Parameter Alpha Research Articles

Stochastic Learning Strategy with Varying Regularization Activation Layer-Based Diabetes Prediction Using Multilayer Perceptron

Diabetes makes people more susceptible to disorders like heart disease, kidney disease, Parkinson’s, cataracts, respiratory issues, and other illnesses. Numerous tests are employed to capture essential data for diagnosing diabetes, and the assessment is then used to decide the most appropriate course of action. Nevertheless, it is still challenging to identify the precise traits essential for the emergence of diabetes. Finding the critical factors that influence the target diabetes condition is the primary goal of this research. The Stochastic Learning Strategy with Varying Regularization Logistic Activation Multilayer Perceptron (MLP) model has been implemented with the Indian Diabetes patient dataset extracted from the UCI database repository. Data preprocessing was accomplished for the patients of the Indian diabetes dataset. This research tries to extract the correlation and distribution of all features from the Indian Diabetes Patient dataset. All classifiers demonstrate an accuracy of less than 80% when applied to the Indian Diabetes patient dataset with and without feature scaling, except the Random Forest classifier, which is found to have an accuracy of 78%, which is not the best. The Indian Diabetes patient dataset is fitted with MLP with various learning rates such as Constant learning, Constant momentum and Constant Nesterov momentum learning rate, Invasive Scaling, Invasive Scaling momentum and Invasive Scaling Nesterov momentum learning rate, Adaptive learning, Adaptive momentum and Adaptive Nesterov momentum learning rate for various activation layers like ReLU (Rectified Linear Unit), Identity, tanh and logistic activation layer and the performance is analyzed. The MLP with a logistic activation layer under the Adaptive momentum Learning rate strategy is examined with varying regularization parameter alpha to strengthen the prediction accuracy. The proposed Stochastic Learning Strategy with Varying Regularization Logistic Activation MLP model was further analyzed with the performance metrics precision, recall, FScore, runtime and accuracy and the experimental results exhibit 97% accuracy for MLP with logistic activation layer under Adaptive momentum Learning rate toward predicting the diabetes disease.

Key controls on water transit times along a tropical precipitation gradient

The tropics are one of the most diverse and dynamic regions on Earth. Despite their importance, our understanding of tropical hydrological processes remains a significant challenge, mostly due to limited monitoring. Here, we used high-resolution daily input–output isotope data sets from seven tropical catchments ranging in size from 1.6 to 990 km2, situated within a precipitation gradient ranging from on average 958 to 5117 mm year−1, in Australia, Costa Rica, and Ecuador. For these catchments, we estimated and compared streamflow mean transit times (MTT) with potential explanatory hydro-geomorphological and climate variables. Using a simple lumped convolution integral model with a Gamma distribution as transfer function (best-fit Kling Gupta efficiencies up to 0.92), our tropical dataset resulted in short MTTs from 49 to 497 days (0.13 – 1.4 years), together with considerable presence of new water in the stream and low damping ratios in most of the catchments. The gamma distribution alpha parameter was below the previously identified quasi-global pattern of around 0.5 in 5 out of 7 catchments. Principal Component Analysis (PCA) and a relative importance test identified rock type (%), days with zero precipitation and the water storage capacity as the most important controls on TT. In addition, the TT distribution as indicated by the Gamma distribution alpha parameter was best explained by annual evapotranspiration, altitude, and Andosol soil cover (%). Our findings identified the key controls on TT and TT distribution in fast-responding tropical catchments compared to other geomorphic and climate zones, emphasizing the value of TT as a coherent descriptor that can be used for catchment characterization.

The Use of Brown's Double Exponential Smoothing Method to Predict Harvest Yields in Horticultural Crops

Agriculture stands as a pivotal sub-sector within the economy of North Aceh. Among its primary commodities are horticultural crops, encompassing the cultivation of vegetables, fruits, medicinal plants, and ornamental flora. In endeavors to boost agricultural productivity and efficiency, the utilization of harvest prediction methodologies has grown increasingly indispensable. This study relies on historical harvest data spanning from 2017 to 2022 to forecast crops such as leafy greens, fruits, and medicinal plants. The selected plants for prediction include spinach, water spinach, cucumber, banana, durian, rambutan, ginger, lesser galangal, and turmeric. Data analysis employs Brown's double exponential smoothing method, selecting the α (alpha) parameter that minimizes the Mean Absolute Percentage Error (MAPE) for accurate forecasting. Spinach is anticipated to yield 1239.9508 quintals, with an α (alpha) parameter of 0.9 and a MAPE of 38.46%. Water spinach is forecasted to yield 2069.75 quintals, with an α (alpha) parameter of 0.5 and a MAPE of 18.14%. Cucumber is projected to yield 1023.22432 quintals, with an α (alpha) parameter of 0.4 and a MAPE of 31.51%. Consequently, the highest projected yield is for water spinach at 2069,75 quintals.

The Comparison of Performance Double Exponential Smoothing and Double Moving Average Methods in Seasonal Meat Stock Demand

The demand for beef supplies depended on seasonal patterns because it depends on feed supplies, especially in rural areas that still rely on natural feed. Beef supplies were part of government regulations because they were highly demanded commodities. They are livestock products that contain nutritional value to meet the protein needs of the community. Beef stocks were influenced by factors such as beef production, beef consumption, and people's income levels. In anticipating the increasing demand for beef, it is necessary to carry out forecasting to estimate the demand for meat in the future. In forecasting, various methods were used to choose the method with the lowest error rate. This research will compare the Double Exponential Smoothing (DES) with the Double Moving Average (DMA) based on the Mean Absolute Percentage Error (MAPE) measurement, one method for finding the minor error value. Based on the test results with beef supplies in Madura and conducting analysis, it can be concluded that the method with the smallest MAPE value is the Double Exponential Smoothing method, with the smallest MAPE value of 9.50% at an alpha parameter of 0.5. In testing with the Double Moving Average method, by determining the best MAPE value, the best time order in the Double Moving Average method is at time order parameter 2 with a MAPE value of 29.8408%. After finding the parameter with the smallest MAPE value, that parameter is used for data testing. In the measurement, data testing for data of 1 year, two years, three years, and four years. Each method has a level of error value that increases the same; a lot of data entered can affect the size of the MAPE value. So, the more data entered, the smaller the resulting error value.

The Role of Surface Roughening in Improving the Selectivity of Copper for CO2 Electroreduction

Electroreduction of CO2 (eCO2RR) has emerged as a promising alternative for production of renewable fuels and important commodity chemicals such as ethylene and ethanol. Simultaneously, it opens an economically attractive path to mitigate point sources of CO2, a potent greenhouse gas. Despite decades of research efforts, Cu remains the only catalyst capable of producing significant amounts of desirable C2+ products, containing more than one carbon atom. However, further improvement is needed for industrial applications. It is known that roughened copper-based electrocatalysts surfaces exhibit enhanced selectivity towards C2+ products during electroreduction of CO2. This poses a challenge for modeling efforts aiming to find a rationale for the enhanced selectivity. In this work, we use an innovative modeling approach to investigate roughened copper surfaces derived from cuprous oxide on structures of 100x100 nm2 size with atomic resolution and accuracy compared to density functional theory (DFT). We combine semilocal DFT (RPBE) in VASP, effective medium theory (EMT), and linear scaling relationships via the recently proposed alpha parameter scheme to investigate the site-resolved catalytic selectivity of copper sites. Electrode potential-dependence is included via the use of the VASPsol implicit solvation and a grand canonical potential approach. The approach allows us to investigate the effect of varied macroscopic roughness on the intrinsic catalytic activity of copper by comparing surfaces exposed to simulated sputtering and electropolishing, respectively. The study highlights i) the challenge for designing improved copper catalysts for CO2RR with monodisperse active site selectivity, ii) the need for careful experimentation when comparing to theory, and iii) the limitations of macroscopic roughness in controlling atomic scale activity.

PENERAPAN DOUBLE EXPONENTIAL SMOOTHING UNTUK MEMPREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU

This research aims to find out or predict the number of new students who will register for admission at the UNTRIB Kalabahi campus in 2024. The data collection technique used in this research is secondary data obtained from BAAK (Student Academic Administration Bureau) UNTRIB Kalabahi from 2007 until 2023 there are 17 data. The method used in this research is the Double Exponential Smoothing method from Brown to analyze the number of new students in 2024 based on new student data in 2007-2023 by testing using 9 alpha value parameters, namely 0.1 to 0.9 by looking at the Mean value The smallest Absolute Percentage Error (MAPE). The research results show that the smallest MAPE value is 6.35% with an alpha parameter value of 0.5. The estimated number of new student admissions in the 2024/2025 academic year is 590 people

The behaviour of eccentric sub-pc massive black hole binaries embedded in massive discs

Using the 3D smoothed-particle hydrodynamics code PHANTOM, we investigated the evolution of the orbital properties of massive black hole binaries embedded in massive discs where gravitational instabilities (GIs) triggered by the disc’s self-gravity are the only significant source of angular momentum transport. In particular, we investigated the evolution of binaries with different initial eccentricities of e0 = 0.05, 0.5, 0.8 and mass ratios of q = 0.1, 0.3, 0.9. Previous studies indicate an equilibrium eccentricity of around 0.6 < e < 0.8, where the binary tidal torques and disc self-gravity torque are in dynamical balance. Our simulations suggest that there might not be a universal value of critical eccentricity. We find that binaries that are initially more eccentric attain higher asymptotic eccentricity than more circular ones do. This implies that there is a range of critical eccentricity values that depend on the initial condition and microphysics (initial eccentricity, mass ratio, cooling) of the system. In particular, we find the width of this range to be narrower for more unequal binaries. We furthermore measured preferential accretion on one of the binary components, only finding more accretion onto the primary for the mass ratio q = 0.3 and eccentricity e = 0.8. We discuss how this might have implications for the amplitude of the gravitational wave background detected by pulsar timing array (PTA) experiments. We finally measure the corresponding value of the viscosity parameter α due to GIs in our simulations and discuss how this depends on the binary properties.

A Compartmental Approach to Modeling the Measles Disease: A Fractional Order Optimal Control Model

Measles is the most infectious disease with a high basic reproduction number (R0). For measles, it is reported that R0 lies between 12 and 18 in an endemic situation. In this paper, a fractional order mathematical model for measles disease is proposed to identify the dynamics of disease transmission following a declining memory process. In the proposed model, a fractional order differential operator is used to justify the effect and success rate of vaccination. The total population of the model is subdivided into five sub-compartments: susceptible (S), exposed (E), infected (I), vaccinated (V), and recovered (R). Here, we consider the first dose of measles vaccination and convert the model to a controlled system. Finally, we transform the control-induced model to an optimal control model using control theory. Both models are analyzed to find the stability of the system, the basic reproduction number, the optimal control input, and the adjoint equations with the boundary conditions. Also, the numerical simulation of the model is presented along with using the analytical findings. We also verify the effective role of the fractional order parameter alpha on the model dynamics and changes in the dynamical behavior of the model with R0=1.

Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student’s t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies.

Optimization of Double Exponential Smoothing Using Particle Swarm Optimization Algorithm in Electricity Load

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.

A two Identical Unit Cold Standby System with two Repairmen and Correlated Failure and Repair Times

The paper deals with the stochastic analysis of a two identical unit cold standby system model. As soon as a unit fails it is attended first by assistant repairman, who completes its repair to make it as good as new with fixed known probability p. Otherwise, with probability q( =1-p), the unit repaired by assistant repairman goes to expert repairman so as to make it as good as new. Both the repairmen are always available with the system and they work their jobs simultaneously. The failure time and repair time by assistant repairman are correlated random variables having their joint distribution as Bivariate exponential. Various measures of system effectiveness useful to system designers and operation managers are obtained by using regenerative point technique. The MTSF and steady state profit have also been studied through graphs in respect of parameter alpha for three different values of repair rate by expert repairman and two different values of correlation coefficient between failure and repair times and important conclusions are drawn.. KEYWORDS :Bivariate exponential distribution, Mean time to system failure (MTSF), Regenerative point, Laplace transform.

Enhancing convergence and accuracy: A comparative study of preconditioning for steady, laminar, and high gradient flows over a wide range of Mach numbers

This paper introduces a novel platform that integrates three preconditioning matrices based on conservative variables: the Turkel, Choi–Merkle and Onur matrices. The platform aims to compare these matrices in terms of accuracy and robustness by investigating their performance in solving three distinct and challenging high-gradient laminar flow problems: (i) Bi-Plane NACA0012 airfoil, (ii) lid-driven flow in a square cavity and (iii) flow in a planar T-junction. These problems serve as new and challenging test cases to accurately determine the abilities of the preconditioning matrices. The preconditioning matrices are applied to evaluate the numerical solutions, and their performance in complex flow fields is assessed in terms of accuracy and efficiency. By solving these flow problems, the effectiveness of the preconditioning matrices is thoroughly analyzed. By integrating these preconditioning matrices into a single platform, this paper significantly contributes to the field. The approach enables a direct and meaningful comparison of the performance of the Turkel, Choi–Merkle and Onur matrices in solving these new and challenging laminar flow problems. While all three matrices demonstrate comparable accuracy in predicting flow characteristics like pressure coefficients, Turkel’s method shows superior convergence acceleration across the almost test cases due to alpha parameter and modifications of the momentum equations. In the external flow case, Turkel converges 22–53% faster than the other matrices by adjusting momentum terms with an alpha parameter. For the internal lid-driven cavity case, Turkel again accelerates convergence up to 38% over Choi–Merkle as Reynolds and Mach numbers increase. However, Onur’s method stalls at high Reynolds/Mach numbers. At low values of Reynolds and Mach numbers, Onur reduces computational cost by 17%. Finally, for the T-junction case, Turkel and Choi–Merkle perform almost identically, decreasing CPU time by 55% vs Onur, which lacks convergence robustness. While the preconditioning matrices have similar accuracy, Turkel offers the best convergence improvement by accounting for momentum effects. Onur works well at low Reynolds/Mach numbers but shows limitations at higher values. The selection of the optimal preconditioning matrix should be based on whether momentum or viscosity dominates in the flow field, as well as the intensity of the viscosity gradient rate.

Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms

The purpose of this research was to improve the accuracy of stock price prediction by implementing optimization algorithms on forecasting methods, in this case, the exponential smoothing method. This research implemented the Particle Swarm Optimization (PSO) and Bat Algorithm metaheuristic optimization algorithms to determine the single-exponential smoothing method’s smoothing parameters. Before implementing the optimization algorithm, the way to determine the smoothing parameters was by trial-and-error method, which is considered less effective. Therefore, the novelty of this research is tuning the parameters of the exponential smoothing method using a comparison of two metaheuristic algorithms, namely the particle swarm optimization algorithm compared to the bat algorithm. The Single Exponential Smoothing method with PSO and Bat algorithms was proven to improve accuracy. The alpha parameter found by the PSO algorithm is 0.9346, and the bat algorithm is 0.936465. With a MAPE of 1.0311%, it was better than the MAPE generated in the Single Exponential smoothing method by trial and error of 1.0316%. This research contributes to providing insight that in a highly sensitive stock prediction situation, metaheuristic algorithms can be used to create more accurate and efficient prediction results.

A constitutive model considering creep damage of wood

AbstractThe serviceability of wooden structures involves multiphysical phenomena, notably the interactions among creep, plasticity, and damage. The influence of creep on the initialization of the damage and on its growth and spread can be adjusted by an additional alpha parameter in order to take into account the coupled effect between creep and damage more properly. We integrate an orthotropic viscoelastic model, based on the generalized Kelvin chain, with an orthotropic damage model, capturing both the immediate nonlinear elastic–plastic–damage response and the time-dependent viscous response of timber. The combination of these material models is important to obtain a realistic description of wood behavior, because the timber shows an immediate nonlinear elastic–plastic–damage response, but also the time-dependent viscous response. In this paper, we algorithmize, implement, and validate the concept of ‘creep damage’, a phenomenon observed in wooden structures. Benchmark tests reveal two distinct patterns of damage in beech wood, immediate postload damage that evolves over time and damage that occurs and spreads during the loading period.

VEHICLE VOLUME FORECASTING SYSTEM ON TOLL ROADS USING DOUBLE MOVING AVERAGE AND DOUBLE EXPONENTIAL SMOOTHING METHODS

Forecasting or forecasting has been used as one of the considerations, especially in the field of business and economics. This is done so that company losses can be minimized and company profits can be maximized. Therefore we need a system that can make it easier to get accurate forecasting results. However, in real cases procurement of IT consultants requires quite expensive costs. So the research provides a solution to design a website-based vehicle volume forecasting system on toll roads conducted at PT. Jasamarga Pandaan Tol. This system is expected to facilitate the Traffic Collection team in projecting the volume of vehicles on toll roads in the future. The method used in forecasting vehicle volume is the Double Moving Average and Double Exponential Smoothing methods. The Double Moving Average method is a time series method where the results of this method are influenced by the order or time period. The Exponential Smoothing method is a time series method where the results of this method are influenced by the alpha parameter. The results of these two methods will be compared based on the accuracy of the data so that the user can easily see the results of forecasting vehicle volume in the future. Mean Absolute Percentage Error (MAPE) is the absolute (absolute) average percentage error. Definition of Mean Absolute Percentage Error is a statistical measurement of the accuracy of estimates (predictions) in forecasting methods. The results of the accuracy of applying the Double Moving Average method give an average MAPE value of 30,124% and the Double Exponential Smoothing method gives an average MAPE value of 5,368%.

Prediksi Ekspor Migas Indonesia Dengan Double Exponential Smoothing

In Indonesia, there are several forms of exports, one of which is oil and gas exports. The Double Exponential Smoothing method is a forecasting method that can be used to forecast oil and gas export data. This method uses two smoothing parameters, namely the average smoothing parameter alpha (α), the trend smoothing parameter beta (β). In this study, the population used is data on oil and gas exports in Indonesia. And the sample used is Indonesia's oil and gas export data from January 2020 to October 2021, we get 22 data sourced from the Central Statistics Agency (BPS). The Double Exponential Smoothing method is used because the data to be processed has a trend, both an up and a down trend and gas exports in Indonesia have an up and down pattern. The Double Exponential Smoothing method can also predict the prediction of oil and gas exports in Indonesia in the next months, November 2021 to February 2022. To process the data, the researcher used the Holt Double Exponential Smoothing method .

Alpha Logarithm Transformed Half Logistic Distribution: Properties and Applications

Abstract: In this paper, we introduce a new two parameter Alpha Logarithm Transformed Half Logistic (ALTHL) distribution. A new method has been proposed to introduce an extra parameter to a family of distributions for more flexibility .The proposed family may be named as Alpha Logarithm Transformed Half Logistic. This can be obtained via reparameterizing the exponentiated Kumaraswamy G-logarithmic family and the alpha logarithmic family of distributions. The recommended distribution reveals increasing, decreasing and bath-shaped probability Density, Distribution and Hazard rate function and Survival function. Some distributional properties of new model are investigated which include the Density Function, Distribution Function (DF), Quantile Function(QF), Moments, Moment Generating Function (MGF), Cumulative Generating Function (CGF).

Analysis of neutron monitor count rates and timing distributions from latitude surveys

Neutron monitors continuously record the hadronic part of secondary atmospheric radiation on the ground, which originates from primary cosmic rays. In Thailand, we developed a mobile neutron monitor housed inside a standard-size shipping container named “Changvan.” It contains three neutron-sensitive proportional counters set up in the typical NM64 layout. However, the central counter doesn’t have the lead producer, leading us to refer to it as a “semi-leaded” neutron monitor. We examined cosmic ray spectral variations on two latitude surveys during 2018-2019 and 2019-2020. This work examines the ratio of count rates between leaded and unleaded setups, which shows notable variation based on geomagnetic cutoff rigidity, suggesting a sensitivity to the cosmic ray spectrum. This measurement could be implemented at stationary stations. The unleaded counter, shielded by the reflector with a higher count from nearby lead, may have advantages over a bare one. Furthermore, we explore alternative techniques to identify spectral changes in Galactic cosmic rays using Changvan data. We analyze using time delay histograms to determine the leader fraction (L) of neutrons that are not preceded by another neutron from the same primary cosmic ray. We also examine other parameters, including the alpha (α) parameter and pulse rate (PR), which can be compared with count rates (CR). Our findings indicate that the ratios of L and α are not significantly affected by geomagnetic cutoff rigidity. In contrast, CR and PR exhibit significant dependency and show opposite trends.

Flute Instability Effects for Disk Accretion onto Neutron Stars with Strong Magnetic Fields

The flute instability at the inner edge of a thin diamagnetic accretion disk is analyzed. Themagnetic field configuration model fromAly (1980) is used. We have analyzed a modified dispersion relationfor the flute instability that takes into account the Keplerian disk rotation. We have derived the inner radiusof the accretion disk within our analysis of the flute instability.We show that the inner radius does not differfrom the Alfve´ n radius for spherical accretion to within a dimensionless coefficient, with the proportionalitycoefficient depending only on the turbulence alpha parameter and the relative disk thickness (h/r).

Microsatellite Markers: A Tool to Assess the Genetic Diversity of Yellow Mustard (Sinapis alba L.)

Microsatellite markers were used for the assessment of genetic diversity and genetic structure in a germplasm collection of yellow mustard, Sinapis alba L. The comprehensive collection of genetic resources represented 187 registered varieties, landraces, and breeding materials. Microsatellites generated 44 polymorphic alleles in 15 loci. Eleven of them were medium to highly polymorphic, and the high levels of observed heterozygosity (0.12–0.83) and Nei’s gene diversity index (0.11–0.68) indicated a high level of polymorphism. Based on PCoA and neighbor joining analyses, the genetic resources were divided into two groups. The range of genetic dissimilarity in the analysed collection was in the range of 0.00–1.00. The high level of dissimilarity between the accessions was documented by the high WAM value (33.82%). Bayesian clustering algorithms were performed in the STRUCTURE 2.3.4 software. The number of clusters was estimated at K = 2. The accessions were classified according to Q1/Q2 values. The low average values of the parameters Fst_1 (0.3482), Fst_2 (0.1916), and parameter alpha (0.0602) indicated substantial mating barriers between varieties and reproductive isolation due to the limited exchange of genetic resources between breeders. These results demonstrated the importance of extensive collections of genetic resources for the maintenance of genetic diversity and indicated considerable genetic differentiation among accessions.