Search results for: sample average approximation

Authors: K. Vandenboer, L. Dolphen, A. Bezuijen

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Backward erosion piping is an important failure mechanism for water-retaining structures, a phenomenon that results in the formation of shallow pipes at the interface of a sandy or silty foundation and a cohesive cover layer. This paper studies the effect of two soil types on backward erosion piping; both in case of a homogeneous sand layer, and in a vertically layered sand sample, where the pipe is forced to subsequently grow through the different layers. Two configurations with vertical sand layers are tested; they both result in wider pipes and higher critical gradients, thereby making this an interesting topic in research on measures to prevent backward erosion piping failures.

Keywords: backward erosion piping, embankments, physical modeling, sand

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Authors: H. Gholami, M. Jalali Azizpour

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Stress Corrosion Crack (SCC) in pipeline is a type of environmentally assisted cracking (EAC), since its discovery in 1965 as a possible cause of failure in pipeline, SCC has caused, on average, one of two failures per year in the U.S, According to the NACE SCC DA a pipe line segment is considered susceptible to SCC if all of the following factors are met: The operating stress exceeds 60% of specified minimum yield strength (SMYS), the operating temperature exceeds 38°C, the segment is less than 32 km downstream from a compressor station, the age of the pipeline is greater than 10 years and the coating type is other than Fusion Bonded Epoxy(FBE). In this paper as a practical experience in NISOC, Direct Assessment (DA) Method is used for identification SCC defect in unpiggable pipeline located downstream of compressor station.

Keywords: stress corrosion crack, direct assessment, disbondment, transgranular SCC, compressor station

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Authors: Krzysztof T. Piotrowski

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The main goal of the research was to investigate differences in attention in two kinds of musical performance - recall and improvisation. Musical recall is a sample of convergent production that requires intensively focused attention. Inversely, musical improvisation is a divergent task and probably requires a different way of attentional control. The study was designed in dual task paradigm. Participants were to remember a simple melody and then recall or improvise, simultaneously performing the spatial attentional test on computer screen. The result shows that improvising participants find spatial goals in more disperse way. The conclusion is that musical improvisation requires extensification of attention to occur.

Keywords: attention, creativity, divergent task, musical improvisation

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Authors: Okba Belahssen, Lazhar Torchane, Said Benramache, Abdelouahed Chala

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This paper presents corrosion behavior of the plasma-nitrided 32CrMoV13 steel. Different kinds of samples were tested: non-treated, plasma nitrided samples. The structure of layers was determined by X-ray diffraction, while the morphology was observed by scanning electron microscopy (SEM). The corrosion behavior was evaluated by electrochemical techniques (potentiodynamic curves and electrochemical impedance spectroscopy). The corrosion tests were carried out in acid chloride solution (HCl 1M). Experimental results showed that the nitrides ε-Fe2−3N and γ′-Fe4N present in the white layer are nobler than the substrate but may promote, by galvanic effect, a localized corrosion through open porosity. The better corrosion protection was observed for nitrided sample.

Keywords: plasma-nitrided, 32CrMoV13 steel, corrosion, EIS

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Authors: Elham Moniri, Zohre Moradi

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In this study, ion imprinted poly urea-formaldehyde was prepared. The morphology imprinted polymer was studied by scanning electron microscopy. Then, the effects of various parameters on Cr(III) sorption such as pH, contact time were investigated. The optimum pH value for sorption of Cr(III) was 6. The sorption capacity of imprinted poly urea-formaldehyde for Cr(III) were 4 mg.g−1. A Cr(III) removal of 97-98% was obtained. The profile of Cr(III) uptake on this sorbent reflects good accessibility of the chelating sites in the imprinted poly urea-formaldehyde. The developed method was utilized for determination of Cr(III) in environmental water samples by flame atomic absorption spectrometry with satisfactory results.

Keywords: chromium ion, environmental sample, elimination, imprinted poly urea-formaldehyde, polymeric sorbent

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Authors: Janne Engblom, Elias Oikarinen

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A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models are dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Arellano-Bover/Blundell-Bond Generalized method of moments (GMM) estimator which is an extension of the Arellano-Bond model where past values and different transformations of past values of the potentially problematic independent variable are used as instruments together with other instrumental variables. The Arellano–Bover/Blundell–Bond estimator augments Arellano–Bond by making an additional assumption that first differences of instrument variables are uncorrelated with the fixed effects. This allows the introduction of more instruments and can dramatically improve efficiency. It builds a system of two equations—the original equation and the transformed one—and is also known as system GMM. In this study, Finnish housing price dynamics were examined empirically by using the Arellano–Bover/Blundell–Bond estimation technique together with ordinary OLS. The aim of the analysis was to provide a comparison between conventional fixed-effects panel data models and dynamic panel data models. The Arellano–Bover/Blundell–Bond estimator is suitable for this analysis for a number of reasons: It is a general estimator designed for situations with 1) a linear functional relationship; 2) one left-hand-side variable that is dynamic, depending on its own past realizations; 3) independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; 4) fixed individual effects; and 5) heteroskedasticity and autocorrelation within individuals but not across them. Based on data of 14 Finnish cities over 1988-2012 differences of short-run housing price dynamics estimates were considerable when different models and instrumenting were used. Especially, the use of different instrumental variables caused variation of model estimates together with their statistical significance. This was particularly clear when comparing estimates of OLS with different dynamic panel data models. Estimates provided by dynamic panel data models were more in line with theory of housing price dynamics.

Keywords: dynamic model, fixed effects, panel data, price dynamics

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Authors: Masahiro Takahashi, Kosuke Harada, Shihomi Nakao, Mitsuhiro Higashihata, Hiroshi Ikenoue, Daisuke Nakamura, Tatsuo Okada

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Zinc oxide (ZnO) is one of the light emitting materials in ultraviolet (UV) region. In addition, ZnO nanostructures are also attracting increasing research interest as building blocks for UV optoelectronic applications. We have succeeded in synthesizing vertically-aligned ZnO nanostructures by laser interference patterning, which is catalyst-free and non-contact technique. In this study, vertically-aligned ZnO nanowall arrays were synthesized using two-beam interference. The maximum height and average thickness of the ZnO nanowalls were about 4.5 µm and 200 nm, respectively. UV lasing from a piece of the ZnO nanowall was obtained under the third harmonic of a Q-switched Nd:YAG laser excitation, and the estimated threshold power density for lasing was about 150 kW/cm2. Furthermore, UV lasing from the vertically-aligned ZnO nanowall was also achieved. The results indicate that ZnO nanowalls can be applied to random laser.

Keywords: zinc oxide, nanowall, interference laser, UV lasing

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Authors: Al Omari Moahmmed Ahmed

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In the Bayesian, we developed an approach by using non-informative prior with covariate and obtained by using Gauss quadrature method to estimate the parameters of the covariate and reliability function of the Weibull regression distribution with Type-I censored data. The maximum likelihood seen that the estimators obtained are not available in closed forms, although they can be solved it by using Newton-Raphson methods. The comparison criteria are the MSE and the performance of these estimates are assessed using simulation considering various sample size, several specific values of shape parameter. The results show that Bayesian with non-informative prior is better than Maximum Likelihood Estimator.

Keywords: non-informative prior, Bayesian method, type-I censoring, Gauss quardature

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Authors: Pilar Giffenig, Avanna Kotlarz, Taylor Dehring

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Anxiety and depression are common mental illnesses in the U.S today. While there are various treatments for these mental health disorders, many of the medications come with a large variety of side effects that decrease medication compliance. Recent studies have looked at the impact of probiotics on anxiety and depression. Our goal was to determine whether probiotics could help relieve symptoms of anxiety and or depression. We conducted a literature search of three databases focusing on systematic reviews and RTC and found 25 articles, 8 of which were used for our analysis. Seven out of the eight articles showed that probiotics have the potential to significantly reduce symptoms of anxiety and depression. However, larger study sample sizes, type of probiotic, and correct dosage are required in future research to determine the role of probiotics in the treatment of anxiety and depression.

Keywords: probiotics, anxiety, depression, treatment, psychology, nutrition

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Authors: Masood Hussain, Erol Pehlivan, Ahmet Avci, Ecem Guder

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Green synthesis of silver nanoparticles (AgNPs) was carried out by using echinacea flower extract as reducing/protecting agent. The effects of various operating parameters and additives on the dimensions such as stirring rate, temperature, pH of the solution, the amount of extract and concentration of silver nitrate were optimized in order to achieve monodispersed spherical and small size echinacea protected silver nanoparticles (echinacea-AgNPs) through biosynthetic method. The surface roughness and topography of synthesized metal nanoparticles were confirmed by using Atomic Force Microscopy (AFM). High-Resolution Transmission Electron Microscopic (HRTEM) results elaborated the formation of uniformly distributed Echinacea protected AgNPs (Echinacea-AgNPs) having an average size of 30.2±2nm.

Keywords: Echinacea flower extract, green synthesis, silver nanoparticles, morphology

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Authors: Ming-Yeh Lu, Shiao-Shing Chen, Saikat Sinha Ray, Hung-Te Hsu

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Recently, anaerobic membrane bioreactors (AnMBRs) has been widely utilized, which combines anaerobic biological treatment process and membrane filtration, that can be present an attractive option for wastewater treatment and water reuse. Conventional AnMBR is having several advantages, such as improving effluent quality, compact space usage, lower sludge yield, without aeration and production of energy. However, the removal of nitrogen and phosphorus in the AnMBR permeate was negligible which become the biggest disadvantage. In recent years, forward osmosis (FO) is an emerging technology that utilizes osmotic pressure as driving force to extract clean water without additional external pressure. The pore size of FO membrane is kindly mentioned the pore size, so nitrogen or phosphorus could effectively improve removal of nitrogen or phosphorus. Anaerobic bioreactor with FO membrane (AnOMBR) can retain the concentrate organic matters and nutrients. However, phosphorus is a non-renewable resource. Due to the high rejection property of FO membrane, the high amount of phosphorus could be recovered from the combination of AnMBR and FO. In this study, development of novel submerged anaerobic osmotic membrane bioreactor integrated with periodic microfiltration (MF) extraction for simultaneous phosphorus and clean water recovery from wastewater was evaluated. A laboratory-scale AnOMBR utilizes cellulose triacetate (CTA) membranes with effective membrane area of 130 cm² was fully submerged into a 5.5 L bioreactor at 30-35℃. Active layer-facing feed stream orientation was utilized, for minimizing fouling and scaling. Additionally, a peristaltic pump was used to circulate draw solution (DS) at a cross flow velocity of 0.7 cm/s. Magnesium sulphate (MgSO₄) solution was used as DS. Microfiltration membrane periodically extracted about 1 L solution when the TDS reaches to 5 g/L to recover phosphorus and simultaneous control the salt accumulation in the bioreactor. During experiment progressed, the average water flux was achieved around 1.6 LMH. The AnOMBR process show greater than 95% removal of soluble chemical oxygen demand (sCOD), nearly 100% of total phosphorous whereas only partial removal of ammonia, and finally average methane production of 0.22 L/g sCOD was obtained. Therefore, AnOMBR system periodically utilizes MF membrane extracted for phosphorus recovery with simultaneous pH adjustment. The overall performance demonstrates that a novel submerged AnOMBR system is having potential for simultaneous wastewater treatment and resource recovery from wastewater, and hence, the new concept of this system can be used to replace for conventional AnMBR in the future.

Keywords: anaerobic treatment, forward osmosis, phosphorus recovery, membrane bioreactor

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Authors: Liang Zhou, Qinke Sun

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Taking the Yangtze River Economic Belt as an example, using long-term nighttime lighting data from DMSP/OLS from 1992 to 2012, support vector machine classification (SVM) was used to quantitatively extract urban built-up areas of economic belts, and spatial analysis of expansion intensity index, standard deviation ellipse, etc. was introduced. The model conducts detailed and in-depth discussions on the strength, direction, and type of the expansion of the middle and lower reaches of the economic belt and the key node cities. The results show that: (1) From 1992 to 2012, the built-up areas of the major cities in the Yangtze River Valley showed a rapid expansion trend. The built-up area expanded by 60,392 km², and the average annual expansion rate was 31%, that is, from 9615 km² in 1992 to 70007 km² in 2012. The spatial gradient analysis of the watershed shows that the expansion of urban built-up areas in the middle and lower reaches of the river basin takes Shanghai as the leading force, and the 'bottom-up' model shows an expanding pattern of 'upstream-downstream-middle-range' declines. The average annual rate of expansion is 36% and 35%, respectively. 17% of which the midstream expansion rate is about 50% of the upstream and downstream. (2) The analysis of expansion intensity shows that the urban expansion intensity in the Yangtze River Basin has generally shown an upward trend, the downstream region has continued to rise, and the upper and middle reaches have experienced different amplitude fluctuations. To further analyze the strength of urban expansion at key nodes, Chengdu, Chongqing, and Wuhan in the upper and middle reaches maintain a high degree of consistency with the intensity of regional expansion. Node cities with Shanghai as the core downstream continue to maintain a high level of expansion. (3) The standard deviation ellipse analysis shows that the overall center of gravity of the Yangtze River basin city is located in Anqing City, Anhui Province, and it showed a phenomenon of reciprocating movement from 1992 to 2012. The nighttime standard deviation ellipse distribution range increased from 61.96 km² to 76.52 km². The growth of the major axis of the ellipse was significantly larger than that of the minor axis. It had obvious east-west axiality, in which the nighttime lights in the downstream area occupied in the entire luminosity scale urban system leading position.

Keywords: urban space, support vector machine, spatial characteristics, night lights, Yangtze River Economic Belt

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

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Authors: Hindar Panguji, Nichlah Rifqiyah, Irkham Widiono, Pudji Astuti

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Many transportations of livestock in Indonesia is still managed traditionally. Transportation involves several things that may cause stress, from a certain treatment or other factors, either externally or internally, that act as stressors. This study aimed to determine the profile of cortisol and IL-6 in female Bali breeding cattle transported for 120 hours using 100 GT traditional vessels with two floors and a capacity of 300-400 heads. Before transportation, all of the animals have got the vaccination. Blood samples from thirty cows were taken before transportation, during loading, during docking, and after transportation. ELISA method was used to analyze the concentration of cortisol and IL6. The averages of cortisol concentration before transportation, during loading, docking, and after transportation were 78.21±27.96 ng/mL, 90.78±30.91 ng/mL, 69.90±53.92 ng/mL and 69.34±32.03 ng/mL respectively. The average concentration of IL-6 before, during, docking and after transportation were 259.86±70.16 pg/mL, 315.41±64.21 pg/mL, 410.13±247.43 pg/mL dan 424.81±98.86 pg/mL. It was concluded there were no differences in cortisol concentrations and level of IL6 of each cow at different stages of transportation (p > 0.05) It would be possible that vaccination could reduce the fluctuation of cortisol.

Keywords: shipping, Bali's breed cows, vessel, cortisol, IL6

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Authors: Akash Amalan, Rui Wang, Yanqi Qiao, Emmanouil Panaousis, Kaitai Liang

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Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled General Adversarial Networks (GANs) to benefit from the rich distributed training data while preserving privacy. However, in a non-IID setting, current federated GAN architectures are unstable, struggling to learn the distinct features, and vulnerable to mode collapse. In this paper, we propose an architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse, and instability for non-IID datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e., high inception score) on average over 20 clients compared to baseline FLGAN.

Keywords: federated learning, generative adversarial network, inference attack, non-IID data distribution

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Authors: Maali Kachouri, Anis Jarboui

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We focus on the causal relationship between governance and information transparency as well as interrelation among the various governance mechanisms. This paper employs a simultaneous equations approach to show this relationship in the Tunisian context. Based on an 8-year dataset, our sample covers 28 listed companies over 2006-2013. Our findings suggest that internal and external governance mechanisms are interdependent. Moreover, in order to analyze the causal effect between information transparency and governance mechanisms, we found evidence that information transparency tends to increase good corporate governance practices.

Keywords: simultaneous equations approach, transparency, causal relationship, corporate governance

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Authors: Ghaleb Y. Abbasi, Israa Abu Rumman

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This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking.

Keywords: ARIMA models, sales demand forecasting, time series, R code

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Authors: Sung-Wook Shin, Sung-Taek Chung

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Hand grip strength has been utilized as an indicator to evaluate the motor ability of hands, responsible for performing multiple body functions. It is, however, difficult to evaluate other factors (other than hand muscular strength) utilizing the hand grip strength only. In this study, we analyzed the motor ability of hands using EMG and the hand grip strength, simultaneously in order to evaluate concentration, muscular strength reaction time, instantaneous muscular strength change, and agility in response to visual reaction. In results, the average time (and their standard deviations) of muscular strength reaction EMG signal and hand grip strength was found to be 209.6 ± 56.2 ms and 354.3 ± 54.6 ms, respectively. In addition, the onset time which represents acceleration time to reach 90% of maximum hand grip strength, was 382.9 ± 129.9 ms.

Keywords: hand grip strength, EMG, visual reaction, endurance

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Authors: Jagroop Singh Sidhu, Buta Singh

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A low computational deblocking filter including three frequency related modes (smooth mode, intermediate mode, and non-smooth mode for low-frequency, mid-frequency, and high frequency regions, respectively) is proposed. The suggested approach requires zero additions, zero subtractions, zero multiplications (for intermediate region), no divisions (for non-smooth region) and no comparison. The suggested method thus keeps the computation lower and thus suitable for image coding systems based on blocks. Comparison of average number of operations for smooth, non-smooth, intermediate (per pixel vector for each block) using filter suggested by Chen and the proposed method filter suggests that the proposed filter keeps the computation lower and is thus suitable for fast processing algorithms.

Keywords: blocking artifacts, computational complexity, non-smooth, intermediate, smooth

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Authors: Fagner O. Serrano, Danielle R. Bouchard, Todd A. Duhame

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Walking cadence (steps/min) is an effective way to prescribe exercise so an individual can reach a moderate intensity, which is recommended to optimize health benefits. To our knowledge, there is no study on the required walking cadence to reach a moderate intensity for people that present chronic conditions or risk factors for chronic conditions such as Cardiovascular Diseases (CVD). The objectives of this study were: 1- to identify the walking cadence needed for people at risk of CVD to a reach moderate intensity, and 2- to develop and test an equation using clinical variables to help professionals working with individuals at risk of CVD to estimate the walking cadence needed to reach moderate intensity. Ninety-one people presenting a minimum of two risk factors for CVD completed a medically supervised graded exercise test to assess maximum oxygen consumption at the first visit. The last visit consisted of recording walking cadence using a foot pod Garmin FR-60 and a Polar heart rate monitor, aiming to get participants to reach 40% of their maximal oxygen consumption using a portable metabolic cart on an indoor flat surface. The equation to predict the walking cadence needed to reach moderate intensity in this sample was developed as follows: The sample was randomly split in half and the equation was developed with one half of the participants, and validated using the other half. Body mass index, height, stride length, leg height, body weight, fitness level (VO2max), and self-selected cadence (over 200 meters) were measured using objective measured. Mean walking cadence to reach moderate intensity for people age 64.3 ± 10.3 years old at risk of CVD was 115.8  10.3 steps per minute. Body mass index, height, body weight, fitness level, and self-selected cadence were associated with walking cadence at moderate intensity when evaluated in bivariate analyses (r ranging from 0.22 to 0.52; all P values ≤0.05). Using linear regression analysis including all clinical variables associated in the bivariate analyses, body weight was the significant predictor of walking cadence for reaching a moderate intensity (ß=0.24; P=.018) explaining 13% of walking cadence to reach moderate intensity. The regression model created was Y = 134.4-0.24 X body weight (kg).Our findings suggest that people presenting two or more risk factors for CVD are reaching moderate intensity while walking at a cadence above the one officially recommended (116 steps per minute vs. 100 steps per minute) for healthy adults.

Keywords: cardiovascular disease, moderate intensity, older adults, walking cadence

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Authors: Tushnik Sarkar, Mofazzal H. Khondekar, Subrata Banerjee

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This paper investigates the nature of the fluctuation of the daily average Solar wind speed time series collected over a period of 2492 days, from 1^st January, 1997 to 28^th October, 2003. The degree of self-similarity and scalability of the Solar Wind Speed signal has been explored to characterise the signal fluctuation. Multi-fractal Detrended Fluctuation Analysis (MFDFA) method has been implemented on the signal which is under investigation to perform this task. Furthermore, the singularity spectra of the signals have been also obtained to gauge the extent of the multifractality of the time series signal.

Keywords: detrended fluctuation analysis, generalized hurst exponent, holder exponents, multifractal exponent, multifractal spectrum, singularity spectrum, time series analysis

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Authors: Nadjiba Meziou-Chebouti, Amel Merabet, Yahia Chebouti, Nassima Behidj, Salahedine Doumandji

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Among the Anacardiaceae, the fruit (Pistacia vera L.) is the only species that produces edible fruits. The introduction of real pistachio was made in the early sixties by an FAO program in Algeria in several regions in the northern part of Algeria: Tlemcen, Sidi Bel Abbes, Batna, Bouira M'sila. Chemical analyzes of seeds pistachios were made on seeds from an orchard that localizes to Bechloul (Bouira) located in bioclimatic sub-humid temperate winter floor. Analyzes reveal dry matter content of 3.60±0.45%, the water rate is 7.21±0.36%. However, the fat content is 46.00±0.90%, in average blood sugar, it is 4.02±0.47%, the protein reached 29.88±0.76%. Given the very interesting that high-fat food nutritional values, culture pistachio must be considered for its extension in Algeria.

Keywords: pistachio, dry matter, fat, sugar, protein

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Authors: Paola Causin, Andrea Aspri, Alessandro Benfenati

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Diffuse Optical Tomography (DOT) is an emergent medical imaging technique which employs NIR light to estimate the spatial distribution of optical coefficients in biological tissues for diagnostic purposes, in a noninvasive and non-ionizing manner. DOT reconstruction is a severely ill-conditioned problem due to prevalent scattering of light in the tissue. In this contribution, we present our research in adopting hybrid knowledgedriven/data-driven approaches which exploit the existence of well assessed physical models and build upon them neural networks integrating the availability of data. Namely, since in this context regularization procedures are mandatory to obtain a reasonable reconstruction [1], we explore the use of neural networks as tools to include prior information on the solution. 2. Materials and Methods The idea underlying our approach is to leverage neural networks to solve PDE-constrained inverse problems of the form 𝒒 ∗ = 𝒂𝒓𝒈 𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃), (1) where D is a loss function which typically contains a discrepancy measure (or data fidelity) term plus other possible ad-hoc designed terms enforcing specific constraints. In the context of inverse problems like (1), one seeks the optimal set of physical parameters q, given the set of observations y. Moreover, 𝑦̃ is the computable approximation of y, which may be as well obtained from a neural network but also in a classic way via the resolution of a PDE with given input coefficients (forward problem, Fig.1 box ). Due to the severe ill conditioning of the reconstruction problem, we adopt a two-fold approach: i) we restrict the solutions (optical coefficients) to lie in a lower-dimensional subspace generated by auto-decoder type networks. This procedure forms priors of the solution (Fig.1 box ); ii) we use regularization procedures of type 𝒒̂ ∗ = 𝒂𝒓𝒈𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃)+ 𝑹(𝒒), where 𝑹(𝒒) is a regularization functional depending on regularization parameters which can be fixed a-priori or learned via a neural network in a data-driven modality. To further improve the generalizability of the proposed framework, we also infuse physics knowledge via soft penalty constraints (Fig.1 box ) in the overall optimization procedure (Fig.1 box ). 3. Discussion and Conclusion DOT reconstruction is severely hindered by ill-conditioning. The combined use of data-driven and knowledgedriven elements is beneficial and allows to obtain improved results, especially with a restricted dataset and in presence of variable sources of noise.

Keywords: inverse problem in tomography, deep learning, diffuse optical tomography, regularization

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Authors: Ming-Chien Su, Yi-Zih Chen, Nien-Hsin Kao, Hideaki Shibata

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The major component of the atmosphere is nitrogen, yet atmospheric nitrogen has limited availability for biological use. Human activities have produced different types of nitrogen related compounds such as nitrogen oxides from combustion, nitrogen fertilizers from farming, and the nitrogen compounds from waste and wastewater, all of which have impacted the environment. Many studies have indicated the N-footprint is dominated by food, followed by housing, transportation, and goods and services sectors. To solve the impact issues from agricultural land, nitrogen cycle research is one of the key solutions. The study site is located in Hualien County, Taiwan, a major rice and food production area of Taiwan. Importantly, environmentally friendly farming has been promoted for years, and an environmental indicator system has been established by previous authors based on the concept of resilience capacity index (RCI) and environmental performance index (EPI). Nitrogen management is required for food production, as excess N causes environmental pollution. Therefore it is very important to develop a roadmap of the nitrogen footprint, and to integrate it with environmental indicators. The key focus of the study thus addresses (1) understanding the environmental impact caused by the nitrogen cycle of food products and (2) uncovering the trend of the N-footprint of agricultural products in Hualien, Taiwan. The N-footprint model was applied, which included both crops and energy consumption in the area. All data were adapted from government statistics databases and crosschecked for consistency before modeling. The actions involved with agricultural production were evaluated and analyzed for nitrogen loss to the environment, as well as measuring the impacts to humans and the environment. The results showed that rice makes up the largest share of agricultural production by weight, at 80%. The dominant meat production is pork (52%) and poultry (40%); fish and seafood were at similar levels to pork production. The average per capita food consumption in Taiwan is 2643.38 kcal capita−1 d−1, primarily from rice (430.58 kcal), meats (184.93 kcal) and wheat (ca. 356.44 kcal). The average protein uptake is 87.34 g capita−1 d−1, and 51% is mainly from meat, milk, and eggs. The preliminary results showed that the nitrogen footprint of food production is 34 kg N per capita per year, congruent with the results of Shibata et al. (2014) for Japan. These results provide a better understanding of the nitrogen demand and loss in the environment, and the roadmap can furthermore support the establishment of nitrogen policy and strategy. Additionally, the results serve to develop a roadmap of the nitrogen cycle of an environmentally friendly farming area, thus illuminating the nitrogen demand and loss of such areas.

Keywords: agriculture productions, energy consumption, environmental indicator, nitrogen footprint

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Authors: Jin-Hee Ahn, Seok-Hyeon Jeon, Young-Bin Lee, Min-Gyun Ha, Yu-Chan Hong

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Corrosion durability of steel bridges can be generally affected by atmospheric environments of bridge installation, since corrosion problem is related to environmental factors such as humidity, temperature, airborne salt, chemical components as SO₂, chlorides, etc. Thus, atmospheric environment condition should be measured to estimate corrosion condition of steel bridges as well as measurement of actual corrosion damage of structural members of steel bridge. Even in the same atmospheric environment, the corrosion environment may be different depending on the installation direction of structural members. In this study, therefore, atmospheric corrosion monitoring was conducted using atmospheric corrosion monitoring sensor, hygrometer, thermometer and airborne salt collection device to examine the corrosion durability of steel bridges. As a target steel bridge for corrosion durability monitoring, a cable-stayed bridge with truss steel members was selected. This cable-stayed bridge was located on the coast to connect the islands with the islands. Especially, atmospheric corrosion monitoring was carried out depending on structural direction of a cable-stayed bridge with truss type girders since it consists of structural members with various directions. For atmospheric corrosion monitoring, daily average electricity (corrosion current) was measured at each monitoring members to evaluate corrosion environments and corrosion level depending on structural members with various direction which have different corrosion environment in the same installed area. To compare corrosion durability connected with monitoring data depending on corrosion monitoring members, monitoring steel plate was additionally installed in same monitoring members. Monitoring steel plates of carbon steel was fabricated with dimension of 60mm width and 3mm thickness. And its surface was cleaned for removing rust on the surface by blasting, and its weight was measured before its installation on each structural members. After a 3 month exposure period on real atmospheric corrosion environment at bridge, surface condition of atmospheric corrosion monitoring sensors and monitoring steel plates were observed for corrosion damage. When severe deterioration of atmospheric corrosion monitoring sensors or corrosion damage of monitoring steel plates were found, they were replaced or collected. From 3month exposure tests in the actual steel bridge with various structural member with various direction, the rust on the surface of monitoring steel plate was found, and the difference in the corrosion rate was found depending on the direction of structural member from their visual inspection. And daily average electricity (corrosion current) was changed depending on the direction of structural member. However, it is difficult to identify the relative differences in corrosion durability of steel structural members using short-term monitoring results. After long exposure tests in this corrosion environments, it can be clearly evaluated the difference in corrosion durability depending on installed conditions of steel bridges. Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03028755).

Keywords: corrosion, atmospheric environments, steel bridge, monitoring

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Authors: Mohammadali Mokhtari

Abstract:

Developing countries increasingly recognize the need to reduce energy subsidies. Cash transfers are proposed instead of subsidies to mitigate the negative effects of energy subsidy reforms. However, there is little evidence about the redistribution effect of these subsidy-to-cash reforms. We study the redistribution impact of the largest subsidy-to-cash reform in the history of developing countries in Iran in 2010. We find a strong pro-poor impact of the reform using five inequality measures, including the Gini index and the ratio of rich to poor expenditures. Finally, we show this pro-poor impact is on average and discuss other possible mechanisms by which low-income groups objected to these pro-poor reforms in the next round, which took place in November 2019 and led to wide and large protests.

Keywords: energy economics, subsidy reform, inequality, Middle East, Iran

Procedia PDF Downloads 114

Authors: Elvis Nyirenda

Abstract:

This research study work was done to evaluate and propose an optimum measure to enhance the uptake of clean energy technologies such as solar photovoltaics, the study also aims at enhancing the country’s energy mix from the overdependence on hydro power which is susceptible to droughts and climate change challenges The country in the years 2015 - 2016 and 2018 - 2019 had received rainfall below average due to climate change and a shift in the weather pattern; this resulted in prolonged power outages and load shedding for more than 10 hours per day. ZESCO Limited, the utility company that owns infrastructure in the generation, transmission, and distribution of electricity (state-owned), is seeking alternative sources of energy in order to reduce the over-dependence on hydropower stations. One of the alternative sources of energy is Solar Energy from the sun. However, solar power is intermittent in nature and to smoothen the load curve, investment in robust energy storage facilities is of great importance to enhance security and reliability of electricity supply in the country. The methodology of the study looked at the historical performance of the Kafue gorge upper power station and utilised the hourly generation figures as input data for generation modelling in Homer software. The average yearly demand was derived from the available data on the system SCADA. The two dams were modelled as natural battery with the absolute state of charging and discharging determined by the available water resource and the peak electricity demand. The software Homer Energy System is used to simulate the scheme incorporating a pumped storage facility and Solar photovoltaic systems. The pumped hydro scheme works like a natural battery for the conservation of water, with the only losses being evaporation and water leakages from the dams and the turbines. To address the problem of intermittency on the solar resource and the non-availability of water for hydropower generation, the study concluded that utilising the existing Hydro power stations, Kafue Gorge upper and Kafue Gorge Lower to work conjunctively with Solar energy will reduce power deficits and increase the security of supply for the country. An optimum capacity of 350MW of solar PV can be integrated while operating Kafue Gorge power station in both generating and pumping mode to enable efficient utilisation of water at Kafue Gorge upper Dam and Kafue Gorge Lower dam.

Keywords: hydropower, solar power systems, energy storage, photovoltaics, solar irradiation, pumped hydro storage system, supervisory control and data acquisition, Homer energy

Procedia PDF Downloads 117

Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

Abstract:

The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

Procedia PDF Downloads 369

Authors: Atabong Awung Lekeazem

Abstract:

The purpose of this paper is to identify the impact or role of incentives on employee’s performance with a particular emphasis on Korean workers. The process involves defining and explaining the different types of motivation. In defining them, we also bring out the difference between the two major types of motivations. The second phase of the paper shall involve gathering data/information from a sample population and then analyzing the data. In the analysis, we shall get to see the almost similar mentality or value which Koreans attach to motivation, which a slide different view coming only from top management personnel. The last phase shall have us presenting the data and coming to a conclusion from which possible knowledge on how managers and potential managers can ignite the best out of their employees.

Keywords: motivation, employee’s performance, Korean workers, business information systems

Procedia PDF Downloads 414

Authors: Tareq Oshan

Abstract:

Facility location and size decisions are important to any company because they affect profitability and success. However, warehouses are exposed to various risks of failure that affect their activity. This paper presents a mixed-integer non-linear mathematical model that can be used to determine optimal warehouse locations and sizes, which warehouses to fortify, and which branches should be assigned to specific warehouses when there is a risk of warehouse failure. Every branch is assigned to a fortified primary warehouse or a nonfortified primary warehouse and a fortified backup warehouse. The standard method and an introduced method, based on the average probabilities, for linearizing this mathematical model were used. A Canadian case study was used to demonstrate the developed mathematical model, followed by some sensitivity analysis.

Keywords: supply chain network design, fortified warehouse, mixed-integer mathematical model, warehouse failure risk

Procedia PDF Downloads 243