Search results for: brain machine interface (BMI)

Authors: Anthony Chidi Ezika, Gbolahan Joseph Adekoya, Emmanuel Rotimi Sadiku, Yskandar Hamam, Suprakas Sinha Ray

Abstract:

High-energy-density polymer/ceramic composites require a high breakdown strength and dielectric constant. Interface polarization and electric percolation are responsible for the high dielectric constant. In order to create composite dielectrics, high conductivity ceramic particles are combined with polymers to increase the dielectric constant. In this study, bonding and the non-uniform distribution of charges in the ceramic/ceramic interface zone are investigated using density functional theory (DFT) modeling. This non-uniform distribution of charges is intended to improve the ceramic/ceramic interface's dipole polarization (dielectric response). The interfacial chemical bond formation can also improve the structural stability of the hybrid filler and, consequently, of the composite films. To comprehend the electron-transfer process, the density of state and electron localization function of the PPy with hybrid fillers are also studied. The polymer nanocomposite is anticipated to provide a suitable dielectric response for energy storage applications.

Keywords: energy storage, V₂C/ ZnS hybrid, polypyrrole, MXene, nanocomposite, dielectric

Procedia PDF Downloads 98

Authors: Xiao-Yin Liu, Liang-Xue Zhou

Abstract:

Traumatic brain injury (TBI), as a kind of nerve trauma caused by an external force, affects people all over the world and is a global public health problem. Although there are various clinical treatments for brain injury, including surgery, drug therapy, and rehabilitation therapy, the therapeutic effect is very limited. To improve the therapeutic effect of TBI, scaffolds combined with exosomes are a promising but challenging method for TBI repair. In this study, we examined whether a novel 3D-printed collagen/chitosan scaffold/exosomes derived from neural stem cells (NSCs) pretreated with insulin growth factor-1 (IGF-I) scaffolds (3D-CC-INExos) could be used to improve TBI repair and functional recovery after TBI. Our results showed that composite scaffolds of collagen-, chitosan- and exosomes derived from NSCs pretreated with IGF-I (INExos) could continuously release the exosomes for two weeks. In the rat TBI model, 3D-CC-INExos scaffold transplantation significantly improved motor and cognitive function after TBI, as assessed by the Morris water maze test and modified neurological severity scores. In addition, immunofluorescence staining and transmission electron microscopy showed that the recovery of damaged nerve tissue in the injured area was significantly improved by 3D-CC-INExos implantation. In conclusion, our data suggest that 3D-CC-INExos might provide a potential strategy for the treatment of TBI and lay a solid foundation for clinical translation.

Keywords: traumatic brain injury, exosomes, insulin growth factor-1, neural stem cells, collagen, chitosan, 3D printing, neural regeneration, angiogenesis, functional recovery

Procedia PDF Downloads 64

Authors: Jui-Pui Hung, Yong-Run Chen, Wei-Cheng Shih, Shen-He Tsui, Kung-Da Wu

Abstract:

This paper presents the effect on the tooling conditions on the machining stabilities of a milling machine tool. The machining stability was evaluated in different feeding direction in the X-Y plane, which was referred as the orientation-dependent machining stability. According to the machining mechanics, the machining stability was determined by the frequency response function of the cutter. Thus, we first conducted the vibration tests on the spindle tool of the milling machine to assess the tool tip frequency response functions along the principal direction of the machine tool. Then, basing on the orientation dependent stability analysis model proposed in this study, we evaluated the variation of the dynamic characteristics of the spindle tool and the corresponding machining stabilities at a specific feeding direction. Current results demonstrate that the stability boundaries and limited axial cutting depth of a specific cutter were affected to vary when it was fixed in the tool holder with different overhang length. The flute of the cutter also affects the stability boundary. When a two flute cutter was used, the critical cutting depth can be increased by 47 % as compared with the four flute cutter. The results presented in study provide valuable references for the selection of the tooling conditions for achieving high milling performance.

Keywords: tooling condition, machining stability, milling machine, chatter

Procedia PDF Downloads 417

Authors: Chien-Ho Lin, Ho-Ching Yang, Barbara Knowlton, Shin-Leh Huang, Ming-Chang Chiang

Abstract:

Practicing motor tasks arranged in an interleaved order (interleaved practice, or IP) generally leads to better learning than practicing tasks in a repetitive order (repetitive practice, or RP), an example of how desirable difficulty during practice benefits learning. Greater difficulty during practice, e.g. IP, is associated with greater brain activity measured by higher blood-oxygen-level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) in the sensorimotor areas of the brain. In this study resting-state fMRI was applied to investigate whether increase in resting-state brain connectivity immediately after practice predicts the benefit of desirable difficulty to motor learning. 26 healthy adults (11M/15F, age = 23.3±1.3 years) practiced two sets of three sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, fMRI data were acquired in a resting state after practice. The resting-state fMRI data was decomposed using a group-level spatial independent component analysis (ICA), yielding 9 independent components (IC) matched to the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipito-temporal network, and the frontal network. A weighted resting-state functional connectivity (wRSFC) was then defined to incorporate information from within- and between-network brain connectivity. The within-network functional connectivity between a voxel and an IC was gauged by a z-score derived from the Fisher transformation of the IC map. The between-network connectivity was derived from the cross-correlation of time courses across all possible pairs of ICs, leading to a symmetric nc x nc matrix of cross-correlation coefficients, denoted by C = (pᵢⱼ). Here pᵢⱼ is the extremum of cross-correlation between ICs i and j; nc = 9 is the number of ICs. This component-wise cross-correlation matrix C was then projected to the voxel space, with the weights for each voxel set to the z-score that represents the above within-network functional connectivity. The wRSFC map incorporates the global characteristics of brain networks measured by the between-network connectivity, and the spatial information contained in the IC maps measured by the within-network connectivity. Pearson correlation analysis revealed that greater IP-minus-RP difference in wRSFC was positively correlated with the RP-minus-IP difference in the response time on Day 5, particularly in brain regions crucial for motor learning, such as the right dorsolateral prefrontal cortex (DLPFC), and the right premotor and supplementary motor cortices. This indicates that enhanced resting brain connectivity during the early phase of memory consolidation is associated with enhanced learning following interleaved practice, and as such wRSFC could be applied as a biomarker that measures the beneficial effects of desirable difficulty on motor sequence learning.

Keywords: desirable difficulty, functional magnetic resonance imaging, independent component analysis, resting-state networks

Procedia PDF Downloads 192

Authors: S. B. R. Slagmulder

Abstract:

Monitoring inspiratory effort and dynamic lung stress in patients on pressure support ventilation in the ICU is important for protecting against self inflicted lung injury (P-SILI) and diaphragm dysfunction. Strategies to address the detrimental effects of respiratory drive and effort can lead to improved patient outcomes. Two non-invasive estimation methods, occlusion pressure (Pocc) and P0.1, have been proposed for achieving lung and diaphragm protective ventilation. However, their relationship and interpretation in neuro ICU patients is not well understood. P0.1 is the airway pressure measured during a 100-millisecond occlusion of the inspiratory port. It reflects the neural drive from the respiratory centers to the diaphragm and respiratory muscles, indicating the patient's respiratory drive during the initiation of each breath. Occlusion pressure, measured during a brief inspiratory pause against a closed airway, provides information about the inspiratory muscles' strength and the system's total resistance and compliance. Research Objective: Understanding the relationship between Pocc and P0.1 in brain-injured patients can provide insights into the interpretation of these values in pressure support ventilation. This knowledge can contribute to determining extubation readiness and optimizing ventilation strategies to improve patient outcomes. The central goal is to asses a study protocol for determining the relationship between Pocc and P0.1 in brain-injured patients on pressure support ventilation and their ability to predict successful extubation. Additionally, comparing these values between brain-damaged and non-brain-damaged patients may provide valuable insights. Key Areas of Inquiry: 1. How do Pocc and P0.1 values correlate within brain injury patients undergoing pressure support ventilation? 2. To what extent can Pocc and P0.1 values serve as predictive indicators for successful extubation in patients with brain injuries? 3. What differentiates the Pocc and P0.1 values between patients with brain injuries and those without? Methodology: P0.1 and occlusion pressures are standard measurements for pressure support ventilation patients, taken by attending doctors as per protocol. We utilize electronic patient records for existing data. Unpaired T-test will be conducted to compare P0.1 and Pocc values between both study groups. Associations between P0.1 and Pocc and other study variables, such as extubation, will be explored with simple regression and correlation analysis. Depending on how the data evolve, subgroup analysis will be performed for patients with and without extubation failure. Results: While it is anticipated that neuro patients may exhibit high respiratory drive, the linkage between such elevation, quantified by P0.1, and successful extubation remains unknown The analysis will focus on determining the ability of these values to predict successful extubation and their potential impact on ventilation strategies. Conclusion: Further research is pending to fully understand the potential of these indices and their impact on mechanical ventilation in different patient populations and clinical scenarios. Understanding these relationships can aid in determining extubation readiness and tailoring ventilation strategies to improve patient outcomes in this specific patient population. Additionally, it is vital to account for the influence of sedatives, neurological scores, and BMI on respiratory drive and occlusion pressures to ensure a comprehensive analysis.

Keywords: brain damage, diaphragm dysfunction, occlusion pressure, p0.1, respiratory drive

Procedia PDF Downloads 52

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 64

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 79

Authors: Jamal Choudhry, Roland Larsson, Andreas Almqvist

Abstract:

A multi-scale flash temperature model has been developed and validated against existing work. The core strength of the proposed model is that it can be adapted to predict flash contact temperatures occurring in various types of sliding systems. In this paper, it is used to investigate how different surface roughness parameters affect the flash temperatures. The results show that for decreasing Hurst exponents as well as increasing values of the high-frequency cut-off, the maximum flash temperature increases. It was also shown that the effect of surface roughness does not influence the average interface temperature. The model predictions were validated against data from an experiment conducted in a pin-on-disc machine. This also showed the importance of including a wear model when simulating flash temperature development in a sliding system.

Keywords: multiscale, pin-on-disc, finite element method, flash temperature, surface roughness

Procedia PDF Downloads 98

Authors: Shamatha Shetty, Sakshi Dhabadi, Prerana M., Indushree B.

Abstract:

About 66% of firms claim that insider attacks are more likely to happen. The frequency of insider incidents has increased by 47% in the last two years. The goal of this work is to prevent dangerous employee behavior by using keyloggers and the Machine Learning (ML) model. Every keystroke that the user enters is recorded by the keylogging program, also known as keystroke logging. Keyloggers are used to stop improper use of the system. This enables us to collect all textual data, save it in a CSV file, and analyze it using an ML algorithm and the VirusTotal API. Many large companies use it to methodically monitor how their employees use computers, the internet, and email. We are utilizing the SVM algorithm and the VirusTotal API to improve overall efficiency and accuracy in identifying specific patterns and words to automate and offer the report for improved monitoring.

Keywords: cyber security, machine learning, cyclic process, email notification

Procedia PDF Downloads 40

Authors: Ramachandra C. G., Amarnath. M., Prashanth Pai M., Nagesh S. N.

Abstract:

The machine's performance level drops down over a period of time due to the wear and tear of its components. The early detection of an emergent fault becomes very vital in order to obtain uninterrupted production in a plant. Maintenance is an activity that helps to keep the machine's performance at an anticipated level, thereby ensuring the availability of the machine to perform its intended function. At present, a number of modern maintenance techniques are available, such as preventive maintenance, predictive maintenance, condition-based maintenance, total productive maintenance, etc. Condition-based maintenance or condition monitoring is one such modern maintenance technique in which the machine's condition or health is checked by the measurement of certain parameters such as sound level, temperature, velocity, displacement, vibration, etc. It can recognize most of the factors restraining the usefulness and efficacy of the total manufacturing unit. This research work is conducted on a Batch Mill in a tire production unit located in the Southern Karnataka region. The health of the mill is assessed using amplitude of vibration as a parameter of measurement. Most commonly, the vibration level is assessed using various points on the machine bearing. The normal or standard level is fixed using reference materials such as manuals or catalogs supplied by the manufacturers and also by referring vibration standards. The Rio-Vibro meter is placed in different locations on the batch-off mill to record the vibration data. The data collected are analyzed to identify the malfunctioning components in the batch off the mill, and corrective measures are suggested.

Keywords: availability, displacement, vibration, rio-vibro, condition monitoring

Procedia PDF Downloads 64

Authors: Harshkumar K. Desai, Anuj K. Desai, Jay P. Patel, Snehal V. Trivedi, Yogendrasinh Parmar

Abstract:

The need of continuous improvement in a product or process in this era of global competition leads to apply value engineering for functional and aesthetic improvement in consideration with economic aspect too. Solar industries located at G.I.D.C., Makarpura, Vadodara, Gujarat, India; a manufacturer of variety of CNC Machines had a challenge to analyze the structural design of column, base, carriage and table of CNC Milling Machine in the account of reduction of overall weight of a machine without affecting the rigidity and accuracy at the time of operation. The identified task is the first attempt to validate and optimize the proposed design of ribbed structure statically using advanced modeling and analysis tools in a systematic way. Results of stress and deformation obtained using analysis software are validated with theoretical analysis and found quite satisfactory. Such optimized results offer a weight reduction of the final assembly which is desired by manufacturers in favor of reduction of material cost, processing cost and handling cost finally.

Keywords: CNC milling machine, optimization, finite element analysis (FEA), weight reduction

Procedia PDF Downloads 259

Authors: Heramb Somthankar, Anujoy Chakraborty

Abstract:

This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

Procedia PDF Downloads 95

Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

Abstract:

With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

Procedia PDF Downloads 385

Authors: Amit Kumar Haldar, Mark Simms, Ian McDevitt, Anthony Comer

Abstract:

Interface properties of fiber metal laminates (FML) affects the integrity and deformation failure modes. In this paper, the mechanical behaviours of Ti/GFRP/Ti laminates were experimentally investigated through low-velocity impact tests. Two different surface treatments of Titanium (Ti-6Al-4V) alloy sheets were prepared to obtain the composite interface properties based on annealing and sandblast surface treatment processes. The deformation failure modes, impact load sustaining ability and energy absorption capacity of FMLs were analysed. The impact load and modulus were shown to be dependent on the surface treatments of Titanium (Ti-6Al-4V) alloy sheets. It was demonstrated that the impact load performance was enhanced when titanium surfaces were annealed and sandblasted. It has also been shown that the values of the strength and energy absorption were slightly higher when the tests conducted at relatively higher loading rate, as a result of the rate-sensitive effects on the damage resistance of the FML.

Keywords: fiber metal laminates, metal composite interface, indentation, low velocity impact

Procedia PDF Downloads 181

Authors: Arezou Hajhashemi, Karim Asgari, Masoud Etemadifar, Maryam Keyvani, Ali Hekmatnia

Abstract:

Multiple Sclerosis (MS) is one of the most prevalent demyelinating diseases in CNS. As in other chronic cerebral diseases, impairment in cognitive functioning and in memory is popular. Because of the inflammatory and demyelinating nature of the disease, the localization of plaques in different parts of the Prefrontal and Limbic System, may lead to memorial symptoms. This investigation was intended to study relationship between frequency of plaques and memorial symptoms arising from dysfunction limbic system and prefrontal of patients with MS. The sample was selected randomly from patients with MS with memory problem, who have been referred to Isfahan Multiple Sclerosis Society. Brain System Test and Memory Test was administered to the sample, and their MRI's were analyzed by specialist in order to indentify two different parts of plaques. The data was analyzed by SPSS. The results showed that there were significant relationship between MS plaques and prefrontal's dysfunction and memorial symptom related to prefrontal area; however, there were no significant relationship between MS plaques and limbic system's dysfunction and memorial symptoms related to limbic system area. The results of this study suggest that memorial symptoms due to injury regions of the brain have the most significant relationship to prefrontal. Better judgment about these results needs more studies in future.

Keywords: multiple sclerosis, magnetic image, brain injury, behavior disorder

Procedia PDF Downloads 498

Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

Abstract:

Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

Procedia PDF Downloads 135

Authors: Sanja Seljan, Ivan Dunđer

Abstract:

The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

Procedia PDF Downloads 468

Authors: Ranjeet Kumar, Pravin Suryakantrao Deshmukh, Sonal Sharma, Basudev Banerjee

Abstract:

With the tremendous increase in exposure to radiofrequency microwaves emitted by mobile phones, globally public awareness has grown with regard to the potential health hazards of microwaves on the nervous system in the brain. India alone has more than one billion mobile users out of 4.3 billion globally. Our studies have suggested that radio frequency able to affect neuronal alterations in the brain, and hence, affecting cognitive behaviour. However, adverse effect of low-intensity microwave exposure with endoplasmic reticulum stress and autophagy has not been evaluated yet. In this study, we explore whether low-intensity microwave induces endoplasmic reticulum stress and autophagy with varying frequency and time duration in Wistar rat. Ninety-six male Wistar rat were divided into 12 groups of 8 rats each. We studied at 900 MHz, 1800 MHz, and 2450 MHz frequency with reference to sham-exposed group. At the end of the exposure, the rats were sacrificed to collect brain tissue and expression of CHOP, ATF-4, XBP-1, Bcl-2, Bax, LC3 and Atg-4 gene was analysed by real-time PCR. Significant fold change (p < 0.05) of gene expression was found in all groups of 1800 MHz and 2450 MHz exposure group in comparison to sham exposure group. In conclusion, the microwave exposure able to induce ER stress and modulate autophagy. ER (endoplasmic reticulum) stress and autophagy vary with increasing frequency as well as the duration of exposure. Our results suggested that microwave exposure is harmful to neuronal health as it induces ER stress and hampers autophagy in neuron cells and thereby increasing the neuron degeneration which impairs cognitive behaviour of experimental animals.

Keywords: autophagy, ER stress, microwave, nervous system, rat

Procedia PDF Downloads 118

Authors: Vincent Iacobellis, Kamran Behdinan

Abstract:

A finite element cohesive zone model is used to predict the temperature dependent material properties of a polyimide matrix composite with unidirectional carbon fiber arrangement. The cohesive zone parameters have been obtained from previous research involving an atomistic-to-continuum multiscale simulation of the fiber-matrix interface using the bridging cell multiscale method. The goal of the research was to both investigate the effect of temperature change on the composite behavior with respect to transverse loading as well as the validate the use of cohesive parameters obtained from atomistic-to-continuum multiscale modeling to predict fiber-matrix interfacial cracking. From the multiscale model cohesive zone parameters (i.e. maximum traction and energy of separation) were obtained by modeling the interface between the coarse-grained polyimide matrix and graphite based carbon fiber. The cohesive parameters from this simulation were used in a cohesive zone model of the composite microstructure in order to predict the properties of the macroscale composite with respect to changes in temperature ranging from 21 ˚C to 316 ˚C. Good agreement was found between the microscale RUC model and experimental results for stress-strain response, stiffness, and material strength at low and high temperatures. Examination of the deformation of the composite through localized crack initiation at the fiber-matrix interface also agreed with experimental observations of similar phenomena. Overall, the cohesive zone model was shown to be both effective at modeling the composite properties with respect to transverse loading as well as validated the use of cohesive zone parameters obtained from the multiscale simulation.

Keywords: cohesive zone model, fiber-matrix interface, microscale damage, multiscale modeling

Procedia PDF Downloads 465

Authors: S. H. Jang

Abstract:

Posterior reversible encephalopathy syndrome (PRES) is characterized by acute neurologic symptoms (visual loss, headache, altered mentality and seizures) and by typical imaging findings (bilateral subcortical and cortical edema with predominatly posterior distribution). Nephrotic syndrome is a syndrome comprising signs of proteinuria, hypoalbuminemia, and edema. It is well known that hypertension predispose patient with nephrotic syndrome to PRES. A 45-year old male was referred for suddenly developed vertigo, disequilibrium. He had previous history of nephrotic syndrome. His medical history included diabetes controlled with medication. He was hospitalized because of generalized edema a few days ago. His vital signs were stable. On neurologic examination, his mental state was alert. Horizontal nystagmus to right side on return to primary position was observed. He showed good grade motor weakness and ataxia in right upper and lower limbs without other sensory abnormality. Brain MRI showed increased signal intensity in FLAIR image, decreased signal intensity in T1 image and focal enhanced lesion in T1 contrast image at whole midbrain, pons and cerebellar peduncle symmetrically, which was compatible with vasogenic edema. Laboratory findings showed severe proteinuria and hypoalbuminemia. He was given intravenous dexamethasone and diuretics to reduce vasogenic edema and raise the intra-vascular osmotic pressure. Nystagmus, motor weakness and limb ataxia improved gradually over 2 weeks; He recovered without any neurologic symptom and sign. Follow-up MRI showed decreased vasogenic edema fairly. We report a case of brain stem PRES in normotensive, nephrotic syndrome patient.

Keywords: posterior reversible encephalopathy syndrome, MRI, nephrotic syndrome, vasogenic brain edema

Procedia PDF Downloads 261

Authors: Hsiao-Wen Tsai

Abstract:

Motor vehicle accident is the first cause of head injury in the world; severe head injury cases may cause conscious disturbance and death. This is a report about a case of a young adult patient suffering from motor vehicle accident leading to severe head injury who passed through three time surgical procedures, and his mother (who is the informal caregiver). This case was followed from 28th January to 15th February 2011 by using Gordon’s 11 functional health patterns. Patient’s cognitive-perceptual and self-perception-self-concept patterns were altered. Anxiety was also noted on his informal caregiver due to patients’ condition. During the intensive care period, maintaining patient’s vital signs and cerebral perfusion pressure were essential to avoid secondary neuronal injury. Multi-sensory stimulation, caring accompanying, supporting, listening and encouraging patient’s family involved in patient care were very important to reduce informal caregiver anxiety. Finally, the patient consciousness improved from GCS 4 to GCS 11 before discharging from ICU. Patient’s primary informal caregiver, his mother, also showed anxiety improvement. This is was successful case with traumatic brain injury recovered from coma.

Keywords: anxiety, multi-sensory stimulation, reduce intracranial adaptive capacity, traumatic brain injury

Procedia PDF Downloads 250

Authors: Gor Muradyan

Abstract:

The paper is about friction and wear, including mechanisms, modeling, characterization, measurement and testing case in Bangladesh. Bangladesh is a country under development, A lot of people live here, approximately 145 million. The territory of this country is very small. Therefore buildings are very close to each other. As the pipe lines are very old, and people get almost dirty water, there are a lot of ongoing projects under ADB. In those projects the contractors using HDD machines (Horizontal Directional Drilling ) and grundoburst. These machines are working underground. As ground in Bangladesh is very sludge, machine can't work relevant because of big friction in the soil. When drilling works are finished machine is pulling the pipe underground. Very often the pulling of the pipes becomes very complicated because of the friction. Therefore long section of the pipe laying can’t be done because of a big friction. In that case, additional problems rise, as well as additional work must be done. As we mentioned above it is not possible to do big section of the pipe laying because of big friction in the soil, Because of this it is coming out that contractors must do more joints, more pressure test. It is always connected with additional expenditure and losing time. This machine can pull in 75 mm to 500 mm pipes connected with the soil condition. Length is possible till 500m related how much friction it will had on the puller. As less as much it can pull. Another machine grundoburst is not working at this soil condition at all. The machine is working with air compressor. This machine are using for the smaller diameter pipes, 20 mm to 63 mm. Most of the cases these machines are being used for the installing of the house connection pipes, for making service connection. To make a friction less contractors using bigger pulling had then the pipe. It is taking down the friction, But the problem of this machine is that it can't work at sludge. Because of mentioned reasons the friction has a big mining during this kind of works. There are a lot of ways to reduce the friction. In this paper we'll introduce the ways that we have researched during our practice in Bangladesh.

Keywords: Bangladesh, friction and wear, HDD machines, reducing friction

Procedia PDF Downloads 293

Authors: Yujiang Wu

Abstract:

As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

Procedia PDF Downloads 80

Authors: Ehsan Tuzcuoğlu, Muhammed Emin Çoban, Songül Byraktar

Abstract:

One of the main targets of the washing machine is to remove any dirt and stains from the clothes. Especially, the stain removal is significantly important in the Far East market, where the high percentage of the consumers use the top load washing machines as washing appliance. They use all pretreatment methods (i.e. soaking, prewash, and heavy functions) to eliminate the stains from their clothes. Therefore, with this study it is aimed to study experimentally the stain removal performance of 3 different Top-Loading washing machines of the Far East market with 24 different types of stains which are mostly related to Far East culture. In the meanwhile, the mechanical damge on laundry is examined for each machine to see the mechanical effect of the related stain programs on the textile load of the machines. The test machines vary according to have a heater, moving part(s)on their impeller, and to be in different height/width ratio of the drum. The results indicate that decreasing the water level inside the washing machine might result in better soil removal as well as less textile damage. Beside this, the experimental results reveal that heating has the main effect on stain removal. Two-step (or delayed) heating and a lower amount of water can also be considered as the further parameters

Keywords: laundry, washing machine, top load washing machine, stain removal, textile damage, mechanical textile damage

Procedia PDF Downloads 110

Authors: Zenah Gheblawi, Susan Armijo-Olivo, Elisa B. Pelai, Vaishali Sharma, Musa Tashfeen, Angela Fung, Francisca Claveria

Abstract:

Due to physiological differences between men and women, pain is experienced differently between the two sexes. Chronic pain disorders, notably temporomandibular disorders (TMDs), disproportionately affect women in diagnosis, and pain severity in opposition of their male counterparts. TMDs are a type of musculoskeletal disorder that target the masticatory muscles, temporalis muscle, and temporomandibular joints, causing considerable orofacial pain which can usually be referred to the neck and back. Therapeutic methods are scarce, and are not TMD-centered, with the latest research suggesting that subjects with chronic musculoskeletal pain disorders have abnormal alterations in the grey matter of their brains which can be remedied with exercise, and thus, decreasing the pain experienced. The aim of the study is to investigate the effects of exercise therapy in TMD female patients experiencing chronic jaw pain and to assess the consequential effects on brain activity. In a randomized controlled trial, the effectiveness of an exercise program to improve brain alterations and clinical outcomes in women with TMD pain will be tested. Women with chronic TMD pain will be randomized to either an intervention arm or a placebo control group. Women in the intervention arm will receive 8 weeks of progressive exercise of motor control training using visual feedback (MCTF) of the cervical muscles, twice per week. Women in the placebo arm will receive innocuous transcutaneous electrical nerve stimulation during 8 weeks as well. The primary outcomes will be changes in 1) pain, measured with the Visual Analogue Scale, 2) brain structure and networks, measured by fractional anisotropy (brain structure) and the blood-oxygen level dependent signal (brain networks). Outcomes will be measured at baseline, after 8 weeks of treatment, and 4 months after treatment ends and will determine effectiveness of MCTF in managing TMD, through improved clinical outcomes. Results will directly inform and guide clinicians in prescribing more effective interventions for women with TMD. This study is underway, and no results are available at this point. The results of this study will have substantial implications on the advancement in understanding the scope of plasticity the brain has in regards with pain, and how it can be used to improve the treatment and pain of women with TMD, and more generally, other musculoskeletal disorders.

Keywords: exercise therapy, musculoskeletal disorders, physical therapy, rehabilitation, tempomandibular disorders

Procedia PDF Downloads 278

Authors: M. E. Kolpakova, A. A. Jakovleva, L. S. Poliakova, H. El Amghari, S. Soliman, D. R. Faizullina, V. V. Sharoyko

Abstract:

Brain neuroplasticity is associated with blood-brain barrier vascular endothelial proteoglycans and post-stroke microglial activation. The study of the mechanisms of reperfusion injury limitation by remote ischemic postconditioning (RC) is of interest due to the effects on functional recovery after cerebral ischemia. The goal of the study is the assessment of the role of syndecan-1 (SDC-1) in restriction of ischemic-reperfusion injury on middle cerebral artery model in rats using RC protocol. Randomized controlled trials were conducted. Ischemia was performed by middle cerebral artery occlusion by Belayev L. (1996) on the Wistar rat-males (n= 87) weighting 250 ± 50 g. under general anesthesia (Zoletil 100 и Xylazine 2%). Syndecan-1 (SDC-1) concentration difference in plasma samples of false operated animals and animals with brain ischemia was 30% (30 min. МСАо: 41.4 * ± 1.3 ng/ml). SDC-1 concentration in animal plasma samples with ischemia + RC protocol was 112% (30 min МСАо+ RC): 67.8**± 5.8 ng/ml). Calculation of infarction volume in the ischemia group revealed brain injury in 31.97 ± 2.5%; the volume of infarction was 13.6 ± 1.3% in 30 min. МCАо + RC group. Swelling of tissue in the group 30 min. МCАо + RC was 16 ± 2.1%; it was 47 ± 3.3%. in 30 min. МCАо group. Correlation analysis showed a high direct correlation relationship between infarct area and muscle strength in the right forelimb (КК=0.72) in the 30 min. МCАо + RC group. Correlation analysis showed very high inverse correlation between infarct area and capillary blood flow in the 30 min. МCАо + RC group (p <0.01; r = -0.98). We believe the SDC-1 molecule in blood plasma may play role of potential messenger of ischemic-reperfusion injury restriction mechanisms. This leads to infarct-limiting effect of remote ischemic postconditioning and early functioning recovery.

Keywords: ischemia, МСАо, remote ischemic postconditioning, syndecan-1

Procedia PDF Downloads 42

Authors: Caroline Kim

Abstract:

Scholars have long disputed the relationship between the origins of language and human behavior. Historically, behaviorist psychologist B. F. Skinner argued that language is one instance of the general stimulus-response phenomenon that characterizes the essence of human behavior. Another, more recent approach argues, by contrast, that language is an innate cognitive faculty and does not arise from behavior, which might develop and reinforce linguistic facility but is not its source. Pinker, among others, proposes that linguistic defects arise from damage to the brain, both congenital and acquired in life. Much of his argument is based on case studies in which damage to the Broca’s and Wernicke’s areas of the brain results in loss of the ability to produce coherent grammatical expressions when speaking or writing; though affected speakers often utter quite fluent streams of sentences, the words articulated lack discernible semantic content. Pinker concludes on this basis that language is an innate component of specific, classically language-correlated regions of the human brain. Taking a notorious 1970s case of linguistic maladaptation, this paper queries the dominant materialist paradigm of language-correlated regions. Susan “Genie” Wiley was physically isolated from language interaction in her home and beaten by her father when she attempted to make any sort of sound. Though without any measurable resulting damage to the brain, Wiley was never able to develop the level of linguistic facility normally achieved in adulthood. Having received a negative reinforcement of language acquisition from her father and lacking the usual language acquisition period, in adulthood Wiley was able to develop language only at a quite limited level in later life. From a contemporary behaviorist perspective, this case confirms the possibility of language deficiency without brain pathology. Wiley’s potential language-determining areas in the brain were intact, and she was exposed to language later in her life, but she was unable to achieve the normal level of communication skills, deterring socialization. This phenomenon and others like it in the case limited literature on linguistic maladaptation pose serious clinical, scientific, and indeed philosophical difficulties for both of the major competing theories of language acquisition, innateness, and linguistic stimulus-response. The implications of such cases for future research in language acquisition are explored, with a particular emphasis on the interaction of innate capacity and stimulus-based development in early childhood.

Keywords: behaviorism, innateness hypothesis, language, Susan "Genie" Wiley

Procedia PDF Downloads 280

Authors: Anton Hristozov, Eric Matson, Eric Dietz, Marcus Rogers

Abstract:

Components are heavily used in many software systems, including robotics systems. The growth of sophistication and diversity of new capabilities for robotic systems presents new challenges to their architectures. Their complexity is growing exponentially with the advent of AI, smart sensors, and the complex tasks they have to accomplish. Such complexity requires a more rigorous approach to the creation, use, and interoperability of software components. The issue is exacerbated because robotic systems are becoming more and more reliant on third-party components for certain functions. In order to achieve this kind of interoperability, including dynamic component replacement, we need a way to standardize their interfaces. A formal approach is desperately needed to specify what an interface of a robotic software component should contain. This study performs an analysis of the issue and presents a universal and generic approach to standardizing component interfaces for robotic systems. Our approach is inspired by well-established robotic architectures such as ROS, PX4, and Ardupilot. The study is also applicable to other software systems that share similar characteristics with robotic systems. We consider the use of JSON or Domain Specific Languages (DSL) development with tools such as Antlr and automatic code and configuration file generation for frameworks such as ROS and PX4. A case study with ROS2 is presented as a proof of concept for the proposed methodology.

Keywords: CPS, robots, software architecture, interface, ROS, autopilot

Procedia PDF Downloads 71

Authors: Manpreet Kaur

Abstract:

When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

Procedia PDF Downloads 91

Authors: Darwin Junnior Sabino Diego, Brando Burgos Guerrero, Diego Arroyo Villanueva

Abstract:

Additive manufacturing, commonly known as 3D printing, has revolutionized modern manufacturing by enabling the agile creation of complex objects. However, challenges persist in the consistency and quality of printed parts, particularly in their mechanical properties. This study focuses on addressing these challenges through the optimization of printing parameters in FDM technology, using Machine Learning techniques. Our aim is to improve the mechanical properties of printed objects by optimizing parameters such as speed, temperature, and orientation. We implement a methodology that combines experimental data collection with Machine Learning algorithms to identify relationships between printing parameters and mechanical properties. The results demonstrate the potential of this methodology to enhance the quality and consistency of 3D printed products, with significant applications across various industrial fields. This research not only advances understanding of additive manufacturing but also opens new avenues for practical implementation in industrial settings.

Keywords: 3D printing, additive manufacturing, machine learning, mechanical properties

Procedia PDF Downloads 31