Search results for: breast cancer detection

Authors: Davit Mirzoyan, Ararat Khachatryan

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A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: detection, monitoring, process corner, process variation

Procedia PDF Downloads 506

Authors: Kirti Gosavi, Anita Bhole

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The paper demonstrates simulation and steady-state performance of three phase squirrel cage induction motor and detection of rotor broken bar fault using MATLAB. This simulation model is successfully used in the fault detection of rotor broken bar for the induction machines. A dynamic model using PWM inverter and mathematical modelling of the motor is developed. The dynamic simulation of the small power induction motor is one of the key steps in the validation of the design process of the motor drive system and it is needed for eliminating advertent design errors and the resulting error in the prototype construction and testing. The simulation model will be helpful in detecting the faults in three phase induction motor using Motor current signature analysis.

Keywords: squirrel cage induction motor, pulse width modulation (PWM), fault diagnosis, induction motor

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Authors: Sema Şenoğlu, Sevgi Karakuş

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Hydrazone scaffold is important to design new drug groups and is found to possess numerous uses in pharmaceutical chemistry. Besides, hydrazone derivatives are also known for biological activities such as anticancer, antimicrobial, antiviral, and antifungal. Hydrazone derivatives are promising anticancer agents because they inhibit cancer proliferation and induce apoptosis. Human carbonic anhydrase IX has a high potential to be an antiproliferative drug target, and targeting this protein is also important for obtaining potential anticancer inhibitors. The protein construct was retrieved as a PDB file from the RCSB protein database. This binding interaction of proteins and ligands was performed using Discovery Studio Visualizer. In vitro inhibitory activity of hydrazone derivatives was tested against enzyme carbonic anhydrase IX on the PyRx programme. Most of these molecules showed remarkable human carbonic anhydrase IX inhibitory activity compared to the acetazolamide. As a result, these compounds appear to be a potential target in drug design against human carbonic anhydrase IX.

Keywords: cancer, carbonic anhydrase IX enzyme, docking, hydrazone

Procedia PDF Downloads 63

Authors: Ippei Kamihira, Takashi Nakajima, Taiyo Matsumura, Hikaru Miura, Takashi Ono

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In this research, the goal was construction of a system by which multiple sensors were used to observe the daily life behavior of persons living alone (while respecting their privacy). Using this information to judge such conditions as a bad physical condition or falling in the home, etc., so that these abnormal conditions can be made known to relatives and third parties. The daily life patterns of persons living alone are expressed by the number of responses of sensors each time that a set time period has elapsed. By comparing data for the prior two weeks, it was possible to judge a situation as 'normal' when the person was in a good physical condition or as 'abnormal' when the person was in a bad physical condition.

Keywords: sensors, elderly living alone, abnormality detection, iifestyle habit

Procedia PDF Downloads 235

Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: C. S. Marques, M. J. Reis Lima, J. Oliveira, E. Teixeira-Lemos

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The growing concerns for physical wellbeing and health have been reflected in the way we choose food in our table. Nowadays, we are all more informed consumers and choose healthier foods. On the other hand, stroke, cancer and atherosclerosis may be somehow minimized by the intake of some bioactive compounds present in food, the so-called nutraceuticals and functional foods. The aim of this work was to make a revision of the published studies about the effects of some bioactive compounds, namely lycopene in human health, in the prevention of diseases, thus playing the role of a functional food. Free radical in human body can induce cell damage and consequently can be responsible for the development of some cancers and chronic diseases. Lycopene is one of the most powerful antioxidants known, being the predominant carotenoid in tomato. The respective chemistry, bioavailability, and its functional role in the prevention of several diseases will be object of this work. On the other hand the inclusion of lycopene in some foods can also be made by biotechnology and represents a way to recover the wastes in the tomato industry with nutritional positive effects in health.

Keywords: tomato, lycopene, bioavailability, functional foods, carotenoids, cancer and antioxidants

Procedia PDF Downloads 585

Authors: Hyun-jin Kim, Gumgyung Gu, Dae-Hyun Ko, Woochang Lee, Sail Chun, Won-Ki Min

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Background: Ovarian carcinoma is the fourth most common cause of cancer-related death in women worldwide. HE4, a novel marker for ovarian cancer could be used for monitoring recurrence or progression of disease in patients with invasive epithelial ovarian carcinoma. It is further intended to be used in conjunction with CA 125 to estimate the risk of epithelial ovarian cancer in women presenting with an adnexal mass. In this study, we aim to evaluate the analytical performance and clinical utility of HE4 assay using Architect i 2000SR(Abbott Diagnostics, USA). Methods: The precision was evaluated according to Clinical and Laboratory Standards Institute(CLSI) EP5 guideline. Three levels of control materials were analyzed twice a day in duplicate manner over 20 days. We calculated within run and total coefficient of variation (CV) at each level of control materials. The linearity was evaluated based on CLSI EP6 guideline. Five levels of calibrator were prepared by mixing high and low level of calibrators. For 43 women with adnexal masses, HE4 and CA 125 were measured and Risk of ovarian malignancy (ROMA) scores were calculated. The patients’ medical records were reviewed to determine the clinical utility of HE4 and ROMA score. Results: In a precision study, the within-run and total CV were 2.0 % and 2.3% for low level of control material, 1.9% and 2.4% for medium level and 0.5 % and 1.1% for high level, respectively. The linear range of HE4 was 14.63 to 1475.15pmol/L. Of the 43 patients, two patients in pre-menopausal group showed the ROMA score above the cut-off level (7.3%). One of them showed CA 125 level within the reference range, while the HE4 was higher than the cut-off. Conclusion: The overall analytical performance of HE4 assay using Architect showed high precision and good linearity within clinically important range. HE4 could be an useful marker for managing patients with adnexal masses.

Keywords: HE4, CA125, ROMA, evaluation, performance

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Authors: Jarek Krajewski, David Daxberger, Luzi Beyer

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Finding objective and non-intrusive measurements of emotional and psychopathological states (e.g., post-traumatic stress disorder, PTSD) is an important challenge. Thus, the proposed approach here uses Photoplethysmographic imaging (PPGI) applying facial RGB Cam videos to estimate heart rate levels. A pipeline for the signal processing of the raw image has been proposed containing different preprocessing approaches, e.g., Independent Component Analysis, Non-negative Matrix factorization, and various other artefact correction approaches. Under resting and constant light conditions, we reached a sensitivity of 84% for pulse peak detection. The results indicate that PPGI can be a suitable solution for providing heart rate data derived from these indirectly post-traumatic stress states.

Keywords: heart rate, PTSD, PPGI, stress, preprocessing

Procedia PDF Downloads 110

Authors: Nyimas Annissa Mutiara Andini

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Every year, children develop hydrocephalus during the first year of life. If it is not treated, hydrocephalus can lead to brain damage, a loss in mental and physical abilities, and even death. To be treated, first, we have to do a proper diagnosis using some examinations especially to detect hydrocephalus earlier. One of the examination that could be done is using a head circumference measurement. Increased head circumference is a first and main sign of hydrocephalus, especially in infant (0-1 year age). Head circumference is a measurement of a child's head largest area. In this measurement, we want to get the distance from above the eyebrows and ears and around the back of the head using a measurement tape. If the head circumference of an infant is larger than normal, this infant might potentially suffer hydrocephalus. If early diagnosis and timely treatment of hydrocephalus could be done most children can recover successfully. There are some problems with early detection of hydrocephalus using regular tape for head circumference measurement. One of the problem is the infant’s comfort. We need to make the infant feel comfort along the head circumference measurement to get a proper result of the examination. For that, we can use a helpful stuff, like a hat. This paper is aimed to describe the possibility of using a head circumference measuring instrument for early detection of hydrocephalus in an infant with a mike hat, coloured-tape-in-hat. In the first life, infants’ head size is about 35 centimeters. First three months after that infants will gain 2 centimeters each month. The second three months, infant’s head circumference will increase 1 cm each month. And for the six months later, the rate is 0.5 cm per month, and end up with an average of 47 centimeters. This formula is compared to the WHO’s head circumference growth chart. The shape of this tape-in-hat is alike an upper arm measurement. This tape-in-hat diameter is about 47 centimeters. It contains twelve different colours range by age. If it is out of the normal colour, the infant potentially suffers hydrocephalus. This examination should be done monthly. If in two times of measurement there still in the same range abnormal of head circumference, or a rapid growth of the head circumference size, the infant should be referred to a pediatrician. There are the pink hat for girls and blue hat for boys. Based on this paper, we know that this measurement can be used to help early detection of hydrocephalus in an infant.

Keywords: head circumference, hydrocephalus, infant, mike hat

Procedia PDF Downloads 248

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

Procedia PDF Downloads 523

Authors: Guorong Sui, Xingwei Jia, Fei Tong, Xiumin Gao

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Camera calibration is a fundamental issue of high precision noncontact measurement. And it is necessary to analyze and study the reliability and application range of its linear model which is often used in the camera calibration. According to the imaging features of monocular cameras, a camera model which is based on the image pixel coordinates and three dimensional space coordinates is built. Using our own customized template, the image pixel coordinate is obtained by the subpixel corner detection method. Without considering the aberration of the optical system, the feature extraction and linearity analysis of the line segment in the template are performed. Moreover, the experiment is repeated 11 times by constantly varying the measuring distance. At last, the linearity of the camera is achieved by fitting 11 groups of data. The camera model measurement results show that the relative error does not exceed 1%, and the repeated measurement error is not more than 0.1 mm magnitude. Meanwhile, it is found that the model has some measurement differences in the different region and object distance. The experiment results show this linear model is simple and practical, and have good linearity within a certain object distance. These experiment results provide a powerful basis for establishment of the linear model of camera. These works will have potential value to the actual engineering measurement.

Keywords: camera linear model, geometric imaging relationship, image pixel coordinates, three dimensional space coordinates, sub-pixel corner detection

Procedia PDF Downloads 265

Authors: Joshi Manisha, Shivaram, Anand Vinekar, Tanya Susan Mathews, Yeshaswini Nagaraj

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Retinopathy of prematurity (ROP) is abnormal blood vessel development in the retina of the eye in a premature infant. The principal object of the invention is to provide a technique for detecting demarcation line and ridge detection for a given ROP image that facilitates early detection of ROP in stage 1 and stage 2. The demarcation line is an indicator of Stage 1 of the ROP and the ridge is the hallmark of typically Stage 2 ROP. Thirty Retcam images of Asian Indian infants obtained during routine ROP screening have been used for the analysis. A graphical user interface has been developed to detect demarcation line/ridge and to extract ground truth. This novel algorithm uses multilevel vessel enhancement to enhance tubular structures in the digital ROP images. It has been observed that the orientation of the demarcation line/ridge is normal to the direction of the blood vessels, which is used for the identification of the ridge/ demarcation line. Quantitative analysis has been presented based on gold standard images marked by expert ophthalmologist. Image based analysis has been based on the length and the position of the detected ridge. In image based evaluation, average sensitivity and positive predictive value was found to be 92.30% and 85.71% respectively. In pixel based evaluation, average sensitivity, specificity, positive predictive value and negative predictive value achieved were 60.38%, 99.66%, 52.77% and 99.75% respectively.

Keywords: ROP, ridge, multilevel vessel enhancement, biomedical

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Authors: Damião Sousa, Hasan Turkez, Ozlem Tozlu, Tamires Lima

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Glioblastoma (GBM) is an aggressive cancer from the brain and with high prevalence and significant morbimortality. Therefore, it is necessary to investigate new therapeutic options against this pathology. Thus, the purpose of this study was to evaluate the antitumor activity from Mentha crispa essential oil (MCEO), its major constituent rotundifolone (ROT) and a series of six analogues on human U87MG glioblastoma cell line. The antitumor effects of the compounds on human U87MG-GBM cell line were assessed using in vitro cell viability assays. In addition, biosafety tests were performed on cultured human blood cells. The data show that MCEO, 1,2-perillaldehyde epoxide (EPER1) and perillaldehyde (PALD) were the most cytotoxic compounds against the U87MG cells, with IC50 values of 16.263, 15.087 and 14.888 μg/mL, respectively. The treatment with MCEO, EPER1 and PALD did not lead to damage in blood cells. These chemical analogues may be useful as prototypes for development of novel antitumor drugs due to their promising activities and toxicological safety.

Keywords: antitumor activity, cancer, natural products, terpenes

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: Nima Samie, Sekaran Muniandy, Zahurin Mohamed, M. S. Kanthimathi

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Although number of indole containing compounds have been reported to have anticancer properties in vitro but only a few of them show potential as anticancer compounds in vivo. The current study was to evaluate the mechanism of cytotoxicity of selected indolic compound in vivo and in vitro. In this context, we determined the potency of the compound in the induction of apoptosis, cell cycle arrest, and cytoskeleton rearrangement. HT-29, WiDr, CCD-18Co, human monocyte/macrophage CRL-9855, and B lymphocyte CCL-156 cell lines were used to determine the IC50 of the compound using the MTT assay. Analysis of apoptosis was carried out using immunofluorescence, acridine orange/ propidium iodide double staining, Annexin-V-FITC assay, evaluation of the translocation of NF-kB, oxygen radical antioxidant capacity, quenching of reactive oxygen species content, measurement of LDH release, caspase-3/-7, -8 and -9 assays and western blotting. The cell cycle arrest was examined using flowcytometry and gene expression was assessed using qPCR array. Results displayed a potent suppressive effect on HT-29 and WiDr after 24 h of treatment with IC50 value of 2.52±0.34 µg/ml and 2.13±0.65 µg/ml respectively. This cytotoxic effect on normal, monocyte/macrophage and B-cells was insignificant. Dipping in the mitochondrial membrane potential and increased release of cytochrome c from the mitochondria indicated induction of the intrinsic apoptosis pathway by the compound. Activation of this pathway was further evidenced by significant activation of caspase-9 and 3/7. The compound was also shown to activate the extrinsic pathways of apoptosis via activation of caspase-8 which is linked to the suppression of NF-kB translocation to the nucleus. Cell cycle arrest in the G1 phase and up-regulation of glutathione reductase, based on excessive ROS production were also observed. These findings were further investigated for inhibitory efficiency of the compound on colonic aberrant crypt foci in male rats. Rats were divided in to 5 groups: vehicle, cancer control, positive control groups and the groups treated with 25 and 50 mg/kg of compounds for 10 weeks. Administration of compound suppressed total colonic ACF formation up to 73.4%. The results also showed that treatment with the compound significantly reduced the level of malondialdehyde while increasing superoxide dismutase and catalase activities. Furthermore, the down-regulation of PCNA and Bcl2 and the up-regulation of Bax was confirmed by immunohistochemical staining. The outcome of this study suggest sthat the indolic compound is a potent anti-cancer agent against colon cancer and can be further evaluated by animal trial.

Keywords: indolic compound, chemoprevention, crypt, azoxymethane, colon cancer

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Authors: Soheil Zorofchian Moghadamtousi, Habsah Abdul Kadir, Mohammadjavad Paydar, Elham Rouhollahi, Hamed Karimian

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The present study was designed to evaluate the molecular mechanisms of Annona muricata leaves ethyl acetate extract (AMEAE) against lung cancer A549 cells. Cell viability analysis revealed the selective cytotoxic effect of AMEAE towards A549 cells. Treatment of A549 cells with AMEAE significantly elevated the reactive oxygen species formation, followed by attenuation of mitochondrial membrane potential via upregulation of Bax and downregulation of Bcl-2, accompanied by cytochrome c release to the cytosol. The released cytochrome c triggered the activation of caspase-9 followed by caspase-3. In addition, AMEAE-induced apoptosis was accompanied by cell cycle arrest at G1 phase. Our data showed for the first time that AMEAE inhibited the proliferation of A549 cells, leading to cell cycle arrest and programmed cell death through activation of the mitochondrial-mediated signaling pathway.

Keywords: Annona muricata, lung cancer, apoptosis, mitochondria

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Authors: Rims Janeliukstis, Deniss Mironovs, Andrejs Kovalovs

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Operational Modal Analysis (OMA) is widely applied as a method for Structural Health Monitoring for structural damage identification and assessment by tracking the changes of the identified modal parameters over time. Unfortunately, modal parameters also depend on such external factors as temperature and loads. Any structural condition assessment using modal parameters should be done taking into consideration those external factors, otherwise there is a high chance of false positives. A method of structural reliability assessment based on anomaly detection technique called Machalanobis Squared Distance (MSD) is proposed. It requires a set of reference conditions to learn healthy state of a structure, which all future parameters are compared to. In this study, structural modal parameters (natural frequency and mode shape), as well as ambient temperature and loads acting on the structure are used as features. Numerical tests were performed on a finite element model of a carbon fibre reinforced polymer composite beam with delamination damage at various locations and of various severities. The advantages of the demonstrated approach include relatively few computational steps, ability to distinguish between healthy and damaged conditions and discriminate between different damage severities. It is anticipated to be promising in reliability assessment of massively produced structural parts.

Keywords: operational modal analysis, reliability assessment, anomaly detection, damage, mahalanobis squared distance

Procedia PDF Downloads 90

Authors: Navpreet Kaur, Himkusha Thakur, Nirmal Prabhakar

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One of the most publicized and controversial issue in crop production is the use of agrichemicals- also known as pesticides. This is evident in many reports that Organophosphate (OP) insecticides, among the broad range of pesticides are mainly involved in acute and chronic poisoning cases. Therefore, detection of OPs is very necessary for health protection, food and environmental safety. In our study, a nanocomposite of poly (3,4 ethylenedioxythiophene) (PEDOT) and multi-walled carbon nanotubes (MWCNTs) has been deposited electrochemically onto the surface of fluorine doped tin oxide sheets (FTO) for the analysis of malathion OP. The -COOH functionalization of MWCNTs has been done for the covalent binding with amino groups of AChE enzyme. The use of PEDOT-MWCNT films exhibited an excellent conductivity, enables fast transfer kinetics and provided a favourable biocompatible microenvironment for AChE, for the significant malathion OP detection. The prepared PEDOT-MWCNT/FTO and AChE/PEDOT-MWCNT/FTO nano-biosensors were characterized by Fourier transform infrared spectrometry (FTIR), Field emission-scanning electron microscopy (FE-SEM) and electrochemical studies. Electrochemical studies were done using Cyclic Voltammetry (CV) or Differential Pulse Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS). Various optimization studies were done for different parameters including pH (7.5), AChE concentration (50 mU), substrate concentration (0.3 mM) and inhibition time (10 min). The detection limit for malathion OP was calculated to be 1 fM within the linear range 1 fM to 1 µM. The activity of inhibited AChE enzyme was restored to 98% of its original value by 2-pyridine aldoxime methiodide (2-PAM) (5 mM) treatment for 11 min. The oxime 2-PAM is able to remove malathion from the active site of AChE by means of trans-esterification reaction. The storage stability and reusability of the prepared nano-biosensor is observed to be 30 days and seven times, respectively. The application of the developed nano-biosensor has also been evaluated for spiked lettuce sample. Recoveries of malathion from the spiked lettuce sample ranged between 96-98%. The low detection limit obtained by the developed nano-biosensor made them reliable, sensitive and a low cost process.

Keywords: PEDOT-MWCNT, malathion, organophosphates, acetylcholinesterase, nano-biosensor, oxime (2-PAM)

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Authors: Rameez Ahmad, Aqib Mehmood, Imran Khan

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Self-directed car (SDC) that can drive itself from one fact to another without support from a driver. Certain trust that self-directed car obligate the probable to transform the transportation manufacturing while essentially removing coincidences, and cleaning up the environment. This study realizes the effects that SDC (also called a self-driving, driver or robotic) vehicle travel demands and ride scheme is likely to have. Without the typical obstacles that allows detection of a audio vision based hardware and software construction (It (SDC) and cost benefits, the vehicle technologies, Gold (Generic Obstacle and Lane Detection) to a knowledge-based system to predict their potential and consider the shape, color, or balance) and an organized environment with colored lane patterns, lane position ban. Discovery the problematic consequence of (SDC) on GT (grand trunk road) road and brand the car further effectual.

Keywords: SDC, gold, GT, knowledge-based system

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

Abstract:

Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Ugo Rovigatti

Abstract:

Neuroblastoma (NBL) has epitomized for at least 40 years our understanding of cancer cellular and molecular biology and its potential applications to novel therapeutic strategies. This includes the discovery of the very first oncogene aberrations and tumorigenesis suppression by differentiation in the 80s; the potential role of suppressor genes in the 90s; the relevance of immunotherapy in the millennium first, and the discovery of additional mutations by NGS technology in the millennium second decade. Similar discoveries were achieved in the majority of human cancers, and similar therapeutic interventions were obtained subsequently to NBL discoveries. Unfortunately, targeted therapies suggested by specific mutations (such as MYCN amplification –MNA- present in ¼ or 1/5 of cases) have not elicited therapeutic successes in aggressive NBL, where the prognosis is still dismal. The reasons appear to be linked to Tumor Heterogeneity, which is particularly evident in NBL but also a clear hallmark of aggressive human cancers generally. The new avenue of cancer immunotherapy (CIT) provided new hopes for cancer patients, but we still ignore the cellular or molecular targets. CIT is emblematic of high-risk disease (HR-NBL) since the mentioned GD2 passive immunotherapy is still providing better survival. We recently critically reviewed and evaluated the literature depicting the genomic landscapes of HR-NBL, coming to the qualified conclusion that among hundreds of affected genes, potential targets, or chromosomal sites, none correlated with anti-GD2 sensitivity. A better explanation is provided by the Micro-Foci inducing Virus (MFV) model, which predicts that neuroblasts infection with the MFV, an RNA virus isolated from a cancer-cluster (space-time association) of HR-NBL cases, elicits the appearance of MNA and additional genomic aberrations with mechanisms resembling chromothripsis. Neuroblasts infected with low titers of MFV amplified MYCN up to 100 folds and became highly transformed and malignant, thus causing neuroblastoma in young rat pups of strains SD and Fisher-344 and larger tumor masses in nu/nu mice. An association was discovered with GD2 since this glycosphingolipid is also the receptor for the family of MFV virus (dsRNA viruses). It is concluded that a dsRNA virus, MFV, appears to provide better explicatory mechanisms for the genesis of i) specific genomic aberrations such as MNA; ii) extensive tumor heterogeneity and chromothripsis; iii) the effects of passive immunotherapy with anti-GD2 monoclonals and that this and similar models should be further investigated in both pediatric and adult cancers.

Keywords: neuroblastoma, MYCN, amplification, viruses, GD2

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Authors: Hitesh Gupta, Surender Dabas

Abstract:

TORS is increasingly gaining widespread use and has been explored as minimally invasive surgery for the treatment of supraglottic cancer (SGC). Being a central critical role of Supraglottis in deglutition, swallowing outcomes post TORS remain a most important factor. Available published studies show inconsistent swallowing outcomes and are deficient in standardized outcome measures, description of swallowing recovery and rehabilitation. So, the objective of this study is to find out swallowing outcomes in SGC patients after TORS provided with early dysphagia management using standardized measures. Prospectively 16 patients were recruited in the study who underwent TORS for primary tumor of Supraglottis, involving one or more sub-sites or invading to sites other than Supraglottis at the BLK Super Specialty Hospital, New Delhi from March 2019 to June 2020. All patients were evaluated for dysphagia with subsequent swallowing rehabilitation on post operative day 3 in the hospital or at the time of discharge, whichever was earlier. Functional oral intake scale (FOIS) and penetration-aspiration score (PAS) were used as outcome measures to quantify swallowing recovery at one month and six month post operatively. Post TORS, patients achieved functional swallow in less than one month, where resection was limited to Supraglottis, while the recovery was delayed in patients with extended resection to tongue base or hypopharynx. Overall, out of Total 16 cases including all supraglottis sub-catagories, 13 (81%) could remove their NG tube (FOIS ≥5 and PAS=1 ) within 6 months. In which 8 cases(62%) achieved functional swallow in less than one month. Swallowing outcomes post TORS supraglottic laryngectomy are favorable if provided with early dysphagia management (or swallowing rehabilitation).

Keywords: dysphagia, supraglottic cancer, swallowing, TORS

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Authors: Shireen Ibish

Abstract:

Physical inactivity and unhealthy diets have contributed to a global burden of disease with increased relation to non-communicable diseases, increased risk of colon and breast cancer, high prevalence of depression, reduced quality of life and early death. The World Health Organisation’s facts on Obesity show a tripling in prevalence across the European Region since the 1980s. This has lead to a huge public health burden, being responsible for and 10-13% of deaths (fourth largest cause of global mortality) and 2-8% of health costs in the Region. In the UK alone, the present cost of physical inactivity has been estimated to be £8.2 billion. In 2002 a paper published in the International Journal of Epidemiology on ‘sedentary’ lifestyle, put into figures the increasingly worrying statistics across European countries. “Percentages of sedentary lifestyles across European countries ranged between 43.3% (Sweden) and 87.8% (Portugal)”. This was especially so amongst obese subjects, less- educated people, and smokers. While in the UK’s “50% of adult population in the UK is predicted to be obese by 2050.” Sports and Exercise Medicine, as a specialty, has a lot to offer in targeting this globally increasing epidemic. The worrying figures and the increasing knowledge of combating and preventing this issue have lead to increased awareness amongst the medical profession and more targeted interventions to reduce the burden of disease. “The public health element of the specialty is critical – this is not simply a specialty for the management of elite athletes’ medical conditions – it is central to the promotion of exercise as a means of disease prevention, to enhance well-being and in the management of disease.” WHO advised on creating National policies, encouraging and providing opportunities for greater physical activity, and improve the affordability, availability and accessibility of healthy foods. In the UK various different movements have been established to target this problem. The Motivate2Move, Move Eat Treat and guidelines advising specialties on targeting and encouraging exercise in the population (Sport and Exercise Medicine A Fresh Approach).

Keywords: sedentary lifestyle, obesity, public health burden, medicine

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Y. Kourd, D. Lefebvre, N. Guersi

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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. In this paper we propose a method of fault diagnosis based on neuro-fuzzy technique and the choice of a threshold. The validation of this method on a test bench "Actuator Electro DAMADICS Benchmark". In the first phase of the method, we construct a model represents the normal state of the system to fault detection. With residuals analysis generated and the choice of thresholds for signatures table. These signatures provide us with groups of non-detectable faults. In the second phase, we build faulty models to see the flaws in the system that are not located in the first phase.

Keywords: residuals analysis, threshold, neuro-fuzzy system, residual evaluation

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Authors: Gong Zhang, Hong Cai, Bin Dong, Jifang Tao, Aiqun Liu, Dim-Lee Kwong, Yuandong Gu

Abstract:

An on-chip refractive index sensor with high sensitivity and large measurement range is demonstrated in this paper. The sensing structures are based on Mach-Zehnder interferometer configuration, built on the SOI substrate. The wavelength sensitivity of the sensor is estimated to be 3129 nm/RIU. Meanwhile, according to the interference pattern period changes, the measured period sensitivities are 2.9 nm/RIU (TE mode) and 4.21 nm/RIU (TM mode), respectively. As such, the wavelength shift and the period shift can be used for fine index change detection and larger index change detection, respectively. Therefore, the sensor design provides an approach for large index change measurement with high sensitivity.

Keywords: Mach-Zehnder interferometer, nanotechnology, refractive index sensing, sensors

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Authors: M. Abuqebitah, H. Tanyildizi, N. Yeyin, I. Cavdar, M. Demir, L. Kabasakal

Abstract:

Aim: Targeted radionuclide therapy (TRT) is an increasingly used treatment modality for wide range of cancers. Presently dosimetry is highly required either to plan treatment or to ascertain the absorbed dose delivered to critical organs during treatment. Methods and Materials: The study comprised 7 patients suffered from prostate cancer with progressive disease and candidate to undergo Lu-177-DOTA-617 therapy following to PSMA- PET/CT imaging for all patients. (5.2±0.3 mCi) was intravenously injected. To evaluate bone marrow absorbed dose 2 cc blood samples were withdrawn in short variable times (3, 15, 30, 60, 180 minutes) after injection. Furthermore, whole body scans were performed using scintillation gama camera in 4, 24, 48, and 120 hours after injection and in order to quantify the activity taken up in the body, kidneys , liver, right parotid, and left parotid the geometric mean of anterior and posterior counts were determined through ROI analysis, after that background subtraction and attenuation correction were applied using patients PSMA- PET/CT images taking in a consideration: organ thickness, body thickness, and Hounsfield unites from CT scan. OLINDA/EXM dosimetry program was used for curve fitting, residence time calculation, and absorbed dose calculations. Findings: Absorbed doses of bone marrow, left kidney, right kidney, liver, left parotid, right parotid, total body were 1.28±0.52, 32.36±16.36, 32.7±13.68, 10.35±3.45, 38.67±21.29, 37.55±19.77, 2.25±0.95 (mGy/mCi), respectively. Conclusion: Our first results clarify that Lu-177-DOTA-617 is safe and reliable therapy as there were no complications seen. In the other hand, the observable variation in the absorbed dose of the critical organs among the patients necessitate patient-specific dosimetry approach to save body organs and particularly highly exposed kidneys and parotid gland.

Keywords: Lu-177-PSMA, prostate cancer, radionuclide therapy

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Authors: Refilwe Moeletsi

Abstract:

Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

Procedia PDF Downloads 292