Search results for: brain machine interface (BMI)

Authors: Harsh Sadawarti, Kamal Malik

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Humans are the unique and the most powerful creature on this planet just because of the high level of intelligence gifted by nature. Computational Intelligence is highly influenced by the term natural intelligence, neurosciences and mathematics. To deal with the in-depth study of computational intelligence and to utilize it in real-life applications, it is quite important to understand its simulation with the human brain. In this paper, the three important parts, Frontal Lobe, Occipital Lobe and Parietal Lobe of the human brain, are compared with the ANN(Artificial Neural Network), CNN(Convolutional Neural network), and RNN(Recurrent Neural Network), respectively. Intelligent computational systems are created by combining deductive reasoning, logical concepts and high-level algorithms with the simulation and study of the human brain. Human brain is a combination of Physiology, Psychology, emotions, calculations and many other parameters which are of utmost importance that determines the overall intelligence. To create intelligent algorithms, smart machines and to simulate the human brain in an effective manner, it is quite important to have an insight into the human brain and the basic concepts of biological neurosciences.

Keywords: computational intelligence, neurosciences, convolutional neural network, recurrent neural network, artificial neural network, frontal lobe, occipital lobe, parietal lobe

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Authors: Jungkyun Im

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Nonsteroidal anti-inflammatory drugs (NSAIDs) are effective for relieving pain and reducing inflammation. They are nonselective inhibitors of two isoforms of COX, cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2), and thereby inhibiting the production of hormone-like lipid compounds such as, prostaglandins and thromboxanes which cause inflammation, pain, fever, platelet aggregation, etc. In addition, recently there are many research articles reporting the neuroprotective effect of NSAIDs in neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). However, the clinical use of NSAIDs in these diseases is limited by low brain distribution. Therefore, in order to assist the in-depth investigation on the pharmaceutical mechanism of flurbiprofen in neuroprotection and to make flurbiprofen a more potent drug to prevent or alleviate neurodegenerative diseases, delivery of flurbiprofen to brain should be effective and sufficient amount of flurbiprofen must penetrate the BBB thus gaining access into the patient’s brain. We have recently developed several types of guanidine-rich molecular carriers with high molecular weights and good water solubility that readily cross the blood-brain barrier (BBB) and display efficient distributions in the mouse brain. The G8 (having eight guanidine groups) molecular carrier based on D-sorbitol was found to be very effective in delivering anticancer drugs to a mouse brain. In the present study, employing the same molecular carrier, we prepared the flurbiprofen conjugate and studied its BBB permeation by mouse tissue distribution study. Flurbiprofen was attached to a molecular carrier with a fluorescein probe and multiple terminal guanidiniums. The conjugate was found to internalize into live cells and readily cross the BBB to enter the mouse brain. Our novel synthetic flurbiprofen conjugate will hopefully delivery NSAIDs into brain, and is therefore applicable to the neurodegenerative diseases treatment or prevention.

Keywords: flurbiprofen, drug delivery, molecular carrier, organic synthesis

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Authors: Namita Mittal, Himakshi Shekhawat, Ankit Vidyarthi

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In medical research study, doctors and radiologists face lot of complexities in analysing the brain tumors in Magnetic Resonance (MR) images. Brain tumor detection is difficult due to amorphous tumor shape and overlapping of similar tissues in nearby region. So, radiologists require one such clinically viable solution which helps in automatic segmentation of tumor inside brain MR image. Initially, segmentation methods were used to detect tumor, by dividing the image into segments but causes loss of information. In this paper, a hybrid method is proposed which detect Region of Interest (ROI) on the basis of difference in intensity values and texture values of tumor region using nearby tissues with Gradient Vector Flow (GVF) technique in the identification of ROI. Proposed approach uses both intensity and texture values for identification of abnormal section of the brain MR images. Experimental results show that proposed method outperforms GVF method without any loss of information.

Keywords: brain tumor, GVF, intensity, MR images, segmentation, texture

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Authors: Abdelkader Hachemi & Boualem Remini

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Seawater intrusion is a significant challenge faced by coastal aquifers in the Mediterranean basin. This study aims to determine the position of the sharp interface between seawater and freshwater in the aquifer of Wadi Nador, located in the Wilaya of Tipaza, Algeria. A numerical areal sharp interface model using the finite element method is developed to investigate the spatial and temporal behavior of seawater intrusion. The aquifer is assumed to be homogeneous and isotropic. The simulation results are compared with geophysical prospection data obtained through electrical methods in 2011 to validate the model. The simulation results demonstrate a good agreement with the geophysical prospection data, confirming the accuracy of the sharp interface model. The position of the sharp interface in the aquifer is found to be approximately 1617 meters from the sea. Two scenarios are proposed to predict the interface position for the year 2024: one without pumping and the other with pumping. The results indicate a noticeable retreat of the sharp interface position in the first scenario, while a slight decline is observed in the second scenario. The findings of this study provide valuable insights into the dynamics of seawater intrusion in the Wadi Nador aquifer. The predicted changes in the sharp interface position highlight the potential impact of pumping activities on the aquifer's vulnerability to seawater intrusion. This study emphasizes the importance of implementing measures to manage and mitigate seawater intrusion in coastal aquifers. The sharp interface model developed in this research can serve as a valuable tool for assessing and monitoring the vulnerability of aquifers to seawater intrusion.

Keywords: seawater, intrusion, sharp interface, Algeria

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Ozgu Hafizoglu

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An analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with physical, behavioral, principal relations that are essential to learning, discovery, and innovation. The Cognitive Model of Analogy (CMA) leads and creates patterns of pathways to transfer information within and between domains in science, just as happens in the brain. The connectome of the brain shows how the brain operates with mental leaps between domains and mental hops within domains and the way how analogical reasoning mechanism operates. This paper demonstrates the CMA as an evolutionary approach to science, technology, and life. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions in the new era, especially post-pandemic. In this paper, we will reveal how to draw an analogy to scientific research to discover new systems that reveal the fractal schema of analogical reasoning within and between the systems like within and between the brain regions. Distinct phases of the problem-solving processes are divided thusly: stimulus, encoding, mapping, inference, and response. Based on the brain research so far, the system is revealed to be relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain’s mechanism in macro context; brain and spinal cord, and micro context: glia and neurons, relative to matching conditions of analogical reasoning and relational information, encoding, mapping, inference and response processes, and verification of perceptual responses in four-term analogical reasoning. Finally, we will relate all these terminologies with these mental leaps, mental maps, mental hops, and mental loops to make the mental model of CMA clear.

Keywords: analogy, analogical reasoning, brain connectome, cognitive model, neurons and glia, mental leaps, mental hops, mental loops

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Authors: Ali Azeem, Seung-Cheol Hong

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This study focuses on the epidemiological association between childhood leukaemia & adult brain cancer to offer strong evidence that extremely low-frequency magnetic field (ELF-MF) produced from power lines caused cancer. It also gives a comprehensive literature review on epidemiological studies of ELF-MF risk associated with HVTL and childhood leukaemia & adult brain cancer. From the literature review, it is concluded that there is a weak association present between ELF-MF and childhood leukaemia. No consistent association was present between brain cancer and ELF-MF. This study is done on Scielo data and PubMed using the terms extremely low-frequency magnetic field (ELF-MF+cancer), adult brain cancer, high power transmission lines, etc., for the past 10 years.

Keywords: childhood leukaemia, high voltage transmission lines, acute lymphoblastic leukaemia, power lines

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Authors: Bing Li, Tong Lu, Lei Qiang

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Stoneley waves are interface waves that propagate at the interface between two solid media. In this study, the dispersion characteristics and wave structures of Stoneley waves in elastic multilayered plates are displayed and investigated. With a perspective of bulk wave, a reasonable assumption of the potential function forms of the expansion wave and shear wave in nth layer medium is adopted, and the characteristic equation of Stoneley waves in a three-layered plate is given in a determinant form. The dispersion curves and wave structures are solved and presented in both numerical and simulation results. It is observed that two Stoneley wave modes exist in a three-layered plate, that conspicuous dispersion occurs on low frequency band, that the velocity of each Stoneley wave mode approaches the corresponding Stoneley wave velocity at interface between two half infinite spaces. The wave structures reveal that the in-plane displacement of Stoneley waves are relatively high at interfaces, which shows great potential for interface defects detection.

Keywords: characteristic equation, interface waves, potential function, Stoneley waves, wave structure

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Authors: Sagheer A. Onaizi

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The adsorption of anionic biosurfactant (surfactin) and anionic synthetic surfactant (sodium dodecylbenzenesulphonate, abbreviated as SDOBS) from phosphate buffer containing high concentrations of co- and counter-ions to the air-buffer interface has been investigated. The self-assembly of the two surfactants at the interface has been monitored through dynamic surface tension measurements. The equilibrium surface pressure-surfactant concentration data in the premicellar region were regressed using Gibbs adsorption equation. The predicted surface saturations for SDOBS and surfactin are and, respectively. The occupied area per an SDOBS molecule at the interface saturation condition is while that occupied by a surfactin molecule is. The surface saturations reported in this work for both surfactants are in a very good agreement with those obtained using expensive techniques such as neutron reflectometry, suggesting that the surface tension measurements coupled with appropriate theoretical analysis could provide useful information comparable to those obtained using highly sophisticated techniques.

Keywords: adsorption, air-liquid interface, biosurfactant, surface tension

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Authors: Yun-An Chen, Hung-Jun Lin, Tai-Horng Young, Der-Zen Liu

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Decellularized brain extracellular matrix had been shown that it has the ability to influence on cell proliferation, differentiation and associated cell phenotype. However, this scaffold is thought to have poor mechanical properties and rapid degradation, it is hard for cell recellularization. In this study, we used decellularized brain extracellular matrix combined with chitosan, which is naturally occurring polysaccharide and non-cytotoxic polymer, forming a 3-D scaffold for neural stem/precursor cells (NSPCs) regeneration. HE staining and DAPI fluorescence staining confirmed decellularized process could effectively vanish the cellular components from the brain. GAGs and collagen I, collagen IV were be showed a great preservation by Alcain staining and immunofluorescence staining respectively. Decellularized brain extracellular matrix was well mixed in chitosan to form a 3-D scaffold (DB-C scaffold). The pore size was approximately 50±10 μm examined by SEM images. Alamar blue results demonstrated NSPCs had great proliferation ability in DB-C scaffold. NSPCs that were cultured in this complex scaffold differentiated into neurons and astrocytes, as reveled by NSPCs expression of microtubule-associated protein 2 (MAP2) and glial fibrillary acidic protein (GFAP). In conclusion, DB-C scaffold may provide bioinformatics cues for NSPCs generation and aid for CNS injury functional recovery applications.

Keywords: brain, decellularization, chitosan, scaffold, neural stem/precursor cells

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Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.

Keywords: brain-computer interface, electroencephalography, finger motion decoding, independent component analysis, pseudo real-time motion decoding

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Authors: Lal Hussain, Wajid Aziz, Sajjad Ahmed Nadeem, Saeed Arif Shah, Abdul Majid

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Brain electrical activity as reflected in Electroencephalography (EEG) have been analyzed and diagnosed using various techniques. Among them, complexity measure, nonlinearity, disorder, and unpredictability play vital role due to the nonlinear interconnection between functional and anatomical subsystem emerged in brain in healthy state and during various diseases. There are many social and economical issues of alcoholic abuse as memory weakness, decision making, impairments, and concentrations etc. Alcoholism not only defect the brains but also associated with emotional, behavior, and cognitive impairments damaging the white and gray brain matters. A recently developed signal analysis method i.e. Multiscale Permutation Entropy (MPE) is proposed to estimate the complexity of long-range temporal correlation time series EEG of Alcoholic and Control subjects acquired from University of California Machine Learning repository and results are compared with MSE. Using MPE, coarsed grained series is first generated and the PE is computed for each coarsed grained time series against the electrodes O1, O2, C3, C4, F2, F3, F4, F7, F8, Fp1, Fp2, P3, P4, T7, and T8. The results computed against each electrode using MPE gives higher significant values as compared to MSE as well as mean rank differences accordingly. Likewise, ROC and Area under the ROC also gives higher separation against each electrode using MPE in comparison to MSE.

Keywords: electroencephalogram (EEG), multiscale permutation entropy (MPE), multiscale sample entropy (MSE), permutation entropy (PE), mann whitney test (MMT), receiver operator curve (ROC), complexity measure

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Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Amira Ben Rabeh, Faouzi Benzarti, Hamid Amiri, Mouna Bouaziz

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Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work; we proposed an application to detect Alzheimer’s diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Keywords: Alzheimer Diseases (AD), Computer Assisted Diagnostic(CAD), hippocampus, Corpus Callosum (CC), cortex, Support Vector Machine (SVM)

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: Y. J. Chen, T. M. Yue, Z. N. Guo

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This paper reports the effects of laser energy on the characteristics of bubbles generated in the weld zone and the formation of new chemical bonds at the Polyethylene Terephthalate (PET)/Ti joint interface in laser joining of PET to Ti. The samples were produced by using different laser energies ranging from 1.5 J – 6 J in steps of 1.5 J, while all other joining parameters remained unchanged. The types of chemical bonding at the joint interface were analysed by the x-ray photoelectron spectroscopy (XPS) depth-profiling method. The results show that the characteristics of the bubbles and the thickness of the chemically bonded interface, which contains the laser generated bonds of Ti–C and Ti–O, increase markedly with increasing laser energy input. The tensile failure load of the joint depends on the combined effect of the amount and distribution of the bubbles formed and the chemical bonding intensity of the joint interface.

Keywords: laser direct joining, Ti/PET interface, laser energy, XPS depth profiling, chemical bond, tensile failure load

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Authors: J. Constanzo, B. Paquette, G. Charest, L. Masson-Côté, M. Guillot

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Targeted and whole-brain irradiation in humans can result in significant side effects causing decreased patient quality of life. To adequately investigate structural and functional alterations after stereotactic radiosurgery, preclinical studies are needed. The first step is to establish a robust standardized method of targeted irradiation on small regions of the rat brain. Eleven euthanized male Fischer rats were imaged in a stereotactic bed, by computed tomographic (CT), to estimate positioning variations regarding to the bregma skull reference point. Using a rat brain atlas and the stereotactic bregma coordinates assessed from CT images, various regions of the brain were delimited and a treatment plan was generated. A dose of 37 Gy at 30% isodose which corresponds to 100 Gy in 100% of the target volume (X = 98.1; Y = 109.1; Z = 100.0) was set by Leksell Gamma Plan using sectors number 4, 5, 7, and 8 of the Gamma Knife unit with the 4-mm diameter collimators. Effects of positioning accuracy of the rat brain on the dose deposition were simulated by Gamma Plan and validated with dosimetric measurements. Our results showed that 90% of the target volume received 110 ± 4.7 Gy and the maximum of deposited dose was 124 ± 0.6 Gy, which corresponds to an excellent relative standard deviation of 0.5%. This dose deposition calculated with the Gamma Plan was validated with the dosimetric films resulting in a dose-profile agreement within 2%, both in X- and Z-axis,. Our results demonstrate the feasibility to standardize the irradiation procedure of a small volume in the rat brain using a Gamma Knife.

Keywords: brain irradiation, dosimetry, gamma knife, small-animal irradiation, stereotactic radiosurgery (SRS)

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Authors: Fatima S. Rodriguez, Victorino T. Taylan, Manolito C. Bulaong, Helen F. Gavino, Vitaliana U. Malamug

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In sundrying, the quality of the grains are greatly reduced when paddy grains were caught by the rain unsacked and unstored resulting to reduced profit. The objectives of this study were to design and fabricate a quick covering machine for grain drying pavement to test and evaluate the operating characteristics of the machine according to its deployment speed, recovery speed, deployment time, recovery time, power consumption, aesthetics of laminated sack, conducting partial budget, and cost curve analysis. The machine was able to cover the grains in a 12.8 m x 22.5 m grain drying pavement at an average time of 17.13 s. It consumed 0 .53 W-hr for the deployment and recovery of the cover. The machine entailed an investment cost of $1,344.40 and an annual cost charge of $647.32. Moreover, the savings per year using the quick covering machine was $101.83.

Keywords: quick, covering machine, grain, drying pavement

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Authors: Joanna Maj

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Awe inspiring is the idea that emotions could be present in human-machine interactions, both on the human side as well as the machine side. Human factors present intriguing components and are examined in detail while discussing this controversial topic. Mood, attention, memory, performance, assessment, causes of emotion, and neurological responses are analyzed as components of the interaction. Problems in computer-based technology, revenge of the system on its users and design, and applications comprise a major part of all descriptions and examples throughout this paper. It also allows for critical thinking while challenging intriguing questions regarding future directions in research, dealing with emotion in human-machine interactions.

Keywords: biocomputing, biomedical engineering, emotions, human-machine interaction, interfaces

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Semyachkina-Glushkovskaya Oxana, Fedosov Ivan, Blokhina Inna, Terskov Andrey, Evsukova Arina, Elovenko Daria, Adushkina Viktoria, Dubrovsky Alexander, Jürgen Kurths

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In modern neurobiology, sleep is considered a novel biomarker and a promising therapeutic target for brain diseases. This is due to recent discoveries of the nighttime activation of the brain lymphatic system (BLS), playing an important role in the removal of wastes and toxins from the brain and contributes neuroprotection of the central nervous system (CNS). In our review, we discuss that night stimulation of BLS might be a breakthrough strategy in a new treatment of Alzheimer’s and Parkinson’s disease, stroke, brain trauma, and oncology. Although this research is in its infancy, however, there are pioneering and promising results suggesting that night transcranial photostimulation (tPBM) stimulates more effectively lymphatic removal of amyloid-beta from mouse brain than daily tPBM that is associated with a greater improvement of the neurological status and recognition memory of animals. In our previous study, we discovered that tPBM modulates the tone and permeability of the lymphatic endothelium by stimulating NO formation, promoting lymphatic clearance of wastes and toxins from the brain tissues. We also demonstrate that tPBM can also lead to angio- and lymphangiogenesis, which is another mechanism underlying tPBM-mediated stimulation of BLS. Thus, photo-augmentation of BLS might be a promising therapeutic target for preventing or delaying brain diseases associated with BLS dysfunction. Here we present pioneering technology for simultaneous tPBM in humans and sleep monitoring for stimulation of BLS to remove toxins from CNS and modulation of brain immunity. The wireless-controlled gadget includes a flexible organic light-emitting diode (LED) source that is controlled directly by a sleep-tracking device via a mobile application. The designed autonomous LED source is capable of providing the required therapeutic dose of light radiation at a certain region of the patient’s head without disturbing of sleeping patient. To minimize patients' discomfort, advanced materials like flexible organic LEDs were used. Acknowledgment: This study was supported by RSF project No. 23-75-30001.

Keywords: brain diseases, brain lymphatic system, phototherapy, sleep

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Authors: Chai Mingming, Li Lei, Lu Xiaoxia

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In this paper, the mechanism of stratified liquids’ mixing in a cylinder is investigated. It is focused on the effects of Rayleigh-Taylor Instability (RTI) and rotation of the cylinder on liquid interface mixing. For miscible liquids, Planar Laser Induced Fluorescence (PLIF) technique is applied to record the concentration field for one liquid. Intensity of Segregation (IOS) is used to describe the mixing status. For immiscible liquids, High Speed Camera is adopted to record the development of the interface. The experiment of RTI indicates that it plays a great role in the mixing process, and meanwhile the large-scale mixing is triggered, and subsequently the span of the stripes decreases, showing that the mesoscale mixing is coming into being. The rotation experiments show that the spin-down process has a great role in liquid mixing, during which the upper liquid falls down rapidly along the wall and crashes into the lower liquid. During this process, a lot of interface instabilities are excited. Liquids mix rapidly in the spin-down process. It can be concluded that no matter what ways have been adopted to speed up liquid mixing, the fundamental reason is the interface instabilities which increase the area of the interface between liquids and increase the relative velocity of the two liquids.

Keywords: interface instability, liquid mixing, Rayleigh-Taylor Instability, spin-down process, spin-up process

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Authors: Joon Ho Kim, Tae Kwon Ha

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Effect of aging treatment on microstructural aspects of interfacial layers of the Cu/Al clad sheet produced by Differential Speed Rolling (DSR) process were studied by Electron Back Scattered Diffraction (EBSD). Clad sheet of Al/Cu has been fabricated by using DSR, which caused severe shear deformation between Al and Cu plate to easily bond to each other. Rolling was carried out at 100°C with speed ratio of 2, in which the total thickness reduction was 45%. Interface layers of clad sheet were analyzed by EBSD after subsequent annealing at 400°C for 30 to 120 min. With increasing annealing time, thickness of interface layer and fraction of high angle grain boundary were increased and average grain size was decreased.

Keywords: aluminium/copper clad sheet, differential speed rolling, interface layer, microstructure, annealing, electron back scattered diffraction

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Authors: Minh Tu Le, Quang Huy Nguyen, Mai Lan Nguyen

Abstract:

Bonding characteristics between pavement layers have an important influence on responses of pavement structures. This paper deals with analytical solution for the stresses, strains, and deflections of double-layered asphalt pavement structure. This solution is based on the homogeneous half-space of layered theory developed by Burmister (1943). The partial interaction between the layers is taken into account by considering an interface bonding behavior which is obtained by push-out shear test. Numerical applications considering three cases of bonding (unbonded, partially bonded, and fully bonded overlays) are carried out to the influence of the interface bonding on the structural behavior of asphalt pavement under static loading. Further, it was observed that numerical results indicate that the horizontal shear reaction modulus at the interface (Ks) will significantly affect pavement structure behavior.

Keywords: analytical solution, interface bonding, shear test keyword, double-layered asphalt, shear reaction modulus

Procedia PDF Downloads 204

Authors: Kwaku Damoah

Abstract:

This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

Procedia PDF Downloads 45

Authors: Valentin P. Velikov

Abstract:

Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: computer science, graphical user interface, user dialog interface, dialog frames, data modeling, subject area modeling

Procedia PDF Downloads 286

Authors: Walter Evaldo Kuchenbecker, Julio Carlos Teixeira

Abstract:

Since efficiency became a challenge to reduce energy consumption of all electrical machines applications, the permanent magnet machine raises up as a better option, because its performance, robustness and simple control. Even though, the electrical machine was developed through analyses of magnetism effect, permanent magnet machines still not well dominated. As permanent magnet machines are becoming popular in most applications, the pressure to standardize this type of electrical machine increases. However, due limited domain, it is still nowadays without any standard to manufacture, test and application. In order to determine an inductance of the machine, a new method is proposed.

Keywords: permanent magnet generators (pmg), synchronous machine parameters, test procedures, inductances

Procedia PDF Downloads 280

Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

Abstract:

An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

Procedia PDF Downloads 393