Average Square Research Articles

Numerical framework of magnetized thermal Casson nanoliquid flow with time-dependent stretching channel enclosing chemical reaction effect and variable fluid properties: A Particle Swarm Optimization with stability

Nanofluids are a relatively new class of materials that have gained attention in recent years due to their unique physical and thermal properties. They are used as contrast agents in biomedicine, including for magnetic resonance imaging (MRI) and computed tomography (CT). Keeping the above applications in mind, this theoretical study intended to explore the consequence of transient magnetized Casson nanoliquid driven by a permeable bi-direction time-dependent stretching platform positioned inclined subject to nonlinear heat source/sink. The Brownian movement, thermophoretic force, chemical reaction, and variable internal heating impacts are incorporated into the proposed flow problem. The leading constitutive PDEs are diminished into coupled nonlinear self-similar dimensionless ODEs through pertinent non-dimensional quantities. The resulting problems are addressed utilizing “Particle Swarm Optimization (PSO)” and a hybrid shooting methodology inspired by the Runge–Kutta four (RK4) method. The novelty of this work is the investigation of the numerical solution of magnetized Casson nanoliquid enclosing thermal radiation and chemical reaction via PSO along with hybrid shooting technique over time-dependent stretching sheet, which has not been elaborated on to date. The physical appearance of relevant physical factors on different flow phenomena are evaluated via graphs. For the stability purpose of the numerical method Average Square Residue Error (ASRE) and Total Average Square Residue Error (TASRE) are computed. For the validation purpose the present work is associated with the available work and great correlation is found.

Biometric Characteristics of Live Weight in Reindeer Populations of the Eastern Arctic and Subarctic

Purpose: the study of biometric data on live weight and their significance for artificial selection in the population of the northern deer (Rangifertarandus L.) ..Materials and methods. Information was used from annual reports of reindeer herding of the North of the Far East. The methods of variation statistics that are generally accepted in zootechnical research were applied. Extreme (LIM) and average values (M) of the sign, trust boundaries of the general average (M ± t ∙ m), the average square deviation (σ), the coefficient of variation (CV), the boundaries of the extreme class variation (M ± 2σ) are determined. Associations of a live mass of sexual age groups in the population of the northern deer were established by calculating the correlation coefficients (R) and rectilinear regression (B).Results. The consolidated correlation coefficient (RSV), reflecting the level of direct linear connection of the live weight of calves with a mass of uterus and manufacturers, was ≈ 0.7, which relates to the degree of close dependence. Using the regression equation (y = a+bx) calculated the predicted average size of the live weight of young animals in the average live weight of the importance. The data allow you to establish the threshold of selection of tribal animals in order to obtain offspring with a given value of the sign. In the study, the impact on the live mass of the season, year, range, and farms is leveled. It was established that throughout the generations a living mass is a stable phenotypic sign. Biometric data on live weight, as a quantitative attitude, indirectly reflect the state and movement of group genetic information in the populations of deer of the Far North-East of Russia.

SVM Model Against Telecom Card Fraud Using GA Optimised Ten-Fold Cross-Testing

The increased number of payment methods also makes it easier for personal information to be stolen by criminals, and for criminals to take over financial payment accounts and steal money. With trillions of bank card transactions occurring every day, Credit Card Fraud Detection (CCFD) is a serious challenge, so this paper predicts "whether or not fraud occurs" by using six types of machine learning models. For problem 1, firstly, "mean, maximum, minimum, median, variance, standard deviation, quartile" are calculated for each indicator; secondly, data cleaning is carried out, and the data set is found to be free of missing values and outliers. Then the data preprocessing work was carried out, min_max normalisation and z-score standardisation were performed on the data. After that, correlation analysis was carried out, and the first four indicators were classified as negative indicators and the last three as positive indicators according to the characteristics of the indicators themselves. It can be found by calculating the Pearson correlation coefficient value after two data processing. Using the coefficient of variation method to calculate the weight of the seven "influence whether fraud" indicators. Finally, BP neural network model, decision tree model, random forest classification model, ELM model, SVM model, logistic regression model are established. For Problem 2, the four models constructed in Problem 1 are solved; to solve the BP neural network model: the data set is divided into training set and testing set according to the ratio of 6:4, and the sigmod function is used as the activation function. For BP neural network, "output >0.5" is recorded as 1, i.e. fraudulent behaviour; "output <0.5" is recorded as 0, i.e. non-fraudulent behaviour. Adjusting the learning rate and the number of iterations, the optimal average mean square error after optimal gradient descent is smaller. To solve the SVM model, the data set is divided into ten groups using the improved ten-fold cross-test, with one group as the training set and nine groups as the validation set, so as to obtain the model with the highest accuracy and the corresponding training data, and then the genetic algorithm is used to search for the optimisation of the kernel parameters in the SVM model on this basis. To solve the decision tree model, the training set and prediction set are divided into 7:3 and solved, and the number of leaf nodes is optimised. Solve the random forest classification model, divided into training set and prediction set according to 7:3 and solved, for similar accuracy choose the random forest classifier when the decision tree is less.

A Perceptual Machine Model Based Approach to Recommending Online Learning Resources

Abstract The bias values of various learning resources are computed using neuron excitation functions based on the perceptual machine model in this paper. Each learning sample is calculated using the weight vector value of each layer in the learning resources. The difference between the output result of the network and the expected value is calculated and converted into the minimum value of the loss function for solving the normalized processing of the weight matrix of the learning resources. It is found that the average square root error in the online learning resources is 0.0897, the decreasing rate is 35.28% compared with the empirical mixing method, and the bias of the online resource recommendation model is 0.2453, which indicates that the proposed model can learn the mixing weight matrix more quickly and obtain a better mixing analysis field for more accurate and personalized learning resource recommendation.

Platoon dispersion model in the mixed traffic environment

To describe the phenomenon of platoon dispersion in the mixed traffic environment and provide a theoretical basis for signal timing optimization in the mixed traffic environment. Based on Robertson’s platoon dispersion model, this paper fully considers the characteristics of mixed traffic conditions, and models the platoon dispersion in the mixed traffic environment. Firstly, considering the influence of the percentage of non-motor vehicles on the platoon dispersion, the functional relationship between the percentage of non-motor vehicles and the parameters of the platoon dispersion model is derived, and the parameters of the Robertson platoon dispersion model are calibrated. Secondly, through the data collected from Zitai Road in the city of Kunming, the required model parameters are calculated, and the arrival flow rate distribution downstream of the intersection is obtained. Finally, according to the measured data, the proposed model is compared with the classical Robertson model. The results show that compared with the Robertson model, the average prediction mean square error of the proposed model is reduced by 5.89%, which can better describe the dispersion law of the mixed traffic under the influence of different percentage of non-motorized vehicles.

Comparing Black-Scholes and GARCH Models in Long Strangle Option Strategies for LQ45 Index

This study compared the Black-Scholes and GARCH models in a long strangle strategy applied to the LQ45 index using closing price data from 1998 to 2021. It aimed to assess the benefits, calculate returns during crises and non-crisis periods, and evaluate performance through Average Mean Square Error (AMSE). The Black-Scholes model consistently outperformed GARCH in one- and three-month options. One-month options had an average return of 28.64%, and three-month options, 43.31%. In crises, Black-Scholes delivered average profits of 43.36% for one-month and 45.14% for three-month options. In non-crisis conditions, profits averaged 26% for one-month and 42.84% for three-month options. Model performance varied by option type and market context. Black-Scholes excelled in one-month call options (1.268% error), while GARCH performed better in one-month put options (1.0981% error). For three-month options, GARCH outperformed in call options (1.270% error), and Black-Scholes dominated put options (3.117% error). In summary, the choice between models should consider market conditions, favoring GARCH during crises and Black-Scholes in non-crisis scenarios.

A simple solution to overcome large errors due to unknown masked layers in refraction seismics

The issue regarding errors in the depth of a refractor owing to masked or undetected layers in the overburden is a drawback of the seismic refraction method. The solutions available for eliminating these errors require prior knowledge of the masked layers. These solutions are not useful when several unknown masked layers occur, such as in sedimentary basins. The use of the average velocity of the overburden above the refractor has been suggested by early investigators to reduce these errors. However, no quantitative relationship between the average velocity-based depth and the actual depth has been established. Subsequent studies have shown that the average velocity-based depth is invariably lower than the actual depth. Therefore, it can be considered the minimum depth, irrespective of the number, type, velocity, and thickness of the masked layers present in the overburden.This study shows that the minimum depth to a refractor obtained using the average velocity of the multilayer overburden and the intercept time closely approximates the actual depth with a minor (< 10%) error. The maximum limit of error for all practical purposes is −14.6%, but not substantially higher as previously expected. These findings hold fundamental significance. The expression derived for the error in the three-layer case clearly indicates that it varies (a) directly as the square of the difference between the velocities of the top and intermediate layers and (b) inversely as the velocity of the refractor below them. The error also has an inverse relationship with the composite term comprising the sum of the, velocity ratio to the thickness ratio of the intermediate layer to the top layer, as well as its reciprocal. Numerical modeling with synthetic models and a multilayer field example with a hidden layer were used to illustrate these findings. This study highlights the vital role that the average or root mean square velocity can play in refraction seismics.

Disentangling environmental drivers of subarctic dinocyst assemblage compositional change during the Holocene

Abstract. Analysis of compositional changes in fossil organic-walled dinoflagellate cyst (dinocysts) assemblages is a widely used tool for the quantitative reconstruction of past environmental parameters. This approach assumes that the assemblage composition is significantly related to the reconstructed environmental parameter, which requires an independent correlation between the assemblage and the variable, meaning that the variable explains a dimension of the assemblage variance that is not also explained by other parameters. Theoretically, dinocyst assemblages can be used to reconstruct multiple environmental variables. However, there is evidence that primary and subordinate drivers for assemblage compositions regionally differ, and it remains unclear whether a significant relationship to specific parameters in the present ocean always implies that this relationship is significant in fossil assemblages, questioning if past changes in these multiple parameters can be reconstructed simultaneously from fossil assemblages. Here, we analysed a local subset of the Northern Hemisphere dinocyst calibration dataset (n=1968), including samples from the Baffin Bay area (n=421), and evaluated the benefits of a local versus a more regional or global calibration for the environmental reconstruction of Baffin Bay oceanography during the Holocene. We determined the dimensionality of the dinocyst ecological response and identified environmental drivers in the Baffin Bay area for the modern dataset. We analysed four existing Holocene records along a north–south transect in the area and evaluated the statistical significance of downcore reconstructions by applying the local and global datasets with different techniques: the modern analogue technique (MAT), the weighted average partial least square (WA-PLS) and maximum likelihood (ML). We evaluated reconstructions tested as significant in light of the existing state of knowledge about West Greenland's Holocene palaeoceanography. Our analyses imply that present-day and Holocene dinocyst assemblages in the Baffin Bay area are primarily driven by salinity changes; other parameters were less important in driving assemblage compositions, and their contribution differed among the studied records. We show that the objectively occurring and temporally coherent shifts in dinocyst assemblages in the Holocene of Baffin Bay can be robustly interpreted only by transfer functions that are locally calibrated. Transfer functions based on the broad Northern Hemisphere calibration yielded many insignificant environmental reconstructions. At the same time, we show that even in the local calibration, not all parameters that appear to significantly affect dinocyst assemblages in the calibration dataset can be meaningfully reconstructed in the fossil record. A thorough evaluation of the calibration dataset and the significance of downcore applications is necessary to reveal the region-specific information contained in dinocyst assemblage composition.

Особенности геопространственного мониторинга бугров пучения многолетне-мерзлых пород на этапе инженерных изысканий

The features of the permafrost rocks’ heave mounds geospatial monitoring arrangement are revealed. A classification of heave mounds was developed, models of temperature fields for a search and evaluation well were constructed, and formulas for determining the average square error of constructing a complete heave hill model were proposed. In the normative and technical literature, as a note, it is recommended to make changes at passing a corridor of linear communications in the immediate vicinity of the heave mounds with the possibility of changing the distances between earth prisms. An example of the route passage between them is given

End-to-End Deep Learning of Joint Geometric Probabilistic Shaping Using a Channel-Sensitive Autoencoder

In this paper, we propose an innovative channel-sensitive autoencoder (CSAE)-aided end-to-end deep learning (E2EDL) technique for joint geometric probabilistic shaping. The pretrained conditional generative adversarial network (CGAN) is introduced in the CSAE which performs differentiable substitution of the optical fiber channel model under variable input optical power (IOP) levels. This enables the CSAE-aided E2EDL to design optimal joint geometric probabilistic shaping schemes for optical fiber communication systems at varying IOPs. The results of the proposed CSAE-aided E2EDL technique show that for a dual-polarization 64-Gbaud signal with a transmission distance of 5 × 80 km, when the modulation format is a 64-quadrature amplitude modulation (QAM) or a 128-QAM, the maximum generalized mutual information (GMI) level learned via CSAE-aided E2EDL is 5.9826 or 6.8384 bits/symbol under varying IOPs, respectively. In addition, the pretrained CGAN, as a substitution for optical fiber transmission model, accurately characterizes the distortion of signals with different IOPs, with an average bit error ratio (BER) difference of only 1.83%, an average mean square error (MSE) of 0.0041 and an average K-L divergence of 0.0046. In summary, this paper delivers new insights into the application of E2EDL and demonstrates the feasibility of joint geometric probabilistic shaping-based E2EDL for fiber optic communication systems with varying IOPs.

Pattern Recognition of Food Security in Indonesia Using Biclustering Plaid Model

Biclustering come in various algorithms, selecting the most suitable biclustering algorithm can be a challenging task. The performance of algorithms can vary significantly depending on the specific data characteristics. The Plaid model is one of popular biclustering algorithms, has gained recognition for its efficiency and versatility across various applications, including food security. Indonesia deals with complex food security challenges. The nation's unique geographic and socioeconomic diversity demands region-specific food security solutions. Identifying province-specific food security patterns is crucial for effective policymaking and resource allocation, ultimately promoting food sufficiency and stability at the regional level. This study assesses the performance of the Plaid model in identifying food security patterns at the provincial level in Indonesia. To optimize biclusters, we explore various parameter tuning scenarios (the choice of model, the number of layers, and the threshold value for row and column releases). The selection criteria are based on the change ratio of the initial matrix's mean square residue to the mean square residue of the Plaid model, the average mean square residue, and the number of biclusters. The constant column model was selected with a mean square residue change ratio of 0.52, an average mean square plaid model residue of 4.81, and it generates 6 overlapping biclusters. The results show each bicluster has unique characteristics. Notably, Bicluster 1 that consist of 2 provinces, exhibits the lowest food security levels, marked by variables X1, X2, X4, and X7. Furthermore, the variables X1, X4, and X7 consistently appear across several biclusters. This highlights the importance of prioritizing these three variables to improve the food security status of the regions.

A Tremor Suppression and Noise Removal Algorithm for Microscopic Robot-Assisted Cataract Surgery

Ophthalmic surgery consists of a series of precise manipulations performed using a surgical microscope. A surgical robot with a master–slave paradigm is commonly adopted to improve the precision and guidance intelligence for ophthalmic surgery; however, the surgeon's hand tremor, misoperation, inherent characteristics of the robot, and environmental interference may cause tremors of the end-effector of the surgical manipulator, thus increasing the risk of soft tissue damage and poor prognosis for the patient. A symmetrical threshold noise reduction combiner (STIC) is proposed to suppress hand tremors and reduce environmental noise interference. Additionally, the inverse kinematic model of the eight-degrees-of-freedom surgical robot for cataract surgery is developed, with the constraints of continuous circular capsulorhexis manipulation. Simulation results show that the signal-to-noise ratio of the STIC is increased from 35.3907 to 57.0158 dB, whereas the maximum, average, standard deviation, and root mean square of the output of the STIC are decreased by 64.79%, 77.24%, 74.50%, and 75.86%, respectively. Three sets of in vitro experimental results show that these four indicators of the STIC performance are reduced by 59.73%, 76.57%, 57.23%, and 65.02%, respectively. These results validate the feasibility and effectiveness of the proposed method on hand tremor suppression and inherent high-frequency disturbance elimination, thereby enhancing the accuracy of the microsurgical operation.

COSMO-RS solute partition ratios for solvent mixtures of unknown composition: Henry's law constants as descriptors for mixture sigma profiles

Henry's law constants (H) for selected probe molecules have been used as descriptors to estimate the COSMO-RS sigma profiles of solvents and solvent mixtures. Henry's law constants were calculated with COSMOtherm for small sets of probe molecules in 155 organic solvents (training set), and these constants subsequently used as descriptors to model the solvent sigma profiles with 61 multiple linear regression (MLR) equations. Subsequent input into COSMOtherm of weighted basis molecule solvent mixtures whose sigma profiles closely matched those modelled for the training set solvents allowed estimation of air-solvent and water-solvent partition ratios for solutes in solvents and solvent mixtures without input of the solvent or solvent mixture identity. The best performing model had 16 descriptors and gave both a training and test set average root-mean square error (RMSE) of 0.008 and an average relative square error (RSE) of 0.07. Partition ratios (K) were then generated for a test set of 251 additional organic solute molecules in solvent/water media where solvents were test set compounds and H constants for the same probe molecules were used as descriptors. The best performing sigma profile model yielded log K RMSE values ranging from 0.17 to 0.92. Finally, this approach was applied to several mixtures ranging from simple binary mixtures to two mixtures considered to be of unknown or variable composition, complex reaction productions or biological materials (UVCBs), namely gasoline and an essential oil mixture. Mixture/water partition ratios were estimated for 251 solutes giving log K RMSE values ranging from 0.24 to 0.88.

Lagrangian Particle Dispersion in a Poor Man’s Magnetohydrodynamic Turbulence Model

Lagrangian dispersion of fluid particle pairs refers to the study of how individual fluid particles disperse and move in a fluid flow, providing insights to understand transport phenomena in various environments, from laminar to turbulent conditions. Here, we explore this phenomenon in synthetic velocity and magnetic fields generated through a reduced-order model of the magnetohydrodynamic equations, which is able to mimic both a laminar and a turbulent environment. In the case of laminar conditions, we find that the average square distance between particle pairs increases linearly with time, implying a dispersion pattern similar to Brownian motion at all time steps. On the other hand, under turbulent conditions, surprisingly enough we observe a Richardson scaling, indicating a super-ballistic dispersion pattern, which aligns with the expected scaling properties for a turbulent environment. Additionally, our study reveals that the magnetic field plays an organizing role. Lastly, we explore a purely hydrodynamic case without magnetic field effects, showing that, even in a turbulent environment, the behavior remains Brownian-like, highlighting the crucial role of the magnetic field in generating the Richardson scaling observed in our model.

Determinants of housing affordability in the USA

Purpose This study aims to identify the determinants of housing affordability in an effort to inform policy. Design/methodology/approach The authors use econometric analysis to determine variables that impact housing affordability in the USA. Findings The authors find that affordability depends on a number of demographic factors as well as physical characteristics of properties, including average age of homeowner, family size and average dwelling square footage. The authors also find that vacancy rates, increase in house price and median family income also have a significant impact on housing affordability. Additionally, the authors find that households with high-cost burdens are more vulnerable to mortgage rates and property taxes than those with moderate-cost burdens. As a result, changes in economic or policy variables tend to have a disproportionate impact on high-cost-burdened households, and they are more vulnerable to economic and policy shocks. Originality/value To date, the literature has not done a systematic investigation of housing affordability using detailed census data.

The Support Vector Regression Method Performance Analysis in Predicting National Staple Commodity Prices

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.

Liquid Composition Identification and Characteristic Measurement Using Ultrasonic Transmission Technique via Neural Network

This project is to determine the composition of liquids solvent by using the ultrasonic frequency signal from echoscope scan machine. The transmission technique of ultrasonic signal is focused. On the research experiment, studies on mixing of distilled water with control sodium chloride (Kitchen Salt), kitchen sugar and monosodium glutamate (MSG). The Parameters such as Fast Fourier Transform (FFT) which is the parameters are using to identify the ratio of composition of liquid solvent. The feature extraction of median, average and root mean square (RMS) from FFT is represented with different result analysis such as sensitivity, specificity, accuracy, Area under curve, kappa, F-measure and precision. The results performed more than 90% with Neural Network.

Spatial variability of Cu, Zn and Mn contents in Non-anthropized soils in the Santa Catarina state

The aim of this study was to evaluate the spatial variability of Cu, Zn and Mn in non-anthropic soils in the Santa Catarina state, using ordinary kriging and ordinary cokriging models. The Cu, Mn and Zn contents were taken from the database of the Department of Soils and Natural Resources of the State University of Santa Catarina. The geostatistical methods of ordinary kriging and ordinary cokriging were applied to fit the theoretical models to the experimental semivariograms. The model choice with the best fit was based on the parameters of Average Square Root, Average Standard Error, Akaike Criterion and Spatial Dependency Index. For ordinary kriging the Gaussian model (R² 0.998) explains the spatial variability of Cu, while the circular model describes the variations of Mn (R² 0.894) and Zn (R² 0.991). For ordinary cokriging, the exponential model describes the behavior of trace elements versus land elevation. Geostatistical methods are effective in the spatializing of Cu, Mn and Zn contents in non-anthropic soils in the Santa Catarina state. Finally, land elevation does not show a linear relationship with the trace elements levels, which are concentrated in the western region, correlating with the source material.

The Prediction of The Drought Index in The Indragiri Watershed Using SARIMA and SPI Methods

Farmers are concerned about the problem of drought in agriculture because it will disrupt the production system and cause losses. In 2015, the Indragiri Hulu area experienced a drought on 273 hectares of agricultural land. This area is part of the Indragiri River Watershed in Riau Province. To prevent the impact of future droughts, forecasting is required. The level of drought is influenced by rainfall, so the Seasonal Auto Regressive Integrated Moving Average (SARIMA) method is used to model rainfall data. In addition, the Standardized Precipitation Index (SPI) is also used to analyze the drought index. After the analysis, it was found that the best model in forecasting is SARIMA model (1,0,1)(2,1,1)12 with an average residual square of 18.0175, and the drought index in the Indragiri watershed is currently included in the "Normal" category. The results of this forecasting are expected to assist the Riau Province National Disaster Management Agency in anticipating and mitigating the impact of future drought disasters.

CHALLANGES AND PROSPECTS FOR TOMATO PRODUCTIVITY IN RESPONSE TO CLIMATIC VARIATIONS: EVIDNECES FROM KHYBER PAKHTUNKHWA-PAKISTAN

Climate change is the momentous and persisting change in the world’s temperature, precipitation, humidity, and other climatic variables. This study, therefore estimated the impact of climatic variations on tomato productivity across agro ecological zones of Khyber Pakhtunkhwa, Pakistan. Panel data for 28 years (1991-2018) across the six districts of the agro ecological was used due to availability of data on tomato productivity and climatic variables. Yield of tomato, area, maximum temperature and rainfall were included in the final estimated model. The results indicate that the average maximum temperature and average maximum temperature square have a significant impact on tomato yield. Average maximum temperature has positive coefficient while the average maximum temperature square has a negative coefficient. This demonstrates that, at first, the tomato yield increases as the temperature rises. It reaches the maximum at the critical temperature (34.95ºC) but shows a decline once the temperature rises from the critical value.