Search results for: edge detection method

Authors: Meenal Surawar, Rajashree Kotharkar

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Urban Heat Island (UHI) is found more pronounced as a prominent urban environmental concern in developing cities. To study the UHI effect in the Indian context, the Nagpur urban area has been explored in this paper using Landsat 7 ETM+ satellite images through Remote Sensing and GIS techniques. This paper intends to study the effect of LU/LC pattern on daytime Land Surface Temperature (LST) variation, contributing UHI formation within the Nagpur Urban area. Supervised LU/LC area classification was carried to study urban Change detection using ENVI 5. Change detection has been studied by carrying Normalized Difference Vegetation Index (NDVI) to understand the proportion of vegetative cover with respect to built-up ratio. Detection of spectral radiance from the thermal band of satellite images was processed to calibrate LST. Specific representative areas on the basis of urban built-up and vegetation classification were selected for observation of point LST. The entire Nagpur urban area shows that, as building density increases with decrease in vegetation cover, LST increases, thereby causing the UHI effect. UHI intensity has gradually increased by 0.7°C from 2000 to 2006; however, a drastic increase has been observed with difference of 1.8°C during the period 2006 to 2013. Within the Nagpur urban area, the UHI effect was formed due to increase in building density and decrease in vegetative cover.

Keywords: land use/land cover, land surface temperature, remote sensing, urban heat island

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Authors: Di Luo, Maria Buchberger, Anja Pickhard

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Objectives: The purpose of this study was to assess and compare the diagnostic accuracy of four common morphological approaches, including sonography, computed tomography (CT), positron emission tomography/computed tomography (PET/CT), and magnetic resonance imaging (MRI) for the evaluation of cervical lymph node metastases in head and neck squamous cell carcinoma (HNSCC) patients. Material and Methods: Included in this retrospective study were 26 patients diagnosed with HNSCC between 2010 and 2011 who all underwent sonography, CT, PET/CT, and MRI imaging before neck dissection. Morphological data were compared to the corresponding histopathological results. Statistical analysis was performed with SPSS statistic software (version 26.0), calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for detection of cervical lymph node metastases. Results: The 5-year survival rate of the patient collective was 55.5%.Risk factors for survival included initial primary tumor stage, initial lymph node stage, initial metastasis status, and therapeutic approaches. Cox regression showed initial metastasis status(HR 8.671, 95%CI 1.316-57.123, p=0.025) and therapeutic approaches(HR 6.699, 95%CI 1.746-25.700, p=0.006)to be independent predictive risk factors for survival. Sensitivity was highest for MRI (96% compared to 85% for sonography and 89% for CT and PET/CT). Specificity was comparable with 95 % for CT and 98 % for sonography and PET/CT, but only 68% for MRI. While the MRI showed the least PPV (34%) compared to all other methods (85% for sonography,75% for CT, and 86% for PET/CT), the NPV was comparable in all methods(98-99%). The overall accuracy of cervical lymph node metastases detection was comparable for sonography, CT, and PET/CT with 96%,97%,94%, respectively, while MRI had only 72% accuracy. Conclusion: Since the initial status of metastasis is an independent predictive risk factor for patients’ survival, efficient detection is crucial to plan adequate therapeutic approaches. Sonography, CT, and PET/CT have better diagnostic accuracy than MRI for the evaluation of cervical lymph node metastases in HNSCC patients.

Keywords: cervical lymph node metastases, diagnostic accuracy, head and neck squamous carcinoma, risk factors, survival

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

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Authors: Anahita Izadyar, My Ni Van, Kayleigh Amber Rodriguez, Ilwoo Seok, Elizabeth E. Hood

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Using a recombinant enzyme derived from corn and a simple modification, we fabricated a facile, fast, and cost-beneficial biosensor to measure glucose. The Nafion/ Plant Produced Mn Peroxidase (PPMP)– glucose oxidase (GOx)- Bovine serum albumin (BSA) /Au electrode showed an excellent amperometric response to detect glucose. This biosensor is capable of responding to a wide range of glucose—20.0 µM−15.0 mM and has a lower detection limit (LOD) of 2.90µM. The reproducibility response using six electrodes is also very substantial and indicates the high capability of this biosensor to detect a wide range of 3.10±0.19µM to 13.2±1.8 mM glucose concentration. Selectivity of this electrode was investigated in an optimized experimental solution contains 10% diet green tea with citrus containing ascorbic acid (AA), and citric acid (CA) in a wide concentration of glucose at 0.02 to 14.0mM with an LOD of 3.10µM. Reproducibility was also investigated using 4 electrodes in this sample and shows notable results in the wide concentration range of 3.35±0.45µM to of 13.0 ± 0.81 mM. We also used other voltammetry methods to evaluate this biosensor. We applied linear sweep voltammetry (LSV) and this technique shows a wide range of 0.10−15.0 mM to detect glucose with a lower detection limit of 19.5µM. The performance and strength of this enzyme biosensor were the simplicity, wide linear ranges, sensitivities, selectivity, and low limits of detection. We expect that the modified biosensor has the potential for monitoring various biofluids.

Keywords: plant-produced manganese peroxidase, enzyme-based biosensors, glucose, modified gold electrode, glucose oxidase

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Authors: A. Boulanoir, B. Ould Bouamama, A. Debiane, N. Achour

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The paper deals with robust Fault Detection and isolation with respect to parameter uncertainties based on linear fractional transformation form (LFT) Bond graph. The innovative interest of the proposed methodology is the use only one representation for systematic generation of robust analytical redundancy relations and adaptive residual thresholds for sensibility analysis. Furthermore, the parameter uncertainties are introduced graphically in the bond graph model. The methodology applied to the nonlinear industrial Electro-Mechanical Actuators (EMA) used in avionic systems, has determined first the structural monitorability analysis (which component can be monitored) with given instrumentation architecture with any need of complex calculation and secondly robust fault indicators for online supervision.

Keywords: bond graph (BG), electro mechanical actuators (EMA), fault detection and isolation (FDI), linear fractional transformation (LFT), mechatronic systems, parameter uncertainties, avionic system

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Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: T. V. Dinh, I. Y. Choi, J. W. Ahn, Y. H. Oh, G. Bo, J. Y. Lee, J. C. Kim

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A Non-Dispersive Infrared (NDIR) multi-gas analyzer has been developed to monitor the emission of carbon monoxide (CO) and sulfur dioxide (SO2) from various industries. The NDIR technique for gas measurement is based on the wavelength absorption in the infrared spectrum as a way to detect particular gasses. NDIR analyzers have popularly applied in the Tele-Monitoring System (TMS). The advantage of the NDIR analyzer is low energy consumption and cost compared with other spectroscopy methods. However, zero/span drift and interference are its urgent issues to be solved. Multi-pathway technique based on optical White cell was employed to improve the sensitivity of the analyzer in this work. A pyroelectric detector was used to detect the Infrared radiation. The analytical range of the analyzer was 0 ~ 200 ppm. The instrument response time was < 2 min. The detection limits of CO and SO2 were < 4 ppm and < 6 ppm, respectively. The zero and span drift of 24 h was less than 3%. The linearity of the analyzer was less than 2.5% of reference values. The precision and accuracy of both CO and SO2 channels were < 2.5% of relative standard deviation. In general, the analyzer performed well. However, the detection limit and 24h drift should be improved to be a more competitive instrument.

Keywords: analyzer, CEMS, monitoring, NDIR, TMS

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Authors: Atiwit Sinyoo, Sekh Thanprasertsuk, Sithiporn Agthong, Pasakorn Watanatada, Shaun Peter Qureshi, Saknan Bongsebandhu-Phubhakdi

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Background: Since the SARS-CoV-2 virus became extensively disseminated throughout the world, online learning has become one of the most hotly debated topics in educational reform. While some studies have already shown the advantage of online learning, there are still questions concerning how online learning affects students’ learning behavior and academic achievement when each student learns in a different way. Hence, we aimed to develop a guide for preclinical medical students to avoid drawbacks and get benefits from online learning that possibly a double-edged sword. Methods: We used a multiple-choice questionnaire to evaluate the learning behavior of second-year Thai medical students in the neuroscience course. All traditional face-to-face lecture classes were video-recorded and promptly posted to the online learning platform throughout this course. Students could pick and choose whatever classes they wanted to attend, and they may use online learning as often as they wished. Academic performance was evaluated as summative score, spot exam score and pre-post-test improvement. Results: More frequently students used online learning platform, the less they attended lecture classes (P = 0.035). High proactive online learners (High PO) who were irregular attendee (IrA) had significantly lower summative scores (P = 0.026), spot exam score (P = 0.012) and pre-post-test improvement (P = 0.036). In the meanwhile, conditional attendees (CoA) who only attended classes with attendance check had significantly higher summative score (P = 0.025) and spot exam score (P = 0.001) if they were in the High PO group. Conclusions: The benefit and drawbacks edges of using an online learning platform were demonstrated in our research. Based on this double-edged sword effect, we believe that online learning is a valuable learning strategy, but students must carefully plan their study schedule to gain the “benefit edge” meanwhile avoiding its “drawback edge”.

Keywords: academic performance, assessment, attendance, online learning, preclinical medical students

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Authors: Balthazar Temu, Zhao Yan, Bogdan-Petrin Ratiu, Sang Soon Oh, Qiang Li

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Nanowire based Photonic Crystal Surface Emitting Lasers (PCSELs) reported in the literature have been designed using a triangular, square or honeycomb patterns. The triangular and square pattern PCSELs have limited degrees of freedom in tuning the design parameters which hinders the ability to design high quality factor (Q-factor) devices. Nanowire based PCSELs designed using triangular and square patterns have been reported with the lasing thresholds of 130 kW/〖cm〗^2 and 7 kW/〖cm〗^2 respectively. On the other hand the honeycomb pattern gives more degrees of freedom in tuning the design parameters, which can allow one to design high Q-factor devices. A deformed honeycomb pattern device was reported with lasing threshold of 6.25 W/〖cm〗^2 corresponding to a simulated Q-factor of 5.84X〖10〗^5.Despite this achievement, the design principles which can lead to realization of even higher Q-factor honeycomb pattern PCSELs have not yet been investigated. In this work we show that through deforming the honeycomb pattern and tuning the heigh and lattice constants of the nanowires, it is possible to achieve even higher Q-factor devices. Considering three different band edge modes, we investigate how the resonance wavelength changes as the device is deformed, which is useful in designing high Q-factor devices in different wavelength bands. We eventually establish the design and simulation of honeycomb PCSELs operating around the wavelength of 960nm , in the O and the C band with Q-factors up to 7X〖10〗^7. We also investigate the Q-factors of undeformed device, and establish that the mode at the band edge close to 960nm can attain highest Q-factor of all the modes when the device is undeformed and the Q-factor degrades as the device is deformed. This work is a stepping stone towards the fabrication of very high Q-factor, nanowire based honey comb PCSELs, which are expected to have very low lasing threshold.

Keywords: designing nanowire PCSEL, designing PCSEL on silicon substrates, low threshold nanowire laser, simulation of photonic crystal lasers

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Ebru Cubukcu, Gozde Eksioglu Cetintahra, Burcin Hepguzel Hatip, Mert Cubukcu

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Obesity and overweight cause serious health problems. Public and private organizations aim to encourage walking in various ways in order to cope with the problem of obesity and overweight. This study aims to understand how the spatial characteristics of urban street pattern, connectivity and complexity influence young people’s choice to walk or not. 185 public university students in Izmir, the third largest city in Turkey, participated in the study. Each participant had worn an accelerometer and a global positioning (GPS) device for a week. The accelerometer device records data on the intensity of the participant’s activity at a specified time interval, and the GPS device on the activities’ locations. Combining the two datasets, activity maps are derived. These maps are then used to differentiate the participants’ walk trips and motor vehicle trips. Given that, the frequency of walk and motor vehicle trips are calculated at the street segment level, and the street segments are then categorized into two as ‘preferred by pedestrians’ and ‘preferred by motor vehicles’. Graph Theory-based accessibility indices are calculated to quantify the spatial characteristics of the streets in the sample. Six different indices are used: (I) edge density, (II) edge sinuosity, (III) eta index, (IV) node density, (V) order of a node, and (VI) beta index. T-tests show that the index values for the ‘preferred by pedestrians’ and ‘preferred by motor vehicles’ are significantly different. The findings indicate that the spatial characteristics of the street network have a measurable effect on young people’s choice to walk or not. Policy implications are discussed. This study is funded by the Scientific and Technological Research Council of Turkey, Project No: 116K358.

Keywords: graph theory, walkability, accessibility, street network

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Authors: B. Chikh, L. Moussa, H. Bechtoula, Y. Mehani, A. Zerzour

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This study presents a new method, applicable to evaluation and design of structures has been developed and illustrated by comparison with the capacity spectrum method (CSM, ATC-40). This method uses inelastic spectra and gives peak responses consistent with those obtained when using the nonlinear time history analysis. Hereafter, the seismic demands assessment method is called in this paper DSM, Ductility Spectrum Method. It is used to estimate the seismic deformation of Single-Degree-Of-Freedom (SDOF) systems based on DDRS, Ductility Demand Response Spectrum, developed by the author.

Keywords: seismic demand, capacity, inelastic spectra, design and structure

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Authors: Ching-Sung Wang, Teng-Wei Wang, Zhen-Ting Zheng

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This study mainly concerns a safety device designed for child care. When children are out of sight or the caregivers cannot always pay attention to the situation, through the functions of this device, caregivers can immediately be informed to make sure that the children do not get lost or hurt, and thus, ensure their safety. Starting from this concept, a device is produced based on the relatively low-cost Bluetooth piconet system and a three-axis gyroscope sensor. This device can transmit data to a mobile phone app through Bluetooth, in order that the user can learn the situation at any time. By simply clipping the device in a pocket or on the waist, after switching on/starting the device, it will send data to the phone to detect the child’s fall and distance. Once the child is beyond the angle or distance set by the app, it will issue a warning to inform the phone owner.

Keywords: children care, piconet system, three-axis gyroscope, distance detection, falls detection

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Mahshid Arabi

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With the rapid expansion of digital technologies and the internet, information security has become a critical priority for organizations and individuals. The widespread use of digital tools such as smartphones and internet networks facilitates the storage of vast amounts of data, but simultaneously, vulnerabilities and security threats have significantly increased. The aim of this study is to examine and analyze modern methods of information security management and to develop a comprehensive model to counteract threats and information misuse. This study employs a mixed-methods approach, including both qualitative and quantitative analyses. Initially, a systematic review of previous articles and research in the field of information security was conducted. Then, using the Delphi method, interviews with 30 information security experts were conducted to gather their insights on security challenges and solutions. Based on the results of these interviews, a comprehensive model for information security management was developed. The proposed model includes advanced encryption techniques, machine learning-based intrusion detection systems, and network security protocols. AES and RSA encryption algorithms were used for data protection, and machine learning models such as Random Forest and Neural Networks were utilized for intrusion detection. Statistical analyses were performed using SPSS software. To evaluate the effectiveness of the proposed model, T-Test and ANOVA statistical tests were employed, and results were measured using accuracy, sensitivity, and specificity indicators of the models. Additionally, multiple regression analysis was conducted to examine the impact of various variables on information security. The findings of this study indicate that the comprehensive proposed model reduced cyber-attacks by an average of 85%. Statistical analysis showed that the combined use of encryption techniques and intrusion detection systems significantly improves information security. Based on the obtained results, it is recommended that organizations continuously update their information security systems and use a combination of multiple security methods to protect their data. Additionally, educating employees and raising public awareness about information security can serve as an effective tool in reducing security risks. This research demonstrates that effective and up-to-date information security management requires a comprehensive and coordinated approach, including the development and implementation of advanced techniques and continuous training of human resources.

Keywords: data protection, digital technologies, information security, modern management

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Authors: Shin Yoneda, Mikiya Kato, Kosuke Murate, Kodo Kawase

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In recent years, there have been numerous attempts to smuggle narcotic drugs and chemicals by concealing them in international mail. Combatting this requires a non-destructive technique that can identify such illicit substances in mail. Terahertz (THz) waves can pass through a wide variety of materials, and many chemicals show specific frequency-dependent absorption, known as a spectral fingerprint, in the THz range. Therefore, it is reasonable to investigate non-destructive mail inspection techniques that use THz waves. For this reason, in this work, we tried to identify reagents under high attenuation shielding materials using injection-seeded THz-wave parametric generator (is-TPG). Our THz spectroscopic imaging system using is-TPG consisted of two non-linear crystals for emission and detection of THz waves. A micro-chip Nd:YAG laser and a continuous wave tunable external cavity diode laser were used as the pump and seed source, respectively. The pump beam and seed beam were injected to the LiNbO₃ crystal satisfying the noncollinear phase matching condition in order to generate high power THz-wave. The emitted THz wave was irradiated to the sample which was raster scanned by the x-z stage while changing the frequencies, and we obtained multispectral images. Then the transmitted THz wave was focused onto another crystal for detection and up-converted to the near infrared detection beam based on nonlinear optical parametric effects, wherein the detection beam intensity was measured using an infrared pyroelectric detector. It was difficult to identify reagents in a cardboard box because of high noise levels. In this work, we introduce improvements for noise reduction and image clarification, and the intensity of the near infrared detection beam was converted correctly to the intensity of the THz wave. A Gaussian spatial filter is also introduced for a clearer THz image. Through these improvements, we succeeded in identification of reagents hidden in a 42-mm thick cardboard box filled with several obstacles, which attenuate 56 dB at 1.3 THz, by improving analysis methods. Using this system, THz spectroscopic imaging was possible for saccharides and may also be applied to cases where illicit drugs are hidden in the box, and multiple reagents are mixed together. Moreover, THz spectroscopic imaging can be achieved through even thicker obstacles by introducing an NIR detector with higher sensitivity.

Keywords: nondestructive inspection, principal component analysis, terahertz parametric source, THz spectroscopic imaging

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Authors: Katherine Madero Valencia, Carlos Jaramillo, Elida Dueñas, Carlos Torres, María Del Pilar Delgado

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Asthma, described as a chronic inflammatory condition of the airways, courses accompanied by episodes known as exacerbations, characterized by a worsening of symptoms. Among the triggers, some allergen-irritative and infectious agents are found, including Chlamydophila pneumoniae which seems to play an increasingly important role. In this paper a PCR was used to detect C. pneumoniae in order to estimate the frequency of infections caused by this agent in pediatric patients with asthma exacerbations. C. pneumoniae distribution throughout the study period was also evaluated. 175 nasopharyngeal aspirates from children with asthma exacerbations were analyzed by PCR and sequencing. A global prevalence of C. pneumoniae of 53.71% was obtained. This study highlights a high circulation of C. pneumoniae during the study period, in children of all ages and especially in children under 5 years old. Molecular tests applied permit a rapid detection and improved our knowledge about these infections in children with asthma.

Keywords: Chlamydophila pneumoniae, detection, molecular techniques, pediatric asthma

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Authors: Hanouf A. S. Al Nuwaysir, Nadine Moubayed, Abir Ben Bacha, Islem Abid

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Consumption of waste water continues to cause significant problems for human health in both developed and developing countries. Many regulations have been implied by different world authorities controlling water quality for the presence of coliforms used as standard indicators of water quality deterioration and historically leading health protection concept. In this study, the European directive for the detection of Escherichia coli, ISO 9308-1, was applied to examine and monitor coliforms in water samples collected from Wadi Hanifa and neighboring wells, Riyadh governorate, kingdom of Saudi Arabia, which is used for irrigation and industrial purposes. Samples were taken from different locations for 8 months consecutively, chlorine concentration ranging from 0.1- 0.4 mg/l, was determined using the DPD FREE CHLORINE HACH kit. Water samples were then analyzed following the ISO protocol which relies on the membrane filtration technique (0.45µm, pore size membrane filter) and a chromogenic medium TTC, a lactose based medium used for the detection and enumeration of total coliforms and E.coli. Data showed that the number of bacterial isolates ranged from 60 to 300 colonies/100ml for well and surface water samples respectively; where higher numbers were attributed to the surface samples. Organisms which apparently ferment lactose on TTC agar plates, appearing as orange colonies, were selected and additionally cultured on EMB and MacConkey agar for a further differentiation among E.coli and coliform bacteria. Two additional biochemical tests (Cytochrome oxidase and indole from tryptophan) were also investigated to detect and differentiate the presence of E.coli from other coliforms, E. coli was identified in an average of 5 to 7colonies among 25 selected colonies.On the other hand, a more rapid, specific and sensitive analytical molecular detection namely single colony PCR was also performed targeting hha gene to sensitively detect E.coli, giving more accurate and time consuming identification of colonies considered presumptively as E.coli. Comparative methodologies, such as ultrafiltration and direct DNA extraction from membrane filters (MoBio, Grermany) were also applied; however, results were not as accurate as the membrane filtration, making it a technique of choice for the detection and enumeration of water coliforms, followed by sufficiently specific enzymatic confirmatory stage.

Keywords: coliform, cytochrome oxidase, hha primer, membrane filtration, single colony PCR

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Authors: Jignasha Derasari, Patel Krishna M, Modi Jignasa G.

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Chemical stability of pharmaceutical molecules is a matter of great concern as it affects the safety and efficacy of the drug product.Stability testing data provides the basis to understand how the quality of a drug substance and drug product changes with time under the influence of various environmental factors. Besides this, it also helps in selecting proper formulation and package as well as providing proper storage conditions and shelf life, which is essential for regulatory documentation. The ICH guideline states that stress testing is intended to identify the likely degradation products which further help in determination of the intrinsic stability of the molecule and establishing degradation pathways, and to validate the stability indicating procedures. A simple, accurate and precise stability indicating RP- HPLC method was developed and validated for simultaneous estimation of Amiloride Hydrochloride and Furosemide in tablet dosage form. Separation was achieved on an Phenomenexluna ODS C18 (250 mm × 4.6 mm i.d., 5 µm particle size) by using a mobile phase consisting of Ortho phosphoric acid: Acetonitrile (50:50 %v/v) at a flow rate of 1.0 ml/min (pH 3.5 adjusted with 0.1 % TEA in Water) isocratic pump mode, Injection volume 20 µl and wavelength of detection was kept at 283 nm. Retention time for Amiloride Hydrochloride and Furosemide was 1.810 min and 4.269 min respectively. Linearity of the proposed method was obtained in the range of 40-60 µg/ml and 320-480 µg/ml and Correlation coefficient was 0.999 and 0.998 for Amiloride hydrochloride and Furosemide, respectively. Forced degradation study was carried out on combined dosage form with various stress conditions like hydrolysis (acid and base hydrolysis), oxidative and thermal conditions as per ICH guideline Q2 (R1). The RP- HPLC method has shown an adequate separation for Amiloride hydrochloride and Furosemide from its degradation products. Proposed method was validated as per ICH guidelines for specificity, linearity, accuracy; precision and robustness for estimation of Amiloride hydrochloride and Furosemide in commercially available tablet dosage form and results were found to be satisfactory and significant. The developed and validated stability indicating RP-HPLC method can be used successfully for marketed formulations. Forced degradation studies help in generating degradants in much shorter span of time, mostly a few weeks can be used to develop the stability indicating method which can be applied later for the analysis of samples generated from accelerated and long term stability studies. Further, kinetic study was also performed for different forced degradation parameters of the same combination, which help in determining order of reaction.

Keywords: amiloride hydrochloride, furosemide, kinetic study, stability indicating RP-HPLC method validation

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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Authors: Paul Okonkwo, Howard Smith

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A study to estimate the size of the cabin and major aircraft components as well as detect and avoid interference between internally placed components and the external surface, during the conceptual design synthesis and optimisation to explore the design space of a BWB, was conducted. Sizing of components follows the Bradley cabin sizing and rubber engine scaling procedures to size the cabin and engine respectively. The interference detection and avoidance algorithm relies on the ability of the Class Shape Transform parameterisation technique to generate polynomial functions of the surfaces of a BWB aircraft configuration from the sizes of the cabin and internal objects using few variables. Interference detection is essential in packaging of non-conventional configuration like the BWB because of the non-uniform airfoil-shaped sections and resultant varying internal space. The unique configuration increases the need for a methodology to prevent objects from being placed in locations that do not sufficiently enclose them within the geometry.

Keywords: packaging, optimisation, BWB, parameterisation, aircraft conceptual design

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Authors: Petra Hypšová, Martin Seitl

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One of the challenges of forensic psychology is to detect deception during a face-to-face interview. In addition to the classical approaches of monitoring the utterance and its components, detection is also sought by observing behavioral and physiological changes that occur as a result of the increased emotional and cognitive load caused by the production of distorted information. Typical are changes in facial temperature, eye movements and their fixation, pupil dilation, emotional micro-expression, heart rate and its variability. Expanding technological capabilities have opened the space to detect these psychophysiological changes and behavioral manifestations through non-contact technologies that do not interfere with face-to-face interaction. Non-contact deception detection methodology is still in development, and there is a lack of studies that combine multiple non-contact technologies to investigate their accuracy, as well as studies that show how different types of lies produced by different interviewers affect physiological and behavioral changes. The main objective of this study is to apply a specific non-contact technology for deception detection. The next objective is to investigate scenarios in which non-contact deception detection is possible. A series of psychophysiological experiments using infrared thermal imaging, eye tracking and behavioral analysis with FaceReader 9.0 software was used to achieve our goals. In the laboratory experiment, 16 adults (12 women, 4 men) between 18 and 35 years of age (SD = 4.42) were instructed to produce alternating prepared and spontaneous truths and lies. The baseline of each proband was also measured, and its results were compared to the experimental conditions. Because the personality of the examiner (particularly gender and facial appearance) to whom the subject is lying can influence physiological and behavioral changes, the experiment included four different interviewers. The interviewer was represented by a photograph of a face that met the required parameters in terms of gender and facial appearance (i.e., interviewer likability/antipathy) to follow standardized procedures. The subject provided all information to the simulated interviewer. During follow-up analyzes, facial temperature (main ROIs: forehead, cheeks, the tip of the nose, chin, and corners of the eyes), heart rate, emotional expression, intensity and fixation of eye movements and pupil dilation were observed. The results showed that the variables studied varied with respect to the production of prepared truths and lies versus the production of spontaneous truths and lies, as well as the variability of the simulated interviewer. The results also supported the assumption of variability in physiological and behavioural values during the subject's resting state, the so-called baseline, and the production of prepared and spontaneous truths and lies. A series of psychophysiological experiments provided evidence of variability in the areas of interest in the production of truths and lies to different interviewers. The combination of technologies used also led to a comprehensive assessment of the physiological and behavioral changes associated with false and true statements. The study presented here opens the space for further research in the field of lie detection with non-contact technologies.

Keywords: emotional expression decoding, eye-tracking, functional infrared thermal imaging, non-contact deception detection, psychophysiological experiment

Procedia PDF Downloads 101

Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

Procedia PDF Downloads 133

Authors: Shiuh-Jer Huang, Yu-Sheng Hsu

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On-board available parking space detecting system, parking trajectory planning and tracking control mechanism are the key components of vehicle backward auto-parking system. Firstly, pair of ultrasonic sensors is installed on each side of vehicle body surface to detect the relative distance between ego-car and surrounding obstacle. The dimension of a found empty space can be calculated based on vehicle speed and the time history of ultrasonic sensor detecting information. This result can be used for constructing the 2D vehicle environmental map and available parking type judgment. Finally, the auto-parking controller executes the on-line optimal parking trajectory planning based on this 2D environmental map, and monitors the real-time vehicle parking trajectory tracking control. This low cost auto-parking system was tested on a model car.

Keywords: vehicle auto-parking, parking space detection, parking path tracking control, intelligent fuzzy controller

Procedia PDF Downloads 246

Authors: Rabie Fadil, Parshuram Aarotale, Shubha Majumder, Bijay Guargain

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Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 ± 6 year, weight:73 ± 11 kg, height:172 ± 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85±4 bpm vs 68 ± 5bpm, p < 0.01), ST (191 ± 10 ms vs 253 ± 31 ms, p < 0.05), LVET (350 ± 14 ms vs 479 ± 47 ms, p < 0.05) and DT (551 ± 22 ms vs 683 ± 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage.

Keywords: blood pressure, hemorrhage, lower-body negative pressure, LBNP, machine learning

Procedia PDF Downloads 168

Authors: Dalwinder Singh, Amandeep Verma, Manpreet Kaur, Birmohan Singh

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The detection of pre-cancerous changes using a Pap smear test of cervical cell is the important step for the early diagnosis of cervical cancer. The Pap smear test consists of a sample of human cells taken from the cervix which are analysed to detect cancerous and pre-cancerous stage of the given subject. The manual analysis of these cells is labor intensive and time consuming process which relies on expert cytotechnologist. In this paper, a computer assisted system for the automated analysis of the cervical cells has been proposed. We propose a morphology based approach to the nucleus detection and segmentation of the cytoplasmic region of the given single or multiple overlapped cell. Further, various texture and region based features are calculated from these cells to classify these into normal and abnormal cell. Experimental results on public available dataset show that our system has achieved satisfactory success rate.

Keywords: cervical cancer, cervical tissue, mathematical morphology, texture features

Procedia PDF Downloads 530

Authors: Jaeyoung Kim, Heeju Lee, Huijin Jung, Heesoo Pyo, Seungki Kim, Joonseok Lee

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Avian influenza viruses (AIV) are the primary cause of highly contagious respiratory diseases caused by type A influenza viruses of the Orthomyxoviridae family. AIV are categorized on the basis of types of surface glycoproteins such as hemagglutinin and neuraminidase. Certain H5 and H7 subtypes of AIV have evolved to the high pathogenic avian influenza (HPAI) virus, which has caused considerable economic loss to the poultry industry and led to severe public health crisis. Several commercial kits have been developed for on-site detection of AIV. However, the sensitivity of these methods is too low to detect low virus concentrations in clinical samples and opaque stool samples. Here, we introduced a background-free near-infrared (NIR)-to-NIR upconversion nanoparticle-based lateral flow immunoassay (NNLFA) platform to yield a sensor that detects AIV within 20 minutes. Ca²⁺ ion in the shell was used to enhance the NIR-to-NIR upconversion photoluminescence (PL) emission as a heterogeneous dopant without inducing significant changes in the morphology and size of the UCNPs. In a mixture of opaque stool samples and gold nanoparticles (GNPs), which are components of commercial AIV LFA, the background signal of the stool samples mask the absorption peak of GNPs. However, UCNPs dispersed in the stool samples still show strong emission centered at 800 nm when excited at 980 nm, which enables the NNLFA platform to detect 10-times lower viral load than a commercial GNP-based AIV LFA. The detection limit of NNLFA for low pathogenic avian influenza (LPAI) H5N2 and HPAI H5N6 viruses was 10² EID₅₀/mL and 10³.⁵ EID₅₀/mL, respectively. Moreover, when opaque brown-colored samples were used as the target analytes, strong NIR emission signal from the test line in NNLFA confirmed the presence of AIV, whereas commercial AIV LFA detected AIV with difficulty. Therefore, we propose that this rapid and background-free NNLFA platform has the potential of detecting AIV in the field, which could effectively prevent the spread of these viruses at an early stage.

Keywords: avian influenza viruses, lateral flow immunoassay on-site detection, upconversion nanoparticles

Procedia PDF Downloads 166

Authors: Hiroyuki Aoki

Abstract:

The physical properties of polymer materials are dependent on the conformation and molecular motion of a polymer chain. Therefore, the structure and dynamic behavior of the single polymer chain have been the most important concerns in the field of polymer physics. However, it has been impossible to directly observe the conformation of the single polymer chain in a bulk medium. In the current work, the novel techniques to study the conformation and dynamics of a single polymer chain are proposed. Since a fluorescence method is extremely sensitive, the fluorescence microscopy enables the direct detection of a single molecule. However, the structure of the polymer chain as large as 100 nm cannot be resolved by conventional fluorescence methods because of the diffraction limit of light. In order to observe the single chains, we developed the labeling method of polymer materials with a photo-switchable dye and the super-resolution microscopy. The real-space conformational analysis of single polymer chains with the spatial resolution of 15-20 nm was achieved. The super-resolution microscopy enables us to obtain the three-dimensional coordinates; therefore, we succeeded the conformational analysis in three dimensions. The direct observation by the nanometric optical microscopy would reveal the detailed information on the molecular processes in the various polymer systems.

Keywords: polymer materials, single molecule, super-resolution techniques, conformation

Procedia PDF Downloads 308

Authors: Hadi Rouhi Belvirdi

Abstract:

The construction of underground structures using the traditional method, which begins with excavation and the implementation of the foundation of the underground structure, continues with the construction of the main structure from the ground up, and concludes with the completion of the final ceiling, is known as the Bottom-Up Method. In contrast to this method, there is an advanced technique called the Top-Down Method, which has practically replaced the traditional construction method in large projects in industrialized countries in recent years. Unlike the traditional approach, this method starts with the construction of surrounding walls, columns, and the final ceiling and is completed with the excavation and construction of the foundation of the underground structure. Some of the most significant advantages of this method include the elimination or minimization of formwork surfaces, the removal of temporary bracing during excavation, the creation of some traffic facilities during the construction of the structure, and the possibility of using it in limited and high-traffic urban spaces. Despite these numerous advantages, unfortunately, there is still insufficient awareness of this method in our country, to the extent that it can be confidently stated that most stakeholders in the construction industry are unaware of the existence of such a construction method. However, it can be utilized as a very important execution option alongside other conventional methods in the construction of underground structures. Therefore, due to the extensive practical capabilities of this method, this article aims to present a methodology for constructing underground structures based on the aforementioned advanced method to the scientific community of the country, examine the advantages and limitations of this method and their impacts on time and costs, and discuss its application in urban spaces. Finally, some underground structures executed in the Ahvaz urban rail, which are being implemented using this advanced method to the best of our best knowledge, will be introduced.

Keywords: top-down method, bottom-up method, underground structure, construction method

Procedia PDF Downloads 17