High Coefficient Of Determination Research Articles

Mathematical Modeling and Optimization of Enzyme‐Based Amperometric Screen‐Printed Biosensors Using Horseradish Peroxidase as a Model

AbstractIn this study, a mathematical model was formulated to characterize the functioning of third‐generation enzyme‐based amperometric biosensors. This study utilized a horseradish peroxidase (HRP)‐based, screen‐printed biosensor. The model parameters were determined using the Environment for Modeling Simulation and Optimization (EMSO), validated with experimental data, and exhibited high determination coefficients, indicating the accuracy of the models in capturing biosensor behavior. Simulations based on this model emphasize diffusion as a limiting factor. Further sensitivity analysis was conducted using simulated response surfaces and biosensor performance optimization, which confirmed that higher concentrations of HRP in the thinnest polymeric film resulted in enhanced biosensor sensitivity.

From dishwasher to river: how to adapt a low-cost turbidimeter for water quality monitoring.

This study presents the process of design and development of a low-cost turbidimeter for monitoring water quality, facilitating rigorous spatial-temporal variability analysis within large-scale hydrological systems. We propose a low-cost optical turbidimeter, modifying the existent SEN0189 turbidity sensor, Arduino boards, and additional sensors for temperature compensation. We compared a low-cost system with high-tech sensors, modifying the original low-cost SEN0189 probe for enhanced environmental performance. The three-step methodological framework involved prototype development, compensation for environmental factors, and preparation for future field deployment. Calibration equations with a high coefficient of determination and a temperature correction equation were established. We made adaptations to overcome field deployment challenges, including a 3-D printed sensor case, defining the relationship between measurement uncertainty and energy consumption, and specifying field installation guidelines. In summary, this study presents a comprehensive approach to a low-cost optical turbidity system, demonstrating its potential for accurate and affordable field deployment. We aim to address the critical need for sustainable inland water management tools, making this system a valuable contribution to environmental monitoring practices. We also aim to inspire similar development of open-source monitoring systems within our community.

Sonophotocatalytic degradation of rhodamine B dye using Zr doped ZnO nanoparticles: Kinetic study and toxicity assessment

This study investigates sonophotocatalysis for the oxidation of Rhodamine B (RhB) dye using Zr doped ZnO nanoparticles. The synthesized nanoparticles were characterized by scanning electron microscope, Fourier-transform infrared, X-ray diffraction, Brunauer Emmett-Teller, thermogravimetric analysis, and photoluminescence. The hybrid sonophotocatalysis system achieved 98.73 ± 1.25% degradation of RhB under the conditions: RhB dye concentration = 10 ppm, 4 wt% of Zr doped ZnO = 0.1 g, reaction temperature = 30 °C, time = 60 min, pH = 7. A kinetic model was developed to predict the efficiency of RhB degradation through intrinsic elementary chemical reactions. The high coefficient of determination (R2 = 0.96) indicates the model's prediction accuracy in describing the degradation kinetics of RhB within sonophotocatalysis system. The electrical energy per order (EEO) analysis demonstrated that US + UVC + 4 wt% of Zr doped ZnO significantly reduced EEO (1055 kWh m−3 order−1). The synergy index for this combination was approximately 1.60, demonstrating a significant synergistic effect. Density Functional Theory (DFT) studies were utilized to propose the most probable degradation pathway, identifying reactive sites and potential byproducts. The simulations revealed the central carbon atom (1C site) as highly susceptible to radical attacks, indicating potential decolorization pathways such as N-de-ethylation, chromophore cleavage, and ring opening. Toxicity assessments of both the parent dye and its intermediates were conducted using ECOSAR (Ecological Structure Activity Relationship) to evaluate their ecological impacts. The combination of experimental results and kinetic modeling and simulations offers a deep understanding of RhB degradation complexities, driving advancements in sustainable water treatment technologies.

Development of FTIR-ATR spectra and PLS regression combination model for discrimination of pure and adulterated acacia honey

A Fourier Transform Infrared Spectroscopy and attenuated total reflectance (FTIR-ATR)-based chemometric model was evaluated for the rapid identification and estimation of adulterants in honey. Two types of honey, bee honey and stingless bee honey, were adulterated with acid adulterants (acetic acid, citric acid, and tamarind extract) at different concentrations of 1%, 3%, 5%, and 7% and sugar adulterants (liquid corn syrup, cane sugar, palm sugar, and inverted sugar) at different concentrations of 1%, 2%, and 3%. The physicochemical properties (Brix, moisture content, pH, and free acidity) and sugar contents (glucose, fructose, and sucrose) of each type of honey and their adulterated samples were determined before FTIR analysis. For all the samples, FTIR spectra were acquired in the mid-infrared range (400-3600 cm−1) and the spectra obtained were subjected to partial least squares (PLS) analysis to develop the model for the determination of adulterants in honey samples. The PLS model produced high coefficient of determination (R2) for bee honey adulterated with acetic acid (0.95), citric acid (0.98), tamarind extract (0.97), and stingless bee honey adulterated with liquid corn syrup (0.99), inverted sugar (0.91), cane sugar (0.86), and palm sugar (0.77). A recognition model was developed for mixture detection and verified through the physicochemical analysis. The combination of FTIR-ATR spectra and the PLS regression model can be a fast, reliable, nondestructive method to detect honey adulteration.

Optimising the production of PLGA nanoparticles by combining design of experiment and machine learning

Poly(lactic-co-glycolic acid) (PLGA) is a widely used biodegradable polymer in drug delivery and nanoparticle (NP) formulation due to its controlled drug release properties and safety profiles. Among the methods available for NP production, nanoprecipitation is distinguished by its simplicity and scalability. However, it requires careful optimisation to achieve the desired NP characteristics, making the process potentially lengthy and costly. This study aimed to assess and compare the predictive performance of Design of Experiments (DOE) and Machine Learning (ML) models for the optimisation of PLGA nanoparticle size and zeta potential produced by nanoprecipitation. Various ML methods were employed to predict particle size, with Extreme Gradient Boosting (XGBoost) identified as the best performing. The key finding is that integrating ML with DOE provides deeper insights into the dataset than either method alone. While ML outperformed DOE in predictive performance, as evidenced by lower root mean squared error values and higher coefficients of determination, both methods struggled to accurately predict zeta potential, generating models with high errors. However, ML proved more effective in identifying the parameters that most significantly influence NP size, even with a smaller DOE dataset. Combining DOE datasets with ML for parameter importance was particularly advantageous in situations where data is limited, offering superior predictive power and the potential to streamline experimental design and optimisation. These results suggest that the synergistic use of ML and DOE can lead to more robust feature analysis and improved optimisation outcomes, particularly for NP size. This integrated approach can enhance the accuracy of predictions and supports more efficient experimental design, streamlining nanoparticle production processes, especially under resource-constrained conditions.

Use Artificial Neural Networks to Predict Seepage in the Hilla Canal Regulator

The variation in the seepage under hydraulic structures significantly impacts their stability and effective water management, especially considering recent water scarcity challenges. This paper aims to calculate seepage and investigate the hydraulic performance of the Al-Hilla canal regulator's foundation. The methodology involves constructing a model (SEEP/W) and comparing its results with one-site piezometer readings. Using a mixed-methods approach, this study integrates software modelling and statistical analysis techniques. This study integrates software modelling and statistical analysis techniques. The Geo-studio program facilities modelling of seepage flow, while JASP software is used for statistical analysis and predictive equation development. The methodology consists of data collection, Geo-Studio modelling, JASP analysis, and validation of equation accuracy. After verifying the models’ efficiency, the data for the seepage equation was established under various upstream and downstream conditions, incorporating artificial intelligence algorithms. This data was analyzed to drive a predictive equation for seepage with a high coefficient of determination (R2) OF 97%. Additionally, another equation was formulated to determine the total water pressure head, achieving an R2 value of 95 %. These equations are invaluable tools for predicting the total water pressure head and seepage and enhancing the management of hydraulic structure.

A novel colorimetric assay for the detection of urinary N1, N12-diacetylspermine, a known biomarker for colorectal cancer

Urinary N1, N12-diacetylspermine (DAS) is a known biomarker for colorectal cancer (CRC). However, DAS levels in both healthy and CRC patients’ urine samples are extremely low and often challenging to quantify. Complex and expensive methods do exist to detect DAS in urine, but simpler, less expensive methods to detect DAS are needed, especially in low resource settings. Here we describe a highly efficient, fast, precise, and inexpensive colorimetric assay to detect low levels of DAS in human urine samples. We used recombinant diacetylspermine oxidase (rDAS Ox), expressed and extracted from E. coli, to oxidize DAS, producing three products including hydrogen peroxide (H2O2). The level of DAS present, which correlates with H2O2 levels, was measured using horseradish peroxidase (HRP), which together with H2O2, oxidized Amplex™ Red to produce the pink-colored resorufin. The concentration of resorufin is directly proportional to H2O2 (and DAS) levels. As urine contains metabolites which interfere with these oxidation reactions, we developed a simple two column-based protocol using ion exchange resins to remove these compounds and concentrate the DAS. With this novel cleaning and concentrating method, DAS was concentrated 15 times (confirmed by nuclear magnetic resonance (NMR) spectroscopy) and <1 μM DAS could be detected. Correlation graphs of urine samples spiked with known DAS concentrations versus assay-determined DAS concentrations had high coefficients of determination (R2) for 0-10 μM DAS (0.94) and for 0-1 μM DAS (0.91), clearly demonstrating the excellent performance of the two-column protocol with the rDAS Ox reaction mixture. To the best of our knowledge, this is first reported colorimetric enzymatic assay that quantitates DAS in urine.

The short-term wind power prediction based on a multi-layer stacked model of BO-CNN-BiGRU-SA

Wind power generation is influenced by various meteorological factors, exhibiting significant volatility and unpredictability. This variability presents considerable challenges for accurate wind power forecasting. In this study, we propose an innovative method for short-term wind power prediction that integrates a Bayesian-optimized Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Units (BiGRU), and a Self-Attention Mechanism (SA) within a multi-layer architecture. Initially, we preprocess features using Pearson correlation analysis and input them into the CNN to investigate complex nonlinear spatial relationships among multiple feature variables and the current load. Subsequently, the BiGRU captures long-term dependencies from both forward and backward time sequences. Finally, we implement the Self-Attention Mechanism to weigh the features and generate the predicted wind power. We optimize the model's numerous hyperparameters utilizing a Bayesian algorithm. Through comparative ablation experiments with varying time segment lengths on wind farm datasets from four regions, our method significantly outperforms 11 models, including Long Short-Term Memory (LSTM), and surpasses several state-of-the-art (SOTA) prediction models, such as iTransformer, PatchTST, Non-stationary Transformers, TSMixer, and DLinear. The highest coefficient of determination (R²) achieved was 0.981, with the Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) decreasing by 11.22% to 62.04% compared to other models. The results demonstrate the predictive accuracy and generalization performance of our proposed model.

Colorimetric and fluorometric sensing of polar E120 in juice and environmental water samples using mannitol-functionalized magnetic nanoparticles and nitrogen-doped carbon dots

In this study, mannitol-functionalized magnetic nanoparticles (MMNPs) as a unique nanosorbent and N-doped fluorescent carbon dots (N-CDs) as a cost-effective nanosensor were created and utilized, for the first time, for dispersive micro-solid-phase extraction (Dµ-SPE) to determine carmine (E120) dye in water samples and juices. The modification of the magnetic nanoparticles with mannitol was designed to enhance the responsive potential for adsorption of the polar E120 dye from complex sample matrices through electrostatic interaction. The as-fabricated N-CDs fluorescent probe exhibited a high fluorescence quantum yield (Φs) of 43.1 %, allowing for accurate fluorometric detection of E120 dye. The as-synthesized MMNPs nanosorbent and fluorescent N-CDs nanoprobe were characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR), Transmission electron microscopy (TEM), Energy Dispersive X-ray (EDX), Thermogravimetric analysis (TGA), and Vibrating-sample magnetometer (VSM). Density Functional Theory (DFT) studied the E120 dye structure using Gaussian 09 to explore the interactions between E 120 dye molecules and MMNPs/N-CDs. The impact of the critical adsorption and detection experimental factors was investigated and adjusted. A minimal amount of MMNPs nanosorbent (150 mg) is sufficient for E120 extraction in an acceptable time of 15 min. Furthermore, with a high determination coefficient, the adsorption characteristics fit with the models of Langmuir isotherm and first-order kinetics. The adsorption capacity (qe) of the as-fabricated MMNPs was 87.7 mg.g−1. After adsorption, E120 dye was fluorometrically analyzed using nitrogen-doped carbon dots as a fluorescent nanosensor via the inner filter effect (IFE) mechanism. Under the optimized conditions, the proposed fluorometric procedures showed a linear increase in the fluorescence ratio with increasing the E120 concentration in the range of 1.0 – 160.0 μg.mL−1 with detection (LOD) and quantitation (LOQ) limits of 0.27 and 0.83 μg.mL−1, respectively. The relative standard deviation (%RSD) did not exceed 2.34 %. The proposed methodology was successfully applied to determine E120 dye in juice and environmental water samples with % recovery ranged from 89.2-106.1 % and 92.9–107.2 %, respectively offering a reliable and environmentally friendly alternative to traditional detection methods with potential applications across various industries.

Temperature-Dependent Compressive Strength Modeling of Geopolymer Blocks Utilizing Glass Powder and Steel Slag

This article investigates the development of geopolymers as a modern, environmentally sustainable binder with ceramic-like properties, offering exceptional thermal and fire-resistant characteristics. The study primarily utilized fly ash (FA) in combination with glass powder (GP) and steel slag (SS). The SS content varied between 30% and 40%, while the molarity of NaOH was set at 10M, 12M, and 14M. Based on these variables, a total of 18 mixes incorporating GP and SS were formulated. The samples were subjected to elevated temperatures of 200 °C, 400 °C, 600 °C, and 800 °C, after which their compressive strength was measured. To better understand the material formation, scanning electron microscopy and energy dispersive X-ray spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and thermogravimetry differential thermal analysis analyses were conducted. The study aimed to examine the influence of oxide ratios (Na/Si, Si/Al, H2O/Na2O, and Na/Al) on compressive strength at elevated temperatures. Additionally, the research sought to develop a predictive model, elucidating the relationship between these oxide ratios and compressive strength of geopolymers. To achieve this, ten machine learning techniques were applied, revealing the complex connection between oxide ratios and the strength properties of geopolymers. The support vector regressor (SVR) model outperformed other regression and boosting models, achieving a high coefficient of determination (R2) value of 0.95, indicating superior predictive accuracy. The reduced error levels and high R2 values highlighted the enhanced performance of the SVR model. A sensitivity analysis was done to understand the contributions of each parameter to the outcome predictions further. Employing machine learning techniques to forecast compressive strength of geopolymer blocks under various elevated temperature conditions improves predictive accuracy and optimizes resource utilization, leading to significant time savings.

Development of Luminescent and Photosensitive Cellulose Microfibers From Palm Sprout Fibers: Antibacterial Efficacy and Dye Adsorption Potential.

A key contemporary challenge is enhancing the value of agro-industrial byproducts. Cellulose, the most abundant renewable resource, offers significant industrial potential due to its versatile properties. Produced in its pure form by various bacteria, cellulose is increasingly utilized in microscale and nanoscale fibers for composite reinforcement. In this study, cellulose microfibers were derived from palm sprout fibers through a series of treatments like pretreatment, high-pressure hydrolysis in an autoclave, and bleaching. These fibers were then characterized using techniques such as UV spectroscopy, FTIR, SEM, and XRD. The study evaluated the antibacterial properties of the treated cellulose microfibers against both gram-positive Staphylococcus aureus (07%) and gram-negative Escherichia coli (10%), finding higher effectiveness against E.coli. Additionally, the microfibers exhibited a lower EC50 value of 0.101 mg/mL, suggesting that although the microfiber extract is effective, the standard antioxidant is somewhat more potent. Adsorption studies revealed that the cellulose microfibers followed the Langmuir isotherm model, with a high determination coefficient of 0.998, and reached maximum dye adsorption for RB 160 within 120 min. In this adsorption study, 50 mg of dye was removed. These results indicate that the treated cellulose microfibers are a promising biosorbent for improving dye removal processes.

Modeling and optimization of adsorption behavior of lactic acid onto zirconium-based metal-organic framework: response surface methodology (RSM)

ABSTRACT Lactic acid is readily generated in bacterial fermentations; however, a significant amount of processing and expenses are required for the purification of lactic acid in the fermentation broth. In this work, the sorption of lactic acid from a simulated broth has been studied by utilizing zirconium-based metal-organic framework (Zr-UiO-66 MOF) that acts as a prominent adsorbent due to its specific geometries and functional groups. The morphological, and structural properties of the prepared MOF are investigated using different characterization techniques such as Field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD) pattern, Fourier transform infrared (FTIR) analysis, and Brunauer-Emmett-Teller (BET) analysis. The characterization results demonstrate the accuracy of the synthesis procedure. In order to achieve an efficient lactic acid separation process, it is essential to determine the optimal operational conditions. To address this, response surface methodology (RSM) was employed to devise an approach for optimizing five crucial process parameters for achieving the optimal response in lactic acid recovery removal. These parameters include initial LA concentration (ranging from 1 to 36 mg.L−1), pH (3–10), temperature (25 to 55 °C), adsorbent mass (0.25 to 1 g), and contact time (10 to 240 min). The RSM analysis revealed that the quadratic model provided the best fit for the experimental data, with a high coefficient of determination (R2). Three validation trials were carried out to determine the precision of the optimization process, leading to a maximum adsorption efficiency of 97.7% being reached under the optimized conditions (initial concentration = 18.5 mg.L−1, pH = 6.5, adsorbent mass = 0.625 g, contact time = 240 min, and temperature = 40 °C).

Magnetic sludge-derived biochar for Cu (II) removal: RSM-based preparation and BP artificial neural network adsorption modeling

Magnetic sludge-derived biochar (MSDB) was synthesized using response surface methodology (RSM) optimization, with sludge as the raw material and sodium pyrophosphate as the modifier. The structure and surface properties of MSDB were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), vibrating sample magnetometer (VSM), and Brunauer-Emmett-Teller (BET) analysis. These analyses revealed that MSDB is rich in functional groups and exhibits magnetic properties, facilitating the adsorption of Cu(II) via complexation and easy separation of MSDB through magnetic means. Batch adsorption experiments showed that under conditions of 25 °C and pH 3.0, with an MSDB dosage of 0.75 g L−1, a Cu(II) removal efficiency of 95.67 % was achieved. Thermodynamic studies demonstrated maximum adsorption capacities of 173.69 mg g−1 at 25 °C, 183.26 mg g−1 at 35 °C, and 197.96 mg g−1 at 45 °C. Additionally, the BP neural network model showed a high coefficient of determination (R2 = 0.9396) and a low mean squared error (MSE = 0.0268) in simulating the MSDB-Cu(II) adsorption process, confirming the robustness of the model in both data simulation and predictive accuracy. This method offers a promising approach for kinetic modeling and predicting adsorption behavior in related fields of study.

Advancements in renewable energy: Achieving milder reaction conditions in biodiesel synthesis from green seed canola oil with pristine ZIF-8

This study explores the enhancement of reaction conditions for biodiesel production from green seed canola oil through the methanolysis reaction using pristine ZIF-8 as a catalyst. The research focused on the effects of varying the methanol-to-oil molar ratio, temperature, and reaction time as well as their combined effects on biodiesel yield. To analyze these factors, Response Surface Methodology (RSM) paired with Central Composite Design (CCD) was employed. The quadratic model was identified as the best fit for the experimental data, evidenced by a high determination coefficient (R²) of 0.99, with all model parameters proving significant. A comprehensive characterization of the catalyst was conducted using various techniques. The surface and pore properties were examined using N2 adsorption-desorption measurements. X-ray diffractometry (XRD) was employed for structural analysis, while X-ray photoelectron spectroscopy (XPS) presented the binding information of the sample. Fourier transform infrared spectroscopy (FT-IR) provided insights into the functional groups present. Thermal gravimetric analysis (TGA) assessed the thermal stability of the catalyst. Results revealed that ZIF-8 significantly improved biodiesel production, with optimal conditions identified at a reaction temperature of 160 °C, a duration of 2.5 h, and methanol to oil (M/O) molar ratio of 26, resulting in a biodiesel yield of 85 %. This yield is close to the predicted value, demonstrating the reliability of the developed model. The kinetic analysis was performed under optimal reaction conditions. From this, the activation energy (Ea) and the pre-exponential factor (A) were obtained to be 14.5 kJ/mol and 3.4×10−7 min−1, respectively. The use of pristine ZIF-8 in biodiesel production from a low-quality and cheap oil offers a more efficient and milder reaction conditions, with potential for significant reductions in production costs and environmental impact.

Indirect estimation of compressive strength of cement-stabilized mine tailings by using ultrasonic pulse velocity for road construction

This study proposes a correlation formula between the compressive strength, ultrasonic pulse velocity UPV, and dynamic elastic modulus of cement-stabilized mine tailings CSMT. Samples were tested in curing times (1, 7, 14, 28, and 90 days) by cement amounts (2%, 4%, 6%). The findings demonstrated that the strength and wave velocities of CSMT were enhanced due to increased cement content and extended curing periods. A regression equation with a high coefficient of determination (R-square &gt;0.95) was developed to establish a relationship between compressive strength and ultrasonic pulse velocity UPV for tailings containing 6% cement content in both treated types. The study uncovered a strong relationship between pulse velocity and compressive strength in different CSMT mixtures. These findings indicate that UPV tests can accurately, rapidly, and without causing damage, and evaluate the compressive strength of CSMT for road paving. The results improve the advancement of environmentally friendly building materials and showcase the efficacy of non-destructive ultrasonic testing in assessing structural characteristics. The investigation has profound consequences in enhancing the quality and management of road construction industry, and the management of mining waste.

A Novel Technique in Determining Mud Cake Permeability in SiO2 Nanoparticles and KCl Salt Water Based Drilling Fluid using Deep Learning Algorithm

The permeability of the mud cake formed at the formation-wellbore interface is an important factor in the designing of water-based drilling fluids. This study presents a novel approach to utilizing experimental thixotropic and rheological parameters of polymeric water-based drilling fluids having varying concentrations of SiO2 nanoparticles and KCl salt. A fully connected feed-forward multi-layered neural network, more commonly known as a Multilayer Perceptron (MLP) was developed to predict the mud cake permeability using input parameters such as SiO2 &amp; KCl concentration, differential pressure, temperature, mud cake thickness, API LPLT and HPHT filter loss volume and spurt loss volume. The results suggested that the developed Multilayer Perceptron model effectively determined the mud cake permeability based on the input parameters of the WBDF mentioned above. The model converged on the global minima, minimizing the loss function using the Gradient descent algorithm. A higher Coefficient of Determination (R2) value i.e., 0.8781, and a lesser Root Mean Square Error (RMSE) value i.e., 0.04378 indicates the higher accuracy of the model. Pearson’s Coefficient of Correlation obtained via the heatmap indicates that mud cake permeability is strongly influenced by the differential pressure followed by filter loss volume, spurt loss volume, mud cake thickness, and temperature. Previous similar studies have focused on using machine learning algorithms, this study utilized a robust deep learning algorithm i.e., Multilayer Perceptron (MLP) neural network to simultaneously model the combined effects of SiO2 nanoparticles and KCl salt concentrations on mud cake permeability, offering an unprecedented level of accuracy in predicting key WBDF performance parameters

A novel spine tester TO GO.

Often after large animal experiments in spinal research, the question arises-histology or biomechanics? While biomechanics are essential for informed decisions on the functionality of the therapy being studied, scientists often choose histological analysis alone. For biomechanical testing, for example, flexibility, specimens must be shipped to institutions with special testing equipment, as spine testers are complex and immobile. The specimens must usually be shipped frozen, and, thus, biological and histological investigations are not possible anymore. To allow both biomechanical and biological investigations with the same specimen and, thus, to reduce the number of required animals, the aim of the study was to develop a spine tester that can be shipped worldwide to test on-site. The "Spine Tester TO GO" was designed consisting of a frame with three motors that initiate pure moments and rotate the specimen in three motion planes. A load cell and an optical motion tracking system controlled the applied loads and measured range of motion (ROM) and neutral zone (NZ). As a proof of concept, the new machine was validated and compared under real experimental conditions with an existing testing machine already validated employing fresh bovine tail discs CY34 (n = 10). The new spine tester measured reasonable ROM and NZ from hysteresis curves, and the ROM of the two testing machines formed a high coefficient of determination R 2 = 0.986. However, higher ROM results of the new testing machine might be explained by the lower friction of the air bearings, which allowed more translational motion. The spine tester TO GO now opens up new opportunities for on-site flexibility tests and contributes hereby to the 3R principle by limiting the number of experimental animals needed to obtain full characterization of spine units at the macroscopic, biomechanical, biochemical, and histological level.

Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques

ABSTRACT Cloud properties are pivotal in analyzing rainfall patterns globally, especially in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought-prone and flood-prone regions of western India, utilizing satellite data and employing various machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties along with the aerosol property in the drought-prone area. Additionally, a moderate correlation exists between certain cloud characteristics of one region and another. Employing ML algorithms for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest (RF) demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can be helpful in predicting future cloud dynamics and climate variables identifying the most suitable model.

LJF of thin-walled gapped uni-planar K-type tubular steel joints with collar under brace balanced axial loads

This paper investigates the efficacy of the collar plate in improving the Local Joint Flexibility (LJF) of thin-walled circular hollow section (CHS) K-joints under axial loads. Initially, a finite element model (FEM) was created and verified against data from 14 available experimental tests. Subsequently, an extensive series of 136 unreinforced and reinforced K-joints was simulated to analyze the influence of parameters such as collar plate dimensions (η and λ), brace angle (θ), and joint geometry (β, ξ, and γ) on the LJF factor (fLJF) and the fLJF ratio (χ). The FEMs incorporated plate-to-member contact and weld modeling. Findings indicate that the utilization of the plate reduces the fLJF by 73 %. Additionally, it was observed that plate length significantly affects the fLJF compared to plate thickness. While the impact of γ, θ, and ξ on χ is marginal, β, η and λ emerge as a noteworthy determinant of χ. Despite the pivotal role of fLJF in joint behavior, there exists a gap in studies and equations specifically addressing fLJF in collar-plate reinforced K-joints under axial loads. To address this gap, leveraging the FE results, a design equation is proposed. This equation is subsequently validated with high coefficients of determination and standards set by the UK Department of Energy.

Reference Ranges and Z-Score Equations for 19 Fetal Cardiac Biometry Structures From 18 to 34 Weeks' Gestation.

To determine equations for calculating the Z-scores of fetal cardiac structures between 18+0 and 34+6 weeks of gestation, create percentile reference tables and curves for the structures, and assess the intra- and inter-observer reproducibility of the measurements. A cross-sectional study was conducted involving 340 normal fetuses from singleton pregnancies between 18 and 34 weeks of gestational age (GA). Nineteen cardiac structures were evaluated: diameters of the mitral, tricuspid, aortic, and pulmonary valve annuli; length, diameter, and area of the left and right ventricles; cardiac area and circumference; and diameters of the ascending aorta, aortic isthmus, main pulmonary artery, right pulmonary artery, left pulmonary artery, and ductus arteriosus. Regression analysis was performed to determine the equations for the mean and standard deviation of all structures using GA, biparietal diameter (BPD), and femur length (FL) as independent variables. All equations had high coefficients of determination (R2). The best performance was achieved using the GA (R2 .819-.944), followed by FL (R2 .813-.937) and BPD (R2 .792-.934). The structure that demonstrated the highest R2 was the cardiac circumference and the smallest was the ductus arteriosus. Reference tables of percentiles 1, 5, 10, 50, 90, 95, and 99, and reference curves of Z-scores were created for all 19 cardiac structures, depending on the GA. All measurements demonstrated good and excellent reproducibility with an inter-observer intraclass correlation coefficient (ICC) of 0.774-0.972 and intra-observer ICC of 0.938-0.993. Equations were produced to calculate Z-scores as well as percentile tables and curves for 19 fetal heart structures. All the measurements demonstrated good reproducibility.