Search results for: root mean square error

Authors: Ric Joseph R. Murillo, Edna N. Gueco, Dennis I. Merino

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Let F be C or R, where C and R are the set of complex numbers and real numbers, respectively, and n be a natural number. An n-by-n matrix A over the field F is called an α-involutory matrix or an α-involution if there exists an α in the field such that the square of the matrix is equal to αI, where I is the n-by-n identity matrix. If α is a complex number or a nonnegative real number, then an n-by-n matrix A over the field F can be written as a sum of n-by-n α-involutory matrices over the field F if and only if the trace of that matrix is an integral multiple of the square root of α. Meanwhile, if α is a negative real number, then a 2n-by-2n matrix A over R can be written as a sum of 2n-by-2n α-involutory matrices over R if and only the trace of the matrix is zero. Some other properties of α-involutory matrices are also determined

Keywords: α-involutory Matrices, sum of α-involutory Matrices, Trace, Matrix Theory

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Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara

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For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.

Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter

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Authors: Sai Sankalp Vemavarapu

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This paper discusses the application of machine learning in the field of transportation engineering for predicting engineering properties of pavement more accurately and efficiently. Predicting the elastic properties aid us in assessing the current road conditions and taking appropriate measures to avoid any inconvenience to commuters. This improves the longevity and sustainability of the pavement layer while reducing its overall life-cycle cost. As an example, we have implemented differential evolution (DE) in the back-calculation of the elastic modulus of multi-layered pavement. The proposed DE global optimization back-calculation approach is integrated with a forward response model. This approach treats back-calculation as a global optimization problem where the cost function to be minimized is defined as the root mean square error in measured and computed deflections. The optimal solution which is elastic modulus, in this case, is searched for in the solution space by the DE algorithm. The best DE parameter combinations and the most optimum value is predicted so that the results are reproducible whenever the need arises. The algorithm’s performance in varied scenarios was analyzed by changing the input parameters. The prediction was well within the permissible error, establishing the supremacy of DE.

Keywords: cost function, differential evolution, falling weight deflectometer, genetic algorithm, global optimization, metaheuristic algorithm, multilayered pavement, pavement condition assessment, pavement layer moduli back calculation

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Authors: Alper Taner, Yesim Benal Oztekin, Huseyin Duran

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In this study, an Artificial Neural Network (ANN) was developed in order to classify barley varieties. For this purpose, physical properties of barley varieties were determined and ANN techniques were used. The physical properties of 8 barley varieties grown in Turkey, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, N-l, N-2 and N-3 were constructed. The performances of these models were compared. It was determined that the best-fit model was N-1. In the N-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain were used as input parameter; and varieties as output parameter. R2, Root Mean Square Error and Mean Error for the N-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the N-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in flourmills.

Keywords: physical properties, artificial neural networks, barley, classification

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Authors: Himayatullah Khan

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This study estimates supply response function of sugarcane in Pakistan from 1980-81 to 2012-13. The study uses co-integration approach and error correction mechanism. Sugarcane production, area and price series were tested for unit root using Augmented Dickey Fuller (ADF). The study found that these series were stationary at their first differenced level. Using the Augmented Engle-Granger test and Cointegrating Regression Durbin-Watson (CRDW) test, the study found that “production and price” and “area and price” were co-integrated suggesting that the two sets of time series had long-run or equilibrium relationship. The results of the error correction models for the two sets of series showed that there was disequilibrium in the short run there may be disequilibrium. The Engle-Granger residual may be thought of as the equilibrium error which can be used to tie the short-run behavior of the dependent variable to its long-run value. The Granger-Causality test results showed that log of price granger caused both the long of production and log of area whereas, the log of production and log of area Granger caused each other.

Keywords: co-integration, error correction mechanism, Granger-causality, sugarcane, supply response

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Authors: Mirza Mohibulla Baig, Imil Hamda Imran, Tri Bagus Susilo, Sami El Ferik

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The paper deals with system identification and control a nonlinear model of semi-autonomous underwater vehicle (UUV). The input-output data is first generated using the experimental values of the model parameters and then this data is used to compute the estimated parameter values. In this study, we use the semi-autonomous UUV LAURS model, which is developed by the Sensors and Actuators Laboratory in University of Sao Paolo. We applied three methods to identify the parameters: integral method, which is a classical least square method, recursive least square, and weighted recursive least square. In this paper, we also apply three different inputs (step input, sine wave input and random input) to each identification method. After the identification stage, we investigate the control performance of yaw motion of nonlinear semi-autonomous Unmanned Underwater Vehicle (UUV) using feedback linearization-based controller. In addition, we compare the performance of the control with an integral and a non-integral part along with state feedback. Finally, disturbance rejection and resilience of the controller is tested. The results demonstrate the ability of the system to recover from such fault.

Keywords: system identification, underwater vehicle, integral method, recursive least square, weighted recursive least square, feedback linearization, integral error

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Authors: I. A. Simsek, S. N. Dogan, A. S. Kipcak, E. Morodor Derun, N. Tugrul

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In this study, the drying characteristics of shrimp are studied by using the traditional drying method of oven. Drying temperatures are selected between 60-80°C. Obtained experimental drying results are applied to eleven mathematical models of Alibas, Aghbashlo et al., Henderson and Pabis, Jena and Das, Lewis, Logaritmic, Midilli and Kucuk, Page, Parabolic, Wang and Singh and Weibull. The best model was selected as parabolic based on the highest coefficient of determination (R²) (0.999990 at 80°C) and the lowest χ² (0.000002 at 80°C), and the lowest root mean square error (RMSE) (0.000976 at 80°C) values are compared to other models. The effective moisture diffusivity (Deff) values were calculated using the Fick’s second law’s cylindrical coordinate approximation and are found between 6.61×10⁻⁸ and 6.66×10⁻⁷ m²/s. The activation energy (Ea) was calculated using modified form of Arrhenius equation and is found as 18.315 kW/kg.

Keywords: activation energy, drying, effective moisture diffusivity, modelling, oven, shrimp

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Jia Zhang, Fengmei Yao, Yanjing Tan

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The accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (P < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002-2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.

Keywords: process-based model, C4 crop, maize yield, remote sensing, Northeast China Plain

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Authors: A. Chillali, M. Sahmoudi

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In this paper we will give a method for encoding the elements of the ring of integers of sextic extension, namely L = Q(a,b) which is a rational quadratic over cubic field K =Q(a) where a^{2} is a rational square free integer and b is a root of irreducible polynomiale of degree 3.

Keywords: coding, integral bases, sextic, quadratic

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Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

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This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: El Andari Chifaa, Bouaziz Rached

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The central focus of this paper was to check whether the Okun’s law in Tunisia is valid or not. For this purpose, we have used quarterly time series data during the period 1990Q1-2014Q1. Firstly, we applied the error correction model instead of the difference version of Okun's Law, the Engle-Granger and Johansen test are employed to find out long run association between unemployment, production, and how error correction mechanism (ECM) is used for short run dynamic. Secondly, we used the gap version of Okun’s law where the estimation is done from three band pass filters which are mathematical tools used in macro-economic and especially in business cycles theory. The finding of the study indicates that the inverse relationship between unemployment and output is verified in the short and long term, and the Okun's law holds for the Tunisian economy, but with an Okun’s coefficient lower than required. Therefore, our empirical results have important implications for structural and cyclical policymakers in Tunisia to promote economic growth in a context of lower unemployment growth.

Keywords: Okun’s law, validity, unit root, cointegration, error correction model, bandpass filters

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Authors: Manjinder Bajwa, Mahipal Singh, Manish Nagpal

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This research paper aims to propose a novel approach using ANOVA technique for the strategic investigation of process parameters and their effects on the mechanical properties of Aluminium alloy cast. The two process parameters considered here were permeability of sand and pouring temperature of aluminium alloy. ANOVA has been employed for the first time to determine the effects of these selected parameters on the impact strength of alloy. The experimental results show that this proposed technique has great potential for analyzing sand casting process. Using this approach we have determined the treatment mean square, response mean square and mean square of error as 8.54, 8.255 and 0.435 respectively. The research concluded that at the 5% level of significance, permeability of sand is the more significant parameter influencing the impact strength of cast alloy.

Keywords: aluminium alloy, pouring temperature, permeability of sand, impact strength, ANOVA

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Authors: Z. Ansari, M. R. M. Rejab, D. Bachtiar, J. P. Siregar

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This study focus on the compression and tensile properties of new and recycle square honeycombs structure from sugar palm fibre (SPF) and polylactic acid (PLA) composite. The end data will determine the failure strength and energy absorption for both new and recycle composite. The control SPF specimens were fabricated from short fibre co-mingled with PLA by using a bra-blender set at 180°C and 50 rpm consecutively. The mixture of 30% fibre and 70% PLA were later on the hot press at 180°C into sheets with thickness 3mm consecutively before being assembled into a sandwich honeycomb structure. An INSTRON tensile machine and Abaqus 6.13 software were used for mechanical test and finite element simulation. The percentage of error from the simulation and experiment data was 9.20% and 9.17% for both new and recycled product. The small error of percentages was acceptable due to the nature of the simulation model to be assumed as a perfect model with no imperfect geometries. The energy absorption value from new to recycled product decrease from 312.86kJ to 282.10kJ. With this small decrements, it is still possible to implement a recycle SPF/PLA composite into everyday usages such as a car's interior or a small size furniture.

Keywords: failure modes, numerical modelling, polylactic acid, sugar palm fibres

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: G. Shahzadi, A. Soulaimani

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This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety.

Keywords: Rockfill dam, parameter identification, stochastic analysis, regression, PLAXIS

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Authors: Pitak Keawbunsong, Sathaporn Promwong

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This paper presents an investigation on the efficiency of the optimized Davison path loss model in order to look for a suitable path loss model to design and planning DTTV propagation for small and medium urban areas in southern Thailand. Hadyai City in Songkla Province is chosen as the case study to collect the analytical data on the electric field strength. The optimization is conducted through the least square method while the efficiency index is through the statistical value of relative error (RE). The result of the least square method is the offset and slop of the frequency to be used in the optimized process. The statistical result shows that RE of the old Davidson model is at the least when being compared with the optimized Davison and the Hata models. Thus, the old Davison path loss model is the most accurate that further becomes the most optimized for the plan on the propagation network design.

Keywords: DTTV propagation, path loss model, Davidson model, least square method

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Authors: Naveen Kumar Perumalla, Devadas Kuna, Mohammed Akhter Ali

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Satellite Navigation System such as the United States Global Positioning System (GPS) plays a significant role in determining the user position. Similar to that of GPS, Indian Regional Navigation Satellite System (IRNSS) is a Satellite Navigation System indigenously developed by Indian Space Research Organization (ISRO), India, to meet the country’s navigation applications. This system is also known as Navigation with Indian Constellation (NavIC). The NavIC system’s main objective, is to offer Positioning, Navigation and Timing (PNT) services to users in its two service areas i.e., covering the Indian landmass and the Indian Ocean. Six NavIC satellites are already deployed in the space and their receivers are in the performance evaluation stage. Four NavIC dual frequency receivers are installed in the ‘Advanced GNSS Research Laboratory’ (AGRL) in the Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, India. The NavIC receivers can be operated in two positioning modes: Stand-alone IRNSS and Hybrid (IRNSS+GPS) modes. In this paper, analysis of various parameters such as Dilution of Precision (DoP), three Dimension (3D) Root Mean Square (RMS) Position Error and Horizontal Position Error with respect to Visibility of Satellites is being carried out using the real-time IRNSS data, obtained by operating the receiver in both positioning modes. Two typical days (6th July 2017 and 7th July 2017) are considered for Hyderabad (Latitude-17°24'28.07’N, Longitude-78°31'4.26’E) station are analyzed. It is found that with respect to the considered parameters, the Hybrid mode operation of NavIC receiver is giving better results than that of the standalone positioning mode. This work finds application in development of NavIC receivers for civilian navigation applications.

Keywords: DoP, GPS, IRNSS, GNSS, position error, satellite visibility

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Authors: Wenqi Liu, Reginald Bailey

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This study explores an advanced approach to enhancing B2B sales forecasting by integrating machine learning models with a rule-based decision framework. The methodology begins with the development of a machine learning classification model to predict conversion likelihood, aiming to improve accuracy over traditional methods like logistic regression. The classification model's effectiveness is measured using metrics such as accuracy, precision, recall, and F1 score, alongside a feature importance analysis to identify key predictors. Following this, a machine learning regression model is used to forecast sales value, with the objective of reducing mean absolute error (MAE) compared to linear regression techniques. The regression model's performance is assessed using MAE, root mean square error (RMSE), and R-squared metrics, emphasizing feature contribution to the prediction. To bridge the gap between predictive analytics and decision-making, a rule-based decision model is introduced that prioritizes customers based on predefined thresholds for conversion probability and predicted sales value. This approach significantly enhances customer prioritization and improves overall sales performance by increasing conversion rates and optimizing revenue generation. The findings suggest that this combined framework offers a practical, data-driven solution for sales teams, facilitating more strategic decision-making in B2B environments.

Keywords: sales forecasting, machine learning, rule-based decision model, customer prioritization, predictive analytics

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Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

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The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

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Authors: Akhmadjon Sultanov, Eun-Ho Lee, Hye-Jin Park, Young-Je Cho

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This study tested the anti-inflammatory effect of wild indigo (Baptisia tinctoria) root in Raw 264.7 cells. We prepared two extracts of B. tinctoria; one in water and the other in 50% ethanol. Then we evaluated the toxicities of the B. tinctoria root extracts at 10 to 100 mg mL-1 concentrations in raw 264.7 cells and observed 80% cell viability. The anti-inflammatory effect of B. tinctoria root extract in lipopolysaccharide (LPS)-stimulated Raw 264.7 cells were observed with concentrations at 10, 30, and 50 μg mL-1. The results showed that 77.27-66.82% of nitric oxide (NO) production was inhibited by 50 μg mL-1 B. tinctoria root extract. The protein expression of Inducible NO synthase (iNOS) expression dramatically decreased by 93.14% and 52.65% in raw 264.7 cells treated with water and ethanol extracts of B. tinctoria root, respectively. Moreover, cyclooxygenase-2 (COX-2) protein expression decreased by 42.85% and 69.70% in raw 264.7 cells treated with water and ethanol extracts of B. tinctoria root, respectively. Furthermore, the mRNA expression of pro-inflammatory markers, such as tumor necrosis factor-alpha, interleukin-1β, interleukin-6, monocyte chemoattractant protein-1, and prostaglandin E synthase 2, was significantly suppressed in a concentration-dependent manner. Additionally, the B. tinctoria root extracts effectively inhibited enzymes involved in physiological activities. The B. tinctoria root extracts showed excellent anti-inflammatory effects and can be used as a functional material for biological activities.

Keywords: cytokine, macrophage, pro-inflammatory, protein expression, real-time PCR

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Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif

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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.

Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence

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Authors: Hailemariam Solomon, Taye Tadesse, Daniel Nadew, Firezer Girma

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Sorghum is an important crop in semi-arid regions and has shown resilience to drought stress. However, recurrent drought is affecting its productivity. Therefore, it is necessary to explore genes that contribute to drought stress adaptation to increase sorghum productivity. The aim of this study is to evaluate and determine the effect of root system traits, specifically root angle, on drought stress adaptation and grain yield performance in sorghum genotypes. A total of 428 sorghum genotypes from the Ethiopian breeding program were evaluated in three drought-stress environments. Field trials were conducted using a row-column design with three replications. Root system traits were phenotyped using a high-throughput phenotyping platform and analyzed using a row-column design with two replications. Data analysis was performed using R software and regression analysis. The study found significant variations in root system architecture among the sorghum genotypes. Non-stay-green genotypes had a grain yield ranging from 1.63 to 3.1 tons/ha, while stay-green genotypes had a grain yield ranging from 2.4 to 2.9 tons/ha. The analysis of root angle showed that non-stay-green genotypes had an angle ranging from 8.0 to 30.5 degrees, while stay-green genotypes had an angle ranging from 12.0 to 29.0 degrees. Improved varieties exhibited angles between 14.04 and 19.50 degrees. Positive and significant correlations were observed between leaf areas and shoot dry weight, as well as between leaf width and shoot dry weight. Negative correlations were observed between root angle and leaf area, as well as between root angle and root length. This research highlights the importance of root system architecture, particularly root angle traits, in enhancing grain yield production in drought-stressed conditions. It also establishes an association between root angle and grain yield traits for maximizing sorghum productivity.

Keywords: roor sysytem architecture, root angle, narrow root angle, wider root angle, drought

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Authors: Navdeep Singh Randhawa

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Wireless communication system has been proved as the best for any communication. However, there are some undesirable threats of a wireless communication channel on the information transmitted through it, such as attenuation, distortions, delays and phase shifts of the signals arriving at the receiver end which are caused by its band limited and dispersive nature. One of the threat is ISI (Inter Symbol Interference), which has been found as a great obstacle in high speed communication. Thus, there is a need to provide perfect and accurate technique to remove this effect to have an error free communication. Thus, different equalization techniques have been proposed in literature. This paper presents the equalization techniques followed by the concept of adaptive filter equalizer, its algorithms (LMS and RLS) and applications of adaptive equalization technique.

Keywords: channel equalization, adaptive equalizer, least mean square, recursive least square

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Authors: Rahim Ada, Zamari Temory, Hasan Dalgic

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Germination and seedling responses of seven safflower seed genotypes (Dinçer, Remzibey, Black Sun2 cultivars and A19, F4, I1, J19 lines) to different salinity concentrations (0, 5, 10, and 20 g l-1) and temperatures (10 and 20 oC) evaluated in Completely Randomized Factorial Designs in Department of Field Crops of Selcuk University, Konya, Turkey. Seeds in the control (distilled water) had at 10 and 20 oC the highest germination percentage (93.88 and 94.32 %), shoot length (4.60 and 8.72 cm), root length (4.27 and 6.54 cm), shoot dry weight (22.37 mg and 25.99 mg), and root dry weight (2.22 and 2.47 mg). As the salt concentration increased, values of all characters were decreased. In this experiment, in 20 g l-1 salt concentration found germination percentage (21.28 and 26.66 %), shoot (1.32 and 1.35 cm) and root length (1.04 and 1.10 cm), shoot (8.05 mg and 7.49 mg) and root dry weight (0.83 and 0.98 mg) at 10, and 20 oC.

Keywords: safflower, NaCl, temperature, shoot and root length, salt concentration

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Authors: Melissa Langevin, Natalie Ward, Colleen Fitzgibbons, Christa Ramsey, Melanie Hogue, Anna Theresa Lobos

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Description: Root Cause Analysis (RCA) is used by health care teams to examine adverse events (AEs) to identify causes which then leads to recommendations for prevention Despite widespread use, RCA has limitations. Best practices have not been established for implementing recommendations or tracking the impact of interventions after AEs. During phase 1 of this study, we used simulation to analyze two fictionalized AEs that occurred in hospitalized paediatric patients to identify and understand how the errors occurred and generated recommendations to mitigate and prevent recurrences. Scenario A involved an error of commission (inpatient drug error), and Scenario B involved detecting an error that already occurred (critical care drug infusion error). Recommendations generated were: improved drug labeling, specialized drug kids, alert signs and clinical checklists. Aim: Use simulation to optimize interventions recommended post critical event analysis prior to implementation in the clinical environment. Methods: Suggested interventions from Phase 1 were designed and tested through scenario simulation in the clinical environment (medicine ward or pediatric intensive care unit). Each scenario was simulated 8 times. Recommendations were tested using different, voluntary teams and each scenario was debriefed to understand why the error was repeated despite interventions and how interventions could be improved. Interventions were modified with subsequent simulations until recommendations were felt to have an optimal effect and data saturation was achieved. Along with concrete suggestions for design and process change, qualitative data pertaining to employee communication and hospital standard work was collected and analyzed. Results: Each scenario had a total of three interventions to test. In, scenario 1, the error was reproduced in the initial two iterations and mitigated following key intervention changes. In scenario 2, the error was identified immediately in all cases where the intervention checklist was utilized properly. Independently of intervention changes and improvements, the simulation was beneficial to identify which of these should be prioritized for implementation and highlighted that even the potential solutions most frequently suggested by participants did not always translate into error prevention in the clinical environment. Conclusion: We conclude that interventions that help to change process (epinephrine kit or mandatory checklist) were more successful at preventing errors than passive interventions (signage, change in memory aids). Given that even the most successful interventions needed modifications and subsequent re-testing, simulation is key to optimizing suggested changes. Simulation is a safe, practice changing modality for institutions to use prior to implementing recommendations from RCA following AE reviews.

Keywords: adverse events, patient safety, pediatrics, root cause analysis, simulation

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Authors: Asmaa Al Shafiee, Erdin Ibraim

Abstract:

Stabilizing slopes by using vegetation is considered as a cost-effective and eco-friendly alternative to the conventional methods. The main aim of this study is to investigate the mechanical effect of analogue root systems on the shear strength of different soil types. Three objectives were defined to achieve the main aim of this paper. Firstly, explore the effect of root architectural design to shear strength parameters. Secondly, study the effect of root area ratio (RAR) on the shear strength of two different soil types. Finally, to investigate how different kinds of soil can affect the behavior of the roots during shear failure. 3D printing tool was used to develop different analogue tap root models with different architectural designs. Direct shear tests were performed on Leighton Buzzard (LB) fraction B sand, which represents a coarse sand and Huston sand, which represent medium-coarse sand. All tests were done with the same relative density for both kinds of sand. The results of the direct shear test indicated that using plant roots will increase both friction angle and cohesion of soil. Additionally, different root designs affected differently the shear strength of the soil. Furthermore, the directly proportional relationship was found between root area ratio for the same root design and shear strength parameters of soil. Finally, the root area ratio effect should be combined with branches penetrating the shear plane to get the highest results.

Keywords: leighton buzzard sand, root area ratio, rooted soil, shear strength, slope stabilization

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Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

Abstract:

Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Muhammad Ashfaq, Muhammad Saleem Haider, Muhammad Ali, Muhammad Sajjad, Amna Ali, Urooj Mubashar

Abstract:

Phenotypic diversity was confirmed by measuring different morphological traits i.e. seed traits (seed length, seed width, seed thickness, seed length-width ratio, 1000 grain weight) and root-shoot traits (shoot length, root length, shoot fresh weight, root fresh weight, root-shoot ratio, root numbers and root thickness). Variance and association study of desirable traits determine the genotypic differences among the rice germplasm. All the traits showed significant differences among the genotypes. The traits were studied in Randomized complete block design (RCBD) at different water levels. Some traits showed positive correlation with each other and beneficial for increasing the yield and production of the crop. Seed thickness has positive correlation with seed length and seed width (r= 0.104**, r=0.246**). On the other hand, various root shoot traits showed positive highly significant association at different water levels i.e. root length, fresh root weight, root thickness, shoot thickness and root numbers. Our main focus to study the performance/correlation of root shoots traits under stress condition. Fresh root weight, shoot thickness and root numbers showed positive significant association with shoot length, root length, fresh root and shoot weight (r=0.2530**, r=0.2891**, r=0.4626**, r=0.4515**, r=0.5781**, r=0.7164**, r=0.0603**, r= 0.5570**, r=0.5824**). Long root length genotypes favors and suitable for drought stress conditions and screening of diverse genotypes for the further development of new plant material that performing well under different environmental conditions. After screening genetic diversity of potential rice, lines were studied to check the polymorphism by using some SSR markers. DNA was extracted, and PCR analyses were done to study PIC values and allelic diversity of the genotypes. The main objective of this study is to screen out the genotypes on the basis of various genotypic and phenotypic traits.

Keywords: rice, morphological traits, association, germplasm, genetic diversity, water levels, variation

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Authors: Kang Huang, Wanting Zhou, Shiwei Yuan, Lei Li

Abstract:

Since information technology develops rapidly, the security issue has become an increasingly critical for computer system. In particular, as cloud computing and the Internet of Things (IoT) continue to gain widespread adoption, computer systems need to new security threats and attacks. The Root of Trust (RoT) is the foundation for providing basic trusted computing, which is used to verify the security and trustworthiness of other components. Design a reliable Root of Trust and guarantee its own security are essential for improving the overall security and credibility of computer systems. In this paper, we discuss the implementation of self-security technology based on the RISC-V Root of Trust at the hardware level. To effectively safeguard the security of the Root of Trust, researches on security safeguard technology on the Root of Trust have been studied. At first, a lightweight and secure boot framework is proposed as a secure mechanism. Secondly, two kinds of memory protection mechanism are built to against memory attacks. Moreover, hardware implementation of proposed method has been also investigated. A series of experiments and tests have been carried on to verify to effectiveness of the proposed method. The experimental results demonstrated that the proposed approach is effective in verifying the integrity of the Root of Trust’s own boot rom, user instructions, and data, ensuring authenticity and enabling the secure boot of the Root of Trust’s own system. Additionally, our approach provides memory protection against certain types of memory attacks, such as cache leaks and tampering, and ensures the security of root-of-trust sensitive information, including keys.

Keywords: root of trust, secure boot, memory protection, hardware security

Procedia PDF Downloads 215