Coefficient Of Determination Values Research Articles

The influence of entrepreneurship competence to entrepreneurships orientation on beef cattle farmer in Karanganyar regency

This research aims to analyze the influence and relationship of entrepreneurial competence to entrepreneurship orientation of beef cattle business. The research was conducted in Tasikmadu, Karanganyar, Central Java Indonesia at February-April 2021. Methods of data collection using survey method with questionnaire as primary data retrieval tool. The method of determining the respondents using census method on all farmers who attended the training with the number of respondents 42 people. Method of determining the location by purposive sampling. Data analysis used was test of validity and reliability, multiple linear regression analysis continued test of determination (R2), F test, t test and classical assumption test. Validity test results show valid results rhitung> rtabel is 0.304 and reliability value α > 0.6 on all variables. Multiple linear regression analysis obtained value of equation Y = 0.741 + 0.350X1 + 0.246X2 + 0.264X3 + 0.047X4 + 0.011X5 + 0.038X6. The value of determination coefficient (R2) is 0.608, which means that entrepreneurship competence variable can explain the entrepreneurship orientation variable equal to 60.8%. The entrepreneurial competence variable affects together toward the orientation with the value Fcount> Ftable. It can be concluded that entrepreneurial competence consisting of communication, problem solving, initiative, planning and organization, self-awareness and technology simultaneous influence to entrepreneurial orientation.

A simple quantitative method using TLC-image analysis to determine fructooligosaccharides (FOS) in food samples.

The thin-layer chromatography technique (TLC) is a simple and inexpensive analysis commonly used to identify qualitatively the presence of carbohydrates in food samples such as mono- di and oligosaccharides particularly. TLC assay could be improved using image processing software for the semiquantitative determination of this type of compound. In the present work, TLC-image analysis with Silica Gel 60 TLC plates was used for the semiquantitative determination of 6 standards of carbohydrates (glucose, fructose, sucrose, 1-kestose, nystose, and fructofuranosylnystose). Subsequently, the areas of the spots of each compound were determined by digitizing in a conventional office scanner. Then, the segmentation of the images is carried out using software for image processing. The calibration curves were plotted in the Excel software using the average of the areas of the pigmentations obtained in pixels. In this study, the technique of thin-layer chromatography was also used to quantitatively determine the presence of carbohydrates in food samples such as honey, garlic, and onion. Values of determination coefficient (R2) greater than 0.97 in all the calibration curves were obtained. This technique could be useful for detecting carbohydrates (monosaccharides, disaccharides, and oligosaccharides) in analytical assays and food samples without needing specialized analytical equipment. In this work, it was possible to determine the concentration of carbohydrates in samples of garlic and onion that showed the presence of prebiotic carbohydrates in addition to sucrose, glucose, and fructose.

The Influence of Excellence Service, Integrity of Tax Officer, and Electronic Tax Reporting on Tax Revenue of Large Taxpayer Office Two

The research is about The Influence of Excellence Service, Integrity of Tax Officer, and Electronic Tax Reporting on Tax Revenue of Large Taxpayer Office Two. The sample in this research is 150 taxpayers selected from 250 taxpayers registered at Large Taxpayer Office Two. This research is using questionnaire as an instrument of data collection that is related to Excellence Service, Integrity of Tax Officer, and Electronic Tax Reporting to Tax Revenue. Based on the result of this research is found, there is a positive effect of Excellence Service, Integrity of Tax Officer, and Electronic Tax Reporting to Tax Revenue. Through significance test by using F distribution, it is obtained the value of Fcalculate is 55.345. Meanwhile Ftabel is 2.670. So, Fcalculate is 55.345 > Ftabel is 2.670. Therefore, H0 is rejected and H1 is accepted. This case showed that excellence service, integrity of tax officer, and electronic tax reporting simultaneously can improve tax revenue of Large Taxpayer Office Two. The value of Determination Coefficient that is obtained from 0.729. This research showed that 53.2% variable on Tax Revenue is caused by Excellence Service, Integrity of Tax Officer, and Electronic Tax Reporting. Thus, 46.8% remains caused by others.

Investigation of the synergistic effect of nonionic surfactants on emulsion resolution using response surface methodology.

The production of crude oil is always accompanied by water production, which may create severe separation problems. It is important to understand the stabilization mechanism and parameters contributing to the formation of emulsion, specifically the synergy mixing of surfactants. These factors have not been studied primarily in previous studies. The main objective of the current work was to assess the influence of synergy mixing of nonionic surfactants, sorbitan monooleate (hexitol) and polysorbate 80 (glycol), which are mainly affecting the stability of oil-in-water emulsions. Several factors, such as the mixing rate, mixing time, and aging time of the studied emulsions were also investigated. Response surface methodology (RSM), and central composite design (CCD) were employed to design the experiments. Emulsion stability was measured through a static bottle test over a range of time (1-7 days) at a temperature of 60 °C. A model was established with a coefficient of determination value at 0.8814 and the highest emulsion stability achieved was 42.83%. The least water separation was observed at 0.5 v/v% hexitol, 1.5 v/v% glycol, 15 000 rpm mixing rate in 5 minutes, and seven-day ageing time to achieve ∼41.56% emulsion stability. The minimum emulsion stability of ∼25.0% was observed using 0.5 v/v% of sorbitan monooleate and polysorbate 80 at 5000 rpm of mixing rate in 15 min and under seven days of observation. The results also revealed that the mixing time and ageing time do not affect the stability of the prepared emulsions. Hexitol, mixing rate, synergy mixing of nonionic surfactants and polysorbate 80, and mixing speed significantly influence emulsion stability. The R 2 value of 88.14% verified that the model is well-fitted and the optimal values for the input variables were successfully obtained using RSM.

PENGARUH GAYA KEPEMIMPINAN DAN MOTIVASI KERJA TERHADAP KINERJA PEGAWAI BALAI BESAR TEKNOLOGI AERODINAMIKA, AEROELASTIKA DAN AEROAKUSTIKA BPPT INDONESIA

This research was conducted with the aim of knowing the effect of leadership style and work motivation simultaneously on employee performance at the Center for Aerodynamics Technology, Aeroelastic Aeroacoustics (BBTA3) Puspiptek Serpong-South Tangerang. The research method used is descriptive research with quantitative data analysis. The sampling technique used is Saturated Sample with a total sample of 66 respondents at the Center for Aerodynamics, Aeroelastics, Aeroacoustics Technology (BBTA3). Based on the results of data processing, it is known that leadership style affects employee performance with a significant rate value of 0.000 <0.05, then Ha1 is accepted and H01 is rejected. Work motivation has no effect on employee performance with a significant level value of 0.497 > 0.05, then H02 is accepted and Ha2 is rejected. The results of the simultaneous correlation test, leadership style and work motivation with employee performance are 0.678. The coefficient of determination value is 0.459. This means that leadership style and work motivation contribute 45.9%, while the remaining 54.1% is influenced by other variables outside this study.

THE ECONOMIC AND HEALTH FACTOR ON HAPPINESS INDEX OF INDONESIAN CITIZENS

People who live with poverty lack resources to fulfill their basic needs such as food, clothing, education and health care. On average, their monthly expenses would not exceed the standard poverty line. The quality of education and healthcare can be measured by the Mean Years School (MYS) and Public Health Development Index (PHDI). Poverty, MYS and PHDI play a part in the Human Development Index (HDI) which in turn will affect the Happiness Index (HI). The main discussion in this journal is whether poverty, MYS and PHDI has any influence on the Happiness index of the Indonesian people. This study uses secondary data from 33 provinces and the National data of 2018 that was retrieved from Statistics Indonesia, in conjunction with path analysis as the analysis tool of choice. Research results reflect that based on the regression coefficient value (r) the four independent variables have simultaneous relationships with IKb of 0.534 and the determinant coefficient value (R-Square) of 0.286. Though this relationship is not Significant (0.198) to the IKb. Partially, only poverty has a negative relationship that is not Significant (0.038) to IKb with the parameter coefficient value of - 5.273.

Использование зимних и ранневесенних космоснимков для оценки проективного покрытия березняков на залежах

More than 40 million hectares of agricultural land were abandoned after the collapse of the Soviet Union. A significant part of the land is covered by spontaneously regenerating woody and shrubby vegetation. When identifying the forest regeneration, the stands with a tree cover of more than 50% are accurately identified. It is difficult to identify the initial stages of forest regeneration on the abandoned agricultural lands using summer satellite images because of little difference between the young trees and saplings due to their low height and low density on the one hand, and herbaceous vegetation on the other. The purpose of this work was to apply winter and early-spring satellite images for assessments of the tree cover of birch-dominated stands (Betula pen-dula Roth.) formed on the abandoned agricultural lands (See Fig. 1). We used 189 releves of birch forests on the abandoned agricultural lands in the broad-leaved forest zone of the Republic of Bashkortostan. A regression analysis of the evaluation of the tree cover was carried out using the values of the spectral reflectance of the RED, NIR, SWIR11, and SWIR12 bands, as well as the values of the NDFSI snow index from seven cloudless Sentinel-2 images taken between 04.11.2020 and 13.05.2021 (See Fig. 2, 3). When selecting optimal regression models, the values of correlation coefficients (R) and determination coefficients (R2) were used to assess the model quality. To test the possibility of using the obtained models for assessing the tree cover of the stand at earlier succession stages, we involved the data on the tree cover from 36 geobotanical releves, where the crown density of the stand was visually evaluated in July 2013. Then, the described procedure was applied to calculate the tree cover using the Landsat-8 image taken on 25.03.2014. When creating regression models to calculate the tree cover, the best results were obtained using the red band of early spring images during the period when snowpack is still solid (from mid-March to the first half of April) (See Table 1). The correlation between the tree cover and the spectral reflectance of the red band was -0.90. The model allowed us to determine accurately the tree cover of birch forests aged from 18 to 20 years which prevail in the zone of broad-leaved forests in the Republic of Bashkortostan. The accuracy of the model for determining the tree cover according to the obtained regression models for other dates is unstable and highly likely influenced by the snow depth and the seasonal dynamics of changes in the radiation intensity of the red and infrared bands (See Table 2, 3). To conclude, the equations calculated from modern satellite images can be used to assess the tree cover using retrospective images at earlier succession stages of the abandoned field recovery. When using early-spring images, the snow depth should be taken into account since the snowpack melting dates can vary greatly from year to year. The paper contains 3 Figures, 3 Tables, and 41 References. The Authors declare no conflict of interest.

Isotermas de dessorção e calor isostérico do hidrolisado proteico do subproduto de abate de tilápia

Abstract Due to the by-products generated during the processing of meat, biomolecules derived from these by-products, in the form of protein hydrolysates, have been studied for being used as raw materials to produce food. In the present study, the tilapia slaughtering by-products were hydrolyzed, under 60 oC for 2 hours, and spray-dried under 130 oC. After the drying process, equilibrium isotherms of the by-products were obtained through the dynamic method, under temperatures of 20 oC, 35 oC, and 50 oC. All the equilibrium curves presented type III behavior and in the adjustment of the empirical models, the White and Eyring model represented more properly the experimental data of equilibrium. This model presented the highest value of the determination coefficient and lower values of the Sum of the Squares of Residuals (SSR), Relative Mean Error (RME), and Akaike Information Criteria (AIC). The isosteric desorption heat, calculated by the Othmer method, varied from 2395 to 5682 kJ/kg, for equilibrium moisture contents between 0.09 and 0.30 kg/kg. The equation obtained for the calculation of the isosteric desorption heat of the tilapia by-product hydrolysate can be employed in calculations related to the modeling, simulation, optimization, and control of industrial-scale drying processes.

An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross‐validation, and regression ensemble framework. Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. Different machine learning (ML) approaches, including support vector regression (SVR), extreme learning machine (ELM), and multilayer perceptron neural network (MLP), are adopted as reference models. In order to maximize the determination coefficient (R2) value and reduce the root mean square error (RMSE), hyperparameters are set using the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 (ELM + GBR + XGBR‐SVR) and STACK2 (ELM + GBR + XGBR‐LASSO) models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision‐making, the ensemble method can be used to forecast the demand in a steel industry one month ahead.

A Novel Deep Learning Approach for Short Term Photovoltaic Power Forecasting Based on GRU-CNN Model

The integration of photovoltaic power brings the key to clean energy. However, the increasing proportion of photovoltaic (PV) energy in power systems due to the random and intermittent nature of solar energy resources is causing difficulties for system operators to dispatch PV power stations. To reduce the negative influence of the use of PV power, it is great significant to predict PV power accurately. In this paper, we propose a high-precision hybrid neural network model that employs Gated Recurrent Units (GRU) and Convolution Neural Network (CNN) to build a GRU-CNN model to forecast PV system output power. The proposed framework has two major phases. Firstly, the sample data is divided into training set and test set. For this, the temporal characteristics of the data set are extracted using a GRU model and the spatial characteristics are obtained using the CNN model. Secondly, the final predicted PV power is obtained through the output layer. The forecasting accuracy of GRU-CNN is determined by the mean absolute error (MAE), mean square error (MSE), determination coefficient (R2) and root mean square error (RMSE) values. The findings of the comparison experiments show that the GRU-CNN model has better accuracy than some deep learning methods, including, GRU, CNN and long-short term memory model (LSTM).

МОДЕЛИРОВАНИЕ ВЗАИМОСВЯЗИ ПОВЕРХНОСТНЫХ И ДРЕНАЖНЫХ ВОД

The purpose of the research was to create an adequate mathematical apparatus to establish the interrelation between surface and drainage waters, since the need of interconnection between surface and drainage waters in the field of environmental safety, reclamation and hydrology arises regularly. At the same time, conducting field studies that determine this interrelation is a very costly and time-consuming event, therefore, the development of a mathematical apparatus that adequately describes the relationship between surface and drainage waters is an urgent task. Materials and methods. The developed mathematical apparatus is based on the compilation of the classical theory, namely: the Chezy, Darcy formulas, the water table, the water balance equation and the expression for the water surface slope. Results. As a result of the research, the drainage water runoff zone was divided into a finite number of elements, and the average velocity of the filtration flow interacting between the canal and drainage water was determined. Numerical verification of the modeling results was carried out on the basis of data obtained during field studies at the inter-farm canal section near the village Swamp Belevsky district Tula region. In the process of research, an algorithm and a program were developed for calculating the drainage water flow rate for the considered section of the inter-farm canal. Based on the simulation results, a hydrograph of drainage water flow rate was constructed in the inter-farm canal near the village Swamp, while the hydrograph of drainage water flow, built on the basis of modeling results, practically coincides with the hydrograph, built on the basis of data provided from the results of field studies. Conclusions. Approbation of the results of the developed mathematical apparatus and mathematical model showed the correlation between theoretical and experimental studies with a determination coefficient value of 90.22 %.

Non-Invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard

Hyperspectral imaging offers enormous potential for measuring grape composition with a high degree of representativity, allowing all exposed grapes from the cluster to be examined non-destructively. On-the-go hyperspectral images were acquired using a push broom hyperspectral camera (400–100 nm) that was mounted in the front part of a motorized platform moving at 5 km/h in a commercial Tempranillo vineyard in La Rioja, Spain. Measurements were collected on three dates during grape ripening in 2018 on the east side of the canopy, which was defoliated in the basal fruiting zone. A total of 144 grape clusters were measured for Total soluble solids (TSS), Titratable acidity (TA), pH, Tartaric and Malic acid, Anthocyanins and Total polyphenols, using standard wet chemistry reference methods, throughout the entire experiment. Partial Least Squares (PLS) regression was used to build calibration, cross validation and prediction models for the grape composition parameters. The best performances returned determination coefficients values of external validation (R2p) of 0.82 for TSS, 0.81 for Titratable acidity, 0.61 for pH, 0.62 for Tartaric acid, 0.84 for Malic acid, 0.88 for Anthocyanins and 0.55 for Total polyphenols. The promising results exposed in this work disclosed a notable methodology on-the-go for the non-destructive, in-field assessment of grape quality composition parameters along the ripening period.

BOD5 dynamics in three vertical layers in free-water surface wetlands

This paper introduces a first-order variable residence time (VART) model, the so-called BOD-VART model, to estimate the biochemical oxygen demand (BOD) in built wetlands with a free-water surface. The BOD-VART model mathematically designs a free-water surface wetland into three vertical layers: the vegetated water column layer, the upper root layer dominated by advection, and the lower root layer dominated by diffusion. It also biogeochemically simulates various processes and mechanisms of BOD removal, including mass transfer between the layers through advection, dispersion, and diffusion. The inclusion of a dynamic diffusion-dominated root zone is a unique feature of the BOD-VART model. In this study, the BOD-VART model was tested using data obtained from the Lake Manzala Engineering Wetland, Egypt. The test results showed that the determination coefficient and root mean square error values were 0.74 and 1.97 mg/l, respectively. The BOD-VART model is a reliable and efficient instrument for modeling designed wetlands and determining the effect of different mass transfer processes on the treatment elements in developed wetlands.

Swimming Phase-Based Performance Evaluation Using a Single IMU in Main Swimming Techniques.

Comprehensive monitoring of performance is essential for swimmers and swimming coaches to optimize the training. Regardless of the swimming technique, the swimmer passes various swimming phases from wall to wall, including a dive into the water or wall push-off, then glide and strokes preparation and finally, swimming up to the turn. The coach focuses on improving the performance of the swimmer in each of these phases. The purpose of this study was to assess the potential of using a sacrum-worn inertial measurement unit (IMU) for performance evaluation in each swimming phase (wall push-off, glide, stroke preparation and swimming) of elite swimmers in four main swimming techniques (i.e. front crawl, breaststroke, butterfly and backstroke). Nineteen swimmers were asked to wear a sacrum IMU and swim four one-way 25 m trials in each technique, attached to a tethered speedometer and filmed by cameras in the whole lap as reference systems. Based on the literature, several goal metrics were extracted from the instantaneous velocity (e.g. average velocity per stroke cycle) and displacement (e.g. time to reach 15 m from the wall) data from a tethered speedometer for the swimming phases, each one representing the goodness of swimmer’s performance. Following a novel approach, that starts from swimming bout detection and continues until detecting the swimming phases, the IMU kinematic variables in each swimming phase were extracted. The highly associated variables with the corresponding goal metrics were detected by LASSO (least absolute shrinkage and selection operator) variable selection and used for estimating the goal metrics with a linear regression model. The selected kinematic variables were relevant to the motion characteristics of each phase (e.g. selection of propulsion-related variables in wall push-off phase), providing more interpretability to the model. The estimation reached a determination coefficient (R2) value more than 0.75 and a relative RMSE less than 10% for most goal metrics in all swimming techniques. The results show that a single sacrum IMU can provide a wide range of performance-related swimming kinematic variables, useful for performance evaluation in four main swimming techniques.

Statistical optimisation of esterase from Salinicoccus roseus strain RF1H and its potential application in synthetic dye decolorisation

Halophilic bacteria isolated from Indian Sundarban Wetland, identified as Salinicoccus roseus strain RF1H, were screened for extracellular esterase production. Media components for esterase production under submerged fermentation were optimised using Plackett–Burman Design followed by Box-Behnken Design of Response Surface Methodology (RSM) at pH 4.0, 3.5 g/L of MgSO4, and tributyrin concentration of 1(%v/v). The regression model was significant with a determination coefficient (R 2) value of 0.826. Using RSM, the experimental value of maximum enzyme activity (2.442 UmL−1) was 1.35 fold increased. The kinetic parameters K m and V max for hydrolysis of p-nitrophenyl acetate were 9.98 mM and 2.26 µmol/min/mg respectively, establishing a high affinity for the substrate. Further, the enzyme conferred promising results in decolorisation of synthetic dyes -Malachite Green, Congo Red, Brilliant Yellow, and Orange G. Molecular docking was used to investigate the binding pockets of the esterase, enzyme-ligand interactions, and amino acids which played a key role in dye decolorisation. Docking score results and decolorisation percentage were found in good agreement. This is the first report on the biotransformation of synthetic dyes to non-toxic products using esterase from Salinicoccus roseus.

Determination of Levamisole and Mebendazole and Its Two Metabolite Residues in Three Poultry Species by HPLC-MS/MS.

A high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed to simultaneously analyze levamisole (LMS) and mebendazole (MBZ) and its two metabolites, 5-hydroxymebendazole (HMBZ) and 2-amino-5-benzoylbenzimidazole (AMBZ), in poultry muscle (chicken, duck and goose). In the sample preparation process, basic ethyl acetate was used as the extraction agent, and the extracted samples were back-extracted with hydrochloric acid, purified by Oasis MCX solid-phase extraction (SPE) cartridges, and reconstituted in the initial mobile phase after being blown dry with nitrogen. Chromatographic separation was performed on an Xbridge C18 column (4.6 mm × 150 mm, 5 μm) with 0.1% formic acid in water and acetonitrile as the mobile phases, and gradient elution was performed at a flow rate of 0.6 mL/min and a column temperature of 35 °C. In blank poultry muscle samples, the spiked concentrations of LMS, MBZ, HMBZ, and AMBZ were within the range of the limit of quantitation (LOQ) to 25 μg/kg. The peak areas of the four target drugs had a good linear relationship with the concentration, and the determination coefficient (R2) values were higher than 0.9990. The average recoveries of LMS, MBZ, HMBZ, and AMBZ were 86.77–96.94%; the intraday relative standard deviations (RSDs) were 1.75–4.99% at LOQ, 0.5 maximum residue limit (MRL), 1.0 MRL, and 2.0 MRL; the interday RSDs were 2.54–5.52%; and the LODs and LOQs were 0.04–0.30 μg/kg and 0.12–0.80 μg/kg, respectively.

Numerical investigations of response surface methodology for organic dye adsorption onto Mg-Al LDH -GO Nano Hybrid: An optimization, kinetics and isothermal studies

We developed Mg-Al LDH intercalated with NO3- decorated Graphene Oxide nanohybrid with positive cationic textile dye adsorption, such as methylene blue. Contact time, initial dye concentration, and pH were among the independent variables used in the study. The Response Surface Model (RSM) was used to optimise and describe the interdependencies of the different variables. The method was evaluated using the Box-Behnken design (BBD). A second-order polynomial model was used to understand the experimental results, and the effectiveness of the chosen model was verified by its strong agreement in determination coefficient values. The adsorption results suited the best for the Langmuir isotherm than the D-R and Freundlich isotherms, resulting in efficient adsorption of 187.62 ​mg/g, proving LDH-GO is an effective dye adsorbent. Simultaneously, the kinetics were based on the pseudo-second-order model, and additional kinetic factors such as Elovich, pseudo-first-order, and intra-particular diffusion were examined. According to Thermodynamic studies, the adsorption process is spontaneous, which is exothermic and characterised by increased randomness. According to a regeneration test, even after four cycles, 37% of the removal efficiency for MB can be achieved. Based on this report, these materials can be used as an adsorbent to remove the dye.

Length-weight relationship of four deep-sea chondrichthyans (Elasmobranchii & Holocephali) in Ecuadorian oceanic waters

The present work reported the length-weight relationship of four deep-sea chondrichthyan species (i.e., Hydrolagus spp., E. granulosus, C. owstonii and C. squamosus) captured by the experimental fishery of the depth cod (Dissostichus eleginoides) in Ecuadorian oceanic waters, from 2018 to 2020. The fishing included 592 hauls, starting at 1,000 m depth, using between 1,000 to 1,500 N° 14/0 circle hooks. Length-weight relationship (LWR) was estimated by the power model (y = axb). The determination coefficient (r2) value was always above 0.94. The slope (b) showed negative allometric growth (b < 3) for females of C. owstonii, C. squamosus and males of E. granulosus and Hydrolagus spp. Allometry was positive (b > 3) in females of E. granulosus and Hydrolagus spp. as well as, in males of C. owstonii and C squamosus. In addition, the allometry for the combined sexes was negative in C. owstonii and was positive in C. squamosus and E. granulosus.

Predicting Particulate Matter (PM2.5) in Malaysia using Multiple Linear Regression and Artificial Neural Network

Air pollution is a well-known issue for all countries, including Malaysia. It has been stated that particulate matter that less than 2.5mm known as PM2.5 has a greater effect on health as the smaller particulate size can penetrate deep into the respiratory system and affect the cardiovascular system significantly. Therefore, it is necessary to estimate the concentration of PM2.5 for haze precautions. This study characterizes the pattern of PM2.5 concentrations involving seven stations including Alor Setar, Shah Alam, Pasir Gudang, Ipoh, Kuantan, Kuala Terengganu and Miri with seven indicator parameters (Carbon Monoxide, Ozone, Sulphur Dioxide, Nitrogen Dioxide, Humidity, Temperature and Wind Speed). PM2.5 concentrations were predicted for each station using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). Descriptive and trend analysis using Mann-Kandell Trend analysis was used to describe the haze characteristics and identify significant trends in the haze selected locations in Malaysia. MLR and ANN were fitted for the data. The performance of both prediction models was compared based on R2 and Mean Square Error (MSE). The results show ANN performed better than MLR with a high value of coefficient determination (R2) and low error measure. The ANN model was used to predict the occurrence of haze for the next day in the Air Quality Index (API).

New fuel consumption model considering vehicular speed, acceleration, and jerk

A novel computational model for the volatile state was developed to improve eco-driving in intelligent transportation systems (ITS). First, the volatile state was divided into eight types using vehicle acceleration and jerk as delineating criteria. Data analysis showed that each jerk type had a different proportion and contribution level to fuel consumption. Next, the model was created by considering eight instantaneous driving decisions as represented by vehicle speed, acceleration, and jerk. The model input included vehicle speed multiplied by acceleration, with jerk as a classifier. The model was calibrated using quadratic polynomial fitting, and validated using another portion of the data. Finally, predictions were compared with the widely used Vehicle Specific Power (VSP) model and the Virginia Tech Microscopic (VT-Micro) model to evaluate model performance. The new model thoroughly captured the measured fuel consumption and provided more accurate predictions in new routes than the above-mentioned models. The mean absolute percentage error value of the new model was ∼4.9% and 3.2% lower than those of the VSP and VT-Micro models, respectively. The determinant coefficient value was up to 95.8%, which was ∼4.6% and 8.5% higher than those of the VSP and VT-Micro models, respectively.