Search results for: correlated random damping

Authors: Deniz Peksen

Abstract:

This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Diogen Babuc

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The question that is a motivation of writing is how many devote themselves to discovering something in the world of science where much is discerned and revealed, but at the same time, much is unknown. Methods: The insightful elements of this algorithm are the ciphering and deciphering algorithms of Playfair, Caesar, and Vigenère. Only a few of their main properties are taken and modified, with the aim of forming a specific functionality of the algorithm called Latin Djokovic. Specifically, a string is entered as input data. A key k is given, with a random value between the values a and b = a+3. The obtained value is stored in a variable with the aim of being constant during the run of the algorithm. In correlation to the given key, the string is divided into several groups of substrings, and each substring has a length of k characters. The next step involves encoding each substring from the list of existing substrings. Encoding is performed using the basis of Caesar algorithm, i.e., shifting with k characters. However, that k is incremented by 1 when moving to the next substring in that list. When the value of k becomes greater than b+1, it’ll return to its initial value. The algorithm is executed, following the same procedure, until the last substring in the list is traversed. Results: Using this polyalphabetic method, ciphering and deciphering of strings are achieved. The algorithm also works for a 100-character string. The x character isn’t used when the number of characters in a substring is incompatible with the expected length. The algorithm is simple to implement, but it’s questionable if it works better than the other methods from the point of view of execution time and storage space.

Keywords: ciphering, deciphering, authentic, algorithm, polyalphabetic cipher, random key, methods comparison

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Authors: Augustine Okolie, Johannes Müller, Mirjam Kretzchmar

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We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is the basic reproduction number R0. The estimator is tested in a simulation study and applied to covid-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical covid-19 data, we are able to compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution meet the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency on the reproduction number.

Keywords: stochastic SIR model on graph, contact tracing, branching process, parameter inference

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Naohiro Nakamura, Takuya Kinoshita, Hiroshi Fukuyama

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The 2011 off The Pacific Coast of Tohoku Earthquake caused considerable damage to wide areas of eastern Japan. A large number of earthquake observation records were obtained at various places. To design more earthquake-resistant buildings and improve earthquake disaster prevention, it is necessary to utilize these data to analyze and evaluate the behavior of a building during an earthquake. This paper presents an earthquake response simulation analysis (hereafter a seismic response analysis) that was conducted using data recorded during the main earthquake (hereafter the main shock) as well as the earthquakes before and after it. The data were obtained at a high-rise steel-reinforced concrete (SRC) building in the bay area of Tokyo. We first give an overview of the building, along with the characteristics of the earthquake motion and the building during the main shock. The data indicate that there was a change in the natural period before and after the earthquake. Next, we present the results of our seismic response analysis. First, the analysis model and conditions are shown, and then, the analysis result is compared with the observational records. Using the analysis result, we then study the effect of soil-structure interaction on the response of the building. By identifying the characteristics of the building during the earthquake (i.e., the 1st natural period and the 1st damping ratio) by the Auto-Regressive eXogenous (ARX) model, we compare the analysis result with the observational records so as to evaluate the accuracy of the response analysis. In this study, a lumped-mass system SR model was used to conduct a seismic response analysis using observational data as input waves. The main results of this study are as follows: 1) The observational records of the 3/11 main shock put it between a level 1 and level 2 earthquake. The result of the ground response analysis showed that the maximum shear strain in the ground was about 0.1% and that the possibility of liquefaction occurring was low. 2) During the 3/11 main shock, the observed wave showed that the eigenperiod of the building became longer; this behavior could be generally reproduced in the response analysis. This prolonged eigenperiod was due to the nonlinearity of the superstructure, and the effect of the nonlinearity of the ground seems to have been small. 3) As for the 4/11 aftershock, a continuous analysis in which the subject seismic wave was input after the 3/11 main shock was input was conducted. The analyzed values generally corresponded well with the observed values. This means that the effect of the nonlinearity of the main shock was retained by the building. It is important to consider this when conducting the response evaluation. 4) The first period and the damping ratio during a vibration were evaluated by an ARX model. Our results show that the response analysis model in this study is generally good at estimating a change in the response of the building during a vibration.

Keywords: ARX model, response analysis, SRC building, the 2011 off the Pacific Coast of Tohoku Earthquake

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Authors: Basit Aftab, Zhichao Wang, Feng Zhongke

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Land Use and Land Cover Change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the spatiotemporal dynamics of land use and land cover (LULC) across a three-decade period (1992–2022) in a district area. The goal is to support sustainable land management and urban planning by utilizing the combination of remote sensing, GIS data, and observations from Landsat satellites 5 and 8 to provide precise predictions of the trajectory of urban sprawl. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the Random Forest method with Multilayer Perceptron (MLP) and Markov Chain analysis. To predict the dynamics of LULC change for the year 2035, a hybrid technique based on multilayer Perceptron and Markov Chain Model Analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. The study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will be increased by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. It provides valuable insights for policymakers, land managers, and researchers to support sustainable land use planning, conservation efforts, and climate change mitigation strategies.

Keywords: land use land cover, Markov chain model, multi-layer perceptron, random forest, sustainable land, remote sensing.

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Authors: Ahmed A. M. Shoaib, Heba A. Esaily, Mahmoud M. Emara, Eman A. E. Badr, Amany S. Khalifa, Mayada M. M., Abdel-Raizk

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Background: DM is associated with metabolic bone diseases, osteoporosis, low-impact fractures and falls in geriatrics. Fetuin-A, which is a serum protein produced by the liver and promotes bone mineralization, is an independent risk factor for type 2 diabetes. Aim: Evaluation of fetuin-A level and bone mineral density in postmenopausal Egyptian female patients with type 2 diabetes mellitus and their correlation with each other & with other metabolic parameters. Patients and methods: Seventy postmenopausal female patients with type II diabetes and thirty postmenopausal female as control were included in this study. Measurement of Fetuin-A together with metabolic parameters and DXA in wrist, hip and spine, ALP, CBC, FBS, PP2H and HBA1c was done in all participants. Results: - Fetuin-A level was found to be highly significant (p< 0.001) between diabetic and nondiabetic groups and negatively correlated with BMD in spine. No difference in BMD was found between patients and control groups while significant negative correlation was found between FBS and hip BMD (<0.05) and between 2hpp and HBA1c with spine BMD in the diabetic group (<0.05). Osteoporosis represented 12.9% in spine area and 7.2% in hip and wrist areas in diabetic patients, while osteopenia were found in 58.5%, 57.1%, and 37.1% in diabetic patients in spine, wrist, and hip respectively. Conclusion: - type II diabetes cannot be considered as a risk factor for osteoporosis; while glycemic parameters (FBS, 2hpp & HBA1c) and serum Fetuin-A levels were correlated with BMD in diabetics. Good glycemic control can be protective against osteoporosis in diabetic elderly.

Keywords: fetuin-A, BMD, postmenopausal, DM type II

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Authors: M. Soltan Rezaee, M. R. Ghazavi, A. Najafi, W.-H. Liao

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Generally, different rods in shaft systems can be misaligned based on the mechanical system usages. These rods can be linked together via U-coupling easily. The system is self-stimulated and may cause instabilities due to the inherent behavior of the coupling. In this study, each rod includes an elastic shaft with an angular stiffness and structural damping. Moreover, the mass of shafts is considered via attached solid disks. The impact of the system architecture and shaft mass on the instability of such mechanism are studied. Stability charts are plotted via a method based on Floquet theory. Eventually, the unstable points have been found and analyzed in detail. The results show that stabilizing the driveline is feasible by changing the system characteristics which include shaft mass and architecture.

Keywords: coupling, mechanical systems, oscillations, rotating shafts

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Authors: M. Sasajima, T. Yamaguchi, Y. Hu, Y. Koike

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In this paper, we discuss the propagation of sound in the narrow pathways of an occluded-ear simulator typically used for the measurement of insert-type earphones. The simulator has a standardized frequency response conforming to the international standard (IEC60318-4). In narrow pathways, the speed and phase of sound waves are modified by viscous air damping. In our previous paper, we proposed a new finite element method (FEM) to consider the effects of air viscosity in this type of audio equipment. In this study, we will compare the results from the ear simulator FEM model, and those from a three dimensional human ear canal FEM model made from computed tomography images, with the measured frequency response data from the ear canals of 18 people.

Keywords: ear simulator, FEM, viscosity, human ear canal

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Authors: Hernando Santamaría-García, Sandra Báez, Pablo Reyes, José Santamaría-García, Diana Matallana, Adolfo García, Agustín Ibañez

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The study of moral emotions (i.e., Schadenfreude and envy) is critical to understand the ecological complexity of everyday interactions between cognitive, affective, and social cognition processes. Most previous studies in this area have used correlational imaging techniques and framed Schadenfreude and envy as monolithic domains. Here, we profit from a relevant neurodegeneration model to disentangle the brain regions engaged in three dimensions of Schadenfreude and envy: deservingness, morality, and legality. We tested 20 patients with behavioral variant frontotemporal dementia (bvFTD), 24 patients with Alzheimer’s disease (AD), as a contrastive neurodegeneration model, and 20 healthy controls on a novel task highlighting each of these dimensions in scenarios eliciting Schadenfreude and envy. Compared with the AD and control groups, bvFTD patients obtained significantly higher scores on all dimensions for both emotions. Interestingly, the legal dimension for both envy and Schadenfreude elicited higher emotional scores than the deservingness and moral dimensions. Furthermore, correlational analyses in bvFTD showed that higher envy and Schadenfreude scores were associated with greater deficits in social cognition, inhibitory control, and behavior. Brain anatomy findings (restricted to bvFTD and controls) confirmed differences in how these groups process each dimension. Schadenfreude was associated with the ventral striatum in all subjects. Also, in bvFTD patients, increased Schadenfreude across dimensions was negatively correlated with regions supporting social-value rewards, mentalizing, and social cognition (frontal pole, temporal pole, angular gyrus and precuneus). In all subjects, all dimensions of envy positively correlated with the volume of the anterior cingulate cortex, a region involved in processing unfair social comparisons. By contrast, in bvFTD patients, the intensified experience of envy across all dimensions was negatively correlated with a set of areas subserving social cognition, including the prefrontal cortex, the parahippocampus, and the amygdala. Together, the present results provide the first lesion-based evidence for the multidimensional nature of the emotional experiences of envy and Schadenfreude. Moreover, this is the first demonstration of a selective exacerbation of envy and Schadenfreude in bvFTD patients, probably triggered by atrophy to social cognition networks. Our results offer new insights into the mechanisms subserving complex emotions and moral cognition in neurodegeneration, paving the way for groundbreaking research on their interaction with other cognitive, social, and emotional processes.

Keywords: social cognition, moral emotions, neuroimaging, frontotemporal dementia

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Authors: I-Hui Hsieh, Yu-Hsuan Hu

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The powerful effect of music is often associated with changes in physiological responses such as heart rate and respiration. Previous studies demonstrate that Mayer waves of blood pressure, the spontaneous rhythm occurring at 0.1 Hz, corresponds to a progressive crescendo of the musical phrase. However, music contain dynamic changes in temporal and spectral features. As such, it remains unclear which aspects of musical structures optimally affect synchronization of cardiovascular rhythms. This study investigates the independent contribution of spectral pattern, temporal pattern, and dissonance level on synchronization of cardiovascular rhythms. The regularity of acoustical patterns occurring at a periodic rhythm of 0.1 Hz is hypothesized to elicit the strongest coherence of cardiovascular rhythms. Music excerpts taken from twelve pieces of Western classical repertoire were modulated to contain varying degrees of pattern regularity of the acoustic envelope structure. Three levels of dissonance were manipulated by varying the harmonic structure of the accompanying chords. Electrocardiogram and photoplethysmography signals were recorded for 5 minutes of baseline and simultaneously while participants listen to music excerpts randomly presented over headphones in a sitting position. Participants were asked to indicate the pleasantness of each music excerpt by adjusting via a slider presented on screen. Analysis of the Fourier spectral power of blood pressure around 0.1 Hz showed a significant difference between music excerpts characterized by spectral and temporal pattern regularity compared to the same content in random pattern. Phase coherence between heart rate and blood pressure increased significantly during listening to spectrally-regular phrases compared to its matched control phrases. The degree of dissonance of the accompanying chord sequence correlated with level of coherence between heart rate and blood pressure. Results suggest that low-level auditory features of music can entrain coherence of autonomic physiological variables. These findings have potential implications for using music as a clinical and therapeutic intervention for regulating cardiovascular functions.

Keywords: cardiovascular rhythms, coherence, dissonance, pattern regularity

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Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Mohamad Saberi, Arash Sohrabi

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Tuned liquid damper is one the passive structural control ways which has been used since mid-1980 decade for seismic control in civil engineering. This system is made of one or many tanks filled with fluid, mostly water that installed on top of the high raised structure and used to prevent structure vibration. In this article, we will show how to make seismic table contain TLD system and analysis the result of using this system in our structure. Results imply that when frequency ratio approaches 1 this system can perform its best in both dissipate energy and increasing structural damping. And also results of these serial experiments are proved compatible with Hunzer linear theory behaviour.

Keywords: TLD, seismic table, structural system, Hunzer linear behaviour

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Authors: W. Meron Mebrahtu, R. Absi

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Velocity distribution in turbulent open-channel flows is organized in a complex manner. This is due to the large spatial and temporal variability of fluid motion resulting from the free-surface turbulent flow condition. This phenomenon is complicated further due to the complex geometry of channels and the presence of solids transported. Thus, several efforts were made to understand the phenomenon and obtain accurate mathematical models that are suitable for engineering applications. However, predictions are inaccurate because oversimplified assumptions are involved in modeling this complex phenomenon. Therefore, the aim of this work is to study velocity distribution profiles and obtain simple, more accurate, and predictive mathematical models. Particular focus will be made on the acceptable simplification of the general transport equations and an accurate representation of eddy viscosity. Wide rectangular open-channel seems suitable to begin the study; other assumptions are smooth-wall, and sediment-free flow under steady and uniform flow conditions. These assumptions will allow examining the effect of the bottom wall and the free surface only, which is a necessary step before dealing with more complex flow scenarios. For this flow condition, two ordinary differential equations are obtained for velocity profiles; from the Reynolds-averaged Navier-Stokes (RANS) equation and equilibrium consideration between turbulent kinetic energy (TKE) production and dissipation. Then different analytic models for eddy viscosity, TKE, and mixing length were assessed. Computation results for velocity profiles were compared to experimental data for different flow conditions and the well-known linear, log, and log-wake laws. Results show that the model based on the RANS equation provides more accurate velocity profiles. In the viscous sublayer and buffer layer, the method based on Prandtl’s eddy viscosity model and Van Driest mixing length give a more precise result. For the log layer and outer region, a mixing length equation derived from Von Karman’s similarity hypothesis provides the best agreement with measured data except near the free surface where an additional correction based on a damping function for eddy viscosity is used. This method allows more accurate velocity profiles with the same value of the damping coefficient that is valid under different flow conditions. This work continues with investigating narrow channels, complex geometries, and the effect of solids transported in sewers.

Keywords: accuracy, eddy viscosity, sewers, velocity profile

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Abdulaziz Almustafa

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Considering the critical role permanence has on predictions related to the contextual interference effect on laboratory and field research, this study sought to determine whether the paradigm of the effect depends on the complexity of the skill during the acquisition and transfer phases. The purpose of the present study was to investigate the effects of contextual interference CI by extending previous laboratory and field research with adult athletes through the acquisition and transfer phases. Male (n=60) athletes age 18-22 years-old, were chosen randomly from Eastern Province Clubs. They were assigned to complete blocked, random, or serial practices. Analysis of variance with repeated measures MANOVA indicated that, the results did not support the notion of CI. There were no significant differences in acquisition phase between blocked, serial and random practice groups. During the transfer phase, there were no major differences between the practice groups. Apparently, due to the task complexity, participants were probably confused and not able to use the advantages of contextual interference. This is another contradictory result to contextual interference effects in acquisition and transfer phases in sport settings. One major factor that can influence the effect of contextual interference is task characteristics as the nature of level of difficulty in sport-related skill.

Keywords: contextual interference, acquisition, transfer, task difficulty

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Authors: Nor Zaidi Haron, Fauziyah Salehuddin, Norsuhaidah Arshad, Sani Irwan Salim

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Resistive random access memories (RRAMs) are one of the main candidates for future computer memories. However, due to their tiny size and immature device technology, the quality of the outgoing RRAM chips is seen as a serious issue. Defective RRAM cells might behave differently than existing semiconductor memories (Dynamic RAM, Static RAM, and Flash), meaning that they are difficult to be detected using existing test schemes. This paper presents an enhanced test scheme, referred to as Programmable Short Write Time (PSWT) that is able to improve the detection of faulty RRAM cells. It is developed by applying multiple weak write operations, each with different time durations. The test circuit embedded in the RRAM chip is made programmable in order to supply different weak write times during testing. The RRAM electrical model is described using Verilog-AMS language and is simulated using HSPICE simulation tools. Simulation results show that the proposed test scheme offers better open-resistive fault detection compared to existing test schemes.

Keywords: memory fault, memory test, design-for-testability, resistive random access memory

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Authors: Sy-Wei Lo, Chi-Heng Yu

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A new design of aerostatic thrust bearing is proposed for high static stiffness. The bearing body, which is mead of polymer covered with metallic membrane, is held by a circular ring. Such a support helps form a concave air gap to grasp the air pressure. The polymer body, which can be made rapidly by either injection or molding is able to provide extra damping under dynamic loading. The smooth membrane not only serves as the bearing surface but also protects the polymer body. The restrictor is a capillary inside a silicone tube. It can passively compensate the variation of load by expanding the capillary diameter for more air flux. In the present example, the stiffness soars from 15.85 N/µm of typical bearing to 349.85 N/µm at bearing elevation 9.5 µm; meanwhile the load capacity also enhances from 346.86 N to 704.18 N.

Keywords: aerostatic, bearing, polymer, static stiffness

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Authors: M. K. Hossain, J. Keidel, O. Hensel, M. Diakite

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Fat reduced dairy products are holding a potential market due to health reason. Due to less creamy, and pleasantness, reduced and/or low-fat dairy products are getting less consumer acceptance whereas the fat molecule provides smooth, creamy and a pleasant mouthfeel in dairy products especially yogurt & ice cream. This study was aimed to investigate whether the application of microparticulated whey proteins (MWPs) processed by extrusion cooking, the reduced fat yogurt can achieve similar or higher creaminess compared to whole milk (3.8% fat) and skimmed milk (0.5% fat) yogurt. Full cream and skimmed milk were used to prepare natural stirred yogurt, as well as the dry matter content, also adjusted up to 16% with skimmed milk powder. Whey protein concentrates (WPC80) were used to produce MWPs in particle size of d50 > 5 µm, d50 3<5 µm and d50 < 3 µm through the hot-extrusion process with a screw speed of 400, 600 and 1000 rpm respectively. Furthermore, the commercially available microparticulated whey protein called Simplesse® was also applied in order to compare with extruded MWPs. The rheological and sensory properties of yogurt were assessed, and data were analyzed statistically. The applications of extruded MWPs with 600 and 1000 rpm were achieved significantly (p < 0.05) higher creaminess and preference compared to the whole and skimmed milk yogurt whereas, 400 rpm got lower preference. On the other hand, Simplesse® obtained the lowest creaminess and preference compared to other yogurts, although the contribution of dry matter in yogurt was same as extruded MWPs. The creaminess and viscosities were strongly (r = 0.62) correlated, furthermore, the viscosity from sensory evaluation and the dynamic viscosity of yogurt was also significantly (r = 0.72) correlated which clarifies that the performance of sensory panelists as well as the quality of the products.

Keywords: microparticulation, hot-extrusion, reduced-fat yogurt, whey protein concentrate

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Authors: Pajaree Naramitpanich, Natchanok Jansawang, Panwilai Chomchid

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The aims of this are compared between the students’ logical thinking abilities of their learning for designing the 5-lesson instructional plans of the 2-instructional methods, namely; the STEM Education and the Creative Learning Process (CLP) for developing students’ logical thinking abilities that a sample consisted of 90 students from two chemistry classes of different learning outcomes in Wapi Phathum School with the cluster random sampling technique was used at the 11th grade level. To administer of their learning environments with the 45-experimenl student group by the STEM Education method and the 45-controlling student group by the Creative Learning Process. These learning different groups were obtained using the 5 instruments; the 5-lesson instructional plans of the STEM Education and the Creative Learning Process to enhance the logical thinking tests on Mineral issue were used. The efficiency of the Creative Learning Processes (CLP) Model and the STEM Education’s innovations of these each five instructional lesson plans based on criteria are higher than of 80/80 standard level with the IOC index from the expert educators. The averages mean scores of students’ learning achievement motives were assessed with the Pre and Post Techniques and Logical Thinking Ability Test (LTAT) and dependent t-test analysis were differentiated between the CLP and the STEM, significantly. Students’ perceptions of their chemistry classroom environment inventories with the MCI with the CLP and the STEM methods also were found, differently. Associations between students’ perceptions of their chemistry classroom learning environment inventories on the CLP Model and the STEM Education learning designs toward their logical thinking abilities toward chemistry, the predictive efficiency of R2 values indicate that 68% and 76% of the variances in students’ logical thinking abilities toward chemistry to their controlling and experimental chemistry classroom learning environmental groups with the MCI were correlated at .05 levels, significantly. Implementations of this result are showed the students’ learning by the CLP of the potential thinking life-changing roles in most their logical thinking abilities that it is revealed that the students perceive their abilities to be highly learning achievement in chemistry group are differentiated with the STEM education of students’ outcomes.

Keywords: design, the lesson instructional plans, the stem education, the creative learning process, logical thinking ability, different, learning outcome, student, chemistry class

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Authors: Xiaosheng Chen, Jingjing Chen, Phil Reed, Dan Zhang

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Conscious intention can be a promising point cut to grasp consciousness and orient voluntary action. The current study adopted a random ratio (RR), yoked random interval (RI) reinforcement learning schedule instead of the previous highly repeatable and single decision point paradigms, aimed to induce voluntary action with the conscious intention that evolves from the interaction between short-range-intention and long-range-intention. Readiness potential (RP) -like-EEG amplitude and inter-trial-EEG variability decreased significantly prior to voluntary action compared to cued action for inter-trial-EEG variability, mainly featured during the earlier stage of neural activities. Notably, (RP) -like-EEG amplitudes decreased significantly prior to higher RI-reward rates responses in which participants formed a higher plane of conscious intention. The present study suggests the possible contribution of conscious intention-based processes to the neural activities from the earlier stage prior to voluntary action on reinforcement leanring schedule.

Keywords: Reinforcement leaning schedule, voluntary action, EEG, conscious intention, readiness potential

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Authors: Mehmet Savsar, Majid Aldaihani

Abstract:

Flexible Manufacturing Systems (FMS) are used to produce a variety of parts on the same equipment. Therefore, their utilization is higher than traditional machining systems. Higher utilization, on the other hand, results in more frequent equipment failures and additional need for maintenance. Therefore, it is necessary to carefully analyze operational characteristics and productivity of FMS or Flexible Manufacturing Cells (FMC), which are smaller configuration of FMS, before installation or during their operation. Appropriate models should be developed to determine production rates based on operational conditions, including equipment reliability, availability, and repair capacity. In this paper, a stochastic model is developed for an automated FMC system, which consists of two machines served by two robots and a single repairman. The model is used to determine system productivity and equipment utilization under different operational conditions, including random machine failures, random repairs, and limited repair capacity. The results are compared to previous study results for FMC system with sufficient repair capacity assigned to each machine. The results show that the model will be useful for design engineers and operational managers to analyze performance of manufacturing systems at the design or operational stages.

Keywords: flexible manufacturing, FMS, FMC, stochastic modeling, production rate, reliability, availability

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Authors: Brad Stappenbelt

Abstract:

The phenomenon of vortex-induced vibration (VIV) for elastically restrained cylindrical structures in cross-flows is relatively well investigated. The utility of this mechanism in harvesting energy from marine current and tidal flows is however arguably still in its infancy. With relatively few moving components, a flow-induced vibration-based energy conversion device augers low complexity compared to the commonly employed turbine design. Despite the interest in this concept, a practical device has yet to emerge. It is desirable for optimal system performance to design for a very low mass or mass moment of inertia ratio. The device operating range, in particular, is maximized below the vortex-induced vibration critical point where an infinite resonant response region is realized. An unfortunate consequence of this requirement is large buoyancy forces that need to be mitigated by gravity-based, suction-caisson or anchor mooring systems. The focus of this paper is the testing of a novel VIV marine current energy harvesting configuration that utilizes a symmetrical and balanced pair of horizontal pivoted cylinders. The results of several years of experimental investigation, utilizing the University of Wollongong fluid mechanics laboratory towing tank, are analyzed and presented. A reduced velocity test range of 0 to 60 was covered across a large array of device configurations. In particular, power take-off damping ratios spanning from 0.044 to critical damping were examined in order to determine the optimal conditions and hence the maximum device energy conversion efficiency. The experiments conducted revealed acceptable energy conversion efficiencies of around 16% and desirable low flow-speed operating ranges when compared to traditional turbine technology. The potentially out-of-phase spanwise VIV cells on each arm of the device synchronized naturally as no decrease in amplitude response and comparable energy conversion efficiencies to the single cylinder arrangement were observed. In addition to the spatial design benefits related to the horizontal device orientation, the main advantage demonstrated by the current symmetrical horizontal configuration is to allow large velocity range resonant response conditions without the excessive buoyancy. The novel configuration proposed shows clear promise in overcoming many of the practical implementation issues related to flow-induced vibration marine current energy harvesting.

Keywords: flow-induced vibration, vortex-induced vibration, energy harvesting, tidal energy

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Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Obesity is a clinical state associated with low-grade inflammation. It is also a major risk factor for insulin resistance (IR). In its advanced stages, metabolic syndrome (MetS), a much more complicated disease which may lead to life-threatening problems, may develop. Obesity-mediated IR seems to correlate with the inflammation. Human studies performed particularly on pediatric population are scarce. The aim of this study is to detect possible associations between inflammation and IR in terms of some related ratios. 549 children were grouped according to their age- and sex-based body mass index (BMI) percentile tables of WHO. MetS components were determined. Informed consent and approval from the Ethics Committee for Clinical Investigations were obtained. The principles of the Declaration of Helsinki were followed. The exclusion criteria were infection, inflammation, chronic diseases and those under drug treatment. Anthropometric measurements were obtained. Complete blood cell, fasting blood glucose, insulin, and C-reactive protein (CRP) analyses were performed. Homeostasis model assessment of insulin resistance (HOMA-IR), systemic immune inflammation (SII) index, tense index, alanine aminotransferase to aspartate aminotransferase ratio (ALT/AST), neutrophils to lymphocyte (NLR), platelet to lymphocyte, and lymphocyte to monocyte ratios were calculated. Data were evaluated by statistical analyses. The degree for statistical significance was 0.05. Statistically significant differences were found among the BMI values of the groups (p < 0.001). Strong correlations were detected between the BMI and waist circumference (WC) values in all groups. Tense index values were also correlated with both BMI and WC values in all groups except overweight (OW) children. SII index values of children with normal BMI were significantly different from the values obtained in OW, obese, morbid obese and MetS groups. Among all the other lymphocyte ratios, NLR exhibited a similar profile. Both HOMA-IR and ALT/AST values displayed an increasing profile from N towards MetS3 group. BMI and WC values were correlated with HOMA-IR and ALT/AST. Both in morbid obese and MetS groups, significant correlations between CRP versus SII index as well as HOMA-IR versus ALT/AST were found. ALT/AST and HOMA-IR values were correlated with NLR in morbid obese group and with SII index in MetS group, (p < 0.05), respectively. In conclusion, these findings showed that some parameters may exhibit informative differences between the early and late stages of obesity. Important associations among HOMA-IR, ALT/AST, NLR and SII index have come to light in the morbid obese and MetS groups. This study introduced the SII index and NLR as important inflammatory markers for the discrimination of normal and obese children. Interesting links were observed between inflammation and IR in morbid obese children and those with MetS, both being late stages of obesity.

Keywords: children, inflammation, insulin resistance, metabolic syndrome, obesity

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Authors: Srinivasan Chandrasekaran, R. Nagavinothini

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Offshore platforms for ultra-deep waters are form-dominant by design; hybrid systems with large flexibility in horizontal plane and high rigidity in vertical plane are preferred due to functional complexities. Offshore triceratops is relatively a new-generation offshore platform, whose deck is partially isolated from the supporting buoyant legs by ball joints. They allow transfer of partial displacements of buoyant legs to the deck but restrain transfer of rotational response. Buoyant legs are in turn taut-moored to the sea bed using pre-tension tethers. Present study will discuss detailed dynamic analysis and preliminary design of the chosen geometric, which is necessary as a proof of validation for such design applications. A detailed numeric analysis of triceratops at 2400 m water depth under random waves is presented. Preliminary design confirms member-level design requirements under various modes of failure. Tether configuration, proposed in the study confirms no pull-out of tethers as stress variation is comparatively lesser than the yield value. Presented study shall aid offshore engineers and contractors to understand suitability of triceratops, in terms of design and dynamic response behaviour.

Keywords: offshore structures, triceratops, random waves, buoyant legs, preliminary design, dynamic analysis

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Authors: Ashit Syngle, Inderjit Verma, Pawan Krishan

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Objective: Endothelial progenitor cells (EPCs) have reparative potential in overcoming the endothelial dysfunction and reducing cardiovascular risk. EPC depletion has been demonstrated in the setting of established atherosclerotic diseases. With this background, we evaluated whether reduced EPCs population are associated with endothelial dysfunction, subclinical atherosclerosis and inflammatory markers in ankylosing spondylitis (AS) patients without any known traditional cardiovascular risk factor in AS patients. Methods: Levels of circulating EPCs (CD34+/CD133+), brachial artery flow-mediated dilatation, carotid intima-media thickness (CIMT) and inflammatory markers i.e erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), tissue necrosis factor (TNF)–α, interleukin (IL)-6, IL-1 were assessed in 30 AS patients (mean age33.41 ± 10.25; 11 female and 19 male) who fulfilled the modified New York diagnostic criteria with 25 healthy volunteers (mean age 29.36± 8.64; 9 female and 16 male) matched for age and sex. Results: EPCs (CD34+/CD133+) cells were significantly (0.020 ± 0.001% versus 0.040 ± 0.010%, p<0.001) reduced in patients with AS compared to healthy controls. Endothelial function (7.35 ± 2.54 versus 10.27 ±1.73, p=0.002), CIMT (0.63 ± 0.01 versus 0.35 ± 0.02, p < 0.001) and inflammatory markers were also significantly (p < 0.01) altered as compared to healthy controls. Specifically, CD34+CD133+cells were inversely multivariate correlated with CRP and TNF-α and endothelial dysfunction was positively correlated with reduced number of EPC. Conclusion: Depletion of EPCs population is an independent predictor of endothelial dysfunction and early atherosclerosis in AS patients and may provide additional information beyond conventional risk factors and inflammatory markers.

Keywords: endothelial progenitor cells, atherosclerosis, ankylosing spondylitis, cardiovascular

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Authors: Tiao Chang, Ivan Caballero, Hong Zhou

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Streams carry water and sediment naturally by maintaining channel dimensions, pattern, and profile over time. Watershed erosion as a natural process has occurred to contribute sediment to streams over time. The formation of channel dimensions is complex. This study is to relate quantifiable and consistent channel dimensions at the bankfull stage to the corresponding watershed erosion estimation by the Revised Universal Soil Loss Equation (RUSLE). Twelve sites of which drainage areas range from 7 to 100 square miles in the Hocking River Basin of Ohio were selected for the bankfull geometry determinations including width, depth, cross-section area, bed slope, and drainage area. The twelve sub-watersheds were chosen to obtain a good overall representation of the Hocking River Basin. It is of interest to determine how these bankfull channel dimensions are related to the soil erosion of corresponding sub-watersheds. Soil erosion is a natural process that has occurred in a watershed over time. The RUSLE was applied to estimate erosions of the twelve selected sub-watersheds where the bankfull geometry measurements were conducted. These quantified erosions of sub-watersheds are used to investigate correlations with bankfull channel dimensions including discharge, channel width, channel depth, cross-sectional area, and pebble distribution. It is found that drainage area, bankfull discharge and cross-sectional area correlates strongly with watershed erosion well. Furthermore, bankfull width and depth are moderately correlated with watershed erosion while the particle size, D50, of channel bed sediment is not well correlated with watershed erosion.

Keywords: watershed, stream, sediment, channel

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Authors: Carla E. O. de Moraes, Gladson O. Antunes, Mauro A. Rincon

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The aim of this paper is to study the internal stabilization of the Bernoulli-Euler equation numerically. For this, we consider a square plate subjected to a feedback/damping force distributed only in a subdomain. An algorithm for obtaining an approximate solution to this problem was proposed and implemented. The numerical method used was the Finite Difference Method. Numerical simulations were performed and showed the behavior of the solution, confirming the theoretical results that have already been proved in the literature. In addition, we studied the validation of the numerical scheme proposed, followed by an analysis of the numerical error; and we conducted a study on the decay of the energy associated.

Keywords: Bernoulli-Euler plate equation, numerical simulations, stability, energy decay, finite difference method

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Authors: Selim M. Khan

Abstract:

Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Liu Shengnan, Sun Liping, Zhu Jianxun

Abstract:

Based on three dimensional potential flow theory and hinged rigid body motion equations, structure RAOs of Pelamis wave energy converter is analyzed. Analysis of numerical simulation is carried out on Pelamis in the irregular wave conditions, and the motion response of structures and total generated power is obtained. The paper analyzes influencing factors on the average power including diameter of floating body, section form of floating body, draft, hinged stiffness and damping. The optimum parameters are achieved in Zhejiang Province. Compared with the results of the pelamis experiment made by Glasgow University, the method applied in this paper is feasible.

Keywords: Pelamis, hinge, floating multibody, wave energy

Procedia PDF Downloads 458