Search results for: neural smith predictor

Authors: Jossitt Williams Vargas Cruz, Aura Jazmín Pérez Ríos

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The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation.

Keywords: correlations, cosmic rays, sun, sunspots and variations.

Procedia PDF Downloads 74

Authors: Anteneh Berhane Yeyi, Tefera Belachew

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Any woman who could become pregnant is at risk of having a baby with neural tube defects (NTDs). A spontaneous aborted women with immediately preceding pregnancy may have an increased risk of develop NTDs. Ethiopia has one of the highest rates of micronutrient deficiencies, including folate and iron deficiency. Currently, in Ethiopia, NTDs is emerged as a public health concern. Even if Ethiopia, has implement different strategies for reducing maternal and neonatal mortality and morbidity, there is no room in the health care system and lack of integration for preventing the risk of NTDs for those women who aborted spontaneously and women who discontinue long acting contraception to become pregnant. The purpose of this study was to evaluate the effect of preconception picture-based nutrition education on knowledge and adherence to iron-folic acid supplement (IFAS) intake to reduce the risk of developing neural tube defects (NTDs) and iron deficiency anemia (IDA) among women who had a planned to pregnancy in Ethiopia, a country with a high burden of NTDs. Methodology: This study was conducted in Eastern Ethiopia. A double blinded parallel randomized controlled trial design was employed among women in the age group of 18-45 years who requested to interrupt modern contraceptive who have an intention to be pregnant and women with spontaneous abortion who refused to take a contraceptive. The interventional arm (n=122) received a preconception picture-based nutrition education with iron-folic acid supplement, and the control arm (n=122) received only preconception IFAS. In this study male partners were participated. Result: After three months of intervention the proportion of adherence to IFAS was 23% (n=56). With regard to adherence within the groups, 42.6% (n=52) in the intervention group and 3.3% (n=4) in the control group and the intervention group were significantly higher than in control group. In the intervention group the proportion of adherence to IFAS intake among participants increased by 40.1% and there were statistically difference (P<0.0001). The difference in difference between the two groups of adherence to IFAS intake was 37.6% and there were a statistical significance (P<0.0001). Level of knowledge between the groups did differ before and after intervention (P= 0.87 Vs P<0.0001). The overall the mean change in knowledge Mean (+SE) between group was 0.9 (+3.04 SE) and there were significant differences between two groups (P<0.001). Conclusion: In general this intervention is effective toward adherence to IFAS and a critical milestone to improve maternal health and reduce the neonate mortality due to NTDs and other advert effect of pregnancy and birth outcomes. This intervention is very short, simple, and cost effective and has potential for adaptation, feasible development to large-scale implementation in the existing health care system. Furthermore, this type of interventional approach has the potential to reduce the country's ANC program dropout rates and increase male partner’s participation on reproductive health.

Keywords: NTDs, IFAS, WRA, Ethiopia

Procedia PDF Downloads 34

Authors: Deanne McArthur, Melinda Wall, Claire Hanlon, Dana Agnolin, Krista Davis, Melanie Dennis, Elizabeth Glidden, Anne Marie Smith, Claudette Thomson

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In Canada, as prevalence rates and severity of illness have increased among patients suffering from eating disorders, wait times have grown substantially. Patients in Canada often face wait times in excess of 12 months. It is known that delaying treatment for eating disorders contributes to poor patient outcomes and higher rates of symptom relapse. Improving interim services for adult patients awaiting outpatient treatment is a priority for an outpatient eating disorders clinic in Ontario, Canada. The clinical setting currently provides care for adults diagnosed with anorexia nervosa, bulimia nervosa and binge eating disorder. At present, the only support provided while patients are on the waitlist consists of communication with primary care providers regarding parameters for medical monitoring. The significance of this study will be to test the feasibility, acceptability and efficacy of an intervention to support adult patients awaiting outpatient eating disorder treatment for anorexia nervosa, bulimia nervosa and binge eating disorder. Methods: An intervention including psychoeducation, supportive resources, self-monitoring, and auxiliary referral will be pilot-tested with a group of patients in the summer of 2022 and detailed using a prospective cohort case study research design. The team will host patient focus groups in May 2022 to gather input informing the content of the intervention. The intervention will be pilot tested with newly-referred patients in June and July 2022. Patients who participate in the intervention will be asked to complete a survey evaluating the utility of the intervention and for suggestions, they may have for improvement. Preliminary findings describing the existing literature pertaining to waitlist interventions for patients with eating disorders, data gathered from the focus groups and early pilot testing results will be presented. Data analysis will continue throughout 2022 and early 2023 for follow-up publication and presentation in the summer of 2023. The aim of this study is to contribute to the body of knowledge pertaining to providing interim support to those patients waiting for treatment for eating disorders and, by extension, to improve outcomes for this population.

Keywords: eating disorders, waitlist management, intervention study, pilot test

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Authors: Sarah Alamolhoda, Kevin J. Smith, Xiaotao Bi, Naoko Ellis

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For millennials, biomass has been the most important source of fuel used to produce energy. Energy derived from biomass is renewable by re-growth of biomass. Various technologies are used to convert biomass to potential renewable products including combustion, gasification, pyrolysis and fermentation. Gasification is the incomplete combustion of biomass in a controlled environment that results in valuable products such as syngas, biooil and biochar. Syngas is a combustible gas consisting of hydrogen (H₂), carbon monoxide (CO), carbon dioxide (CO₂), and traces of methane (CH₄) and nitrogen (N₂). Cleaned syngas can be used as a turbine fuel to generate electricity, raw material for hydrogen and synthetic natural gas production, or as the anode gas of solid oxide fuel cells. In this work, syngas as a product of woody biomass gasification in British Columbia, Canada, was introduced to two consecutive fixed bed reactors to perform a catalytic water gas shift reaction followed by a catalytic methanation reaction. The water gas shift reaction is a well-established industrial process and used to increase the hydrogen content of the syngas before the methanation process. Catalysts were used in the process since both reactions are reversible exothermic, and thermodynamically preferred at lower temperatures while kinetically favored at elevated temperatures. The water gas shift reactor and the methanation reactor were packed with Cu-based catalyst and Ni-based catalyst, respectively. Simulated syngas with different percentages of CO, H₂, CH₄, and CO₂ were fed to the reactors to investigate the effect of operating conditions in the unit. The water gas shift reaction experiments were done in the temperature of 150 ˚C to 200 ˚C, and the pressure of 550 kPa to 830 kPa. Similarly, methanation experiments were run in the temperature of 300 ˚C to 400 ˚C, and the pressure of 2340 kPa to 3450 kPa. The Methanation reaction reached 98% of CO conversion at 340 ˚C and 3450 kPa, in which more than half of CO was converted to CH₄. Increasing the reaction temperature caused reduction in the CO conversion and increase in the CH₄ selectivity. The process was designed to be renewable and release low greenhouse gas emissions. Syngas is a clean burning fuel, however by going through water gas shift reaction, toxic CO was removed, and hydrogen as a green fuel was produced. Moreover, in the methanation process, the syngas energy was transformed to a fuel with higher energy density (per volume) leading to reduction in the amount of required fuel that flows through the equipment and improvement in the process efficiency. Natural gas is about 3.5 times more efficient (energy/ volume) than hydrogen and easier to store and transport. When modification of existing infrastructure is not practical, the partial conversion of renewable hydrogen to natural gas (with up to 15% hydrogen content), the efficiency would be preserved while greenhouse gas emission footprint is eliminated.

Keywords: renewable natural gas, methane, hydrogen, gasification, syngas, catalysis, fuel

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Authors: Afshan Channa, Sameeha Aleem, Harim Mohsin

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First rank symptoms (FRS) are considered to be pathognomic for Schizophrenia. However, FRS is not a distinctive feature of Schizophrenia. It has also been noticed in affective disorder, albeit not inclusive in diagnostic criteria. The presence of FRS in Mania leads to misdiagnosis of psychotic illness, further complicating the management and delay of appropriate treatment. FRS in Mania is associated with poor clinical and functional outcome. Its existence in the first episode of bipolar disorder may be a predictor of poor short-term outcome and decompensating course of illness. FRS in Mania is studied in west. However, the cultural divergence and detriments make it pertinent to study the frequency of FRS in affective disorder independently in Pakistan. Objective: The frequency of first rank symptoms in manic patients, who were under treatment at psychiatric services of tertiary care hospital. Method: The cross sectional study was done at psychiatric services of Aga Khan University Hospital, Karachi, Pakistan. One hundred and twenty manic patients were recruited from November 2014 to May 2015. The patients who were unable to comprehend Urdu or had comorbid psychiatric or organic disorder were excluded. FRS was assessed by administration of validated Urdu version of Present State Examination (PSE) tool. Result: The mean age of the patients was 37.62 + 12.51. The mean number of previous manic episode was 2.17 + 2.23. 11.2% males and 30.6% females had FRS. This association of first rank symptoms with gender in patients of mania was found to be significant with a p-value of 0.008. All-inclusive, 19.2% exhibited FRS in their course of illness. 43.5% had thought broadcasting, made feeling, impulses, action and somatic passivity. 39.1% had thought insertion, 30.4% had auditory perceptual distortion, and 17.4% had thought withdrawal. However, none displayed delusional perception. Conclusion: The study confirms the presence of FRS in mania in both male and female, irrespective of the duration of current manic illness or previous number of manic episodes. A substantial difference was established between both the genders. Being married had no protective effect on the presence of FRS.

Keywords: first rank symptoms, Mania, psychosis, present state examination

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

Procedia PDF Downloads 191

Authors: Habtamu Wolde Engudai

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Background: Sedation can be provided to facilitate a procedure or to stabilize patients admitted in pediatric intensive care unit (PICU). Sedation is often necessary to maintain optimal care for critically ill children requiring mechanical ventilation. However, if sedation is too deep or too light, it has its own adverse effects, and hence, it is important to monitor the level of sedation and maintain an optimal level. Objectives: The objective is to assess the level of sedation and associated factors among intubated critically ill children admitted to PICU of JUMC, Jimma. Methods: A prospective observation study was conducted in the PICU of JUMC in September 2021 in 105 patients who were going to be admitted to the PICU aged less than 14 and with GCS >8. Data was collected by residents and nurses working in PICU. Data entry was done by Epi data manager (version 4.6.0.2). Statistical analysis and the creation of charts is going to be performed using SPSS version 26. Data was presented as mean, percentage and standard deviation. The assumption of logistic regression and the result of the assumption will be checked. To find potential predictors, bi-variable logistic regression was used for each predictor and outcome variable. A p value of <0.05 was considered as statistically significant. Finally, findings have been presented using figures, AOR, percentages, and a summary table. Result: in this study, 105 critically ill children had been involved who were started on continuous or intermittent forms of sedative drugs. Sedation level was assessed using a comfort scale three times per day. Based on this observation, we got a 44.8% level of suboptimal sedation at the baseline, a 36.2% level of suboptimal sedation at eight hours, and a 24.8% level of suboptimal sedation at sixteen hours. There is a significant association between suboptimal sedation and duration of stay with mechanical ventilation and the rate of unplanned extubation, which was shown by P < 0.05 using the Hosmer-Lemeshow test of goodness of fit (p> 0.44).

Keywords: level of sedation, critically ill children, Pediatric intensive care unit, Jimma university

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

Procedia PDF Downloads 147

Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

Procedia PDF Downloads 251

Authors: Yair Bleiberg, Michael Werman

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Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper hyperparameter, just like batch normalization and dropout do. We believe that under certain circumstances, PE is not necessary, and a smarter construction of the network architecture together with a smart training method is sufficient to achieve similar results. In this paper, we show that very simple MLPs can quite easily output a frequency when given input of the half-frequency and quarter-frequency. Using this, we design a network architecture in blocks, where the input to each block is the output of the two previous blocks along with the original input. We call this a Fibonacci Network. By training each block on the corresponding frequencies of the signal, we show that Fibonacci Networks can reconstruct arbitrarily high frequencies.

Keywords: neural networks, positional encoding, high frequency intepolation, fully connected

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Authors: Micheal Olaolu Arowolo, Muhammad Azam, Fei He, Mihail Popescu, Dong Xu

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As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy.

Keywords: biological pathway, gene identification, object detection, Siamese network

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Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

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Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

Procedia PDF Downloads 476

Authors: Ahmet Erdoğan, Şahin Kesici, Mustafa Baloğlu

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Information technologies have lead to changes in the areas of communication, learning, and teaching. Besides offering many opportunities to the learners, these technologies have changed the teaching methods and beliefs of teachers. What the Technological Pedagogical Content Knowledge (TPACK) means to the teachers is considerably important to integrate technology successfully into teaching processes. It is necessary to understand how to plan and apply teacher training programs in order to balance students’ pedagogical and technological knowledge. Because of many inefficient teacher training programs, teachers have difficulties in relating technology, pedagogy and content knowledge each other. While providing an efficient training supported with technology, understanding the three main components (technology, pedagogy and content knowledge) and their relationship are very crucial. The purpose of this study is to determine whether motivational beliefs and self-regulated learning strategies are significant predictors of mathematics teacher candidates' TPACK perceptions. A hundred seventy five Turkish mathematics teachers candidates responded to the Motivated Strategies for Learning Questionnaire (MSLQ) and the Technological Pedagogical And Content Knowledge (TPACK) Scale. Of the group, 129 (73.7%) were women and 46 (26.3%) were men. Participants' ages ranged from 20 to 31 years with a mean of 23.04 years (SD = 2.001). In this study, a multiple linear regression analysis was used. In multiple linear regression analysis, the relationship between the predictor variables, mathematics teacher candidates' motivational beliefs, and self-regulated learning strategies, and the dependent variable, TPACK perceptions, were tested. It was determined that self-efficacy for learning and performance and intrinsic goal orientation are significant predictors of mathematics teacher candidates' TPACK perceptions. Additionally, mathematics teacher candidates' critical thinking, metacognitive self-regulation, organisation, time and study environment management, and help-seeking were found to be significant predictors for their TPACK perceptions.

Keywords: candidate mathematics teachers, motivational beliefs, self-regulated learning strategies, technological and pedagogical knowledge, content knowledge

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

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The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

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Authors: Gül Fatma Türker, Hatice Akman

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Portable muscle stimulators for real-time applications has first introduced by Liberson in 1961. Now these systems has been advanced. In this study, FES (Functional Electrical Stimulator) and NMES (Neuromuscular Electrostimulator) systems are analyzed through their hardware and their quality of life improvements for foot drop patients. FES and NMES systems are used for people whose leg muscles and leg neural connections are healty but not able to walk properly because of their injured central nervous system like spinal cord injuries. These systems are used to stimulate neurons or muscles by getting information from other movements and programming these stimulations to get natural walk and it is accepted as a rehabilitation method for the correction of drop foot. This systems support person to approach natural form of walking. Foot drop is characterized by steppage gait. It is a gait abnormality. This systems helps to person for plantar and dorse reflection movements which are hard to done for foot drop patients.

Keywords: FES, foot drop, NMES, stimulator

Procedia PDF Downloads 388

Authors: Aydin Teymourifar, Gurkan Ozturk, Ozan Bahadir

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NSGA-II is one of the most well-known and most widely used evolutionary algorithms. In addition to its new versions, such as NSGA-III, there are several modified types of this algorithm in the literature. In this paper, a hybrid NSGA-II algorithm has been suggested for solving the multi-objective flexible job shop scheduling problem. For a better search, new neighborhood-based crossover and mutation operators are defined. To create new generations, the neighbors of the selected individuals by the tournament selection are constructed. Also, at the end of each iteration, before sorting, neighbors of a certain number of good solutions are derived, except for solutions protected by elitism. The neighbors are generated using a constraint-based neural network that uses various constructs. The non-dominated sorting and crowding distance operators are same as the classic NSGA-II. A comparison based on some multi-objective benchmarks from the literature shows the efficiency of the algorithm.

Keywords: flexible job shop scheduling problem, multi-objective optimization, NSGA-II algorithm, neighborhood structures

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

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Authors: Maggie Mosher, Adam Carreon, Sean Smith

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A systematic literature review was conducted to explore the evidence base on the use of augmented reality (AR), virtual reality (VR), mixed reality (MR), and extended reality (XR) to present social skill instruction to school-age students with autism spectrum disorder (ASD). Specifically, the systematic review focus was on a. the participants and intervention agents using AR, VR, MR, and XR for social skill acquisition b. the social skills taught through these mediums and c. the social validity measures (i.e., goals, procedures, and outcomes) reported in these studies. Forty-one articles met the inclusion criteria. Researchers in six studies taught social skills to students through AR, in 27 studies through non-immersive VR, and in 10 studies through immersive VR. No studies used MR or XR. The primary targeted social skills were relationship skills, emotion recognition, social awareness, cooperation, and executive functioning. An intervention to improve many social skills was implemented by 73% of researchers, 17% taught a single skill, and 10% did not clearly state the targeted skill. The intervention was considered effective in 26 of the 41 studies (63%), not effective in four studies (10%), and 11 studies (27%) reported mixed results. No researchers reported information for all 17 social validity indicators. The social validity indicators reported by researchers ranged from two to 14. Social validity measures on the feelings toward and use of the technology were provided in 22 studies (54%). Findings indicated both AR and VR are promising platforms for providing social skill instruction to students with ASD. Studies utilizing this technology show a number of social validity indicators. However, the limited information provided on the various interventions, participant characteristics, and validity measures, offers insufficient evidence of the impact of these technologies in teaching social skills to students with ASD. Future research should develop a protocol for training treatment agents to assess the role of different variables (i.e., whether agents are customizing content, monitoring student learning, using intervention specific vocabulary in their day to day instruction). Sustainability may be increased by providing training in the technology to both treatment agents and participants. Providing scripts of instruction occurring within the intervention would provide the needed information to determine the primary method of teaching within the intervention. These variables play a role in maintenance and generalization of the social skills. Understanding the type of feedback provided would help researchers determine if students were able to feel rewarded for progressing through the scenarios or if students require rewarding aspects within the intervention (i.e., badges, trophies). AR has the potential to generalize instruction and VR has the potential for providing a practice environment for performance deficits. Combining these two technologies into a mixed reality intervention may provide a more cohesive and effective intervention.

Keywords: autism, augmented reality, social and emotional learning, social skills, virtual reality

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Authors: Mohamed El Horri, Amine Mouden, Reda Messaoudi, Mohamed Chekkal, Driss Benlaldj, Malika Baghdadi, Lahcene Benmahdi, Fatima Seghier

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Background/aim: Recently, the Von Willebrand factor antigen (vWF-Ag)has been identified as a new marker of portal hypertension (PH) and its complications. Few studies talked about its role in the prediction of esophageal varices. VWF-Ag is considered a non-invasive approach, In order to avoid the endoscopic burden, cost, drawbacks, unpleasant and repeated examinations to the patients. In our study, we aimed to evaluate the ability of this marker in the prediction of another complication of portal hypertension, which is portal hypertensive gastropathy (PHG), the one that is diagnosed also by endoscopic tools. Patients and methods: It is about a prospective study, which include 124 cirrhotic patients with no history of bleeding who underwent screening endoscopy for PH-related complications like esophageal varices (EVs) and PHG. Routine biological tests were performed as well as the VWF-Ag testing by both ELFA and Immunoturbidimetric techniques. The diagnostic performance of our marker was assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curves. Results: 124 patients were enrolled in this study, with a mean age of 58 years [CI: 55 – 60 years] and a sex ratio of 1.17. Viral etiologies were found in 50% of patients. Screening endoscopy revealed the presence of PHG in 20.2% of cases, while for EVsthey were found in 83.1% of cases. VWF-Ag levels, were significantly increased in patients with PHG compared to those who have not: 441% [CI: 375 – 506], versus 279% [CI: 253 – 304], respectively (p <0.0001). Using the area under the receiver operating characteristic curve (AUC), vWF-Ag was a good predictor for the presence of PHG. With a value higher than 320% and an AUC of 0.824, VWF-Ag had an 84% sensitivity, 74% specificity, 44.7% positive predictive value, 94.8% negative predictive value, and 75.8% diagnostic accuracy. Conclusion: VWF-Ag is a good non-invasive low coast marker for excluding the presence of PHG in patients with liver cirrhosis. Using this marker as part of a selective screening strategy might reduce the need for endoscopic screening and the coast of the management of these kinds of patients.

Keywords: von willebrand factor, portal hypertensive gastropathy, prediction, liver cirrhosis

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

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Authors: Fatemeh Abdollahi, Munn-Sann Lye, Jamshid Yazdani Charati, Mehran Zarghami

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Fathers are at risk of depression during the postpartum period. Some studies have been reported maternal depression is the key predictor of paternal postpartum depression (PPD). This study aimed to estimate the prevalence and predictors of parental PPD and its association with maternal PPD. In a cross-sectional study, via a stratified random and convenience sampling method, participants referring to health centers during 2-8 weeks postpartum were recruited from March to October 2017. Paternal PPD and its relation to maternal PPD and other related factors were assessed using multiple logistic regression. Participants were 591 literate couples who referred to Mazandaran province primary health centers during to study period. Couples were screened for depression using Edinburgh Postnatal Depression Scale (EPDS). Fathers provided information on socio-demographic characteristics, life events, neonatal stressor, perceived stress (Perceived Stress Scale), social support (Multidimensional Scale of Perceived Social Support), and general health status using General Health Questionnaire (GHQ) as well. Data on mothers ‘demographic characteristics and obstetrics factors was also gathered. Overall, 93 fathers (15.7%) and 188 mothers (31.8%) reported depressive symptoms above the cut-off EPDS score of 12. In the multiple logistic regression model, older age [OR=1.20, (95%CI: 1.05- 1.36)], maternal depressive symptoms [OR=1.15, (95%CI: 1.04-1.27)], higher GHQ scores [OR=1.21, (95%CI: 1.11-1.33)] and increased recent life events [OR=1.42, (95%CI: 1.01-1.2.00)] were related to paternal PPD. A significant inverse association was found between number of children and paternal PPD [OR=0.20, (95%CI: 0.07-0.53)]. Depressive symptoms, especially in first-time fathers following the birth of a child, are not uncommon. Maternal depressive symptoms and paternal well-being were strong predictors of parental PPD. Creating opportunities for men to access special health care services, parental education to help adapting to parenthood, screening programs, and psychiatric/psychosocial interventions to decrease the suffering of depression for both depressed parents are recommended.

Keywords: depression, men, postpartum, risk factors, women

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Authors: Craig Mahlasi

Abstract:

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

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

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Authors: Evgeny Pokushalov, Andrey Ponomarenko, John Smith, Michael Johnson, Claire Garcia, Inessa Pak, Evgenya Shrainer, Dmitry Kudlay, Sevda Bayramova, Richard Miller

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Background: Obesity is a complex, multifactorial chronic disease that poses significant health risks. Recent advancements in artificial intelligence (AI) offer the potential for more personalized and effective dietary supplement (DS) regimens to promote weight loss. This study aimed to evaluate the efficacy of AI-guided DS prescriptions compared to standard physician-guided DS prescriptions in obese patients. Methods: This randomized, parallel-group pilot study enrolled 60 individuals aged 40 to 60 years with a body mass index (BMI) of 25 or greater. Participants were randomized to receive either AI-guided DS prescriptions (n = 30) or physician-guided DS prescriptions (n = 30) for 180 days. The primary endpoints were the percentage change in body weight and the proportion of participants achieving a ≥5% weight reduction. Secondary endpoints included changes in BMI, fat mass, visceral fat rating, systolic and diastolic blood pressure, lipid profiles, fasting plasma glucose, hsCRP levels, and postprandial appetite ratings. Adverse events were monitored throughout the study. Results: Both groups were well balanced in terms of baseline characteristics. Significant weight loss was observed in the AI-guided group, with a mean reduction of -12.3% (95% CI: -13.1 to -11.5%) compared to -7.2% (95% CI: -8.1 to -6.3%) in the physician-guided group, resulting in a treatment difference of -5.1% (95% CI: -6.4 to -3.8%; p < 0.01). At day 180, 84.7% of the AI-guided group achieved a weight reduction of ≥5%, compared to 54.5% in the physician-guided group (Odds Ratio: 4.3; 95% CI: 3.1 to 5.9; p < 0.01). Significant improvements were also observed in BMI, fat mass, and visceral fat rating in the AI-guided group (p < 0.01 for all). Postprandial appetite suppression was greater in the AI-guided group, with significant reductions in hunger and prospective food consumption, and increases in fullness and satiety (p < 0.01 for all). Adverse events were generally mild-to-moderate, with higher incidences of gastrointestinal symptoms in the AI-guided group, but these were manageable and did not impact adherence. Conclusion: The AI-guided dietary supplement regimen was more effective in promoting weight loss, improving body composition, and suppressing appetite compared to the physician-guided regimen. These findings suggest that AI-guided, personalized supplement prescriptions could offer a more effective approach to managing obesity. Further research with larger sample sizes is warranted to confirm these results and optimize AI-based interventions for weight loss.

Keywords: obesity, AI-guided, dietary supplements, weight loss, personalized medicine, metabolic health, appetite suppression

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Authors: Sachin Singh, Thomas Penzel, Dinesh Nandan

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Accurate identification of Sleep Apnea Hypopnea Syndrom Diagnosis is difficult problem for human expert because of variability among persons and unwanted noise. This paper proposes the diagonosis of Sleep Apnea Hypopnea Syndrome (SAHS) using airflow, ECG, Pulse and SaO2 signals. The features of each type of these signals are extracted using statistical methods and ANN learning methods. These extracted features are used to approximate the patient's Apnea Hypopnea Index(AHI) using sample signals in model. Advance signal processing is also applied to snore sound signal to locate snore event and SaO2 signal is used to support whether determined snore event is true or noise. Finally, Apnea Hypopnea Index (AHI) event is calculated as per true snore event detected. Experiment results shows that the sensitivity can reach up to 96% and specificity to 96% as AHI greater than equal to 5.

Keywords: neural network, AHI, statistical methods, autoregressive models

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Authors: Munkhtuul G., Bolortsetseg Z., Lutzul M., Sugar N., Nyamdorj D., Nomundari B., Zesemdorj O., Erdenebayar N., Lkhagvasuren T. S., Munkhbayarlakh S., Bayasgalan T. Uurtuya S. H.

Abstract:

Background: Elevated serum uric acid (SUA) levels are observed in metabolic and cardiovascular conditions as an early predictor of metabolic syndrome (MS). Hyperuricemia, characterised by high uric acid levels in serum, increases the risk of developing MS by 1.6 times. Being overweight and obese significantly contributes to developing MS and cardiovascular disorders. In Mongolia, the prevalence of overweight and obesity is reaching 48.8% among individuals aged 15 to 49 years, indicating a potential surge in the incidence of MS, cardiovascular disorders, diabetes mellitus, and gout.Objective: This study aimed to determine the SUA levels in men diagnosed with MS and investigate the factors influencing these levels.Methods: A total of 119 men aged 30-60, who underwent preventive examinations and resided in Ulaanbaatar city, were included in the study. The criteria established by the International Diabetes Federation (IDF), American Heart Association (AHA), and the National Heart, Lung, and Blood Institute (NHLBI) were employed to define metabolic syndrome. Hyperuricemia was defined as SUA levels ≥7 mg/dL. Dietary intake was evaluated through the 24-hour recall method.Results: The study revealed that the prevalence of MS among the participants was 42.9% (n=51), with hyperuricemia observed in 16.8% (n=20) of the individuals. Among men diagnosed with MS, 21.3% (n=10) exhibited hyperuricemia. The mean SUA levels were as follows: 4.7±0.8 mg/dL in the healthy group, 5.9±1.1 mg/dL in men without MS but presenting central obesity, and 6.2±1.3 mg/dL in men with MS. After adjusting for age and body mass index (BMI), a positive correlation was observed between SUA levels and triglycerides (β=0.93) as well as lipid accumulation product (LAP) (β=0.92) in men with MS. In the central obesity group, SUA levels exhibited a positive correlation with triglycerides (β=0.91), visceral adiposity index (VAI) (β=0.73), LAP (β=0.92), and cardiometabolic index (CMI) (β=0.69). The risk of hyperuricemia increased by 3.29 times with elevated triglycerides and 3.53 times with an increased LAP.Conclusion: The findings indicate that abdominal fat accumulation, as indicated by elevated triglyceride levels and LAP, is associated with increased SUA levels in men with MS. However, no significant relationship was observed between SUA levels and dietary intake.

Keywords: central obesity, obesity, triglycerides, hyperuricemia

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Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

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Authors: Eugene Stepanov, Arcady Ponosov

Abstract:

To model gene regulatory networks one uses ordinary differential equations with switching nonlinearities, where the initial value problem is known to be well-posed if the trajectories cross the discontinuities transversally. Otherwise, the initial value problem is usually ill-posed, which lead to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid dynamical systems, rather than switched ones, to regularize the problem. 'Hybridization' of the switched system means that one attaches a dynamic discrete component ('automaton'), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness of the initial value problem making it well-posed. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. Several examples are provided in the presentation, which support the suggested analysis. The method can also be of interest in other applied fields, where differential equations contain switchings, e.g. in neural field models.

Keywords: hybrid dynamical systems, ill-posed problems, singular perturbation analysis, switching nonlinearities

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