Squared Correlation Coefficient Research Articles

ARCnet: A Multi-Feature-Based Auto Radio Check Model

Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need for dependable ground-to-air communication. By employing a multi-scale feature fusion approach, including the consideration of audio’s frequency domain, comprehensibility, and temporal information within the radio check scoring network, ARCnet integrates manually designed features with self-supervised features and utilizes a transformer network to enhance speech segment analysis. Utilizing the NISQA open-source dataset and the proprietary RadioCheckSpeech dataset, ARCnet demonstrates superior performance in predicting speech quality, showing a 12% improvement in both the Pearson correlation coefficient and root mean square error (RMSE) compared to existing models. This research not only highlights the significance of applying multi-scale attributes and deep neural network parameters in speech quality assessment but also emphasizes the crucial role of the temporal network in capturing the nuances of voice data. Through a comprehensive comparison of the ARCnet approach to traditional methods, this study underscores its innovative contribution to enhancing communication efficiency and safety in civil aviation.

Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China

The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications.

A correlational study to assess the relationship between body mass index and selected life style practices among high school students in selected schools at Dakshina Kannada district, Karnataka, India

People in the 18.5-22.4 BMI range with high scores on the alternate healthy eating index and high levels of physical activity have the lowest risk of early mortality, while those with higher BMIs can still have a lower risk if they also have at least one low risk lifestyle component. To assess the body mass index of high school students in a selected school, lifestyle practice, find the association between selected lifestyle practices with demographic variables in high school students and association between Body Mass Index with demographic variables in high school students. Prior to starting the data gathering process, approval from BEO and the headmistresses of the chosen high schools was obtained. Canara High School and St. Aloysius High School in Mangalore hosted the primary study. The data gathering period ran from October 121, 2017, to January 1, 2018. All participants were told of the study's goal and gave their informed permission. The respondents received guarantees on the privacy and confidentiality of the data they submitted. Data from the samples were gathered by the researcher herself. In order to gather data, questionnaires about baseline data were used, along with a self-made rating scale to evaluate lifestyle behaviours and measures of height and weight to determine BMI. Karl Pearson ‘s correlation coefficient and Chi square test.The majority of samples, or 70%, had normal BMIs. Merely 20% of those surveyed were overweight. The least number of respondents (10%) were underweighted. Eighty-two percent of the respondents followed moderate lifestyles. Good living habits are present in 15% of the samples. The fewest responders (3%) led unhealthy lifestyles. Level of lifestyle habits broken down by area: The average lifestyle practice of students was 60.657.68, indicating that the majority of students follow a moderate lifestyle.A noteworthy correlation was observed between the mother's work and certain baseline factors, such as the body mass index score. Consequently, the null hypothesis was disproved. The score of a few chosen lifestyle activities and a few chosen baseline characteristics, such gender, were significantly correlated. They rejected the null hypothesis.Teenagers are the future citizens of this country, and their future depends on them. It is crucial to regularly assess students' body mass index and lifestyle choices so that those who lead unhealthy lifestyles can be quickly identified and treated appropriately to reduce their chance of developing health issues later on. Nurses will be able to teach parents and pupils about healthy lifestyle practices since they are aware of the relationship. The present study's findings have been taken into consideration for drawing the following conclusions.

Subgrid-Scale Topographic Effects on Radiation for Global Weather Forecast Models

The incoming solar radiation arriving the Earth’s surface is strongly influenced by surface terrain. Conventional global weather forecast models, with grid scales of about 10 km, lack the resolution to accurately capture terrain-induced variations. We devised a new parameterization method to incorporate high-resolution subgrid-scale terrain data into grid-scale radiative flux calculations without averaging or smoothing topographic features. Utilizing a 15″ digital elevation model from the Shuttle Radar Topography Mission, we computed subgrid-scale data and transformed them onto the Korean Integrated Model’s cubed sphere grid using a Voronoi diagram to maintain geographical accuracy. The new scheme was initially evaluated through offline ideal tests and case studies. The results demonstrated that the scheme accurately captured the variations in downward shortwave flux, keeping the mean flux on a global scale nearly constant. The global mean flux difference in all skies was less than 0.01%. Statistical analyses demonstrated improved temperature and geopotential height predictions compared to reanalysis data. The anomaly correlation coefficient for East Asia at 850 hPa increased by 0.036 at 240 forecast hours. Overall, the anomaly correlation coefficient and root mean square error of geopotential height and temperature showed enhancements, particularly in the Northern Hemisphere and tropics. Importantly, the scheme introduces negligible additional memory and CPU requirements, making it suitable for both regional and global models. Only a 0.58% increase in CPU time was observed for the 10-day forecast.

Optimizing Activated Carbon Production from Waste Cashew Nut Shell with Zinc Chloride: A Box-Behnken Design and Group Method of Data Handling (GMDH) Application

In this study, Response surface methodology (RSM) and innovative Group Method of Data Handling (GMDH) approaches are applied to investigate the optimal process conditions of zinc chloride activated cashew nut production process. The effects of activation conditions (i.e. activation temperature, activation time, and impregnation ratio) on the achievable BET surface areas were studied with the aid of Box Behnken Design (BBD) and GMDH. Comparative analyses of RSM and GMDH-type neural models were further researched. During the process, the polynomial model equations developed were modified and fine-tuned to predict the highest BET surface area(s) using regression analysis and GMDH multi-layered iterative algorithm (MIA). Analysis of Variance (ANOVA) revealed that the significant factor(s) were impregnation ratio, impregnation ratio product, and the 2-way interactions (activation temperature and impregnation ratio) for ZnCl2 activated cashew nut shell. The best activation conditions for producing highest BET surface area of 504 m2.g-1 was activation temperature (873K), activation time (60 min), and impregnation ratio (1.50).The proposed GMDH-type BET model was ascertained to be the best model with average correlation coefficient (R) and root mean square error (RMSE) of 0.925 and 32.0 respectively. Sensitivity analysis conducted for GMDH-type neural network also revealed that the activation temperature and activation time with sensitivity values of 90.6% and 74.1% respectively were the most influential parameters in the basic (ZnCl2) activation process. The results of this study show that RSM and GMDH-type neural network could be applied as effective analytical tools for optimizing the ZCNS (zinc chloride-activated cashew nut shells) manufacturing process.

Trace Level Quantification of Genotoxic Impurities through Atmospheric Pressure Chemical Ionization (APCI) Coupled with Triple Quardrapole Analyzer in Combination Diabetic Drug Product

N-Nitrosamine impurities have been found in several drug products, showing a concern for regulatory aspects. The study presents the development and validation of a sensitive LC-MS/MS method for detecting eight nitrosamines in vildagliptin and metformin drug products. The developed method was specific and linearity was ranged 3.53-55.92 ppb for NDMA, 3.18-50.37 ppb for NMBA, 0.97-15.41 ppb for NDEA, 0.98-15.52 ppb for NEIPA, 1.00-15.86 ppb for NDIPA, 0.98-15.46 ppb for NDBA, 0.98-15.58 ppb for NMPA and 0.7-15.32 ppb for NDPA with correlation coefficient (r) was more than 0.98 and square of correlation coefficient (r2) was found to be greater than 0.96. The LOQ were obtained in the range of 0.97-3.53 ppb with %RSD in the range of 2.5-10.9% for eight nitrosamines showed good sensitivity. Accuracy was found in the range of 98.63 ± 5.77-125.17 ± 3.68% at LOQ level, 74.77 ± 0.83-88.17 ± 0.15% at 50% level, 79.93 ± 8.47-89.00 ± 0.61% at 100% level and 74.67 ± 2.31-84.03 ± 0.93% at 150% level for the eight nitrosamine impurities that were well within acceptance criteria of not less than 70% and not more than 130%. The method validation results demonstrated that the method is precise, accurate and linear, can be applied to quantify the nitrosamines in vildagliptin and metformin drug products.

Machine Learning-Based Predictions of Porosity during Cold Spray Deposition of High Entropy Alloy Coatings

Porosity poses a challenge to the mechanical properties of cold sprayed coatings, especially when it is open or surface-connected, limiting the coatings’ capabilities to act as a barrier. The porosity formation is dependent on the feedstock powder characteristics and the cold spray process parameters. We present a machine learning-based approach to predict porosity based on the above-mentioned factors. Nine different machine learning models based on linear regression (LR), decision trees, random forests, gradient boosting, support vector machine (SVM), and neural networks were explored. Considering the excellent properties of high entropy alloys, Cantor alloy was taken as the consumable. Our dataset, derived from the literature and experiments, identified SVM with a linear kernel and LR as the top-performing models based on the Pearson correlation coefficient (PCC) and root mean square error, where the PCC values exceeded 0.8. The SHapley Additive exPlanations method helped in identifying that the type of gas and powder are the top two factors in pore formation.

The impact of supplementary cementitious materials on the rheological and mechanical properties of mortars based on quarry waste sand

Mineral substances used as additives in cement plants or as additives in the making of concrete contribute through their physical, hydraulic, and pozzolanic activity to improving the behavior of cements in both the fresh and hardened states. Several types of additions are well known, such as natural pozzolans, fly ash, blast furnace slag, and silica fume. These products become more active in the alkaline solutions of cement and give rise to new hydrates that impart greater mechanical strength and better durability to concretes. Through their surface activity and granular distribution, they play a fundamental role in the rheological and mechanical behavior of mortars and concretes. Quarry waste sand (QWS) is generally stockpiled to be eventually sold at very low prices. For this reason, its use in the production of concrete and mortar is increasingly becoming a necessity to protect the environment and meet the needs of the construction and public works sector.This study aims to investigate the effect of using both supplementary cementitious materials (SCM) and quarry waste sand(QWS) to improve some properties of mortar. Ordinary cement is replaced by 10%, 20% and 30% of silica fume (SF), natural pozzolan (NP) or ground blast-furnace slag (GBFS) by weight and the properties of the QWS sand -based mortar are compared to those of natural sand (NS) based mortar. In this study, the slump, superplasticizer requirement, rheological parameters, mechanical strength, and water absorption are investigated. The results obtained show that QWS sand mix has the best workability and requires less superplasticizer dosage. When SCM were used, a drop-in workability is shown and more superplasticizer is required. Also, QWS sand makes the mortar strength 2 and 1.5 times higher than that of NS and becomes 42% higher with 10% SF. Adequate relationships have been established to predict mechanical strengths as a function of test parameters with high correlation coefficient and low root mean square error.

Bézier Cubics and Neural Network Agreement along a Moderate Geomagnetic Storm

The discussion models the IRI-2012 TEC map over a moderate geomagnetic storm period (5 days) in February 2015 and compares the yield of the models. The models are constructed with the help of cubic Bézier curves and machine learning. In a sense, the comparison of a classical and mechanical approach with a modern and computer-based one is a considerable experience for the paper. The parametric curve approach governs models of piecewise continuous Bézier cubics, while the models employ only the TEC map. The design is separated into curve components at every five-hour curvature point, and each component is handled independently. Instead of the traditional least squares method for finding control points of cubics, it utilizes the mean of every five-hour of the piecewise curves of the TEC data. Accordingly, the prediction error can be controlled at a rate that can compete with the modern network approach. In the network model, 120 hours of the solar wind parameters and the TEC map of the storm are processed. The reliability of the network model is assessed by the (R) correlation coefficient and mean square error. In modeling the TEC map with the classical approach, the mean absolute error is 0.0901% and the correlation coefficient (R) score is 99.9%. The R score of the network model is 99.6%, and the mean square error is 0.71958 (TECU) (at epoch 47). The results agree with the literature.

Modeling magnetic refrigeration capacity of doped EuTiO3 magnetocaloric compounds using swarm based intelligent computational method

This work models the magnetic refrigeration capacity (MRC) of doped europium titanate (EuTiO3) at different applied magnetic field using hybrid particle swarm optimization based support vector regression (SW-SVR) algorithm with ionic radii dopant descriptors. The developed model with Gaussian function (SW-SVR-Gas) shows better performance as compared with polynomial based model (SW-SVR-Pol) with improvement of 35.76 %, 0.13 % and 45.76 % using mean absolute error (MAE), correlation coefficient (CC) and root mean square error (RMSE) performance yardsticks, respectively. The developed SW-SVR-Gas model investigates the behavior of Eu1-xBaxTiO3 and EuTi1-xNixO3 doped europium titanate at different dopants concentration as well as applied field. Novelties of the developed model include circumvention of experimental difficulties associated with magnetic refrigeration capacity determination, implementation of simple molecular descriptors with high precision and accuracy. The demonstrated uniqueness of the developed model would strengthen exploration of refrigeration capacity of europium titanate based compounds for addressing global refrigeration crisis.

Application of deep learning in predicting suspended sediment concentration: A case study in Jiaozhou Bay, China

Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harnessed Convolutional Neural Networks (CNNs) to create a deep learning model, which has been applied to the remote sensing data procured from the Geostationary Ocean Color Imager (GOCI) spanning April 2011 to March 2021. Our research indicates that on a small time scale, wind and hydrodynamic forces both have a significant impact on the prediction results of CNNs model. Considering both wind and hydrodynamic forces can effectively improve the model's prediction efficiency for SSC. Moreover, we have employed CNNs to interpolate absent values within the remote sensing datasets, yielding enhancements superior to those attained via linear or multivariate regression techniques. Finally, the correlation coefficient between CNN-derived SSC estimates for Jiaozhou Bay (JZB) and its corresponding remote sensing data is 0.72. Correlation coefficient and root mean square error differ in different regions. In the shallow water of JZB, due to water level changes, there is limited data, and the correlation coefficient in this area is about 0.5–0.6. In the central region of JZB with sufficient data, the correlation coefficient is generally higher than 0.75. Therefore, we believe that this CNNs model can be used to predict the hourly variation of SSC. When juxtaposed with alternative methodologies, the CNN approach is found to economize computational resources and enhance processing efficiency.

Sample Size Optimization for Digital Soil Mapping: An Empirical Example

In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation.

Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment

Quantitative structure-activity relationship (QSAR) is a cost-effective solution to directly and accurately estimating the environmental safety thresholds (ESTs) of pollutants in the ecological risk assessment due to the lack of toxicity data. In this study, QSAR models were developed for estimating the Predicted No-Effect Concentrations (PNECs) of petroleum hydrocarbons and their derivatives (PHDs) under dietary exposure, based on the quantified molecular descriptors and the obtained PNECs of 51 PHDs with given acute or chronic toxicity concentrations. Three high-reliable QSAR models were respectively developed for PHDs, aromatic hydrocarbons and their derivatives (AHDs), and alkanes, alkenes and their derivatives (ALKDs), with excellent fitting performance evidenced by high correlation coefficient (0.89–0.95) and low root mean square error (0.13–0.2 mg/kg), and high stability and predictive performance reflected by high internal and external verification coefficient (Q2LOO, 0.66–0.89; Q2F1, 0.62–0.78; Q2F2, 0.60–0.73). The investigated quantitative relationships between molecular structure and PNECs indicated that 18 autocorrelation descriptors, 3 information index descriptors, 4 barysz matrix descriptors, 6 burden modified eigenvalues descriptors, and 1 BCUT descriptor were important molecular descriptors affecting the PNECs of PHDs. The obtained results supported that PNECs of PHDs can be accurately estimated by the influencing molecular descriptors and the quantitative relationship from the developed QSAR models, that provided a new feasible solution for ESTs derivation in the ecological risk assessment.

Data-driven design approach for the lateral-distortional buckling in steel-concrete composite cellular beams using machine learning models

Composite steel-concrete beams are presented as an interesting solution for covering long spans. These profiles are characterized by the existence of sequentially spaced openings along the beam web. A very common particular use of this type of structure is in continuous or semi-continuous floors, in which the existence of intermediate supports in the span of the structural member leads to the occurrence of points with negative bending moment. Steel structures in this condition can present different types of instability due to the compression of the bottom flange, such as lateral-distortional buckling and web-post buckling. In this context, a comprehensive parametric study was carried out through finite element simulation aiming to obtain the resistance capacity of this type of structural system. It was observed that different analytical procedures aimed at calculating the ultimate moment of this type of structure fail to predict the bearing capacity of the elements successfully. Thus, aiming to promote more precise design approaches, the modeling of the resistant load of these structures was performed using different supervised machine learning techniques. It was found that machine learning techniques have the potential to correctly predict the bearing capacity of the studied members, having a better agreement with the numerical data obtained, when compared to the approaches presented by analytical procedures described in the literature and design codes. Different machine learning algorithms were trained based on a dataset obtained via FEM: artificial neural networks, support vector machines, XGBoost and Random Forest. All models showed considerable effectiveness, with statistical determination coefficients (R²) higher than 0.9. Among these, the best prediction capacity was obtained with the XGBoost model in terms of correlation coefficient and Mean Square Error, presenting itself as the model that came closest to the set of numerical data used as reference.

Mentha spicata drying via infrared: Mathematical modeling and evaluation of two‐step temperature potential for improving drying characteristics

AbstractTo assess the effects of a two‐step temperature method (TTM) on dehydration parameters, color deterioration of dried samples, and specific energy consumption (SEC), spearmint leaves were dried using infrared (IR) undergoing the TTM. Moreover, 14 mathematical models were compared based on the adjusted correlation coefficient, reduced chi‐square, and root mean square error to estimate drying curves. Changes in the color of the dried product at two‐step temperatures of 70to60°C, 60to50°C, and 50to40°C led to decreases of 2.69, .81, and 3.34 compared to fixed temperatures of 70, 60, and 50°C, respectively. On the other hand, the specific energy consumption (SEC) increased by 5.43, 10.92, and 22 MJ.g−1 H2O at 70to60°C, 60to50°C, and 50to40°C compared to 70, 60, and 50°C, respectively. To interpret the results from another point of view, three basic objectives of shortening drying time, reducing energy consumption, and raising the quality of the dried product are achieved in the TTM. For instance, the drying time and energy consumption are reduced at a two‐step temperature of 70to60°C compared to the fixed temperature of 60°C, along with a decrease in the color change compared to the fixed temperature of 70°C. The results indicated that Midilli et al. model resulted in the best fitness in explaining the IR drying behavior of spearmint leaves.Practical applicationsDuring the falling rate period in drying process and especially at higher temperatures and powers, there is a potential for a decrease in the quality of the dried product, and even the risk of thermal runaway due to excessive temperature increase. Certainly, at lower powers and temperatures, the quality of the dried product can be enhanced. However, in this situation, the duration of the process and the energy consumed, which are two crucial factors in the drying process, increase, leading to financial losses for the producer. In the two‐step temperature method (TTM), due to the temperature reduction during the critical period of falling rate, the quality of the dried product improves by considering the processing time and energy consumption.

Notice of Violation of IEEE Publication Principles: A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids

Notice of Violation of IEEE Publication Principles A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids, by H. R. Baghaee; M. Mirsalim; G. B. Gharehpetian; H. A. Talebi; A. Niknam-Kumle, in the IEEE Transactions on Industrial Electronics After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This authorship of this paper was revised without providing attribution to, or permission from, the coauthors of an earlier version of the manuscript. Alireza Niknam-Kumle was responsible for removing the original coauthor names and replacing them with new coauthors, and then resubmitting the content in a new manuscript to IEEE Transactions on Industrial Electronics Due to the nature of this violation, reasonable effort should be made to remove all past and future references to this paper. Control of electronically-coupled distributed energy resources and topology/switching strategy of power converters have the essential roles to provide high quality power for microgrids including unbalanced and nonlinear loads. Multi-level inverters (MLIs) are used to act as high quality converters with more conversion efficiency, and less switching-frequency/switching-losses. Nevertheless their nonlinearity, complexity, and solvability features of selective harmonic elimination strategy that has affected its industrial applications, it can provide desirable output waveform by keeping fundamental component in its desired value as well as eliminating/minimizing of low-order harmonics. This paper presents adaptive neuro-fuzzy inference system (ANFIS) as a switching angle estimator for harmonic optimization problem in cascade MLIs, which is trained based on the database including optimal switching angles obtained for inverter with non-equal DC sources by artificial bee colony (ABC) algorithm. The reliable search ability, accuracy and convergence time of ABC have been compared with particle swarm optimization algorithm. Also, performance of ANFIS is compared with artificial neural network based on correlation coefficient and root mean square error. Simulation results, experimental tests and real-time simulations reveals the effectiveness of proposed estimator for online elimination of low-order harmonics of MLIs in microgrids and its superiority over other methods.

Prediction of the removal of solid suspensions and chemical oxygen demand from a pharmaceutical wastewater plant using a neural network approach

This study aimed to model the removal efficiency of chemical oxygen demand (COD) and solid suspension (SS) from a real pharmaceutical wastewater treatment plant (WWTP) using artificial neural network-multilayer perception (ANN-MLP). The ANN model was developed using experimental data which were collected during four years. The input variables of the neural network are water pH, temperature, SS, and COD. The percentages of removal of COD (CODRE) and of SS (SSRE) are considered as output variables. The Levenberg–Marquardt algorithm was utilized to train ANN. It was found that the ANN architecture has two hidden layers with 8 neurons. The results of ANN models were compared with the measured data based on the correlation coefficient (R2) and mean square error (MSE). It was noticed that the best ANN model provides good accurate results with R2 values of 0.9783 for SSRE and 0.9826 for CODRE, and a value of MSE equal to 1.695 10−3. This study may aid the users to adjust operational parameters in recovering COD and SS in the case of the process treatment of industrial effluents.

Assessment of desertification in the brazilian semiarid region using time series of climatic and biophysical variables

The municipality of Irauçuba, located in the Semiarid region of Ceará, Brazil, has faced the effects of desertification resulting from the scarcity of rainfall. This study aimed to assess the progression of desertification in the area from 2003 to 2020, using climatic and biophysical data collected during this period. We used supplementary data from automatic meteorological stations operated by the National Agency of Water and Basic Sanitation to determine the rainfall anomaly index of the historical series. Additionally, we performed annual scale validation of the CHIRPS data using statistical indicators such as Pearson Correlation Coefficient, Mean Error, Percentage BIAS, and Root Mean Square Error. Subsequently, we analyzed land cover and land use changes over the 17 years (2003-2020) in the municipality by processing CHIRPS, MODIS product (MOD16A2 and MOD11A2), and MapBiomas data in the Google Earth Engine digital platform and QGIS software. We analyzed land cover and land use changes using MapBiomas data. Finally, we analyzed the aridity index for the same 17-year period. The research findings revealed a significant correlation between rainfall and biophysical parameters, with the lowest aridity index of 0.255 observed between 2012 and 2014 and the highest of 0.464 between 2018 and 2020. The area's susceptibility to the desertification process was classified as high, which is consistent with the Semiarid climate of the region. The methodology employed during the research proved to be a sustainable technology of low economic cost and telling and easy access, which enabled the creation of a scientific contribution for future works in the region, besides being able to add knowledge for studies of environmental degradation.

Development of Reverse Phase HPLC Method and Validation: Sofosbuvir and Velpatasvir Quantification in Bulk and Tablets

A new reverse-phase high-performance liquid chromatography (RP-HPLC) method for measuring the amount of sofosbuvir and velpatasvir in active pharmaceutical ingredients (API) and tablet forms has been developed and the method was also validated. Discovery column C18 150 mm x 4.6 mm, pore size of 5 μm was used for the analysis of drugs. Elution of drugs was achieved with 0.1% OPA Buffer: Acetonitrile mixed in the proportion of 40:60 v/v was used as mobile phase, maintaining 1.0 mL/min as flow rate, column temperature maintained at 30°C and uv detector wavelength was fixed at 272 nm. Sofosbuvir and velpatasvir were eluted at retention time (RT) of 2.183 and 3.038 minutes, respectively. Linearity parameter was performed in the levels from 25 to 150% and the square of correlation coefficient (r2) was found to be 0.9992. The %recovery obtained for sofosbuvir was 99.6 and for velpatasvir was 99.4. In precision %RSD for sofosbuvir was found to be 0.4 and for velpatasvir was 0.9. Validation was performed for the developed method and all of the validated parameter results obtained was within the ICH Q2 guidelines acceptance range. So, the developed RP-HPLC method can be used in pharmaceutical industry for quantification of sofosbuvir as well as velpatasvir at once in API and tablet dosage form.

Social Biases and Equity Investment Decisions of Individual Investors: Behavior Finance Perspective

Individual investors may not always pose a rational decision on stock market and heavily guided by their behavioral and social biases. This study is delineated to examine the role of social factors in the decision-making process of individual investors. This study is based on quantitative research approach and utilized a 5-points Likert type closed-end structured questionnaire survey to collect the data from 269 individuals, invested in the firms listed at Nepal Stock Exchange (NEPSE). This study utilized reliability analysis, descriptive statistics, Pearson correlation coefficient and ordinary least square (OLS) method. Evidence indicates that herding, social interaction and media effect shows the positive influences on equity investment decision process of the investors. Finally, this study justified that social biases play the significant role for equity investment decisions of the individual investors through excessive follow the humor of the market place, over or under reaction of the information about securities, follow the suggestions and recommendations of peers, friends, family members and neighbors along with highly depend upon news, social medias and information of so-called experts of capital market that leads to investment mistakes. Hence, investors should strongly follow the proper information about stock and market, utilize their own knowledge about information processing and analysis, and try to reduce the biases while making decisions through information analysis and processing that may downsize the irrational decisions.