Search results for: advanced radiation detection and measurement

Authors: Amin Amini

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A Kinect sensor has been utilized as a cheap and accurate alternative to 3D laser scanners and electronic distance measurement (EDM) systems. This research presents an inexpensive and easy-to-setup system that utilizes the Microsoft Kinect v2 sensor as a surveying and measurement tool and investigates the possibility of using such a device as a replacement for conventional theodolite systems. The system was tested in an indoor environment where its accuracy in distance and angle measurements was tested using virtual markers in a 3D Cartesian environment. The system has shown an average accuracy of 97.94 % in measuring distances and 99.11 % and 98.84 % accuracy for area and perimeter, respectively, within the Kinect’s surveying range of 1.5 to 6 meters. The research also tested the system competency for relative angle determination between two objects.

Keywords: kinect v2, 3D measurement, depth map, ToF

Procedia PDF Downloads 65

Authors: Mette Kjerholt, Thora Grothe Thomsen, Connie Bøttcher Berthelsen, Bibi Hølge Hazelton

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Academic nursing is a relatively new phenomenon in Denmark. Leadership and management training in nursing does not prepare Danish nurse leaders to become leaders for nurses with academic background, and some leaders may feel estranged with including this kind of nursing staff in clinical settings. Currently there is a debate regarding what academic nurses can contribute with in clinical practice, and some managers express concern regarding whether this will lead to less focus on clinical practice and more focus on theoretical issues that may not seem so relevant in a busy everyday clinical setting. The paper will present the experiences of integrating three advanced nurse practitioners with Ph.D. degrees (ANP) in three different clinical departments at a regional hospital in Denmark with no prior experiences with such profiles among its staff.

Keywords: leadership, advanced nurse practitioners, clinical practice, academic nursing

Procedia PDF Downloads 569

Authors: Fei Jia, Xingjian Bai, Xiaowei Zhang, Wenjie Yan, Ruitong Dai, Xingmin Li, Jozef Kokini

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Pseudomonas aeruginosa is a Gram-negative, aerobic, opportunistic human pathogen that is present in the soil, water, and food. This microbe has been recognized as a representative food-borne spoilage bacterium that can lead to many types of infections. Considering the casualties and property loss caused by P. aeruginosa, the development of a rapid and reliable technique for the detection of P. aeruginosa is crucial. The whole-cell aptasensor, an emerging biosensor using aptamer as a capture probe to bind to the whole cell, for food-borne pathogens detection has attracted much attention due to its convenience and high sensitivity. Here, a low-field magnetic resonance imaging (LF-MRI) aptasensor for the rapid detection of P. aeruginosa was developed. The basic detection principle of the magnetic relaxation switch (MRSw) nanosensor lies on the ‘T₂-shortening’ effect of magnetic nanoparticles in NMR measurements. Briefly speaking, the transverse relaxation time (T₂) of neighboring water protons get shortened when magnetic nanoparticles are clustered due to the cross-linking upon the recognition and binding of biological targets, or simply when the concentration of the magnetic nanoparticles increased. Such shortening is related to both the state change (aggregation or dissociation) and the concentration change of magnetic nanoparticles and can be detected using NMR relaxometry or MRI scanners. In this work, two different sizes of magnetic nanoparticles, which are 10 nm (MN₁₀) and 400 nm (MN₄₀₀) in diameter, were first immobilized with anti- P. aeruginosa aptamer through 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) chemistry separately, to capture and enrich the P. aeruginosa cells. When incubating with the target, a ‘sandwich’ (MN₁₀-bacteria-MN₄₀₀) complex are formed driven by the bonding of MN400 with P. aeruginosa through aptamer recognition, as well as the conjugate aggregation of MN₁₀ on the surface of P. aeruginosa. Due to the different magnetic performance of the MN₁₀ and MN₄₀₀ in the magnetic field caused by their different saturation magnetization, the MN₁₀-bacteria-MN₄₀₀ complex, as well as the unreacted MN₄₀₀ in the solution, can be quickly removed by magnetic separation, and as a result, only unreacted MN₁₀ remain in the solution. The remaining MN₁₀, which are superparamagnetic and stable in low field magnetic field, work as a signal readout for T₂ measurement. Under the optimum condition, the LF-MRI platform provides both image analysis and quantitative detection of P. aeruginosa, with the detection limit as low as 100 cfu/mL. The feasibility and specificity of the aptasensor are demonstrated in detecting real food samples and validated by using plate counting methods. Only two steps and less than 2 hours needed for the detection procedure, this robust aptasensor can detect P. aeruginosa with a wide linear range from 3.1 ×10² cfu/mL to 3.1 ×10⁷ cfu/mL, which is superior to conventional plate counting method and other molecular biology testing assay. Moreover, the aptasensor has a potential to detect other bacteria or toxins by changing suitable aptamers. Considering the excellent accuracy, feasibility, and practicality, the whole-cell aptasensor provides a promising platform for a quick, direct and accurate determination of food-borne pathogens at cell-level.

Keywords: magnetic resonance imaging, meat spoilage, P. aeruginosa, transverse relaxation time

Procedia PDF Downloads 150

Authors: Tadeus Atraskevic, Asch Dalbajobas, Mazahistas Pukuotukas

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The novel ferroelectric properties appeared in the recent ten years. Many scientists consider them as non-statement science. Nevertheless, many papers are published. The Mean field theory takes an important place in the theory of ferroelectric materials which can be applied for Boson induced laser systems for ‘Star Track’ soldiers. The novel Laser, which was produced in The Vilnius Bambalio University is a ‘now-how’ among other laser systems. The laser can produce power of 30 kW during 15 seconds. Its size and compatibility distinguishes it among other devices and safety gadgets. Scientists of Bambalio University have already patented the device. The most interesting in this innovations is the process of operation. Merely it may be operated through a bottle a beer what makes the measurement so convenient, that an ordinary scientist can process all stuff without significant effort just by taking pleasure by drinking a bottle of beer. Here we would like to report on the laser system and present our unique developments.

Keywords: laser, boson, ferroelectrics, mean field theory

Procedia PDF Downloads 172

Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

Procedia PDF Downloads 79

Authors: A. Anandha Selvan, B. Malarvizhi

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In recent year the problem of space debris have become very serious. The mass of the artificial objects in orbit increased quite steadily at the rate of about 145 metric tons annually, leading to a total tally of approximately 7000 metric tons. Now most of space debris object orbiting in LEO region about 97%. The catastrophic collision can be mostly occurred in LEO region, where this collision generate the new debris. Thus, we propose a concept for cleaning the space debris in the region of thermosphere by passing the Extreme Ultraviolet (EUV) radiation to in front of space debris object from the re-orbiter. So in our concept the Extreme Ultraviolet (EUV) radiation will create the thermosphere expansion by reacting with atmospheric gas particles. So the drag is produced in front of the space debris object by thermosphere expansion. This drag force is high enough to slow down the space debris object’s relative velocity. Therefore the space debris object gradually reducing the altitude and finally enter into the earth’s atmosphere. After the first target is removed, the re-orbiter can be goes into next target. This method remove the space debris object without catching debris object. Thus it can be applied to a wide range of debris object without regard to their shapes or rotation. This paper discusses the operation of re-orbiter for removing the space debris in thermosphere region.

Keywords: active space debris removal, space debris, LEO, extreme ultraviolet, re-orbiter, thermosphere

Procedia PDF Downloads 458

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

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

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

Procedia PDF Downloads 229

Authors: Jui-Chien Hsieh

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Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

Procedia PDF Downloads 113

Authors: Lina Pan

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In non-Gaussian clutter of a spherically invariant random vector, in the cases that a certain estimated covariance matrix could become singular, the adaptive target detection of high-range-resolution radar is addressed. Firstly, the restricted maximum likelihood (RML) estimates of unknown covariance matrix and scatterer amplitudes are derived for non-Gaussian clutter. And then the RML estimate of texture is obtained. Finally, a novel detector is devised. It is showed that, without secondary data, the proposed detector outperforms the existing Kelly binary integrator.

Keywords: non-Gaussian clutter, covariance matrix estimation, target detection, maximum likelihood

Procedia PDF Downloads 462

Authors: Camarena Rodriguez C. S., Portocarrero Bonifaz A., Palma Esparza R., Romero Carlos N. A.

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Adobe is a construction material that fulfills the same function as a conventional brick. Widely used since ancient times, it is present in an appreciable percentage of buildings in Latin America. Adobe is a mixture of clay and sand. The interest in the study of the properties of this material arises due to its presence in the infrastructure of hospital´s radiological services, located in places with low economic resources, for the attenuation of radiation. Some materials such as lead and concrete are the most used for shielding and are widely studied in the literature. The present study will determine the mass attenuation coefficient of Adobe. The minimum required thicknesses for the primary and secondary barriers will be estimated for the shielding of radiological facilities where conventional and dental X-rays are performed. For the experimental procedure, an X-ray source emitted direct radiation towards different thicknesses of an Adobe barrier, and a detector was placed on the other side. For this purpose, an UNFORS Xi solid state detector was used, which collected information on the difference of radiation intensity. The initial parameters of the exposure started at 45 kV; and then the tube tension was varied in increments of 5 kV, reaching a maximum of 125 kV. The X-Ray tube was positioned at a distance of 0.5 m from the surface of the Adobe bricks, and the collimation of the radiation beam was set for an area of 0.15 m x 0.15 m. Finally, mathematical methods were applied to determine the mass attenuation coefficient for different energy ranges. In conclusion, the mass attenuation coefficient for Adobe was determined and the approximate thicknesses of the most common Adobe barriers in the hospital buildings were calculated for their later application in the radiological protection.

Keywords: Adobe, attenuation coefficient, radiological protection, shielding, x-rays

Procedia PDF Downloads 156

Authors: Ka Ho Yau, Jan Frederick Engels, Kwok Kei Lai, Reinhard Renneberg

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Lateral flow test is a prevalent technology in various sectors such as food, pharmacology and biomedical sciences. Colloidal gold (CG) is widely used as the signalling molecule because of the ease of synthesis, bimolecular conjugation and its red colour due to intrinsic SPRE. However, the production of colloidal gold is costly and requires vigorous conditions. The stability of colloidal gold are easily affected by environmental factors such as pH, high salt content etc. Silica nanoparticles are well known for its ease of production and stability over a wide range of solvents. Using reverse micro-emulsion (w/o), silica nanoparticles with different sizes can be produced precisely by controlling the amount of water. By incorporating different water-soluble dyes, a rainbow colour of the silica nanoparticles could be produced. Conjugation with biomolecules such as antibodies can be achieved after surface modification of the silica nanoparticles with organosilane. The optimum amount of the antibodies to be labelled was determined by Bradford Assay. In this work, we have demonstrated the ability of the dye-doped silica nanoparticles as a signalling molecule in lateral flow test, which showed a semi-quantitative measurement of the analyte. The image was further analysed for the LOD=10 ng of the analyte. The working range and the linear range of the test were from 0 to 2.15μg/mL and from 0 to 1.07 μg/mL (R2=0.988) respectively. The performance of the tests was comparable to those using colloidal gold with the advantages of lower cost, enhanced stability and having a wide spectrum of colours. The positives lines can be imaged by naked eye or by using a mobile phone camera for a better quantification. Further research has been carried out in multicolour detection of different biomarkers simultaneously. The preliminary results were promising as there was little cross-reactivity being observed for an optimized system. This approach provides a platform for multicolour detection for a set of biomarkers that enhances the accuracy of diseases diagnostics.

Keywords: colorimetric detection, immunosensor, paper-based biosensor, silica

Procedia PDF Downloads 380

Authors: Nir Nissim, Ran Yahalom, Tomer Lancewiki, Yuval Elovici, Boaz Lerner

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Background: Attackers increasingly take advantage of innocent users who tend to use USB devices casually, assuming these devices benign when in fact they may carry an embedded malicious behavior or hidden malware. USB devices have many properties and capabilities that have become the subject of malicious operations. Many of the recent attacks targeting individuals, and especially organizations, utilize popular and widely used USB devices, such as mice, keyboards, flash drives, printers, and smartphones. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched via USB devices. Significance: We propose USBWARE, a project that focuses on the vulnerabilities of USB devices and centers on the development of a comprehensive detection framework that relies upon a crucial attack repository. USBWARE will allow researchers and companies to better understand the vulnerabilities and attacks associated with USB devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The framework of USBWARE is aimed at accurate detection of both known and unknown USB-based attacks by a process that efficiently enhances the framework's detection capabilities over time. The framework will integrate two main security approaches in order to enhance the detection of USB-based attacks associated with a variety of USB devices. The first approach is aimed at the detection of known attacks and their variants, whereas the second approach focuses on the detection of unknown attacks. USBWARE will consist of six independent but complimentary detection modules, each detecting attacks based on a different approach or discipline. These modules include novel ideas and algorithms inspired from or already developed within our team's domains of expertise, including cyber security, electrical and signal processing, machine learning, and computational biology. The establishment and maintenance of the USBWARE’s dynamic and up-to-date attack repository will strengthen the capabilities of the USBWARE detection framework. The attack repository’s infrastructure will enable researchers to record, document, create, and simulate existing and new USB-based attacks. This data will be used to maintain the detection framework’s updatability by incorporating knowledge regarding new attacks. Based on our experience in the cyber security domain, we aim to design the USBWARE framework so that it will have several characteristics that are crucial for this type of cyber-security detection solution. Specifically, the USBWARE framework should be: Novel, Multidisciplinary, Trusted, Lightweight, Extendable, Modular and Updatable and Adaptable. Major Findings: Based on our initial survey, we have already found more than 23 types of USB-based attacks, divided into six major categories. Our preliminary evaluation and proof of concepts showed that our detection modules can be used for efficient detection of several basic known USB attacks. Further research, development, and enhancements are required so that USBWARE will be capable to cover all of the major known USB attacks and to detect unknown attacks. Conclusion: USBWARE is a crucial detection framework that must be further enhanced and developed.

Keywords: USB, device, cyber security, attack, detection

Procedia PDF Downloads 391

Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control

Procedia PDF Downloads 492

Authors: M. Z. Kurian, M. V. Chidananda Murthy, H. S. Guruprasad

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An approach to super-resolve the low-resolution (LR) image is presented in this paper which is very useful in multimedia communication, medical image enhancement and satellite image enhancement to have a clear view of the information in the image. The proposed Advanced B-Spline method generates a high-resolution (HR) image from single LR image and tries to retain the higher frequency components such as edges in the image. This method uses B-Spline technique and Crispening. This work is evaluated qualitatively and quantitatively using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The method is also suitable for real-time applications. Different combinations of decimation and super-resolution algorithms in the presence of different noise and noise factors are tested.

Keywords: advanced b-spline, image super-resolution, mean square error (MSE), peak signal to noise ratio (PSNR), resolution down converter

Procedia PDF Downloads 395

Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Gwi Hyun Lee, Mun Soo Na

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Solar energy has many advantages of infinite and clean source, and also it can be used for reduction of greenhouse gases and environment pollution. Concentrated solar system is a very useful to achieve reasonably high thermal efficiency. Different types of solar concentrating systems have been developed such as parabolic trough and parabolic dish. Conical solar concentrator is one of the most reliable and promising renewable energy systems for higher temperature applications. The objectives of this study were to investigate the influence of flow rate affecting the thermal efficiency of a conical solar collector, which has a double tube absorber placed at focal axis for collecting solar radiation. A conical solar concentrator consists of a conical reflector, which reflects direct solar radiation into an absorber. A double tube absorber was placed at the center of focal axis for collecting the solar radiation reflected from a conical reflector. A dual tracking system consists of a linear actuator and slew drive with driving cycle of 6 seconds. Water was used as circulating fluid, which flows from inlet to outlet of an absorber for collecting solar radiation. Three identical conical solar concentrator systems were installed side by side at the same place for the accurate performance analysis under the same environmental conditions. Performance evaluations were carried out with different volumetric flow rate of 2, 4 and 6 L/min to find the influence of flow rate affecting on thermal efficiency. The results indicated that average thermal efficiency was 73.24%, 81.96%, and 79.78% for each flow rate of 2 L/min, 4 L/min, and 6 L/min. It shows that the flow rate of circulating water has a significant effect on the thermal efficiency of the conical solar concentrator. It is concluded that an optimum flow rate of conical solar concentrator is 6 L/min.

Keywords: conical solar concentrator, performance evaluation, solar energy, solar energy system

Procedia PDF Downloads 272

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Mosab kh. A. A. Bashir, E. Mohamed-Ahmed

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The aim of the study is to compare the results of the external exposure and the range of the dose spread by the patients, hospitalized in two different groups of 3-5 d receiving radioiodine therapy because of thyroid cancer, and one of group were giving the local diuretic plant (barley) as local juice. The control group was 28 patients they were isolated as international precautions after taken I-131 capsule 100 mCi, and their external exposure was recorded day by day after first 24 hrs. and the distance for external measurement was 1 m at the abdominal level. The mean of external exposure values of patients at fourth day were 30.24±12.92 µSv h−1. The second group after taking I-131 capsule 100 mCi we were given barley juice (250 mL) after every meal three times on day and their external exposure was recorded day by day after first 24 hrs. The mean of external exposure values of patients of this group at third day was 26.92±9.89 (14-55) µSv h−1. It was observed that the external exposure from the second group clearly decreased to low levels which contributed to the decrease in patient dose and also to the decrease in the exposure from the patient to his/her family.

Keywords: local diuretic juice, therapy, radiation medicine, diuretic plant

Procedia PDF Downloads 311

Authors: Siyu Wang, Anthony Ward

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Emotional intelligence (EI) is a well-established personal characteristic. It has been viewed as a critical factor which can influence an individual's academic achievement, ability to work and potential to succeed. When working in a group, EI is fundamentally connected to the group members' interaction and ability to work as a team. The ability of a group member to intelligently perceive and understand own emotions (Intrapersonal EI), to intelligently perceive and understand other members' emotions (Interpersonal EI), and to intelligently perceive and understand emotions between different groups (Cross-boundary EI) can be considered as Group emotional intelligence (Group EI). In this research, a more representative Group EI measurement approach, which incorporates the information of the composition of a group and an individual’s role in that group, is proposed. To demonstrate the claim of being more representative Group EI measurement approach, this study adopts a multi-method research design, involving a combination of both qualitative and quantitative techniques to establish a metric of Group EI. From the results, it can be concluded that by introducing the weight coefficient of each group member on group work into the measurement of Group EI, Group EI will be more representative and more capable of understanding what happens during teamwork than previous approaches.

Keywords: case study, emotional intelligence, group EI, multi-method research

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Authors: Zuzana Chaloupková, Zdeňka Marková, Václav Ranc, Radek Zbořil

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Prostate cancer is now one of the most serious oncological diseases in men with an incidence higher than that of all other solid tumors combined. Diagnosis of prostate cancer usually involves detection of related genes or detection of marker proteins, such as PSA. One of the new potential markers is PSMA (prostate specific membrane antigen). PSMA is a unique membrane bound glycoprotein, which is considerably overexpressed on prostate cancer as well as neovasculature of most of the solid tumors. Commonly applied methods for a detection of proteins include techniques based on immunochemical approaches, including ELISA and RIA. Magnetically assisted surface enhanced Raman spectroscopy (MA-SERS) can be considered as an interesting alternative to generally accepted approaches. This work describes a utilization of MA-SERS in a detection of PSMA in human blood. This analytical platform is based on magnetic nanocomposites Fe3O4@Ag, functionalized by a low-molecular selector labeled as JB303. The system allows isolating the marker from the complex sample using application of magnetic force. Detection of PSMA is than performed by SERS effect given by a presence of silver nanoparticles. This system allowed us to analyze PSMA in clinical samples with limits of detection lower than 1 ng/mL.

Keywords: diagnosis, cancer, PSMA, MA-SERS, Ag nanoparticles

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Authors: Sungho Kim, Jaechoul Shin, Yujin Baek, Nakseok Kim, Kyungnam Kim, Shinhaeng Jo

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The climate change and increase in heavy traffic have been accelerating damages that cause the problems such as pothole on asphalt pavement. Pothole causes traffic accidents, vehicle damages, road casualties and traffic congestion. A quick and efficient maintenance method is needed because pothole is caused by stripping and accelerates pavement distress. In this study, we propose a rapid and systematic pothole management by developing a pothole automated repairing equipment including a volume measurement system of pothole. Three kinds of cold mix asphalt mixture were investigated to select repair materials. The materials were evaluated for satisfaction with quality standard and applicability to automated equipment. The volume measurement system of potholes was composed of multi-sensor that are combined with laser sensor and ultrasonic sensor and installed in front and side of the automated repair equipment. An algorithm was proposed to calculate the amount of repair material according to the measured pothole volume, and the system for releasing the correct amount of material was developed. Field test results showed that the loss of repair material amount could be reduced from approximately 20% to 6% per one point of pothole. Pothole rapid automated repair equipment will contribute to improvement on quality and efficient and economical maintenance by not only reducing materials and resources but also calculating appropriate materials. Through field application, it is possible to improve the accuracy of pothole volume measurement, to correct the calculation of material amount, and to manage the pothole data of roads, thereby enabling more efficient pavement maintenance management. Acknowledgment: The author would like to thank the MOLIT(Ministry of Land, Infrastructure, and Transport). This work was carried out through the project funded by the MOLIT. The project name is 'development of 20mm grade for road surface detecting roadway condition and rapid detection automation system for removal of pothole'.

Keywords: automated equipment, management, multi-beam sensor, pothole

Procedia PDF Downloads 222

Authors: Amizawati Mohd Amir, Ruhanita Maelah, Zaidi Mohd Noor

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As part of the Malaysia Higher Institutions' Strategic Plan in promoting high-quality research and education, the Ministry of Higher Education has introduced various instrument to assess the universities performance. The aims are that university will produce more commercially-oriented research and continue to contribute in producing professional workforce for domestic and foreign needs. Yet the spirit of the success lies in the commitment of university particularly the academic staff to translate the vision into reality. For that reason, the element of fairness and justice in assessing individual academic staff performance is crucial to promote directly linked between university and individual work goals. Focusing on public research universities (RUs) in Malaysia, this study observes at the issue through the practice of university contemporary performance measurement system. Accordingly management control theory has conceptualized that contemporary performance measurement consisting of three dimension namely strategic, comprehensive and dynamic building upon equity theory, the relationships between contemporary performance measurement system and organizational justice and in turn the effect on academic staff work performance are tested based on online survey data administered on 365 academic staff from public RUs, which were analyzed using statistics analysis SPSS and Equation Structure Modeling. The findings validated the presence of strategic, comprehensive and dynamic in the contemporary performance measurement system. The empirical evidence also indicated that contemporary performance measure and procedural justice are significantly associated with work performance but not for distributive justice. Furthermore, procedural justice does mediate the relationship between contemporary performance measurement and academic staff work performance. Evidently, this study provides evidence on the importance of perceptions of justice towards influencing academic staff work performance. This finding may be a fruitful input in the setting up academic staff performance assessment policy.

Keywords: comprehensive, dynamic, distributive justice, contemporary performance measurement system, strategic, procedure justice, work performance

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Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

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Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

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Authors: Sthanu Mahadev, Wen Chan, Melanie Lim

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Anisotropy dominated continuous-fiber composite materials have garnered attention in numerous mechanical and aerospace structural applications. Tailored mechanical properties in advanced composites can exhibit superiority in terms of stiffness-to-weight ratio, strength-to-weight ratio, low-density characteristics, coupled with significant improvements in fatigue resistance as opposed to metal structure counterparts. Extensive research has demonstrated their core potential as more than just mere lightweight substitutes to conventional materials. Prior work done by Mahadev and Chan focused on formulating a modified composite shell theory based prognosis methodology for investigating the structural response of thin-walled circular cylindrical shell type composite configurations under in-plane mechanical loads respectively. The prime motivation to develop this theory stemmed from its capability to generate simple yet accurate closed-form analytical results that can efficiently characterize circular composite shell construction. It showcased the development of a novel mathematical framework to analytically identify the location of the centroid for thin-walled, open cross-section, curved composite shells that were characterized by circumferential arc angle, thickness-to-mean radius ratio, and total laminate thickness. Ply stress variations for curved cylindrical shells were analytically examined under the application of centric tensile and bending loading. This work presents a cost-effective, small-platform experimental methodology by taking advantage of the full-field measurement capability of digital image correlation (DIC) for an accurate assessment of key mechanical parameters such as in-plane mechanical stresses and strains, centroid location etc. Mechanical property measurement of advanced composite materials can become challenging due to their anisotropy and complex failure mechanisms. Full-field displacement measurements are well suited for characterizing the mechanical properties of composite materials because of the complexity of their deformation. This work encompasses the fabrication of a set of curved cylindrical shell coupons, the design and development of a novel test-fixture design and an innovative experimental methodology that demonstrates the capability to very accurately predict the location of centroid in such curved composite cylindrical strips via employing a DIC based strain measurement technique. Error percentage difference between experimental centroid measurements and previously estimated analytical centroid results are observed to be in good agreement. The developed analytical modified-shell theory provides the capability to understand the fundamental behavior of thin-walled cylindrical shells and offers the potential to generate novel avenues to understand the physics of such structures at a laminate level.

Keywords: anisotropy, composites, curved cylindrical shells, digital image correlation

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Authors: Juneseok You, Kuewhan Jang, Chanho Park, Jaeyeong Choi, Hyunjun Park, Sehyun Shin, Changsoo Han, Sungsoo Na

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Nanomaterials occur toxic effects to human being or ecological systems. Some sensors have been developed to detect toxic materials and the standard for toxic materials has been established. Zinc oxide nanowire (ZnO NW) is known for toxic material. By ionizing in cell body, ionized Zn ions are overexposed to cell components, which cause critical damage or death. In this paper, we detected ZnO NW in water using QCM (Quartz Crystal Microbalance) and ssDNA (single strand DNA). We achieved 30 minutes of response time for real time detection and 100 pg/mL of limit of detection (LOD).

Keywords: zinc oxide nanowire, QCM, ssDNA, toxic material, biosensor

Procedia PDF Downloads 423

Authors: Craig Mahlasi

Abstract:

The Subtropical Thicket Biome has been in peril of transformation. Estimates indicate that as much as 63% of the Subtropical Thicket Biome is severely degraded. Agricultural expansion is the main driver of transformation. While several studies have sought to document and map the long term transformations, there is a lack of information on disturbance events that allow for timely intervention by authorities. Furthermore, tools that seek to perform continuous land cover change detection are often developed for forests and thus tend to perform poorly in thicket ecosystems. This study investigates the utility of Earth Observation data for continuous land cover change detection in Subtropical Thicket ecosystems. Temporal Neural Networks are implemented on a time series of Sentinel-2 observations. The model obtained 0.93 accuracy, a recall score of 0.93, and a precision score of 0.91 in detecting Thicket disturbances. The study demonstrates the potential of continuous land cover change in Subtropical Thicket ecosystems.

Keywords: remote sensing, land cover change detection, subtropical thickets, near-real time

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Authors: R. Loukil, M. Chtourou, T. Damak

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In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.

Keywords: fault detection and isolation FDI, fault tolerant control FTC, sliding mode observer, nonlinear system, robustness, stability

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Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, a residual generation filter with finite memory structure is proposed for fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

Keywords: residual generation filter, finite memory structure, kalman filter, fast detection

Procedia PDF Downloads 694

Authors: Liu Lin Xin

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With the increasing complexity of systems, log parsing and anomaly detection have become crucial for maintaining system stability. However, traditional methods often struggle with adaptability and accuracy, especially when dealing with rapidly evolving log content and unfamiliar domains. To address these challenges, this paper proposes approach that integrates Retrieval Augmented Generation (RAG) technology with Prompt Engineering for Large Language Models, applied specifically in LogPrompt. This approach enables dynamic log parsing and intelligent anomaly detection by combining real-time information retrieval with prompt optimization. The proposed method significantly enhances the adaptability of log analysis and improves the interpretability of results. Experimental results on several public datasets demonstrate the method's superior performance, particularly in scenarios lacking training data, where it significantly outperforms traditional methods. This paper introduces a novel technical pathway for log parsing and anomaly detection, showcasing the substantial theoretical value and practical potential.

Keywords: log parsing, anomaly detection, RAG, prompt engineering, LLMs

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Authors: Said Benkaciali, Mourad Haddadi, Abdallah Khellaf, Kacem Gairaa, Mawloud Guermoui

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In this study, many experiments were carried out to assess the influence of the input parameters on the performance of multilayer perceptron which is one the configuration of the artificial neural networks. To estimate the daily global solar radiation on the horizontal surface, we have developed some models by using seven combinations of twelve meteorological and geographical input parameters collected from a radiometric station installed at Ghardaïa city (southern of Algeria). For selecting of best combination which provides a good accuracy, six statistical formulas (or statistical indicators) have been evaluated, such as the root mean square errors, mean absolute errors, correlation coefficient, and determination coefficient. We noted that multilayer perceptron techniques have the best performance, except when the sunshine duration parameter is not included in the input variables. The maximum of determination coefficient and correlation coefficient are equal to 98.20 and 99.11%. On the other hand, some empirical models were developed to compare their performances with those of multilayer perceptron neural networks. Results obtained show that the neural networks techniques give the best performance compared to the empirical models.

Keywords: empirical models, multilayer perceptron neural network, solar radiation, statistical formulas

Procedia PDF Downloads 340