Search results for: sleep latency
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 437

Search results for: sleep latency

167 Statistical Shape Analysis of the Human Upper Airway

Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

Abstract:

The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

Procedia PDF Downloads 484
166 Effects of Heart Rate Variability Biofeedback to Improve Autonomic Nerve Function, Inflammatory Response and Symptom Distress in Patients with Chronic Kidney Disease: A Randomized Control Trial

Authors: Chia-Pei Chen, Yu-Ju Chen, Yu-Juei Hsu

Abstract:

The prevalence and incidence of end-stage renal disease in Taiwan ranks the highest in the world. According to the statistical survey of the Ministry of Health and Welfare in 2019, kidney disease is the ninth leading cause of death in Taiwan. It leads to autonomic dysfunction, inflammatory response and symptom distress, and further increases the damage to the structure and function of the kidneys, leading to increased demand for renal replacement therapy and risks of cardiovascular disease, which also has medical costs for the society. If we can intervene in a feasible manual to effectively regulate the autonomic nerve function of CKD patients, reduce the inflammatory response and symptom distress. To prolong the progression of the disease, it will be the main goal of caring for CKD patients. This study aims to test the effect of heart rate variability biofeedback (HRVBF) on improving autonomic nerve function (Heart Rate Variability, HRV), inflammatory response (Interleukin-6 [IL-6], C reaction protein [CRP] ), symptom distress (Piper fatigue scale, Pittsburgh Sleep Quality Index [PSQI], and Beck Depression Inventory-II [BDI-II] ) in patients with chronic kidney disease. This study was experimental research, with a convenience sampling. Participants were recruited from the nephrology clinic at a medical center in northern Taiwan. With signed informed consent, participants were randomly assigned to the HRVBF or control group by using the Excel BINOMDIST function. The HRVBF group received four weekly hospital-based HRVBF training, and 8 weeks of home-based self-practice was done with StressEraser. The control group received usual care. We followed all participants for 3 months, in which we repeatedly measured their autonomic nerve function (HRV), inflammatory response (IL-6, CRP), and symptom distress (Piper fatigue scale, PSQI, and BDI-II) on their first day of study participation (baselines), 1 month, and 3 months after the intervention to test the effects of HRVBF. The results were analyzed by SPSS version 23.0 statistical software. The data of demographics, HRV, IL-6, CRP, Piper fatigue scale, PSQI, and BDI-II were analyzed by descriptive statistics. To test for differences between and within groups in all outcome variables, it was used by paired sample t-test, independent sample t-test, Wilcoxon Signed-Rank test and Mann-Whitney U test. Results: Thirty-four patients with chronic kidney disease were enrolled, but three of them were lost to follow-up. The remaining 31 patients completed the study, including 15 in the HRVBF group and 16 in the control group. The characteristics of the two groups were not significantly different. The four-week hospital-based HRVBF training combined with eight-week home-based self-practice can effectively enhance the parasympathetic nerve performance for patients with chronic kidney disease, which may against the disease-related parasympathetic nerve inhibition. In the inflammatory response, IL-6 and CRP in the HRVBF group could not achieve significant improvement when compared with the control group. Self-reported fatigue and depression significantly decreased in the HRVBF group, but they still failed to achieve a significant difference between the two groups. HRVBF has no significant effect on improving the sleep quality for CKD patients.

Keywords: heart rate variability biofeedback, autonomic nerve function, inflammatory response, symptom distress, chronic kidney disease

Procedia PDF Downloads 155
165 Asynchronous Low Duty Cycle Media Access Control Protocol for Body Area Wireless Sensor Networks

Authors: Yasin Ghasemi-Zadeh, Yousef Kavian

Abstract:

Wireless body area networks (WBANs) technology has achieved lots of popularity over the last decade with a wide range of medical applications. This paper presents an asynchronous media access control (MAC) protocol based on B-MAC protocol by giving an application for medical issues. In WBAN applications, there are some serious problems such as energy, latency, link reliability (quality of wireless link) and throughput which are mainly due to size of sensor networks and human body specifications. To overcome these problems and improving link reliability, we concentrated on MAC layer that supports mobility models for medical applications. In the presented protocol, preamble frames are divided into some sub-frames considering the threshold level. Actually, the main reason for creating shorter preambles is the link reliability where due to some reasons such as water, the body signals are affected on some frequency bands and causes fading and shadowing on signals, therefore by increasing the link reliability, these effects are reduced. In case of mobility model, we use MoBAN model and modify that for some more areas. The presented asynchronous MAC protocol is modeled by OMNeT++ simulator. The results demonstrate increasing the link reliability comparing to B-MAC protocol where the packet reception ratio (PRR) is 92% also covers more mobility areas than MoBAN protocol.

Keywords: wireless body area networks (WBANs), MAC protocol, link reliability, mobility, biomedical

Procedia PDF Downloads 348
164 Online Yoga Asana Trainer Using Deep Learning

Authors: Venkata Narayana Chejarla, Nafisa Parvez Shaik, Gopi Vara Prasad Marabathula, Deva Kumar Bejjam

Abstract:

Yoga is an advanced, well-recognized method with roots in Indian philosophy. Yoga benefits both the body and the psyche. Yoga is a regular exercise that helps people relax and sleep better while also enhancing their balance, endurance, and concentration. Yoga can be learned in a variety of settings, including at home with the aid of books and the internet as well as in yoga studios with the guidance of an instructor. Self-learning does not teach the proper yoga poses, and doing them without the right instruction could result in significant injuries. We developed "Online Yoga Asana Trainer using Deep Learning" so that people could practice yoga without a teacher. Our project is developed using Tensorflow, Movenet, and Keras models. The system makes use of data from Kaggle that includes 25 different yoga poses. The first part of the process involves applying the movement model for extracting the 17 key points of the body from the dataset, and the next part involves preprocessing, which includes building a pose classification model using neural networks. The system scores a 98.3% accuracy rate. The system is developed to work with live videos.

Keywords: yoga, deep learning, movenet, tensorflow, keras, CNN

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163 Confirmatory Factor Analysis of Smartphone Addiction Inventory (SPAI) in the Yemeni Environment

Authors: Mohammed Al-Khadher

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Currently, we are witnessing rapid advancements in the field of information and communications technology, forcing us, as psychologists, to combat the psychological and social effects of such developments. It also drives us to continually look for the development and preparation of measurement tools compatible with the changes brought about by the digital revolution. In this context, the current study aimed to identify the factor analysis of the Smartphone Addiction Inventory (SPAI) in the Republic of Yemen. The sample consisted of (1920) university students (1136 males and 784 females) who answered the inventory, and the data was analyzed using the statistical software (AMOS V25). The factor analysis results showed a goodness-of-fit of the data five-factor model with excellent indicators, as RMSEA-(.052), CFI-(.910), GFI-(.931), AGFI-(.915), TLI-(.897), NFI-(.895), RFI-(.880), and RMR-(.032). All within the ideal range to prove the model's fit of the scale’s factor analysis. The confirmatory factor analysis results showed factor loading in (4) items on (Time Spent), (4) items on (Compulsivity), (8) items on (Daily Life Interference), (5) items on (Craving), and (3) items on (Sleep interference); and all standard values of factor loading were statistically significant at the significance level (>.001).

Keywords: smartphone addiction inventory (SPAI), confirmatory factor analysis (CFA), yemeni students, people at risk of smartphone addiction

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162 The Latency-Amplitude Binomial of Waves Resulting from the Application of Evoked Potentials for the Diagnosis of Dyscalculia

Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of the acquisition of new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used, is the analysis of the dynamics of different areas of the brain during a cognitive activity to find the relationships between the different areas analyzed in order to better understand the functioning of neural networks. Also, the latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neuro-developmental difficulties for their subsequent assessment and cure. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, Learning disabilities, neural networks

Procedia PDF Downloads 111
161 Survey of Potential Adverse Health Effects of Mobile Phones, and Wireless Base Stations in Nigeria

Authors: Nureni A. Yekini, Isaac T. Babalola, Edwin E. Aighokhan, Agnes K. Akinwole, N. Stephen Igwe

Abstract:

Survey was conducted to gather information on potential adverse health effects of Mobile Phones, and Telecommunication Tower Base Stations in Nigeria. Data was sourced from two sampled populations. Firstly from the people living in close proximity to base stations, and secondly from cell phone users. Questionnaire was used to gathered information from 574 people on thirteen non-specific health symptoms. Data obtained was presented and analyzed. The analysis shows that people living close to the based stations over a long period of time with or without cell phone, and also the heavy phone users with close proximity to the base stations are liable to have some potential health hazards, such as fatigue, sleep disturbances, headaches, feeling of discomfort, difficulty in concentrating, depression, memory loss, visual disruptions, irritability, hearing disruptions, skin problems, cardiovascular disorders, and dizziness.

Keywords: health hazards, wireless base stations, phone users, mobile phones, Nigeria

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160 Prevention of the Post – Intensive Care Syndrome (PICS) by Implementation of an ICU Delirium Prevention Strategy (DPB)

Authors: Paul M. H. J. Roekaerts

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In recent years, it became clear that much intensive care (ICU) survivors develop a post-intensive care syndrome (PICS) consisting of psychiatric, cognitive and physical problems for a prolonged period after their ICU stay. Physical inactivity and delirium during the ICU stay are the main determinants of the post-ICU PICS. This presentation will focus on delirium, its epidemiology, prevalence, effect on outcome, risk factors and the current standard of care for managing delirium. Because ICU delirium is a predictor of prolonged length-of-stay in the ICU and of death, the use of a delirium prevention bundle (DPB) becomes mandatory in every ICU. In this presentation, a DPB bundle will be discussed consisting of six components: pain, sedation, sleep, sensory and intellectual stimulation, early mobilization, and hydration. For every of the six components, what to do and what not to do will be discussed. The author will present his own institutional policy on pharmacological and non-pharmacological interventions in the management of delirium. The component ‘early mobilization’ will be discussed more in detail, as this component is extremely important in the prevention of delirium as well as in the prevention of the PICS. The author will conclude his presentation with the remaining areas of uncertainties/work and research to be done.

Keywords: delirium, delirium prevention bundle, early mobilisation in intensive care (ICU), post-intensive care syndrome (PICS)

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159 The Role of Physical Activities in Improving the Psychological State, Reducing Stress and Anxiety Resulting from the Corona (Covid-19) Pandemic

Authors: Saidia Houari

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The current coronavirus pandemic (COVID-19) is a special and unusual reality. It can affect people physically, but also psychologically. Indeed, in such a context, many people will experience reactions of stress, anxiety and depression, and Sports is known to be a great in improving the effectiveness of the nervous system and mental health. Professor Ango Frubuze“many studies proved that sports play an important role in fighting psychological tension and some other psychological problems, such as depression and sleep difficulties, but on condition of practicing them properly,choosing the kind that generates comfort and happiness for man “ .The sports university professor in the German city of Cologne added that the effort exerted during the exercise works on restoring balance to the stress hormones like cortisol.The case report provides an insight into the COVID-19 current situation and represents a picture of the current state of mental health and an overview of novel coronavirus (Covid-19) outbreaks in some countries of the world. Some procedures taken to combat the coronavirus. We proposed the practice of physical activities during the quarantine period, and we showed their importance and their positive effects.

Keywords: COVID-19, psycholiqical impacts, stress, physical activities

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158 The Quranic Case for Resurrection

Authors: Maira Farooq Maneka

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Death has increasingly caused humans to investigate its reality and what lies after it, if something at all, with personal conviction and concern. Till date it remains a matter of speculation. We do not encounter arguments other than ‘faith’ from major world religions when justifying claims about life after death (LAD) as it is an unseen phenomenon. This paper attempts to analyse the Islamic idea of resurrection (after death) and its justification that is distinct from faith but instead contemplative in nature. To do this a legal lens was adopted which allowed the categorisation of selected Quranic arguments under the heading of direct evidence, indirect evidence and intuitive reasoning. Results: Four kinds of direct evidences are discussed under the themes of sleep, droughts, predictions and Quranic challenge. The section of indirect evidences narrows its scope only to two, out of many, broad possible signs that pointed towards the reality of resurrection. These include the signs found in nature such as sun and water as well as signs one finds within the human body such as the creation and function of human fingertips. Finally the last section tries to amalgamate Quran’s appeal to human rationality that facilitates the reader in accepting the possibility of resurrection and hence a final Day of Judgement. These include the notion of accountability, pleasure, pain and human agency.

Keywords: Islam, life after death, Quran, resurrection

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157 Using Hidden Markov Chain for Improving the Dependability of Safety-Critical Wireless Sensor Networks

Authors: Issam Alnader, Aboubaker Lasebae, Rand Raheem

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Wireless sensor networks (WSNs) are distributed network systems used in a wide range of applications, including safety-critical systems. The latter provide critical services, often concerned with human life or assets. Therefore, ensuring the dependability requirements of Safety critical systems is of paramount importance. The purpose of this paper is to utilize the Hidden Markov Model (HMM) to elongate the service availability of WSNs by increasing the time it takes a node to become obsolete via optimal load balancing. We propose an HMM algorithm that, given a WSN, analyses and predicts undesirable situations, notably, nodes dying unexpectedly or prematurely. We apply this technique to improve on C. Lius’ algorithm, a scheduling-based algorithm which has served to improve the lifetime of WSNs. Our experiments show that our HMM technique improves the lifetime of the network, achieved by detecting nodes that die early and rebalancing their load. Our technique can also be used for diagnosis and provide maintenance warnings to WSN system administrators. Finally, our technique can be used to improve algorithms other than C. Liu’s.

Keywords: wireless sensor networks, IoT, dependability of safety WSNs, energy conservation, sleep awake schedule

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156 Artificial Intelligent-Based Approaches for Task ‎Offloading, ‎Resource ‎Allocation and Service ‎Placement of ‎Internet of Things ‎Applications: State of the Art

Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

Abstract:

In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎.

Keywords: mobile edge computing, multi-objective optimization, artificial ‎intelligence ‎approaches, task offloading, resource allocation, ‎ service placement

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155 Test of Moisture Sensor Activation Speed

Authors: I. Parkova, A. Vališevskis, A. Viļumsone

Abstract:

Nocturnal enuresis or bed-wetting is intermittent incontinence during sleep of children after age 5 that may precipitate wide range of behavioural and developmental problems. One of the non-pharmacological treatment methods is the use of a bed-wetting alarm system. In order to improve comfort conditions of nocturnal enuresis alarm system, modular moisture sensor should be replaced by a textile sensor. In this study behaviour and moisture detection speed of woven and sewn sensors were compared by analysing change in electrical resistance after solution (salt water) was dripped on sensor samples. Material of samples has different structure and yarn location, which affects solution detection rate. Sensor system circuit was designed and two sensor tests were performed: system activation test and false alarm test to determine the sensitivity of the system and activation threshold. Sewn sensor had better result in system’s activation test – faster reaction, but woven sensor had better result in system’s false alarm test – it was less sensitive to perspiration simulation. After experiments it was found that the optimum switching threshold is 3V in case of 5V input voltage, which provides protection against false alarms, for example – during intensive sweating.

Keywords: conductive yarns, moisture textile sensor, industry, material

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154 Light-Weight Network for Real-Time Pose Estimation

Authors: Jianghao Hu, Hongyu Wang

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The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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153 mm-Wave Wearable Edge Computing Module Hosted by Printed Ridge Gap Waveguide Structures: A Physical Layer Study

Authors: Matthew Kostawich, Mohammed Elmorsy, Mohamed Sayed Sifat, Shoukry Shams, Mahmoud Elsaadany

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6G communication systems represent the nominal future extension of current wireless technology, where its impact is extended to touch upon all human activities, including medical, security, and entertainment applications. As a result, human needs are allocated among the highest priority aspects of the system design and requirements. 6G communications is expected to replace all the current video conferencing with interactive virtual reality meetings involving high data-rate transmission merged with massive distributed computing resources. In addition, the current expansion of IoT applications must be mitigated with significant network changes to provide a reasonable Quality of Service (QoS). This directly implies a high demand for Human-Computer Interaction (HCI) through mobile computing modules in future wireless communication systems. This article proposes the utilization of a Printed Ridge Gap Waveguide (PRGW) to host the wearable nodes. To the best of our knowledge, we propose for the first time a physical layer analysis within the context of a complete architecture. A thorough study is provided on the impact of the distortion of the guiding structure on the overall system performance. The proposed structure shows small latency and small losses, highlighting its compatibility with future applications.

Keywords: ridge gap waveguide, edge computing module, 6G, multimedia IoT applications

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152 A Prediction Model Using the Price Cyclicality Function Optimized for Algorithmic Trading in Financial Market

Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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151 Empirical Study of Health Behaviors of Employees in Information Technology and Business Process Outsourcing

Authors: Yogesh Pawar

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The purpose of this paper is to investigate the behaviors of information technology (IT) and business process outsourcing (BPO) employees in relation to diet, exercise, sleep, stress, and social habits. This was a qualitative research study, using in-depth,semi-structured interviews. Descriptive data were collected from a two-stage purposive sample of 28 IT-BPO employees from two IT companies and one BPOs in Pune. The majority of interviewees reported having an unhealthy diet and/or sedentary lifestyle. Lack of time due to demanding work schedules was the largest barrier to diet and exercise. Given the qualitative study design and limited sampling frame, results may not be generalizable. However, the qualitative data suggests that Pune’s young IT-BPO employees may be at greater risk of lifestyle-related diseases than the general population. The data also suggests that interventions incorporating social influence may be a promising solution, particularly at international call centers. The results from this study provide qualitative insight on the motives for health behaviors of IT-BPO employees, as well as the barriers and facilitators for leading a healthy lifestyle in this industry. The findings provide the framework for future workplace wellness interventions.

Keywords: exercise, information technology, qualitative research, wellness

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150 Acoustics Barrier Design to Reduce Railway Noise by Using Maekawa's Method

Authors: Malinda Sabrina, Khoerul Anwar

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Railway noise generated by pass-by train has been described as a form of environmental pollutants especially for the residential area near the railway. Many studies have shown, that environmental noise particularly transportation noise has negative effects on people which resulting in annoyance and specific health problems such as cardiovascular disease, cognitive impairment and sleep disturbance. Therefore, various attempts are made to reduce the noise. One method of reducing such noise to acceptable noise levels is to build acoustically barrier walls. The objective of this study was to review the method of reducing railway noise and obtain the preliminary design of the acoustics barrier on the edge of railway tracks close to the residential area. The design of this barrier is using the Maekawa's method. Measurements have been performed in residential areas around the railroads in the Karawang - Indonesia with the absence of an acoustical barrier. From the observation, it was found that the railway was passed by five trains within thirty minutes. With the limited distance between the railway tracks and the location of the residential area as well as the street of residents, then it was obtained that a reduction in sound pressure level is 25 dBA. Maximum sound pressure level obtained is 86.9 dBA then by setting the barrier as high as 4 m at a distance, 2.5 m from the railway, the noise level received by residents in the settlement around the railway line becomes 61.9 dBA.

Keywords: acoustics barrier, Maekawa's method, noise attenuation, railway noise

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149 Event Related Brain Potentials Evoked by Carmen in Musicians and Dancers

Authors: Hanna Poikonen, Petri Toiviainen, Mari Tervaniemi

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Event-related potentials (ERPs) evoked by simple tones in the brain have been extensively studied. However, in reality the music surrounding us is spectrally and temporally complex and dynamic. Thus, the research using natural sounds is crucial in understanding the operation of the brain in its natural environment. Music is an excellent example of natural stimulation, which, in various forms, has always been an essential part of different cultures. In addition to sensory responses, music elicits vast cognitive and emotional processes in the brain. When compared to laymen, professional musicians have stronger ERP responses in processing individual musical features in simple tone sequences, such as changes in pitch, timbre and harmony. Here we show that the ERP responses evoked by rapid changes in individual musical features are more intense in musicians than in laymen, also while listening to long excerpts of the composition Carmen. Interestingly, for professional dancers, the amplitudes of the cognitive P300 response are weaker than for musicians but still stronger than for laymen. Also, the cognitive P300 latencies of musicians are significantly shorter whereas the latencies of laymen are significantly longer. In contrast, sensory N100 do not differ in amplitude or latency between musicians and laymen. These results, acquired from a novel ERP methodology for natural music, suggest that we can take the leap of studying the brain with long pieces of natural music also with the ERP method of electroencephalography (EEG), as has already been made with functional magnetic resonance (fMRI), as these two brain imaging devices complement each other.

Keywords: electroencephalography, expertise, musical features, real-life music

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148 Embedded System of Signal Processing on FPGA: Underwater Application Architecture

Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

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The purpose of this paper is to study the phenomenon of acoustic scattering by using a new method. The signal processing (Fast Fourier Transform FFT Inverse Fast Fourier Transform iFFT and BESSEL functions) is widely applied to obtain information with high precision accuracy. Signal processing has a wider implementation in general-purpose pro-cessors. Our interest was focused on the use of FPGAs (Field-Programmable Gate Ar-rays) in order to minimize the computational complexity in single processor architecture, then be accelerated on FPGA and meet real-time and energy efficiency requirements. Gen-eral-purpose processors are not efficient for signal processing. We implemented the acous-tic backscattered signal processing model on the Altera DE-SOC board and compared it to Odroid xu4. By comparison, the computing latency of Odroid xu4 and FPGA is 60 sec-onds and 3 seconds, respectively. The detailed SoC FPGA-based system has shown that acoustic spectra are performed up to 20 times faster than the Odroid xu4 implementation. FPGA-based system of processing algorithms is realized with an absolute error of about 10⁻³. This study underlines the increasing importance of embedded systems in underwater acoustics, especially in non-destructive testing. It is possible to obtain information related to the detection and characterization of submerged cells. So we have achieved good exper-imental results in real-time and energy efficiency.

Keywords: DE1 FPGA, acoustic scattering, form function, signal processing, non-destructive testing

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147 Efficacy of Nasya in Alcohol Withdrawal Syndrome

Authors: Sandip Tambare, Revati Ghadge

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Alcohol withdrawal syndrome continue to be concerning health issue worldwide in alcoholics. Many current option for treating alcohol withdrawal signs are habit forming causing dependency of sedatives. The divine science of Ayurveda recommends Nasya for improvement of alcohol withdrawal signs. As per the latest reports 1/3 of the Indian population is using alcohol in an unhealthy manner, the complication being wide and varied among which, the Alcohol Withdrawal Syndrome is the dominant one. The presentation varies from mild sleep loss or anxiety to delirium. Ayurveda has given utmost in the context of Madatyaya(Alcoholism). Various protocols based on the identification of the status of tridoshas are explained which includes sodhana, samana and satwavachaya chikitsa. Various medications are being used, with appreciated effects in the clinical practice. As per reports, the panchakarma procedure nasya seems highly effective, in managing of the alcohol withdrawal syndrome. Nasya with Ksheerabala Taila is given for 7 days in the condition of Alcohol Withdrawal syndrome and it was the non Randomized trial with 30 subjects satisfying the DSM V criteria for alcohol withdrawl and the assessment was done using the Clinical Institute Withdrawal Assessment for Alcohol Scale revised (CIWA-Ar). Conclusion: Among the symptoms which were studied after the interventions, it was found that there was high significant response in almost all the symptoms in the given subjects. The eternal science of Ayurveda is able to answer the existing problem of alcohol and its abuse in the society.

Keywords: nasya, alcohol withdrawal, madatyaya, ksheerabala taila

Procedia PDF Downloads 109
146 Evaluation of the Diagnostic Potential of IL-2 after Specific Antigen Stimulation with PE35 (Rv3872) and PPE68 (Rv3873) for the Discrimination of Active and Latent Tuberculosis

Authors: Shima Mahmoudi, Babak Pourakbari, Setareh Mamishi, Mostafa Teymuri, Majid Marjani

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Although cytokine analysis has greatly contributed to the understanding of tuberculosis (TB) pathogenesis, data on cytokine profiles that might distinguish progression from latency of TB infection are scarce. Since PE/PPE proteins are known to induce strong humoral and cellular immune responses, the aim of this study was to evaluate the diagnostic potential of interleukin-2 (IL-2) as biomarker after specific antigen stimulation with PE35 and PPE68 for the discrimination of active and latent tuberculosis infection (LTBI). The production of IL-2 was measured in the antigen-stimulated whole-blood supernatants following stimulation with recombinant PE35 and PPE68. All the patients with active TB and LTBI had positive QuantiFERON-TB Gold in Tube test. The level of IL-2 following stimulation with recombinant PE35 and PPE68 were significantly higher in LTBI group than in patients with active TB infection or control group. The discrimination performance (assessed by the area under ROC curve) for IL-2 following stimulation with recombinant PE35 and PPE68 between LTBI and patients with active TB were 0.837 (95%CI: 0.72-0.97) and 0.75 (95%CI: 0.63-0.89), respectively. Applying the 12.4 pg/mL cut-off for IL-2 induced by PE35 in the present study population resulted in sensitivity of 78%, specificity of 78%, PPV of 78% and NPV of 100%. In addition, a sensitivity of 81%, specificity of 70%, PPV of 67% and 87% of NPV was reported based on the 4.4 pg/mL cut-off for IL-2 induced by PPE68. In conclusion, peptides of the antigen PE35 and PPE68, absent from commonly used BCG strains, stimulated strong IL-2- positive T cell responses in patients with LTBI. This study confirms IL-2 induced by PE35 and PPE68 as a sensitive and specific biomarker and highlights IL-2 as new promising adjunct markers for discriminating of LTBI and Active TB infection.

Keywords: IL-2, PE35, PPE68, tuberculosis

Procedia PDF Downloads 382
145 The Patterns of Cross-Sentence: An Event-Related Potential Study of Mathematical Word Problem

Authors: Tien-Ching Yao, Ching-Ching Lu

Abstract:

Understanding human language processing is one of the main challenges of current cognitive neuroscience. The aims of the present study were to use a sentence decision task combined with event-related potentials to investigate the psychological reality of "cross-sentence patterns." Therefore, we take the math word problems the experimental materials and use the ERPs' P600 component to verify. In this study, the experimental material consisted of 200 math word problems with three different conditions were used ( multiplication word problems、division word problems type 1、division word problems type 2 ). Eighteen Mandarin native speakers participated in the ERPs study (14 of whom were female). The result of the grand average waveforms suggests a later posterior positivity at around 500ms - 900ms. These findings were tested statistically using repeated measures ANOVAs at the component caused by the stimulus type of different questions. Results suggest that three conditions present significant (P < 0.05) on the Mean Amplitude, Latency, and Peak Amplitude. The result showed the characteristic timing and posterior scalp distribution of a P600 effect. We interpreted these characteristic responses as the psychological reality of "cross-sentence patterns." These results provide insights into the sentence processing issues in linguistic theory and psycholinguistic models of language processing and advance our understanding of how people make sense of information during language comprehension.

Keywords: language processing, sentence comprehension, event-related potentials, cross-sentence patterns

Procedia PDF Downloads 126
144 Impact of Diet and COVID-19 Policies on Osteopenia in a Hispanic White Adolescent Girl

Authors: Maria Angelica Trak-Fellermeier, Alison K. Macchi, Rodolfo Galvan, Yolangel Hernandez, Thresia Gambon, Rebeca Martinez, Cristina Palacios

Abstract:

Poor lifestyle habits, vitamin D deficiency, and inadequate calcium intake, particularly during the COVID-19 pandemic, may contribute to severe osteopenia in childhood, increasing future fractures and osteoporosis risk. We here present a case of osteopenia in a 13-year-old white, Hispanic, premenarchal girl who completed the baseline visit of the MetA-Bone Trial during the COVID-19 pandemic. The premenarchal girl has a family history of osteoporosis (maternal grandfather) but no previous fractures; moderate outdoor activity was <1 hour/day 3 times/week with 8 hours/day of sleep. Consumption of dairy products and vegetables was <1 serving/day. Lab blood tests confirmed vitamin D deficiency (serum 25(OH)D: 9 ng/L) and hyperphosphatemia (5.2 mg/dL); other tests were normal. DXA scan Z‐score was ‐2.2 SD (indicative of osteopenia by age and sex). The premenarchal girl was referred to a pediatrician, who confirmed the results, and prescribed a daily supplement with 2000 IU of vitamin D and 1000 mg of calcium. Seclusion during the COVID-19 pandemic may have contributed to the severity of the findings. Therefore, we recommend screening children undergoing growth spurts for vitamin D, calcium, and poor lifestyle habits during and after the pandemic.

Keywords: bone mass, vitamin D, puberty, Hispanic

Procedia PDF Downloads 100
143 The Effect of Head Posture on the Kinematics of the Spine During Lifting and Lowering Tasks

Authors: Mehdi Nematimoez

Abstract:

Head posture is paramount to retaining gaze and balance in many activities; its control is thus important in many activities. However, little information is available about the effects of head movement restriction on other spine segment kinematics and movement patterns during lifting and lowering tasks. The aim of this study was to examine the effects of head movement restriction on relative angles and their derivatives using the stepwise segmentation approach during lifting and lowering tasks. Ten healthy men lifted and lowered a box using two styles (stoop and squat), with two loads (i.e., 10 and 20% of body weight); they performed these tasks with two instructed head postures (1. Flexing the neck to keep contact between chin and chest over the task cycle; 2. No instruction, free head posture). The spine was divided into five segments, tracked by six cluster markers (C7, T3, T6, T9, T12, and L5). Relative angles between spine segments and their derivatives (first and second) were analyzed by a stepwise segmentation approach to consider the effect of each segment on the whole spine. Accordingly, head posture significantly affected the derivatives of the relative angles and manifested latency in spine segments movement, i.e., cephalad-to-caudad or caudad-to-cephalad patterns. The relative angles for C7-T3 and T3-T6 increased over the cycle of all lifting and lowering tasks; nevertheless, in lower segments increased significantly when the spine moved into upright standing. However, these effects were clearer during lifting than lowering. Conclusively, the neck flexion can unevenly increase the flexion angles of spine segments from cervical to lumbar over lifting and lowering tasks; furthermore, stepwise segmentation reveals potential for assessing the segmental contribution in spine ROM and movement patterns.

Keywords: head movement restriction, spine kinematics, lifting, lowering, stepwise segmentation

Procedia PDF Downloads 210
142 An IoT-Enabled Crop Recommendation System Utilizing Message Queuing Telemetry Transport (MQTT) for Efficient Data Transmission to AI/ML Models

Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

Procedia PDF Downloads 33
141 Trading off Accuracy for Speed in Powerdrill

Authors: Filip Buruiana, Alexander Hall, Reimar Hofmann, Thomas Hofmann, Silviu Ganceanu, Alexandru Tudorica

Abstract:

In-memory column-stores make interactive analysis feasible for many big data scenarios. PowerDrill is a system used internally at Google for exploration in logs data. Even though it is a highly parallelized column-store and uses in memory caching, interactive response times cannot be achieved for all datasets (note that it is common to analyze data with 50 billion records in PowerDrill). In this paper, we investigate two orthogonal approaches to optimize performance at the expense of an acceptable loss of accuracy. Both approaches can be implemented as outer wrappers around existing database engines and so they should be easily applicable to other systems. For the first optimization we show that memory is the limiting factor in executing queries at speed and therefore explore possibilities to improve memory efficiency. We adapt some of the theory behind data sketches to reduce the size of particularly expensive fields in our largest tables by a factor of 4.5 when compared to a standard compression algorithm. This saves 37% of the overall memory in PowerDrill and introduces a 0.4% relative error in the 90th percentile for results of queries with the expensive fields. We additionally evaluate the effects of using sampling on accuracy and propose a simple heuristic for annotating individual result-values as accurate (or not). Based on measurements of user behavior in our real production system, we show that these estimates are essential for interpreting intermediate results before final results are available. For a large set of queries this effectively brings down the 95th latency percentile from 30 to 4 seconds.

Keywords: big data, in-memory column-store, high-performance SQL queries, approximate SQL queries

Procedia PDF Downloads 234
140 A Multi-Dimensional Neural Network Using the Fisher Transform to Predict the Price Evolution for Algorithmic Trading in Financial Markets

Authors: Cristian Pauna

Abstract:

Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

Procedia PDF Downloads 136
139 Feasibility of Using Musical Intervention to Promote Growth in Preterm Infants in the Neonatal Intensive Care Unit (NICU)

Authors: Yutong An

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Premature babies in the Neonatal Intensive Care Unit (NICU) are usually protected in individual incubators to ensure a constant temperature and humidity. Accompanied by 24-hour monitoring by medical equipment, this provides a considerable degree of protection for the growth of preterm babies. However, preterm babies are still continuously exposed to noise at excessively high decibels (>45dB). Such noise has a highly damaging effect on the growth and development of preterm babies. For example, in the short term, it can lead to sleep deprivation, stress reactions, and difficulty calming emotions, while in the long term, it can trigger endocrine disorders, metabolic disorders, and hearing impairment. Fortunately, musical interventions in the NICU have been shown to provide calmness to newborns. This article integrates existing research on three types of music that are beneficial for preterm infants and their respective advantages and disadvantages. This paper aims to present a possibility, based on existing NICU equipment and experimental data related to musical interventions, to reduce the impact of noise on preterm babies in the NICU through a system design approach that incorporates a personalized adjustable music system in the incubator and an overall music enhancement in the open bay of the NICU.

Keywords: music interventions, neonatal intensive care unit (NICU), premature babies, neonatal nursing

Procedia PDF Downloads 38
138 Occupational Health Assessment in a Telco Account: A Workplace Integrated Safety and Health and Cornell Musculoskeletal Discomfort Questionnaire Analysis Among Diverse Employees at Alorica

Authors: Karl Bryant Buan, Owaida Macadadaya Jr., Mon Eleazar Nonato, Zeke Andrew Palabrica, Charistabelle Mae Santiago

Abstract:

This study explored the occupational health risks faced by employees in the Business Process Outsourcing (BPO) industry, particularly in the Telco Account department of Alorica. The study used a stratified sampling method and a diagnostic tool called Workplace Integrated Safety and Health (WISH) Assessment to measure and evaluate the employees' perception of workplace health and safety. The results showed that more than 50% of call center workers reported feeling emotionally drained, sleep deprived, burnt out, and in need of anxiety or stress medication due to their work. Additionally, there was a significant difference in the perception of employee diversity, specifically in terms of leadership commitment, participation, policies, programs, and practices. The Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) results revealed that most employees complained of discomfort in their lower back, shoulder, upper back, neck, and hip. The researchers recommended an implementation plan for alternative work set-up, a satisfaction survey for employees, team-building activities or programs, and motivational approaches through benefits, incentives, and rewards.

Keywords: WISH assessment, CMDQ, ANOVA, diverse SOGIESC

Procedia PDF Downloads 52