Search results for: phosphate detection

Authors: Lorico D. S. Lapitan Jr., Yihan Xu, Yuan Guo, Dejian Zhou

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The ability of ultrasensitive detection of specific genes and discrimination of single nucleotide polymorphisms is important for clinical diagnosis and biomedical research. Herein, we report the development of a new ultrasensitive approach for label-free DNA detection using magnetic nanoparticle (MNP) assisted rapid target capture/separation in combination with signal amplification using poly-enzyme tagged polymer nanobead. The sensor uses an MNP linked capture DNA and a biotin modified signal DNA to sandwich bind the target followed by ligation to provide high single-nucleotide polymorphism discrimination. Only the presence of a perfect match target DNA yields a covalent linkage between the capture and signal DNAs for subsequent conjugation of a neutravidin-modified horseradish peroxidase (HRP) enzyme through the strong biotin-nuetravidin interaction. This converts each captured DNA target into an HRP which can convert millions of copies of a non-fluorescent substrate (amplex red) to a highly fluorescent product (resorufin), for great signal amplification. The use of polymer nanobead each tagged with thousands of copies of HRPs as the signal amplifier greatly improves the signal amplification power, leading to greatly improved sensitivity. We show our biosensing approach can specifically detect an unlabeled DNA target down to 10 aM with a wide dynamic range of 5 orders of magnitude (from 0.001 fM to 100.0 fM). Furthermore, our approach has a high discrimination between a perfectly matched gene and its cancer-related single-base mismatch targets (SNPs): It can positively detect the perfect match DNA target even in the presence of 100 fold excess of co-existing SNPs. This sensing approach also works robustly in clinical relevant media (e.g. 10% human serum) and gives almost the same SNP discrimination ratio as that in clean buffers. Therefore, this ultrasensitive SNP biosensor appears to be well-suited for potential diagnostic applications of genetic diseases.

Keywords: DNA detection, polymer beads, signal amplification, single nucleotide polymorphisms

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Authors: Evgenia Iakovleva, Juha Koivisto, Pasi Karppinen, J. Inkinen, Mikko Alava

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Preventing the spread of infectious diseases throughout the worldwide is one of the most important tasks of modern health care. Infectious diseases not only account for one fifth of the deaths in the world, but also cause many pathological complications for the human health. Touch surfaces pose an important vector for the spread of infections by varying microorganisms, including antimicrobial resistant organisms. Further, antimicrobial resistance is reply of bacteria to the overused or inappropriate used of antibiotics everywhere. The biggest challenges in bacterial detection by existing methods are non-direct determination, long time of analysis, the sample preparation, use of chemicals and expensive equipment, and availability of qualified specialists. Therefore, a high-performance, rapid, real-time detection is demanded in rapid practical bacterial detection and to control the epidemiological hazard. Among the known methods for determining bacteria on the surfaces, Hyperspectral methods can be used as direct and rapid methods for microorganism detection on different kind of surfaces based on fluorescence without sampling, sample preparation and chemicals. The aim of this study was to assess the relevance of such systems to remote sensing of surfaces for microorganisms detection to prevent a global spread of infectious diseases. Bacillus subtilis and Escherichia coli with different concentrations (from 0 to 10x8 cell/100µL) were detected with hyperspectral camera using different filters as visible visualization of bacteria and background spots on the steel plate. A method of internal standards was applied for monitoring the correctness of the analysis results. Distances from sample to hyperspectral camera and light source are 25 cm and 40 cm, respectively. Each sample is optically imaged from the surface by hyperspectral imaging system, utilizing a JAI CM-140GE-UV camera. Light source is BeamZ FLATPAR DMX Tri-light, 3W tri-colour LEDs (red, blue and green). Light colors are changed through DMX USB Pro interface. The developed system was calibrated following a standard procedure of setting exposure and focused for light with λ=525 nm. The filter is ThorLabs KuriousTM hyperspectral filter controller with wavelengths from 420 to 720 nm. All data collection, pro-processing and multivariate analysis was performed using LabVIEW and Python software. The studied human eye visible and invisible bacterial stains clustered apart from a reference steel material by clustering analysis using different light sources and filter wavelengths. The calculation of random and systematic errors of the analysis results proved the applicability of the method in real conditions. Validation experiments have been carried out with photometry and ATP swab-test. The lower detection limit of developed method is several orders of magnitude lower than for both validation methods. All parameters of the experiments were the same, except for the light. Hyperspectral imaging method allows to separate not only bacteria and surfaces, but also different types of bacteria, such as Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Developed method allows skipping the sample preparation and the use of chemicals, unlike all other microbiological methods. The time of analysis with novel hyperspectral system is a few seconds, which is innovative in the field of microbiological tests.

Keywords: Escherichia coli, Bacillus subtilis, hyperspectral imaging, microorganisms detection

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Authors: Julia R. Beulig, Stefan Ohla, Detlev Belder

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In contrast to off-line analytical methods, lab-on-a-chip technology delivers direct information about the observed reaction. Therefore, microfluidic devices make an important scientific contribution, e.g. in the field of synthetic chemistry. Herein, the rapid generation of analytical data can be applied for the optimization of chemical reactions. These microfluidic devices enable a fast change of reaction conditions as well as a resource saving method of operation. In the presented work, we focus on the investigation of multiphase regimes, more specifically on a biphasic microfluidic droplet systems. Here, every single droplet is a reaction container with customized conditions. The biggest challenge is the rapid qualitative and quantitative readout of information as most detection techniques for droplet systems are non-specific, time-consuming or too slow. An exception is the electrospray mass spectrometry (ESI-MS). The combination of a reaction screening platform with a rapid and specific detection method is an important step in droplet-based microfluidics. In this work, we present a novel approach for synthesis optimization on the nanoliter scale with direct ESI-MS detection. The development of a droplet-based microfluidic device, which enables the modification of different parameters while simultaneously monitoring the effect on the reaction within a single run, is shown. By common soft- and photolithographic techniques a polydimethylsiloxane (PDMS) microfluidic chip with different functionalities is developed. As an interface for the MS detection, we use a steel capillary for ESI and improve the spray stability with a Teflon siphon tubing, which is inserted underneath the steel capillary. By optimizing the flow rates, it is possible to screen parameters of various reactions, this is exemplarity shown by a Domino Knoevenagel Hetero-Diels-Alder reaction. Different starting materials, catalyst concentrations and solvent compositions are investigated. Due to the high repetition rate of the droplet production, each set of reaction condition is examined hundreds of times. As a result, of the investigation, we receive possible reagents, the ideal water-methanol ratio of the solvent and the most effective catalyst concentration. The developed system can help to determine important information about the optimal parameters of a reaction within a short time. With this novel tool, we make an important step on the field of combining droplet-based microfluidics with organic reaction screening.

Keywords: droplet, mass spectrometry, microfluidics, organic reaction, screening

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Authors: Chen Zhang, Nan Chen

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High-dimensional process monitoring becomes increasingly important in many application domains, where usually the process distribution is unknown and much more complicated than the normal distribution, and the between-stream correlation can not be neglected. However, since the process dimension is generally much bigger than the reference sample size, most traditional nonparametric multivariate control charts fail in high-dimensional cases due to the curse of dimensionality. Furthermore, when the process goes out of control, the influenced variables are quite sparse compared with the whole dimension, which increases the detection difficulty. Targeting at these issues, this paper proposes a new nonparametric monitoring scheme for high-dimensional processes. This scheme first projects the high-dimensional process into several subprocesses using random projections for dimension reduction. Then, for every subprocess with the dimension much smaller than the reference sample size, a local nonparametric control chart is constructed based on the spatial rank test to detect changes in this subprocess. Finally, the results of all the local charts are fused together for decision. Furthermore, after an out-of-control (OC) alarm is triggered, a diagnostic framework is proposed. using the square-root LASSO. Numerical studies demonstrate that the chart has satisfactory detection power for sparse OC changes and robust performance for non-normally distributed data, The diagnostic framework is also effective to identify truly changed variables. Finally, a real-data example is presented to demonstrate the application of the proposed method.

Keywords: random projection, high-dimensional process control, spatial rank, sequential change detection

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Authors: Maariyah Irfan

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Intro: Current cardiovascular (CV) care faces challenges such as low budgets and high hospital admission rates. This review aims to evaluate Big Data in CV healthcare management through the use of wearable devices in atrial fibrillation (AF) detection. AF may present intermittently, thus it is difficult for a healthcare professional to capture and diagnose a symptomatic rhythm. Methods: The iRhythm ZioPatch, AliveCor portable electrocardiogram (ECG), and Apple Watch were chosen for review due to their involvement in controlled clinical trials, and their integration with smartphones. The cost-effectiveness and AF detection of these devices were compared against the 12-lead ambulatory ECG (Holter monitor) that the NHS currently employs for the detection of AF. Results: The Zio patch was found to detect more arrhythmic events than the Holter monitor over a 2-week period. When patients presented to the emergency department with palpitations, AliveCor portable ECGs detected 6-fold more symptomatic events compared to the standard care group over 3-months. Based off preliminary results from the Apple Heart Study, only 0.5% of participants received irregular pulse notifications from the Apple Watch. Discussion: The Zio Patch and AliveCor devices have promising potential to be implemented into the standard duty of care offered by the NHS as they compare well to current routine measures. Nonetheless, companies must address the discrepancy between their target population and current consumers as those that could benefit the most from the innovation may be left out due to cost and access.

Keywords: atrial fibrillation, big data, cardiovascular healthcare management, wearable devices

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Authors: U. Datta

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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection

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Authors: Kayode A. Olaniyi, Olabanji F. Omotoye, Adeola A. Ogunleye

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The skew detection and correction form an important part of digital document analysis. This is because uncompensated skew can deteriorate document features and can complicate further document image processing steps. Efficient text document analysis and digitization can rarely be achieved when a document is skewed even at a small angle. Once the documents have been digitized through the scanning system and binarization also achieved, document skew correction is required before further image analysis. Research efforts have been put in this area with algorithms developed to eliminate document skew. Skew angle correction algorithms can be compared based on performance criteria. Most important performance criteria are accuracy of skew angle detection, range of skew angle for detection, speed of processing the image, computational complexity and consequently memory space used. The standard Hough Transform has successfully been implemented for text documentation skew angle estimation application. However, the standard Hough Transform algorithm level of accuracy depends largely on how much fine the step size for the angle used. This consequently consumes more time and memory space for increase accuracy and, especially where number of pixels is considerable large. Whenever the Hough transform is used, there is always a tradeoff between accuracy and speed. So a more efficient solution is needed that optimizes space as well as time. In this paper, an improved Hough transform (HT) technique that optimizes space as well as time to robustly detect document skew is presented. The modified algorithm of Hough Transform presents solution to the contradiction between the memory space, running time and accuracy. Our algorithm starts with the first step of angle estimation accurate up to zero decimal place using the standard Hough Transform algorithm achieving minimal running time and space but lacks relative accuracy. Then to increase accuracy, suppose estimated angle found using the basic Hough algorithm is x degree, we then run again basic algorithm from range between ±x degrees with accuracy of one decimal place. Same process is iterated till level of desired accuracy is achieved. The procedure of our skew estimation and correction algorithm of text images is implemented using MATLAB. The memory space estimation and process time are also tabulated with skew angle assumption of within 00 and 450. The simulation results which is demonstrated in Matlab show the high performance of our algorithms with less computational time and memory space used in detecting document skew for a variety of documents with different levels of complexity.

Keywords: hough-transform, skew-detection, skew-angle, skew-correction, text-document

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Authors: Anil P. Dewani, Ravindra L. Bakal, Anil V. Chandewar

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Present work demonstrates the applicability of high-performance liquid chromatography (HPLC) with UV detection for the determination of malondialdehyde as malondialdehyde-thiobarbituric acid complex (MDA-TBA) in-vivo in rats. The HPLC-UV 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 UV detection at 278 nm. The chromatographic conditions were optimized by varying the concentration and pH 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 (80:20 % v/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. The method was linear for MDA spiked in plasma and subjected to derivatization at concentrations ranging from 100 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 ciprofloxacin (CFL) (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 CFL of 21 days.

Keywords: MDA, TBA, ciprofloxacin, HPLC-UV

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Authors: Gamal Shiha, Waleed Samir, Zahid Azam, Premashis Kar, Saeed Hamid, Shiv Sarin

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A simple, rapid and non-invasive electromagnetic sensor (C-FAST device) was- patented; for diagnosis of HCV RNA. Aim: To test the validity of the device compared to standard HCV PCR. Subjects and Methods: The first phase was done as pilot in Egypt on 79 participants; the second phase was done in five centers: one center from Egypt, two centers from Pakistan and two centers from India (800, 92 and 113 subjects respectively). The third phase was done nationally as multicenter study on (1600) participants for ensuring its representativeness. Results: When compared to PCR technique, C-FAST device revealed sensitivity 95% to 100%, specificity 95.5% to 100%, PPV 89.5% to 100%, NPV 95% to 100% and positive likelihood ratios 21.8% to 38.5%. Conclusion: It is practical evidence that HCV nucleotides emit electromagnetic signals that can be used for its identification. As compared to PCR, C-FAST is an accurate, valid and non-invasive device.

Keywords: C-FAST- a valid and reliable device, distant cellular interaction, electromagnetic signal detection, non-invasive diagnosis of HCV

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Authors: Minh Dong Le, Viet Dung Nguyen, Do Huu Viet, Nguyen Huu Tu

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In this paper, we present a new method for circumscribed masses detection in mammograms. Our method is evaluated on 23 mammographic images of circumscribed masses and 20 normal mammograms from public Mini-MIAS database. The method is quite sanguine with sensitivity (SE) of 95% with only about 1 false positive per image (FPpI). To achieve above results we carry out a progression following: Firstly, the input images are preprocessed with the aim to enhance key information of circumscribed masses; Next, we calculate and evaluate statistically basic features of abnormal regions on training database; Then, mammograms on testing database are divided into equal blocks which calculated corresponding features. Finally, using priority order of basic features to classify blocks as an abnormal or normal regions.

Keywords: mammograms, circumscribed masses, evaluated statistically, priority order of basic features

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Authors: Meenal Surawar, Rajashree Kotharkar

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

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

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Authors: Di Luo, Maria Buchberger, Anja Pickhard

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

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

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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

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

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

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

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

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

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

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

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Authors: Reza Rezaeipour Honarmandzad

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

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

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

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

Keywords: analyzer, CEMS, monitoring, NDIR, TMS

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Authors: Meng-Ying Chiu, Yu-Fang Huang, Pei-Wei Wang, Yi-Ru Wang, Yi-Shuan Shao, Mei-Lien Chen

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Background: Organophosphate pesticides (OPs) are the most heavily used pesticides in agriculture in Taiwan. Therefore, they are commonly detected in general public including pregnant women and children. These compounds are proven endocrine disrupters that may affect the neural development in humans. The aim of this study is to assess the OPs exposure of children in 2 years of age and to examine the association between the exposure concentrations and neurodevelopmental effects in children. Methods: In a prospective cohort of 280 mother-child pairs, urine samples of prenatal and postnatal were collected from each participant and analyzed for metabolites of OPs by using gas chromatography-mass spectrometry. Six analytes were measured including dimethylphosphate (DMP), dimethylthiophosphate (DMTP), dimethyldithiophosphate (DMDTP), diethylphosphate (DEP), diethylthiophosphate (DETP), and diethyldithiophosphate (DEDTP). This study created a combined concentration measure for dimethyl compounds (DMs) consisting of the three dimethyl metabolites (DMP, DMTP, and DMDTP), for diethyl compounds (DEs) consisting of the three diethyl metabolites (DEP, DETP, and DEDTP) and six dialkyl phosphate (DAPs). The Bayley Scales of Infant and Toddler Development (Bayley-III) was used to assess children's cognitive behavior at 2 years old. The association between OPs exposure and Bayley-III scale score was determined by using the Mann-Whitney U test. Results: The measurements of urine samples are still on-going. This preliminary data are the report of 56 children aged 2 from the cohort. The detection rates for DMP, DMTP, DMDTP, DEP, DETP, and DEDTP are 80.4%, 69.6%, 64.3%, 64.3%, 62.5%, and 75%, respectively. After adjusting the creatinine concentrations of urine, the median (nmol/g creatinine) of urinary DMP, DMTP, DMDTP, DEP, DETP, DEDTP, DMs, DEs, and DAPs are 153.14, 53.32, 52.13, 19.24, 141.65, 192.17, 308.8, 311.6, and 702.11, respectively. The concentrations of urine are considerably higher than that in other countries. Children’s cognitive behavior was used three scales for Bayley-III, including cognitive, language and motor. In Mann-Whitney U test, the higher levels of DEs had significantly lower motor score (p=0.037), but no significant association was found between the OPs exposure levels and the score of either cognitive or language. Conclusion: The limited sample size suggests that Taipei children are commonly exposed to OPs and OPs exposure might affect the cognitive behavior of young children. This report will present more data to verify the results. The predictors of OPs concentrations, such as dietary pattern will also be included.

Keywords: biomonitoring, children, neurodevelopment, organophosphate pesticides exposure

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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

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

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Authors: Ting-Ying Su, Meng-Shiou Lee, Shyang-Chwen Sheu

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Garlic is used commonly as a seasoning around the world. But some people suffer from allergy to garlic. Garlic may also cause burning of mouth, stomach, and throat. In some Buddhist traditions, consuming garlic is not allowed. The objective of this study is to develop a LAMP based method for detection of garlic in food. We designed specific primers targeted on ITS1-5.8S rRNA-ITS2 sequence of garlic DNA. The LAMP assay was performed using a set of four different primers F3, B3, FIP and BIP at 60˚C in less than 60 mins. Results showed that the primer was not cross-reactive to other commonly used spice including Chinese leek, Chinese onion, green onion, onion, pepper, basil, parsley, pepper and ginger. As low as 2% of garlic DNA could be detected. Garlic still could be detected by developed LAMP after boiled at 100˚C for 80 minutes and autoclaved at 121˚C for 60 minutes. Commercial products labeled with garlic ingredient could be identified by the developed method.

Keywords: garlic, loop-mediated isothermal amplification, processing, DNA

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

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

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

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Authors: Radhia Toujani, Jalel Akaichi

Abstract:

In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: social network, community detection, agglomerative hierarchical clustering, divisive hierarchical clustering, similarity, modularity, metaheuristic, bee colony

Procedia PDF Downloads 379

Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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

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

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

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

Abstract:

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

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

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

Abstract:

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

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

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Authors: Mamoon Masud, Suleman Mazhar

Abstract:

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

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

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Authors: Paul Okonkwo, Howard Smith

Abstract:

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

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

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Authors: A. K. Singh, Chhotu Ram

Abstract:

The present work relates to the corrosivity of distillery effluent and corrosion performance of mild steel and stainless steels SS304L, SS316L, and 2205. The report presents the results and conclusions drawn on the basis of (i) electrochemical polarization tests performed in distillery effluent and laboratory prepared solutions having composition similar to that of the effluent (ii) the surface examination by scanning electron microscope (SEM) of the corroded steel samples. It is observed that pH and presence of chloride, phosphate, calcium, nitrite and nitrate in distillery effluent enhance corrosion, whereas presence of sulphate and potassium inhibits corrosion. Among the materials tested, mild steel is observed to experience maximum corrosion followed by stainless steels SS304L, SS316L, and 2205.

Keywords: corrosion, distillery effluent, electrochemical polarization, steel

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Authors: Nor Asrina Binti Ramlee

Abstract:

Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately.

Keywords: power quality, voltage sag, voltage swell, wavelet transform

Procedia PDF Downloads 372