Correlation Coefficient R2 Values Research Articles

Excavating trajectory planning of electric shovel based on dynamic excavating volume prediction

Mining electric shovels are one of the core equipments for open-pit mining, and are currently moving towards intelligent and unmanned transformation, with intelligent mining instead of traditional manual operation. In the excavation operation process, due to the complexity and changeability of the material surfaces, different excavation strategies should be adopted to achieve the optimal excavation trajectory. It is an important research direction to realize the unmanned excavation of electric shovels by studying a trajectory planning method that is not limited to fixed resting angle surface, can comprehensively consider the type of material surfaces and aim at the minimum excavation energy consumption per unit volume. Therefore, an electric shovel excavation trajectory planning method based on material surface perception is proposed, which firstly obtains the point cloud data of the material surface through laser radar to perceive the excavation environment, and then carries out horizontal calibration and filtering processing on the point cloud data, and adopts Delaunay triangulation rule to realize the calculation of the dynamic excavation volume of the material. Then, the trajectory model of the dipper tooth tip is established by using the 6th polynomial interpolation method, and the minimum excavation energy consumption per unit volume is taken as the optimization objective, and the whale optimization algorithm is used to plan the excavation trajectories for four kinds of complex stacking surfaces (concave, convex, convex–concave and stepped stacking surfaces). Finally, an electric shovel scaled model test bench is built to experimentally verify the trajectory planning results of the simulation. The correlation coefficient R2 values between the test results and the simulation results are both greater than 0.85, and the deviation between the material amount in the bucket and the planned excavation volume is about 5%, which verifies the reliability of the trajectory optimization model and the accuracy of dynamic modeling.

Quantitative analysis of excipients to the permeability of BCS class III drugs

BCS III drugs exhibit high solubility and low permeability, and some excipients were reported to increase their permeability. Although some permeability-enhancing excipients were investigated, permeability-enhancing strategy still need to be improved. Firstly, we established a database and analyzed the possible effects of excipients. Sodium lauryl sulfate (SLS) was found to be the most-used permeability-enhancing excipients. Moreover, the quantitative models for predicting Papp and Peff of BCS III drugs with SLS were developed, and statistically meaningful descriptors include molecular weight (MW), pKa, logP, solubility, hydrogen bond (HB) count, rotatable bond count (RBC), and topological polar surface area. The models demonstrated a good fit and effective predictive capability with all the correlation R2 values over 0.7. Hydrogen bonding remains the most significant factor in enhancing drug permeability with SLS, while hydrophilicity is also vital in this process. It was also found that MW, logP, pKa, and RBC play significant roles in paracellular transport. In summary, current research did the systematic and quantitative analysis of BCS III drugs and their excipients, which may accelerate formulation research on BCS III products.

Method Development and Validation for the Simultaneous Estimation of Lobeglitazone Sulphate and Glimepiride by HPLC Method.

A simple, precise, and accurate reverse-phase high-performance liquid chromatography (RP-HPLC) method was developed for the simultaneous estimation of lobeglitazone sulfate and glimepiride in bulk and tablet dosage forms. The method involved selecting various chromatographic parameters. Specifically, a new method was developed using a 250 mm × 4.6 mm (HSS) reverse-phase C18 column with 5 µm particle size (Shim-pack C18, 250 × 4.6 mm; 5 µm). The mobile phase consisted of 40 volumes of phosphate buffer (pH 3) and 60 volumes of acetonitrile, with methanol as the diluent, running as an isocratic elution. The flow rate was set at 1.0 mL/min, and UV detection was performed at 254 nm. The injection volume was 2 µL, and the total runtime was 10 minutes. The recoveries for lobeglitazone sulfate were found to be in the range of 101% to 100.6%, while those for glimepiride were in the range of 101.5% to 100%. The method’s accuracy is evident from these results. The inter-day and intra-day precision of the new method were both below the maximum allowable limit (RSD% ≤ 2.0), as per International Council on Harmonisation (Q2 R1) guidelines. The method exhibited a linear response, with correlation coefficient (r2) values of 0.9972% for pioglitazone and 0.998 for glimepiride. The validation parameters, such as accuracy, precision, linearity, specificity, stability in the analytical solution and robustness, met the acceptance criteria. Hence, this method can be effectively used for the simultaneous estimation of lobeglitazone sulfate and glimepiride in both bulk and pharmaceutical dosage forms.

Stability-Indicating HPLC Method Development and Validation for the Simultaneous Determination of Vildagliptin and Dapagliflozin in Pharmaceutical Dosage Form.

Vildagliptin (VD) and Dapagliflozin (DP) were determined simultaneously by RP-HPLC using an ultraviolet (UV) detector, a Hypersil Gold C18 (250 × 4.6 mm) column, 5 µm and a mobile phase of acetonitrile: water (adjusted with O-Phosphoric acid to pH 3) in ratio 40: 60% v/v. The estimation wavelength was chosen to be 213 nm. VD and DP were shown to have retention times of 2.9 and 8.3 minutes, respectively. Q2 (R1) ICH guidelines were followed during the validation process. VD and DP correlation coefficient R2 values are 0.9993 and 0.999, respectively. While DP’s linearity range is 1 to 6 µg/mL, VD’s is 10 to 60 µg/mL. The precision and accuracy studies show %relative standard deviation (%RSD) below 2%. Recovery% was assessed to meet the ICH Q2 (R1) guidelines criteria. The presented research on forced degradation showed stability indicating studies. The findings also demonstrated that the suggested method is suitable for determining VD and DP precisely and accurately.

Simultaneous determination of Rosuvastatin calcium and Bempedoic acid in fixed-dose combination pharmaceutical dosage forms using RP-HPLC method

A precise, simple, and, accurate method for simultaneous estimation of Rosuvastatin calcium and Bempedoic acid was developed by Reversed phase High-Performance Liquid Chromatographic (RP-HPLC). The validation of the developed method is done as per ICH guidelines. The method was developed on Waters HPLC using a Kromasil 100-5-C18 (4.6 × 250 mm) column with a detection wavelength of 220 nm. The mobile phase consists of 0.1 % OPA in water: Acetonitrile (50:50 v/v). The retention time for Rosuvastatin calcium and Bempedoic acid was found as 5.4 and 10.7 min respectively. The linearity range was established between 10.0–100.0 µg/ml for Rosuvastatin calcium and 45.0–450.0 µg/ml for Bempedoic acid. The correlation coefficients (r2 values) for Rosuvastatin calcium and Bempedoic Acid were 0.9985 and 0.9988 respectively, indicating a strong concentration–response relationship. The method was found to be specific as there was no interference with the peak of Rosuvastatin calcium and Bempedoic acid in presence of excipients. The % Recovery of the method was found to be 98.64 %, 99.35 %, and 98.27 % for Rosuvastatin calcium and 99.11 %, 99.16 %, and 99.46 % for Bempedoic Acid. Therefore, the method has good trueness. Repeatability analysis of 6 samples revealed %RSD values of 0.92 % and 1.41 % for Rosuvastatin calcium and Bempedoic Acid respectively. The method is precise as the mean %RSD for Intraday studies of Rosuvastatin calcium and Bempedoic acid was 0.6064 % and 0.5289 % respectively while the Interday studies were 0.8763 % and 0.4955 % respectively. The LOD and LOQ for Rosuvastatin calcium are 0.054 µg/ml and 0.163 µg/ml and for Bempedoic acid are 0.127 µg/ml and 0.387 µg/ml respectively. Also, the method is robust as there was no significant change in result upon changing the method parameter. The developed method is environment friendly and is suitable for the quality control analysis of Rosuvastatin calcium and Bempedoic acid either alone or in combination.

The Novel Method in Validating the Spectral Wavelength Optimization to Determine Archaeological Proxies by the Integration of Aerial and Ground Platforms

This research proposed a novel method for evaluating the ideal spectral wavelength by integrating the aerial remote sensing image and geophysical techniques to improve the detection of buried archaeological proxies via the spectral wavelength. Eight different wavelength ranges were compared to eight distinct spectral indices to conduct a more thorough vegetation analysis. The spectral information from eight various Vegetation Indices and two types of resistivity data was then analyzed using a Linear Regression model. Then, the novel "Constant Experimental Evaluation" method (CEE) was developed to improve the preliminary result. Subsequently, the results outlined newly optimal spectral wavelengths by enhancing correlation coefficient R2 values, before (R2=0.595) to (R2=0.86) after CEE application, with the spectral wavelengths values of (NIRnew=0.783µm) and (Rednew=0.627µm) along with the 50% of image enhancement. It suggests that the technique may be significant to any archaeological site that presents spectrally differently from its immediate environment.

Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging

The misuse of herbicides in fields can cause severe toxicity in maize, resulting in significant reductions in both yield and quality. Therefore, it is crucial to develop early and efficient methods for assessing herbicide toxicity, protecting maize production, and maintaining the field environment. In this study, we utilized maize crops treated with the widely used nicosulfuron herbicide and their hyperspectral images to develop the HerbiNet model. After 4 d of nicosulfuron treatment, the model achieved an accuracy of 91.37 % in predicting toxicity levels, with correlation coefficient R² values of 0.82 and 0.73 for soil plant analysis development (SPAD) and water content, respectively. Additionally, the model exhibited higher generalizability across datasets from different years and seasons, which significantly surpassed support vector machines, AlexNet, and partial least squares regression models. A lightweight model, HerbiNet-Lite, exhibited significantly low complexity using 18 spectral wavelengths. After 4 d of nicosulfuron treatment, the HerbiNet-Lite model achieved an accuracy of 87.93 % for toxicity prediction and R² values of 0.80 and 0.71 for SPAD and water content, respectively, while significantly reducing overfitting. Overall, this study provides an innovative approach for the early and accurate detection of nicosulfuron toxicity within maize fields.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a “V” -shaped oscillation change.

Abstract 335: BD®OMICS-Guard Sample Preservation Buffer preserves mRNA and cell surface epitopes for single-cell sequencing applications

Abstract Multiomics has emerged as a powerful tool to derive insights on the transcriptomic and proteomic data of human cancer samples and preclinical models of blood and tumor malignancies. Due to the sensitivity of sequencing techniques, sample quality and stability can potentially interfere with results. BD® OMICS-Guard Sample Preservation Buffer (SPB), a novel preservation solution, allows cells and bulk tissues to be stored for up to 72h at 4 °C, while reliably capturing the true biology of cells. This reagent gently preserves mRNA and protein integrity without the traditional cross-linking and harsh fixatives, increasing flexibility and expanding options for study designs. Here, we showcase the preservation properties ofthis reagent for multiomic readouts in human cells and mouse tissue across three different timepoints.BD® OMICS-Guard SPB’s preservation of mRNA from human PBMC and bulk mouse spleen tissues wasevidenced by WTA for differentially expressed genes in samples preserved for 24, 48 and 72h comparedto fresh/unpreserved controls. Protein epitope preservation was demonstrated via CITE-Seq assay withthe BD® AbSeq Human Immune Discovery Panel (human PBMC) or a 30-plex panel of anti-mouse BD®AbSeq Antibody-Conjugated Oligonucleotides (mouse splenocytes) and confirmed with multicolor flowcytometry. For both sample types, high correlation R2 values (>0.87) were calculated for the preservation time points versus fresh controls. Concomitant flow cytometry analysis of major cell types and surface protein expression was consistent with sequencing data for all samples. Specificity and sensitivity of major cell type markers and cell surface proteins across cell types and time is visualized in the BD®AbSeq Antibody-generated heatmap for each sample. Additionally, BD® OMICS-Guard SPB maintains mRNA integrity in targeted gene assays and VDJ full-length assays, suggesting robust preservation of select transcripts readily interrogated in single-cell profiling. Batch effect across time and technical replicates for both sample types was limited with the BD Rhapsody™ HT Xpress System and delineated by donor/sample specific tSNEs across time. We demonstrate that the BD® OMICS-Guard Sample Preservation Buffer effectively preserves both mRNA and surface proteins for single-cell experiments. BD® OMICS-Guard SPB allows the convenient preservation of samples such as tumor biopsies to be collected and stored for up to 72h at 4 °C while maintaining mRNA and protein integrity, allowing the flexibility for precious samples to be potentially shipped to a central sequencing site to ensure standardized sequencing workflows. For Research Use Only. Not for use in diagnostic or therapeutic procedures.BD and BD Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2023 BD. All rights reserved. Citation Format: Samuel X. Shi, Cynthia Sakofsky, Zhiqi Zhang, Samatha Vadrevu, Xiaoshan Shi, Hye-Won Song, Aruna Ayer. BD®OMICS-Guard Sample Preservation Buffer preserves mRNA and cell surface epitopes for single-cell sequencing applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 335.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Study on Deformation Behavior and Mechanical Properties of 42CrMo High-Strength Steel with Multi-Station Warm Upsetting

In order to find out the deformation behavior and mechanical properties of 42CrMo steel under warm upsetting conditions, the Gleeble-3500 thermal simulation testing machine was used to carry out a warm upsetting physical simulation experiment on 42CrMo steel. By controlling deformation temperature, strain rate, and constant temperature deformation pass, the microstructure evolution rule under different warm upsetting conditions was analyzed, and its hardness value was measured. Then, the simulation experiment is carried out based on the Deform-3D finite element platform. The results show that, with the increase in deformation temperature, 42CrMo steel has a temperature rise softening effect, which significantly reduces the peak value of rheological stress. At 650 °C, the maximum peak value of rheological stress is only 45.3% of that of cold upsetting deformation at room temperature, and the stress-strain curve tends to be gentle at the plastic deformation stage, which is the most suitable temperature for warm upsetting deformation. The maximum peak flow stress of 42CrMo steel increases with the increase in strain rate, but the number of deformation channels has little influence on the stress-strain curve. The warm, upsetting deformation can refine the internal grain structure significantly, and the grain refinement mechanism is mechanical crushing. When the temperature is slightly higher, the broken grain will recover, and the grain size will grow. During the process of warm upsetting, the strain rate has a great influence on the microhardness of the sample. The deformation pass has little influence on the hardness, and the hardness increases slightly with the increase in the deformation pass. Through the Deform-3D simulation, the correlation coefficient R and the average absolute relative error (AARE) between the simulation value and the experimental value were calculated, and the correlation coefficient R-value was 0.9948, and the average absolute relative error (AARE) was 2.05%, indicating that the simulation can accurately reflect the relationship between displacement and applied load.

A novel method to quantify chitosan in aqueous solutions by ultrahigh-performance liquid chromatography–tandem mass spectrometry

In this study, a novel and accurate quantitative analysis method for the direct determination of chitosan (CS) in aqueous solutions using ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) is presented. By detecting the mass spectrum response intensity of a series of CS characteristic ion pairs, the sample concentration (abscissa) was linearly fitted with the total ion current (TIC) response intensity of its characteristic ion pairs (ordinate). A reliable standard curve was derived for quantifying CS in the range of 125–4000 ng/mL. Under the detection conditions, this CS quantification method yielded acceptable specificity (no interference peak), linearity (with correlation coefficient (r2) values >0.999), precision (acceptable limit RSDr < 3 %, RSDR < 6 %), accuracy (RE within the acceptable limits of ±5 %), and stability (acceptable limit RE within ±5 %, RSDr < 3 %). Moreover, the applicability of measurement was verified when a series of substrates did not interact with CS in the solution. Results have verified the applicability of this method for determining CS content in different composites. This study provides a method for determining CS content with significant practical value and economic benefit.

Production and evaluation of activated carbon from Saudi Arabian Acacia Tortilis tree bark by microwave and low temperature activation process

In the present work, activated carbon was prepared from Acacia Tortilis tree bark, utilizing phosphoric acid chemical activation, and microwave irradiation (AC-MWI). Activated carbon was also prepared by conventional phosphoric acid chemical activation and low temperature carbonization (AC-CA). Characterization of the activated carbons were performed by proximate analysis adopting ASTM standard procedures. BET-specific surface area, total ash content, bulk density, moisture content, pH, pore volume and iodine number were determined. Comparison of characteristics for both carbons, with the characteristics of AC available in the literature and commercially available in the market was performed. Comparison showed that activated carbon produced from Acacia Tortilis tree bark is well comparable with the reported characteristics of AC in literature and commercially available in market. It was found that AC-MWI has higher BET-specific surface area than AC-CA (836 m2/g and 687 m2/g respectively). Results depict that there is an increase of 22.3% in microspores component and 21.7% BET- specific surface area obtained in AC-MWI as compared to AC-CA. Similarly, the corresponding pore volume obtained (4.97 cm3/g and 4.07 cm3/g respectively) demonstrating better values as compared to the commercial ACs (&lt;1 cm3/g). Additionally, it was found that AC can be produced by microwave irradiation in about 48.5% less time when compared to conventional low temperature heating. The adsorption study of produced ACs was performed using methylene blue as a contaminant. Study showed that MB removal rate initially increased with increase in contact time, which decreased with time until steady state was reached. Adsorption data of MB was fitted to Freundlich, Langmuir and Temkin adsorption isotherm models. All models show reasonable correlation, however, Freundlich isotherm is best to describe the MB adsorption on AC-MWI based on correlation coefficient R2 value (0.9985). The results revealed the feasibility of microwave heating for preparation of high surface area activated carbons from Acacia Tortilis tree bark.

Production and evaluation of activated carbon from Saudi Arabian Acacia Tortilis tree bark by microwave and low temperature activation process

In the present work, activated carbon was produced from Acacia Tortilis tree bark, utilizing phosphoric acid chemical activation, and microwave irradiation (AC-MWI). Activated carbon was also produce by conventional phosphoric acid chemical activation and low temperature carbonization (AC-CA). Characterization of produced activated carbons performed by proximate analysis adopting ASTM standard procedures. BET-specific surface area, total ash content, bulk density, moisture content, pH, pore volume and iodine number were determined. Comparison of characteristics for both carbon, with the characteristics of AC available in the literature and commercially available in the market was performed. Comparison showed that activated carbon produced from Acacia Tortilis tree bark is well comparable with the reported characteristics of AC in literature and commercially available in market. It was found that AC-MWI has higher BET-specific surface area than AC-CA (836 m2/g and 687 m2/g respectively). Results depict that there is an increase of 22.3% in microspores component and 21.7% BET- specific surface area obtained in AC-MWI as compared to AC-CA. Similarly, the corresponding pore volume obtained (4.97 cm3/g and 4.07 cm3/g respectively) demonstrating better values as compared to the commercial ACs (&lt;1 cm3/g). Additionally, it was found that AC can be produced by microwave irradiation in about 48.5% less time when compared to conventional low temperature heating. The adsorption study of produced ACs was performed using methylene blue as a contaminant. Study showed that MB removal rate initially increased with increase in contact time, which decreased with time until steady state was reached. Adsorption data of MB was fitted to Freundlich, Langmuir and Temkin adsorption isotherm models. All models show reasonable correlation, however, Freundlich isotherm is best to describe the MB adsorption on AC-MWI based on correlation coefficient R2 value (0.9985). The results revealed the feasibility of microwave heating for preparation of high surface area activated carbons from Acacia Tortilis tree bark.

Euphorbia grantii Oliv. standardized extract and its fraction ameliorate doxorubicin-induced cardiomyopathy in Ehrlich carcinoma bearing mice

Ethnopharmacological relevanceEuphorbia plants have long been used as traditional medicine in China, Europe, America, Turkey, India, Africa, Iran, and Pakistan because of its high medicinal value and health advantages especially as a remedy for several types of cancer. Aim of the studyDoxorubicin (DOX) is one of the most frequently prescribed drugs in cancer chemotherapy, with dose-limiting cardiotoxicity. The development of medicinal approaches to attenuate drug's toxicity represents an area of great concern in cancer research. Because research on this topic is still disputed and limited, we aim to investigate the potential of supplementation with Euphorbia grantii Oliv. on DOX-induced cardiomyopathy in Ehrlich carcinoma bearing mice. Materials and methodsThe high-performance thin layer chromatography (HPTLC) analysis of total methanolic extract (TE), and its bioactive dichloromethane fraction (DCMF) was applied for the determination of friedelin. Male BALB/c mice were used to keep the Ehrlich ascites tumor cells. The experiment was performed for a 2-weeks period. ResultsA good linearity relationship was found to be with correlation coefficient (r2) value of 0.9924 for the isolated friedelin. Limit of detection (LOD) and limit of quantitation (LOQ) was found to be 0.00179, and 0.000537 ng/band respectively for friedelin. The amount of friedelin in the TE and DCMF were determined by using calibration curve of standard as 106.32 ± 5.69 μg, and 159.2 ± 4.24 μg friedelin/mg extract, respectively. DOX-induced cardiomyopathy by decreasing the ejection fraction (EF) compared to the Ehrlich and negative control groups. It resulted in a decrease in the EF by 30 and 39% compared to the other groups. High and low doses of the TE and DCMF did not result in significantly different ejection fractions compared to the Ehrlich group. Co-administration of DCMF with DOX ameliorated the alteration in the serum CKMB and LDH levels. As revealed from histopathological study, DOX impairs viability of cardiac myocytes and DCMF could effectively and extensively counteract this action of DOX and potentially protect the heart from severe toxicity of DOX. ConclusionsFinally, our results indicated that Euphorbia grantii Oliv. would be the best option to reduce DOX adverse effects.

A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

Alfalfa (Medicago sativa L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation R2 values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an R2 value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa R2 values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m2 and 332.32 g/m2, and the biases were −0.47 g/m2 and −2.24 g/m2, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa.

Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

Focusing on sustainable development, the demand for alternative materials in concrete, especially for Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates on developing SCC by incorporating Rice Husk Ash (RHA) and Marble Powder (MP) as cement and filler replacements, respectively, while applying Machine Learning (ML) and Deep Learning (DL) techniques to forecast the CS of RHA/MP-based SCC. The research further evaluates material characteristics, with a strong emphasis on ML and DL for CS prediction. Concrete samples with various mixed ratios were cast and examined after 91 days to collect data for model application. In the experimental technique, 133 samples were gathered, and CS was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine aggregate, and water) in an 80:20 ratio. Various ML algorithms, including linear regression, ridge regression, lasso regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and boosting methods such as gradient boost (GB), XG boost (XGB), and adaptive boosting (ADB) are employed, along with the DL technique of backpropagation neural network (BPNN) with different optimizer algorithms (Adam, SGD, RMSprop) to predict CS in RHA/MP-based SCC. The predicted data is further validated using evaluation parameters such as R-squared (R2), mean squared error (MSE), normalized root mean squared error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Comparatively, ML ensemble algorithms and BPNN using Adam and RMSprop optimizers demonstrate high accuracy in predicting CS outcomes, indicated by their high coefficient correlation R2 values and low error values.

RP-HPLC Method Development, Validation Including Stability Indicating and Forced Degradation Studies for the Estimation of Pemetrexed in API and Pharmaceutical Dosage Form

Stability indicating analytical RP-HPLC (Reverse Phase- High performance Liquid Chromatography) method was developed for the analysis of the drug in the presence of its degradation products and subsequent validation of Pemetrexed that was conducted in API and pharmaceutical dosage form. Pemetrexed was subjected to different stress conditions as per International Conference on Harmonization (ICH) guidelines, stress conditions applied were including the effect of acid, base, oxidative, hydrolysis, thermal and photolytic degradation conditions. Chromatographic separation of API (Active Pharmaceutical ingredient) was achieved on Water’s HPLC instrument and ODS C18, 250x4.6 mm, 5µm particle size column with gradient elution of 0.1% ortho phosphoric acid buffer: Acetonitrile 80:20 ratio was taken as the eluent (mobile phase). The buffer pH was maintained at 3, the flow rate was 1.0 ml/min and wavelength were detected at 225 nm. The Retention time (RT) of Pemetrexed was Found at 2.9 minutes, column temperature was kept ambient and the runtime was 6 min. System suitability parameters like theoretical plates, tailing factor, and Asymmetry values were showed at 3391, 1.41, and 1.101 respectively. The percentage drug purity was found to be 99.99%. Validation of the method was proved that linear relationship over the range of 25-150 µg/mL, with linear regression curve (correlation coefficient) r2 value was noticed at 0.999 and LOD (limit of detection), LOQ (limit of quantification) values was established at 0.33µg/mL, 0.99µg/mL respectively. Recovery studies carried out at the concentrations of 50%, 100% and 150% with percentage recovery values of 100.06, 99.8 and 99.87 respectively. The precision of intraday was 0.64% and inter-day was 0.95%. Robustness of flow rate and mobile phase was changed even though the method was Robust. Forced degradation studies were also conducted to check the stability and suitability of this method to resolve the degradation products. Method validation was proved to be accurate, precise, linear, repeatable and robust. Thus, the accepted method can be proceeded for the estimation of Pemetrexed in pharmaceutical dosage form in a daily basis, due to their simplicity, suitability, accuracy, robustness and reproducibility.

The mediating role of engagement in the relationship between equal opportunities and employee performance

This study aimed to explore the relationship between employee engagement, equal opportunities, and employee performance in Bahraini retail businesses. Using a cross-sectional survey, data were collected from 231 employees and analyzed via SPSS and Smart-PLS, uncovering significant results. Out of the 249 participants invited to the survey, 231 responded. All the variables of the study showed different effects, with equal opportunity and employee engagement (EO → EE) considered the strongest hypotheses in this study at the significance level of 0.01 (β = 0.722, = 0.724, SD = 0.070, t = 10.324, p = 0.000), with f2 = 1.087 above 0.15 and less than 0.35, which was considered a high effect size and strongly supported the hypothesis proposed by the researcher. All hypotheses were supported. The R2 values of independent variables were above 38% for the variance of employee performance, which is considered an excellent fit to the data as evidenced by the squared multiple correlation R2 values for the dependent variables. Finally, EO → EE → JP mediating role indirectly affects JP through EO = 24%, which is considered an excellent effect size.The recent study confirmed a strong positive link between equal opportunities and employee performance, with employee engagement acting as a significant mediator. This suggests that promoting equal opportunities can boost performance, especially when coupled with employee involvement. These findings are important for HRM experts and executives, as they indicate that prioritizing fairness and fostering participation can enhance productivity.

Optimization of port injection of n‐decanol in a PCCI engine using response surface methodology

AbstractThis work aims to define the optimum n‐decanol fraction in the inlet port and the corresponding engine load for the better emissions and performance characteristics of a partially premixed charged compression ignition (PCCI) engine by response surface methodology (RSM). The numerical model based on multi‐linear regression was established using experimental data. For this, the influence of various proportions of n‐decanol through intake port including 10%, 20%, 30%, and 40% were experimentally investigated besides the primary injection of neem oil biodiesel in the volumetric ratio of 80% diesel and 20% neem biodiesel, namely NB20. The optimization using RSM is exploited to capitalize the brake thermal efficiency (BTE) and diminish the emissions including oxides of nitrogen (NOx), carbon monoxide (CO) emission, smoke opacity, and hydrocarbon (HC) emission. The n‐decanol fraction in the port injection of 31.43% and the engine load in terms of brake power of 2.950 kW were found to be optimum parameters with the maximum desirability of 0.752. The optimal responses for brake‐specific fuel consumption (BSFC), BTE, CO, HC, smoke, and NOx under these operating conditions were found to be 0.305 kg/kWh, 28.8%, 0.145%, 19.61%, 54.85 ppm, and 837.7 ppm, respectively. Likewise, the correlation coefficient R2 values for BSFC, BTE, CO, HC, smoke and NOx have been found to be 99.85%, 99.95%, 93.58%, 90.32%, 99.97%, and 99.93%, respectively. According to the study's findings, the RSM is a realistic method for calculating and enhancing a diesel engine's emission and performance values operating in PCCI mode and using n‐decanol and NB20 as fuels.