High Level Of Prediction Accuracy Research Articles

Experimental measurement of thermal conductivity and viscosity of Al2O3-GO (80:20) hybrid and mono nanofluids: A new correlation

This study examines the thermophysical properties of Aluminum oxide (Al2O3) and graphene oxide (GO) based mono and hybrid nanofluid mixture at different volume concentrations for heat transfer application. Firstly, Al2O3 and GO nanoparticles were mixed with base fluid (deionized water) separately at 1.0 % of volume concentration for mono nanofluid preparation. Then, Al2O3-GO hybrid nanofluid (80:20) was prepared for volume concentrations of 0.25 %, 0.5 %, 0.75 % and 1.0 %. The stability of both nanofluids was evaluated based on Zeta potential and pH measurements. Meanwhile, the viscosity and thermal conductivity were investigated for temperatures starting from 30 °C to 50 °C. The experimental thermal conductivity and viscosity measurements were correlated using the analytical regression method to predict the thermal conductivity and dynamic viscosity of Al2O3-GO hybrid nanofluid. The maximum thermal conductivity improvement of hybrid Al2O3-GO nanofluid (1 %) was about 4.30 % and 4.34 % higher than Al2O3 and GO mono nanofluid, respectively. In contrast, the viscosity of 1 % Al2O3-GO hybrid nanofluid showed the least reduction of 4.6 %, which is 4.1 % and 6.6 % less than both Al2O3 and GO mono nanofluids. Compared to experimental values, the new model resulted in a high level of predictive accuracy for both thermal conductivity and viscosity, with a maximum error of 6 % and 4 %.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Transfer Learning and DAE-LSTM

Predicting the Remaining Useful Life (RUL) of lithium-ion batteries is one of the core tasks in battery health management. This study aims to enhance the accuracy of RUL prediction by proposing a battery RUL prediction method that integrates source domain battery iteration transfer with Long Short-Term Memory (LSTM) neural networks. Firstly, multiple batteries with known degradation trends are utilized as source domain batteries, and combined with full-lifetime capacity degradation data to construct the Source Domain Battery Iterations Module (SIM) to obtain the optimal LSTM pre-training model. Secondly, the pre-trained model is transferred to the target domain and fine-tuned using the target domain training set. Finally, the fine-tuned LSTM pre-training model is applied to the task of predicting the capacity of target domain batteries, thereby completing the RUL prediction. The effectiveness of the algorithm is validated on three open-source datasets. Experimental results demonstrate that in scenarios where the source domain and target domain battery types are the same, the absolute error of RUL prediction is less than 2 cycles. Moreover, in cases where the battery types differ, except for the 20% prediction starting point, the RUL prediction error is less than 10 cycles, indicating a high level of prediction accuracy.

A dual-scale hybrid prediction model for UAV demand power: Based on VMD and SSA optimization algorithm

The prediction of power demand for unmanned aerial vehicles (UAV) is an essential basis to ensure the rational distribution of the energy system and stable economic flight. In order to accurately predict the demand power of oil-electric hybrid UAV, a method based on variational mode decomposition (VMD) and Sparrow Search Algorithm (SSA) is proposed to optimize the hybrid prediction model composed of long-short term memory (LSTM) and Least Squares Support Vector Machine (LSSVM). Firstly, perform VMD decomposition on the raw demand power data and use the sample entropy method to classify the feature-distinct mode components into high-frequency and low-frequency categories. Then, each modality component was separately input into the mixed model for rolling prediction. The LSSVM model and LSTM model were used to process low-frequency and high-frequency components, respectively. Finally, the predicted values for each modal component are linearly combined to obtain the final predicted value for power demand. Compared with the current models, the prediction model constructed in this paper stands out for its superior ability to track the changing trends of power demand and achieve the highest level of prediction accuracy.

Evaluation of the strain response of FRP partially confined concrete using FEM and DIC testing

PurposeThis study aims to enhance the understanding of fiber-reinforced polymer (FRP) applications in partially confined concrete, with a specific focus on improving economic value and load-bearing capacity. The research addresses the need for a more comprehensive analysis of non-uniform vertical strain responses and precise stress–strain models for FRP partially confined concrete.Design/methodology/approachDIC and strain gauges were employed to gather data during axial compression tests on FRP partially confined concrete specimens. Finite element analysis using ABAQUS was utilized to model partial confinement concrete with various constraint area ratios, ranging from 0 to 1. Experimental findings and simulation results were compared to refine and validate the stress–strain model.FindingsThe experimental results revealed that specimens exhibited strain responses characterized by either hardening or softening in both vertical and horizontal directions. The finite element analysis accurately reflected the relationship between surface constraint forces and axial strains in the x, y and z axes under different constraint area ratios. A proposed stress–strain model demonstrated high predictive accuracy for FRP partially confined concrete columns.Practical implicationsThe stress–strain curves of partially confined concrete, based on Teng's foundation model for fully confined stress–strain behavior, exhibit a high level of predictive accuracy. These findings enhance the understanding of the mechanical behavior of partially confined concrete specimens, which is crucial for designing and assessing FRP confined concrete structures.Originality/valueThis research introduces innovative insights into the superior convenience and efficiency of partial wrapping strategies in the rehabilitation of beam-column joints, surpassing traditional full confinement methods. The study contributes methodological innovation by refining stress–strain models specifically for partially confined concrete, addressing the limitations of existing models. The combination of experimental and simulated assessments using DIC and FEM technologies provides robust empirical evidence, advancing the understanding and optimization of FRP-concrete structure performance. This work holds significance for the broader field of concrete structure reinforcement.

An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis

The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov–Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of 97.72%pm 1.57, a sensitivity of 97.76%pm 4.08, and a specificity of 98.87%pm 2.09, indicating a high level of prediction accuracy.

Research on a Multi-Objective Optimization Design for the Durability of High-Performance Fiber-Reinforced Concrete Based on a Hybrid Algorithm

To achieve durable high-performance fiber-reinforced concrete that meets economic requirements, this paper introduces a hybrid intelligent framework based on the Latin hypercube experimental design, response surface methodology (RSM), and the NSGA-III algorithm for optimizing the mix design of high-performance fiber-reinforced concrete. The developed framework allows for the prediction of concrete performance and obtains a series of Pareto optimal solutions through multi-objective optimization, ultimately identifying the best mix proportion. The decision variables in this optimization are the proportions of various materials in the concrete mix, with concrete’s frost resistance, chloride ion permeability resistance, and cost as the objectives. The feasibility of this framework was subsequently validated. The results indicate the following: (1) The RSM model exhibits a high level of predictive accuracy, with coefficient of determination (R-squared) values of 0.9657 for concrete frost resistance and 0.9803 for chloride ion permeability resistance. The RSM model can be employed to construct the fitness function for the optimization algorithm, enhancing the efficiency of multi-objective optimization. (2) The NSGA-III algorithm effectively balances durability and cost considerations to determine the optimal mix proportion for the concrete. After multi-objective optimization, the chloride ion permeability resistance and frost resistance of the high-performance fiber-reinforced concrete improved by 38.1% and 6.45%, respectively, compared to the experimental averages, while the cost decreased by 2.53%. The multi-objective optimization method proposed in this paper can be applied to mix design for practical engineering projects, improving the efficiency of concrete mix design.

Evaluating the Effectiveness of Proportional Nodes Method in Curve Fitting for Surfaces: Application to Data of Dynamic Viscosity of Ammonia-Water Solution

This study used the proportional nodes method, a novel curve fitting approach to correlate data for dynamic viscosity of ammonia-water solution. The approach integrates polynomial equations, generated at various temperatures, with those calculated at selected mole fraction nodes. These nodes are scaling factors that account for variations in dynamic viscosity at different temperatures at selected mole fractions. The accuracy of the polynomial equations ensures a high degree of fitting accuracy. The proportional nodes, computed systematically, were integrated into a robust and highly accurate polynomial model to generate correlations that fit the data for the surface. This model exhibited minimal average percentage differences between predicted and actual viscosity values (±0.2614293 for temperature range, 273.15K to 303.15K and ±1.11 for temperature range, 303.15K to 423.15 K), indicating a high level of predictive accuracy. The proportional nodes method offers a significant contribution to both academic research and industry. It provides a more precise and adaptive model for predicting the dynamic viscosity of ammonia-water solution, which is critical for optimizing and designing various industrial applications, including refrigeration systems.

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.

Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning

As agricultural land expansion is the primary driver of deforestation, agroforestry could be an optimal land use strategy for climate change mitigation and reducing pressure on forests. Agroforestry is a promising method for carbon sequestration. With recent advancements in geospatial and data science technology, the ability to predict aboveground biomass (AGB) and assess ecosystem services in agroforestry is rapidly expanding. This study was conducted in the Belpada Block of Balangir, Odisha, a forest-dominated region of eastern India. We recorded species occurrence and measured plant parameters, including Circumference at Breast Height (CBH), height, and geolocation, in 196 plots (0.09 ha) in agroforestry intervention sites and noted the tree species. This study used Sentinel-1 and Sentinel-2 multi sensor data to achieve data synergy in AGB estimation. Three machine learning models were used: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The RF model exhibited the highest level of prediction accuracy (R2 = 0.69 and RMSE = 17.07 Mg/ha), followed by the ANN model (R2 = 0.63 and RMSE = 19.35 Mg/ha), SVM model (R2 = 0.54, RMSE = 21.97 Mg/ha. The spectral vegetation indices that are (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Modified Simple Ratio (MSR), Modified Soil-Adjusted Vegetation Index (MSAVI), Difference Vegetation Index (DVI), and SAR backscatter values, were found important variables for AGB prediction. The findings revealed that agroforestry interventions and plantations resulted in an average carbon stock increase of 15 Mg/ha over five years in the study area. The Plant Value Index (PVI), which indicates the importance of species in the local economy and biomass carbon storage, showed that Tectona grandis was the dominant species with the highest PVI value (88.35), followed by Eucalyptus globulus (56.87), Mangifera indica (53.75), and Azadirachta indica (15.45). This approach enables the expansion of monitoring efforts to assess carbon stock in agroforestry systems, thereby promoting effective management strategies.

Habitat suitability assessment for Saunders's Gull (Saundersilarus saundersi) in the Yellow River Delta, China

The Yellow River Delta (YRD) is a key breeding place for Saunders's Gull (Saundersilarus saundersi), one of the vulnerable birds in the world. Thus, a thorough understanding of the key environmental factors influencing its suitable habitat holds great value for its conservation. Previous researches focused on Saunders's Gull's population changes and habitat features of breeding places in the nature reserve. However, its habitat suitability in the whole YRD has not been studied systematically, hindering the formulation of macro-protection policies to a certain extent. On the basis of occurrence records and environmental variables, we constructed an optimized MaxEnt model using the ‘kuenm’ R package to investigate the potential distribution of suitable habitat for the Saunders's Gull in the YRD. Results showed that the ideal hyperparameters for the MaxEnt model were a feature combination of linear and quadratic terms, and the regularization multiplier of 0.6 after optimization, indicated a high level of prediction accuracy. The environmental factors that had a significant effect on the Saunders's Gull's distribution were land use and land cover, percentage of beach saline–alkali land (PLAND_55) and distance to coastline (D_coastline). The highly suitable habitat had an area of 258.56 km2, of which 66.63% was located within the YRD National Nature Reserve. The management of constructed wetlands should be combined with the protection of natural wetlands to avoid the disadvantage of ‘isolated island-type’ protection. The study provides insights into the quantitative relationship between waterfowl and their habitat, providing support for the sustainable development of the YRD, with certain practical significance.

PSVII-3 The Impact of Genotyping Strategies on Accuracy of Genomic Prediction in Sheep Populations: Preliminary Results of a Simulation Study

Abstract Genomic predictions provide more accurate estimated breeding values (EBV) in younger animals, enabling greater rates of genetic gains over time. However, genomic selection in U.S. sheep is still incipient due to the higher genotyping cost relative to animal cost. Our objective was to compare genotyping strategies differing in the proportion of animals genotyped and the extent of pedigree error on prediction accuracy. These genetic evaluations were based on single-step Genomic Best Linear Unbiased Prediction. We used the AlphaSimR package to simulate a composite sheep population mimicking the Katahdin breeds’ formation. We then simulated four further generations based on the structure of Katahdin flocks engaged in the National Sheep Improvement Program. Thirty-five flocks were simulated with an effective population size of 170. Flock size ranged from 31 to 680 animals and, by design, was skewed towards smaller flocks. Average ram to ewe ratio, mortality rate per generation, and number of lambs born per ewe lambing was 1:16, 10%, and 1.8 lambs, respectively. We simulated a 50k SNP panel for a moderately heritable (0.30) trait. Flocks had low genetic connectedness. Selection criteria differed among flocks: 3 selected randomly, 10 selected based on highest EBV, and 22 selected based on highest phenotypes. All combinations of 5, 40, or 100% of males or females genotyped, 5 or 20% of animals missing pedigree, and 5 or 20% of sires misidentified were compared. We also considered a scenario with no pedigree errors (i.e., no missing pedigree or misidentified sires). Animals with the best phenotype were chosen for genotyping. The first three generations were used as the reference population (7,317 animals) to validate the 4th generation (2,055 animals). Each scenario was replicated 5 times. A linear model was used to estimate differences in prediction accuracy between scenarios, tested with Tukey’s test (alpha 0.05). For the scenario without pedigree errors, prediction accuracy ranged from 0.50 ± 0.06 for 5% of males and females genotyped to 0.75 ± 0.03 for 100% of animals genotyped (Figure 1). For scenarios with 5% pedigree errors, prediction accuracy ranged from 0.33 ± 0.02 for 5% of males and females genotyped to 0.71 ± 0.02 for 100% of animals genotyped. For the scenario with 20% pedigree errors, prediction accuracy ranged from 0.26 ± 0.06 for 5% of males and females genotyped to 0.67 ± 0.03 for 100% of animals genotyped. Across pedigree error scenarios, trends were similar. Importantly, genotyping 5% of one sex and 100% of the other resulted in similar accuracies to genotyping 100% of animals. In scenarios with few animals genotyped, accuracies were higher when errors in pedigree were avoided. These preliminary results reinforce the importance of genomics in correcting pedigree errors, with high levels of prediction accuracy when at least most animals of one sex are genotyped.

Application of tree-based methods in predicting the surface settlement arising from the tunnel excavation with large mix-shield

Surface settlement due to tunnel excavation is affected by several factors. However, no explicit mapping correlation exists between surface settlement and the main influencing factors. In this study, three tree-based methodologies, including classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBRT), were implemented to predict the tunneling-induced surface settlement of the South Hong-Mei Road tunnel in Shanghai, where a large mix-shield was used. Thirteen influencing factors within three categories (tunnel geometry, geological conditions, and shield operation factors) were employed as input variables. Results show that the ensemble methods (RF and GBDT) provide superior performance over the single-tree model (CART). Moreover, GBDT has the highest level of prediction accuracy among the three statistical learning methods. The importance of influencing factors on the tunneling-induced surface settlement was probed. The tunnel geometry had the greatest effect on surface settlement. It is followed by the influencing factors in shield operation factors. Moreover, geological conditions were not as influential as the other influencing factors. The outcomes of this study may provide a reference for evaluating tunneling-induced surface settlement in other similar tunnel projects.

Prediction of High-Temperature Creep Life of Austenitic Heat-Resistant Steels Based on Data Fusion

The creep life prediction of austenitic heat-resistant steel is necessary to guarantee the safe operation of the high-temperature components in thermal power plants. This work presents a machine learning model that can be applied to predict the creep life of austenitic steels, offering a novel method and approach for such predictions. In this paper, creep life data from six typical austenitic heat-resistant steels are used to predict their creep life using various machine learning models. Moreover, the dissimilarities between the machine learning model and the conventional lifetime prediction method are compared. Finally, the influence of different input characteristics on creep life is discussed. The results demonstrate that the prediction accuracy of machine learning depends on both the model and the dataset used. The Gaussian model based on the second dataset achieves the highest level of prediction accuracy. Additionally, the accuracy and the generalization ability of the machine learning model prediction are significantly better than those of the traditional model. Lastly, the effect of the input characteristics on creep life is generally consistent with experimental observations and theoretical analyses.

Predicting health related deprivation using loyalty card digital footprints

Predicting health related deprivation using loyalty card digital footprints

Prognostic Factors and a Predictive Nomogram of Cancer-Specific Survival of Epithelial Ovarian Cancer Patients with Pelvic Exenteration Treatment.

The aim of this study was to explore prognostic factors, develop and internally validate a prognostic nomogram model, and predict the cancer-specific survival (CCS) of epithelial ovarian cancer (EOC) patients with pelvic exenteration (PE) treatment. A total of 454 EOC patients from the Surveillance, Epidemiology, and End Results (SEER) database were collected according to the inclusion criteria and randomly divided into the training (n = 317) and validation (n = 137) cohorts. Prognostic factors of EOC patients with PE treatment were explored by univariate and multivariate stepwise Cox regression analyses. A predictive nomogram was constructed based on selected risk factors. The predictive power of the constructed nomogram was assessed by the time-dependent receiver operating characteristic (ROC) curve. Kaplan-Meier (KM) curve stratified by patients' nomoscore was also plotted to assess the risk stratification of the established nomogram. In internal validation, the C index, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical utility of the models, respectively. In the training cohort, age, histological type, Federation of Gynecology and Obstetrics (FIGO) stage, number of examined lymph nodes, and number of positive lymph nodes were found to be independent prognostic factors of postoperative CSS. A practical nomogram model of EOC patients with PE treatment was constructed based on these selected risk factors. Time-dependent ROC curves and KM curves showed the superior predictive capability and excellent clinical stratification of the nomogram in both training and validation cohorts. In the internal validation, the C index, calibration plots, and DCA in the training and validation cohorts confirmed that the nomogram presents a high level of prediction accuracy and clinical applicability. Our nomogram exhibited satisfactory survival prediction and prognostic discrimination. It is a user-friendly tool with high clinical pragmatism for estimating prognosis and guiding the long-term management of EOC patients with PE treatment.

Machine learning for predicting the outcome of terminal ballistics events

Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of four popular ML-based regression models, extreme gradient boosting (XGBoost), artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GP), on two common terminal ballistics’ problems: (a) predicting the V50 ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments, and (b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness. To achieve this we utilise two datasets, each consisting of approximately 1000 samples, collated from public release sources. We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range. Although extrapolation is not advisable for ML-based regression models, for applications such as lethality/survivability analysis, such capability is required. To circumvent this, we implement expert knowledge and physics-based models via enforced monotonicity, as a Gaussian prior mean, and through a modified loss function. The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models, providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not. The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types, target materials and thicknesses, and impact conditions significantly more diverse than that achievable from any existing analytical approach. Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster. We provide some general guidelines throughout for the development, application, and reporting of ML models in terminal ballistics problems.

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization.

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine

Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy.

Vector Autoregressive (VAR) Method in Analyzing the Effect of Inflation on Food Price Volatility (FPV) in Palopo City, Indonesia

Purpose: This study aimed to analyze the effect of Inflation on food prices in Palopo City, where the inflation rate is calculated based on index numbers collected from several commodities traded at each price level in the market, namely the Consumer Price Index (CPI). Theoretical framework: The Vector Autoregressive (VAR) method is simultaneous equation modeling that has several endogenous variables simultaneously, but each endogenous variable is explained by lags of its own value and other endogenous variables in the model. Forecasting using the VAR method is in some cases better than complex simultaneous equations. Design/methodology/approach: The stationarity test in this study uses the Augmented Dickey-Fuller (ADF) method, followed by determining lag optimal Akaike Information Criteria (AIC) and Schwarz information Criterion (SC) analysis. The stability test of the VAR model was carried out through the Johansen Cointegration Test at a 5% significance level which produced six equations with a trace statistically more significant than the critical value. Then an analysis of Impulse Response Functions (IRF) was carried out to see the dynamic response of the CPI variable to food commodity prices. The data used are time series for January 2018 to December 2020. Findings: The inflation response to food price fluctuations in Palopo City resulted in three conditions. The Palopo City CPI responded positively to the price fluctuations of garlic and red chili. In contrast, shallots and chicken meat price fluctuations responded negatively to the Palopo City CPI. The Palopo City CPI did not permanently affect the price fluctuations of cayenne pepper and chicken eggs. Research, Practical & Social implications: The practical implication of this research is that food commodity prices still have an influence on inflation movements. Local governments should cooperate with city governments in the production of food commodities by implementing cropping patterns on these commodities to overcome excess production, and it is necessary to carry out off-season production to achieve harvest deficits so that prices remain stable. Improvements to the trade system also need to be improved in addition to the production system. This is intended so that the distribution of food commodity products can be more efficient by implementing supply chain management. Originality/value: Collecting time series data of the Consumer Price Index (CPI) for 6 commodities, namely shallots, garlic, chili peppers, cayenne pepper, broiler meat, and broiler eggs, in a modern model, namely a multivariate time series model with a high level of predictive accuracy of the model among these variables to predict and analyze the effects among these series and estimate the interactions among these series with the latest data is the value of this research, and then provide recommendations for the government in making economic strategies, especially for those related to inflation and food prices.

Role clarity, perceived cohesion and felt responsibility as antecedents of altruism and conscientiousness among college teachers in Kerala

PurposeLiterature evidences that altruism and conscientiousness are very important discretionary behaviours within the broader framework of Organizational Citizenship Behaviour (OCB) among teaching community. The present study is intended to examine the effect of role clarity, perceived cohesion and felt responsibility on altruism and conscientiousness among college teachers in Kerala.Design/methodology/approachA questionnaire-based survey was conducted among 354 college teachers, and the causal effect was examined using Partial Least Square-based structural equation modelling.FindingsValidity and reliability of the model were established through measurement model evaluation. Explanatory power of the model was established. Cohesion and felt responsibility significantly predicted altruism, but the effect of role clarity on altruism was not significant. Effect of cohesion, felt responsibility and role clarity on conscientiousness was significant.Originality/valueThe study contributed to the existing theory on antecedents of OCB. The model has high levels of predictive accuracy – role clarity, cohesiveness and felt responsibility – capable of explaining the discretionary behaviour among college teachers.