Precise Estimation Method Research Articles

Robustness of Actual Evapotranspiration Predicted by Random Forest Model Integrating Remote Sensing and Meteorological Information: Case of Watermelon (Citrullus lanatus, (Thunb.) Matsum. & Nakai, 1916)

Water scarcity, exacerbated by climate change and increasing agricultural water demands, highlights the necessity for efficient irrigation management. This study focused on estimating actual evapotranspiration (ETa) in watermelons under semi-arid Mediterranean conditions by integrating high-resolution satellite imagery and agro-meteorological data. Field experiments were conducted in Rutigliano, southern Italy, over a 2.80 ha area. ETa was measured with the eddy covariance (EC) technique and predicted using machine learning models. Multispectral reflectance data from Planet SuperDove satellites and local meteorological records were used as predictors. Partial least squares, the generalized linear model and three machine learning algorithms (Random Forest, Elastic Net, and Support Vector Machine) were evaluated. Random Forest yielded the highest predictive accuracy with an average R2 of 0.74, RMSE of 0.577 mm, and MBE of 0.03 mm. Model interpretability was performed through permutation importance and SHAP, identifying the near-infrared and red spectral bands, average daily temperature, and relative humidity as key predictors. This integrated approach could provide a scalable, precise method for watermelon ETa estimation, supporting data-driven irrigation management and improving water use efficiency in Mediterranean horticultural systems.

Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding.

Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.

Comparison of original and new South African formulae for age estimation in African Black and white children and adolescents using dental and hand-wrist methods.

Methods for age estimation in children involve measurements of: (1) the projections of open apices and tooth heights (T), (2) the total area of the carpal bones and the epiphyses of the ulna and radius (HW), and (3) a combination of these parameters (THW). This study aimed to compare the accuracy of the original formulae for T, HW, and THW in Black South African (BSA) and White South African (WSA) samples of children and adolescents. The sample comprised 556 subjects, 164 BSA (71 males and 93 females) and 392 WSA (171 males and 221 females) aged 6 to 16 years. The main sample comprised 556 subjects and was divided into two parts. The first part was used to develop new formulae specific to the South African population, while the second part, consisting of 183 subjects and referred to as the test sample, was used to evaluate the accuracy of these formulae and to quantify their uncertainty when applied to real cases. Adjusted R2 and standard errors of the estimate (SEEs) were calculated for each regression model. The mean absolute difference or error (MAE) between estimated and chronological age and the percentage of individuals within specific age ranges of ± 0.25, ± 0.5, ± 1, and ± 2 years of chronological age was used to compare the accuracy of formulae. Teeth showed the most significant underestimation in the training sample, -0.49 years in males and - 0.29 years in females, as much as -0.70 years in WSA males, while BSA males were overestimated by + 0.03 years. Combining teeth and hand-wrist, the new formula showed the best performance in age estimation compared to only teeth or hand-wrist, except in WSA males (-0.51 years). New South African-specific formula improved accuracy, especially in males, suggesting their suitability alongside the original Cameriere et al. methods for precise age estimation. The original regression formulae by Cameriere et al., along with the newly developed formulaespecific to South African populations, shows promise for age estimation, considering potential estimation errors and the accuracy rates demonstrated in this study.

Validation of dosimetry programs (Olinda & IDAC) for evaluation of absorbed dose in 177LuPSMA therapy of metastatic castration-resistant prostate cancer (mCRPC) using Monte Carlo simulation

PurposeClinical trials have yielded promising results for 177Lutetium Prostate Specific Membrane Antigen (177Lu-PSMA) therapy in metastatic castration resistant prostate cancer (mCRPC) patients. However, the development of precise methods for internal dosimetry and accurate dose estimation has been considered ongoing research. This study aimed to calculate the absorbed dose to the critical organs and metastasis regions using GATE 9.0 Monte Carlo simulation (MCS) as a gold standard to compare the OLINDA 1.1 and IDAC 2.1 software.Material and MethodsThis study investigated absorbed doses to different organs in 9 mCRPC patients during their first treatment cycle. Whole-body planar images were acquired at 1 ± 0.5, 4 ± 0.5, 24 ± 2, 48 ± 2, 72 ± 2, and 144 ± 2 h post-injection, with SPECT/CT images obtained at 24 ± 2 h. Absorbed doses were calculated for five organs and the entire metastasis regions using GATE, OLINDA, and IDAC platforms. The spherical method was used to determine and compare the absorbed doses in metastatic regions and undefined organs in OLINDA and IDAC Phantom.ResultsThe organ-absorbed dose calculations produced by GATE were consistent with those obtained from OLINDA and IDAC. The average percentage differences in absorbed dose for all organs between Monte Carlo calculations and the estimated from IDAC and OLINDA were -0.24 ± 2.14% and 5.16 ± 5.66%, respectively. There was a significant difference between GATE and both IDAC (17.55 ± 29.1%) and OLINDA (25.86 ± 18.04%) in determining absorbed doses to metastatic areas using the spherical model.ConclusionThe absorbed dose of organs in the first treatment cycle remained below tolerable limits. However, cumulative absorbed doses should be considered for the administered activities in the next cycles of treatment. While Monte Carlo, IDAC, and OLINDA results were aligned for organ dose calculations, patient-specific dosimetry may be necessary due to anatomical and functional changes. Accurate dose estimation for undefined organs and metastatic regions using the spherical model is significantly influenced by tissue density, highlighting the value of CT imaging.

Electrical conductivity as a precise method for salt content estimation in raw and cooked tuna meat

This study evaluates electrical conductivity (EC) for estimating salt content in raw and cooked Skipjack tuna (Katsuwonus pelamis) across various sizes and preparation methods. Tuna sizes ranged from 0.10 to 9.00 kg, and the analysis included moisture, fat, protein, ash, EC, and salt content. In raw tuna, EC exhibited a high correlation with salt content (R² = 0.84), with the prediction equation Y = 0.112X - 0.525. For cooked tuna, which had reduced moisture, fat, and salt compared to raw tuna, EC showed a strong relationship with salt content (R² = 0.89) and the prediction equation Y = 0.169X - 0.623. Canned tuna in brine demonstrated high EC effectiveness for predicting salt content (R² = 0.93 and 0.95 for with and without media, respectively). However, for tuna in oil, EC correlations were weak (R² = 0.06 and 0.01), indicating that oil interferes with ion movement. After validating the estimated salt content using EC against the actual salt content obtained by auto-titration, the results confirm that EC is a reliable and cost-effective tool for estimating salt content in raw, cooked, and brine tuna, although its applicability is limited for oil-based canned products.

Advancing nitrogen nutrition index estimation in summer maize using continuous wavelet transform.

Rapid and non-destructive diagnosis of plant nitrogen (N) status is crucial to optimize N management during the growth of summer maize. This study aimed to evaluate the effectiveness of continuous wavelet analysis (CWA) in estimating the nitrogen nutrition index (NNI), to determine the most suitable wavelet analysis method, and to identify the most sensitive wavelet features across the visible to near-infrared spectrum (325-1,025 nm) for accurate NNI estimation. Field experiments were conducted across two sites (Kaifeng and Weishi) during the 2022 and 2023 growing seasons using four summer maize cultivars (XD20, ZD958, DH661, and DH605) under varying N application rates (0, 80, 160, 240, and 320 kg N ha-1). Canopy reflectance spectra and plant samples were collected from the V6 to V12 growth stages. The wavelet features for each spectral band were calculated across different scales using the CWA method, and their relationships with NNI, plant dry matter (PDM), and plant N concentration (PNC) were analyzed using four regression models. The results showed that NNI varied from 0.61 to 1.19 across different N treatments during the V6 to V12 stages, and the Mexican Hat wavelet was identified as the most suitable mother wavelet, achieving an R ² value of 0.73 for NNI assessment. The wavelet features derived from the Mexican Hat wavelet were effective in estimating NNI, PDM, and PNC under varying N treatments, with the most sensitive wavelet features identified as 745 nm at scale 7 for NNI, 819 nm at scale 5 for PDM, and 581 nm at scale 6 for PNC using linear regression models. The direct and indirect methods for NNI estimation were compared using independent field data sets. Both methods proved valid to predict NNI in summer maize, with relative root mean square errors of 10.8% for the direct method and 13.4% for the indirect method. The wavelet feature at 745 nm, scale 7, from the direct method (NNI = 0.14 WF (745 nm, 7) + 0.3) was found to be simpler and more accurate for NNI calculation. These findings provide new insights into the application of the CWA method for precise spectral estimation of plant N status in summer maize.

Precise position estimation methods for differential-IRNSS using iterative algorithm

Precise position estimation methods for differential-IRNSS using iterative algorithm

More precise method of low-density lipoprotein cholesterol estimation for tobacco and electronic cigarette smokers: A cross-sectional study

More precise method of low-density lipoprotein cholesterol estimation for tobacco and electronic cigarette smokers: A cross-sectional study

A Novel Method and System Implementation for Precise Estimation of Single-Axis Rotational Angles.

Accurately estimating single-axis rotational angle changes is crucial in many high-tech domains. However, traditional angle measurement techniques are often constrained by sensor limitations and environmental interferences, resulting in significant deficiencies in precision and stability. Moreover, current methodologies typically rely on fixed-axis rotation models, leading to substantial discrepancies between measured and actual angles due to axis misalignment. To address these issues, this paper proposes an innovative method for single-axis rotational angle estimation. It introduces a calibration technique for installation errors between inertial measurement units and the overall measurement system, effectively translating dynamic rotational inertial outputs to system enclosure outputs. Subsequently, the method employs triaxial accelerometers combined with zero-velocity detection technology to estimate the rotation axis position. Finally, it delves into analyzing the relationship between quaternion and axis-angle, aimed at reducing noise interference for precise rotational angle estimation. Based on this proposed methodology, a Low-Cost, a High Accuracy Measurement System (HAMS) integrating sensor fusion was designed and implemented. Experimental results demonstrate static measurement errors below ±0.15° and dynamic measurement errors below ±0.5° within a ±180° range.

ESTIMATION OF TENOFOVIR BY UV VISIBLE SPECTROSCOPY

In view of the need for a suitable Spectroscopic method for routine analysis of Tenofovir(antiretroviral medicine) in formulations, attempts were made to develop simple, precise and accurate analytical method for estimation of Tenofovir in formulation. As Validation is a necessary and important step in both framing and documenting the capabilities of the developed method it was done in accordance with USP and ICH guideline for the assay of active ingredient. The method was validated for parameters like linearity, precision, accuracy, specificity, and robustness, limit of detection and limit of quantification. This method provides means to quantify the component. This proposed method was suitable for the analysis of Pharmaceutical dosage forms.The utility of the developed method to determine the content of drug in commercial formulation was also demonstrated. KEY WORDS: Tinofovir,Validation,UV Visible spectroscopy.

Evaluation of input variable selection methods in artificial neural networks for estimating missing daily precipitation

ABSTRACT This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation.

Precise and low-complexity method for underwater Doppler estimation based on acoustic frequency comb waveforms

Ocean observation has advanced rapidly in recent decades due to its crucial role in resource exploration and scientific research, with the Doppler factor being widely utilized. However, the precision of Doppler estimation is frequently constrained by frequency resolution. Traditional frequency estimation methods using single-tone signals face considerable challenges with low accuracy and poor robustness. In response, this paper introduces a novel Doppler-sensitive Acoustic Frequency Comb (AFC) for estimating the Doppler factor, enabling multiple measurements with a single transmission and reception of the signal. The proposed Combined Uneven Uncertainty (CUU) method based on AFC achieves a bias of less than 1.1×10-5, significantly surpassing the optimal result of 3.2×10-5 attained by other frequency estimation methods in the absence of noise. Compared to traditional single-tone methods, the AFC approach improves spectral leakage performance and enhances estimation accuracy without increasing computational complexity. Experimental results demonstrate that the CUU method realizes a difference performance of less than 3.4×10-6, notably lower than that of 3.2×10-5 induced by coherent spectral leakage in fast Fourier Transform (FFT).

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning.

Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.

Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method.

Development and validation of simple stability indicating RP-HPLC method for piperine estimation in Piper nigrum, marketed formulations and nanoformulation

The objective of the current research is to develop simple, rapid, precise and sensitive RP-HPLC method for piperine estimation and its application to marketed Ayurvedic and herbal formulations. A reversed-phase C-18 column (5 mm, 4.6 mm, 250 mm, ZORBAX) was used to achieve the separation of piperine. Optimal chromatographic conditions were established by using mobile phase consisting of methanol and water in a 75:25 ratio with flow rate of 1 mL/min and detection wavelength of 342 nm. The developed HPLC method to estimate piperine was validated in compliance with ICH Q2 (R1) guidelines. The newly developed method demonstrated linearity in the range of 0.5-16 μg/mL, with a regression coefficient R2 > 0.999. The accuracy of the developed method was confirmed by a recovery rate ranging from 98.20% to 99.42%. Precision was also ensured, with a relative standard deviation (%RSD) less than 2%. Additionally, the method exhibited robustness. The limit of detection and limit of quantification for piperine was found to be 0.00056 μg/ml and 0.0016 μg/ml respectively. Moreover, the degradation characteristics of piperine were examined in compliance with ICH Q1A (R2) guidelines. Piperine demonstrated pronounced degradation behaviour in both oxidative and thermal investigations. The developed analytical technique was successfully employed to quantify piperine in Piper nigrum, in commercially available Ayurvedic and herbal products and in nanotransfersomes. Also, the developed method was used to estimate the percent drug entrapped within nanotransfersomes. Therefore, it can be concluded that the established approach is well-suited for performing routine quality analysis of formulations containing piperine.

METHOD DEVELOPMENT AND VALIDATION OF RAPID ISOCRATIC RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF PARACETAMOL, CAFFEINE, AND PROPYPHENAZONE IN PHARMACEUTICAL FORMULATION

Objective: The objective is to develop a novel, rapid, simple, precise, accurate, and reproducible RP-HPLC method for simultaneous estimation of paracetamol, caffeine, and propyphenazone in bulk and a pharmaceutical dosage form. Method: Optimized chromatographic conditions were used for isocratic elution with Shimadzu C18 (4.6×250 mm, 5 μm), methanol, and 20mM phosphate buffer (60:40, v/v, pH 2.5) as mobile phase, flow rate 1.2 mL/min, and UV detector at λ max 272 nm. The method was validated for specificity, precision, linearity, accuracy, robustness, and solution stability as per the International Council for Harmonization (ICH) guidelines. Result: It was discovered that the retention time for paracetamol, caffeine, and propyphenazone was found to be 2.6 min, 3.0 min, and 7.5 min. The method proved to be rapid, simple, linear (R2>0.999), precise {relative standard deviation (RSD<2.0%)}, accurate (recovery 98–102%), sensitive, and robust. Conclusion: The proposed novel isocratic RP-HPLC method was found to be rapid (short run time of 10 min), highly selective, accurate, and sensitive. The method has been successfully applied to the simultaneous analysis of paracetamol, caffeine, and propyphenazone in a pharmaceutical formulation.

Diode Array Detector Based RP-UPLC Method for Simultaneous Estimation of Dapagliflozin Propanediol Monohydrate and Metformin

A simple, rapid, accurate, precise, and reproducible diode array detector-based RP-UPLC method for simultaneous estimation of dapagliflozin propanediol monohydrate and metformin hydrochloride in bulk and tablet dosage form has been developed and validated. The chromatographic analysis was carried out using Acquity UPLC HSS T3 C18 (50 × 2.1mm, 1.8μm) column with a mobile phase consisting of orthophosphoric acid (pH 3.00) and acetonitrile (55:45% V/V) in the isocratic mode, at a flow rate of 0.2mL/min, and eluents were the simultaneous measurement of dapagliflozin propanediol monohydrate and metformin hydrochloride in bulk and tablet dosage form using a diode array detector-based RP-UPLC technique has been developed and validated. The chromatographic analysis was carried out using an Acquity UPLC HSS T3 C18 (50 2.1mm, 1.8 m) column and an orthophosphoric acid (pH 3.00) and acetonitrile (55:45% V/V) mobile phase in the isocratic mode.at a flow rate of 0.2mL/min. Eluents were measured at 222nm. Metformin hydrochloride and dapagliflozin propanediol monohydrate were shown to have retention times of 0.845 minutes and 1.889 minutes, respectively. The linearity was established in the range of 5-15μg/mL for dapagliflozin propanediol monohydrate and 250-750μg/mL for metformin hydrochloride, which had a regression coefficient (r2) greater than 0.999. The % Recovery was found to be 98.77% - 99.57% and 99.15% -101.2% for dapagliflozin propanediol monohydrate and metformin hydrochloride respectively. In a short period,a larger number of samplescan be analyzed due to their short retention time and, therefore, will be economical for routine analysis in the pharmaceutical industry.

A technique for accurate FTIR determination of boron concentration in CVD homoepitaxial diamond layers

The significant technology progress for fabrication of semiconducting CVD diamonds poses an important task of developing a precise and non-destructive method for estimation the boron content in epitaxial layers. We propose a novel two-step technique to determine the boron concentration in homoepitaxial diamond layers from FTIR measurements. The essence of the method is to evaluate the epilayer thickness from reflectance spectra at the first step, and to recalculate and analyze FTIR spectra in terms of effective optical density at the second step. Spectra are divided into 3 wavelength regions with accurate accounting of passing radiation through a multilayered structure with different thicknesses of absorbing media for various absorbing mechanisms. We have demonstrated the benefit of the method for a set of samples with CVD homoepitaxial layers grown on various HPHT substrates. The data obtained from FTIR spectra are thoroughly compared to the charge carrier concentration derived from electrical capacitance–voltage measurements.

Development and Validation of Bioanalytical Method for Estimation of Rivaroxaban using RP-HPLC with Liquid liquid extraction in Human Blood Plasma and its application in Bioequivalence Study

Rivaroxaban is andirect acting oralanticoagulant and factor Xa inhibitor. A simple, selective, precise and rapid RP-HPLC method for estimation of Rivaroxaban (RIVA) in human blood plasma was developed and validated. The sample spike in plasma was extracted using liquid liquid extraction were extracted with the organic solvent ethyl acetate as organic solvent. Apixaban as an internal standard. The compounds were analysed by Agilent HPLC was used with control panel software using UV detector on a Inertsil ODS (250mm x 4.6mm ID;5μ) column with an Flow rate of 1.2mL/min, an isocratic mobile phase consisting of 0.02M Ammonium acetate buffer: Acetonitrile (70:30%v/v). Different sample pre-treatment techniques were evaluated, but Liquid Liquid extraction was found to be satisfactory, with good recovery values of 93.70% for RIVA. The developed method is validated by ICHM10 and USFDA guidelines over the concentration range of 5.00 to 200.00 ng/ml in human blood plasma with R² =0.9993. Within-day precisions and accuracy for RIVA were found in 0.36% to 4.73% and 92.58% to101.82% respectively. The validated RP-HPLC method has been used successfully for both preliminary pharmacokinetic studies and therapeutic drug monitoring

FractalRG: Advanced fractal region growing using Gaussian mixture models for left atrium segmentation

This paper presents an advanced region growing method for precise left atrium (LA) segmentation and estimation of atrial wall thickness in CT/MRI scans. The method leverages a Gaussian mixture model (GMM) and fractal dimension (FD) analysis in a three-step procedure to enhance segmentation accuracy. The first step employs GMM for seed initialization based on the probability distribution of image intensities. The second step utilizes fractal-based texture analysis to capture image self-similarity and texture complexity. An enhanced approach for generating 3D fractal maps is proposed, providing valuable texture information for region growing. In the last step, fractal-guided 3D region growing is applied for segmentation. This process expands seed points iteratively by adding neighboring voxels meeting specific similarity criteria. GMM estimations and fractal maps are used to restrict the region growing process, reducing the search space for global segmentation and enhancing computational efficiency. Experiments on a dataset of 10 CT scans with 3,947 images resulted in a Dice score of 0.85, demonstrating superiority over traditional techniques. In a dataset of 30 MRI scans with 3,600 images, the proposed method achieved a competitive Dice score of 0.89±0.02, comparable to Deep Learning-based models. These results highlight the effectiveness of our approach in accurately delineating the LA region across diverse imaging modalities.