Multilinear Regression Method Research Articles

On the Employment of a Chloride or Floride Salt Fuel System in Advanced Molten Salt Reactors, Part 2; Core Inventory, Fuel Burnup, and Salt Clean-Up System

Breed and Burn (B&B) fuel cycle in molten salt reactors (MSRs) qualifies this reactor type as one of the best candidates to be developed for the Gen-IV R&D program. This feature can be approached by employing a closed fuel cycle and application of a molten salt reactor as a spent nuclear fuel burner; the features promise sustainable and clean energy in the future. In this study, a complete package has been developed to calculate core inventory, fuel burnup, and salt clean-up systems of molten salt reactors during their lifetime. To achieve this, the iMAGINE-3BIC package (“iMAGINE 3D-Reg Burnup & Inventory Calculator package”) has been developed in MATLAB R2023a by employing a CINDER90 module of MCNPX 2.7 for burnup-calculation and multi-linear regression method (MLR). The package can estimate the core inventory (concentration of 25 actinides and 245 non-actinides elements) and the burnup of the reactor core during MSR lifetime (up to 100 years) while optimizing the computational resources (time, CPU and RAM), and it can even be hassle-freely executed on standalone PCs in an appropriate time due to its generous database. In addition, the salt clean-up module of the iMAGINE-3BIC package can be employed to evaluate the effects of the salt clean-up system on the above parameters over the MSRs’ lifetime. Finally, the iMAGINE-3BIC package has been applied to an iMAGINE reactor core design (University of Liverpool, UK—chloride-based salt fuel system) and an EVOL reactor core design (CNRS, Grenoble, France, fluoride-based salt fuel system) to evaluate and compare the performance of chloride/fluoride-based salt fuel MSRs from the point of burnup, core inventory, and salt clean-up systems. The results confirm that while a chloride-based salt fuel system has some advantages in less dependency on the salt clean-up system and fewer poisoning elements inventory, the fluoride-based system can achieve higher burnup during the reactor lifetime. The outcome of this study, along with the first part of this article, provides evidence to support the neutronic decision matrix as well as the pros and cons of employing chloride- or fluoride-based fuel systems in MSR cores.

Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in-vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor

A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule’s binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R 2 = 0.8521, Q 2 LOO = 0.8438, and Q 2 LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: −8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies. Communicated by Ramaswamy H. Sarma

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

Pollution characteristics, sources, and photochemical roles of ambient carbonyl compounds in summer of Beijing, China

Ambient carbonyls are important precursors of radicals and ground-level ozone (O3). In this study, sources, precursors, and impacts on radicals and O3 of carbonyls were investigated based on online observations of volatile organic compounds (VOCs) at an urban site in Beijing during June 2021. Carbonyls accounted for 36% and 42% of mixing ratios and OH reactivity for total measured VOCs, respectively. Formaldehyde was the most abundant carbonyl, with the mean level of 4.13 ± 2.28 ppb. Source apportionment results based on the multi linear regression (MLR) method suggested that secondary production contributed 41%, 25%, 36%, and 30% of formaldehyde, acetaldehyde, propanal, and acetone, respectively. Key precursors of carbonyls were then identified based on the calculation of their production rates. It was found that alkenes contributed 59%–80% of aldehydes production. Impacts of carbonyls on HOx radicals (OH and HO2) and O3 production were explored using a box model based on observations (OBM). Photolysis of HONO, formaldehyde, and O3 were the dominant primary sources of HOx radicals during daytime of O3 pollution days, with average relative contributions of 52%, 28%, and 19% to the total primary production rate of HOx, respectively. Aldehydes accounted for 32% (20% from formaldehyde) of average HOx removal rates. The relative incremental reactivity (RIR) values of NOx determined by the OBM were negative, suggesting that the O3–VOCs-NOx sensitivity was in the VOCs-limited regime. Using the observed concentrations of carbonyls as constraints of OBM, the absolute values of RIR for NOx tended to increase but those for anthropogenic VOCs tended to decrease. Formaldehyde showed the largest RIR value for anthropogenic VOCs during O3 pollution days. These findings indicated the important impacts of carbonyls on O3 production and O3–VOCs-NOx sensitivity.

Thermal and mechanical properties of a new insulation composite material

Abstract The Taguchi optimization method was used to optimize waste and natural different components such as waste marble dust, expanded perlite, perlite aggregate size, cement, and molten tragacanth in the production of new insulation composite material. Compressive strength, thermal conductivity, abrasion loss, and water adsorption properties of the developed composite material were investigated. Taguchi’s standard L18 array was chosen for optimization of these four components with different levels. Response plots were created using the Taguchi and the optimum test condition was determined. The insulation composite material with the best thermal and mechanical properties was obtained under the condition of waste marble dust (1), expanded perlite (1), perlite aggregate size (1) and molten tragacanth (1). In addition, using the anova (Analysis of Variance), percentage impacts on the mechanical and thermal properties of the test parameters were determined. Statistical values obtained from anova and mathematical models are developed by using multi-linear regression method. It was found that the mathematical model and the experimental results were quite compatible. The optimum test conditions detected were verified by confirmation experiments. Confirmation experiment results were obtained between 99.9 % confidence interval values.

Performance optimization of a heat exchanger with coiled-wire turbulator insert by using various machine learning methods

In present study, heat transfer augmentation and pressure loss in a double pipe concentric type heat exchanger with a coiled-wire turbulator inserted in it, are discussed in terms of regression analysis by using various Machine Learning (ML) methods. The non-dimensional design parameters in question are; the Reynolds number (Re), the thickness (e/d), the pitch (p/d) and the length (l/d) of the coiled-wire. Nusselt number (Nu), friction factor (f) and thermal performance factor (η) are the extracted results of the experiments, namely the outputs of the system.In the regression analysis four well-known methods are applied; Supported Vector Regression (SVR), Gaussian Process Regression (GPR), Random Forest (RF) and Multilayer Perceptron Network (MLP) (a kind of Artificial Neural Network (ANN)). The Multi Linear Regression (MLR) method is also applied to the results for comparison. The regression coefficient (R2), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) are the performances obtained by each method.The findings demonstrated that, among the suggested techniques, the MLP and GPR models in particular can be effective tools for calculating Nusselt number, friction factor and thermal performance factor for the selected heat exchanger. The R2 values for Nusselt number, friction factor and thermal performance factor in the MLP model are respectively found to be 1, 0.95 and 0.98. In terms of all performance criteria, the order from best to worst-performing method is as follows: MLP, GPR, SVR and RF. In addition, it was observed that all four methods applied give better results than MLR does.

Phase equilibria in the quaternary NaBr–MgBr2–Na2SO4–MgSO4–H2O system at (323.15 and 348.15) K and their application

To explore the process of extracting bromide salts directly from concentrated bittern, the phase equilibria in the quaternary NaBr–MgBr2–Na2SO4–MgSO4–H2O system at (323.15 and 348.15) K were investigated using the isothermal dissolution equilibrium method. The corresponding equilibrium phase diagrams and water content diagrams were depicted. The phase diagram at 323.15 K consists of eight invariant points, fifteen univariant curves and nine crystallization zones. The phase diagram at 348.15 K has four invariant points, nine univariant curves and six crystallization regions. Double salts were formed in both cases, and either belongs to complex type. The solubility products of the double salts 6Na2SO4·7MgSO4·15H2O and 3Na2SO4·MgSO4 were fitted by adopting the multilinear regression method. The predicted phase diagrams established with the solubilities calculated based on the Pitzer model show good agreement with the measured ones. A new circulation process was proposed to extract NaBr from the concentrated bittern, which provided a theoretical basis for the separation of bromide salts from bittern straightly.

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

Structural Weight Prediction Modelling for a Wide-body Aircraft by Implementing Real-time Case Study

Research studies on portraits of the middle-of-the-market aircraft are the most promising research for future aircraft design. This research aims to investigate the superiority of aircraft design technology, which results in reduced gross weight and increased structural integrity. Using a multi-linear regression method, this article offers to estimate the Maximum Take-Off Weight (MTOW) for wide-body transport aircraft. A real-time case study is carried out for the design configurations, propulsive systems, performance characteristics and structural integrity parameters of 26 distinct middle-of-the-market aircraft. Furthermore, a sensitivity analysis is carried out in order to validate the MTOW model. The model provides the best fitness with R2 = 0.9676. This R2 result is around 5% higher than the lowest R2 achieved from the literature. In addition, a clear-cut insights and managerial views towards future aircraft design are discussed.

Figures-of-Merit Analysis Using the Thermophysical Properties of Water and Ethylene Glycol Based Reduced Graphene Oxide/Nanodiamond Hybrid Nanofluids

The water and ethylene glycol based stable rGO/nanodiamond hybrid nanofluids were prepared and used for thermophysical properties analysis. The thermophysical properties were measured experimentally at various particle loadings from 0.2% to 1.0% and various temperatures ranging from 20 to 60 °C. From the measured thermophysical properties the figures-of-merit were analyzed by assuming constant heat flux and turbulent boundary conditions by using different models, and also by assuming the hybrid nanofluids flows through a tube. Results indicate that the thermal conductivity of water and ethylene glycol based hybrid nanofluids at Φ = 1.0% vol. is enhanced about 27.87% and 18.8% at 60 °C; viscosity of water and ethylene glycol based hybrid nanofluids at Φ = 1.0% vol. is also enhanced by 72.15% and 86.62% compared to their base fluids at 20 °C. The density is also increased with an increase of particle loadings, whereas, the specific heat is decreased with a rise of particle loadings. Experimental thermophysical properties are fitted into regression equations by using multi linear regression method. The figures-of-merit of all the prepared hybrid nanofluids show its value is less than one under the used particle loadings and temperatures.

QSAR Study of N-((3-Benzamido-4-oxo-3,4-Dihydroquinazolin 2-yl)methyl)-N-(Substituted) Phenyl Benzamide as Antiulcer Agents

Suppression of gastric acid secretion by use of proton pump inhibitors is an efficient way to control hyperacidity complications. An inhibitory activity of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides on H+/K+-ATPase was established and reported earlier. Thus, it is significant to develop more promising agents by quantitative structure-activity relationship (QSAR) study of 37 ligands by multi-linear regression method to link the structures of molecules with inhibitory activity on H+/K+-ATPase (pIc50). QSAR model was built using genetic function approximation protocol of the software Discovery Studio Version 2.1 using training set carrying 23 compounds. The remaining 14 compounds were used as a test set. The generated model was showing satisfying statistical qualities, r2=0.84 and predicted correlation coefficient r2pred=0.88. The theoretical approach indicates that an increase in Log D, Shadow_XZ and SC 2, and reduction of Shadow_Z length causes more inhibition of enzyme by molecule.

Measurement report: Production and loss of atmospheric formaldehyde at a suburban site of Shanghai in summertime

Abstract. Formaldehyde (HCHO) is an important trace gas that affects the abundance of HO2 radicals and ozone, leads to complex photochemical processes, and yields a variety of secondary atmospheric pollutants. In a 2021 summer campaign at the Dianshan Lake (DSL) Air Quality Monitoring Supersite in a suburban area of Shanghai, China, we measured atmospheric formaldehyde (HCHO) by a commercial Aero-Laser formaldehyde monitor, methane, and a range of non-methane hydrocarbons (NMHCs). Ambient HCHO showed a significant diurnal cycle with an average concentration of 2.2 ± 1.8 ppbv (parts per billion by volume). During the time period with the most intensive photochemistry (10:00–16:00 LT), secondary production of HCHO was estimated to account for approximately 69.6 % according to a multi-linear regression method based on ambient measurements of HCHO, acetylene (C2H2), and ozone (O3). The average secondary HCHO production rate was estimated to be 0.73 ppbv h−1 during the whole campaign (including daytime and nighttime), with a dominant contribution from reactions between alkenes and OH radicals (66.3 %), followed by OH-radical-initiated reactions with alkanes and aromatics (together 19.0 %), OH-radical-initiated reactions with oxygenated volatile organic compounds (OVOCs; 8.7 %), and ozonolysis of alkenes (6.0 %). An overall HCHO loss, including HCHO photolysis, reactions with OH radicals, and dry deposition, was estimated to be 0.49 ppbv h−1. Calculated net HCHO production rates were in relatively good agreement with the observed rates of HCHO concentration change throughout the sunny days, indicating that HCHO was approximately produced by oxidation of the 24 hydrocarbons we took into account at the DSL site during the campaign, whereas calculated net HCHO production rates prevailed over the observed rates of HCHO concentration change in the morning/midday hours on the cloudy and rainy days, indicating a missing loss term, most likely due to HCHO wet deposition. Our results suggest the important role of secondary pollution in the suburbs of Shanghai, where alkenes are likely key precursors for HCHO.

Perbandingan Akurasi Metode Empiris untuk Pemetaan Batimetri Perairan Benoa, Bali, Menggunakan Citra Satelit SPOT

Satellite Derived Bathymetry (SDB) is an alternative method to obtain bathymetry information data developed by utilizing image data as data sources. This study aimed to compare the accuracy of five empiric methods: the Stumpf Method, Polynomial Method, Multilinear Regression Method (MLR), Lyzenga Method, and Van Hengel and Spitzer Methods (VHS). This research was located in Benoa, Denpasar, and Bali using SPOT 6 satellite imagery with a spatial resolution of 6 meters as the data source. The acquisition was on August 12, 2017, in situ data. The accuracy test was carried out by calculating the coefficient of determination (R2) and the RMSE value. The SPOT 6 image requires an image interpretation process, including radiometric correction and atmospheric correction using DOS and land and water masking using the NDWI equation to obtain accuracy test and bathymetric information. Stumpf method has an RMSE of 5.72 meters, R2 of 0.27. The polynomial method has an RMSE of 6.99 meters R2 of 0.01. The Multilinear Regression method has an RMSE of 5.75 meters R2 of 0.34. The Lyzenga method has an RMSE of 7.66 meters R2 of 0.09. The Van Hengel and Spitzer method has an RMSE of 6.97 meters R2 of 0.03. Based on the results of calculations from this study, the Stumpf method has the highest accuracy with an RMSE of 5.72.

Experimental determination of thermophysical properties and figures-of-merit analysis of 80:20% water and ionic liquid mixture based MXene nanofluid

Novel material of MXene was synthesized and prepared their ionanofluids while dispersing them into 20:80% (by mass) of ionic liquid of 1,3-Dimethylimidazolium dimethyl phosphate [MMIM][DMP] and water mixture. The thermophysical properties were studied experimentally at different particle weight loadings from 0.2% to 1.0% and at different temperatures over 20°C to 60°C. By utilizing the evaluated thermophysical properties of ionanofluids their figures-of-merit were analyzed by using three models under turbulent flow conditions. It is understood from the results, the thermal conductivity of 1.0 wt% of ionanofluid is enhanced about 56.62% at a temperature of 60°C; whereas, the dynamic viscosity is enhanced to 17.66% at 20°C compared to base fluid alone. Moreover the density of ionanofluid is raised and specific heat ionanofluid is lowered with rise of particle weight concentrations. The obtained thermophysical properties were fitted into regression equations based on the multi-linear regression method. The evaluated three models figures-of-merit show all the prepared ionanofluids in the measured weight concentrations and temperatures are beneficial heat transfer fluids because their value is greater than 1.

In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images

Crop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices (VIs) derived from time series PlanetScope and Sentinel-2 images were used to investigate the potential to estimate corn grain yield with different regression methods. A field-scale spatial crop yield prediction model was developed and used to produce yield maps depicting spatial variability in the field. Results from this study clearly showed that high-resolution PlanetScope satellite data could be used to detect the corn yield variability at field level, which could explain 15% more variability than Sentinel-2A data at the same spatial resolution of 10 m. Comparison of the model performance and variable importance measure between models illustrated satisfactory results for assessing corn productivity with VIs. The green chlorophyll vegetation index (GCVI) values consistently produced the highest correlations with corn yield, accounting for 72% of the observed spatial variation in corn yield. More reliable quantitative yield estimation could be made using a multi-linear stepwise regression (MSR) method with multiple VIs. Good agreement between observed and predicted yield was achieved with the coefficient of determination value being 0.81 at 86 days after seeding. The results would help farmers and decision-makers generate predicted yield maps, identify crop yield variability, and make further crop management practices timely.

Fabrication and simulation studies on sound absorption coefficient of natural microfibers reinforced silicone rubber

Silicone rubber (SR) is known as stable and able to get through an extreme environment. Moreover, the combination of SR and natural fiber as the composite for the sound absorption application is still limited. For this reason, this study was aimed to fabricate the natural fiber-reinforced porous SR for the sound absorption material. Then, the composites were simulated using multilinear regression method to predict the sound absorption coefficient and its factor influence. The composite fabrication was started with the alkalization of oil palm fiber treatment. Furthermore, the porous SR were prepared using NaCl filler to form the pores. Then, the specimen containing NaCl was soaked in warm water and all specimens were dried at 110 °C to remove the remaining water. The addition of fibers enhances the density value to the highest value of 1.061 g/cm3 with 6 wt% microfibers. The higher the microfiber addition led to the higher α value in low frequency, it occurred at SR/6 wt% microfibers with the α value of 0.356. Additionally, the most suitable equation with the smallest error is Equation 6 which has the RMSE and values of 0.05234 and 0.6138, respectively.

Dynamical linear modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa/Davos, Switzerland

Abstract. Six collocated spectrophotometers based in Arosa/Davos, Switzerland, have been measuring ozone profiles continuously since 1956 for the oldest Dobson instrument and since 2005 for the Brewer instruments. The datasets of these two ground-based triads (three Dobsons and three Brewers) allow for continuous intercomparisons and derivation of long-term trend estimates. Mainly, two periods in the post-2000 Dobson D051 dataset show anomalies when compared to the Brewer triad time series: in 2011–2013, an offset has been attributed to technical interventions during the renewal of the spectrophotometer acquisition system, and in 2018, an offset with respect to the Brewer triad has been detected following an instrumental change on the spectrophotometer wedge. In this study, the worldwide longest Umkehr dataset (1956–2020) is carefully homogenized using collocated and simultaneous Dobson and Brewer measurements. A recently published report (Garane et al., 2022) described results of an independent homogenization of the same dataset performed by comparison to the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) Global Modeling Initiative (M2GMI) model simulations. In this paper, the two versions of homogenized Dobson D051 records are intercompared to analyze residual differences found during the correction periods. The Aura Microwave Limb Sounder (MLS) station overpass record (2005–2020) is used as an independent reference for the comparisons. The two homogenized data records show common correction periods, except for the 2017–2018 period, and the corrections are similar in magnitude. In addition, the post-2000 ozone profile trends are estimated from the two homogenized Dobson D051 time series by dynamical linear modeling (DLM), and results are compared with the DLM trends derived from the collocated Brewer Umkehr time series. By first investigating the long-term Dobson ozone record for trends using the well-established multilinear regression (MLR) method, we find that the trends obtained by both MLR and DLM techniques are similar within their uncertainty ranges in the upper and middle stratosphere but that the trend's significances differ in the lower stratosphere. Post-2000 DLM trend estimates show a positive trend of 0.2 to 0.5 % yr−1 above 35 km, significant for Dobson D051 but lower and therefore nonsignificantly different from zero at the 95 % level of confidence for Brewer B040. As shown for the Dobson D051 data record, the trend only seems to become significantly positive in 2004. Moreover, a persistent negative trend is estimated in the middle stratosphere between 25 and 30 km. In the lower stratosphere, the trend is negative at 20 km, with different levels of significance depending on the period and on the dataset.

The Influence of Innovation in Product Excellence, Product Price, and Product Credibility Towards Customer Purchase Intention in Herbal Tea Products

The development of herbal tea products in Indonesia has great potential due to plenty of natural commodities found in this country and the habit of Indonesian citizens that make drinking tea a lifestyle. However, the growth of business that is significantly increased shows a very strict competition. To survive and win the market, companies including the herbal tea industry must conduct product innovation based on customer preferences to improve the quality of goods or services offered. By using the multi linear regression method, the influence of product innovation towards purchase intention in herbal tea products is positively significant at 77.9%. Moreover, the product innovation variables in the form of product excellence, product price, and product credibility can explain about 60.1% towards the purchase intention. The product credibility variable has the greatest influence when compared to others. Using simple linear regression, it is also found that the dimensions of product scent, suitability between product price and benefits, and product information are the most significant dimensions compared to the others. The results of this study can be used to build business strategies for herbal tea companies to innovate products that are in accordance with what their customers preferences.

Multivariate calibration technique for spectrophotometric quantification of ondansetron in bulk drug and pharmaceutical formulations

The aim of the present work to develop an accurate, simple, sensitive, and validated Ultra violet (UV) spectrophotometric procedure using multilinear regression method for the analysis of Ondansetron in its bulk and commercial dosage form. The multivariate calibration approach was based on equations constructed using linear regression analysis using the correlation between absorbance and concentration at selected five different wavelengths. Ondansetron had a maximum wavelength of 246 nm. The findings were statistically analysed. A linear plot in the concentration range of 5-15 µg/mL, with a regression co-efficient of 0.999 was obtained. The % RSD for intra-day and Inter day precision were 0.0409 and 0.0228, respectively. The assay was determined and found to be 98.38 % – 101.10 % w/w.

FINANCIAL REPORTING MANIPULATION ON MINING COMPANIES IN INDONESIA: FRAUD DIAMOND THEORY APPROACH

Forensic accounting helps to discover fraud practices where the fraud diamond theory is one of the popular theories in this issue. It puts concerns on budget priorities, financial stability, inefficient monitoring, replacement of auditor and directors. As the dependent variable, financial statement manipulation funded by income control is used. The nine mining entities listed on the Indonesian Stock Exchange in 2017-2019 were chosen using the purposive sampling method, resulting in 27 observations. By using the multi-linear regression method, This study showed that financial targets and financial stability affect the occurrence of fraud in financial reports. Oppositely, insufficient monitoring, replacement of auditors and directors have no impact on the fraudulent reporting.