Coefficient Of Determination Research Articles

PENGARUH AUDIT INTERNAL TERHADAP PENCEGAHAN FRAUD PADA PT POS INDONESIA (PERSERO) KOTA BANDUNG

This study aims to determine whether there is an influence of internal audit on fraud prevention at PT Pos Indonesia (Persero). To determine the influence between the variables studied, researchers use quantitative methods with descriptive and verifiative analysis. Data collection techniques were used through the distribution of questionnaires to 48 respondents. The data analysis methods used include: simple linear regression analysis, correlation coefficient analysis, hypothesis test with t test, and determination coefficient analysis. The results showed that the internal audit variable had an effect on fraud prevention by 63.6% with the category of strong relationships between variables

Eutrophication and HAB Occurrence Control in Lakes of Different Origins: A Multi-Source Remote Sensing Detection Strategy

Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins, leveraging water quality samples and data from the Sentinel-2 and Landsat 5.7–8 platforms. Chl-a, a well-correlated indicator of phytoplankton biomass abundance and eutrophication, was estimated using ordinary least squares linear regression to calibrate surface reflectance with chl-a concentrations. Temporal gaps between sample and image acquisition were considered, and atmospheric correction dedicated to water surfaces was implemented using ACOLITE and those specific to each satellite platform. The developed models achieved determination coefficients higher than 0.69 with mean square errors close to 3 mg/m3 for water bodies with low turbidity. Furthermore, the time series described by the models portray the seasonal variations in the lakes water bodies.

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.

Comparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase

Central Composite Rotatable Design (CCRD), a type of factorial design of experiment is among the most traditional methods used for optimizing bioprocesses, but, in recent years artificial neural networks (ANNs) have emerged as a promising approach for data modeling in bioprocesses. A comparative study between CCRD and ANN modeling for data treatment in the optimization of geranyl cinnamate biosynthesis using Novozym® 435 lipase was conducted. The most effective ANN architecture identified for predicting and maximizing the enzymatic synthesis of geranyl cinnamate was a 3-3-1 neurons model utilizing logsig activation function in the hidden layer. The ANN was trained using all available experimental data and demonstrated a strong fit to the experimental data, coefficient of determination near 1 (R2 = 0.9948) and low Sum-squared Error (SSE = 43.06). The polynomial model (CCRD; R2 = 0.9806; SSE = 161.56) indicated the same optimal conditions as the ANN model, predicting a temperature of 90.2°C, molar ratio of 1:5.68, and enzyme concentration of 18.6% w/w. Comparison of R2 and SSE values due to lack of fit between both models suggests that ANN predictions closely align with experimental conversion values of geranyl cinnamate. Although the polynomial model is feasible to be applied in enzymatic synthesis, it may be less precise in its predictions than ANN models.

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.

Analysis of the Influence of Brand Awareness and Service Quality on Customer Interest in Using Home Ownership Products (KPR) Griya Hasanah Study on: BSI KC Customer Tanjung Karang

Awareness of a brand provides expectations of customers, while service quality is a measure of the difference between consumer expectations and the service provided. Expectations and perceived service quality generate customer interest. However, the reality is that the brand awareness and service quality provided are good, but customer interest is still unstable, still experiencing increases and decreases in using Sharia KPR products. This study aims to determine whether brand awareness, service quality, and customer interest partially and simultaneously affect customer interest in using Sharia KPR at BSI KC Tanjung Karang and how Islamic economics views brand awareness and service quality. This research uses a quantitative approach method. Data were collected directly through distributing questionnaires to KPR customers. The sample used was 86 respondents using the Slovin formula taken by purposive sampling. The data obtained was processed using SPSS version 24. This analysis includes validity and reliability tests, preliminary analysis tests, multiple linear regression analysis tests, and hypothesis testing (t-test, f-test, and coefficient of determination (R2)). The results of the t-test show that brand awareness and service quality partially have a positive and significant effect on customer interest. And the results of the f test show that brand awareness and service quality simultaneously have a positive and significant effect on customer interest in using Sharia KPR products with an effect of count 82.524> ftabel 2.71 with a significance value of 0.00 < 0.05. Based on the results of the determination test (R2) of 0.657, this explains that the influence of brand awareness and service quality variables on customer interest is 65.7%. In comparison, the remaining 34.3% were influenced by other factors outside the research model. Keywords: brand awareness, service quality, customer interest

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g−1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin–Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R 2) and root mean square error (RMSE), the optimal network configuration was identified as a 5–11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

Feasibility of using in situ UV/Vis spectrometer probe for measuring COD in WWTP based on UASB reactor

The aim of this work is to report the use of a UV/Vis spectrometer for real time, in situ monitoring of the chemical oxygen demand (COD) in a municipal wastewater treatment plant (WWTP) operating with upflow anaerobic sludge blanket (UASB) reactor, installed in south of Brazil. The spectrometer was used according to the factory calibration procedures: global, local offset and local linear. COD probe measurements were compared to laboratory analyses for the calibration procedures. Continuous operation led to increasing errors, which was related to grime deposition on the probe window and avoided with periodic cleaning. Performance of the probe measurements versus laboratory analyses was evaluated with coefficient of determination, root mean squared error (RMSE), relative RMSE, RMSE to standard deviation ratio (RSR) and Student’s t-test. It is concluded that the best calibration procedures is the local linear, for the influent and the effluent of UASB reactor. The probe consists of an improvement to real time and in situ monitoring of South America WWTPs.

Advancements in Glucose Monitoring: A Thin Film ZnO-Nanoflakes Based Highly Sensitive Wearable Biosensor for Noninvasive Sweat-Based Point-of-Care Monitoring for Diabetes

In the contemporary global landscape, diabetes is a concerning presence that contributes to noteworthy mortality rates. The etiology of this condition involves a subtle interaction between insulin deficiency and elevated blood sugar levels. Against this backdrop, the necessity for prompt, accurate, and continuous noninvasive monitoring of glucose concentrations becomes notably evident. The electrochemical flexible glucose biosensor was fabricated by immobilizing glucose oxidase (GOx) on ZnO nanoflakes, possessing a 20nm thickness, and synthesized on an Au-coated stretchable PET film. The sensitivity of the flexible biosensor was determined by 29.97μA/decade/cm², and the minimum detection limit was 1μM. The coefficient of determination was 97.76%, which is significantly linear.

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

<p>Understanding the retention of organic compounds (OCs) is critical for membrane applications in water recycling. The objective of this study was to create an optimized model using Artificial Neural Networks for Quantitative Structure-Property Relationship (QSPR-ANN) to predict the effect of adsorption on the retention of organic compounds (OCs) by nanofiltration (NF) and reverse osmosis (RO). An optimal model (QSPR-ANNoptimal) characterized by a similar structure (13 neurons in the inputs layer, 11 neurons in the hidden layer, and 1 neuron in the output layer) is constructed to predict the effect of adsorption on the retention of organic compounds by membranes. A set of 273 data points was used to test the neural network. the data set was used 70% for training, 15% for validation, and 15% for testing. For the most promising neural network model, the calculated retention values were compared to the experimental retention values, and good correlations were found (the determination coefficient "R2 = 0.9872" and the root mean squared error "RMSE = 2.2743%" for the test phase). This indicates the good robustness of the established QSPR-ANN model and the possibility of predicting the various parameters that characterize the retention of OCs by RO/NF. Sensitivity analysis revealed that the effect of adsorption retention of organic compounds by reverses osmosis and nanofiltration membranes depends more precisely on two important interactions (hydrophobic/adsorption and steric hindrance).</p>

Reconstruction of Hourly FY-4A AGRI Land Surface Temperature under Cloud-Covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information

Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution.

Prediction of Dexterous Finger Forces with Forearm Rotation using Motoneuron Discharges.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination (R2) and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area.In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8mg/g and 100.7mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data.

Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment

Abstract. Air pollution monitoring using mobile ground-based measurement platforms can provide high-quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller-scale air quality sensors, it is important to address the need to assess these measurements in a scalable manner. We explore the collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches: a standard collocation assessment technique, in which the mobile platform is parked near the fixed regulatory site for a period of time, and an expanded approach, which uses measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed-reference-site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, CO, USA, and in the San Francisco Bay Area, CA, USA. Our 1-month Denver dataset includes both parked collocation periods near the fixed regulatory sites and general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, to the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (yearlong) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40 h running median converges to within ±4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be employed during large-scale monitoring campaigns using small sensors to identify potential measurement biases.

The Influence of E-Service Quality, Price, and Brand Image on Purchase Intention Islamic Perspective (Case Study on Shopee Customers in Cilegon City)

This study aims to investigate how consumers’ purchase decisions are influenced by the quality, cost, and brand perception of e-services. The sampling strategy utilized in this study is simple random sampling because it is not possible to sample every consumer in the city of Cilegon. The study’s sample consisted of 140 participants. The measurement method used is the Likert scale, and the data collection was done using a questionnaire. Among the analytical methods used are validity and reliability checks, normality checks, multicollinearity checks, heteroscedasticity checks, multiple linear regression analysis, hypothesis testing (t-test and f-test), and R2 tests for the coefficient of determination, using IBM SPSS Statistics 23 as a statistical tool. The results showed a strong correlation between brand perception, pricing, purchase intent, and e-service excellence. Keywords: E-Service quality, price, brand image and purchase intention

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management.

Machine Learning-based Extrapolation of Crop Cultivation Cost

It is important to comprehend the relation between operational expenses such as labour, seed, irrigation, insecticides, fertilizers and manure costs necessary for the cultivation of crops. A precise cost for the cultivation of crops can offer vital information for agricultural decision-making. The main goal of the study is to compare machine learning (ML) techniques to measure relationships among operational cost characteristics for predicting crop cultivation costs before the start of the growing season using the dataset made available by the Ministry of Agriculture and Farmer Welfare of the Government of India. This paper describes various ML regression techniques, compares various learning algorithms as well as determines the most efficient regression algorithms based on the data set, the number of samples and attributes. The data set used for predicting the cost with 1680 instances includes varying costs for 14 different crops for 12 years (2010-2011 to 2021-2022). Ten different ML algorithms are considered and the crop cultivation cost is predicted. The evaluation results show that Random Forest (RF), Decision Tree (DT), Extended gradient boosting (XR) and K-Neighbours (KN) regression provide better performance in terms of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) rate while training and testing time. This study also compares different ML techniques and showed significant differences using the statistical analysis of variance (ANOVA) test. The optimal hyperparameters for the ML models are found using the gridsearchCV and randomizedsearchCV functions, which improves the model's capacity for generalisation.

The Influence of Halal Labeling, Product Quality, and Price on Purchasing Decisions for Tteokbokki Products

The goal is to find out the influence of halal labeling, product quality, and price on purchasing decisions on tteokbokki products. This study used literature studies and questionnaires with data collection techniques using a Likert scale. The population of this study were students of Sharia Economics, University of Sultan Ageng Tirtayasa. This type of research uses correlational research, which is a type of research with a problem characteristic in the form of a correlation relationship between two or more variables. As a result of data processing with SPSS Version 26, the r square value at the coefficient of determination of .374 or 37.4% of the variables of halal labeling, product quality, and price affect the variables of purchase good decisions. The regression equation obtained KP = -3.293+.721 LH- .076 KPr+.679+e. The calculated F value is 7.379 and the resulting Ftable is 2.86. Fcount>Ftable (7.379>2.86), which means that Ho4 was rejected and Ha4 was accepted. It can be seen that simultaneously the variables of halal labeling, product quality, and price have a positive effect on purchasing decisions. But partially, product quality does not affect purchasing decisions. Keywords: halal labeling, product quality, price, purchase decision

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis

Objectives This study aims to provide a comprehensive overview of the role of quadratic polynomials in data modeling and analysis, particularly in representing the curvature of natural phenomena. Methods We begin with a fundamental explanation of quadratic polynomials and describe their general forms and theoretical significance. We then explored the application of these polynomials in regression analysis, detailing the process of fitting quadratic models to the data using Python libraries NumPy and Matplotlib. The methodology also included calculation of the coefficient of determination (R-squared) to evaluate the polynomial model fit. This study utilizes illustratively generated data to demonstrate the application of quadratic polynomials in Python for robust data analysis. Results Using practical examples accompanied by Python scripts, this study demonstrated the application of quadratic polynomials to analyze data patterns. These examples illustrate the utility of quadratic models in applied analytics. Conclusions This study bridges the gap between theoretical mathematical concepts and practical data analysis, thereby enhancing the understanding and interpretation of the data patterns. Furthermore, its implementation in Python, released under MIT’s license, offers an accessible tool for public use.

Evaluation of pesticide residues in rice marketed in Cape Verde by QuEChERS-LC/GC-MS analysis

In this work, a multiresidue pesticide analysis using both gas chromatography and ultra-high performance liquid chromatography coupled to mass spectrometry systems has been conducted to determine 151 pesticides in rice samples consumed by the population of Cape Verde. The extraction and clean-up procedures were carried out by the QuEChERS method. The validation of both methodologies achieved determination coefficients higher than 0.9902 in matrix-matched calibration, relative recovery values in the range 58–120% with relative standard deviation lower than 20%, and limits of quantification of the method in the range 0.008–0.017 mg/kg, which agreed with the legal limits established by both EU regulations and the Codex Alimentarius guidelines. Among the total of 98 rice samples analysed, 91 samples were contaminated by pesticides at concentrations up to 18.3 mg/kg, with pirimiphos-methyl, propiconazole, tebuconazole, and imidacloprid being the most frequently detected. Following EU regulations, 14 non-compliances were found in 13 samples, whereas only one non-compliance was identified according to the Codex. Besides, a risk assessment based on the dietary exposure was performed for the pesticides found in the analysed rice samples. The results showed the need to carry out stricter controls to ensure the security of food agricultural products to protect consumers’ health.