, simplified, Canny edge detection, balsa wood, red prepreg, chitin, chitosan, tannic acid

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 96

Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Raouf Mbarki, Fadi Al Khatib, Malek Adouni

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Knee collateral ligaments play a significant role in restraining excessive frontal motion (varus/valgus rotations). In this investigation, a multiscale frame was developed based on structural hierarchies of the collateral ligaments starting from the bottom (tropocollagen molecule) to up where the fibred reinforced structure established. Experimental data of failure tensile test were considered as the principal driver of the developed model. This model was calibrated statistically using Bayesian calibration due to the high number of unknown parameters. Then the model is scaled up to fit the real structure of the collateral ligaments and simulated under realistic boundary conditions. Predications have been successful in describing the observed transient response of the collateral ligaments during tensile test under pre- and post-damage loading conditions. Collateral ligaments maximum stresses and strengths were observed near to the femoral insertions, a results that is in good agreement with experimental investigations. Also for the first time, damage initiation and propagation were documented with this model as a function of the cross-link density between tropocollagen molecules.

Keywords: multiscale model, tropocollagen, fibrils, ligaments commas

Procedia PDF Downloads 142

Authors: Ahmed Saeed Mohamed, Sadok Sassi, Mohammad Roshun Paurobally

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Gears are important machine components that are widely used to transmit power and change speed in many rotating machines. Any breakdown of these vital components may cause severe disturbance to production and incur heavy financial losses. One of the most common causes of gear failure is the tooth fatigue crack. Early detection of teeth cracks is still a challenging task for engineers and maintenance personnel. So far, to analyze the vibration behavior of gears, different approaches have been tried based on theoretical developments, numerical simulations, or experimental investigations. The objective of this study was to develop a numerical model that could be used to simulate the effect of teeth cracks on the resulting vibrations and hence to permit early fault detection for gear transmission systems. Unlike the majority of published papers, where only one single crack has been considered, this work is more realistic, since it incorporates the possibility of multiple simultaneous cracks with different lengths. As cracks significantly alter the gear mesh stiffness, we performed a finite element analysis using SolidWorks software to determine the stiffness variation with respect to the angular position for different combinations of crack lengths. A simplified six degrees of freedom non-linear lumped parameter model of a one-stage gear system is proposed to study the vibration of a pair of spur gears, with and without tooth cracks. The model takes several physical properties into account, including variable gear mesh stiffness and the effect of friction, but ignores the lubrication effect. The vibration simulation results of the gearbox were obtained via Matlab and Simulink. The results were found to be consistent with the results from previously published works. The effect of one crack with different levels was studied and very similar changes in the total mesh stiffness and the vibration response, both were observed and compared to what has been found in previous studies. The effect of the crack length on various statistical time domain parameters was considered and the results show that these parameters were not equally sensitive to the crack percentage. Multiple cracks are introduced at different locations and the vibration response and the statistical parameters were obtained.

Keywords: dynamic simulation, gear mesh stiffness, simultaneous tooth cracks, spur gear, vibration-based fault detection

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Authors: Nisha James, Shubha Ranganathan

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Sex trafficking is a traumatic ongoing process which includes human rights violations against the victims. Majority of the trafficked individuals in India are from families with low socioeconomic status, from rural areas, unmarried or married off at a very young age. Many of the sex trafficked feel that it is necessary to make sacrifices, for the benefit of their families. The combination of these cultural family values with the stigma of rape and prostitution are manipulated and used as a tool in the abuse of power against the sex trafficked. The rescue, rehabilitation and reintegration of these individuals are usually difficult due to the stigma and social exclusion that they face. In these circumstances, social support is very effective in social inclusion of these individuals. The present study was a qualitative one, using semi-structured interviews with 29 Indian survivors of sex trafficking and a few sex workers. Thematic analysis was done on the data derived from the semi-structured interviews. The major findings indicate that the family can be seen as both the ‘cause’ for being sex trafficked, and the factor in victim continuing to be sex trafficked. At the same time, it can also become a driver for getting rescued, rehabilitated and reintegrated. The study also explores the social construction about ‘family’ among the survivors of sex trafficking, reflecting on who they refer to as ‘family’, what they mean by the term ‘family’ and how these families emerge. Therefore the analytic concept of ‘family’ is a crucial element in sex trafficking and cannot be defined only in terms of its conventional definition of a basic unit of society.

Keywords: sex-trafficking, survivor, family, social construction

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Authors: Chih-Wei Pai, Rong-Chang Jou

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Using A1 A2 police-reported accident data for years 2003–2010 in Taiwan, the paper examines anatomic injuries and crash characteristics specific to pedestrians in “facing traffic” and “back to traffic” crashes. There were 2768 and 7558 accidents involving pedestrians walking along with/against traffic respectively. Injuries sustained by pedestrians and crash characteristics in these two crash types were compared with those in other crash types (nearside crash, nearside dart-out crash, offside crash, offside dart-out crash). Main findings include that “back to traffic” crashes resulted in more severe injuries, and pedestrians in “back to traffic” crashes had increased head, neck, and spine injuries than those in other crash types; and there was an elevated risk of head injuries in unlit darkness and NBU (non-built-up) roadways. Several crash features (e.g. unlit darkness, overtaking maneuvers, phone use by pedestrians and drivers, intoxicated drivers) appear to be over-involved in “back to traffic” crashes. The implications of the research findings regarding pedestrian/driver education, enforcement, and remedial engineering design are discussed.

Keywords: pedestrian accident, crash characteristics, injury, facing traffic, back to traffic

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Authors: Getachew Demeissie Ayalew, Yongtao Sun, Yang Yang

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The spiral welded pipe manufacturing industry requires strict production standards for automated grinders for welding seams. However, traditional grinding machines in this sector are insufficient due to a lack of quality control protocols and inconsistent performance. This research aims to improve the quality of spiral welded pipes by developing intelligent automated abrasive belt grinding equipment. The system has equipped with six degrees of freedom (6 DOF) KUKA KR360 industrial robots, enabling concurrent grinding operations on both internal and external welds. The grinding robot control system is designed with a PLC, and a human-machine interface (HMI) system is employed for operations. The system includes an electric speed controller, data connection card, DC driver, analog amplifier, and HMI for input data. This control system enables the grinding of spiral welded pipe. It ensures consistent production quality and cost-effectiveness by reducing the product life cycle and minimizing risks in the working environment.

Keywords: Intelligent Systems, Spiral Welded Pipe, Grinding, Industrial Robot, End-Effector, PLC Controller System, 3D Laser Sensor, HMI.

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Authors: Baffoe Kingsley

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The consequence of the migration of young people from rural farming communities in the global south to the global north is a well-known phenomenon. While climate change and its accompanying socio-economic structures continue to be the driver, what is not really known is how left behinds are compelled to convert lands meant for the production of traditional staples such as cereals, vegetables, and tubers to the production of export-driven cashew plantations due to youth migration. The consequence of such migration on the development of Ghana and its food security is multidimensional. Using an ethnographic research design, the study revealed that the majority of farmers in the area are now aged, and farm labor has become scarce, which has impeded the cultivation of traditional staples for the population. It has also been established that in the absence of farm labor, most farmers have reduced farm sizes for the production of staples and increased the production of cashews. The practice has, in tend, resulted in a scarcity of land for the cultivation of staples. The study recommends further inquiry into how the effects of migration and cashew production as diversification in agriculture influence national development in Ghana.

Keywords: staple food crops, cashew plantations, climate change, migration

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Authors: Samaneh Nabavi, Hadi Shirzad, Arash Ghoorchian, Maryam Shanesaz, Reza Naderi

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Morphine (MO), the most effective painkiller, is considered the reference by which analgesics are assessed. It is very necessary for the biomedical applications to detect and maintain the MO concentrations in the blood and urine with in safe ranges. To date, there are many expensive techniques for detecting MO. Recently, many electrochemical sensors for direct determination of MO were constructed. The molecularly imprinted polymer (MIP) is a polymeric material, which has a built-in functionality for the recognition of a particular chemical substance with its complementary cavity.This paper reports a sensor for MO using a combination of a molecularly imprinted polymer (MIP) and differential-pulse voltammetry (DPV). Electropolymerization of MO doped polypyrrole yielded poor quality, but a well-doped, nanostructure and increased impregnation has been obtained in the pH=12. Above a pH of 11, MO is in the anionic forms. The effect of various experimental parameters including pH, scan rate and accumulation time on the voltammetric response of MO was investigated. At the optimum conditions, the concentration of MO was determined using DPV in a linear range of 7.07 × 10−6 to 2.1 × 10−4 mol L−1 with a correlation coefficient of 0.999, and a detection limit of 13.3 × 10-8 mol L−1, respectively. The effect of common interferences on the current response of MO namely ascorbic acid (AA) and uric acid (UA) is studied. The modified electrode can be used for the determination of MO spiked into urine samples, and excellent recovery results were obtained. The nanostructured polypyrrole films were characterized by field emission scanning electron microscopy (FESEM) and furrier transforms infrared (FTIR).

Keywords: morphine detection, sensor, polypyrrole, nanostructure, molecularly imprinted polymer

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: M. F. Abreu, A. I. Aguiar, R. Gaio, R. Duarte

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Background: Prison establishments constitute high-risk environments for the transmission and spread of tuberculosis (TB), given their epidemiological context and the difficulty of implementing preventive and control measures. Guidelines for control and prevention of tuberculosis in prisons have been described as incomplete and heterogeneous internationally, due to several identified obstacles, for example scarcity of human resources and funding of prisoner health services. In Portugal, a protocol was created in 2014 with the aim to define and standardize procedures of detection and prevention of tuberculosis within prisons. Objective: The main objective of this study was to identify and describe barriers to tuberculosis detection in prisons of Porto and Lisbon districts in Portugal. Methods: A cross-sectional study was conducted from 2ⁿᵈ January 2018 till 30ᵗʰ June 2018. Semi-structured questionnaires were applied to health care professionals working in the prisons of the districts of Porto (n=6) and Lisbon (n=8). As inclusion criteria we considered having work experience in the area of tuberculosis (either in diagnosis, treatment, or follow up). The questionnaires were self-administered, in paper format. Descriptive analyses of the questionnaire variables were made using frequencies and median. Afterwards, a hierarchical agglomerative clusters analysis was performed. After obtaining the clusters, the chi-square test was applied to study the association between the variables collected and the clusters. The level of significance considered was 0.05. Results: From the total of 186 health professionals, 139 met the criteria of inclusion and 82 health professionals were interviewed (62,2% of participation). Most were female, nurses, with a median age of 34 years, with term employment contract. From the cluster analysis, two groups were identified with different characteristics and behaviors for the procedures of this protocol. Statistically significant results were found in: elements of cluster 1 (78% of the total participants) work in prisons for a longer time (p=0.003), 45,3% work > 4 years while 50% of the elements of cluster 2 work for less than a year, and more frequently answered they know and apply the procedures of the protocol (p=0.000). Both clusters answered frequently the need of having theoretical-practical training for TB (p=0.000), especially in the areas of diagnosis, treatment and prevention and that there is scarcity of funding to prisoner health services (p=0.000). Regarding procedures for TB screening (periodic and contact screening) and procedures for transferring a prisoner with this disease, cluster 1 also answered more frequently to perform them (p=0.000). They also referred that the material/equipment for TB screening is accessible and available (p=0.000). From this clusters we identified as barriers scarcity of human resources, the need to theoretical-practical training for tuberculosis, inexperience in working in health services prisons and limited knowledge of protocol procedures. Conclusions: The barriers found in this study are the same described internationally. This protocol is mostly being applied in portuguese prisons. The study also showed the need to invest in human and material resources. This investigation bridged gaps in knowledge that could help prison health services optimize the care provided for early detection and adherence of prisoners to treatment of tuberculosis.

Keywords: barriers, health care professionals, prisons, protocol, tuberculosis

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Authors: Aldin Justin Sundararaj, Austin Lord Tennyson, Divya Jose, A. N. Subash

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Blast waves are generated due to the explosions of high energy materials. An explosion yielding a blast wave has the potential to cause severe damage to buildings and its personnel. In order to understand the physics of effects of blast pressure on buildings, studies in the shock tube on generic configurations are carried out at various pressures on discrete models. The strength of shock wave is systematically varied by using different driver gases and diaphragm thickness. The basic material of the diaphragm is Aluminum. To simulate the effect of shock waves on discrete models a shock tube was used. Generic models selected for this study are suitably scaled cylinder, cone and cubical blocks. The experiments were carried out with 2mm diaphragm with burst pressure ranging from 28 to 31 bar. Numerical analysis was carried out over these discrete models. A 3D model of shock-tube with different discrete models inside the tube was used for CFD computation. It was found that cone has dissipated most of the shock pressure compared to cylinder and cubical block. The robustness and the accuracy of the numerical model were validation with the analytical and experimental data.

Keywords: shock wave, blast wave, discrete models, shock tube

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Authors: Priya Saha, Sourav Dey Roy Jr., Debotosh Bhattacharjee, Mita Nasipuri, Barin Kumar De, Mrinal Kanti Bhowmik

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Biometric face recognition technology (FRT) has gained a lot of attention due to its extensive variety of applications in both security and non-security perspectives. It has come into view to provide a secure solution in identification and verification of person identity. Although other biometric based methods like fingerprint scans, iris scans are available, FRT is verified as an efficient technology for its user-friendliness and contact freeness. Accurate facial feature localization plays an important role for many facial analysis applications including biometrics and emotion recognition. But, there are certain factors, which make facial feature localization a challenging task. On human face, expressions can be seen from the subtle movements of facial muscles and influenced by internal emotional states. These non-rigid facial movements cause noticeable alterations in locations of facial landmarks, their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper proposes a new hybrid based technique for automatic landmark detection in both neutral and expressive frontal and near frontal face images. The method uses the concept of thresholding, sequential searching and other image processing techniques for locating the landmark points on the face. Also, a Graphical User Interface (GUI) based software is designed that could automatically detect 16 landmark points around eyes, nose and mouth that are mostly affected by the changes in facial muscles. The proposed system has been tested on widely used JAFFE and Cohn Kanade database. Also, the system is tested on DeitY-TU face database which is created in the Biometrics Laboratory of Tripura University under the research project funded by Department of Electronics & Information Technology, Govt. of India. The performance of the proposed method has been done in terms of error measure and accuracy. The method has detection rate of 98.82% on JAFFE database, 91.27% on Cohn Kanade database and 93.05% on DeitY-TU database. Also, we have done comparative study of our proposed method with other techniques developed by other researchers. This paper will put into focus emotion-oriented systems through AU detection in future based on the located features.

Keywords: biometrics, face recognition, facial landmarks, image processing

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Authors: G. Sandhya Devi, G. Suvarna Kumar, S. Chandini

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This research work aims to develop a system that will analyze and identify students who indulge in malpractices/suspicious activities during the course of an academic offline examination. Automated Video Surveillance provides an optimal solution which helps in monitoring the students and identifying the malpractice event immediately. This work is organized into three modules. The first module deals with performing an impersonation check using a PCA-based face recognition method which is done by cross checking his profile with the database. The presence or absence of the student is even determined in this module by implementing an image registration technique wherein a grid is formed by considering all the images registered using the frontal camera at the determined positions. Second, detecting such facial malpractices in which a student gets involved in conversation with another, trying to obtain unauthorized information etc., based on the threshold range evaluated by considering his/her mouth state whether open or closed. The third module deals with identification of unauthorized material or gadgets used in the examination hall by training the positive samples of the object through various stages. Here, a top view camera feed is analyzed to detect the suspicious activities. The system automatically alerts the administration when any suspicious activities are identified, thereby reducing the error rate caused due to manual monitoring. This work is an improvement over our previous work published in identifying suspicious activities done by examinees in an offline examination.

Keywords: impersonation, image registration, incrimination, object detection, threshold evaluation

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

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Authors: M. R. Haji Hajikolaei, A. A. Nikvand, A. R. Ghadrdan, M. Ghorbanpoor, B. Mohammadian

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This study was carried out on water buffalo for detection of Leptospira interrogans in kidney and urine and its relationship with serological findings. Blood, urine and kidney samples were taken immediately after slaughter from 353 water buffalos at Ahvaz abattoir in Khouzestan province, Iran. Sera were initially screened at serum dilution of 1:100 against seven live antigens of Leptospira interrogans: pomona, hardjo, ballum, icterohemorrhagiae, tarasovi, australis and grippotyphosa using the microscopic agglutination test (MAT) and sera with positive results were titrated against reacting antigens in serial twofold dilution from 1:100 to 1:800. The samples of kidney were embedded in paraffin wax and 5µm thick sections were stained routinely with Haematoxylin and Eosin (H&E). Polymerase chain reaction (PCR) examination was done on urine and kidney by using LipL32 gene primers. Antibodies against one or more serovars at dilution >:100 were detected in sera. The most frequent reactor was hardjo (56.2%), followed by pomona (52.3%), australis (9.8%), tarassovi (5.9%), grippotyphosa (4.5%) and icterohaemorrhagiae (3.9%). The L. interrogans were detected in 43 (12.2%) of examined buffaloes, so that 26 (8.2%) of kidney tissues, 14 (4.8%) of urine samples separately and 3 (0.84%) of both kidney and urine samples were positive in PCR. From 153 (43.3%) buffaloes with positive MAT, 24 cases were positive by PCR of kidney and/or urine samples, synchronously. Renal lesions such as interstitial nephritis, acute tubular necrosis (ATN), pyelonephritis, glomerolonephritis, renal fibrosis and hydronephrosis were found in 128 (36.3%) cases. Statistical analysis indicated that there was no significant association between results of MAT, PCR and interstitial nephritis.

Keywords: leptospiral infection, PCR, MAT, histopathology, river buffalo

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Authors: Gaurav Kumar Sinha

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The oil and gas industry is a critical component of the global economy, relying heavily on industrial control systems (ICS) to manage and monitor operations. However, these systems are increasingly becoming targets for cyber-attacks, posing significant risks to operational continuity, safety, and environmental integrity. This paper explores comprehensive cybersecurity strategies for protecting oil and gas industrial control systems. It delves into the unique vulnerabilities of ICS in this sector, including outdated legacy systems, integration with IT networks, and the increased connectivity brought by the Industrial Internet of Things (IIoT). We propose a multi-layered defense approach that includes the implementation of robust network security protocols, regular system updates and patch management, advanced threat detection and response mechanisms, and stringent access control measures. We illustrate the effectiveness of these strategies in mitigating cyber risks and ensuring the resilient and secure operation of oil and gas industrial control systems. The findings underscore the necessity for a proactive and adaptive cybersecurity framework to safeguard critical infrastructure in the face of evolving cyber threats.

Keywords: cybersecurity, industrial control systems, oil and gas, cyber-attacks, network security, IoT, threat detection, system updates, patch management, access control, cybersecurity awareness, critical infrastructure, resilience, cyber threats, legacy systems, IT integration, multi-layered defense, operational continuity, safety, environmental integrity

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Authors: Zatovska T. V., Nesterova N. V., Baranova G. V., Zagorodnya S. D.

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In recent years, biosensor technologies based on the phenomenon of surface plasmon resonance (SPR) are becoming increasingly used in biology and medicine. Their application facilitates exploration in real time progress of binding of biomolecules and identification of agents that specifically interact with biologically active substances immobilized on the biosensor surface (biochips). Special attention is paid to the use of Biosensor analysis in determining the antibody-antigen interaction in the diagnostics of diseases caused by viruses and bacteria. According to WHO, the diseases that are caused by the herpes simplex virus (HSV), take second place (15.8%) after influenza as a cause of death from viral infections. Current diagnostics of HSV infection include PCR and ELISA assays. The latter allows determination the degree of immune response to viral infection and respective stages of its progress. In this regard, the searches for new and available diagnostic methods are very important. This work was aimed to develop Biosensor chip for detection of specific antibodies to HSV-1 in the human blood serum. The proteins of HSV1 (strain US) were used as antigens. The viral particles were accumulated in cell culture MDBK and purified by differential centrifugation in cesium chloride density gradient. Analysis of the HSV1 proteins was performed by polyacrylamide gel electrophoresis and ELISA. The protein concentration was measured using De Novix DS-11 spectrophotometer. The device for detection of antigen-antibody interactions was an optoelectronic two-channel spectrometer ‘Plasmon-6’, using the SPR phenomenon in the Krechman optical configuration. It was developed at the Lashkarev Institute of Semiconductor Physics of NASU. The used carrier was a glass plate covered with 45 nm gold film. Screening of human blood serums was performed using the test system ‘HSV-1 IgG ELISA’ (GenWay, USA). Development of Biosensor chip included optimization of conditions of viral antigen sorption and analysis steps. For immobilization of viral proteins 0.2% solution of Dextran 17, 200 (Sigma, USA) was used. Sorption of antigen took place at 4-8°C within 18-24 hours. After washing of chip, three times with citrate buffer (pH 5,0) 1% solution of BSA was applied to block the sites not occupied by viral antigen. It was found direct dependence between the amount of immobilized HSV1 antigen and SPR response. Using obtained biochips, panels of 25 positive and 10 negative for the content of antibodies to HSV-1 human sera were analyzed. The average value of SPR response was 185 a.s. for negative sera and from 312 to. 1264 a.s. for positive sera. It was shown that SPR data were agreed with ELISA results in 96% of samples proving the great potential of SPR in such researches. It was investigated the possibility of biochip regeneration and it was shown that application of 10 mM NaOH solution leads to rupture of intermolecular bonds. This allows reuse the chip several times. Thus, in this study biosensor chip for detection of specific antibodies to HSV1 was successfully developed expanding a range of diagnostic methods for this pathogen.

Keywords: biochip, herpes virus, SPR

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Authors: Maryam Torabi, Habibi, Abdolahi, Mohammadi, Hassanzadeh, Darban Maghami, Baghi

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Sheeppox Virus (SPPV) and goatpox virus (GTPV) are considerable diseases of sheep, and goats, caused by viruses of the Capripoxvirus (CaPV) genus. They are responsible for economic losses. Animal mortality, morbidity, cost of vaccinations, and restrictions in animal products’ trade are the reasons of economic losses. Control and eradication of CaPV depend on early detection of outbreaks so that molecular detection and genetic analysis could be effective to this aim. This study was undertaken to molecularly characterize SPPV and GTPV strains that have been circulating in Iran. 120 skin papules and nodule biopsies were collected from different regions of Iran and were examined for SPPV, GTPV viruses using TaqMan Real -Time PCR. Some of these amplified genes were sequenced, and phylogenetic trees were constructed. Out of the 120 samples analysed, 98 were positive for CaPV by Real- Time PCR (81.6%), and most of them wereSPPV. then 10 positive samples were sequenced and characterized by amplifying the ORF 103CaPV gene. sequencing and phylogenetic analysis for these positive samples revealed a high percentage of identity with SPPV isolated from different countries in Middle East. In conclusions, molecular characterization revealed nearly complete identity with all recent SPPVs strains in local countries that requires further studies to monitor the virus evolution and transmission pathways to better understand the virus pathobiology that will help for SPPV control.

Keywords: molecular epidemiology, Real-Time PCR, phylogenetic analysis, capripoxviruses

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