Search results for: runoff coefficients

Authors: Mamta Rana, K. K. Singh, Nisha Kumari

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The simulation models with its soil-weather-plant atmosphere interacting system are important tools for assessing the crops in changing climate conditions. The CERES-Wheat & Rice vs. 4.6 DSSAT was calibrated and evaluated for one of the major producers of wheat and rice state- Haryana, India. The simulation runs were made under irrigated conditions and three fertilizer applications dose of N-P-K to estimate crop yield and other growth parameters along with the phenological development of the crop. The genetic coefficients derived by iteratively manipulating the relevant coefficients that characterize the phenological process of wheat and rice crop to the best fit match between the simulated and observed anthesis, physological maturity and final grain yield. The model validated by plotting the simulated and remote sensing derived LAI. LAI product from remote sensing provides the edge of spatial, timely and accurate assessment of crop. For validating the yield and yield components, the error percentage between the observed and simulated data was calculated. The analysis shows that the model can be used to simulate crop yield and yield components for wheat and rice cultivar under different management practices. During the validation, the error percentage was less than 10%, indicating the utility of the calibrated model for climate risk assessment in the selected region.

Keywords: simulation model, CERES-wheat and rice model, crop yield, genetic coefficient

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Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

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Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

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Authors: A. S. Haruna, M. L. Mayanchi

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Pre Nigeria Certificate in Education otherwise called Pre-NCE is an intensive two semester course designed to assist candidates who could not meet the requirements for admission into NCE programme. The task of coping with the stressors in the course can interfere with the students’ ability to regulate attention skills and stay organized. The main objectives of the study were to find out the prevalence of stress; determine the association between stress and ADD and reveal gender difference in the prevalence of ADD among stressed pre-NCE students. Cross–Sectional Correlation Design was employed in which 333 (Male=65%; Female=35%) students were proportionately sampled and administered Stress Assessment Scale [SAS r=0.74) and those identified with stress were thereafter rated with Cognitive Processing Inventory [CPI]. Data collected was used to analyze the three null hypotheses through One-sample Kolmogorov-Smirnov (K-S) Z-score, Pearson Product Moment Correlation Coefficients (PPMCC) and t-test statistics respectively at 0.05 confidence level. Results revealed significant prevalence of stress [Z-calculated =2.24; Z-critical = ±1.96], and a positive relationship between Stress and ADD among Pre-NCE students [r-calculated =0.450; r-critical =0.138]. However, there was no gender difference in the prevalence of ADD among stressed Pre-NCE students in the college [t-calculated =1.49; t-critical =1.645]. The study concludes that while stress and ADD prevail among pre-NCE students, there was no gender difference in the prevalence of ADD. Recommendations offered suggest the use of Learners Assistance Programs (LAP) for stress management, and Teacher-Students ratio of 1:25 be adopted in order to cater for stressed pre-NCE students with ADD.

Keywords: attention deficit disorder, pre-NCE students, stress, Pearson Product Moment Correlation Coefficients (PPMCC)

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Authors: Natália Maria Rego

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ALeibnizn-algebraGis aK-vector space endowed whit a n-linearbracket operation [-,…-] : GG … G→ Gsatisfying the fundamental identity, which can be expressed saying that the right multiplication map Ry2, …, ᵧₙ: Gn→ G, Rᵧ₂, …, ᵧₙn(ˣ¹, …, ₓₙ) = [[ˣ¹, …, ₓₙ], ᵧ₂, …, ᵧₙ], is a derivation. This structure, together with its skew-symmetric version, named as Lie n-algebra or Filippov algebra, arose in the setting of Nambumechanics, an n-ary generalization of the Hamiltonian mechanics. Thefirst goal of this work is to provide a characterization of various classes of central extensions of Leibniz n-algebras in terms of homological properties. Namely, Commutator extension, Quasi-commutator extension, Stem extension, and Stem cover. These kind of central extensions are characterized by means of the character of the map *(E): nHL1(G) → M provided by the five-term exact sequence in homology with trivial coefficients of Leibniz n-algebras associated to an extension E : 0 → M → K → G → 0. For a free presentation 0 →R→ F →G→ 0of a Leibniz n-algebra G,the term M(G) = (R[F,…n.., F])/[R, F,..n-1..,F] is called the Schur multiplier of G, which is a Baer invariant, i.e., it does not depend on the chosen free presentation, and it is isomorphic to the first Leibniz n-algebras homology with trivial coefficients of G. A central extension of Leibniz n-algebras is a short exact sequenceE : 0 →M→K→G→ 0such that [M, K,.. ⁿ⁻¹.., K]=0. It is said to be a stem extension if M⊆[G, .. n.., G]. Additionally, if the induced map M(K) → M(G) is the zero map, then the stem extension Eis said to be a stem cover. The second aim of this work is to analyze the interplay between stem covers of Leibniz n-algebras and the Schur multiplier. Concretely, in the case of finite-dimensional Leibniz n-algebras, we show the existence of coverings, and we prove that all stem covers with finite-dimensional Schur multiplier are isoclinic. Additionally, we characterize stem covers of perfect Leibniz n-algebras.

Keywords: leibniz n-algebras, central extensions, Schur multiplier, stem cover

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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: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Maxime Alex Junior Kuitche, Ruxandra Mihaela Botez, Jean-Chirstophe Maunand

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The evolution of aircraft is closely linked to the study and improvement of propulsion systems. Determining the propulsion performance is a real challenge in aircraft modeling and design. In addition to theoretical methodologies, experimental procedures are used to obtain a good estimation of the propulsion performances. For piston-propeller propulsion, the propeller needs several experimental tests which could be extremely demanding in terms of time and money. This paper presents a new procedure to estimate the performance of a propeller from a numerical approach using computational fluid dynamic analysis. The propeller was initially scanned, and then, its 3D model was represented using CATIA. A structured meshing and Shear Stress Transition k-ω turbulence model were applied to describe accurately the flow pattern around the propeller. Thus, the Partial Differential Equations were solved using ANSYS FLUENT software. The method was applied on the UAS-S45’s propeller designed and manufactured by Hydra Technologies in Mexico. An extensive investigation was performed for several flight conditions in terms of altitudes and airspeeds with the aim to determine thrust coefficients, power coefficients and efficiency of the propeller. The Computational Fluid Dynamics results were compared with experimental data acquired from wind tunnel tests performed at the LARCASE Price-Paidoussis wind tunnel. The results of this comparison have demonstrated that our approach was highly accurate.

Keywords: CFD analysis, propeller performance, unmanned aerial system propeller, UAS-S45

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Authors: Yih-Wenn Laih

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In recent times, the dependence and co-movement among international financial markets have become stronger than in the past, as evidenced by commentaries in the news media and the financial sections of newspapers. Studying the co-movement between returns in financial markets is an important issue for portfolio management and risk management. The realization of co-movement helps investors to identify the opportunities for international portfolio management in terms of asset allocation and pricing. Since the election of the new Prime Minister, Shinzo Abe, in November 2012, the yen has weakened against the US dollar from the 80 to the 120 level. The policies, known as “Abenomics,” are to encourage private investment through a more aggressive mix of monetary and fiscal policy. Given the close economic relations and competitions among Asia markets, it is interesting to discover the co-movement relations, affected by the depreciation of yen, between stock market of Japan and 5 major Asia stock markets, including China, Hong Kong, Korea, Singapore, and Taiwan. Specifically, we devote ourselves to measure the co-movement of stock markets between Japan and each one of the 5 Asia stock markets in terms of rank correlation coefficients. To compute the coefficients, return series of each stock market is first fitted by a skewed-t GARCH (generalized autoregressive conditional heteroscedasticity) model. Secondly, to measure the dependence structure between matched stock markets, we employ the symmetrized Joe-Clayton (SJC) copula to calculate the probability density function of paired skewed-t distributions. The joint probability density function is then utilized as the scoring scheme to optimize the sequence alignment by dynamic programming method. Finally, we compute the rank correlation coefficients (Kendall's  and Spearman's ) between matched stock markets based on their aligned sequences. We collect empirical data of 6 stock indexes from Taiwan Economic Journal. The data is sampled at a daily frequency covering the period from January 1, 2013 to July 31, 2015. The empirical distributions of returns indicate fatter tails than the normal distribution. Therefore, the skewed-t distribution and SJC copula are appropriate for characterizing the data. According to the computed Kendall’s τ, Korea has the strongest co-movement relation with Japan, followed by Taiwan, China, and Singapore; the weakest is Hong Kong. On the other hand, the Spearman’s ρ reveals that the strength of co-movement between markets with Japan in decreasing order are Korea, China, Taiwan, Singapore, and Hong Kong. We explore the effects of “Abenomics” on Asia stock markets by measuring the co-movement relation between Japan and five major Asia stock markets in terms of rank correlation coefficients. The matched markets are aligned by a hybrid method consisting of GARCH, copula and sequence alignment. Empirical experiments indicate that Korea has the strongest co-movement relation with Japan. The strength of China and Taiwan are better than Singapore. The Hong Kong market has the weakest co-movement relation with Japan.

Keywords: co-movement, depreciation of Yen, rank correlation, stock market

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Authors: Zakiul Fuady, Ismail AB, Fauzi, Zulfian

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This research was conducted to analyze the absorption coefficients of sound at several types of materials as well as its combinations. The aim of this research was to find the value of sound absorption coefficients on the materials and its combinations. The materials used in this research were gypsum panel, gypsum-fibre palm, fibre palm-gypsum, and foamed concrete-fibre palm. The test was conducted by using a method of reverberation chamber based on the ISO 354-1985 with the types of the sound source: white noise and pink noise at the frequency of 125 Hz - 8000 Hz. Based on the test results of white noise, it was found that the panel of gypsum-fibre palm has α = 0.93 at low frequency; the panel of fibre palm has α = 0.97 at a medium frequency; and the panel of foamed concrete-fibre palm has α = 0.89 at high frequency. Further, for the sound source of pink noise, it was found that the panel of gypsum-fibre palm has α = 0.99 at low level; the panel of fibre palm-gypsum has α = 0.86 at medium level; and the panel of fibre palm-gypsum has α = 0.64 at high level. The fibre palm panel could absorb the sounds well since this material has bigger airspace (pore) than the foamed concrete and gypsum. Consequently, when the sounds wave enters to this material it will be trapped in the space. The panel of fibre palm affected an increasing of sound absorption coefficient value at the combination materials when the panel of fibre palm was placed under another panel. However, the absorption coefficient values of both fibre palm and fibre palm-gypsum panels are about the same.

Keywords: coefficient of sound absorption, pink noise, white noise, palm

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Authors: K. A. Mistry, I. B. Patel, A. H. Prajapati

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In the present work, the alloy of Bismuth-lead is prepared on the basis of percentage of molecular weight 9:1, 5:5 and 1:9 ratios and grown by Zone- Refining Technique under a vacuum atmosphere. The EDAX of these samples are done and the results are reported. Micro hardness test has been used as an alternative test for measuring material’s tensile properties. The effect of temperature and load on the hardness of the grown alloy has been studied. Further the comparative studies of work hardening coefficients are reported. In the present work, the alloy of Bismuth-lead is prepared on the basis of percentage of molecular weight 9:1, 5:5 and 1:9 ratios and grown by Zone- Refining Technique under a vacuum atmosphere. The EDAX of these samples are done and the results are reported. Micro hardness test has been used as an alternative test for measuring material’s tensile properties. The effect of temperature and load on the hardness of the grown alloy has been studied. Further the comparative studies of work hardening coefficients are reported.

Keywords: EDAX, hardening coefficient, micro hardness, Bi-Pb alloy

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Emi̇ne Avşar Aydin, Nezahat Günenç Tuncel, A. Hami̇t Serbest

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The electromagnetic scattering from a canonical structure is an important issue in electromagnetic theory. In this study, the electromagnetic scattering from a dielectric sphere with oblique incidence is investigated. The incident field is considered as a plane wave with H polarized. The scattered and transmitted field expressions with unknown coefficients are written. The unknown coefficients are obtained by using exact boundary conditions. Then, the sphere is considered as having frequency dependent dielectric permittivity. The frequency dependence is shown by Cole-Cole model. The far scattered field expressions are found respect to different incidence angles in the 1-8 GHz frequency range. The observation point is the angular distance of pi from an incident wave. While an incident wave comes with a certain angle, observation point turns from 0 to 360 degrees. According to this, scattered field amplitude is maximum at the location of the incident wave, scattered field amplitude is minimum at the across incident wave. Also, the scattered fields are plotted versus frequency to show frequency-dependence explicitly. Graphics are shown for some incident angles compared with the Harrington's solution. Thus, the results are obtained faster and more reliable with reciprocal rotation. It is expected that when there is another sphere with different properties in the outer sphere, the presence and location of the sphere will be detected faster. In addition, this study leads to use for biomedical applications in the future.

Keywords: scattering, dielectric sphere, oblique incidence, reciprocal rotation

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Authors: Mohammad H. Fattahi

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Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.

Keywords: chaotic behavior, wavelet, noise reduction, river flow

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Authors: Usman A. Usman

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Undeniably, unlike the Classical side, the Keynesian perspective of the aggregate demand side indeed has a significant position in the policy, growth, and welfare of Nigeria due to government involvement and ineffective demand of the population living with poor per capita income. This study seeks to investigate the effect of Government Final Consumption Expenditure, Financial Deepening on Households, and NPISHs Final consumption expenditure using data on Nigeria from 1981 to 2019. This study employed the ADF stationarity test, Johansen Cointegration test, and Vector Error Correction Model. The results of the study revealed that the coefficient of Government final consumption expenditure has a positive effect on household consumption expenditure in the long run. There is a long-run and short-run relationship between gross fixed capital formation and household consumption expenditure. The coefficients cpsgdp (financial deepening and gross fixed capital formation posit a negative impact on household final consumption expenditure. The coefficients money supply lm2gdp, which is another proxy for financial deepening, and the coefficient FDI have a positive effect on household final consumption expenditure in the long run. Therefore, this study recommends that Gross fixed capital formation stimulates household consumption expenditure; a legal framework to support investment is a panacea to increasing hoodmold income and consumption and reducing poverty in Nigeria. Therefore, this should be a key central component of policy.

Keywords: government final consumption expenditure, household consumption expenditure, vector error correction model, cointegration

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Authors: M. Abbasi

Abstract:

A spillway structure is used to pass excess water and floods from upstream or upstream to downstream or tributary. The spillway is considered one of the most key members of the dam, and the failure of many dams is attributed to the inefficiency of their spillway. Weirs should be selected as strong, reliable and high-performance structures, and weirs should be ready for use in all conditions and able to drain the flood so that we do not witness many casualties and financial losses when a flood occurs. The purpose of this study is to simulate the flow pattern passing over the peak spillway in order to optimize and adjust the height of the spillway walls. In this research, the effect of the shape of the side wings on the flow pattern over the peak spillways of the Gotvand reservoir dam was simulated and modelled using Flow3D software. In this research, side wings with rounded walls with six different approach angles were used. In addition, the different value of H/Hd was used to check the effect of the tank head. The results showed that with the constant H/Hd ratio and the increase of the approach angle of the side wing, the flow depth first decreases and then increases. These changes were the opposite regarding the depth average speed of the flow and the depth average concentration of the air entering the flow. At the same time, with the constant angle of approach of the side wing and with the increase of H/Hd ratio, the flow depth increases. In general, a correct understanding of the operation of overflows and a correct design can significantly reduce construction costs and solve flooding problems.

Keywords: effect of the shape, gotvand reservoir dam, narrowing coefficients, supports of the gates

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Authors: Barenten Suciu

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Classical matrix calculus and Routh-Hurwitz stability conditions, applied to the snake-like motion of the conical wheel axle, lead to the conclusion that the hunting mode is inherently unstable, and its natural frequency is a complex number. In order to analytically solve such a complicated vibration model, either the inertia terms were neglected, in the model designated as geometrical, or restrictions on the creep coefficients and yawing diameter were imposed, in the so-called dynamical model. Here, an alternative solution is proposed to solve the hunting mode, based on the observation that the bullet train wheel axle is equipped with cylindrical wheels. One argues that for such wheel treads, the geometrical hunting is irrelevant, since its natural frequency becomes nil, but the dynamical hunting is significant since its natural frequency reduces to a real number. Moreover, one illustrates that the geometrical simplification of the wheel causes the stabilization of the hunting mode, since the characteristic quartic equation, derived for conical wheels, reduces to a quadratic equation of positive coefficients, for cylindrical wheels. Quite simple analytical expressions for the damping ratio and natural frequency are obtained, without applying restrictions into the model of contact. Graphs of the time-depending hunting lateral perturbation, including the maximal and inflexion points, are presented both for the critically-damped and the over-damped wheel axles.

Keywords: bullet train, creep, cylindrical wheels, damping, dynamical hunting, stability, vibration analysis

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Authors: Emery A. Coppola Jr., Suna Cinar, Ferenc Szidarovszky

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Over-pumping of groundwater resources is a serious problem world-wide. In addition to depleting this valuable resource, hydraulically connected sensitive ecological resources like wetlands and surface water bodies are often impacted and even destroyed by over-pumping. Effectively managing groundwater in a way that satisfy human demand while preserving natural resources is a daunting challenge that will only worsen with growing human populations and climate change. As presented in this paper, a numerical flow model developed for a hypothetical but realistic groundwater/surface water system was combined with formal optimization. Response coefficients were used in an optimization management model to maximize groundwater pumping in a complex, multi-layered aquifer system while protecting against groundwater over-draft, streamflow depletion, and wetland impacts. Pumping optimization was performed for different constraint sets that reflect different resource protection preferences, yielding significantly different optimal pumping solutions. A sensitivity analysis on the optimal solutions was performed on select response coefficients to identify differences between wet and dry periods. Stochastic optimization was also performed, where uncertainty associated with changing irrigation demand due to changing weather conditions are accounted for. One of the strengths of this optimization approach is that it can efficiently and accurately identify superior management strategies that minimize risk and adverse environmental impacts associated with groundwater pumping under different hydrologic conditions.

Keywords: numerical groundwater flow modeling, water management optimization, groundwater overdraft, streamflow depletion

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Authors: Takashi Kaburagi, Masashi Takenaka, Yosuke Kurihara, Takashi Matsumoto

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Major depressive disorder (MDD) is one of the most common mental illnesses today. It is believed to be caused by a combination of several factors, including stress. Stress can be quantitatively evaluated using the State-Trait Anxiety Inventory (STAI), one of the best indices to evaluate anxiety. Although STAI scores are widely used in applications ranging from clinical diagnosis to basic research, the scores are calculated based on a self-reported questionnaire. An objective evaluation is required because the subject may intentionally change his/her answers if multiple tests are carried out. In this article, we present a modified index called the “multi-channel Laterality Index at Rest (mc-LIR)” by recording the brain activity from a wider area of the frontal lobe using multi-channel functional near-infrared spectroscopy (fNIRS). The presented index aims to measure multiple positions near the F_pz defined by the international 10-20 system positioning. Using 24 subjects, the dependencies on the number of measuring points used to calculate the mc-LIR and its correlation coefficients with the STAI scores are reported. Furthermore, a simple linear regression was performed to estimate the STAI scores from mc-LIR. The cross-validation error is also reported. The experimental results show that using multiple positions near the Fpz will improve the correlation coefficients and estimation than those using only two positions.

Keywords: frontal lobe, functional near-infrared spectroscopy, state-trait anxiety inventory score, stress

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: Usman A. Usman

Abstract:

Undeniably, unlike the Classical side, the Keynesian perspective of the aggregate demand side indeed has a significant position in the policy, growth, and welfare of Nigeria due to government involvement and ineffective demand of the population living with poor per capita income. This study seeks to investigate the effect of Government Final Consumption Expenditure, Financial Deepening on Households, and NPISHs Final consumption expenditure using data on Nigeria from 1981 to 2019. This study employed the ADF stationarity test, Johansen Cointegration test, and Vector Error Correction Model. The results of the study revealed that the coefficient of Government final consumption expenditure has a positive effect on household consumption expenditure in the long run. There is a long-run and short-run relationship between gross fixed capital formation and household consumption expenditure. The coefficients cpsgdp financial deepening and gross fixed capital formation posit a negative impact on household final consumption expenditure. The coefficients money supply lm2gdp, which is another proxy for financial deepening, and the coefficient FDI have a positive effect on household final consumption expenditure in the long run. Therefore, this study recommends that Gross fixed capital formation stimulates household consumption expenditure; a legal framework to support investment is a panacea to increasing hoodmold income and consumption and reducing poverty in Nigeria. Therefore, this should be a key central component of policy.

Keywords: household, government expenditures, vector error correction model, johansen test

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Authors: Eko Adhi Setiawan, Duli Asih Siregar, Aiman Setiawan

Abstract:

Solar energy is abundant in nature, particularly in the tropics which have peak sun hour that can reach 8 hours per day. In the fabrication process, Photovoltaic’s (PV) performance are tested in standard test conditions (STC). It specifies a module temperature of 25°C, an irradiance of 1000 W/ m² with an air mass 1.5 (AM1.5) spectrum and zero wind speed. Thus, the results of the performance testing of PV at STC conditions cannot fully represent the performance of PV in the tropics. For example Indonesia, which has a temperature of 20-40°C. In this paper, the effect of temperature on the choice of the 5 kW AC inverter topology on the PV system such as the Central Inverter, String Inverter and AC-Module specifically for the tropics will be discussed. The proper inverter topology can be determined by analysis of the effect of temperature and irradiation on the PV panel. The effect of temperature and irradiation will be represented in the characteristics of I-V and P-V curves. PV’s characteristics on high temperature would be analyzed using Solar panel modeling through MATLAB Simulink based on mathematical equations that form Solar panel’s characteristic curve. Based on PV simulation, it is known then that temperature coefficients of short circuit current (ISC), open circuit voltage (VOC), and maximum output power (PMAX) consecutively as high as 0.56%/oC, -0.31%/oC and -0.4%/oC. Those coefficients can be used to calculate PV’s electrical parameters such as ISC, VOC, and PMAX in certain earth’s surface’s certain point. Then, from the parameters, the utility of the 5 kW AC inverter system can be determined. As the result, for tropical area, string inverter topology has the highest utility rates with 98, 80 %. On the other hand, central inverter and AC-Module Topology has utility rates of 92.69 % and 87.7 % eventually.

Keywords: Photovoltaic, PV-Inverter Configuration, PV Modeling, Solar Panel Characteristics.

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Authors: Rana Alkattan

Abstract:

Aim: This study aims to identify and analyze the factors predicting the professional clinical competency among newly recruited registered nurses. In addition, it aims to explore factors significantly correlated with high and low professional clinical competency score. Method: A descriptive analytical is applied in this study, cross-sectional which conducted between June 2012 and June 2013 at King Abdulaziz University Hospital, as one of the largest governmental university tertiary Hospital in Saudi Arabia. A survey questionnaire was designed to collect data. And then, data were analyzed using the SPSS. Results: A total of the 86 nurses provided valid responses. 69 were female and 17 were male. The majority of the participants in this study were married, from the Philippines, between 20-29 years old. The majority had certified university bachelor’s degree in nursing, as well as had prior experience in nursing between 1 to 5 years. There are two categories emerged from the data, which significantly correlated with nurses' professional competence and development. The first was the newly employed registered nurses demographic characteristic (correlation coefficients 0.154 to 0.470, P < 0.05), while the second was the list of studied environmental factors except 'job rotation factor' (correlation coefficients 0.122 to 0.540, P < 0.01). However, nurses' attitude including motivation and confidence were not associated with nurse's professional competency. Conclusion: that nurses' professional competence development is a process affected by certain personal demographic and environmental factors which will enable newly graduates nurses to provide safe effective patients' care and maintain their career responsibilities.

Keywords: clinical, competence, development nurses professional, registered

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Authors: Robel Legese Meko

Abstract:

Natural fibers have been used as end-of-life textiles and made into textile products which have become a well-proven and effective way of processing. Nowadays, resources to make primary synthetic fibers are becoming less and less as the world population is rising. Hence it is necessary to develop processes to fabricate textiles that are easily converted to composite materials. Acoustic comfort is closely related to the concept of sound absorption and includes protection against noise. This research paper presents an experimental study on sound absorption coefficients, for natural fiber composite materials: a natural fiber (Coir/Jute) with different blend proportions of raw materials mixed with rigid polyurethane foam as a binder. The natural fiber composite materials were characterized both acoustically (sound absorption coefficient SAC) and also in terms of heat transfer (thermal conductivity). The acoustic absorption coefficient was determined using the impedance tube method according to the ASTM Standard (ASTM E 1050). The influence of the structure of these materials on the sound-absorbing properties was analyzed. The experimental results signify that the porous natural coir/jute composites possess excellent performance in the absorption of high-frequency sound waves, especially above 2000 Hz, and didn’t induce a significant change in the thermal conductivity of the composites. Thus, the sound absorption performances of natural fiber composites based on coir/jute fiber materials promote environmentally friendly solutions.

Keywords: coir/jute fiber, sound absorption coefficients, compression molding, impedance tube, thermal insulating properties, SEM analysis

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Authors: Kazuki Kohama, Hiroko Ono

Abstract:

The rainfall in Japan is gradually increasing every year according to Japan Meteorological Agency and Intergovernmental Panel on Climate Change Fifth Assessment Report. It means that the rainfall difference between rainy season and non-rainfall is increasing. In addition, the increasing trend of strong rain for a short time clearly appears. In recent years, natural disasters have caused enormous human injuries in various parts of Japan. Regarding water disaster, local heavy rain and floods of large rivers occur frequently, and it was decided on a policy to promote hard and soft sides as emergency disaster prevention measures with water disaster prevention awareness social reconstruction vision. Okinawa prefecture in subtropical region has torrential rain and water disaster several times a year such as river flood, in which is caused in specific rivers from all 97 rivers. Also, the shortage of capacity and narrow width are characteristic of river in Okinawa and easily cause river flood in heavy rain. This study focuses on Kohatu River that is one of the specific rivers. In fact, the water level greatly rises over the river levee almost once a year but non-damage of buildings around. On the other hand in some case, the water level reaches to ground floor height of house and has happed nine times until today. The purpose of this research is to figure out relationship between precipitation, surface outflow and total treatment water quantity of Kohatu River. For the purpose, we perform hydrological analysis although is complicated and needs specific details or data so that, the method is mainly using Geographic Information System software and outflow analysis system. At first, we extract watershed and then divided to 23 catchment areas to understand how much surface outflow flows to runoff point in each 10 minutes. On second, we create Unit Hydrograph indicating the area of surface outflow with flow area and time. This index shows the maximum amount of surface outflow at 2400 to 3000 seconds. Lastly, we compare an estimated value from Unit Hydrograph to a measured value. However, we found that measure value is usually lower than measured value because of evaporation and transpiration. In this study, hydrograph analysis was performed using GIS software and outflow analysis system. Based on these, we could clarify the flood time and amount of surface outflow.

Keywords: disaster prevention, water disaster, river flood, GIS software

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Authors: Saima Kalsoom, Sobia Masood, Muhammad Faran

Abstract:

This study aimed to examine the association between emotional intelligence and gender role attitudes of married individuals. Another aim of this study was to test the moderating role of gender work status of married individuals for predicting gender role attitudes from emotional intelligence. A sample of (N = 500) married working men and women (both working & housewives) was approached through purposive convenience sampling technique. The data was collected employing cross-sectional research design. The indigenous versions of the Gender Role Attitudes Scale and perceived Emotional Intelligence Scale were used. The results of alpha coefficients for both the scales and subscales used in this study designated satisfactory evidence for internal consistency and reliability. Assessment of correlation coefficients showed significant positive correlation between gender role attitudes and emotional intelligence, subfactors of emotional intelligence i.e., emotional self-regulation, emotional self-awareness, and interpersonal skills with gender role attitudes. Results of model testing revealed that gender (the effect was significant for women) and work status (the effect was more significant for married working women than married working men and housewives) of the married individuals significantly moderated the relationship between emotional intelligence and gender role attitudes into the positive direction. Further, it was also found that gender and work status also moderated the relationship between emotional self-regulation (as sub factor of emotional intelligence) and gender role attitudes in a positive direction. In conclusion, this empirical evidence is vital contribution derived from the traditional and collectivistic socio-cultural background of Pakistan.

Keywords: gender role attitudes, emotional intelligence, emotional self-regulation, gender, work status, married working women

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Authors: Mario Perez-Won, Roberto Lemus-Mondaca, Fernanda Marin, Constanza Olivares

Abstract:

This study presents the simultaneous application of high hydrostatic pressure (HHP) and osmotic dehydration of jumbo squid (Dosidicus gigas) slice. Diffusion coefficients for both components water and solids were improved by the process pressure, being influenced by pressure level. The working conditions were different pressures such as 100, 250, 400 MPa and pressure atmospheric (0.1 MPa) for time intervals from 30 to 300 seconds and a 15% NaCl concentration. The mathematical expressions used for mass transfer simulations both water and salt were those corresponding to Newton, Henderson and Pabis, Page and Weibull models, where the Weibull and Henderson-Pabis models presented the best fitted to the water and salt experimental data, respectively. The values for water diffusivity coefficients varied from 1.62 to 8.10x10⁻⁹ m²/s whereas that for salt varied among 14.18 to 36.07x10⁻⁹ m²/s for selected conditions. Finally, as to quality parameters studied under the range of experimental conditions studied, the treatment at 250 MPa yielded on the samples a minimum hardness, whereas springiness, cohesiveness and chewiness at 100, 250 and 400 MPa treatments presented statistical differences regarding to unpressurized samples. The colour parameters L* (lightness) increased, however, but b* (yellowish) and a* (reddish) parameters decreased when increasing pressure level. This way, samples presented a brighter aspect and a mildly cooked appearance. The results presented in this study can support the enormous potential of hydrostatic pressure application as a technique important for compounds impregnation under high pressure.

Keywords: colour, diffusivity, high pressure, jumbo squid, modelling, texture

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Authors: Ahmed Abbas

Abstract:

Land degradation in the Lower Cheliff region leads to loss of their fertility, physical and chemical properties by secondary salinization and film forming surface or surface crust. The main factor related to runoff and soil erosion is their susceptibility to crusting caused by the impact of raindrops, which causes the reduction of the filterability of the soil. The present study aims to investigate the hydrodynamic behavior of five types of soil taken from the plain of low Cheliff under simulated rainfall by using two intensities, one moderate, and others correspond to heavy rains at low kinetic energies. Experimental results demonstrate the influence of chemical and mechanical physical properties of soils on their hydrodynamic behavior and the influence of heavy rain on the modality of the reduction in the filterability and the amount of transported sediment.

Keywords: erosion, hydrodynamic behavior, rain simulation, soil

Procedia PDF Downloads 254

Authors: Weiyao Zhu, Qian Qi, Ming Yue, Dongxu Ma

Abstract:

Due to the nano-micro pore structures and the massive multi-stage multi-cluster hydraulic fracturing in shale gas reservoirs, the multi-scale seepage flows are much more complicated than in most other conventional reservoirs, and are crucial for the economic development of shale gas. In this study, a new multi-scale non-linear flow model was established and simplified, based on different diffusion and slip correction coefficients. Due to the fact that different flow laws existed between the fracture network and matrix zone, a three-zone composite model was proposed. Then, according to the conformal transformation combined with the law of equivalent percolation resistance, the productivity equation of a horizontal fractured well, with consideration given to diffusion, slip, desorption, and absorption, was built. Also, an analytic solution was derived, and the interference of the multi-cluster fractures was analyzed. The results indicated that the diffusion of the shale gas was mainly in the transition and Fick diffusion regions. The matrix permeability was found to be influenced by slippage and diffusion, which was determined by the pore pressure and diameter according to the Knudsen number. It was determined that, with the increased half-lengths of the fracture clusters, flow conductivity of the fractures, and permeability of the fracture network, the productivity of the fractured well also increased. Meanwhile, with the increased number of fractures, the distance between the fractures decreased, and the productivity slowly increased due to the mutual interference of the fractures. In regard to the fractured horizontal wells, the free gas was found to majorly contribute to the productivity, while the contribution of the desorption increased with the increased pressure differences.

Keywords: multi-scale, fracture network, composite model, productivity

Procedia PDF Downloads 248

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: A. Moussouni, L. Mouzai, M. Bouhadef

Abstract:

Soil erosion is a very complex phenomenon, resulting from detachment and transport of soil particles by erosion agents. The kinetic energy of raindrop is the energy available for detachment and transport by splashing rain. The soil erodibility is defined as the ability of soil to resist to erosion. For this purpose, an experimental study was conducted in the laboratory using rainfall simulator to study the effect of the kinetic energy of rain (Ec) on the soil erodibility (K). The soil used was a sandy agricultural soil of 62.08% coarse sand, 19.14% fine sand, 6.39% fine silt, 5.18% coarse silt and 7.21% clay. The obtained results show that the kinetic energy of raindrops evolves as a power law with soil erodibility.

Keywords: erosion, runoff, raindrop kinetic energy, soil erodibility, rainfall intensity, raindrop fall velocity

Procedia PDF Downloads 471