Search results for: fault detection

Authors: D. S. Mohale, A. P. Dewani, A. S.tripathi, A. V. Chandewar

Abstract:

Present work demonstrates the applicability of high-performance liquid chromatography (HPLC) with UV-Vis detection for the quantification of malondialdehyde as malondialdehyde-thiobarbituric acid complex (MDA-TBA) in-vivo in rats. The HPLC method for MDA-TBA was achieved by isocratic mode on a reverse-phase C18 column (250mm×4.6mm) at a flow rate of 1.0mLmin−1 followed by detection at 532 nm. The chromatographic conditions were optimized by varying the concentration and pH of water followed by changes in percentage of organic phase optimal mobile phase consisted of mixture of water (0.2% triethylamine pH adjusted to 2.3 by ortho-phosphoric acid) and acetonitrile in ratio (80:20v/v). The retention time of MDA-TBA complex was 3.7 min. The developed method was sensitive as limit of detection and quantification (LOD and LOQ) for MDA-TBA complex were (standard deviation and slope of calibration curve) 110 ng/ml and 363 ng/ml respectively. Calibration studies were done by spiking MDA into rat plasma at concentrations ranging from 500 to 1000 ng/ml. The precision of developed method measured in terms of relative standard deviations for intra-day and inter-day studies was 1.6–5.0% and 1.9–3.6% respectively. The HPLC method was applied for monitoring MDA levels in rats subjected to chronic treatment of levofloxacin (LEV) (5mg/kg/day) for 21 days. Results were compared by findings in control group rats. Mean peak areas of both study groups was subjected for statistical treatment to unpaired student t-test to find p-values. The p value was <0.001 indicating significant results and suggesting increased MDA levels in rats subjected to chronic treatment of LEV of 21 days.

Keywords: malondialdehyde-thiobarbituric acid complex, levofloxacin, HPLC, oxidative stress

Procedia PDF Downloads 334

Authors: Ouahab Kadri, Leila Hayet Mouss

Abstract:

In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

Procedia PDF Downloads 298

Authors: Narmina Ismayilova, Shamil Zabitov, Fuad Askerzadeh, Raqif Seyfullayev

Abstract:

Tulallar volcano-tectonic structure is located in the conjugation zone of the Gekgel horst-uplift, Dashkesan, and Agzhakend synclinorium. Regionally, these geological structures are an integral part of the Lok-Karabakh island arc system. Tulallar field is represented by three areas (Central, East, West). The area of the ore field is located within a partially eroded oblong volcano-tectonic depression. In the central part, the core is divided by the deep Tulallar-Chiragdara-Toganalinsky fault with arcuate fragments of the ring structure into three blocks -East, Central, and West, within which the same areas of the Tulallar field are located. In general, for the deposit, the position of both ore-bearing vein zones and ore-bearing blocks is controlled by fractures of two systems - sub-latitudinal and near-meridional orientations. Mineralization of gold-sulfide ores is confined to these zones of disturbances. The zones have a northwestern and northeastern (near-meridian) strike with a steep dip (70-85◦) to the southwest and southeast. The average thickness of the zones is 35 m; they are traced for 2.5 km along the strike and 500 m along with the dip. In general, for the indicated thickness, the zones contain an average of 1.56 ppm Au; however, areas enriched in noble metal are distinguished within them. The zones are complicated by postore fault tectonics. Gold mineralization is localized in the Kimmeridgian volcanics of andesi-basalt-porphyritic composition and their vitrolithoclastic, agglomerate tuffs, and tuff breccias. For the central part of the Tulallar ore field, a map of geochemical anomalies was built on the basis of analysis data carried out in an international laboratory. The total gold content ranges from 0.1-5 g/t, and in some places, even more than 5 g/t. The highest gold content is observed in the monoquartz facies among the secondary quartzites with quartz veins. The smallest amount of gold content appeared in the quartz-kaolin facies. And also, anomalous values of gold content are located in the upper part of the quartz vein. As a result, an en-echelon arrangement of anomalous values of gold along the strike and dip was revealed.

Keywords: geochemical anomaly, gold deposit, mineralization, Tulallar

Procedia PDF Downloads 191

Authors: Manal M. Kame, Hanan G. El-Baz, Zeinab A.Demerdash, Engy M. Abd El-Moneem, Mohamed A. Hendawy, Ibrahim R. Bayoumi

Abstract:

There is a constant need to improve the performance of current diagnostic assays of schistosomiasis as well as develop innovative testing strategies to meet new testing challenges. This study aims at increasing the diagnostic efficiency of monoclonal antibody (MAb)-based antigen detection assays through gold nanoparticles conjugated with specific anti-Schistosoma mansoni monoclonal antibodies. In this study, several hybidoma cell lines secreting MAbs against adult worm tegumental Schistosoma antigen (AWTA) were produced at Immunology Department of Theodor Bilharz Research Institute and preserved in liquid nitrogen. One MAb (6D/6F) was chosen for this study due to its high reactivity to schistosome antigens with highest optical density (OD) values. Gold nanoparticles (AuNPs) were functionalized and conjugated with MAb (6D/6F). The study was conducted on serum samples of 116 subjects: 71 patients with S. mansoni eggs in their stool samples group (gp 1), 25 with other parasites (gp2) and 20 negative healthy controls (gp3). Patients in gp1 were further subdivided according to egg count in their stool samples into Light infection {≤ 50 egg per gram(epg) (n= 17)}, moderate {51-100 epg (n= 33)} and severe infection {>100 epg(n= 21)}. Sandwich ELISA was performed using (AuNPs -MAb) for detection of circulating schistosomal antigen (CSA) levels in serum samples of all groups and the results were compared with that after using MAb/ sandwich ELISA system. Results Gold- MAb/ ELISA system reached a lower detection limit of 10 ng/ml compared to 85 ng/ml on using MAb/ ELISA and the optimal concentrations of AuNPs -MAb were found to be 12 folds less than that of MAb/ ELISA system for detection of CSA. The sensitivity and specificity of sandwich ELISA for detection of CSA levels using AuNPs -MAb were 100% & 97.8 % respectively compared to 87.3% &93.38% respectively on using MAb/ ELISA system. It was found that CSA was detected in 9 out of 71 S.mansoni infected patients on using AuNPs - MAb/ ELISA system and was not detected by MAb/ ELISA system. All those patients (9) was found to have an egg count below 50 epg feces (patients with light infections). ROC curve analyses revealed that sandwich ELISA using gold-MAb was an excellent diagnostic investigator that could differentiate Schistosoma patients from healthy controls, on the other hand it revealed that sandwich ELISA using MAb was not accurate enough as it could not recognize nine out of 71 patients with light infections. Conclusion Our data demonstrated that: Loading gold nanoparticles with MAb (6D/6F) increases the sensitivity and specificity of sandwich ELISA for detection of CSA, thus active (early) and light infections could be easily detected. Moreover this binding will decrease the amount of MAb consumed in the assay and lower the coast. The significant positive correlation that was detected between ova count (intensity of infection) and OD reading in sandwich ELISA using gold- MAb enables its use to detect the severity of infections and follow up patients after treatment for monitoring of cure.

Keywords: Schistosomiasis, nanoparticles, gold, monoclonal antibodies, ELISA

Procedia PDF Downloads 371

Authors: Abanob Nady Wasef Moawed

Abstract:

In more complicated systems, inclusive of automotive gearboxes, a rigorous remedy of the data is essential because there are several transferring elements (gears, bearings, shafts, and many others.), and in this way, there are numerous viable sources of mistakes and also noise. The fundamental goal of these elements are the detection of damage in car gearbox. The detection strategies used are the wavelet technique, the bispectrum, advanced filtering techniques (selective filtering) of vibrational alerts and mathematical morphology. Gearbox vibration assessments were achieved (gearboxes in proper circumstance and with defects) of a manufacturing line of a huge car assembler. The vibration indicators have acquired the use of five accelerometers in distinct positions of the sample. The effects acquired using the kurtosis, bispectrum, wavelet and mathematical morphology confirmed that it's far possible to identify the lifestyles of defects in automobile gearboxes.

Keywords: 3D-shaped electronics, electronic components, thermoforming, component positioningautomotive gearbox, mathematical morphology, wavelet, bispectrum

Procedia PDF Downloads 29

Authors: M. Salvador, J. C. Martinez-Garcia, A. Moyano, M. C. Blanco-Lopez, M. Rivas

Abstract:

Biosensors play a crucial role in the detection of molecules nowadays due to their advantages of user-friendliness, high selectivity, the analysis in real time and in-situ applications. Among them, Lateral Flow Immunoassays (LFIAs) are presented among technologies for point-of-care bioassays with outstanding characteristics such as affordability, portability and low-cost. They have been widely used for the detection of a vast range of biomarkers, which do not only include proteins but also nucleic acids and even whole cells. Although the LFIA has traditionally been a positive/negative test, tremendous efforts are being done to add to the method the quantifying capability based on the combination of suitable labels and a proper sensor. One of the most successful approaches involves the use of magnetic sensors for detection of magnetic labels. Bringing together the required characteristics mentioned before, our research group has developed a biosensor to detect biomolecules. Superparamagnetic nanoparticles (SPNPs) together with LFIAs play the fundamental roles. SPMNPs are detected by their interaction with a high-frequency current flowing on a printed micro track. By means of the instant and proportional variation of the impedance of this track provoked by the presence of the SPNPs, quantitative and rapid measurement of the number of particles can be obtained. This way of detection requires no external magnetic field application, which reduces the device complexity. On the other hand, the major limitations of LFIAs are that they are only qualitative or semiquantitative when traditional gold or latex nanoparticles are used as color labels. Moreover, the necessity of always-constant ambient conditions to get reproducible results, the exclusive detection of the nanoparticles on the surface of the membrane, and the short durability of the signal are drawbacks that can be advantageously overcome with the design of magnetically labeled LFIAs. The approach followed was to coat the SPIONs with a specific monoclonal antibody which targets the protein under consideration by chemical bonds. Then, a sandwich-type immunoassay was prepared by printing onto the nitrocellulose membrane strip a second antibody against a different epitope of the protein (test line) and an IgG antibody (control line). When the sample flows along the strip, the SPION-labeled proteins are immobilized at the test line, which provides magnetic signal as described before. Preliminary results using this practical combination for the detection and quantification of the Prostatic-Specific Antigen (PSA) shows the validity and consistency of the technique in the clinical range, where a PSA level of 4.0 ng/mL is the established upper normal limit. Moreover, a LOD of 0.25 ng/mL was calculated with a confident level of 3 according to the IUPAC Gold Book definition. Its versatility has also been proved with the detection of other biomolecules such as troponin I (cardiac injury biomarker) or histamine.

Keywords: biosensor, lateral flow immunoassays, point-of-care devices, superparamagnetic nanoparticles

Procedia PDF Downloads 231

Authors: Goutam Kumar Ghorai, Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, G. Sarkar, Ashis K. Dhara

Abstract:

Diabetes mellitus is one of the most common chronic diseases in all countries and continues to increase in numbers significantly. Diabetic retinopathy (DR) is damage to the retina that occurs with long-term diabetes. DR is a major cause of blindness in the Indian population. Therefore, its early diagnosis is of utmost importance towards preventing progression towards imminent irreversible loss of vision, particularly in the huge population across rural India. The barriers to eye examination of all diabetic patients are socioeconomic factors, lack of referrals, poor access to the healthcare system, lack of knowledge, insufficient number of ophthalmologists, and lack of networking between physicians, diabetologists and ophthalmologists. A few diabetic patients often visit a healthcare facility for their general checkup, but their eye condition remains largely undetected until the patient is symptomatic. This work aims to focus on the design and development of a fully automated intelligent decision system for screening retinal fundus images towards detection of the pathophysiology caused by microaneurysm in the early stage of the diseases. Automated detection of microaneurysm is a challenging problem due to the variation in color and the variation introduced by the field of view, inhomogeneous illumination, and pathological abnormalities. We have developed aconvolutional neural network for efficient detection of microaneurysm. A loss function is also developed to handle severe class imbalance due to very small size of microaneurysms compared to background. The network is able to locate the salient region containing microaneurysms in case of noisy images captured by non-mydriatic cameras. The ground truth of microaneurysms is created by expert ophthalmologists for MESSIDOR database as well as private database, collected from Indian patients. The network is trained from scratch using the fundus images of MESSIDOR database. The proposed method is evaluated on DIARETDB1 and the private database. The method is successful in detection of microaneurysms for dilated and non-dilated types of fundus images acquired from different medical centres. The proposed algorithm could be used for development of AI based affordable and accessible system, to provide service at grass root-level primary healthcare units spread across the country to cater to the need of the rural people unaware of the severe impact of DR.

Keywords: retinal fundus image, deep convolutional neural network, early detection of microaneurysms, screening of diabetic retinopathy

Procedia PDF Downloads 141

Authors: Manikanta Prasad Banda, Che Hua Yang

Abstract:

Anti-symmetric wave propagation along the particle motion of the wedge waves is known as anti-symmetric flexural (ASF) modes which travel along the wedge tips of the mid-plane apex with a small truncation. This paper investigates the characteristics of the ASF modes propagation with the wedge tip crack. The simulation and experimental results obtained by a three-dimensional (3-D) finite element model explained the contact acoustic non-linear (CAN) behavior in explicit dynamics in ABAQUS and the ultrasonic non-destructive testing (NDT) method is used for defect detection. The effect of various parameters on its high and low-level conversion modes are known for complex reflections and transmissions involved with direct reflections and transmissions. The results are used to predict the location of crack through complex transmission and reflection coefficients.

Keywords: ASF mode, crack detection, finite elements method, laser ultrasound technique, wedge waves

Procedia PDF Downloads 136

Authors: Shafqat Ullah Khan, Ammar Nasir

Abstract:

Using planar antenna array (PAA) in radars, Broadcasting, satellite antennas, and sonar for the detection of targets, Helps provide instant beam pattern control. High flexibility and Adaptability are achieved by multiple beam steering by using a Planar array and are particularly needed in real-life Sanrio’s where the need arises for several high-directivity beams. Faulty sensors in planar arrays generate asymmetry, which leads to service degradation, radiation pattern distortion, and increased levels of sidelobe. The POA, a nature-inspired optimization algorithm, accurately determines faulty sensors within an array, enhancing the reliability and performance of planar array antennas through extensive simulations and experiments. The analysis was done for different types of faults in 7 x 7 and 8 x 8 planar arrays in MATLAB.

Keywords: Planar antenna array, , Pelican optimisation Algorithm, , Faculty sensor, Antenna arrays

Procedia PDF Downloads 79

Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

Abstract:

Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

Procedia PDF Downloads 177

Authors: Marco Lopes, Pedro D. Gaspar, Maria P. Simões

Abstract:

This paper reviews the different concepts that have been investigated concerning the mechanization of fruit thinning as well as multiple working principles and solutions that have been developed for feature extraction of horticultural products, both in the field and industrial environments. The research should be committed towards selective methods, which inevitably need to incorporate some kinds of sensor technology. Computer vision often comes out as an obvious solution for unstructured detection problems, although leaves despite the chosen point of view frequently occlude fruits. Further research on non-traditional sensors that are capable of object differentiation is needed. Ultrasonic and Near Infrared (NIR) technologies have been investigated for applications related to horticultural produce and show a potential to satisfy this need while simultaneously providing spatial information as time of flight sensors. Light Detection and Ranging (LIDAR) technology also shows a huge potential but it implies much greater costs and the related equipment is usually much larger, making it less suitable for portable devices, which may serve a purpose on smaller unstructured orchards. Portable devices may serve a purpose on these types of orchards. In what concerns sensor methods, on-tree fruit detection, major challenge is to overcome the problem of fruits’ occlusion by leaves and branches. Hence, nontraditional sensors capable of providing some type of differentiation should be investigated.

Keywords: fruit thinning, horticultural field, portable devices, sensor technologies

Procedia PDF Downloads 139

Authors: Mohamed Sadok Bensalem, Soulef Amamria, Khaled Lazzez, Mohamed Ghanmi

Abstract:

The southern-central Tunisian Atlas presents a typical example of external zone. It occupies a particular position in the North African chains: firstly, it is the eastern limit of atlassicstructures; secondly, it is the edges between the belts structures to the north and the stable Saharan platform in the south. The evolution of deformation studyis based on several methods such as classical or numerical methods. The principals parameters controlling the genesis of folds in the southern central Tunisian Atlas are; the reactivation of pre-existing faults during later compressive phase, the evolution of decollement level, and the relation between thin and thick-skinned. One of the more principal characters of the southern-central Tunisian Atlas is the variation of belts structures directions determined by: NE-SW direction named the attlassic direction in Tunisia, the NW-SE direction carried along the Gafsa fault (the oriental limit of southern atlassic accident), and the E-W direction defined in the southern Tunisian Atlas. This variation of direction is the result of an important variation of deformation during different tectonics phases. A classical modeling of the Jebel ElKebar anticline, based on faults throw of the pre-existing faults and its reactivation during compressive phases, shows the importance of extensional deformation, particular during Aptian-Albian period, comparing with that of later compression (Alpine phases). A numerical modeling, based on the software Rampe E.M. 1.5.0, applied on the anticline of Jebel Orbata confirms the interpretation of “fault related fold” with decollement level within the Triassic successions. The other important parameter of evolution of deformation is the vertical migration of decollement level; indeed, more than the decollement level is in the recent series, most that the deformation is accentuated. The evolution of deformation is marked the development of duplex structure in Jebel AtTaghli (eastern limit of Jebel Orbata). Consequently, the evolution of deformation is proportional to the depth of the decollement level, the most important deformation is in the higher successions; thus is associated to the thin-skinned deformation; the decollement level permit the passive transfer of deformation in the cover.

Keywords: evolution of deformation, pre-existing faults, decollement level, thin-skinned

Procedia PDF Downloads 132

Authors: Seung-Jun Yu, Young-Min Ko, Hyoung-Kyu Song

Abstract:

Recently developed cooperative diversity scheme enables a terminal to get transmit diversity through the support of other terminals. However, most of the introduced cooperative schemes have a common fault of decreased transmission rate because the destination should receive the decodable compositions of symbols from the source and the relay. In order to achieve high data rate, we propose a cooperative scheme that employs hierarchical modulation. This scheme is free from the rate loss and allows seamless cooperative communication.

Keywords: cooperative communication, hierarchical modulation, high data rate, transmission scheme

Procedia PDF Downloads 425

Authors: Fahad Alamoudi, Yaser Miaji, Fawzy Jalalah

Abstract:

The growing popularity of swimming pools and other activities in the water for sport, fitness, therapy or just enjoyable relaxation have led to the increased use of swimming pools and the establishment of a variety of specific-use pools such as spa pools, Waterslides and more recently, hydrotherapy and wave pools. In this research a few simple equipments are used for test, Detect and alert for detection of water cleanness and pollution. YSI Photometer Systems, TDSTestr High model, rio 12HF, and Electrode A1. The researchers used electrolysis as a method of separating bonded elements and compounds by passing an electric current through them. The results which use 41 experiments show the higher the salt concentration, the more efficient the electrode and the smaller the gap between the plates and The lower the electrode voltage. Furthermore, it is proved that the larger the surface area, the lower the cell voltage and the higher current used the more chlorine produced.

Keywords: photometer, electrode, electrolysis, swimming pool chlorination

Procedia PDF Downloads 349

Authors: Ragu Sasikumar, Palraj Ranganathan, Shen-Ming Chen, Syang-Peng Rwei

Abstract:

A highly sensitive, and selective α-Lipoic acid (ALA) sensor based on a functionalized multi-walled carbon nanotubes-polyindole/Ti2O3 (f-MWCNTs-PIN/Ti2O3) nanocomposite modified glassy carbon electrode (GCE) was developed. The fabricated f-MWCNTs-PIN/Ti2O3/GCE displayed an enhanced voltammetric response for oxidation towards ALA relative to that of a f-MWCNTs/GCE, f-MWCNTs-PIN/GCE, Ti2O3/GCE, and a bare GCE. Under optimum conditions, the f-MWCNTs-PIN/Ti2O3/GCE showed a wide linear range at ALA concentrations of 0.39-115.8 µM. The limit of detection of 12 nM and sensitivity of about 6.39 µA µM-1cm-2. The developed sensor showed anti-interference, reproducibility, good repeatability, and operational stability. Applied possibility of the sensor has been confirmed in vegetable samples.

Keywords: f-MWCNT, polyindole, Ti2O3, Alzheimer’s diseases, ALA sensor

Procedia PDF Downloads 225

Authors: Monisha Elumalai, Joana Guerreiro, Joana Carvalho, Marta Prado

Abstract:

DNA biosensors popularity has been increasing over the past few years. Traditional analytical techniques tend to require complex steps and expensive equipment however DNA biosensors have the advantage of getting simple, fast and economic. Additionally, the combination of DNA biosensors with nanomaterials offers the opportunity to improve the selectivity, sensitivity and the overall performance of the devices. DNA biosensors are based on oligonucleotides as sensing elements. These oligonucleotides are highly specific to complementary DNA sequences resulting in the hybridization of the strands. DNA biosensors are not only an advantage in the clinical field but also applicable in numerous research areas such as food analysis or environmental control. Zebra Mussels (ZM), Dreissena polymorpha are invasive species responsible for enormous negative impacts on the environment and ecosystems. Generally, the detection of ZM is made when the observation of adult or macroscopic larvae's is made however at this stage is too late to avoid the harmful effects. Therefore, there is a need to develop an analytical tool for the early detection of ZM. Here, we present a portable plasmonic biosensor for the detection of environmental DNA (eDNA) released to the environment from this invasive species. The plasmonic DNA biosensor combines gold nanoparticles, as transducer elements, due to their great optical properties and high sensitivity. The detection strategy is based on the immobilization of a short base pair DNA sequence on the nanoparticles surface followed by specific hybridization in the presence of a complementary target DNA. The hybridization events are tracked by the optical response provided by the nanospheres and their surrounding environment. The identification of the DNA sequences (synthetic target and probes) to detect Zebra mussel were designed by using Geneious software in order to maximize the specificity. Moreover, to increase the optical response enzyme amplification of DNA might be used. The gold nanospheres were synthesized and characterized by UV-visible spectrophotometry and transmission electron microscopy (TEM). The obtained nanospheres present the maximum localized surface plasmon resonance (LSPR) peak position are found to be around 519 nm and a diameter of 17nm. The DNA probes modified with a sulfur group at one end of the sequence were then loaded on the gold nanospheres at different ionic strengths and DNA probe concentrations. The optimal DNA probe loading will be selected based on the stability of the optical signal followed by the hybridization study. Hybridization process leads to either nanoparticle dispersion or aggregation based on the presence or absence of the target DNA. Finally, this detection system will be integrated into an optical sensing platform. Considering that the developed device will be used in the field, it should fulfill the inexpensive and portability requirements. The sensing devices based on specific DNA detection holds great potential and can be exploited for sensing applications in-loco.

Keywords: ZM DNA, DNA probes, nicking enzyme, gold nanoparticles

Procedia PDF Downloads 245

Authors: Tolulope Aremu

Abstract:

The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

Procedia PDF Downloads 18

Authors: Naoum Abderrahmane, Boumehed Meriem, Alshaqaqi Belal

Abstract:

Background subtraction algorithm is a larger used technique for detecting moving objects in video surveillance to extract the foreground objects from a reference background image. There are many challenges to test a good background subtraction algorithm, like changes in illumination, dynamic background such as swinging leaves, rain, snow, and the changes in the background, for example, moving and stopping of vehicles. In this paper, we propose an efficient and accurate background subtraction method for moving object detection in video surveillance. The main idea is to use a developed fast-independent component analysis (ICA) algorithm to separate background, noise, and foreground masks from an image sequence in practical environments. The fast-ICA algorithm is adapted and adjusted with a matrix calculation and searching for an optimum non-quadratic function to be faster and more robust. Moreover, in order to estimate the de-mixing matrix and the denoising de-mixing matrix parameters, we propose to convert all images to YCrCb color space, where the luma component Y (brightness of the color) gives suitable results. The proposed technique has been verified on the publicly available datasets CD net 2012 and CD net 2014, and experimental results show that our algorithm can detect competently and accurately moving objects in challenging conditions compared to other methods in the literature in terms of quantitative and qualitative evaluations with real-time frame rate.

Keywords: background subtraction, moving object detection, fast-ICA, de-mixing matrix

Procedia PDF Downloads 96

Authors: Andreea Campu, Ilinica Muresan, Simona Cainap, Simion Astilean, Monica Focsan

Abstract:

Acute myocardial infarction (AMI) is the most severe cardiovascular disease, which has threatened human lives for decades, thus a continuous interest is directed towards the detection of cardiac biomarkers such as cardiac troponin I (cTnI) in order to predict risk and, implicitly, fulfill the early diagnosis requirements in AMI settings. Microfluidics is a major technology involved in the development of efficient sensing devices with real-time fast responses and on-site applicability. Microfluidic devices have gathered a lot of attention recently due to their advantageous features such as high sensitivity and specificity, miniaturization and portability, ease-of-use, low-cost, facile fabrication, and reduced sample manipulation. The integration of gold nanoparticles into the structure of microfluidic sensors has led to the development of highly effective detection systems, considering the unique properties of the metallic nanostructures, specifically the Localized Surface Plasmon Resonance (LSPR), which makes them highly sensitive to their microenvironment. In this scientific context, herein, we propose the implementation of a novel detection device, which successfully combines the efficiency of gold bipyramids (AuBPs) as signal transducers and thermal generators with the sample-driven advantages of the microfluidic channels into a miniaturized, portable, low-cost, specific, and sensitive test for the dual LSPR-thermographic cTnI detection. Specifically, AuBPs with longitudinal LSPR response at 830 nm were chemically synthesized using the seed-mediated growth approach and characterized in terms of optical and morphological properties. Further, the colloidal AuBPs were deposited onto pre-treated silanized glass substrates thus, a uniform nanoparticle coverage of the substrate was obtained and confirmed by extinction measurements showing a 43 nm blue-shift of the LSPR response as a consequence of the refractive index change. The as-obtained plasmonic substrate was then integrated into a microfluidic “Y”-shaped polydimethylsiloxane (PDMS) channel, fabricated using a Laser Cutter system. Both plasmonic and microfluidic elements were plasma treated in order to achieve a permanent bond. The as-developed microfluidic plasmonic chip was further coupled to an automated syringe pump system. The proposed biosensing protocol implicates the successive injection inside the microfluidic channel as follows: p-aminothiophenol and glutaraldehyde, to achieve a covalent bond between the metallic surface and cTnI antibody, anti-cTnI, as a recognition element, and target cTnI biomarker. The successful functionalization and capture of cTnI was monitored by LSPR detection thus, after each step, a red-shift of the optical response was recorded. Furthermore, as an innovative detection technique, thermal determinations were made after each injection by exposing the microfluidic plasmonic chip to 785 nm laser excitation, considering that the AuBPs exhibit high light-to-heat conversion performances. By the analysis of the thermographic images, thermal curves were obtained, showing a decrease in the thermal efficiency after the anti-cTnI-cTnI reaction was realized. Thus, we developed a microfluidic plasmonic chip able to operate as both LSPR and thermal sensor for the detection of the cardiac troponin I biomarker, leading thus to the progress of diagnostic devices.

Keywords: gold nanobipyramids, microfluidic device, localized surface plasmon resonance detection, thermographic detection

Procedia PDF Downloads 129

Authors: Hiroshi Nakayama, Yuji Ito

Abstract:

Synthetic cannabinoids have attracted much public attention recently in Japan. 1-pentyl-3-(1-naphthoyl)-indole (JWH-018), 1-pentyl-2-methyl-3-(1-naphthoyl) indole (JWH-015), 1-(5-fluoropentyl)-3- (1-(2,2,3,3- tetramethylcyclopropyl)) indole (XLR-11) and 1-methyl-3- (1-admantyl) indole (JWH-018 adamantyl analog) are known as synthetic cannabinoids and are also considered dangerous illegal drugs in Japan. It has become necessary to develop sensitive and useful methods for detection of synthetic cannabinoids. We produced two monoclonal antibodies (MAb) against synthetic cannabinoids, named NT1 (IgG1) and NT2 (IgG1), using Hybridoma technology. The cross-reactivity of these produced MAbs was evaluated using a competitive enzyme-linked immunosorbent assay (ELISA). In the results, we found both of these antibodies recognize many kinds of synthetic cannabinoids analog. However, neither of these antibodies recognizes naphtoic acid, 1-methyl-indole and indole known as a raw material of synthetic cannabinoid. Thus, the MAbs produced in this study could be a useful tool for the detection of synthetic cannabinoids.

Keywords: ELISA, monoclonal antibody, sensor, synthetic cannabinoid

Procedia PDF Downloads 355

Authors: Xinglu Nie, Feifei Tang, Chuntao Wei, Zhimin Ruan, Qianhong Zhu

Abstract:

Current method of manual-style inspection can not fully meet the requirement of the urban rail transit security in China. In this paper, an intelligent inspection method using unmanned aerial vehicle (UAV) is utilized. A series of orthophoto of rail transit monitored area was collected by UAV, image correction and registration were operated among multi-phase images, then the change detection was used to detect the changes, judging the engineering activities and human activities that may become potential threats to the security of urban rail. Not only qualitative judgment, but also quantitative judgment of changes in the security control area can be provided by this method, which improves the objectives and efficiency of the patrol results. The No.6 line of Chongqing Municipality was taken as an example to verify the validation of this method.

Keywords: rail transit, control of protected areas, intelligent inspection, UAV, change detection

Procedia PDF Downloads 369

Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader

Abstract:

The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.

Keywords: condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network

Procedia PDF Downloads 276

Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

Abstract:

This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

Procedia PDF Downloads 63

Authors: Parvin Samadi Pakchin, Reza Saber, Hossein Ghanbari, Yadollah Omidi

Abstract:

Ovarian cancer is one of the leading cause of mortality among the gynecological malignancies, and it remains the one of the most prevalent cancer in females worldwide. Tumor markers are biochemical molecules in blood or tissues which can indicates cancers occurrence in the human body. So, the sensitive and specific detection of cancer markers typically recruited for diagnosing and evaluating cancers. Recently extensive research efforts are underway to achieve a simple, inexpensive and accurate device for detection of cancer biomarkers. Compared with conventional immunoassay techniques, electrochemical immunosensors are of great interest, because they are specific, simple, inexpensive, easy to handling and miniaturization. Moreover, in the past decade nanotechnology has played a crucial role in the development of biosensors. In this study, a signal-off electrochemical immunosensor for the detection of CA125 antigen has been developed using chitosan-gold nanoparticles (CS-AuNP) and multi-wall carbon nanotubes (MWCNT) composites. Toluidine blue (TB) is used as redox probe which is immobilized on the electrode surface. CS-AuNP is synthesized by a simple one step method that HAuCl4 is reduced by NH2 groups of chitosan. The CS-AuNP-MWCNT modified electrode has shown excellent electrochemical performance compared with bare Au electrode. MWCNTs and AuNPs increased electrochemical conductivity and accelerate electrons transfer between solution and electrode surface while excessive amine groups on chitosan lead to the effective loading of the biological material (CA125 antibody) and TB on the electrode surface. The electrochemical, immobilization and sensing properties CS-AuNP-MWCNT-TB modified electrodes are characterized by cyclic voltammetry, electrochemical impedance spectroscopy, differential pulse voltammetry and square wave voltammetry with Fe(CN)63−/4−as an electrochemical redox indicator.

Keywords: signal-off electrochemical biosensor, CA125, ovarian cancer, chitosan-gold nanoparticles

Procedia PDF Downloads 290

Authors: Mohamed Sadok Bensalem, Soulef Amamria, Khaled Lazzez, Mohamed Ghanmi

Abstract:

The southern-central Tunisian Atlas presents a typical example of an external zone. It occupies a particular position in the North African chains: firstly, it is the eastern limit of atlassic structures; secondly, it is the edges between the belts structures to the north and the stable Saharan platform in the south. The evolution of deformation study is based on several methods, such as classical or numerical methods. The principals parameters controlling the genesis of folds in the southern central Tunisian Atlas are; the reactivation of pre-existing faults during the later compressive phase, the evolution of decollement level, and the relation between thin and thick-skinned. One of the more principal characters of the southern-central Tunisian Atlas is the variation of belts structures directions determined by: NE-SW direction, named the attlassic direction in Tunisia, the NW-SE direction carried along the Gafsa fault (the oriental limit of southern atlassic accident), and the E-W direction defined in the southern Tunisian Atlas. This variation of direction is the result of important variation of deformation during different tectonics phases. A classical modelling of the Jebel ElKebar anticline, based on faults throw of the pre-existing faults and its reactivation during compressive phases, shows the importance of extensional deformation, particular during Aptian-Albian period, comparing with that of later compression (Alpine phases). A numerical modelling, based on the software Rampe E.M. 1.5.0, applied on the anticline of Jebel Orbata confirms the interpretation of “fault related fold” with decollement level within the Triassic successions. The other important parameter of evolution of deformation is the vertical migration of decollement level; indeed, more than the decollement level is in the recent series, most that the deformation is accentuated. The evolution of deformation is marked the development of duplex structure in Jebel At Taghli (eastern limit of Jebel Orbata). Consequently, the evolution of deformation is proportional to the depth of the decollement level, the most important deformation is in the higher successions; thus, is associated to the thin-skinned deformation; the decollement level permit the passive transfer of deformation in the cover.

Keywords: evolution of deformation, pre-existing faults, decollement level, thin-skinned

Procedia PDF Downloads 126

Authors: Muhammad SAFDAR, Mehmet Ozaslan, Musarrat Abbas Khan

Abstract:

Babesiosis is a haemoparasitic disease due to the multiplication of protozoan’s parasite, Babesia ovis in the red blood cells of the host, and contributes numerous economical losses, including sheep and goat ruminants. The early identification and successful treatment of Babesia Ovis spp. belong to the key steps of control and health management of livestock resources. The objective of this study was to construct a polymerase chain reaction (PCR) based method for the detection of Babesia spp. in small ruminants and to determine the risk factors involved in the spreading of babesiosis infections. A total of 100 blood samples were collected from 50 sheep and 50 goats along with different areas of Muzaffargarh, Pakistan, from randomly selected herds. Data on the characteristics of sheep and goats were collected through questionnaires. Of 100 blood samples examined, 18 were positive for Babesia ovis upon microscopic studies, whereas 11 were positive for the presence of Babesia spp. by PCR assay. For the recognition of parasitic DNA, a set of 500bp oligonucleotide was designed by PCR amplification with sequence 18S rRNA gene for B. ovis. The prevalence of babesiosis in small ruminant’s sheep and goat detected by PCR was significantly higher in female animals (28%) than male herds (08%). PCR analysis of the reference samples showed that the detection limit of the PCR assay was 0.01%. Taken together, all data indicated that this PCR assay was a simple, fast, specific detection method for Babesia ovis species in small ruminants compared to other available methods.

Keywords: Babesia ovis, PCR amplification, 18S rRNA, sheep and goat

Procedia PDF Downloads 126

Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

Abstract:

Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

Procedia PDF Downloads 91

Authors: S. Mani, M. Kafil, E. Asadi

Abstract:

Condition Monitoring (CM) of electrical equipment has gained remarkable importance during the recent years; due to huge production losses, substantial imposed costs and increases in vulnerability, risk and uncertainty levels. Power cables feed numerous electrical equipment such as transformers, motors, and electric furnaces; thus their condition assessment is of a very great importance. This paper investigates electrical, structural and environmental failure sources, all of which influence cables' performances and limit their uptimes; and provides a comprehensive framework entailing practical CM guidelines for maintenance of cables in industries. The multilevel CM framework presented in this study covers performance indicative features of power cables; with a focus on both online and offline diagnosis and test scenarios, and covers short-term and long-term threats to the operation and longevity of power cables. The study, after concisely overviewing the concept of CM, thoroughly investigates five major areas of power quality, Insulation Quality features of partial discharges, tan delta and voltage withstand capabilities, together with sheath faults, shield currents and environmental features of temperature and humidity; and elaborates interconnections and mutual impacts between those areas; using mathematical formulation and practical guidelines. Detection, location, and severity identification methods for every threat or fault source are also elaborated. Finally, the comprehensive, practical guidelines presented in the study are presented for the specific case of Electric Arc Furnace (EAF) feeder MV power cables in Mobarakeh Steel Company (MSC), the largest steel company in MENA region, in Iran. Specific technical and industrial characteristics and limitations of a harsh industrial environment like MSC EAF feeder cable tunnels are imposed on the presented framework; making the suggested package more practical and tangible.

Keywords: condition monitoring, diagnostics, insulation, maintenance, partial discharge, power cables, power quality

Procedia PDF Downloads 228

Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

Abstract:

This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability.

Keywords: ultrasonic distance measurement, accuracy and consistency, flight controller comparisons, ESP32 vs arduino nano

Procedia PDF Downloads 58

Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal

Abstract:

Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.

Keywords: affective computing, deep learning, emotion recognition, multimodal

Procedia PDF Downloads 156