Coefficient Of Determination R2 Research Articles

MULLER-WEISS DISEASE: PROPOSED CLASSIFICATION AND TREATMENT ALGORITHM

Muller-Weiss disease is an uncommon condition with unclear etiology and no gold standard treatment. The question arises; which joints to fuse? Although no consensuses prevail, one must postulate fusion should include those affected. Consequently, to establish an algorithm for its surgical management we set out to study clinical and radiographic features with use of SPECT-CT and a literature review.57 consecutive feet presenting with Muller-Weiss disease analysed; 15 men, 25 women, age 22–84. Condition bilateral in 17, left side 16, right in 7 patients. Specific history and examination by senior author. Radiographic series and SPECT-CT obtained with surgery performed on significantly symptomatic feet. Measurements of Meary-Tomeno angles, anteroposterior thickness of navicular at the midpoint of each naviculo-cuneiform, alongside the medial extrusion distance and percentage of compression in each case performed. Poor correlation between Meary's angle and 1) degree of compression at naviculo-cuneiform joints, 2) degree of extrusion 3) compression vs extrusion using R2 coefficient of determination (invalidating Maceira et al. classification). In unilateral cases, extrusion significantly greater on affected side 94.7% (P< 0.001 Fisher exact test). Degree of extrusion significantly greater in bilateral than unilateral cases (p=0.004 unpaired T test). Valgus hindfoot and Meary's negative most common pattern with no correlation between heel alignment and Meary's R2 = 0.003. SPECT-CT useful to determine subtalar involvement in ‘stage 2 disease.’Following review of cases and published literature we propose the following classification for Muller-Weiss disease with treatment algorithm. 3 Stage delineation; Stage 1 (Normal hindfoot alignment); 1A. Talonavicular disease only - Isolated Talonavicular arthrodesis 1B. Talonavicular + Subtalar; double medial or triple arthrodesis. Stage 2. Talonavicular + Naviculocuneiform; 2A. Adequate bone stock - Talo-naviculo-cuneiform arthrodesis, 2B. Inadequate bone stock +- subtalar disease; Talo-naviculo-cuneiform arthrodesis with tricortical bone graft (Mayich). Stage 3; Asymmetric ankle varus. Pantalar arthrodesis Double/triple/TNC/TAR arthrodesis with hindfoot re-alignment.

Developing interpretable machine learning model for evaluating young modulus of cemented paste backfill

Cemented paste backfill (CPB), a mixture of wet tailings, binding agent, and water, proves cost-effective and environmentally beneficial. Determining the Young modulus during CPB mix design is crucial. Utilizing machine learning (ML) tools for Young modulus evaluation and prediction streamlines the CPB mix design process. This study employed six ML models, including three shallow models Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF) and three hybrids Extreme Gradient Boosting-Particle Swarm Optimization (XGB-PSO), Gradient Boosting-Particle Swarm Optimization (GB-PSO), Random Forest-Particle Swarm Optimization (RF-PSO). The XGB-PSO hybrid model exhibited superior performance (coefficient of determination R2 = 0.906, root mean square error RMSE = 19.535 MPa, mean absolute error MAE = 13.741 MPa) on the testing dataset. Shapley Additive Explanation (SHAP) values and Partial Dependence Plots (PDP) provided insights into component influences. Cement/Tailings ratio emerged as the most crucial factor for enhancing Young modulus in CPB. Global interpretation using SHAP values identified six essential input variables: Cement/Tailings, Curing age, Cc, solid content, Fe2O3 content, and SiO2 content.

Enhanced biodegradation of phenol under Cr(VI) stress by microbial collaboration and potential application of machine learning for phenol biodegradation

ABSTRACT Cr(VI) and phenol commonly coexist in wastewater, posing a great threat to the environment and human health. However, it is still a challenge for microorganisms to degrade phenol under high Cr(VI) stress. In this study, the phenol-degrading strain Bacillus cereus ZWB3 was co-cultured with the Cr(VI)-reducing strain Bacillus licheniformis MZ-1 to enhance phenol biodegradation under Cr(Ⅵ) stress. Compared with phenol-degrading strain ZWB3, which has weak tolerance to Cr(Ⅵ), and Cr(Ⅵ)-reducing strain MZ-1, which has no phenol-degrading ability, the co-culture of two strains could significantly increase the degraded rate and capacity of phenol. In addition, the co-cultured strains exhibited phenol degradation ability over a wide pH range (7–10). The reduced content of intracellular proteins and polysaccharides produced by the co-cultured strains contributed to the enhancement of phenol degradation and Cr(Ⅵ) tolerance. The determination coefficients R2, RMSE, and MAPE showed that the BP-ANN model could predict the degradation of phenol under various conditions, which saved time and economic cost. The metabolic pathway of microbial degradation of phenol was deduced by metabolic analysis. This study provides a valuable strategy for wastewater treatment containing Cr(Ⅵ) and phenol.

Bacterial cellulose-supported dual-layered nanofibrous adsorbent for thin-film micro-solid-phase extraction of antibiotics in municipal wastewaters

In this study, bacterial cellulose was coated with composite nanofibers of polyvinyl alcohol doped with beta cyclodextrin and alginate (PVA-SA-βCD), constructed using the electrospinning technique. This novel material served as an effective adsorbent for thin-film micro-solid-phase extraction (TF-μSPE) of antibiotics from water samples, followed by HPLC-UV analysis. The adsorbent was subjected to a comprehensive characterization using ATR-FTIR, FE-SEM, and BET techniques. These analyses provided valuable insights into its physicochemical structure and properties. Several key parameters that affect the performance of the TF-μSPE method were investigated including electrospinning factors (voltage, flow rate, needle tip-collector distance, and electrospinning time), desorption solvent type and volume, adsorbent dose, adsorption and desorption times, pH value, and salt percentage. Under the optimized conditions, the limits of detections and quantifications for target antibiotics were obtained in the ranges of 0.02–0.03 and 0.07–0.1 μg L⁻1, respectively. The linear range was 0.07–1000 μg L⁻1 with satisfactory determination coefficients (r2) of 0.9944–0.9984. The intra-day and inter-day precisions were obtained as 1.1–1.7 % and 2.2–3.5 %, respectively. The developed method was successfully applied to determine antibiotics in municipal wastewater samples, yielding recoveries within the range of 70–100 % (RSD%<3.7). The green features of the method were also assessed based on AGREE tool. This is the first report on the fabrication of a double-layered nanofibrous adsorbent and its application for the adsorption of antibiotics in wastewater. This robust approach combines efficiency with analytical accuracy, making it a valuable tool for antibiotic analysis in environmental samples.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Ultrasonic-induced grafted lanthanum sulfide decorated multi-walled carbon nanotube onto bacterial cellulose applied for adsorption of pesticides in environmental waters

A new biosorbent was fabricated by modification of bacterial cellulose biopolymer grafted with lanthanum sulfide decorated carboxylated multiwall carbon nanotube (La2S3@MWCNT@BC). The sorbent was employed in a green alternative dispersive-solid phase extraction of a variety of 14 pesticides in environmental water samples. The analyses were performed using GC-µECD. The properties and structure of La2S3@MWCNT@BC nanocomposite were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and adsorption-desorption isotherms. The composition of the sorbent was also investigated to evaluate the adsorptive properties of its constituents. The impact of various parameters influencing extraction efficacies such as sorbent dose, adsorption time, sample pH, ionic strength, and desorption conditions was investigated. The method was validated by specificity, matrix effect% (-0.4 to -7.4), enrichment factor (4-10), limits of quantification (0.007–0.31 μg L−1), matrix-matched calibration linearity (0.01-200 µg L−1), determination coefficients (r2=0.9921-0.9998), and precision. The optimized method was applied for the analysis of multiclass pesticides in seven environmental and drinking waters and the recoveries were obtained in the 81-108 % range with RSDs of 2.5-4.7%. This paper is the first report on the synthesis and use of La2S3@MWCNT@BC nanocomposite to extract pesticides from different water samples. The greenness of the procedure was evaluated by the AGREE protocols.

The effect of applying the internal control system COBIT5 framework on tax performance

        The purpose of this research is to analyze the effect of applying the internal control system according to the COBIT5 framework on tax performance. Data was collected from the research sample using the questionnaire form, and the number of distributed questionnaires was (55), distributed to each of the officials and workers in the Iraqi General tax authority, Professionals, and Academics at the University of Baghdad. A set of statistical methods were used to study the relationship between the two research variables, using the statistical program (SPSS) to analyze the data and calculate (the arithmetic mean, correlation coefficient, coefficient of variation, F test, R2 coefficient of determination, T test, Cronbach's alpha coefficient). The research has a significant positive relationship and influence between the framework (COBIT5) and the tax performance. The most influential dimensions of COBIT5 in tax performance dimension delivery, service and support (DSS), followed by evaluation, guidance and control (EDM), while the least influential dimensions in tax performance dimension compliance are planning and organization (APO). The research recommended adopting the COBIT5 internal control framework as a guide for auditors to advise senior management on the efficiency of the internal control system in the tax institution. This includes a governance framework for information technology, which is one of the most important pillars of the corporate governance system. This research also contributes by providing theoretical and practical implications for tax administration to improve the reality of tax performance.

Automated determination of the ion-recombination correction factor (ksat) in ultra-high dose rate electron radiation therapy.

Plane-parallel ionization chambers are the recommended secondary standard systems for clinical reference dosimetry of electrons. Dosimetry in high dose rate and dose-per-pulse (DPP) is challenging as ionization chambers are subject to ion recombination, especially when dose rate and/or DPP is increased beyond the range of conventional radiotherapy. The lack of universally accepted models for correction of ion recombination in UDHR is still an issue as it is, especially in FLASH-RT research, which is crucial in order to be able to accurately measure the dose for a wide range of dose rates and DPPs. The objective of this study was to show the feasibility of developing an Artificial Intelligence model to predict the ion-recombination factor-ksat for a plane-parallel Advanced Markus ionization chamber for conventional and ultra-high dose rate electron beams based on machine parameters. In addition, the predicted ksat of the AI model was compared with the current applied analytical models for this correction factor. A total number of 425 measurements was collected with a balanced variety in machine parameter settings. The specific ksat values were determined by dividing the output of the reference dosimeter (optically stimulated luminescence [OSL]) by the output of the AM chamber. Subsequently, a XGBoost regression model was trained, which used the different machine parameters as input features and the corresponding ksat value as output. The prediction accuracy of this regression model was characterized by R2-coefficient of determination, mean absolute error and root mean squared error. In addition, the model was compared with the Two-Voltage (TVA) method and empirical Petersson model for 19 different dose-per-pulse values ranging from conventional to UDHR regimes. The Akiake Information criterion (AIC) was calculated for the three different models. The XGBoost regression model reached a R2-score of 0.94 on the independent test set with a MAE of 0.067 and RMSE of 0.106. For the additional 19 random data points, the ksat values predicted by the XGBoost model showed to be in agreement, within the uncertainties, with the ones determined by the Petersson model and better than the TVA method for doses per pulse>3.5Gy with a maximum deviation from the ground truth of 14.2%, 16.7%, and -36.0%, respectively, for DPP>4Gy. The proposed method of using AI for ksat determination displays efficiency. For the investigated DPPs, the ksat values obtained with the XGBoost model were in concurrence with the ones obtained with the current available analytical models within the boundaries of uncertainty, certainly for the DPP characterizing UDHR. But the overall performance of the AI model, taking the number of free parameters into account, lacked efficiency. Future research should optimize the determination of the experimental ksat, and investigate the determination the ksat for DPPs higher than the ones investigated in this study, while also evaluating the prediction of the proposed XGBoost model for UDHR machines of different centers.

Three-Dimensional Meso-Structure-Based Model for Evaluating the Complex Permittivity of Asphalt Concrete.

Microwave heating is an emerging alternative pretreatment method for road maintenance in cold seasons. The thermal behavior of asphalt pavement under microwave heating is mainly determined by the complex permittivity of the asphalt mixture. In this study, an innovative approach for calculating the complex permittivity of an asphalt mixture based on a three-dimensional meso-scale heterogeneous structure was proposed. A series of experiments was conducted to verify the accuracy of this approach. The effect of porosity, void size, moisture content and aggregate gradation on the complex permittivity for an asphalt mixture were computationally analyzed based on the validated approach. Moreover, the applicability of commonly used classical dielectric models was analyzed. The classical Lichtenecker-Rother (LR) dielectric model was modified on the basis of simulation data for various conditions. The results showed that the real part of the complex permittivity decreased with the increase in porosity. Some sudden change in the imaginary part of the complex permittivity was observed within the frequency range from 2.6 GHz to 3.9 GHz. A larger air void size would lead to a larger frequency at which sudden change occurs. The real part and imaginary part of the complex permittivity tend to be smaller when more coarse aggregates are replaced with fine aggregates. Both the real part and the imaginary part of the complex permittivity increase with higher moisture content due to the stronger dielectric property of water. Each 1% increase in moisture content would lead to about a 3~4% increase in the real part of the complex permittivity. The determination coefficients R2 for the real part and the imaginary part of the complex permittivity fitted by the modified Brown model were the maximum values, which were 0.922 and 0.980, respectively. The method presented in this study is useful for transportation agencies to optimize microwave heating during winter maintenance.

Novel virtual sensors development based on machine learning combined with convolutional neural-network image processing-translation for feedback control systems of internal combustion engines

Physical sensors are commonly used to record performance data of internal combustion engines (ICEs) for online feedback control and calibration, but they are prone to diagnostic and increased development costs. Lookup tables are commonly used in conventional calibration and feedback control; however, the table parameters increase with the advancement of ICE technologies under transient operations. Consequently, the calibration and control systems are time-consuming. This work proposes novel virtual sensors to address these issues by predicting the combustion, performance, and emission of ICEs using neural networks and image processing/translation. The novel sensors are targeted for onboard feedback control systems under transient driving. Firstly, a virtual diesel engine (VDE) was developed and calibrated against experimental data taken from a production 2.2 L turbocharged diesel engine. The VDE was calibrated under WLTC, JC08, and NEDC transient operations and was used to generate teaching data. Next, the virtual sensors are developed using five machine learning (ML) regressors. The result shows that the coefficient of determination R2 from all ML regressors exceeded 0.94, and the XG-Boost outperforms other ML techniques with R2 > 0.977. XG-Boost parameter estimations were 8 times faster than that on a desktop simulation. Then, an image classification model using a deep convolutional neural network (D-CNN) is constructed, and the dependency of performance parameters and exhaust emissions with the rate of heat release (R.H.R) and in-cylinder pressure profile is confirmed. The performance parameters and emissions dependency was compared individually with R.H.R. and the in-cylinder pressure profile. As a result, a strong correlation between the performance and R.H.R. was observed. Finally, a generative adversarial network (GAN) model was constructed to translate the in-cylinder pressure profile to R.H.R. profile. A novel method to develop virtual sensors for advanced feedback control of any type of ICEs is proposed for the first time.

A physics-based PSO-BPNN model for civil aircraft noise assessment

With the increasingly severe problem of aircraft noise pollution around airports, it is urgent to explore accurate and effective aircraft noise assessment methods. This article proposes a physics-based PSO-BPNN model based on the European Civil Aviation Conference (ECAC) best practice model, backpropagation neural network (BPNN), and particle swarm optimization (PSO) to enrich the methodology system of aircraft noise assessment. The primary modeling process is as follows. Firstly, based on airport parameters, flight information, and other data, the theoretical noise levels of ground monitoring point are calculated using the ECAC dynamic model. Then, a dataset containing measured noise values, theoretical noise values, trajectory data, and meteorological data is constructed to train the physics-based BPNN model, in order to correct the theoretical noise level. Finally, the PSO algorithm is introduced to optimize the parameters of the BPNN model and the construction of the physics-based PSO-BPNN model is completed. By taking Hefei Xinqiao International Airport (HFE) as a research case, the experimental results show that the physics-based PSO-BPNN model, which combines best practice model and machine learning model, demonstrates better performance than the ECAC model and physics-based BPNN model because of its balance between stability and flexibility. In the validation set, the error of 74.77 % of the predicted results was within ±3 dB(A), and the coefficient of determination R2 between all predicted values and measured values reached 0.9450, which indicates the physics-based PSO-BPNN model a potential aircraft noise assessment solution.

Some criminological indicators of land mismanagement

Based on official statistical sources, this article presents a long-term criminological analysis of the situation with land mismanagement in Ukraine spanning the period from 2002 to 2023. The author determines absolute, average and relative indicators, in particular, the rate and dynamics of crimes in this category. The total number of recorded crimes in the category under study (1,240 crimes), their average annual occurrence (56 crimes), and the average growth rate (22.9 %) are calculated. Employing a 5th degree polynomial function, a trend depicting the dynamics of these crimes is constructed. The chosen model aptly captures the development trend of the process with high precision (coefficient of determination R2 = 0.916) and facilitates the identification and characterization of four distinct periods, each reflecting the main trends of land mismanagement in Ukraine.&#x0D; A paradoxical situation is revealed by the analysis of the practical application of the criminal law provisions which have supplemented Art. 254 of the Ukrainian Criminal Code « Land Mismanagement» in accordance with Law of Ukraine No. 402-IX of December 19, 2019 « On Amendments to Certain Legislative Acts of Ukraine Regarding Improvement of Legislation on Mining of Amber and Other Minerals». Notably, no crimes were recorded under Part 2 of Article 254 of the Criminal Code of Ukraine (the primary corpus delicti). Conversely, 15 crimes were recorded under Parts 3 and 4 of Article 254 (qualifying elements of the crime). The author concludes that this correlation would be impossible in the case of objective recording of crimes, and it also underscores a noteworthy latent aspect of land mismanagement, in particular, deliberate evasion of mandatory reclamation of lands damaged in the course of pilot commercial development of amber deposits or amber mining under a special subsoil use permit, resulting in considerable damage. The conducted analysis holds an intrinsic value both for scientific research and for law enforcement, ensuring an appropriate information support base for countering land mismanagement, firstly, by providing a clear understanding of the current situation with this phenomenon. Furthermore, it lays the groundwork for the development of the relevant system of criminal preventive measures tailored to address this specific issue.

Selection of Spectral Parameters and Optimization of Estimation Models for Soil Total Nitrogen Content during Fertilization Period in Apple Orchards

The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, was taken as the experimental area. The optimal method for extracting spectral characteristic bands and screening spectral characteristic indices (SCIs) of soil total nitrogen (TN) for independent and comprehensive fertilization periods was explored. Independent and comprehensive soil TN content estimation models were constructed and optimized for each and the entire fertilization period, respectively. The results show that compared with the correlation coefficient method, stepwise multiple linear regression (SMLR) performs better in extracting hyperspectral characteristic bands of soil TN content. It helps to achieve a higher modeling accuracy, smaller root mean square error (RMSE), and is more conducive to avoiding the influence of multicollinearity of model variables. The sensitive areas of soil TN content in the SCI do not undergo significant changes due to different fertilization periods. Among them, the ratio spectral indices (RSIs) are in the range of 800–900 nm, 1900–1950 nm, and 2200–2300 nm, while the sensitive areas of the difference spectral index (DI) and Normalized difference spectral index (NDSI) are in the range of 1900–1950 nm and 2200–2300 nm. The combination of SCI and characteristic bands significantly improves the prediction accuracy of soil TN estimation models. The independent and comprehensive estimation models for each fertilization period based on the BP (back propagation) neural network optimized by the Mind Evolution Algorithm (MEA-BPNN) can achieve a more stable and accurate estimation of soil TN. Finally, using soil spectral characteristic bands selected through continuum removal (CR) transformation and SMLR, combined with SCI, the model based on the MEA-BPNN (CR-SCI-MEA-BPNN) has the best prediction performance. The modeling determination coefficients R2 for each fertilization period reached 0.94, 0.95, 0.92, and 0.94, respectively, with RMSE of 0.0032, 0.0024, 0.0035, and 0.0027. The R2 and RMSE of the modeling and validation set of the entire fertilization period comprehensive model are 0.899, 0.0038, and 0.89, 0.0041, respectively. The results of this article provide technical support for promoting the timely monitoring of soil TN content and guiding rational fertilization in apple orchards.

Correlation between the sound speed ratio and physical properties of seafloor sediments in the northwestern shelf of the south China sea

Based on a substantial number of seabed sediment samples and corresponding acoustic characteristic and physical property test data collected in the continental shelf area of the northwestern South China Sea, this study employed a sound speed ratio correction model to analyze the correlation between the sound speed ratio of seabed sediments and parameters such as porosity, density, mean grain size, and water content. This study resulted in the development of both single-parameter and double-parameter empirical formulas for the sound speed ratio of seabed sediments in this marine region. The determination coefficients (r2) for the single-parameter empirical formulas based on physical properties, excluding the mean grain size (r2 ≤ 0.75), were all above 0.78, indicating a strong correlation. Additionally, the fitting curve at 100 kHz was slightly greater than that at 50 kHz, consistent with the acoustic dispersion characteristics of sediments. To assess the applicability of the empirical formulas to different maritime areas, the formulas derived from the South Yellow Sea, East China Sea, and South China Sea were compared with those of the study area. The results indicated that the empirical formulas for the sound speed ratio established in this study have general applicability, especially for sediments with low porosity, high density, and low mean grain size. In addition, the determination coefficient of the dual-parameter empirical formula for the sound speed ratio–porosity–mean grain size is superior to that of the single-parameter empirical formula for the sound speed ratio–mean grain size. Through comparative studies with other marine areas, it was found that there are certain differences in the relationships between porosity and mean grain size established in different marine areas. Therefore, based on the rough sphere model, this study transforms the GS model into a single-parameter model built on either porosity or mean grain size. The error analysis results indicate that the single-parameter GS model has a smaller error than the measured data. However, the GS model controlled by a single parameter performs poorly compared to the predicted curves of the sound speed ratio–porosity and the sound speed ratio–mean grain size fitted in this study. This discrepancy may be attributed to the fact that the input parameters of the GS model are reference values, emphasizing the need for further research to determine suitable input parameters for the GS model in this marine area.

Effect of operating conditions and surface roughness on grease lubrication efficiency

The aim of this paper is to investigate the effect of operating parameters (load and rotational speed) and arithmetic mean deviation (Ra) roughness value on temperature rise and tribological behavior of greased point contact under heavy load. The 3-factor, 4-level orthogonal friction experiments are designed and carried out. The moving point heat source integration method is applied to calculate the transient temperature rise of interfaces. The results show that the case with load 1900 N, rotational speed 260 rpm and Ra 0.4 μm has the highest friction coefficient and temperature rise. The grease fails and layered spalling and oxidative wear occur on the wear scar. Four second-order regression models are established to predict friction coefficient and the maximum temperature rise under different load, rotational speed and surface roughness. And the determination coefficients R2 of them are higher than 97 %. The models are helpful to determine if the working conditions fall within effective lubrication range and avoid severe damage.

Application of fourier transform infrared photoacoustic spectroscopy for quantification of nutrient contents and their plant availability in manure and digestate

In this study, we assess the feasibility of using Fourier Transform Infrared Photoacoustic Spectroscopy (FTIR-PAS) to predict macro- and micro-nutrients in a diverse set of manures and digestates. Furthermore, the prediction capabilities of FTIR-PAS were assessed using a novel error tolerance-based interval method in view of the accuracy required for application in agricultural practices. Partial Least-Squares Regression (PLSR) was used to correlate the FTIR-PAS spectra with nutrient contents. The prediction results were then assessed with conventional assessment methods (root mean square error (RMSE), coefficient of determination R2, and the ratio of prediction to deviation (RPD)). The results show the potential of FTIR-PAS to be used as a rapid analysis technique, with promising prediction results (R2 > 0.91 and RPD >2.5) for all elements except for bicarbonate-extractable P, K, and NH4+-N (0.8 < R2 < 0.9 and 2 < RPD <2.5). The results for nitrogen and phosphorus were further evaluated using the proposed error tolerance-based interval method. The probability of prediction for nitrogen within the allowed limit is calculated to be 94.6 % and for phosphorus 83.8 %. The proposed error tolerance-based interval method provides a better measure to decide if the FTIR-PAS in its current state could be used to meet the required accuracy in agriculture for the quantification of nutrient content in manure and digestate.

Prediction of Rheological Parameters of Polymers by Machine Learning Methods

Introduction. All polymer materials and composites based on them are characterized by pronounced rheological properties, the prediction of which is one of the most critical tasks of polymer mechanics. Machine learning methods open up great opportunities in predicting the rheological parameters of polymers. Previously, studies were conducted on the construction of predictive models using artificial neural networks and the CatBoost algorithm. Along with these methods, due to the capability to process data with highly nonlinear dependences between features, machine learning methods such as the k-nearest neighbor method, and the support vector machine (SVM) method, are widely used in related areas. However, these methods have not been applied to the problem discussed in this article before. The objective of the research was to develop a predictive model for evaluating the rheological parameters of polymers using artificial intelligence methods by the example of polyvinyl chloride.Materials and Methods. This paper used k-nearest neighbor method and the support vector machine to determine the rheological parameters of polymers based on stress relaxation curves. The models were trained on synthetic data generated from theoretical relaxation curves constructed using the nonlinear Maxwell-Gurevich equation. The input parameters of the models were the amount of deformation at which the experiment was performed, the initial stress, the stress at the end of the relaxation process, the relaxation time, and the conditional end time of the process. The output parameters included velocity modulus and initial relaxation viscosity coefficient. The models were developed in the Jupyter Notebook environment in Python.Results. New predictive models were built to determine the rheological parameters of polymers based on artificial intelligence methods. The proposed models provided high quality prediction. The model quality metrics in the SVR algorithm were: MAE – 1.67 and 0.72; MSE – 5.75 and 1.21; RMSE – 1.67 and 1.1; MAPE – 8.92 and 7.3 for the parameters of the initial relaxation viscosity and velocity modulus, respectively, with the coefficient of determination R2 – 0.98. The developed models showed an average absolute percentage error in the range of 5.9 – 8.9%. In addition to synthetic data, the developed models were also tested on real experimental data for polyvinyl chloride in the temperature range from 20° to 60°C.Discussion and Conclusion. The approbation of the developed models on real experimental curves showed a high quality of their approximation, comparable to other methods. Thus, the k-nearest neighbor algorithm and SVM can be used to predict the rheological parameters of polymers as an alternative to artificial neural networks and the CatBoost algorithm, requiring less effort to preset adjustment. At the same time, in this research, the SVM method turned out to be the most preferred method of machine learning, since it is more effective in processing a large number of features

Successful controlled release of cyclophosphamide from tert-butylamine-functionalized large-pore SBA-15

ABSTRACT Controlled drug delivery systems operate effectively using the correct carrier/host design that respects the physicochemical characteristics of the drug. LP-SBA-15 shows exceptional qualities as a new host for cyclophosphamide (CP) delivery due to its low toxicity, high biocompatibility, and biodegradability in vivo. Cyclophosphamide is a phosphoramide-derived alkylating compound currently used for treating autoimmune diseases and slowing or restraining cancer cell growth. LP-SBA-15 was synthesized and functionalized using 0%–15%–25% tert-Butylamine (TBA) to incorporate the drug. The composite was characterized by XRD, N2 physisorption, UV-VIS, and FTIR spectroscopy. Absorption and release of CP were assessed by UV-VIS spectrophotometry. The release mechanism from the functionalized LP-SBA-15 matrix was evaluated by fitting experimental data with different mathematical models. The Ritger – Peppas, Weibull, and First-Order models were the most appropriate, as confirmed by the coefficient of determination R2 and other statistics. We designed LP-SBA-15 functionalized with 15% TBA as a suitable system that achieves a moderate initial release rate and maintains a constant rate over long periods, enabling the nanodrug (CP) to concentrate on tumor tissue and not on normal cells while simultaneously reaching adequate levels over time and avoiding harmful side effects due to their deposition.

Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology.

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

Pengaruh Kepercayaan, Kualitas Layanan, Lokasi Dan Pengaruhnya Terhadap Kepuasan Anggota BMT Assafiyah Kota Gajah Lampung Tengah

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