Search results for: biomarker detection

Authors: Naoum Abderrahmane, Boumehed Meriem, Alshaqaqi Belal

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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

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Authors: Hiroshi Nakayama, Yuji Ito

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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 356

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

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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

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

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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

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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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

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Authors: Muhammad SAFDAR, Mehmet Ozaslan, Musarrat Abbas Khan

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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

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

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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

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Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

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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

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

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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

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Y. Kourd, D. Lefebvre

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The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. This paper proposes a method of fault diagnosis based on a neuro-fuzzy hybrid structure. This hybrid structure combines the selection of threshold and decision tree. The validation of this method is obtained with the DAMADICS benchmark. In the first phase of the method, a model will be constructed that represents the normal state of the system to fault detection. Signatures of the faults are obtained with residuals analysis and selection of appropriate thresholds. These signatures provide groups of non-separable faults. In the second phase, we build faulty models to see the flaws in the system that cannot be isolated in the first phase. In the latest phase we construct the tree that isolates these faults.

Keywords: decision tree, residuals analysis, ANFIS, fault diagnosis

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Authors: Tien S. H. Pham, Peter J. Mahon, Aimin Yu

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The detection of drug molecules by voltammetry has attracted great interest over the past years. However, many drug molecules exhibit poor electrochemical signals at common electrodes which result in low sensitivity in detection. An efficient way to overcome this problem is to modify electrodes with functional materials. Since discovered in 2004, graphene (or reduced graphene oxide) has emerged as one of the most studied two-dimensional carbon materials in condensed matter physics, electrochemistry, and so on due to its exceptional physicochemical properties. Additionally, the continuous development of technology has opened the new window for the successful fabrications of many novel graphene-based nanomaterials to serve in electrochemical analysis. This research aims to synthesize and characterize gold nanoparticle coated beta-cyclodextrin functionalized reduced graphene oxide (Au NP–β-CD–RGO) nanocomposites with highly conductive and strongly electro-catalytic properties as well as excellent supramolecular recognition abilities for the modification of electrodes. The electrochemical responses of ciprofloxacin at the as-prepared nanocomposite modified electrode was effectively amplified was much higher in comparison with that at the bare electrode. The linear concentration range was from 0.01 to 120 µM, with a detection limit of 2.7 nM using differential pulse voltammetry. Thus, Au NP–β-CD–RGO nanocomposite has great potential as an ideal material to construct sensitive sensors for the electrochemical determination of ciprofloxacin or similar antibacterial drugs in the future based on its excellent stability, selectivity, and reproducibility.

Keywords: Au nanoparticles, β-CD, ciprofloxacin, electrochemical determination, graphene based nanomaterials

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Authors: Antonella Cartoni, Mattea Carmen Castrovilli

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A biosensor for lactate detection has been developed using an environmentally friendly approach. The biosensor is based on lactate oxidase (LOX) and has remarkable capabilities for reuse and storage at room temperature. The manufacturing technique employed is ambient electrospray deposition (ESD), which enables efficient and sustainable immobilization of the LOX enzyme on a cost-effective com-mercial screen-printed Prussian blue/carbon electrode (PB/C-SPE). The study demonstrates that the ESD technology allows the biosensor to be stored at ambient pressure and temperature for extended periods without affecting the enzymatic activity. The biosensor can be stored for up to 90 days without requiring specific storage conditions, and it can be reused for up to 24 measurements on both freshly prepared electrodes and electrodes that are three months old. The LOX-based biosensor exhibits a lin-ear range of lactate detection between 0.1 and 1 mM, with a limit of detection of 0.07±0.02 mM. Ad-ditionally, it does not exhibit any memory effects. The immobilization process does not involve the use of entrapment matrices or hazardous chemicals, making it environmentally sustainable and non-toxic compared to current methods. Furthermore, the application of a electrospray deposition cycle on previously used biosensors rejuvenates their performance, making them comparable to freshly made biosensors. This highlights the excellent recycling potential of the technique, eliminating the waste as-sociated with disposable devices.

Keywords: green friendly, reuse, storage performance, immobilization, matrix-free, electrospray deposition, biosensor, lactate oxidase, enzyme

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Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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Authors: Ahmed Noor Al-qayyim, Barlas Özden Çağlayan

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Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on obtaining very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. This study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using “Two Points - Condensation (TPC) technique”. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices are obtained from optimization of the equation of motion using the measured test data. The current stiffness matrices are compared with original (undamaged) stiffness matrices. High percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element. Where two cases are considered, the method detects the damage and determines its location accurately in both cases. In addition, the results illustrate that these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can also be used for big structures.

Keywords: damage detection, optimization, signals processing, structural health monitoring, two points–condensation

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

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Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Masauso Moses Phiri

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Frequent glucose monitoring is essential to the management of diabetes. Plasmonic enzyme-based glucose biosensors have the advantages of greater specificity, simplicity and rapidity. The aim of this study was to develop a rapid plasmonic colorimetric glucose biosensor based on biocatalytic enlargement of AuNS guided by GOx. Gold nanoparticles of 18 nm in diameter were synthesized using the citrate method. Using these as seeds, a modified seeded method for the synthesis of monodispersed gold nanostars was followed. Both the spherical and star-shaped nanoparticles were characterized using ultra-violet visible spectroscopy, agarose gel electrophoresis, dynamic light scattering, high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy. The feasibility of a plasmonic colorimetric assay through growth of AuNS by silver coating in the presence of hydrogen peroxide was investigated by several control and optimization experiments. Conditions for excellent sensing such as the concentration of the detection solution in the presence of 20 µL AuNS, 10 mM of 2-(N-morpholino) ethanesulfonic acid (MES), ammonia and hydrogen peroxide were optimized. Using the optimized conditions, the glucose assay was developed by adding 5mM of GOx to the solution and varying concentrations of glucose to it. Kinetic readings, as well as color changes, were observed. The results showed that the absorbance values of the AuNS were blue shifting and increasing as the concentration of glucose was elevated. Control experiments indicated no growth of AuNS in the absence of GOx, glucose or molecular O₂. Increased glucose concentration led to an enhanced growth of AuNS. The detection of glucose was also done by naked-eye. The color development was near complete in ± 10 minutes. The kinetic readings which were monitored at 450 and 560 nm showed that the assay could discriminate between different concentrations of glucose by ± 50 seconds and near complete at ± 120 seconds. A calibration curve for the qualitative measurement of glucose was derived. The magnitude of wavelength shifts and absorbance values increased concomitantly with glucose concentrations until 90 µg/mL. Beyond that, it leveled off. The lowest amount of glucose that could produce a blue shift in the localized surface plasmon resonance (LSPR) absorption maxima was found to be 10 – 90 µg/mL. The limit of detection was 0.12 µg/mL. This enabled the construction of a direct sensitivity plasmonic colorimetric detection of glucose using AuNS that was rapid, sensitive and cost-effective with naked-eye detection. It has great potential for transfer of technology for point-of-care devices.

Keywords: colorimetric, gold nanostars, glucose, glucose oxidase, plasmonic

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Authors: Hanna Lu, Sandra S. M. Chan, Linda C. W. Lam

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Objectives: Intra-individual variability (IIV) has been considered as a biomarker of healthy ageing. However, the composite role of IIV in attention, as an impending indicator for neurocognitive disorders warrants further exploration. This study aims to investigate the IIV, as well as their relationships with attention network functions in adults with neurocognitive disorders (NCD). Methods: 36adults with NCD due to Alzheimer’s disease(NCD-AD), 31adults with NCD due to vascular disease (NCD-vascular), and 137 healthy controls were recruited. Intraindividual standard deviations (iSD) and intraindividual coefficient of variation of reaction time (ICV-RT) were used to evaluate the IIV. Results: NCD groups showed greater IIV (iSD: F= 11.803, p < 0.001; ICV-RT:F= 9.07, p < 0.001). In ROC analyses, the indices of IIV could differentiateNCD-AD (iSD: AUC value = 0.687, p= 0.001; ICV-RT: AUC value = 0.677, p= 0.001) and NCD-vascular (iSD: AUC value = 0.631, p= 0.023;ICV-RT: AUC value = 0.615, p= 0.045) from healthy controls. Moreover, the processing speed could distinguish NCD-AD from NCD-vascular (AUC value = 0.647, p= 0.040). Discussion: Intra-individual variability in attention provides a stable measure of cognitive performance, and seems to help distinguish the senior adults with different cognitive status.

Keywords: intra-individual variability, attention network, neurocognitive disorders, ageing

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Authors: Roberta Pecoraro, Elena Maria Scalisi

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Engineered Nanoparticles (ENPs) and Nanomaterials (ENMs) concern an active research area and a sector in full expansion. They have physical-chemical characteristics and small size that improve their performance compared to common materials. Due to the increase in their production and their subsequent release into the environment, new strategies are emerging to assess risk of nanomaterials. NPs can be released into the environment through aquatic systems by human activities and exert toxicity on living organisms. We evaluated the potential toxic effect of cerium oxide (CeO2) nanoparticles because it’s used in different fields due to its peculiar properties. In order to assess nanoparticles toxicity, Fish Embryo Toxicity (FET) test was performed. Powders of CeO2 NPs supplied by the CNR-IMM of Catania are indicated as CeO2 type 1 (as-prepared) and CeO2 type 2 (modified), while CeO2 type 3 (commercial) is supplied by Sigma-Aldrich. Starting from a stock solution (0.001g/10 ml dilution water) of each type of CeO2 NPs, the other concentration solutions were obtained adding 1 ml of the stock solution to 9 ml of dilution water, leading to three different solutions of concentration (10-4, 10-5, 10-6 g/ml). All the solutions have been sonicated to avoid natural tendency of NPs to aggregate and sediment. FET test was performed according to the OECD guidelines for testing chemicals using our internal protocol procedure. A number of eight selected fertilized eggs were placed in each becher filled with 5 ml of each concentration of the three types of CeO2 NPs; control samples were incubated only with dilution water. Replication was performed for each concentration. During the exposure period, we observed four endpoints (embryo coagulation, lack of formation of somites, failure to lift the yolk bag, no heartbeat) by a stereomicroscope every 24 hours. Immunohistochemical analysis on treated larvae was performed to evaluate the expression of metallothioneins (MTs), Heat Shock Proteins 70 (HSP70) and 7-ethoxyresorufin-O-diethylase (EROD). Our results have not shown evident alterations on embryonic development because all embryos completed the development and the hatching of the eggs, started around the 48th hour after exposure, took place within the last observation at 72 hours. A good reactivity, both in the embryos and in the newly hatched larvae, was found. The presence of heartbeat has also been observed in embryos with reduced mobility confirming their viability. A higher expression of EROD biomarker was observed in the larvae exposed to the three types of CeO2, showing a clear difference with the control. A weak positivity was found for MTs biomarker in treated larvae as well as in the control. HSP70 are expressed homogeneously in all the type of nanoparticles tested but not too much greater than control. Our results are in agreement with other studies in the literature, in which the exposure of Danio rerio larvae to other metal oxide nanoparticles does not show adverse effects on survival and hatching time. Further studies are necessary to clarify the role of these NPs and also to solve conflicting opinions.

Keywords: Danio rerio, endpoints, fish embryo toxicity test, metallic nanoparticles

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Authors: Jessie Rapoza, Petr Moroshkin, Jimmy Xu

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Chirality is omnipresent, in nature, in life, and in the field of physics. One intriguing example is the homochirality that has remained a great secret of life. Another is the pairs of mirror-image molecules – enantiomers. They are identical in atomic composition and therefore indistinguishable in the scalar physical properties. Yet, they can be either therapeutic or toxic, depending on their chirality. Recent studies suggest a potential link between abnormal levels of certain D-amino acids and some serious health impairments, including schizophrenia, amyotrophic lateral sclerosis, and potentially cancer. Although indistinguishable in their scalar properties, the chirality of a molecule reveals itself in interaction with the surrounding of a certain chirality, or more generally, a broken mirror-symmetry. In this work, we report on a system for chiral molecule detection, in which the mirror-symmetry is doubly broken, first by asymmetric structuring a nanopatterned plasmonic surface than by the incidence of circularly polarized light (CPL). In this system, the incident circularly-polarized light induces a surface plasmon polariton (SPP) wave, propagating along the asymmetric plasmonic surface. This SPP field itself is chiral, evanescently bound to a near-field zone on the surface (~10nm thick), but with an amplitude greatly intensified (by up to 104) over that of the incident light. It hence probes just the molecules on the surface instead of those in the volume. In coupling to molecules along its path on the surface, the chiral SPP wave favors one chirality over the other, allowing for chirality detection via the change in an optical rectification current measured at the edges of the sample. The asymmetrically structured surface converts the high-frequency electron plasmonic-oscillations in the SPP wave into a net DC drift current that can be measured at the edge of the sample via the mechanism of optical rectification. The measured results validate these design concepts and principles. The observed optical rectification current exhibits a clear differentiation between a pair of enantiomers. Experiments were performed by focusing a 1064nm CW laser light at the sample - a gold grating microchip submerged in an approximately 1.82M solution of either L-arabinose or D-arabinose and water. A measurement of the current output was then recorded under both rights and left circularly polarized lights. Measurements were recorded at various angles of incidence to optimize the coupling between the spin-momentums of the incident light and that of the SPP, that is, spin-momentum locking. In order to suppress the background, the values of the photocurrent for the right CPL are subtracted from those for the left CPL. Comparison between the two arabinose enantiomers reveals a preferential signal response of one enantiomer to left CPL and the other enantiomer to right CPL. In sum, this work reports on the first experimental evidence of the feasibility of chiral molecule detection via optical rectification in a metal meta-grating. This nanoscale interfaced electrical detection technology is advantageous over other detection methods due to its size, cost, ease of use, and integration ability with read-out electronic circuits for data processing and interpretation.

Keywords: Chirality, detection, molecule, spin

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Authors: Seyda Donmez, Oya Aydin Urucu, Ece Kok Yetimoglu

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Solidified floating organic drop microextraction was used for a preconcentration method of trace amount of lead. The analyte was complexed with 1-(2-pyridylazo)-2-naphtol and 1-undecanol, acetonitrile was added as an extraction and dispersive solvent respectively. The influences of some analytical parameters pH, volumes of extraction and disperser solvent, concentration of chelating agent, and concentration of salt were optimized. Under the optimum conditions the detection limits of Pb (II) was determined. The procedure was validated for the analysis of NCS DC 73347a hair standard reference material with satisfactory result. The developed procedure was successfully applied to food and water samples for detection of Pb (II) ions.

Keywords: analytical methods, graphite furnace atomic absorption spectrometry, heavy metals, solidified floating organic drop microextraction

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Xavier A. Conlan

Abstract:

Speed of analysis is a significant limitation to current high-performance liquid chromatography/mass spectrometry (HPLC/MS) and ultra-high-pressure liquid chromatography (UHPLC)/MS systems both of which are used in many forensic investigations. The flow rate limitations of MS detection require a compromise in the chromatographic flow rate, which in turn reduces throughput, and when using modern columns, a reduction in separation efficiency. Commonly, this restriction is combated through the post-column splitting of flow prior to entry into the mass spectrometer. However, this results in a loss of sensitivity and a loss in efficiency due to the post-extra column dead volume. A new chromatographic column format known as 'parallel segmented flow' involves the splitting of eluent flow within the column outlet end fitting, and in this study we present its application in order to interrogate the provenience of methamphetamine samples with mass spectrometry detection. Using parallel segmented flow, column flow rates as high as 3 mL/min were employed in the analysis of amino acids without post-column splitting to the mass spectrometer. Furthermore, when parallel segmented flow chromatography columns were employed, the sensitivity was more than twice that of conventional systems with post-column splitting when the same volume of mobile phase was passed through the detector. These finding suggest that this type of column technology will particularly enhance the capabilities of modern LC/MS enabling both high-throughput and sensitive mass spectral detection.

Keywords: chromatography, mass spectrometry methamphetamine, parallel segmented outlet flow column, forensic sciences

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Authors: Hiroyuki Aoki

Abstract:

The glass transition phenomenon has been extensively studied for a long time. The glass transition of polymer materials is assigned to the transition of the dynamics of the chain backbone segment. However, the detailed mechanism of the transition behavior of the segmental motion is still unclear. In the current work, the single molecule detection technique was employed to reveal the trajectory of the molecular motion of the single polymer chain. The center segment of poly(butyl methacrylate) chain was labeled by a perylenediimide dye molecule and observed by a highly sensitive fluorescence microscope in a defocus condition. The translational and rotational diffusion of the center segment in a single polymer chain was analyzed near the glass transition temperature. The direct observation of the individual polymer chains revealed the intermittent behavior of the segmental motion, indicating the spatial inhomogeneity.

Keywords: glass transition, molecular motion, polymer materials, single molecule

Procedia PDF Downloads 340

Authors: H. Mahdjoub, B. Hamaidi, B. Zerouali, S. Rouabhia

Abstract:

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. Accordingly, the use of neuro-fuzzy technic seems very promising. This paper treats a diagnosis modeling a strategic equipment of an industrial installation. We propose a diagnostic tool based on adaptive neuro-fuzzy inference system (ANFIS). The neuro-fuzzy network provides an abductive diagnosis. Moreover, it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. This work was carried out with real data of a lubrication circuit from the gas turbine. The machine of interest is a gas turbine placed in a gas compressor station at South Industrial Centre (SIC Hassi Messaoud Ouargla, Algeria). We have defined the zones of good and bad functioning, and the results are presented to demonstrate the advantages of the proposed method.

Keywords: fault detection and diagnosis, lubrication system, turbine, ANFIS, training, pattern recognition

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Authors: Mehmet Hakan Karaata, Ali Hamdan, Omer Yusuf Adam Mohamed

Abstract:

Consider a distributed system that delivers messages from a process to another. Such a system is often required to deliver each message to its destination regardless of whether or not the system components experience arbitrary forms of faults. In addition, each message received by the destination must be a message sent by a system process. In this paper, we first identify the necessary and sufficient conditions to detect some restricted form of Byzantine faults referred to as modifying Byzantine faults. An observable form of a Byzantine fault whose effect is limited to the modification of a message metadata or content, timing and omission faults, and message replay is referred to as a modifying Byzantine fault. We then present a distributed protocol to detect modifying Byzantine faults using optimal number of messages over node-disjoint paths.

Keywords: Byzantine faults, distributed systems, fault detection, network pro- tocols, node-disjoint paths

Procedia PDF Downloads 566

Authors: Iqraq Kamal, Akmal Razif, Sivadas Chandra Sekaran, Ahmad Syazwan Hisaburi

Abstract:

Painting plays a crucial role in the aerospace manufacturing industry, serving both protective and cosmetic purposes for components. However, the traditional manual painting method is time-consuming and labor-intensive, posing challenges for the sector in achieving higher efficiency. Additionally, the current automated robot path planning has been a bottleneck for spray painting processes, as typical manual teaching methods are time-consuming, error-prone, and skill-dependent. Therefore, it is essential to develop automated tool path planning methods to replace manual ones, reducing costs and improving product quality. Focusing on flat panel painting in aerospace manufacturing, this study aims to address issues related to unreliable part identification techniques caused by the high-mixture, low-volume nature of the industry. The proposed solution involves using a spray gun and a UR10 robotic arm with a vision system that utilizes one-shot object detection (OS2D) to identify parts accurately. Additionally, the research optimizes path planning by concentrating on the region of interest—specifically, the identified part, rather than uniformly covering the entire painting tray.

Keywords: aerospace manufacturing, one-shot object detection, automated spray painting, vision-based path optimization, deep learning, automation, robotic arm

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Authors: Aidan Battison, Neliswa Mama

Abstract:

Environmental pollution by ionic species has been identified as one of the biggest challenges to the sustainable development of communities. The widespread use of organic and inorganic chemical products and the release of toxic chemical species from industrial waste have resulted in a need for advanced monitoring technologies for environment protection, remediation and restoration. Some of the disadvantages of conventional sensing methods include expensive instrumentation, well-controlled experimental conditions, time-consuming procedures and sometimes complicated sample preparation. On the contrary, the development of fluorescent chemosensors for biological and environmental detection of metal ions has attracted a great deal of attention due to their simplicity, high selectivity, eidetic recognition, rapid response and real-life monitoring. Coumarin derivatives S1 and S2 (Scheme 1) containing 1,2,3-triazole moieties at position -3- have been designed and synthesized from azide and alkyne derivatives by CuAAC “click” reactions for the detection of metal ions. These compounds displayed a strong preference for Fe3+ ions with complexation resulting in fluorescent quenching through photo-induced electron transfer (PET) by the “sphere of action” static quenching model. The tested metal ions included Cd2+, Pb2+, Ag+, Na+, Ca2+, Cr3+, Fe3+, Al3+, Cd2+, Ba2+, Cu2+, Co2+, Hg2+, Zn2+ and Ni2+. The detection limits of S1 and S2 were determined to be 4.1 and 5.1 uM, respectively. Compound S1 displayed the greatest selectivity towards Fe3+ in the presence of competing for metal cations. S1 could also be used for the detection of Fe3+ in a mixture of CH3CN/H¬2¬O. Binding stoichiometry between S1 and Fe3+ was determined by using both Jobs-plot and Benesi-Hildebrand analysis. The binding was shown to occur in a 1:1 ratio between the sensor and a metal cation. Reversibility studies between S1 and Fe3+ were conducted by using EDTA. The binding site of Fe3+ to S1 was determined by using 13 C NMR and Molecular Modelling studies. Complexation was suggested to occur between the lone-pair of electrons from the coumarin-carbonyl and the triazole-carbon double bond.

Keywords: chemosensor, "click" chemistry, coumarin, fluorescence, static quenching, triazole

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Authors: Rana Tabassum, Ravi Kant, Banshi D. Gupta

Abstract:

Malic acid is an extensively distributed organic acid in numerous horticultural products in minute amounts which significantly contributes towards taste determination by balancing sugar and acid fractions. An enhanced concentration of malic acid is utilized as an indicator of fruit maturity. In addition, malic acid is also a crucial constituent of several cosmetics and pharmaceutical products. An efficient detection and quantification protocol for malic acid is thus highly demanded. In this study, we report a novel detection scheme for malic acid by synergistically collaborating fiber optic surface plasmon resonance (FOSPR) and distinctive features of nanomaterials favorable for sensing applications. The design blueprint involves the deposition of an assembly of malate dehydrogenase enzyme entrapped in ZnO nanowires forming the sensing route over silver coated central unclad core region of an optical fiber. The formation and subsequent decomposition of the enzyme-analyte complex on exposure of the sensing layer to malic acid solutions of diverse concentration results in modification of the dielectric function of the sensing layer which is manifested in terms of shift in resonance wavelength. Optimization of experimental variables such as enzyme concentration entrapped in ZnO nanowires, dip time of probe for deposition of sensing layer and working pH range of the sensing probe have been accomplished through SPR measurements. The optimized sensing probe displays high sensitivity, broad working range and a minimum limit of detection value and has been successfully tested for malic acid determination in real samples of fruit juices. The current work presents a novel perspective towards malic acid determination as the unique and cooperative combination of FOSPR and nanomaterials provides myriad advantages such as enhanced sensitivity, specificity, compactness together with the possibility of online monitoring and remote sensing.

Keywords: surface plasmon resonance, optical fiber, sensor, malic acid

Procedia PDF Downloads 380