Search results for: automatic magnetic dispersive solid-phase extraction

Authors: Sandabad Sara, Sayd Tahri Yassine, Hammouch Ahmed

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The development of science has allowed computer scientists to touch the medicine and bring aid to radiologists as we are presenting it in our article. Our work focuses on the detection and localization of tumors areas in the human brain; this will be a completely automatic without any human intervention. In front of the huge volume of MRI to be treated per day, the radiologist can spend hours and hours providing a tremendous effort. This burden has become less heavy with the automation of this step. In this article we present an automatic and effective tumor detection, this work consists of two steps: the first is the image filtering using the filter Nl-means, then applying the expectation maximization algorithm (EM) for retrieving the tumor mask from the brain MRI and extracting the tumor area using the mask obtained from the second step. To prove the effectiveness of this method multiple evaluation criteria will be used, so that we can compare our method to frequently extraction methods used in the literature.

Keywords: MRI, Em algorithm, brain, tumor, Nl-means

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Authors: M. N. Azian, A. N. Ilia Anisa, Y. Iwai

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The mechanism for extraction bioactive compounds from plant matrix is essential for optimizing the extraction process. As a benchmark technique, a soxhlet extraction has been utilized for discussing the mechanism and compared with an accelerated water extraction. The trends of both techniques show that the process involves extraction and degradation. The highest yields of 6-, 8-, 10-gingerols and 6-shogaol in soxhlet extraction were 13.948, 7.12, 10.312 and 2.306 mg/g, respectively. The optimum 6-, 8-, 10-gingerols and 6-shogaol extracted by the accelerated water extraction at 140oC were 68.97±3.95 mg/g at 3min, 18.98±3.04 mg/g at 5min, 5.167±2.35 mg/g at 3min and 14.57±6.27 mg/g at 3min, respectively. The effect of temperature at 3mins shows that the concentration of 6-shogaol increased rapidly as decreasing the recovery of 6-gingerol.

Keywords: mechanism, ginger bioactive compounds, soxhlet extraction, accelerated water extraction

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: A.Abbad, H.A.Bentounes, W.Benstaali

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The full-potential linearized augmented plane wave method (FP-LAPW) within the Generalized Gradient Approximation (GGA) is used to calculate the magnetic and optical properties of quaternary GaFeMnN. The results show that the compound becomes magnetic and half metallic and there is an apparition of peaks at low frequencies for the optical properties.

Keywords: FP-LAPW, LSDA, magnetic moment, reflectivity

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Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

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Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

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Authors: M. Al Azri, M. Elzain, K. Bouziane, S. M. Chérif

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The electronic and magnetic properties of 6H-SiC with Mn impurities have been calculated using ab-initio calculations. Various configurations of Mn sites and Si and C vacancies were considered. The magnetic coupling between the two Mn atoms at substitutional and interstitials sites with and without vacancies is studied as a function of Mn atoms interatomic distance. It was found that the magnetic interaction energy decreases with increasing distance between the magnetic atoms. The energy levels appearing in the band gap due to vacancies and due to Mn impurities are determined. The calculated DOS’s are used to analyze the nature of the exchange interaction between the impurities. The band coupling model based on the p-d and d-d level repulsions between Mn and SiC has been used to describe the magnetism observed in each configuration. Furthermore, the impacts of applying U to Mn-d orbital on the magnetic moment have also been investigated. The results are used to understand the experimental data obtained on Mn- 6H-SiC (as-implanted and as –annealed) for various Mn concentration (CMn = 0.7%, 1.6%, 7%).

Keywords: ab-initio calculations, diluted magnetic semiconductors, magnetic properties, silicon carbide

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

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In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: evolving learning, knowledge extraction, knowledge graph, text mining

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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Authors: Olayinka Oduwole, Steve Sheard

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The magnetic separation of biological cells using super-magnetic beads has been used widely for various bioassays. These bioassays can further be integrated with other laboratory components to form a biosensor which can be used for cell sorting, mixing, purification, transport, manipulation etc. These bio-sensing applications have also been facilitated by the wide availability of magnetic beads which range in size and magnetic properties produced by different manufacturers. In order to improve the efficiency and separation capabilities of these biosensors, it is important to determine the magnetic force induced velocities and interaction of beads within the magnetic field; this will help biosensor users choose the desired magnetic bead for their specific application. This study presents for the first time the interaction between a pair of different sized super-paramagnetic beads suspended in a static fluid moving within a uniform magnetic field using a modified finite-time-finite-difference scheme. A captured video was used to record the trajectory pattern and a good agreement was obtained between the simulated trajectories and the video data. The model is, therefore, a good approximation for predicting the velocities as well as the interaction between various magnetic particles which differ in size and magnetic properties for bio-sensing applications requiring a low concentration of magnetic beads.

Keywords: biosensor, magnetic field, magnetic separation, super-paramagnetic bead

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Authors: Kinjal Trivedi, Ramesh V. Upadhyay

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Magnetic fluid as a nanolubricant is a most recent field of study due to its unusual properties that can be tuned by applying a magnetic field. In present work, four ball tester has been used to investigate the tribological properties of the magnetic fluid having a 4 wt% of nanoparticles. The structural characterization of fluid shows crystallite size of particle is 11.7 nm and particles are nearly spherical in nature. The magnetic characterization shows the fluid saturation magnetization is 2.2 kA/m. The magnetic field applied using permanent strip magnet (0 to 1.6 mT) on the faces of the lock nut and fixing a solenoid (0 to 50 mT) around a shaft, such that shaft rotates freely. The magnetic flux line for both the systems analyzed using finite elemental analysis. The coefficient of friction increases with the application of magnetic field using permanent strip magnet compared to zero field value. While for the solenoid, it decreases at 20 mT. The wear scar diameter is lower for 1.1 mT and 20 mT when the magnetic field applied using permanent strip magnet and solenoid, respectively. The coefficient of friction and wear scar reduced by 29 % and 7 % at 20 mT using solenoid. The worn surface analysis carried out using Scanning Electron Microscope and Atomic Force Microscope to understand the wear mechanism. The results are explained on the basis of structure formation in a magnetic fluid upon application of magnetic field. It is concluded that the tribological properties of magnetic fluid depend on magnetic field and its applied direction.

Keywords: four ball tester, magnetic fluid, nanolubricant, tribology

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Authors: Angely Sim Jia Wun, Sasa Arsovski

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Numerous studies have been conducted on the segmentation of medical images. Segmenting the lungs is one of the common research topics in those studies. Our research stemmed from the lack of solutions for automatic bone, airway, and vessel segmentation, despite the existence of multiple lung segmentation techniques. Consequently, currently, available software tools used for medical image segmentation do not provide automatic lung, bone, airway, and vessel segmentation. This paper presents segmentation techniques along with an interactive software tool architecture for segmenting bone, lung, airway, and vessel tissues. Additionally, we propose a method for creating binary masks from automatically generated segments. The key contribution of our approach is the technique for automatic image thresholding using adjustable Hounsfield values and binary mask extraction. Generated binary masks can be successfully used as a training dataset for deep-learning solutions in medical image segmentation. In this paper, we also examine the current software tools used for medical image segmentation, discuss our approach, and identify its advantages.

Keywords: lung segmentation, binary masks, U-Net, medical software tools

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Authors: Georgios Alexandropoulos

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The purpose of this study is to examine the historical text of Alexiad by Anna Comnena using computational tools for the extraction of lexical bundles containing the name of her father, Alexius Comnenus. For this reason, in this research we apply corpus linguistics techniques for the automatic extraction of lexical bundles and through them we will draw conclusions about how these lexical bundles serve her support provided to her father.

Keywords: lexical bundles, computational literature, critical discourse analysis, Alexiad

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Authors: Tomasz Borowski, Dawid Sołoducha, Rafał Rakoczy, Marian Kordas

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The growing interest in magnetic fields applications is visible due to the increased number of articles on this topic published in the last few years. In this study, the influence of various magnetic fields (MF) on the mass transfer process was examined. To carry out the prototype set-up equipped with an MF generator that is able to generate a pulsed magnetic field (PMF), oscillating magnetic field (OMF), rotating magnetic field (RMF) and static magnetic field (SMF) was used. To demonstrate the effect of MF’s on mass transfer, the calcium carbonate precipitation process was selected. To the vessel with attached conductometric probes and placed inside the generator, specific doses of calcium chloride and sodium carbonate were added. Electrical conductivity changes of the mixture inside the vessel were measured over time until equilibrium was established. Measurements were conducted for various MF strengths and concentrations of added chemical compounds. Obtained results were analyzed, which allowed to creation of mathematical correlation models showing the influence of MF’s on the studied process.

Keywords: mass transfer, oscillating magnetic field, rotating magnetic field, static magnetic field

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Authors: Edris Rawashdeh, I. Abu-Falahah, H. M. Jaradat

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The variable-coefficient non-linear dispersive wave equation is investigated with the aid of symbolic computation. By virtue of a newly developed simplified bilinear method, multi-soliton solutions for such an equation have been derived. Effects of the inhomogeneities of media and nonuniformities of boundaries, depicted by the variable coefficients, on the soliton behavior are discussed with the aid of the characteristic curve method and graphical analysis.

Keywords: dispersive wave equations, multiple soliton solution, Hirota Bilinear Method, symbolic computation

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Authors: Theresa Slump, Paula Neumeister, Katharina Feldker, Carina Y. Heitmann, Thomas Straube

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A characteristic feature of post-traumatic stress disorder (PTSD) is the automatic processing of disorder-specific stimuli that expresses itself in intrusive symptoms such as intense physical and psychological reactions to trauma-associated stimuli. That automatic processing plays an essential role in the development and maintenance of symptoms. The aim of our study was, therefore, to investigate the behavioral and neural correlates of automatic processing of trauma-related stimuli in PTSD. Although interpersonal traumatization is a form of traumatization that often occurs, it has not yet been sufficiently studied. That is why, in our study, we focused on patients suffering from interpersonal traumatization. While previous imaging studies on PTSD mainly used faces, words, or generally negative visual stimuli, our study presented complex trauma-related and neutral visual scenes. We examined 19 female subjects suffering from PTSD and examined 19 healthy women as a control group. All subjects did a geometric comparison task while lying in a functional-magnetic-resonance-imaging (fMRI) scanner. Trauma-related scenes and neutral visual scenes that were not relevant to the task were presented while the subjects were doing the task. Regarding the behavioral level, there were not any significant differences between the task performance of the two groups. Regarding the neural level, the PTSD patients showed significant hyperactivation of the hippocampus for task-irrelevant trauma-related stimuli versus neutral stimuli when compared with healthy control subjects. Connectivity analyses revealed altered connectivity between the hippocampus and other anxiety-related areas in PTSD patients, too. Overall, those findings suggest that fear-related areas are involved in PTSD patients' processing of trauma-related stimuli even if the stimuli that were used in the study were task-irrelevant.

Keywords: post-traumatic stress disorder, automatic processing, hippocampus, functional magnetic resonance imaging

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Authors: Kapila Warnakulasuriya, Walimuni Janaka Mendis

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In this study, an artificial intelligence-based approach is developed to diagnose abnormal tissues in human or animal bodies by analyzing magnetic resonance signals. As opposed to the conventional method of generating an image from the magnetic resonance signals, which are then evaluated by a radiologist for the diagnosis of abnormalities, in the discussed approach, the magnetic resonance signals are analyzed by an artificial intelligence algorithm without having to generate or analyze an image. The AI-based program compares magnetic resonance signals with millions of possible magnetic resonance waveforms which can be generated from various types of normal tissues. Waveforms generated by abnormal tissues are then identified, and images of the abnormal tissues are generated with the possible location of them in the body for further diagnostic tests.

Keywords: magnetic resonance, artificial intelligence, magnetic waveform analysis, abnormal tissues

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Authors: Evans Belly, Imdad Rizvi, M. M. Kadam

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Satellite imagery is one of the emerging technologies which are extensively utilized in various applications such as detection/extraction of man-made structures, monitoring of sensitive areas, creating graphic maps etc. The main approach here is the automated detection of buildings from very high resolution (VHR) optical satellite images. Initially, the shadow, the building and the non-building regions (roads, vegetation etc.) are investigated wherein building extraction is mainly focused. Once all the landscape is collected a trimming process is done so as to eliminate the landscapes that may occur due to non-building objects. Finally the label method is used to extract the building regions. The label method may be altered for efficient building extraction. The images used for the analysis are the ones which are extracted from the sensors having resolution less than 1 meter (VHR). This method provides an efficient way to produce good results. The additional overhead of mid processing is eliminated without compromising the quality of the output to ease the processing steps required and time consumed.

Keywords: building detection, shadow detection, landscape generation, label, partitioning, very high resolution (VHR) satellite imagery

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Authors: Huai-Cong Liu，Tae Chul Jeong，Ju Lee

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This paper describes characteristic analysis of a synchronous reluctance motor (SynRM)’s rotor with the Multi-segment and Multi-layer structure. The magnetic-saturation phenomenon in SynRM is often appeared. Therefore, when modeling analysis of SynRM the calculation of nonlinear magnetic field needs to be considered. An important influence factor on the convergence process is how to determine the relative permeability. An improved method, which ensures the calculation, is convergence by linear iterative method for saturated magnetic field. If there are inflection points on the magnetic curve,an optimum convergence method of solution for nonlinear magnetic field was provided. Then the equivalent magnetic circuit is calculated, and d,q-axis inductance can be got. At last, this process is applied to design a 7.5Kw SynRM and its validity is verified by comparing with the result of finite element method (FEM) and experimental test data.

Keywords: SynRM, magnetic-saturation, magnetic circuit, analytical modeling

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Sari Aouatef, Larabi Amina

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First-principles calculations within density functional theory based are used to investigate the influence of doped rare earth elements on some properties of perovskite systems Dy₀.₀₆₂₅Y₀.₉₃₇₅FeO₃ and Dy₀.₁₂₅ Y₀.₈₇₅ FeO₃. The electronic and magnetic properties are studied by means of the full-potential linearized augmented plane wave method with Vasp code. The calculated densities of states presented in this work identify the semiconducting behavior for Dy₀.₁₂₅ Y₀.₈₇₅ FeO₃, and the semi-metallic behavior for Dy₀.₀₆₂₅Y₀.₉₃₇₅ FeO₃. Besides, to investigate magnetic properties of several compounds, four magnetic configurations are considered (ferromagnetic (FM), antiferromagnetic type A (A-AFM), antiferromagnetic type C (C-AFM) and antiferromagnetic type G (G-AFM). By doping the Dy element, the system shows different changes in the magnetic order and electronic structure. It is found that Dy₀.₀₆₂₅Y₀.₉₃₇₅ FeO₃ exhibits the strongest magnetic change corresponding to the transition to the ferromagnetic order with the largest magnetic moment of 4.997.

Keywords: DFT, Perovskites, multiferroic, magnetic properties

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Authors: Raghvendra Singh Yadav, Ivo Kuřitka, Jarmila Vilcakova, Jaromir Havlica, Lukas Kalina, Pavel Urbánek, Michal Machovsky, Milan Masař, Martin Holek

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In this work, CoFe₂₋ₓGdₓO₄ (x=0.00, 0.05, 0.10, 0.15, 0.20) spinel ferrite nanoparticles are synthesized by sonochemical method. The structural properties and cation distribution are investigated using X-ray Diffraction (XRD), Raman Spectroscopy, Fourier Transform Infrared Spectroscopy and X-ray photoelectron spectroscopy. The morphology and elemental analysis are screened using field emission scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectroscopy, respectively. The particle size measured by FE-SEM and XRD analysis confirm the formation of nanoparticles in the range of 7-10 nm. The electrical properties show that the Gd³⁺ doped cobalt ferrite (CoFe₂₋ₓGdₓO₄; x= 0.20) exhibit enhanced dielectric constant (277 at 100 Hz) and ac conductivity (20.17 x 10⁻⁹ S/cm at 100 Hz). The complex impedance measurement study reveals that as Gd³⁺ doping concentration increases, the impedance Z’ and Z’ ’ decreases. The influence of Gd³⁺ doping in cobalt ferrite nanoparticles on the magnetic property is examined by using vibrating sample magnetometer. Magnetic property measurement reveal that the coercivity decreases with Gd³⁺ substitution from 234.32 Oe (x=0.00) to 12.60 Oe (x=0.05) and further increases from 12.60 Oe (x=0.05) to 68.62 Oe (x=0.20). The saturation magnetization decreases with Gd³⁺ substitution from 40.19 emu/g (x=0.00) to 21.58 emu/g (x=0.20). This decrease follows the three-sublattice model suggested by Yafet-Kittel (Y-K). The Y-K angle increases with the increase of Gd³⁺ doping in cobalt ferrite nanoparticles.

Keywords: sonochemical method, nanoparticles, magnetic property, dielectric property, electrical property

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Authors: Shalini Rajput, Dinesh Mohan

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Water is the most important and essential resources for existing of life on the earth. Water quality is gradually decreasing due to increasing urbanization and industrialization and various other developmental activities. It can pose a threat to the environment and public health therefore it is necessary to remove hazardous contaminants from wastewater prior to its discharge to the environment. Recently, magnetic iron oxide nanoparticles have been arise as significant materials due to its distinct properties. This article focuses on the synthesis method with a possible mechanism, structure and application of magnetic iron oxide nanoparticles. The various characterization techniques including X-ray diffraction, transmission electron microscopy, scanning electron microscopy with energy dispersive X-ray, Fourier transform infrared spectroscopy and vibrating sample magnetometer are useful to describe the physico-chemical properties of nanoparticles. Nanosized iron oxide particles utilized for remediation of contaminants from aqueous medium through adsorption process. Due to magnetic properties, nanoparticles can be easily separate from aqueous media. Considering the importance and emerging trend of nanotechnology, iron oxide nanoparticles as nano-adsorbent can be of great importance in the field of wastewater treatment.

Keywords: nanoparticles, adsorption, iron oxide, nanotechnology

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Authors: Chao-Ming Su, Pei-Sheng Wu, Yu-Chi Kuo, Yin-Chou Huang, Tan-Yueh Chen, Jefunnie Matahum, Tzong-Rong Ger

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Application of magnetic particles (MPs) has been applied in biomedical field for many years. There are lots of advantages through this mediator including high biocompatibility and multi-diversified bio-applications. However, current techniques for evaluating the quantity of the magnetic-labeled sample assays are rare. In this paper, a Wheatstone bridge giant magnetoresistance (GMR) sensor integrated with a homemade detecting system was fabricated and used to quantify the concentration of MPs. The homemade detecting system has shown high detecting sensitivity of 10 μg/μl of MPs with optimized parameter vertical magnetic field 100 G, horizontal magnetic field 2 G and flow rate 0.4 ml/min.

Keywords: magnetic particles, magnetoresistive sensors, microfluidics, biosensor

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Authors: Neda Azimi, Masoud Rahimi, Faezeh Mohammadi

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Computational fluid dynamics (CFD) simulation was performed to investigate the effect of ferrofluid stimulation on hydrodynamic and mass transfer characteristics of two immiscible liquid phases in a Y-micromixer. The main purpose of this work was to develop a numerical model that is able to simulate hydrodynamic of the ferrofluid flow under magnetic field and determine its effect on mass transfer characteristics. A uniform external magnetic field was applied perpendicular to the flow direction. The volume of fluid (VOF) approach was used for simulating the multiphase flow of ferrofluid and two-immiscible liquid flows. The geometric reconstruction scheme (Geo-Reconstruct) based on piecewise linear interpolation (PLIC) was used for reconstruction of the interface in the VOF approach. The mass transfer rate was defined via an equation as a function of mass concentration gradient of the transported species and added into the phase interaction panel using the user-defined function (UDF). The magnetic field was solved numerically by Fluent MHD module based on solving the magnetic induction equation method. CFD results were validated by experimental data and good agreements have been achieved, which maximum relative error for extraction efficiency was about 7.52 %. It was showed that ferrofluid actuation by a magnetic field can be considered as an efficient mixing agent for liquid-liquid two-phase mass transfer in microdevices.

Keywords: CFD modeling, hydrodynamic, micromixer, ferrofluid, mixing

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Authors: S. Jagadeesh Kumar

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This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

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Authors: Xiao-Li Liu, Ling-Yun Zhao, Xing-Jie Liang, Hai-Ming Fan

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Owing to their unique properties, magnetic nanoparticles have been used as diagnostic and therapeutic agents for biomedical applications. Highly monodispersed magnetic nanoparticles with controlled particle size and surface coating have been successfully synthesized as a model system to investigate the effect of surface coating on the T2 relaxivity and specific absorption rate (SAR) under an alternating magnetic field, respectively. Amongst, by using mPEG-g-PEI to solubilize oleic-acid capped 6 nm magnetic nanoparticles, the T2 relaxivity could be significantly increased by up to 4-fold as compared to PEG coated nanoparticles. Moreover, it largely enhances the cell uptake with a T2 relaxivity of 92.6 mM-1s-1 for in vitro cell MRI. As for hyperthermia agent, SAR value increase with the decreased thickness of PEG surface coating. By elaborate optimization of surface coating and particle size, a significant increase of SAR (up to 74%) could be achieved with a minimal variation on the saturation magnetization (<5%). The 19 nm magnetic nanoparticles with 2000 Da PEG exhibited the highest SAR of 930 W•g-1 among the samples, which can be maintained in various simulated physiological conditions. This systematic work provides a general strategy for the optimization of surface coating of magnetic core for high performance MRI contrast agent and hyperthermia agent.

Keywords: magnetic nanoparticles, magnetic hyperthermia, magnetic resonance imaging, surface modification

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Authors: W. N. L. Mahadi, S. N. Syed Zin, W. A. R. Othman, N. A. Mohd Rasyid, N. Jusoh

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Public concerns on Extremely Low Frequency (ELF) Electromagnetic Field (EMF) exposure have been elongated since the last few decades. Electrical substations and high tension rooms (HT room) in commercial buildings were among the contributing factors emanating ELF magnetic fields. This paper discussed various shielding methods conventionally used in mitigating the ELF exposure. Nevertheless, the standard methods were found to be impractical and incapable of meeting currents shielding demands. In response to that, remarkable researches were conducted in effort to invent novel methods which is more convenient and efficient such as magnetic aqueous shielding or paint, textiles and papers shielding. A mitigation method using magnetic aqueous substrate in shielding application was proposed in this paper for further investigation. using Manganese Zinc Ferrite (Mn0.4Zn0.6Fe2O4). The magnetic field and flux distribution inside the aqueous magnetic material are evaluated to optimize shielding against ELF-EMF exposure, as to mitigate its exposure.

Keywords: ELF shielding, magnetic aqueous substrate, shielding effectiveness, passive shielding, magnetic material

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Authors: Sijo Varghese

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A space craft (E. M. Tractor) which is intended to deflect or tug the asteroids which possesses threat towards the planets is the whole idea behind this paper. In this case "Electro Magnetic Induction" is used where it is known that when two separate circuits are connected to the electro magnet and on application of electric current through the one circuit in to the coil induces magnetic fields which repels the other circuit.( Faraday's law of Electromagnetic Induction). Basically a Spacecraft is used to attach a large sheet of aluminum on to the surface of the asteroid, the Spacecraft acts as an electro magnet and the induced magnetic field would eventually repel the aluminum intern repelling the asteroid. This method would take less time as compared to use of gravity( which requires a larger spacecraft and process will take a long time).

Keywords: asteroids, electro magnetic induction, gravity, electro magnetic tractor

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Authors: Essebti Dhahri

Abstract:

We present a new refrigerant La₀.₇Ca₀.₁₅Sr₀.₁₅Mn₁₋ₓGaₓO₃ (x = 0.0-0.1) manganites. These compounds were prepared by the sol-gel method. The refinement of the X-ray diffraction reveals that all samples crystallize in a rhombohedral structure (space group R3 ̅c). Detailed measurements of the magnetization as a function of temperature and magnetic applied field M (µ₀H, T) were carried out. From the M(µ₀H, T) curves, we have calculated the magnetic entropy change (ΔSM) according to the Maxwell relation. The temperature dependence of the magnetization M(T) reveals a decrease of M when increasing the x content. The magnetic entropy change (ΔSM) reaches a maximum value near room temperature. It was also found that this compound exhibits a large magnetocaloric effect MCE which increases when decreasing Ga concentration. So, the studied compounds could be considered potential materials for magnetic refrigeration application.

Keywords: magnetic measurements, Rietveld refinement, magnetic refrigeration, magnetocaloric effect

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Authors: Redrisse Djoumessi Fobasso, Baidyanath Sahu, Andre M. Strydom

Abstract:

The R₂TX₃ (R = rare-earth, T = transition, and X = s and p block element) series of compounds are interesting owing to their fascinating structural and magnetic properties. In this present work, we have studied the magnetic and physical properties of the new Gd₂AgSi₃ polycrystalline compound. The sample was synthesized by the arc-melting method and confirmed to crystallize in the tetragonal α-ThSi₂-type crystal structure with space group I4/amd. Dc– and ac–magnetic susceptibility, specific heat, electrical resistivity, and magnetoresistance measurements were performed on the new compound. The structure provides a unique position in the unit cell for the magnetic trivalent Gd ion. Two magnetic phase transitions were consistently found in dc- and ac-magnetic susceptibility, heat capacity, and electrical resistivity at temperatures Tₙ₁ = 11 K and Tₙ₂ = 20 K, which is an indication of the complex magnetic behavior in this compound. The compound is found to be metamagnetic over a range of temperatures below and above Tₙ₁. From field-dependent electrical resistivity, it is confirmed that the compound shows unusual negative magnetoresistance in the antiferromagnetically ordered region. These results contribute to a better understanding of this class of materials.

Keywords: complex magnetic behavior, metamagnetic, negative magnetoresistance, two magnetic phase transitions

Procedia PDF Downloads 105