Normalized Root Mean Research Articles

Quantitative Relationship of Plant Height and Leaf Area Index of Spring Maize under Different Water and Nitrogen Treatments Based on Effective Accumulated Temperature

To optimize the growth management of spring maize, it is essential to understand the dynamics of plant height and leaf area index (LAI) under controlled water and nitrogen supply. This study conducted two-year field experiments (2022–2023) in Karamay, Xinjiang. Three irrigation levels (75%, 100%, and 125% of Crop Evapotranspiration (ETc)) and four nitrogen application rates (0, 93, 186, and 279 kg N/ha) were set. A logistic growth model was fitted using accumulated effective temperature as the independent variable to analyze the growth and development characteristics of spring maize under various water and nitrogen conditions. The results demonstrated that the logistic models, based on relative effective accumulated temperature, had a determination coefficient (R2) of over 0.99 and a Normalized Root Mean Square Error (NRMSE) of less than 10%. Irrigation extended the rapid growth phase of plant height, whereas nitrogen application shortened the time to enter this rapid growth phase and prolonged its duration. Irrigation increased the maximum LAI growth rate and shortened and prolonged the rapid growth phase, while nitrogen extended the duration of the rapid growth phase for LAI. The W2N2 treatment, consisting of 100% ETc irrigation and 186 kg N/ha, was identified as the optimal drip irrigation water–nitrogen combination for spring maize in the study area. Under optimal water and nitrogen supply, both the maximum growth rate and the average growth rate during the rapid growth phase were higher, requiring accumulated effective temperatures of 825.16–845.74 °C·d and 856.68–890.00 °C·d, respectively, to reach these rates. The appropriate water and nitrogen supply significantly enhanced the synergistic promotion of growth and development in spring maize. This study provides a theoretical basis for the quantitative analysis of growth dynamics in summer maize using effective accumulated temperature.

Effect of different constraining boundary conditions on simulated femoral stresses and strains during gait

Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µε), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for both healthy and pathological femurs.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m 2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using Pmetrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m 2 (range: 40-70.3 kg/m 2 ). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Relative truth value acquisition method of heterogeneous surface based on the Improved Point Spread Function

Abstract. The acquisition of pixel-scale relative truth value is important for remote sensing validation.The Point Spread Function (PSF) has been widely used in the field of the acquisition of relative truth value of heterogeneous surface. In this study, we propose an Improved Point Spread Function (IPSF) based on the Median Pixel Variability Weighted (MPVW) method and PSF to acquire relative truth value of heterogeneous surface. Firstly, the size of variance and clustering window are confirmed based on the pixel scale information of the satellite product to be validated. Secondly, the PSF suitable for heterogeneous surface is selected from 5 PSFs. Thirdly, the IPSF is constructed according to the MPVW and the PSF suitable for heterogeneous surface. Finally, the IPSF is used to acquire relative true value at pixel-scale of heterogeneous surface from airborne hyperspectral image. This study shows: (1) Good correlation between relative true value obtained using IPSF and reference value is indicated as R2 values among 256 channels reach 0.985, structural similarity (SSIM) ranges from 0.992 to 0.998, and peak signal noise ratio (PSNR) more than 35dB. (2) Better accuracy performance is observed in the acquisition of relative truth value of heterogeneous surface for IPSF than PSF. Compared with conventional PSF, the R2, PSNR and SSIM of the result of IPSF are increased by about 1.34%, 9% and 0.7% on the average. (3) Significant advantage of IPSF is also reported in reducing the deviation compared to PSF, as the average root mean square error (RMSEave) is reduced by 30.37% and the average mean absolute error (MAEave) is reduced by 35.98%, respectively. (4) Comparing the RMSE and MAE of PSF and IPSF result, the RMSE and MAE of the result of IPSF become smaller. The RMSE decreases from 3 ~ 195 to 2 ~ 131. The MAE changes from 2 ~ 138 to 1 ~ 89. Overall, the IPSF proposed in this paper can effectively calculate the relative true value of heterogeneous surface, which can provide reference for the validation of multi-spectral and hyper-spectral satellite products.

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches

The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m3/m3 and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications.

Impact of Real-Time Blind Slat Angle Control on Reducing Total Energy Consumption of a Library Building Using an Optimized Artificial Neural Network Model: A Case Study of the Six Moroccan Thermal Zones

Abstract The prevalence of extensive glazed areas in contemporary buildings contributes significantly to solar radiation infiltration, elevating energy demands and causing discomfort for occupants. Window shading devices play a pivotal role in addressing this challenge. This paper presents the development and optimization of an artificial neural network (ANN) predictive model, designed to enable real-time control of slat angles by predicting total energy loads, specifically during summer (for cooling and lighting purposes). The refined model demonstrates high precision, achieving a normalized root mean square error (nRMSE) of approximately 1.72% and a correlation coefficient (R) of around 0.999, despite utilizing limited meteorological data. Key inputs for the model include solar radiation, solar altitude, and external temperature, with a particular focus on slat reflectivity. The study assesses the efficiency of three slat types based on their reflectivity: high (80%), medium (50%), and low (20%). Additionally, the research explores the impact of window-to-wall ratio (WWR) values on the control system's efficacy, revealing a positive correlation between higher WWR values and improved energy savings through ANN slat angle control. Furthermore, the study extends the applicability of the ANN model to the six thermal zones in Morocco, affirming its generalization across diverse environmental conditions.

Sound pressure level determination around the transformer using coherent and incoherent analytical summation methods

In this paper, coherent and incoherent analytical summation methods from elementary sound sources on the transformer’s tank are presented. They aim to determine a one-third-octave spectrum of sound pressure levels (SPL) in points around the transformer. Each part of the transformer’s surface is treated as a separate plane source so the analytical calculation can be applied to find SPL in the surroundings. Reflections from the ground are also considered by putting an image of the plane sources on the other side of the reflecting plane and adding its contributions to the total pressure in the desired points. Grid-like vibration measurements of vibration velocity on the tank surfaces are used as input parameters. Vibrations and sound pressure levels are measured to validate the method on the 5 MVA transformer experimental object. The SPL around the transformer in short-circuit (SC) and open-circuit (OC) tests is measured in the semi-anechoic chamber to compare it with theoretical results. By analyzing the results, the coherent calculation with reflection provided the most accurate results. In the SC operating condition, the normalized root mean square error (NRMSE) is 17.2%, and in the OC operating condition, it is 10.9%. The novelty of the presented method is that it considers complicated transformer geometry where each surface is calculated as a separate noise source. It calculates noise at a distance from the elementary vibration sources, considering phase and reflection from the hard ground, and provides detailed noise maps that can be used for noise modeling around the substations.

Integrating NSGA-II and CFD for enhanced urban airflow prediction: Recalibration of closure coefficients for a nonlinear eddy viscosity model

Accurately predicting urban environmental airflows using the Reynolds-averaged Navier‒Stokes (RANS) approach presents significant challenges due to unsolved closure issues. Previous attempts to address these limitations through coefficient adjustments were constrained by isotropic assumptions, resulting in limited generalizability and reliability, especially for complex building configurations. In this study, we proposed a recalibration approach within a multi-objective optimization framework. Nondominant sorting genetic algorithm-II (NSGA-II) and computational fluid dynamics (CFD) numerical computations were utilized to optimize the closure coefficients of a nonlinear eddy viscosity (NLEV) model, with a focus on improving the accuracy of the mean velocity, turbulent kinetic energy, and airflow characteristic predictions. To ensure the robustness of the model, appropriate objective functions were carefully defined. The proposed approach was evaluated using three-dimensional (3D) building cases related to urban prototypes, and the results demonstrated the necessity of multi-objective optimization. Our findings indicated that the trade-off solution on the Pareto front consistently outperformed the baseline and single-objective optimal versions across different types of urban airflow, demonstrating superior generalizability. The simulation results of this solution exhibited closer agreement with the wind tunnel experimental data and provided more accurate distributions of the time-averaged values and airflow characteristics. The normalized root mean square errors are reduced by about 33 %, 52 %, 35 % and 9 % in velocity, and 31 %, 25 %, 24 % and 11 % in turbulent kinetic energy, respectively. These results underscore the effectiveness of the multi-objective optimization framework in addressing closure issues and improving the prediction accuracy of complex urban environmental airflows.

Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics

Background and objectiveTreatment planning through the diagnostic dimension of theranostics provides insights into predicting the absorbed dose of RPT, with the potential to individualize radiation doses for enhancing treatment efficacy. However, existing studies focusing on dose prediction from diagnostic data often rely on organ-level estimations, overlooking intra-organ variations. This study aims to characterize the intra-organ theranostic heterogeneity and utilize artificial intelligence techniques to localize them, i.e. to predict voxel-wise absorbed dose map based on pre-therapy PET.Methods23 patients with metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA I&T RPT were retrospectively included. 48 treatment cycles with pre-treatment PET imaging and at least 3 post-therapeutic SPECT/CT imaging were selected. The distribution of PET tracer and RPT dose was compared for kidney, liver and spleen, characterizing intra-organ heterogeneity differences. Pharmacokinetic simulations were performed to enhance the understanding of the correlation. Two strategies were explored for pre-therapy voxel-wise dosimetry prediction: (1) organ-dose guided direct projection; (2) deep learning (DL)-based distribution prediction. Physical metrics, dose volume histogram (DVH) analysis, and identity plots were applied to investigate the predicted absorbed dose map.ResultsInconsistent intra-organ patterns emerged between PET imaging and dose map, with moderate correlations existing in the kidney (r = 0.77), liver (r = 0.5), and spleen (r = 0.58) (P < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R2 = 0.92 for kidney). The DL model improved the mean slope of fitting lines in identity plots (199% for liver), when compared to the theoretical optimal results of the organ-dose approach.ConclusionOur results demonstrated the intra-organ heterogeneity of pharmacokinetics may complicate pre-therapy dosimetry prediction. DL has the potential to bridge this gap for pre-therapy prediction of voxel-wise heterogeneous dose map.

Assessing the long‐term effects of conservation agriculture on cotton production in Northeast Louisiana using the denitrification–decomposition model

AbstractConservation agriculture (CA) aims to sustain agricultural production, soil, and environmental health in agroecosystems and has been promoted throughout the United States. The adoption of CA in cotton (Gossypium hirsutum) systems provides both agronomic and environmental benefits. Yet, there is limited information on the long‐term effects of CA practices on crop yield and adaptation strategies. An integrated CA system, that is, cover crops with no‐tillage (NT) instead of conventional agriculture, was implemented in the long‐term field experiments and assessed with an integrated biogeochemical model. Using the denitrification–decomposition model, this study estimated the effects of four different cover crops, for example, native grass (NG), hairy vetch (Vicia villosa), winter wheat (Triticum aestivum L.), and crimson clover (Trifolium incarnatum), on cotton yield under four different nitrogen (N) levels (e.g., 0, 50, 100, and 150 kg N/ha) and estimated responses on carbon (C) sequestration, and ecosystem functionality over a 10‐year study. The NT‐NG 50 N was used as a calibration dataset to accurately estimate the cotton lint yield with a normalized root mean square error (NRMSE) of 21% and model efficiency of 0.3. The calibration data validated the effects of hairy vetch, winter wheat, and crimson clover under the NT‐50 N with NRMSE of 24%, 21%, and 25%, respectively. According to the scenario analysis, the 50 kg N/ha application with a single‐irrigation event (10‐cm depth) was most beneficial for maximizing the cotton yield with cover crop incorporation at the NT system over the long term. The effects of increasing cover crop biomass (i.e., double seed rate) on C content, regardless of N application rates, varied based on the relationship between the main and cover crop species. Besides, the furrow plow tillage system provided efficient C sequestration. The proposed approach stands to provide agricultural and environmental sustainability with the implementation of cover crop or crop residue incorporation instead of increased N application, seed rates, and irrigation events under NT practices.

Precision Irrigation Soil Moisture Mapper: A Thermal Inertia Approach to Estimating Volumetric Soil Water Content Using Unmanned Aerial Vehicles and Multispectral Imagery

To address the issue of estimating soil moisture at a hyper-resolution scale, a methodology referred to as Precision Irrigation Soil Moisture Mapper (PrISMM), that includes three key components, is developed: high-resolution remotely sensed optical and thermal data, surface energy balance modeling, and site-specific soil analysis. An Unmanned Aerial Vehicle/System (UAV or UAS) collects high-resolution multispectral imagery in the Dallas–Fort Worth metropolitan study area. Orthomosaics are converted to thermal inertia estimates in a spatially distributed format using the remotely sensed data combined with a set of surface energy balance modeling equations. Using thermal and physical properties of soil gained from site-specific soil analysis, thermal inertia estimates were further converted from thermal inertia to daily volumetric soil water content (VSWC) with a horizonal resolution of 8.6 cm. A ground truthing dataset of measured VSWC values taken from a Time Domain Reflectometer was compared with model results, producing a reasonable correlation with an average coefficient of determination of (R2) = 0.79, an average root mean square error (RMSE) = 0.0408, and mean absolute error (MAE) = 0.0308. This study highlights a practical approach of estimating VSWC for irrigation purposes while providing superior spatio-temporal coverage over in situ methods. The authors envision that PrISMM can be implemented in water usage management by relating VSWC with weather forecasts and evapotranspiration rates to develop time-based spatially distributed irrigation management plans.

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predicting in vivo brain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy. Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT. Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations. Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction. Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Mathematical modelling of stem and progenitor cell dynamics during ruxolitinib treatment of patients with myeloproliferative neoplasms

IntroductionThe Philadelphia chromosome-negative myeloproliferative neoplasms are a group of slowly progressing haematological malignancies primarily characterised by an overproduction of myeloid blood cells. Patients are treated with various drugs, including the JAK1/2 inhibitor ruxolitinib. Mathematical modelling can help propose and test hypotheses of how the treatment works.Materials and methodsWe present an extension of the Cancitis model, which describes the development of myeloproliferative neoplasms and their interactions with inflammation, that explicitly models progenitor cells and can account for treatment with ruxolitinib through effects on the malignant stem cell response to cytokine signalling and the death rate of malignant progenitor cells. The model has been fitted to individual patients’ data for the JAK2 V617F variant allele frequency from the COMFORT-II and RESPONSE studies for patients who had substantial reductions (20 percentage points or 90% of the baseline value) in their JAK2 V617F variant allele frequency (n = 24 in total).ResultsThe model fits very well to the patient data with an average root mean square error of 0.0249 (2.49%) when allowing ruxolitinib treatment to affect both malignant stem and progenitor cells. This average root mean square error is much lower than if allowing ruxolitinib treatment to affect only malignant stem or only malignant progenitor cells (average root mean square errors of 0.138 (13.8%) and 0.0874 (8.74%), respectively).DiscussionSystematic simulation studies and fitting of the model to the patient data suggest that an initial reduction of the malignant cell burden followed by a monotonic increase can be recapitulated by the model assuming that ruxolitinib affects only the death rate of malignant progenitor cells. For patients exhibiting a long-term reduction of the malignant cells, the model predicts that ruxolitinib also affects stem cell parameters, such as the malignant stem cells’ response to cytokine signalling.

A molecular and computational study of galbanic acid as a regulator of Sirtuin1 pathway in inhibiting lipid accumulation in HepG2 cells

Introduction Sirtuin1 (SIRT1) plays a crucial role in the pathophysiology of non-alcoholic fatty liver disease. We investigated the mechanistic role of galbanic acid (Gal) as a regulator of SIRT1 in silico and in vitro. Methods HepG2 cells were treated with Gal in the presence or absence of EX-527, a SIRT1-specific inhibitor, for 24 h. Sirtuin1 gene and protein expression were measured by RT-PCR and Western blotting, respectively. It has been docked to the allosteric reign of SIRT1 (PDB ID: 4ZZJ) to study the effect of Gal on SIRT1, and then the protein and complex molecular dynamic (MD) simulations had been studied in 100 ns. Results The semi-quantitative results of Oil red (p < .03) and TG level (p < .009) showed a significant reduction in lipid accumulation by treatment with Gal. Also, a significant increase was observed in the gene and protein expression of SIRT1 (p < .05). MD studies have shown that the average root mean square deviation (RMSD) was about 0.51 Å for protein structure and 0.66 Å for the complex. The average of radius of gyration (Rg) is 2.33 and 2.32 Å for protein and complex, respectively, and the pattern of root mean square fluctuation (RMSF) was almost similar. Conclusion Computational studies show that Gal can be a great candidate to use as a SIRT1 ligand because it does not interfere with the structure of the protein, and other experimental studies showed that Gal treatment with SIRT1 inhibitor increases fat accumulation in HepG2 cells.

Consecutive high-performance removal of Cu2+ metal ions and Deltamethrin using multifunctional pyrolysis cuttlebone/cotton fabric nanocomposite

A simple technique was developed for the modification of cotton materials that is inexpensive, environmentally friendly, and very effective. Waste Cotton fabrics (WCFs) are loaded with propolis extract (PE) for Cu2+ removal. Then, Cu2+ underwent a pyrolysis process with modified cuttlebone (CB) at 900 °C for 5 h. The surface of the prepared materials was characterized using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray (SEM-EDX), Fourier transform infrared (FTIR), BET, particle sizes, thermogravimetric analysis (TGA) and zeta potential analysis. The Cu2+ metal ions from an aqueous solution were removed using WCFs/PE, and DLM was subsequently removed using pyro WCFs/PE/Cu/CB. The as-prepared NPs exhibited the face-centered cubic structure of WCFs/PE/Cu/CB with crystallite sizes ranging from 386.70 to 653.10 nm. FTIR spectra revealed that CB was present on the surface of the resulting WCFs/PE/Cu. SEM revealed the dispersion of a uniformly flower-like morphology over a large area. Sorption studies were performed based on parameters that included pH, dose, contact time, and initial concentration. The adsorption isotherm and the kinetic studies of the DLM adsorption process were applied at a pH of 5.0 and a temperature of 25 °C using several isotherms and kinetic models. The results revealed qmax (20.51 mg/g) with R2 = 0.97, the Langmuir isotherm that best matches the experimental data. Hence, the Langmuir isotherm suggests that it is the model that best describes sorption on homogenous surfaces or surface-supporting sites with various affinities. The correlation coefficient R2, χ2, adjusted correlation coefficient, and error functions like root mean square (RMSE), normalized root mean square error (NRMES), and mean absolute error (MAE) were used to evaluate the best-fit models to the experimental adsorption data. Moreover, cost estimation for the prepared adsorbent WCFs/PE/Cu showed that it costs approximately 3 USD/g, which is a cheap adsorbent compared to other similar adsorbents reported in the literature. The examined WCFs/PE have significant applicability potential for Cu2+-laden wastewater treatment due to their superior Cu2+ metal ions adsorption capability and reusability.The cytotoxicity and safety study showed that at higher concentrations, it resulted in much less cell viability. Additionally, the removal efficiency of Cu2+ metal ions from synthetic, realistic industrial wastewater using WCFs/PE reached up to 96.29 %, demonstrating good adsorption capability. Thus, there is a huge possibility of accomplishing this and performing well. This study paves the way for the reuse and valorization of selected adsorbents following circular economy principles.Two green metrics were applied, the Analytical Eco-scale and the Analytical GREEnness Calculator (AGREE).

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics. Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10min static FDG-PET acquisition, 50min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01. ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMeanand SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

River stream flow prediction through advanced machine learning models for enhanced accuracy

The Narmada River basin, a significant water resource in central India, plays a crucial role in supporting agricultural, industrial, and domestic water supply. Effective management of this basin requires accurate streamflow forecasting, which has become increasingly important. This study delves into streamflow forecasting using historical data from five major river stations, covering the upper reaches in the East and middle sections. The dataset spans from 1978 to 2020 and undergoes rigorous screening and preparation, including normalization using StandardScaler. The research adopts a comprehensive approach, developing models for training on 70 % of historical data, validation on the most current 15 %, and testing against future targets with another 15 % of the data. To achieve precise predictions, a suite of machine learning models is employed, including CatBoost, LGBM (Light Gradient Boosting Machine), Random Forest, and XGBoost. Performance evaluation of these models relies on key indices such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), root mean square percent error (RMSPE), normalized root mean squared error (NRMSE), and R-squared (R2). Notably, among these models, Random Forest emerges as the most robust for streamflow prediction, showcasing its effectiveness in handling the complexities of hydrological forecasting. This research contributes significantly to the field of streamflow forecasting in the Narmada River basin by providing insights into the performance of various machine learning models. The findings not only enhance our understanding of watershed dynamics but also highlight the pivotal role that machine learning can play in improving hydrological forecasting for sustainable watershed management.

Dingo optimization algorithm-based random forests model to evaluate the compressive strength of the concrete at elevated temperatures

The qualities of the materials employed to manufacture concrete are significantly impacted by high temperatures, which results in a noticeable decrease in the material’s strength characteristics. Concrete must be worked very hard and allowed to reach the required compressive strength (fc). Nevertheless, a preliminary estimation of the desired outcome may be made with an outstanding degree of reliability by using supervised machine learning algorithms. The study combined the Dingo optimization algorithm (DOA), Coot bird optimization (COA), and Artificial rabbit optimization (ARO) with Random Forests (RF) evaluation to determine the fc of concrete at high temperatures. The abbreviations used for the combined methods are RFD, RFC, and RFA, respectively. Remarkably, removing the temperature (T) parameter from the input set leads to a remarkable 1100% improvement in the effectiveness index (PI) and normalized root mean squared error (NRMSE), while causing a significant fall in the coefficient of determination (R2). The findings suggest that all RFD, RFC, and RFA have substantial promise in properly forecasting the fc of concrete at high temperatures. More precisely, the RFD algorithm demonstrated exceptional precision with R2 values of 0.9885 and 0.9873 throughout the training and testing stages, respectively. Through a comparison of the error percentages for RFD, RFC, and RFA in error-based measurements, it becomes evident that RFD exhibits an error rate that is about 50% smaller compared to that of RFC and RFA. This prediction is crucial for various industries and applications where concrete structures are subjected to elevated temperatures, such as in fire resistance assessments for buildings, tunnels, bridges, and other infrastructure. By accurately forecasting the compressive strength of concrete under these conditions, engineers and designers can make informed decisions regarding the material’s suitability and performance in high-temperature environments, leading to enhanced safety, durability, and cost-effectiveness of structures.

A novel hybrid optimized incremental relevance vector machine and filtering technique for state of charge estimation of lithium-ion batteries

Despite not requiring a comprehensive understanding of the intricate electrochemical reactions within the battery, the data-driven approach faces challenges regarding state of charge (SOC) fluctuations during abrupt changes in battery current. Currently, research on data-driven filtering methods predominantly focuses on neural network-based algorithms, with limited exploration of the relevance vector machine (RVM) algorithm that possesses advantages in handling small sample sizes, high sparsity, and probabilistic distributions. Drawing inspiration from this premise, the present manuscript introduces an innovative fusion algorithm, founded upon the framework of the adaptive Kalman filtering (AKF) algorithm, which intricately melds the optimized incremental RVM (OIRVM) with the Coulomb counting methodology, called Coulomb_AKF_OIRVM (CFR) algorithm. Experimental findings suggest that the CFR algorithm displays exceptional generalization capacity across different driving cycles and temperatures, exhibiting high estimation accuracy and robustness. The average root mean square error, determination coefficient, mean absolute error and maximum absolute error of the public datasets and experimental datasets are 0.25 %, 0.02 %, 0.20 %, and 0.85 %, respectively.