Coefficient Of Determination Values Research Articles

Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable groundwater

Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L−1) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.

The Influence of Return on Asset, Debt to Total Asset Ratio, and Economic Value Added, on Company Value with Dividends as a Moderating Variable in Industrial Sector Companies

The purpose of this study is to determine the Effect of Return On Assets, Debt To Total Assets Ratio, and Economic Value Added on Company Value with Dividend Policy as a Moderating Variable in Industrial Sector Companies. The sampling technique in this study is purposive sampling. So that the number of samples obtained is 54 companies. The tests used are Normality Test, Multicollinearity Test, Heteroscedasticity Test, Autocorrelation Test, Interaction Test / MRA. The correlation coefficient value is 0.177, meaning that between ROA, DAR, EVA and Dividend Policy on company value, the relationship is low. The determination coefficient value (R²) or R square obtained is 0.031, indicating that 3.1% of the influence on company value is explained by the variables ROA, DAR, EVA and Dividend Policy. While the remaining 96.6% is influenced by other variables not examined in this study.

Pengaruh Kompensasi dan Beban Kerja terhadap Kinerja Karyawan (Studi pada PT. Dipa Puspa Labsains Jakarta Selatan)

The aim of this research is to determine the effect of compensation and workload on the performance of PT Dipa Puspa Labsains South Jakarta employees, both partially and simultaneously. The method used is quantitative. The sampling technique used the Slovin formula and a sample of 99 respondents was obtained. Data analysis uses validity tests, reliability tests, classical assumption tests, regression analysis, correlation coefficients, coefficients of determination and hypothesis testing. The results of this research show that partially there is a positive and significant influence between compensation on employee performance with the simple linear regression equation Y= 24.957 + 0.367X1. The correlation value is 0.576 and the tcount value is 6.948 > ttable 1.985 with a significance of 0.000 <0.05. Partially there is a positive and significant influence between workload on employee performance with the simple linear regression equation Y = 15.317 + 0.602X2. The correlation value is 0.536 and the tcount value is 6.259 > ttable 1.985 with a significance of 0.000 <0.05. Simultaneously there is a positive and significant influence between compensation and workload on employee performance with the multiple linear regression equation Y = 16.210 + 0.253X1 + 0.332X2. The coefficient of determination value is 62.3%. And the Fcount value is 30.428 > Ftable 3.09 with a significance level of 0.000 < 0.05.

Shrinkage and tree-based regression methods for the prediction of the live weight of Akkaraman sheep using morphological traits.

The prediction of live weight (LW) is of critical importance to farmers in a range of applications, including breeding and monitoring animal growth. In this context, Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net as shrinkage methods, and Classification and Regression Trees (CART) and Random Forest (RF) as tree-based regression methods were used in this study to predict LW of Akkaraman Sheep at 6-month age using sex, birth weight (BW) and some morphological traits such as withers height (WH), chest depth (CD), body length (BL), chest width (CW), rump height (RH), and chest circumference (CC). The dataset of 100 sheep, consisting of 44 males and 56 females, was randomly divided into training and test sets with a ratio of 80% and 20%, respectively. 10-fold cross-validation method was implemented using the training set to obtain optimum regression models and avoid overfitting. A test set was used to compare the prediction performance of regression methods based on various comparison criteria. Results revealed that LW was significantly correlated with all morphological traits and BW with coefficients ranging from 0.216 to 0.757. RF outperformed the other regression models with a coefficient of determination value (R2) of 0.865, followed by Ridge (R2 = 0.761), LASSO (R2 = 0.755), Elastic Net (R2 = 0.750), and CART (R2 = 0.654). The results indicated that WH and CD contributed the most, while sex and BW contributed the least in constructing the optimum RF model. In conclusion, the use of RF is recommended for predicting the LW of Akkaraman sheep. These results can provide a data-driven approach to improve decision-making in animal breeding.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

The HPLC-PDA Method for Simultaneous Determination of Regalosides from Bulbs of Lilium lancifolium Thunb. and Their Antioxidant Effects.

Lilium lancifolium Thunb. is a herbal medicine that is widely used to treat inflammation and lung diseases. In this study, a simultaneous quantitative method was developed for the quality control of BLL using high-performance liquid chromatography coupled with a photodiode array detector (HPLC-PDA), and their antioxidant effects were evaluated. Eight regalosides (i.e., regaloside A, B, C, E, F, H, I, and K) were selected as marker substances and separated on a Gemini C18 reversed-phase analytical column by gradient elution with distilled water-acetonitrile mobile phase containing 0.1% (v/v) formic acid. The method was validated with respect to linearity, sensitivities (limit of detection (LOD) and limit of quantitation (LOQ)), accuracy, and precision. The antioxidant effects of the extract and each component were evaluated using the 2,2-diphenyl-1-picrylhydrazyl radical scavenging assay and 2-2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) assay. The coefficients of determination values used as indicators of linearity for all components were ≥0.9999. LOD and LOQ concentrations were 0.10-0.66 μg/mL and 0.29-2.01 μg/mL, respectively. The recovery was 95.39-103.925% (relative standard deviation; RSD ≤ 2.55%), and precision RSD was <2.78%. The HPLC-PDA method was applied to real samples, and all components were detected at 1.12-29.76 mg/freeze-dried g. The evaluation of antioxidant effects showed that regalosides C, E, and K exhibited significant antioxidant effects. Our knowledge will be appropriately utilized in raw material management and conducting clinical and non-clinical studies on L. lancifolium or herbal medicine prescriptions containing L. lancifolium.

Lag Plot: A Novel Method to Determine Viable Bacterial Cell Number via a Single OD Measurement.

Bactericidal activity is a valuable parameter which is considered and measured for antimicrobial compounds. The available standard protocol to evaluate bactericidal activity is based on the direct colony count. Colony counting requires serial dilution, plating, overnight incubation, and direct counting, which is time-and labor-intensive. In regard to eliminate direct plate count, novel techniques were developed based on the real-time growth monitoring which can come with some limitations and drawbacks. These drawbacks encourage us to develop a novel technique with simple procedure to determine viable bacterial cell count. In this procedure, real-time growth monitoring is not required. In fact, after an incubation time, the number of viable bacteria can be determined with a single OD measurement and through an equation. In this regard, four standard bacterial strains, including Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), and Bacillus subtilis (ATCC 23857), were cultured with descending inoculum densities (1.5 × 107 to 15CFU/mL) and growth curves were drawn. As expected, growth in the samples with lower inoculum densities recorded with longer lag phase. Also, a direct relation was observed between the recorded turbidities and initial cell counts. A logarithmic curve (lag plot) was obtained by plotting the OD, after 12-18h incubation, against initial cell counts. In all examined strains, R2 was calculated in the range of 0.96-0.99 which is acceptable value for coefficient of determination. Equation corresponding to the lag plot was obtained and called lag equation. This equation is applicable to calculate the number of viable cells in unknown samples simply by inoculation, incubation, and single OD measurement. Developed technique can be introduced as a simple substitution for labor- and time-intensive direct colony counting.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

Application of Machine Learning Methods in Predicting Lysine-specific Histone Demethylase 1 (LSD1) Inhibitors

Background: Lysine-specific histone demethylase 1 (LSD1) is a well-known anti-cancer target for drug discovery. Novel reversible inhibitors of LSD1 are desirable to be developed. Objective: This study aimed to build reliable predictive models to evaluate the antineoplastic efficacy of compounds and reveal the structural foundation underlying the inhibitory activity of LSD1. Methods: Multiple artificial intelligence algorithms were utilized in the development of quantitative structure-activity relationship (QSAR) models. A dataset comprising 915 compounds with welldefined IC50 values against LSD1 was assembled for analysis. The structures of these compounds were described by different descriptor packages. Principal component analysis (PCA) was performed to explore the chemical space distribution of each dataset. Y-randomization test and applicability domain (AD) analysis were deployed to validate the reliability of models. Results: For regression models, a consensus model was constructed by integrating the predictions of four top-performing individual models (SVM_ECFP4, RFR_PyDescriptor, RFR_RDKIT, and TRANSNNI), resulting in enhanced predictive performance as compared to the individual models. The consensus model achieved a determination coefficient (r2, for the test set) value of 0.82. For classification models, the consensus model was derived from the amalgamation of three individual models (ASNN_RDKIT, ASNN_ECFP4, and RFR_ECFP4), and the overall prediction accuracy of the model was 0.96 for external validation. Conclusion: The reliable models could be used to identify highly active LSD1 inhibitors for the purpose of efficient virtual screening. In addition, the important molecular properties and structural fragments derived from this work can provide guidance for the structural optimization of novel LSD1 inhibitors.

Effect of Implementing the Balanced Scorecard on the Management Performance of PT YKK AP Indonesia

This study aims to determine the effect of a balanced scorecard on the management performance of PT. YKK AP Indonesia. This study used 40 employees of PT. YKK AP Indonesia, who met the criteria to be respondents. This study was conducted using the conventional sampling method on PT employees. YKK AP Indonesia and analyzed using multiple linear regression. The first stage tested the validity and reliability of questions for each variable. The second stage conducted a classical test, and the third stage conducted an F test, t-test, and determination coefficient test. The results of the t-test study showed that the variables of financial perspective, customer perspective, internal business process perspective, and growth and learning perspective had a partial effect on management performance. In the F test, all independent variables simultaneously affected the dependent variable. Meanwhile, the determination coefficient value shows that the independent variables of financial perspective, customer perspective, internal business process perspective, and growth and learning perspective contributed to the dependent variable, namely management performance, by 49%, while other factors outside the model influenced the remaining 51%.

Pengaruh Lingkungan Kerja dan Disiplin Kerja Terhadap Kinerja Karyawan: Penilaian Kinerja Menggunakan Key Performance Indicator

This research aims to find out: 1) How the influence of work environment on employee performance at PT ARBA Group Gorontalo, 2) How the influence of work discipline on employee performance at PT ARBA Group Gorontalo, and 3) How the influence of work environment and work discipline on employee performance at PT ARBA Group Gorontalo. With 35 respondents determined using saturated sampling method. The approach in this study uses a quantitative approach with data collection techniques using a questionnaire (questionnaire). Data analysis techniques using multiple linear regression. Based on the results of the analysis it was found that: 1) Partially, the work environment has a significant effect on employee performance with t count&gt; t table, namely 2.718&gt; 2.036. 2) Work discipline has a significant effect on employee performance with t count&gt; t table, namely 4,770&gt; 2,036 and 3) Simultaneously the work environment and work discipline have a significant effect on employee performance at PT. ARBA Group Gorontalo with f count&gt; f table, namely 38,140&gt; 3,284. With a coefficient determination value of work environment and work discipline of 0.686 or 68.8% strong effect on employee performance at PT. ARBA Group Gorontalo. While 0.314 or 31.4% is influenced by other variables not explained in this study.

Optimization and Analysis of Dry Sliding Wear Behavior of AlCrTiSiN Coated SKD61 Steel Using Response Surface Methodology

The aim of this research is to investigate the wear behavior of SKD61 steel coated with AlCrTiSiN using the Filtered cathodic arc plasma deposition coating method while adhering to VDI 3198 standards for coating adhesion assessment. Dry sliding wear tests, conducted according to ASTM G133-05. The experiment is designed using response surface methodology (RSM) to determine the response parameter as the wear rate, with independent parameters of interest including load, sliding velocity, and sliding distance ranging from 5 to 11N, 0.1 to 0.2 m/s, and 250 to 550 m, respectively. Additionally, wear characteristics are analyzed using SEM-EDS analysis. The wear test results demonstrate that load had the most significant impact on the wear rate, followed by distance and speed, respectively. The target of achieving minimal wear rate was attained with a load of 5 N, a sliding velocity of 0.11 m/s, and a sliding distance of 550 m. Model significance testing revealed that the determination coefficient (R2) and adjusted determination coefficient values, indicating the accuracy of the model, were equal to 93.96% and 88.53%, respectively.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

The Effect of Service Prices and Service Quality on Consumer Satisfaction at PT. Paramita Ban Workshop Cipete, South Jakarta Period 2023

The purpose of this study is to determine the influence of service price and service quality on consumer satisfaction at the PT Paramita Ban Cipete Workshop in South Jakarta. The method used is quantitative. The sampling technique used saturated sampling and a sample of 98 respondents was obtained. Data analysis uses validity tests, reliability tests, classical assumption tests, regression analysis, correlation coefficients, determination coefficients and hypothesis tests. The results of this study are that the price of services has a significant effect on consumer satisfaction with a determination coefficient value of 25.2% and the hypothesis test is obtained t calculation &gt; t table or (4.788 &gt; 1.988). The quality of service did not have a significant effect on consumer satisfaction with a determination coefficient value of 74.8% and the hypothesis test was obtained by calculating t &lt; t table or (1,616 &lt; 1,988). Service price and service quality simultaneously have a significant effect on consumer satisfaction with the regression equation Y = 19,999 + 0.414 (X1) Y = 19,999 + 0.134 (X2). The value of the determination coefficient was 25.2% while the remaining 74.8% was influenced by other factors. The hypothesis test obtained the F value calculated &gt; F table or (17.764 &gt; 3.094).

Pengaruh Ukuran Perusahaan dan Working Capital Turnover terhadap Profitabilitas pada PT Unilever Tbk Tahun 2014-2023

This study purpose to analyze the Effect of Company Size and Working Capital Turnover (WCT) on Profitability (ROA) at PT Unilever Indonesia Tbk in 2014-2023. This study uses a quantitative method. The population and sample in this study are the Quarterly Financial Reports of PT Unilever Indonesia Tbk in 2014-2023. The sample in the study was 40 financial reports taken using saturated sampling techniques. The data analysis used in this study is multiple linear regression analysis using the SPSS 22 application. The results of the study indicate that partially the Working Capital Turnover (WCT) variable has an effect on Profitability (ROA). While the company size variable partially has no effect on Profitability (ROA). Simultaneously, the company size and Working Capital Turnover (WCT) variables have an effect on Profitability (ROA) with a determination coefficient value of 16.1%.

Pengaruh Penggunaan Girlband Blackpink Sebagai Brand Ambassador Terhadap Minat Beli Konsumen

This research is motivated by Shopee's inclusion in the e-commerce category which is the most recommended in the MarkPlus Inc survey and this study aims to find out the influence of Blackpink as a Brand Ambassador during the 12.12 Birthday Sale 2018 promotional event on consumers' buying interest. The results of this study used a quantitative approach using the survey method with non-probability sampling type data collection techniques. The results of this study showed a calculated t value of 7.075 greater than the table t of 1.66008. It states that in this study H1 was accepted, the statement that the Brand Ambassador had an effect on buying interest was strengthened by a correlation value of 0.576 which means that two variables have a significant relationship, with a determination coefficient value of 33% of the buying interest variable can be explained by the brand ambassador variable. In this study, it can be concluded that there is an influence of Blackpink as a Brand Ambassador during the Shopee 12.12 Birthday Sale 2018 promotion.

IMPACT OF PRODUCT QUALITY AND SERVICE QUALITY ON CONSUMER SATISFACTION

The rapid growth of food business in Indonesia also leads to increased competition. Restaurants should have the advantage of being able to compete in order to survive and win the competition in this food business. One way that can be used to achieve such excellence is to implement the right quality of service and product quality so as to improve customer satisfaction and create consumer loyalty. This research aims to analyse the impact of service quality and product quality on customer satisfaction and the customer loyalty in restaurants and to know which factors are influential in the restaurant. This research aims to determine the influence of product quality and service quality on consumer satisfaction at Mie Gacoan Kenjeran 2 Surabaya. The research method uses quantitative methods. The data used are primary and secondary data. Data was obtained by distributing questionnaires to a sample of 97 respondents. This research uses a purposive sampling method and the analytical tool used is multiple linear regression with the help of the SPSS 29 program. Partial test results show that product quality has a significant effect on consumer satisfaction, service quality has a significant effect on consumer satisfaction. For the simultaneous test, it shows that product quality and service quality have a significant effect on customer satisfaction. The coefficient of determination value is 0.665, meaning that the regression model obtained is able to explain that product quality and service quality variables can influence consumer satisfaction at Mie Gacoan Kenjeran 2 Surabaya by 66.5%. Meanwhile, the remaining 33.5% is influenced by other variables not explained in this research.

Keputusan Pembelian melalui Live Streaming E-Commerce Shopee, Online Customer Review, dan Free Gift (Studi Kasus pada Pelanggan MS Glow by Mirza)

This research aims to determine the influence of Shopee e-commerce live streaming, online customer reviews, and free gifts on purchasing decisions for MS Glow by Mirza. The research method uses quantitative methods. The data used are primary and secondary data. Data was obtained by distributing questionnaires to a sample of 100 respondents. This research uses a purposive sampling method and the analytical tool used is multiple linear regression with the help of the SPSS 29 program. Partial test results show that live streaming has a significant influence on purchasing decisions, online customer reviews have no significant influence on purchasing decisions, and free gifts have no significant influence on purchasing decisions. For simultaneous tests, it shows that live streaming, online customer reviews, and free gifts have a significant influence on purchasing decisions. The coefficient of determination value is 0.665, meaning that the regression model obtained is able to explain that the variables live streaming, online customer reviews, and free gifts can influence purchasing decisions for MS Glow by Mirza products by 66.5%. Meanwhile, the remaining 33.5% is influenced by other variables not explained in this research.

Predicting Particle Size and Soil Organic Carbon of Soil Profiles Using VIS-NIR-SWIR Hyperspectral Imaging and Machine Learning Models

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction.

Urine Glucose Detection Via Gold Nanoparticle Formation Using 3D-Connector Microfluidic Paper Based Analytical Devices

A metabolic disorders that have experienced a significant increase in the world are diabetes mellitus. Diabetes is caused by two main factors: the first is damage to pancreatic beta cells, which prevents insulin from being produced, and the second is impaired insulin secretion and function. Chronic diabetes, if not treated properly, can lead to acute complications including eye, kidney, lung, nerve, and even death. Diabetes can be diagnosed through blood and urine. In general, glucose detection is carried out using invasive methods that use blood samples, which can cause pain and discomfort for users. Current research is developing non-invasive glucose detection using urine samples. This research aims to develop non-invasive glucose detection technology using 3D-connector μPADs (Microfluidic Paper Based Analytical Devices) which have the advantages of being safe, easy, and simple. The three-dimensional connector on the device functions as a connector to facilitate the coordination of fluid flow in the sample zone and detection zone. The glucose detection method uses gold (III) chloride as a gold nanoparticle (AuNPs) precursor, an aqueous extract of Acalypha indica Linn as a stabilizing agent, sodium hydroxide (NaOH) as a catalyst, and glucose in artificial urine as a sample. Method validation results using imageJ software indicated linearity with a coefficient of determination value (R2) of 0.9714, precision with a %RSD value (Relative Standard Deviation) of 2.69, and an accuracy level ranging from 92.22-99.23%.