Search results for: stiffness detection
2946 DEEPMOTILE: Motility Analysis of Human Spermatozoa Using Deep Learning in Sri Lankan Population
Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya
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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa
Procedia PDF Downloads 1052945 Evidence Theory Enabled Quickest Change Detection Using Big Time-Series Data from Internet of Things
Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker
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Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.Keywords: CUSUM, evidence theory, kl divergence, quickest change detection, time series data
Procedia PDF Downloads 3342944 Automatic Detection and Classification of Diabetic Retinopathy Using Retinal Fundus Images
Authors: A. Biran, P. Sobhe Bidari, A. Almazroe, V. Lakshminarayanan, K. Raahemifar
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Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.Keywords: diabetic retinopathy, fundus images, STARE, Gabor filter, support vector machine
Procedia PDF Downloads 2932943 Effect of Different Knee-Joint Positions on Passive Stiffness of Medial Gastrocnemius Muscle and Aponeuroses during Passive Ankle Motion
Authors: Xiyao Shan, Pavlos Evangelidis, Adam Kositsky, Naoki Ikeda, Yasuo Kawakami
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The human triceps surae (two bi-articular gastrocnemii and one mono-articular soleus) have aponeuroses in the posterior and anterior aspects of each muscle, where the anterior aponeuroses of the gastrocnemii adjoin the posterior aponeurosis of the soleus, possibly contributing to the intermuscular force transmission between gastrocnemii and soleus. Since the mechanical behavior of these aponeuroses at different knee- and ankle-joint positions remains unclear, the purpose of this study was to clarify this through observations of the localized changes in passive stiffness of the posterior aponeuroses, muscle belly and adjoining aponeuroses of the medial gastrocnemius (MG) induced by different knee and ankle angles. Eleven healthy young males (25 ± 2 yr, 176.7 ± 4.7 cm, 71.1 ± 11.1 kg) participated in this study. Each subject took either a prone position on an isokinetic dynamometer while the knee joint was fully extended (K180) or a kneeling position while the knee joint was 90° flexed (K90), in a randomized and counterbalanced order. The ankle joint was then passively moved through a 50° range of motion (ROM) by the dynamometer from 30° of plantar flexion (PF) to 20° of dorsiflexion (DF) at 2°/s and the ultrasound shear-wave velocity was measured to obtain shear moduli of the posterior aponeurosis, MG belly, and adjoining aponeuroses. The main findings were: 1) shear modulus in K180 was significantly higher (p < 0.05) than K90 for the posterior aponeurosis (across all ankle angles, 10.2 ± 5.7 kPa-59.4 ± 28.7 kPa vs. 5.4 ± 2.2 kPa-11.6 ± 4.1 kPa), MG belly (from PF10° to DF20°, 9.7 ± 2.2 kPa-53.6 ± 18.6 kPa vs. 8.0 ± 2.7 kPa-9.5 ± 3.7 kPa), and adjoining aponeuroses (across all ankle angles, 17.3 ± 7.8 kPa-80 ± 25.7 kPa vs. 12.2 ± 4.5 kPa-52.4 ± 23.0 kPa); 2) shear modulus of the posterior aponeuroses significantly increased (p < 0.05) from PF10° to PF20° in K180, while shear modulus of MG belly significantly increased (p < 0.05) from 0° to PF20° only in K180 and shear modulus of adjoining aponeuroses significantly increased (p < 0.05) across the whole ROM of ankle both in K180 and K90. These results suggest that different knee-joint positions can affect not only the bi-articular gastrocnemius but also influence the mechanical behavior of aponeuroses. In addition, compared to the gradual stiffening of the adjoining aponeuroses across the whole ROM of ankle, the posterior aponeurosis became slack in the plantar flexed positions and then was stiffened gradually as the knee was fully extended. This suggests distinct stiffening for the posterior and adjoining aponeuroses which is joint position-dependent.Keywords: aponeurosis, plantar flexion and dorsiflexion, shear modulus, shear wave elastography
Procedia PDF Downloads 1882942 A Smartphone-Based Real-Time Activity Recognition and Fall Detection System
Authors: Manutchanok Jongprasithporn, Rawiphorn Srivilai, Paweena Pongsopha
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Fall is the most serious accident leading to increased unintentional injuries and mortality. Falls are not only the cause of suffering and functional impairments to the individuals, but also the cause of increasing medical cost and days away from work. The early detection of falls could be an advantage to reduce fall-related injuries and consequences of falls. Smartphones, embedded accelerometer, have become a common device in everyday life due to decreasing technology cost. This paper explores a physical activity monitoring and fall detection application in smartphones which is a non-invasive biomedical device to determine physical activities and fall event. The combination of application and sensors could perform as a biomedical sensor to monitor physical activities and recognize a fall. We have chosen Android-based smartphone in this study since android operating system is an open-source and no cost. Moreover, android phone users become a majority of Thai’s smartphone users. We developed Thai 3 Axis (TH3AX) as a physical activities and fall detection application which included command, manual, results in Thai language. The smartphone was attached to right hip of 10 young, healthy adult subjects (5 males, 5 females; aged< 35y) to collect accelerometer and gyroscope data during performing physical activities (e.g., walking, running, sitting, and lying down) and falling to determine threshold for each activity. Dependent variables are including accelerometer data (acceleration, peak acceleration, average resultant acceleration, and time between peak acceleration). A repeated measures ANOVA was performed to test whether there are any differences between DVs’ means. Statistical analyses were considered significant at p<0.05. After finding threshold, the results were used as training data for a predictive model of activity recognition. In the future, accuracies of activity recognition will be performed to assess the overall performance of the classifier. Moreover, to help improve the quality of life, our system will be implemented with patients and elderly people who need intensive care in hospitals and nursing homes in Thailand.Keywords: activity recognition, accelerometer, fall, gyroscope, smartphone
Procedia PDF Downloads 6912941 The Combination Of Aortic Dissection Detection Risk Score (ADD-RS) With D-dimer As A Diagnostic Tool To Exclude The Diagnosis Of Acute Aortic Syndrome (AAS)
Authors: Mohamed Hamada Abdelkader Fayed
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Background: To evaluate the diagnostic accuracy of (ADD-RS) with D-dimer as a screening test to exclude AAS. Methods: We conducted research for the studies examining the diagnostic accuracy of (ADD- RS)+ D-dimer to exclude the diagnosis of AAS, We searched MEDLINE, Embase, and Cochrane of Trials up to 31 December 2020. Results: We identified 3 studies using (ADD-RS) with D-dimer as a diagnostic tool for AAS, involving 3261 patients were AAS was diagnosed in 559(17.14%) patients. Overall results showed that the pooled sensitivities were 97.6 (95% CI 0.95.6, 99.6) at (ADD-RS)≤1(low risk group) with D-dimer and 97.4(95% CI 0.95.4,, 99.4) at (ADD-RS)>1(High risk group) with D-dimer., the failure rate was 0.48% at low risk group and 4.3% at high risk group respectively. Conclusions: (ADD-RS) with D-dimer was a useful screening test with high sensitivity to exclude Acute Aortic Syndrome.Keywords: aortic dissection detection risk score, D-dimer, acute aortic syndrome, diagnostic accuracy
Procedia PDF Downloads 2142940 Automated Detection of Women Dehumanization in English Text
Authors: Maha Wiss, Wael Khreich
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Animals, objects, foods, plants, and other non-human terms are commonly used as a source of metaphors to describe females in formal and slang language. Comparing women to non-human items not only reflects cultural views that might conceptualize women as subordinates or in a lower position than humans, yet it conveys this degradation to the listeners. Moreover, the dehumanizing representation of females in the language normalizes the derogation and even encourages sexism and aggressiveness against women. Although dehumanization has been a popular research topic for decades, according to our knowledge, no studies have linked women's dehumanizing language to the machine learning field. Therefore, we introduce our research work as one of the first attempts to create a tool for the automated detection of the dehumanizing depiction of females in English texts. We also present the first labeled dataset on the charted topic, which is used for training supervised machine learning algorithms to build an accurate classification model. The importance of this work is that it accomplishes the first step toward mitigating dehumanizing language against females.Keywords: gender bias, machine learning, NLP, women dehumanization
Procedia PDF Downloads 792939 Preliminary Study of Gold Nanostars/Enhanced Filter for Keratitis Microorganism Raman Fingerprint Analysis
Authors: Chi-Chang Lin, Jian-Rong Wu, Jiun-Yan Chiu
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Myopia, ubiquitous symptom that is necessary to correct the eyesight by optical lens struggles many people for their daily life. Recent years, younger people raise interesting on using contact lens because of its convenience and aesthetics. In clinical, the risk of eye infections increases owing to the behavior of incorrectly using contact lens unsupervised cleaning which raising the infection risk of cornea, named ocular keratitis. In order to overcome the identification needs, new detection or analysis method with rapid and more accurate identification for clinical microorganism is importantly needed. In our study, we take advantage of Raman spectroscopy having unique fingerprint for different functional groups as the distinct and fast examination tool on microorganism. As we know, Raman scatting signals are normally too weak for the detection, especially in biological field. Here, we applied special SERS enhancement substrates to generate higher Raman signals. SERS filter we designed in this article that prepared by deposition of silver nanoparticles directly onto cellulose filter surface and suspension nanoparticles - gold nanostars (AuNSs) also be introduced together to achieve better enhancement for lower concentration analyte (i.e., various bacteria). Research targets also focusing on studying the shape effect of synthetic AuNSs, needle-like surface morphology may possible creates more hot-spot for getting higher SERS enhance ability. We utilized new designed SERS technology to distinguish the bacteria from ocular keratitis under strain level, and specific Raman and SERS fingerprint were grouped under pattern recognition process. We reported a new method combined different SERS substrates can be applied for clinical microorganism detection under strain level with simple, rapid preparation and low cost. Our presenting SERS technology not only shows the great potential for clinical bacteria detection but also can be used for environmental pollution and food safety analysis.Keywords: bacteria, gold nanostars, Raman spectroscopy surface-enhanced Raman scattering filter
Procedia PDF Downloads 1652938 Flashover Detection Algorithm Based on Mother Function
Authors: John A. Morales, Guillermo Guidi, B. M. Keune
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Electric Power supply is a crucial topic for economic and social development. Power outages statistics show that discharges atmospherics are imperative phenomena to produce those outages. In this context, it is necessary to correctly detect when overhead line insulators are faulted. In this paper, an algorithm to detect if a lightning stroke generates or not permanent fault on insulator strings is proposed. On top of that, lightning stroke simulations developed by using the Alternative Transients Program, are used. Based on these insights, a novel approach is designed that depends on mother functions analysis corresponding to the given variance-covariance matrix. Signals registered at the insulator string are projected on corresponding axes by the means of Principal Component Analysis. By exploiting these new axes, it is possible to determine a flashover characteristic zone useful to a good insulation design. The proposed methodology for flashover detection extends the existing approaches for the analysis and study of lightning performance on transmission lines.Keywords: mother function, outages, lightning, sensitivity analysis
Procedia PDF Downloads 5852937 Material Response Characterisation of a PolyJet 3D Printed Human Infant Skull
Authors: G. A. Khalid, R. Prabhu, W. Whittington, M. D. Jones
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To establish a causal relationship of infant head injury consequences, this present study addresses the necessary challenges of cranial geometry and the physical response complexities of the paediatric head tissues. Herein, we describe a new approach to characterising and understanding infant head impact mechanics by developing printed head models, using high resolution clinical postmortem imaging, to provide the most complete anatomical representation currently available, and biological material response data-matched polypropylene polymers, to replicate the relative mechanical response properties of immature cranial bone, sutures and fontanelles. Additive manufacturing technology was applied to creating a physical polymeric model of a newborn infant skull, using PolyJet printed materials. Infant skull materials responses, were matched by a response characterisation study, utilising uniaxial tensile testing (1 mm min-1 loading rate), to determine: the stiffness, ultimate tensile strength and maximum strain of rigid and rubber additively manufactured acrylates. The results from the mechanical experiments confirm that the polymeric materials RGD835 Vero White Plus (White), representing the frontal and parietal bones; RGD8510- DM Rigid Light Grey25 (Grey), representing the occipital bone; and FLX9870-DM (Black) representing the suture and fontanelles, were found to show a close stiffness -correlation (E) at ambient temperatures. A 3D physical model of infant head was subsequently printed from the matched materials and subsequently validated against results obtained from a series of Post Mortem Human Surrogate (PMHS) tests. A close correlation was demonstrated between the model impact tests and the PMHS. This study, therefore, represents a key step towards applying printed physical models to understanding head injury biomechanics and is useful in the efforts to predict and mitigate head injury consequences in infants, whether accidental or by abuse.Keywords: infant head trauma, infant skull, material response, post mortem human subjects, polyJet printing
Procedia PDF Downloads 1382936 Heterogeneity, Asymmetry and Extreme Risk Perception; Dynamic Evolution Detection From Implied Risk Neutral Density
Authors: Abderrahmen Aloulou, Younes Boujelbene
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The current paper displays a new method of extracting information content from options prices by eliminating biases caused by daily variation of contract maturity. Based on Kernel regression tool, this non-parametric technique serves to obtain a spectrum of interpolated options with constant maturity horizons from negotiated optional contracts on the S&P TSX 60 index. This method makes it plausible to compare daily risk neutral densities from which extracting time continuous indicators allows the detection traders attitudes’ evolution, such as, belief homogeneity, asymmetry and extreme Risk Perception. Our findings indicate that the applied method contribute to develop effective trading strategies and to adjust monetary policies through controlling trader’s reactions to economic and monetary news.Keywords: risk neutral densities, kernel, constant maturity horizons, homogeneity, asymmetry and extreme risk perception
Procedia PDF Downloads 4832935 A Fast Chemiresistive H₂ Gas Sensor Based on Sputter Grown Nanocrystalline P-TiO₂ Thin Film Decorated with Catalytic Pd-Pt Layer on P-Si Substrate
Authors: Jyoti Jaiswal, Satyendra Mourya, Gaurav Malik, Ramesh Chandra
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In the present work, we have fabricated and studied a resistive H₂ gas sensor based on Pd-Pt decorated room temperature sputter grown nanocrystalline porous titanium dioxide (p-TiO₂) thin film on porous silicon (p-Si) substrate for fast H₂ detection. The gas sensing performance of Pd-Pt/p-TiO₂/p-Si sensing electrode towards H₂ gas under low (10-500 ppm) detection limit and operating temperature regime (25-200 °C) was discussed. The sensor is highly sensitive even at room temperature, with response (Ra/Rg) reaching ~102 for 500 ppm H₂ in dry air and its capability of sensing H₂ concentrations as low as ~10 ppm was demonstrated. At elevated temperature of 200 ℃, the response reached more than ~103 for 500 ppm H₂. Overall the fabricated resistive gas sensor exhibited high selectivity, good sensing response, and fast response/recovery time with good stability towards H₂.Keywords: sputtering, porous silicon (p-Si), TiO₂ thin film, hydrogen gas sensor
Procedia PDF Downloads 2562934 Low-Cost Parking Lot Mapping and Localization for Home Zone Parking Pilot
Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan
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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.Keywords: ADAS, home zone parking pilot, object detection, visual SLAM
Procedia PDF Downloads 662933 A Study of Permission-Based Malware Detection Using Machine Learning
Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud
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Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.Keywords: android malware detection, machine learning, malware, malware analysis
Procedia PDF Downloads 1662932 Quartz Crystal Microbalance Based Hydrophobic Nanosensor for Lysozyme Detection
Authors: F. Yılmaz, Y. Saylan, A. Derazshamshir, S. Atay, A. Denizli
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Quartz crystal microbalance (QCM), high-resolution mass-sensing technique, measures changes in mass on oscillating quartz crystal surface by measuring changes in oscillation frequency of crystal in real time. Protein adsorption techniques via hydrophobic interaction between protein and solid support, called hydrophobic interaction chromatography (HIC), can be favorable in many cases. Some nanoparticles can be effectively applied for HIC. HIC takes advantage of the hydrophobicity of proteins by promoting its separation on the basis of hydrophobic interactions between immobilized hydrophobic ligands and nonpolar regions on the surface of the proteins. Lysozyme is found in a variety of vertebrate cells and secretions, such as spleen, milk, tears, and egg white. Its common applications are as a cell-disrupting agent for extraction of bacterial intracellular products, as an antibacterial agent in ophthalmologic preparations, as a food additive in milk products and as a drug for treatment of ulcers and infections. Lysozyme has also been used in cancer chemotherapy. The aim of this study is the synthesis of hydrophobic nanoparticles for Lysozyme detection. For this purpose, methacryoyl-L-phenylalanine was chosen as a hydrophobic matrix. The hydrophobic nanoparticles were synthesized by micro-emulsion polymerization method. Then, hydrophobic QCM nanosensor was characterized by Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM) and zeta size analysis. Hydrophobic QCM nanosensor was tested for real-time detection of Lysozyme from aqueous solution. The kinetic and affinity studies were determined by using Lysozyme solutions with different concentrations. The responses related to a mass (Δm) and frequency (Δf) shifts were used to evaluate adsorption properties.Keywords: nanosensor, HIC, lysozyme, QCM
Procedia PDF Downloads 3472931 Thermolysin Entrapment in a Gold Nanoparticles/Polymer Composite: Construction of an Efficient Biosensor for Ochratoxin a Detection
Authors: Fatma Dridi, Mouna Marrakchi, Mohammed Gargouri, Alvaro Garcia Cruz, Sergei V. Dzyadevych, Francis Vocanson, Joëlle Saulnier, Nicole Jaffrezic-Renault, Florence Lagarde
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An original method has been successfully developed for the immobilization of thermolysin onto gold interdigitated electrodes for the detection of ochratoxin A (OTA) in olive oil samples. A mix of polyvinyl alcohol (PVA), polyethylenimine (PEI) and gold nanoparticles (AuNPs) was used. Cross-linking sensors chip was made by using a saturated glutaraldehyde (GA) vapor atmosphere in order to render the two polymers water stable. Performance of AuNPs/ (PVA/PEI) modified electrode was compared to a traditional immobilized enzymatic method using bovine serum albumin (BSA). Atomic force microscopy (AFM) experiments were employed to provide a useful insight into the structure and morphology of the immobilized thermolysin composite membranes. The enzyme immobilization method influence the topography and the texture of the deposited layer. Biosensors optimization and analytical characteristics properties were studied. Under optimal conditions AuNPs/ (PVA/PEI) modified electrode showed a higher increment in sensitivity. A 700 enhancement factor could be achieved with a detection limit of 1 nM. The newly designed OTA biosensors showed a long-term stability and good reproducibility. The relevance of the method was evaluated using commercial doped olive oil samples. No pretreatment of the sample was needed for testing and no matrix effect was observed. Recovery values were close to 100% demonstrating the suitability of the proposed method for OTA screening in olive oil.Keywords: thermolysin, A. ochratoxin , polyvinyl alcohol, polyethylenimine, gold nanoparticles, olive oil
Procedia PDF Downloads 5882930 Highly-Sensitive Nanopore-Based Sensors for Point-Of-Care Medical Diagnostics
Authors: Leyla Esfandiari
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Rapid, sensitive detection of nucleic acid (NA) molecules of specific sequence is of interest for a range of diverse health-related applications such as screening for genetic diseases, detecting pathogenic microbes in food and water, and identifying biological warfare agents in homeland security. Sequence-specific nucleic acid detection platforms rely on base pairing interaction between two complementary single stranded NAs, which can be detected by the optical, mechanical, or electrochemical readout. However, many of the existing platforms require amplification by polymerase chain reaction (PCR), fluorescent or enzymatic labels, and expensive or bulky instrumentation. In an effort to address these shortcomings, our research is focused on utilizing the cutting edge nanotechnology and microfluidics along with resistive pulse electrical measurements to design and develop a cost-effective, handheld and highly-sensitive nanopore-based sensor for point-of-care medical diagnostics.Keywords: diagnostics, nanopore, nucleic acids, sensor
Procedia PDF Downloads 4632929 Directly Observed Treatment Short-Course (DOTS) for TB Control Program: A Ten Years Experience
Authors: Solomon Sisay, Belete Mengistu, Woldargay Erku, Desalegne Woldeyohannes
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Background: Tuberculosis is still the leading cause of illness in the world which accounted for 2.5% of the global burden of disease, and 25% of all avoidable deaths in developing countries. Objectives: The aim of study was to assess impact of DOTS strategy on tuberculosis case finding and treatment outcome in Gambella Regional State, Ethiopia from 2003 up to 2012 and from 2002 up to 2011, respectively. Methods: Health facility-based retrospective study was conducted. Data were collected and reported in quarterly basis using WHO reporting format for TB case finding and treatment outcome from all DOTS implementing health facilities in all zones of the region to Federal Ministry of Health. Results: A total of 10024 all form of TB cases had been registered between the periods from 2003 up to 2012. Of them, 4100 (40.9%) were smear-positive pulmonary TB, 3164 (31.6%) were smear-negative pulmonary TB and 2760 (27.5%) had extra-pulmonary TB. Case detection rate of smear-positive pulmonary TB had increased from 31.7% to 46.5% from the total TB cases and treatment success rate increased from 13% to 92% with average mean value of being 40.9% (SD= 0.1) and 55.7% (SD=0.28), respectively for the specified year periods. Moreover, the average values of treatment defaulter and treatment failure rates were 4.2% and 0.3%, respectively. Conclusion: It is possible to achieve the recommended WHO target which is 70% of CDR for smear-positive pulmonary TB, and 85% of TSR as it was already been fulfilled the targets for treatments more than 85% from 2009 up to 2011 in the region. However, it requires strong efforts to enhance case detection rate of 40.9% for smear-positive pulmonary TB through implementing alternative case finding strategies.Keywords: Gambella Region, case detection rate, directly observed treatment short-course, treatment success rate, tuberculosis
Procedia PDF Downloads 3422928 Scientific Recommender Systems Based on Neural Topic Model
Authors: Smail Boussaadi, Hassina Aliane
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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model
Procedia PDF Downloads 962927 Dimensionality Reduction in Modal Analysis for Structural Health Monitoring
Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti
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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement
Procedia PDF Downloads 1222926 Exploring the Role of Building Information Modeling for Delivering Successful Construction Projects
Authors: Muhammad Abu Bakar Tariq
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Construction industry plays a crucial role in the progress of societies and economies. Furthermore, construction projects have social as well as economic implications, thus, their success/failure have wider impacts. However, the industry is lagging behind in terms of efficiency and productivity. Building Information Modeling (BIM) is recognized as a revolutionary development in Architecture, Engineering and Construction (AEC) industry. There are numerous interest groups around the world providing definitions of BIM, proponents describing its advantages and opponents identifying challenges/barriers regarding adoption of BIM. This research is aimed at to determine what actually BIM is, along with its potential role in delivering successful construction projects. The methodology is critical analysis of secondary data sources i.e. information present in public domain, which include peer reviewed journal articles, industry and government reports, conference papers, books, case studies etc. It is discovered that clash detection and visualization are two major advantages of BIM. Clash detection option identifies clashes among structural, architectural and MEP designs before construction actually commences, which subsequently saves time as well as cost and ensures quality during execution phase of a project. Visualization is a powerful tool that facilitates in rapid decision-making in addition to communication and coordination among stakeholders throughout project’s life cycle. By eliminating inconsistencies that consume time besides cost during actual construction, improving collaboration among stakeholders throughout project’s life cycle, BIM can play a positive role to achieve efficiency and productivity that consequently deliver successful construction projects.Keywords: building information modeling, clash detection, construction project success, visualization
Procedia PDF Downloads 2592925 Concept Drifts Detection and Localisation in Process Mining
Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa
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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining
Procedia PDF Downloads 3452924 Novel p22-Monoclonal Antibody Based Blocking ELISA for the Detection of African Swine Fever Virus Antibodies in Serum
Authors: Ghebremedhin Tsegay, Weldu Tesfagaber, Yuanmao Zhu, Xijun He, Wan Wang, Zhenjiang Zhang, Encheng Sun, Jinya Zhang, Yuntao Guan, Fang Li, Renqiang Liu, Zhigao Bu, Dongming Zhao*
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African swine fever (ASF) is a highly infectious viral disease of pigs, resulting in significant economic loss worldwide. As there is no approved vaccines and treatments, the control of ASF entirely depends on early diagnosis and culling of infected pigs. Thus, highly specific and sensitive diagnostic assays are required for accurate and early diagnosis of ASF virus (ASFV). Currently, only a few recombinant proteins have been tested and validated for use as reagents in ASF diagnostic assays. The most promising ones for ASFV antibody detection were p72, p30, p54, and pp62. So far, three ELISA kits based on these recombinant proteins have been commercialized. Due to the complex nature of the virus and variety forms of the disease, robust serodiagnostic assays are still required. ASFV p22 protein, encoded by KP177R gene, is located in the inner membrane of viral particle and appeared transiently in the plasma membrane early after virus infection. The p22 protein interacts with numerous cellular proteins, involved in processes of phagocytosis and endocytosis through different cellular pathways. However, p22 does not seem to be involved in virus replication or swine pathogenicity. In this study, E.coli expressed recombinant p22 protein was used to generate a monoclonal antibody (mAb), and its potential use for the development of blocking ELISA (bELISA) was evaluated. A total of 806 pig serum samples were tested to evaluate the bELISA. Acording the ROC (Reciever operating chracteristic) analysis, 100% sensitivity and 98.10% of specificity was recorded when the PI cut-off value was set at 47%. The novel assay was able to detect the antibodies as early as 9 days post infection. Finaly, a highly sensitive, specific and rapid novel p22-mAb based bELISA assay was developed, and optimized for detection of antibodies against genotype I and II ASFVs. It is a promising candidate for an early and acurate detection of the antibodies and is highly expected to have a valuable role in the containment and prevention of ASF.Keywords: ASFV, blocking ELISA, diagnosis, monoclonal antibodies, sensitivity, specificity
Procedia PDF Downloads 752923 Electrochemical Biosensor for Rutin Detection with Multiwall Carbon Nanotubes and Cerium Dioxide Nanoparticles
Authors: Stephen Rathinaraj Benjamin, Flavio Colmati Junior, Maria Izabel Florindo Guedes, Rosa Amalia Fireman Dutra
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A new enzymatic electrochemical biosensor based on multiwall carbon nanotubes and cerium oxide nanoparticles for the detection of rutin has been developed. The cerium oxide nanoparticles /HRP/ multiwall carbon nanotubes/ carbon paste electrode (HRP/ CeO2/MWCNTs/CPE) was prepared by ensuing addition of MWCNTs and HRP on the CPE, followed by the mixing with cerium oxide nanoparticles. Surface physical characteristics of the modified electrode and the electrochemical properties of the composite were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), cylic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). The HRP/ CeO2/MWCNTs/CPE showed good selectivity, stability and reproducibility, which was further applied to detect rutin tablet and capsule samples with satisfactory results.Keywords: cerium dioxide nanoparticles, horseradish peroxidase, multiwall carbon nanotubes, rutin
Procedia PDF Downloads 3882922 Study the Behavior of Different Composite Short Columns (DST) with Prismatic Sections under Bending Load
Authors: V. Sadeghi Balkanlou, M. Reza Bagerzadeh Karimi, A. Hasanbakloo, B. Bagheri Azar
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In this paper, the behavior of different types of DST columns has been studied under bending load. Briefly, composite columns consist of an internal carbon steel tube and an external stainless steel wall that the between the walls are filled with concrete. Composite columns are expected to combine the advantages of all three materials and have the advantage of high flexural stiffness of CFDST columns. In this research, ABAQUS software is used for finite element analysis then the results of ultimate strength of the composite sections are illustrated.Keywords: DST, stainless steel, carbon steel, ABAQUS, straigh columns, tapered columns
Procedia PDF Downloads 3862921 Graphen-Based Nanocomposites for Glucose and Ethanol Enzymatic Biosensor Fabrication
Authors: Tesfaye Alamirew, Delele Worku, Solomon W. Fanta, Nigus Gabbiye
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Recently graphen based nanocomposites are become an emerging research areas for fabrication of enzymatic biosensors due to their property of large surface area, conductivity and biocompatibility. This review summarizes recent research reports of graphen based nanocomposites for the fabrication of glucose and ethanol enzymatic biosensors. The newly fabricated enzyme free microwave treated nitrogen doped graphen (MN-d-GR) had provided highest sensitivity towards glucose and GCE/rGO/AuNPs/ADH composite had provided far highest sensitivity towards ethanol compared to other reported graphen based nanocomposites. The MWCNT/GO/GOx and GCE/ErGO/PTH/ADH nanocomposites had also enhanced wide linear range for glucose and ethanol detection respectively. Generally, graphen based nanocomposite enzymatic biosensors had fast direct electron transfer rate, highest sensitivity and wide linear detection ranges during glucose and ethanol sensing.Keywords: glucose, ethanol, enzymatic biosensor, graphen, nanocomposite
Procedia PDF Downloads 1242920 Numerical Evaluation of Lateral Bearing Capacity of Piles in Cement-Treated Soils
Authors: Reza Ziaie Moayed, Saeideh Mohammadi
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Soft soil is used in many of civil engineering projects like coastal, marine and road projects. Because of low shear strength and stiffness of soft soils, large settlement and low bearing capacity will occur under superstructure loads. This will make the civil engineering activities more difficult and costlier. In the case of soft soils, improvement is a suitable method to increase the shear strength and stiffness for engineering purposes. In recent years, the artificial cementation of soil by cement and lime has been extensively used for soft soil improvement. Cement stabilization is a well-established technique for improving soft soils. Artificial cementation increases the shear strength and hardness of the natural soils. On the other hand, in soft soils, the use of piles to transfer loads to the depths of ground is usual. By using cement treated soil around the piles, high bearing capacity and low settlement in piles can be achieved. In the present study, lateral bearing capacity of short piles in cemented soils is investigated by numerical approach. For this purpose, three dimensional (3D) finite difference software, FLAC 3D is used. Cement treated soil has a strain hardening-softening behavior, because of breaking of bonds between cement agent and soil particle. To simulate such behavior, strain hardening-softening soil constitutive model is used for cement treated soft soil. Additionally, conventional elastic-plastic Mohr Coulomb constitutive model and linear elastic model are used for stress-strain behavior of natural soils and pile. To determine the parameters of constitutive models and also for verification of numerical model, the results of available triaxial laboratory tests on and insitu loading of piles in cement treated soft soil are used. Different parameters are considered in parametric study to determine the effective parameters on the bearing of the piles on cemented treated soils. In the present paper, the effect of various length and height of the artificial cemented area, different diameter and length of the pile and the properties of the materials are studied. Also, the effect of choosing a constitutive model for cemented treated soils in the bearing capacity of the pile is investigated.Keywords: bearing capacity, cement-treated soils, FLAC 3D, pile
Procedia PDF Downloads 1252919 Automatic Censoring in K-Distribution for Multiple Targets Situations
Authors: Naime Boudemagh, Zoheir Hammoudi
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The parameters estimation of the K-distribution is an essential part in radar detection. In fact, presence of interfering targets in reference cells causes a decrease in detection performances. In such situation, the estimate of the shape and the scale parameters are far from the actual values. In the order to avoid interfering targets, we propose an Automatic Censoring (AC) algorithm of radar interfering targets in K-distribution. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The homogeneous population is then used to estimate the unknown parameters by the classical Method of Moment (MOM). The AC algorithm does not need any prior information about the clutter parameters nor does it require both the number and the position of interfering targets. The accuracy of the estimation parameters obtained by this algorithm are validated and compared to various actual values of the shape parameter, using Monte Carlo simulations, this latter show that the probability of censing in multiple target situations are in good agreement.Keywords: parameters estimation, method of moments, automatic censoring, K distribution
Procedia PDF Downloads 3712918 Detecting Heartbeat Architectural Tactic in Source Code Using Program Analysis
Authors: Ananta Kumar Das, Sujit Kumar Chakrabarti
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Architectural tactics such as heartbeat, ping-echo, encapsulate, encrypt data are techniques that are used to achieve quality attributes of a system. Detecting architectural tactics has several benefits: it can aid system comprehension (e.g., legacy systems) and in the estimation of quality attributes such as safety, security, maintainability, etc. Architectural tactics are typically spread over the source code and are implicit. For large codebases, manual detection is often not feasible. Therefore, there is a need for automated methods of detection of architectural tactics. This paper presents a formalization of the heartbeat architectural tactic and a program analytic approach to detect this tactic in source code. The experiment of the proposed method is done on a set of Java applications. The outcome of the experiment strongly suggests that the method compares well with a manual approach in terms of its sensitivity and specificity, and far supersedes a manual exercise in terms of its scalability.Keywords: software architecture, architectural tactics, detecting architectural tactics, program analysis, AST, alias analysis
Procedia PDF Downloads 1582917 Pharmacokinetic Monitoring of Glimepiride and Ilaprazole in Rat Plasma by High Performance Liquid Chromatography with Diode Array Detection
Authors: Anil P. Dewani, Alok S. Tripathi, Anil V. Chandewar
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Present manuscript reports the development and validation of a quantitative high performance liquid chromatography method for the pharmacokinetic evaluation of Glimepiride (GLM) and Ilaprazole (ILA) in rat plasma. The plasma samples were involved with Solid phase extraction process (SPE). The analytes were resolved on a Phenomenex C18 column (4.6 mm× 250 mm; 5 µm particle size) using a isocratic elution mode comprising methanol:water (80:20 % v/v) with pH of water modified to 3 using Formic acid, the total run time was 10 min at 225 nm as common wavelength, the flow rate throughout was 1ml/min. The method was validated over the concentration range from 10 to 600 ng/mL for GLM and ILA, in rat plasma. Metformin (MET) was used as Internal Standard. Validation data demonstrated the method to be selective, sensitive, accurate and precise. The limit of detection was 1.54 and 4.08 and limit of quantification was 5.15 and 13.62 for GLM and ILA respectively, the method demonstrated excellent linearity with correlation coefficients (r2) 0.999. The intra and inter-day precision (RSD%) values were < 2.0% for both ILA and GLM. The method was successfully applied in pharmacokinetic studies followed by oral administration in rats.Keywords: pharmacokinetics, glimepiride, ilaprazole, HPLC, SPE
Procedia PDF Downloads 367