High Coefficient Of Determination Research Articles

Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans

Abstract This study delves into the ability of near infrared (NIR) techniques by means of a self-developed portable sensing device near-infrared reflectance spectroscopy (NIRS) i16 USK instrument to accurately predict the moisture content (MC) and chlorogenic acid (CGA) within intact coffee beans through the development of calibration models. Spectral absorbance measurements were conducted across the 1,000‒2,500 nm wavelength range. Leveraging two multivariate calibration approaches namely principal component regression and partial least square regression (PLSR) for 74 bulk coffee beans (60 g) in calibration and 36 bulk coffee beans samples in external validation. The results reveal a notably high determination coefficient (R 2) of 0.984 for MC and 0.908 for CGA in calibration using PLSR, indicating the feasibility of rapid, simultaneous, and non-destructive prediction. Furthermore, upon external validation, the PLSR model exhibited consistent predictive performance, with R 2 values for MC and CGA contents reaching 0.978 and 0.846, respectively. Consequently, these outcomes underscore NIR as an effective, concurrent, and non-invasive means to assess the quality parameters and attributes of intact coffee beans, presenting promising prospects for the advancement of coffee quality evaluation.

Optimization of Ethylene Glycol Production Process from Bio Mass (Corn Cob) Using Response Surface Methodology (RSM)

This study focuses on the increasing global demand for Monoethylene Glycol (MEG) and its production from corncob through Catalytic Hydrogenation. The process was optimized using Design of Experiment (DOE) and Response Surface Methodology (RSM). Physiochemical properties of the produced MEG were found to align with ASTM standards. Physiochemical properties of the produced Monoethylene glycol (MEG) was determined to have the following; density 2.19g/cm3, specific gravity 1.077 at 20 oC, flash point 116.20 oC and viscosity 12.32mm2/s at 40 oC, this values is in agreement with the ASTM standard values for monoethylene glycol. The study utilized a central composite design (CCD) with 21 runs to assess the impact of key parameters on biomass hydrogenation, revealing a quadratic model, also Analysis of variance (ANOVA) shows a high coefficient of determination (R2) of >0.9932. Four parameters (mass ratio of binary catalyst, hydrogen pressure, temperature and mass ratio of catalyst to feedstock) were varied with two center points to determine the effects of process parameters and eventually to get optimum monoethylene glycol (MEG) yield. The optimized conditions yielded a MEG yield of 70.2wt.%, demonstrating the potential of corncob as a viable source for MEG production. This finding proved that corncob can be utilized to produce monoethylene glycol (MEG) which could be potentially used in many way.

Understanding the evolution of moisture during isothermal dehydration of glutinous rice through global sensitivity analysis

This study examined the effect of layer thickness and temperature on the evolution of moisture during the isothermal dehydration of glutinous rice. To analyse the moisture evolution, empirical models and secondary models (SM) were employed. Global sensitivity analysis (GSA) was used to estimate the importance of the experimental parameters on the developed SM. According to the dehydration curves, the increase in temperature and reduction in the layer thickness significantly shortened (P<0.05) the dehydration time of glutinous rice. The effective diffusivity, activation energy, and Gibb-free energy were 2.0×10−12m2/s to 2.53×10−11m2/s, 43.03 kJmol−1 to 51.05 kJmol−1, and 145.10 kJmol−1 to 155.85 kJmol−1respectively. The Page and Logarithmic models are suitably considered in evaluating the dehydration kinetics of the glutinous rice with a high coefficient of determination (R2) values of >0.9989. The SM aptly described moisture evolution with higher accuracy of R2=0.9843. The GSA shows that the variation in the SM output is primarily driven by the variation in the input parameters with low reliance on its interaction with other parameters, indicating that the SM can play a significant role in the optimization of the industrial dehydration process with a wide range of applications in the agricultural production system.

Zastosowanie metod uczenia maszynowego do predykcji generowanej energii w małym systemie PV

The article presents data analysis for predicting energy production in photovoltaic (PV) power plant systems. The purpose of long-term forecasts is to determine the effectiveness of preventive actions and manage the power system effectively. Climate variables affecting the production of electricity in photovoltaic systems were analyzed. Forecasting methods using machine learning techniques such as Multi-Layer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were compared. In addition, metrics were selected to determine the quality of forecasts. Determining the quality of forecasts was based on the actual varying conditions, not on the weather forecast data. The way of data preparation to create forecasting models were presented and the models with the best metrics were selected. For this purpose, the Scikit-learn library was used to create scripts in Python. The best results were obtained for regression models: MLPRegressor, CatBoostRegressor and Support Vector Regression. Actual measurement data from a system of optimally-positioned panels with a power of 3.0 kWp were used. For the MLPRegressor model, the highest coefficient of determination 0.605 was achieved with the smallest root-mean-square error of 1.79 KWh.

All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.

Quantification of AICAR and study of metabolic markers after administration.

Objectives: AICAR (5-amino-4-imidazolecarboxyamide ribonucleoside) was reported as the first pharmacological AMPK (adenosine 5'-monophosphate (AMP)-activated protein kinase) activator, and it has been confirmed to exhibit a significant endurance enhancement effect and prohibited for doping by the World Anti-Doping Agency. Due to the fact that the human body can produce such substances, in order to ensure fairness in sports competition, methods for rapid detection and multi-type identification of AICAR drugs taken orally should be established. Methods: to assess AICAR levels, a new rapid, sensitive, efficient, and selective method was reported for the quantitative detection of AICAR in urine using LC-MS/MS. The method was validated for quantitative purposes based on the elemental selectivity, intra- (1.0-15.6%) and inter-day precision (1.3-16.3%), accuracy (99.9-112.8%), matrix effects (88.9-103.6%), recovery (87.4-106.5%), and stability at four different concentrations. The calibration curve was linear over a wide concentration range of 10-10,000 ng mL-1 with a high coefficient of determination (R 2 > 0.998). The limit of detection (LOD) and limit of quantification (LOQ) for the experiment were determined to be 1 and 10 ng mL-1, respectively. Simultaneously, metabolomics analysis was used to obtain the metabolic fingerprint of different populations and biomarkers to distinguish administration cases through partial least squares discriminant analysis (PLS-DA) and a receiver operating characteristic (ROC) curve. Results: the method enables easy quantitation for LC-MS/MS analysis with the best recovery yield maintained, and the method was applied to 122 Asian biological samples with an average concentration of 1310.5 ± 1031.4 ng mL-1. Through drug metabolism research, 734 and 294 variables were extracted for data analysis respectively in the positive and negative ion modes, and more than 100 metabolites with significant up- and down-regulation were found after the test. Conclusions: this research developed a fast, precise, effective, and specific approach for the qualitative and quantitative identification of AICAR in urine. Meanwhile, administration metabolism studies found that there were significant changes in AICAR levels and other compounds, such as PC types PC(18:1/16:0), PC(16:0/18:0), and PC(16:0/16:0), PE types PE(18:0/20:4), and LPE-type 18:1, which could better distinguish samples before and after AICAR administration. The analysis provides a multi-perspective reference for WADA to determine a positive criterion.

Development and Validation of an Enzyme-Linked Immunosorbent Assay-Based Protocol for Evaluation of Respiratory Syncytial Virus Vaccines.

Recently, respiratory syncytial virus (RSV) vaccines based on the prefusion F (pre-F) antigen were approved in the United States. We aimed to develop an enzyme-linked immunosorbent assay (ELISA)-based protocol for the practical and large-scale evaluation of RSV vaccines. Two modified pre-F proteins (DS-Cav1 and SC-TM) were produced by genetic recombination and replication using an adenoviral vector. The protocol was established by optimizing the concentrations of the coating antigen (pre-F proteins), secondary antibodies, and blocking buffer. To validate the protocol, we examined its accuracy, precision, and specificity using serum samples from 150 participants across various age groups and the standard serum provided by the National Institute of Health. In the linear correlation analysis, coating concentrations of 5 and 2.5 μg/mL of DS-Cav1 and SC-TM showed high coefficients of determination (r > 0.90), respectively. Concentrations of secondary antibodies (alkaline phosphatase-conjugated anti-human immunoglobulin G, diluted 1:2000) and blocking reagents (5% skim milk/PBS-T) were optimized to minimize non-specific reactions. High accuracy was observed for DS-Cav1 (r = 0.90) and SC-TM (r = 0.86). Further, both antigens showed high precision (coefficient of variation < 15%). Inhibition ELISA revealed cross-reactivity of antibodies against DS-Cav1 and SC-TM, but not with the attachment (G) protein.

Mo@Ni-MOF nanocomposite: A promising photocatalyst for photodegradation of Methylene blue

This study investigates the efficacy of pure Ni-MOF and Mo@Ni-MOF composite, synthesized via solvothermal approach as photocatalysts for the degradation of Methylene blue (MB) dye. The characterization techniques including XRD, SEM and EDX disclose that Ni-MOF and Mo@Ni-MOF exhibit high crystallinity, uniform grain morphology, and exceptional purity. The reduced band gap values from1.96 to 1.83 eV revealed that Mo@Ni-MOF has the potential to suppress the recombination of photoinduced charges to enhance the photocatalytic activity. It is found that introduction of Mo into Ni-MOF resulted in a superior dye degradation efficiency of 84 %, as compared to pure Ni-MOF (68 %). The reaction follows pseudo first-order kinetics with a high coefficient of determination (R2 = 0.95864). The reactive oxygen species analysis identifies superoxide anion radicals as the primary contributors of dye degradation. Furthermore, reusability analysis confirms photocatalytic efficiency with no significant decrease observed even after four successive runs. Overall, Mo@Ni-MOF emerges as a promising and recyclable photocatalyst for the efficient removal of MB dye from industrial effluents.

Factors determining tropical upper-level cloud radiative effect in the radiative-convective equilibrium framework

Investigation of the major factors determining tropical upper-level cloud radiative effect (TUCRE) is crucial for understanding cloud feedback mechanisms. We examined the TUCRE inferred from the outputs of historical runs and AMIP runs from CMIP6 models employing a radiative-convective equilibrium (RCE). In this study, we incorporated the RCE model configurations of atmospheric dynamics and thermodynamics from the climate models, while simplifying the intricate systems. Using the RCE model, we adjusted the global mean surface temperature to achieve energy balance, considering variations in tropical cloud fraction, regional reflectivity, and emission temperature corresponding to each climate model. Subsequently, TUCRE was calculated as a unit of K/%, representing the change in global mean surface temperature (K) in response to an increment in the tropical upper-level clouds (%). Our RCE model simulation indicates that the major factors determining the TUCRE are the emission temperatures of tropical moist-cloudy and moist-clear regions, as well as the fraction of tropical upper-level clouds. The higher determination coefficients between TUCRE and both the emission temperature of tropical moist regions and the upper-level cloud fraction are attributable to their contribution to the trapping effect on the outgoing longwave radiations, which predominantly determines TUCRE. Consequently, the results of this study underscore the importance of accurately representing the upper-level cloud fraction and emission temperature in tropical moist regions to enhance the representation of TUCRE in climate models.

Optimization of bioethanol production from sugar beet processing by-product molasses using response surface methodology

AbstractBioethanol production from renewable biomass sources has garnered significant interest due to its potential as a sustainable alternative to fossil fuels. In this study, we investigated the optimization of bioethanol production from molasses, a by-product of the sugar production process using Saccharomyces cerevisiae through Response Surface Methodology (RSM). Initially, the fermentation process was optimized using RSM, considering four independent variables: substrate concentration, pH, temperature, and fermentation time. Subsequently, the effects of these variables on bioethanol yield were evaluated, and a quadratic model was developed to predict the optimum conditions. Analysis of variance (ANOVA) indicated a high coefficient of determination (R2) for the model, suggesting its adequacy for prediction. The optimized conditions for bioethanol production were determined as follows: substrate concentration of 200 g L−1, pH of 5.0, temperature of 30 °C and fermentation time of 72 h. Under these conditions, the predicted bioethanol yield was 84%. Overall, this study demonstrates the successful application of RSM for optimizing bioethanol production from molasses using S. cerevisiae, highlighting its potential as a promising feedstock for biofuel production.

Concordance between methods of estimating body fat in pediatric and adolescent age.

Background and objective: The percentage of fat (%BF), essential data in the health or nutrition field, can be estimated by different methods. This work compares the %BF values obtained by anthropometry, Near Infrared Interactance (NIR), and Bioimpedance Analysis (BIA), in childhood and adolescence. Material and Methods: The sample consisted of 1015 schoolchildren aged 9 to 16 years. Height, weight, and four adipose folds were measured: tricipital, bicipital, subscapular, and suprailiac. The %BF by anthropometry was calculated using a specific formula based on body density (BD). A tetrapolar analyzer was used for the BIA calculation. The optical density in the biceps was measured for the one observed by NIR. Results: The high correlation and determination coefficients show that the relationship between %BF by anthropometry, BIA, or NIR is very strong, mainly between the first two. The intraclass correlation coefficient (ICC) results between Anthropometry and BIA show an almost perfect agreement. Its graphical contrast attests that the correlation is higher in the male sex. In general, the agreement between Anthropometry and BIA is higher than between Anthropometry and NIR. Conclusions: The three techniques used to measure %BF show high concordance. All ICC values between pairs of methods are above 0.90. The comparison of %BF by anthropometry with BIA or NIR shows that the affinity is higher in intermediate adiposity percentages than in extreme ones. This trend occurs in both sexes and age groups.

Microbial detoxification of chlorpyrifos, profenofos, monocrotophos, and dimethoate by a multifaceted rhizospheric Bacillus cereus strain PM38 and its potential for the growth promotion in cotton.

Bacillus genera, especially among rhizobacteria, are known for their ability to promote plant growth and their effectiveness in alleviating several stress conditions. This study aimed to utilize indigenous Bacillus cereus PM38 to degrade four organophosphate pesticides (OPs) such as chlorpyrifos (CP), profenofos (PF), monocrotophos (MCP), and dimethoate (DMT) to mitigate the adverse effects of these pesticides on cotton crop growth. Strain PM38 exhibited distinct characteristics that set it apart from other Bacillus species. These include the production of extracellular enzymes, hydrogen cyanide, exopolysaccharides, Indol-3-acetic acid (166.8μg/mL), siderophores (47.3μg/mL), 1-aminocyclopropane-1-carboxylate deaminase activity (32.4μg/mL), and phosphorus solubilization (162.9μg/mL), all observed at higher concentrations. This strain has also shown tolerance to salinity (1200mM), drought (20% PEG-6000), and copper and cadmium (1200mg/L). The amplification of multi-stress-responsive genes, such as acdS, ituC, czcD, nifH, sfp, and pqqE, further confirmed the plant growth regulation and abiotic stress tolerance capability in strain PM38. Following the high-performance liquid chromatography (HPLC) analysis, the results showed striking compatibility with the first kinetic model. Strain PM38 efficiently degraded CP (98.4%), PF (99.7%), MCP (100%), and DMT (95.5%) at a concentration of 300ppm over 48h at 35°C under optimum pH conditions, showing high coefficients of determination (R2) of 0.974, 0.967, 0.992, and 0.972, respectively. The Fourier transform infrared spectroscopy (FTIR) analysis and the presence of opd, mpd, and opdA genes in the strain PM38 further supported the potential to degrade OPs. In addition, inoculating cotton seedlings with PM38 improved root length under stressful conditions. Inoculation of strain PM38 reduces stress by minimizing proline, thiobarbituric acid-reactive compounds, and electrolyte leakage. The strain PM38 has the potential to be a good multi-stress-tolerant option for a biological pest control agent capable of improving global food security and managing contaminated sites.

Statistical investigation of interdependence of trabecular microarchitectural parameters from micro-computed tomography

Standard microarchitectural analysis of bone using micro-computed tomography produces a large number of parameters that quantify the structure of the trabecular network. Analyses that perform statistical tests on many parameters are at elevated risk of making Type I errors. However, when multiple testing correction procedures are applied, the risk of Type II errors is elevated if the parameters being tested are strongly correlated. In this article, we argue that four commonly used trabecular microarchitectural parameters (thickness, separation, number, and bone volume fraction) are interdependent and describe only two independent properties of the trabecular network. We first derive theoretical relationships between the parameters based on their geometric definitions. Then, we analyze these relationships with an aggregated in vivo dataset with 2987 images from 1434 participants and a synthetically generated dataset with 144 images using principal component analysis (PCA) and linear regression analysis. With PCA, when trabecular thickness, separation, number, and bone volume fraction are combined, we find that 92 % to 97 % of the total variance in the data is explained by the first two principal components. With linear regressions, we find high coefficients of determination (0.827–0.994) and fitted coefficients within expected ranges. These findings suggest that to maximize statistical power in future studies, only two of trabecular thickness, separation, number and bone volume fraction should be used for statistical testing.

Effect of dual flocculant by unmodified manihot esculenta starch and aluminium sulphate on the removal of chemical oxygen demand optimized by response surface methodology

Abstract Applying inorganic metal salt-based aluminium sulphate (Al2(SO4)3) or alum in wastewater treatment has gained many concerns regarding the impact on health and environmental implications. Due to the negative consequences, incorporating inorganic and natural flocculants in the coagulation-flocculation process is the alternative way to reduce undesirable effects. In this study, an evaluation performance of Manihot esculenta (ME) starch and alum as a dual flocculant was conducted to remove chemical oxygen demand (COD) in the optimum weight ratio of alum/starch = 0.06. The optimization of coagulation-flocculation by the optimal (custom) design, response surface methodology (RSM) presented that applying a dual flocculant improved the COD removal efficiency by up to 93% compared to a single coagulant (alum) of 85%. Besides the performance of COD removal increased, the dosage of the chemical coagulant was reduced by up to 64% at the optimum condition of 18 mg/L alum dosage, 307 mg/L starch dosage, pH 9, and 27 mins settling time. The analysis of variance (ANOVA) indicated that the quadratic model was significantly developed with a p-value &lt; 0.05. The results were justified by a high coefficient of determination (R2 alum = 0.9641) and (R2 dual flocculants = 0.9335) using single and dual flocculants, respectively. The findings supported ME starch as an alternative approach in minimizing chemical coagulants in wastewater treatment.

Traffic Noise Modeling under Mixed Traffic Condition in Mid-Sized Indian City: A Linear Regression and Neural Network-Based Approach

Noise pollution is a significant concern in urban settings, caused by traffic increases, urban expansion, and industrial activity. The transportation sector is a crucial contributor to overall noise pollution, particularly in India, where different vehicles ply the roads, resulting in highly fluctuating noise levels. Consequently, traffic noise modeling is essential for addressing this severe issue. The present study employs the MLR (Multiple Linear Regression) and Artificial Neural Network (ANN) approach to model and predict traffic-induced noise levels. The ANN approach outperforms the MLR technique. The architecture of the ANN model integrates different vehicle categories and average speeds as input, resulting in precise predictions. Evaluation of the model’s performance reveals an average Root Mean Squared Error (RMSE) of 0.204 and a high Coefficient of determination (R2) value of 0.93, emphasizing its accuracy. Similarly, in the case of MLR model the RMSE for the training and testing dataset are 1.55 and 1.69 dBA, respectively with R2 value of 0.84. Subsequently, sensitivity analysis highlights the substantial impact of 2-wheelers, tractors/trailers, and 3-wheelers on noise predictions. This study contributes valuable insights into noise management, urban planning, and sustainable development. It demonstrates the efficiency of the ANN approach in addressing complex noise pollution challenges, offering a path toward quieter and healthier urban environments.

Constructing a Corrected Stations Network Using Some Geomatic Techniques

Abstract The major objective of this study is to establish a network of Ground Control Points-GCPs which can use it as a reference for any engineering project. Total Station (type: Nikon Nivo 5.C), Optical Level and Garmin Navigator GPS were used to perform traversing. Traversing measurement was achieved by using nine points covered the selected area irregularly. Near Civil Engineering Department at Baghdad University Al-jadiriya, an attempt has been made to assess the accuracy of GPS by comparing the data obtained from the Total Station. The average error of this method is 3.326 m with the highest coefficient of determination (R2) is 0.077 m observed in Northing. While in the Easting the average error is 4.634 m with (R2) 0.099 m observed. The average error 5.513 m with (R2) is 0.071 m is observed in Reduced Level. The accuracy assessments of the interpolation methods (Total Station and Level) have been calculated using the average error 0.240 m with (R2) is 0.735 m is observed in Reduced Level. The GIS 10.3 program can automatically transform the saved data to text format and produce digital plans with attribute.

Predictive modeling of oleuropein release from double nanoemulsions: An analytical study comparing intelligent models and Monte Carlo simulation

The objective of this study was to evaluate the release of oleuropein (OLP) from double nanoemulsions stabilized with polymeric complexes. Initially, W1/O nano-emulsions loaded with OLP were prepared and re-emulsified into an aqueous phase (W2), which included a complex of whey protein concentrate (WPC)/pectin, to form W1/O/W2 emulsions. The microstructure of the final double emulsions was analyzed by scanning electron microscopy (SEM), and particles with smooth, comparatively spherical, and somewhat asymmetrical surfaces with a size range of 100–200 nm were observed, which were compatible with dynamic light scattering (DLS) data. The release trend of OLP was determined by fitting it to several empirical models including zero order, first order, Higuchi, Hixson-Crowell, Korsemeyer-Peppas, Baker-Lonsdale, and utilizing intelligent modeling techniques such as Fuzzy Logic (FL) and Artificial Neural Networks (ANNs). Among the mathematical models, the zero order equation had the highest coefficient of determination (R2 = 0.988), while the first order equation had the lowest root-mean-square error (RMSE = 0.0176) and sum of squared errors (SSE = 0.0009) for the goodness of fit of the model, when considering the release trend of OLP. FL and ANNs proved effective in modeling controlled release of OLP-loaded nanocarriers, achieving high R2 values. Additionally, Monte Carlo (MC) simulation showed potential for evaluating the release process when compared to other methods.

A practical equation for predicting saturated hydraulic conductivity of fine-grained soils

The saturated hydraulic conductivity (ks) of soils plays a crucial role in various agricultural and environmental engineering applications, particularly in relation to pollution prevention and water migration. Firstly, the traditional Kozeny-Carman (KC) equation, which are commonly used to estimate ks, has limitations in predicting ks for different types of fine-grained soils based on the analyses of permeability test data in the literature. Secondly, to tackle this issue, a modified KC equation was proposed by introducing an expression for the shape parameter and establishing a relationship between the tortuosity equation and the plastic limit. Additionally, the validation of the proposed equation was conducted using ks values of 25 fine-grained soils obtained from other sources. Finally, a comparison was made with the four commonly used ks models to evaluate its performance. The results indicate that the proposed equation demonstrates a relatively high coefficient of determination and a low root mean square error. This suggests that the proposed equation has the potential to accurately assess ks for fine-grained soils. The predictions for ks using the modified equation showed good agreement with the experimental data mentioned in the literature.

Magnetic dispersive solid-phase extraction of clarithromycin and azithromycin by Fe3O4@ionic salt g-C3N4 adsorbent and HPLC-UV

In this study, Fe3O4@ionic salt g-C3N4 was synthesized as a new adsorbent for magnetic dispersive solid phase extraction (MDSPE) of two antibiotics (clarithromycin and azithromycin) in biological samples followed by HPLC-UV. The new nanocomposite was characterized by Fourier-transform infrared (FT-IR), field emission-scanning electronic microscope (FE-SEM), transmission electron microscope (TEM), nitrogen adsorption–desorption analysis, X-ray powder diffraction (XRD), and vibrating sample magnetometer (VSM). Box-Behnken design (BBD) and response surface methodology (RSM) were used for the optimization and design of experiments. The method had a suitable linear range (1–300 µg/L), a high coefficient of determination (R2; 0.9988 and 0.9993), and a low detection limit (0.33 and 0.42 µg/L) for clarithromycin and azithromycin, respectively. The applicability of the method was evaluated by estimating the recovery of analytes from urine and blood serum samples. Recovery of more than 90.0 % and RSDs of less than 5.2 % show the acceptable accuracy and precision of the proposed method.

Degree of bending of FRP-reinforced tubular X-joints in offshore jacket-type structures under in-plane bending moment

In offshore jacket structures, the stress within the chord thickness of tubular joints arises from both membrane and bending stresses. The Degree of Bending (DoB)—the ratio of bending stress to the total stress—is key to evaluating these joints' fatigue resistance. This study delves into the DoB in tubular X-joints reinforced with fiber-reinforced polymer (FRP) subjected to in-plane bending moment. It begins by validating finite element (FE) analysis accuracy through comparison with existing theoretical and experimental evidence. Following this, the analysis of 166 joints was conducted to examine how variations in FRP characteristics (such as type, layer count, and layout) and the joints' dimensionless geometric factors influence the DoB and its ratio in reinforced joints compared to their unreinforced counterparts. Findings indicate that FRP layers enhance fatigue resistance by lowering hot spot stresses on the chord's inner and outer surfaces and elevating the DoB value by up to 26.16%. Consequently, the study introduces a parametric formula for calculating the DoB in FRP-reinforced tubular X-joints under an in-plane bending moment. This development is significant, as there was no existing parametric formula for determining DoB in FRP-reinforced joints. The proposed formula stands out for its high determination coefficient and minimal error, effectively addressing a notable gap in the field.