Mathematical Model For Prediction Research Articles

Single and multi‐fractal dimension variation of tight sandstone by using centrifuge T2 spectral curve

AbstractPore‐fracture structure distribution heterogeneity (PFSH) affects dynamic variation of porosity‐permeability of tight sandstone reservoirs, then restricting gas production performance. A fractal model by low‐field nuclear magnetic resonance technology (LF‐NMR) has been used in the quantitative characterization of PFSH. Among some literature, PFSH was studied by using a saturated T2 spectrum. However, there are few studies on fractal characteristics of T2 spectral morphology in a centrifugal state and its influence on porosity‐permeability parameters. In this paper, 30 tight sandstone samples were collected from Taiyuan Formation in Qinshui Basin. Then LF‐NMR technology was used to analyze PFSH, and sample types were divided by using T2 spectra difference under saturated and centrifugal conditions. Meanwhile, single (model 1 and 2) and multi‐fractal model are adopted to calculate fractal parameters of saturated and centrifugal T2 spectra, and then a difference in fractal parameters under different water conditions was compared. Correlation between different fractal parameters, pore structure and T2 cut‐off value are studied, and a mathematical prediction model for T2 cut‐off value by using fractal and pore structure parameters are established. The results are as follows. (1) All the samples are divided into four types A/B/C/D. For example, the type A sample is characterized by a single peak of T2 spectrum and T2 value is less than 10 ms, which indicates that this type belongs a smaller‐pore developed. Type B sample is characterized by a single peak of T2 spectrum and T2 value is10–100 ms, which indicates that this type belongs to mesopore developed. (2) In saturated state (DS), PFSH of type A sample by using model 1 and 2 is stronger than that of type B, followed by type C and D. Then the multifractal model shows that PFSH of type B sample is stronger than that of other sample types. Correlation between fractal dimension calculated by using single fractal and pore structure parameters is stronger than that of multifractal dimension. (3) T2 spectrum in centrifugal state has fractal characteristics (Di), and there are certain correlation Di with Ds. Therefore, a mathematical prediction model for T2 cut‐off value by using fractal and pore structure parameters is established. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

Prediction of environmental pollution hazard index of water conservancy system based on fuzzy logic

The water conservancy framework is a significant foundation for China's financial and social turn of events. Simultaneously monetary and social turn of events, likewise tremendously affect the biological climate. In the prediction method of the environmental pollution hazard index of water conservancy projects, a risk assessment must be carried out to enhance its applicability. This article mainly focuses on traditional mathematical prediction models and combines the characteristics of fuzzy logic mathematics (good nonlinear quality) to establish a new combination prediction method. By comparing the convergence errors of traditional methods with this method, the results show that the algorithm proposed in this paper can effectively reduce prediction errors with fewer iterations, making it stable at around 500 times, with good convergence and high computational accuracy. The fuzzy logic-based prediction method for environmental pollution hazards in water conservancy systems proposed in this article can overcome the difficulty of nonlinear combination prediction. This achieves real-time prediction of environmental pollution hazards in water conservancy systems and shortens response time, greatly improving the accuracy of prediction.

Daily load curve prediction for Jordan based on statistical techniques

Abstract The article proposes a mathematical prediction model for daily load curves (DLCs) in Jordan from 2023–2050. The historical hourly peak loads based on the growth rate statistical method in 1994–2020 and the annual forecasted peak loads during the morning and evening periods taken from the long-term load forecast (LTLF) study of National Electric Power Company (NEPCO) during 2022–2050 are employed in the prediction model. The results show that the actual hourly growth rates, the annual forecasted growth rates, and the hourly peak loads in the reference year 2022 are the main input variables used in the prediction formula. The LTLF study conducted by NEPCO employs various sophisticated methods depending on the end-user sectorial electricity consumption that imply an econometric approach, market survey, and Gomprtz extrapolation techniques. The peak load in Jordan relies upon several climatic and nonclimatic variables, implying the ambient temperature, gross domestic product, income, demographic, urbanization, electricity tariff, average oil prices, and other factors related to technology and new aspects of energy saving and space heating/cooling systems, the DLC in Jordan is variable and changing from year to year. The proposed model considers a variation in the future DLC and suggests three different scenarios of DLC’s prediction based on the time occurrence of the peak load: the first is the daytime peak occurrence scenario, the second is the evening peak occurrence scenario, and finally is the daytime and evening peaks may be close to each other.

Transport properties evaluation of pore-scale GDLs for PEMFC using orthogonal design method

Gas diffusion layer (GDL) is an essential component of proton exchange membrane fuel cells, serving the functions of gas-water transport, thermal-electrical conduction and mechanical support. The various microstructural characteristics of the GDL have coupled and complex impact on transport properties, which is not comprehensively considered in previous studies. This study combines stochastically reconstruction techniques, pore-scale modeling and orthogonal design method to evaluate the coupling effect of multiple microstructural characteristics on transport properties, and determine the impact degree of each microstructural characteristics, including fiber diameter, porosity, GDL thickness, fiber orientation coefficient, binder and PTFE content. Finally, new mathematical models are developed and validated to predict and optimize the anisotropic transport properties accurately and rapidly by considering the coupling effect of multiple microstructural characteristics. The results showed that porosity has higher impact degree on gas diffusion and heat conduction than other microstructural characteristics. The combinations of microstructural parameters are optimized to achieve higher performance, with thermal conductivity and gas diffusivity increased by ≥139% and 62%, respectively. The prediction mathematical models are validated with the error ranging from 1% to 8%, which can predict transport properties and optimize the GDL microstructure accurately and rapidly.

Verification of a Mathematical Model of Mitral Chordae Length in Echocardiography as a Function of Their Manual Measurement

Aim: In this study, we sought to verify a mathematical model, ME = 0.87 * MM + 4, by performing tests on a sample of individuals with known chordae length by echographic measurement. Patients & methods: This report presents a retrospective analysis of 191 patients who underwent mitral valve repair. This validation analysis was carried out with the same sample of participants that we previously published in the “Journal of Cardiothoracic Surgery” under the name “Modeling of mitral chordae’s length in echocardiography as a function of their manual measurement in the operating room”. The entire sample (N = 191) was randomly divided into two subsamples: → Learning data: 70% (134) of the total sample and was used to train and develop the mathematical model. → Test data: 30% (57) of the total sample and was used to evaluate the performance of the predictive mathematical model. Results: In our analysis, we obtained an RMSE of 1.108, indicating good model performance and validity. We demonstrated by Student’s t-test that the model accurately predicts echographic mitral chordae length, and that there is no statistically significant difference between predicted and actual echographic values. We also visually demonstrated a linear relationship with no significant outliers, indicating good alignment between predicted and actual values of echographically measured mitral chordae length. Conclusions: The results of this validation not only improve the applicability of the model, but also provide clinicians with a valuable tool for making informed decisions about patients’ cardiac health.

ПРОГНОЗИРОВАНИЕ РАЗВИТИЯ ВОСТОЧНОЙ ПЛОДОЖОРКИ (GRAPHOLITA MOLESTA) С УЧЁТОМ ПОГОДНЫХ ФАКТОРОВ ДЛЯ РАЗРАБОТКИ СИСТЕМЫ ЗАЩИТЫ РАСТЕНИЙ МНОГОЛЕТНИХ НАСАЖДЕНИЙ

Modern agriculture is impossible without the use of plant protection products against pests and diseases, which, with their free development, significantly reduce the productivity of plantings and the marketability of the harvested crop. Research conducted in this direction abroad is aimed at using predictive mathematical models that link environmental factors that are significant for the pest and its development. At the same time, the system of intellectualization of such a forecast is increasingly being used with the use of artificial intelligence methods that independently form models based on constantly accumulating information databases. The aim of the study was to evaluate the effectiveness of an automated program for forecasting the development of the oriental fruit moth, developed on the basis of the temperature factor of the environment for the subsequent selection of an optimal adaptive plant protection system for perennial crops. To compare the results of the automated calculation of the development of the eastern fruit moth with objective data in the field, we carried out observations of the calendar dates of the butterfly flights and overcoming the economic threshold of harmfulness (ETH) on pheromone traps placed in peach plantations, on which chemical treatments with insecticides were not carried out in the period from 2019 to 2022. In the course of the research, a useful computer program was developed to determine the calendar dates of the development of the oriental fruit moth based on air temperature data using flexible logical algorithms. It is established that the variability of the terms of individual development of the pest provides an increase in the phase periods with each successive generation. The use of a computer program will allow us to develop a system for protecting trees from pests, as well as to adjust the timing of measures depending on objectively changing indicators of the conditions of the year.

Applications of design and reverse engineering for the development of digital and smart tools for composite additive manufacturing

This paper presents a study dedicated to the development of Digital and Smart Tools, based on solved applications of design engineering and reverse engineering. This approach is justified by the fact as well as the need for preparations prior to Composite Additive Manufacturing. Future integration of Digital Smart Tools with Composite Additive Manufacturing will significantly contribute to the efficient and effective support of the green economy, the active, responsible, safe and resilient protection of environment, life and climate. The research involved in this paper contributes to develop Digital and Smart Tools Applications, intended for integrated digital design, development, manufacturing and further predictive maintenance and services, based on robotic systems, extended automatic control, Artificial Intelligence and Machine Learning, including Mathematical Modeling and Numerical Simulations for Performance Prediction at Design Regime and Off-Design Regimes for jet engines, with the best capabilities to generate and integrate improvements, optimizations and potential innovative solutions. This paper presents significant applications of design, concept engineering development and reverse engineering design, as: 1/ the design of a transonic axial compressor rotor blade, 2/ the design of a swept axial compressor rotor, 3/ concept design engineering developments in case of a swept fan rotor blade, 4/ concept design developments and reverse engineering in case of a HP axial compressor rotor blade, part of Spey 512-14 DW turbofan engine, 5/ reverse engineering design of a Cessna 182 Skylane N223IF light aircraft wheel cover. In line with Europe´s vision for sustainable aviation, this research study and INCAS´ TGA Project "Technological Development Platform for "Green" Technologies in Aviation and Ecological Manufacturing with Superior Added Value; TGA - Technologies for Green Aviation" will significantly contribute to the Green Deal, as a production center using "Green" Technologies in aviation and ecological manufacturing, as well as collaborative developer of Digital and Smart Tools for Composite Additive Manufacturing.

Mathematical modelling based on deposition parameters for optical band gap estimation of Sb2S3 thin films

A mathematical prediction model was developed to predict the band gap of Sb2S3 films based on the synthesis parameters. For the optimization of process parameters, a chemometric optimization approach was used instead of the traditional method. The band gaps of chalcogenide films were measured between 1.68 eV and 1.98 eV. The developed model was able to predict the band gap of the optimized Sb2S3 sample, measured as 1.71 eV, as 1.77 eV. The mathematical model achieved the band gap prediction of both the optimized Sb2S3 film and the test sample with a relative error of 3.51% and 2.23%, respectively. The data revealed that the developed model could reliably predict the band gap of Sb2S3 nanoparticles with a relative error of less than 5%. These findings may provide a significant advantage in nanoscale applications in terms of better understanding the predictability of the band gap of Sb2S3 films prior to synthesis.

Location and Extents of Scour Hole around an Erodible Spill-through Abutment under Clear Water Condition and the Abutment Classification

Bridge abutment scour is a complex phenomenon, which significantly affects bridge stability and is responsible for the damage and failures of many bridges over waterways across the world. Given the widespread and devastating human and societal costs, numerous experimental studies have been conducted to find the mechanisms of bridge abutment scour, and several empirical and mathematical prediction models are available. However, the location of the scour hole and its extents have not been investigated in detail, which is one of the important parameters, not only for the bridge stability itself, but also for the safety of structures around the bridge and their design. Thus, in this study, laboratory experiments were carried out using several different lengths of erodible abutment under different flow conditions to suggest a new mathematical criterion for abutment classification with respect to the location of scour holes. Furthermore, additional analysis was conducted to locate the point of the deepest scour depth and extent of the scour hole around the abutment. Both in transverse and flow direction, the location of the scour hole and the point of the deepest scour are governed by the geometric contraction ratio. This research will be useful in analyzing the bridge safety itself as well as safety of the river training works close to the bridge with respect to the location and extents of the scour hole.

Experimental evaluation and neural networks modeling of removal efficiency and volumetric mass transfer coefficient for gas desulfurization in spray tower

Spray towers to can be used to absorb pollutants gases. Due to the complexity of the process, to propose mathematical models for prediction of removal efficiency and volumetric mass transfer coefficient, is a difficult task. This work aimed to evaluate the influence of variables on the SO2 removal process in a spray tower and to obtain an artificial neural network (ANN) to predict the removal efficiency and the volumetric mass transfer coefficient for columns with different heights, gas and liquid flow rates. The tower built in this study reached a maximum efficiency of 99.47% and showed that it was possible to achieve high levels of removal without the need to build high towers. The best ANN obtained presented error of 2.76% for the removal efficiency of the SO2 and 6.79% for the volumetric mass transfer coefficient. The obtained results were very promising in the prediction of important parameters in the process of removing atmospheric pollutants via spray tower.

Theoretical quantification of pH-responsiveness of blend membrane

In this work, a simple predictive mathematical model has been presented that quantifies the membrane parameters, e.g., permeability, effective pore diameter and number of charged units in a polymer chain in a pH-varying environment devoid of added salt. This has been validated experimentally for the case of poly(vinylidene fluoride) (PVDF) membranes blended with poly(methyl methacrylate)-co-poly(acrylic acid) (PMMA-co-PAA) additive copolymer. The effect of Donnan potential was incorporated into the model and estimated using electrokinetic characterizations. It varied considerably from 0.25 to −1.93 units (dimensionless) from pH 2 to 11. Variations of surface morphology and the electronic structure of the blend membrane with pH were studied in detail. Molecular simulations were performed to estimate the effective distance between the neighboring units in the charged/uncharged states and for confirming the copolymer chain behavior in different pH environments. Adsorption experiments were conducted to compare the variation of the number of uncharged units with pH with those estimated using the model. A pH-dependent permeability hysteresis behavior was observed which was explained using an intermolecular hydrogen bonding mechanism. About 80 % variation in the permeability of the blend membrane was observed between the pH limits which was in close agreement with the model results.

Prediction of erosive wear – A novel mathematical model

Several authors in past idealized erodent particles by assuming of spherical shapes. In reality, particles predominantly have irregular and angular shapes. The present study evolved a novel mechanistic mathematical model for prediction of solid particle erosive wear assuming the angular particles of square pyramidal shapes. The proposed model predicts the absolute volume loss as a cumulative contribution of deformation damage and cutting removal. It includes the effect of velocity, impingement angle, mass, abrasiveness factor of striking particles and the properties of target material. It can be effectively used for prediction of erosive wear for jet type of fluid flow.

Co-combustion of methane hydrate granules and liquid biofuel

The use of fossil hydrocarbons is accompanied by such problems as the depletion of energy resources and high levels of anthropogenic emissions. One of the options for solving these problems can be the involvement in the fuel sector of composite mixed fuels using gas hydrates and vegetable liquid bio-fuels. In this research we test a hypothesis that gas hydrate with added rapeseed oil will make an energy-efficient and environmentally friendly composite fuel. According to the experimental findings, the co-combustion of gas hydrates and liquid biofuels shows high potential. It shortens the ignition delay by 1.5 times at 700 °C in the combustion chamber and by half at 800 °C compared with the combustion of methane hydrate alone. It also produces 18–32% less carbon monoxide, 12–22% less nitrogen oxides, and 14–33% less sulfur oxides than the direct combustion of rapeseed oil. On the basis of the results obtained, we have developed a predictive mathematical model simulating the heat transfer in a layer of composite fuel. A methane hydrate and rapeseed oil mixing scheme is proposed for combustion chambers. We also provide recommendations on how to use the research findings in a number of energy-related applications.

Effectiveness of Different Treatment Tactics of Superficial Thrombophlebitis of Lower Limbs (with Development of Mathematical Model for Prediction of Therapeutic Effectiveness)

INTRODUCTION: The main goals of treatment of superficial thrombophlebitis of lower limbs (ST LL) can be achieved using different treatment tactics: conservative treatment, surgical intervention, and their combination.
 AIM: To perform a comparative analysis of the effectiveness of pharmacotherapy, crossectomy in combination with pharmacotherapy and of phlebectomy in combination with pharmacotherapy in patients with ST LL.
 MATERIALS AND METHODS: A comparative analysis of the effectiveness of the therapeutic tactics of ST LL was conducted (86 patients; 36 men and 50 women). Group 1 patients received only conservative treatment, group 2 patients underwent crossectomy and conservative treatment in the postoperative period, group 3 — phlebectomy in combination with conservative treatment. The clinical effectiveness of treatment was evaluated by the recurrence rate and/or progression of the disease within 3 months after treatment and the level of life quality (Chronic Venous Insufficiency Questionnaire, CIVIQ 20, visual analog scale, VAS). Based on the data obtained in the study, a mathematical model was developed to determine the maximally effective treatment method. For mathematical modeling, a ‘random forest’ method was used.
 RESULTS: All the studied treatment methods demonstrated a comparative clinical effectiveness. Analysis of the dynamics of the studied parameters (adjusted for gender and age of the patients) compared with their initial values within each group showed that statistically significant changes in the psychological factor were observed in the pharmacotherapy group already on the 7th control day (p = 0.024), while in the crossectomy and phlebectomy groups only on the 14th day. In the groups of pharmacotherapy (p = 0.001) and phlebectomy (p = 0.005), the improvement in terms of the social factor occurred faster than in the group of crossectomy, since statistically significant differences were found on the 7th day, and in the group of crossectomy only on the 14th day.
 CONCLUSION: All the groups demonstrated comparable clinical effectiveness in normalization of the quality of life and the recurrence rate and/or progression of the disease within three months after completion of treatment. On the basis of the data obtained, predictive models have been constructed that allow, based on the initial characteristics of the patient, to determine the tactics of therapy that can ensure maximum effectiveness in terms of normalization of values, reflecting the quality of life and VAS parameter.

A multi-objective optimization of laser cladding processing parameters of AlCoCrFeNi2.1 eutectic high-entropy alloy coating

In order to obtain the optimal processing parameters for laser cladding (LC) of AlCoCrFeNi2.1 eutectic high-entropy alloy, a multi-objective optimization method based on response surface methodology (RSM) was proposed. In this paper, a one-way pre-test was conducted by varying the processing parameters using a single control variable method. The results show that the porosity and microhardness of the coating are closely related to the processing parameters. The cross-section microhardness of the coating showed instability under certain process parameters, based on which the optimization window of RSM processing parameters was determined. In the RSM model, a mathematical prediction model was developed according to the laser power, scanning speed, powder feeding rate and microhardness, dilution rate and aspect ratio. Microhardness is inversely correlated with laser power and positively correlated with scanning speed and powder feeding rate. The optimal processing parameters (laser power: 1675 W, scanning speed: 7.8 mm/s and powder feeding rateing rate: 12.91 g/min) were determined. The model showed a good agreement with the experimental validation results. The coatings have good shape with an average microhardness of 322 HV0.2. The maximum average error between model predictions and experimental validation was 5.27 %.

Development and Validation of a Predictive Model Based on LASSO Regression: Predicting the Risk of Early Recurrence of Atrial Fibrillation after Radiofrequency Catheter Ablation.

Although recurrence rates after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF) remain high, there are a limited number of novel, high-quality mathematical predictive models that can be used to assess early recurrence after RFCA in patients with AF. To identify the preoperative serum biomarkers and clinical characteristics associated with post-RFCA early recurrence of AF and develop a novel risk model based on least absolute shrinkage and selection operator (LASSO) regression to select important variables for predicting the risk of early recurrence of AF after RFCA. This study collected a dataset of 136 atrial fibrillation patients who underwent RFCA for the first time at Peking University Shenzhen Hospital from May 2016 to July 2022. The dataset included clinical characteristics, laboratory results, medication treatments, and other relevant parameters. LASSO regression was performed on 100 cycles of data. Variables present in at least one of the 100 cycles were selected to determine factors associated with the early recurrence of AF. Then, multivariable logistic regression analysis was applied to build a prediction model introducing the predictors selected from the LASSO regression analysis. A nomogram model for early post-RFCA recurrence in AF patients was developed based on visual analysis of the selected variables. Internal validation was conducted using the bootstrap method with 100 resamples. The model's discriminatory ability was determined by calculating the area under the curve (AUC), and calibration analysis and decision curve analysis (DCA) were performed on the model. In a 3-month follow-up of AF patients (n = 136) who underwent RFCA, there were 47 recurrences of and 89 non-recurrences of AF after RFCA. P, PLR, RDW, LDL, and CRI-II were associated with early recurrence of AF after RFCA in patients with AF (p < 0.05). We developed a predictive model using LASSO regression, incorporating four robust factors (PLR, RDW, LDL, CRI-II). The AUC of this prediction model was 0.7248 (95% CI 0.6342-0.8155), and the AUC of the internal validation using the bootstrap method was 0.8403 (95% CI 0.7684-0.9122). The model demonstrated a strong predictive capability, along with favorable calibration and clinical applicability. The Hosmer-Lemeshow test indicated that there was good consistency between the predicted and observed values. Additionally, DCA highlighted the model's advantages in terms of its clinical application. We have developed and validated a risk prediction model for the early recurrence of AF after RFCA, demonstrating strong clinical applicability and diagnostic performance. This model plays a crucial role in guiding physicians in preoperative assessment and clinical decision-making. This novel approach also provides physicians with personalized management recommendations.

Analytical Study of Interstitial Fluid Extractive Microneedle Arrays Using Differential Transform Method

The computational studies of a predictive mathematical model for the extraction of interstitial (ISF) for transdermal and non-invasive diagnosis using biodegradable and hollow microneedle patch is presented in this paper. Rapid Diagnostic Tests diagnosis, which is non-invasive, affordable, straightforward, and provides results promptly and reliably, has increased access to parasite-based analysis on a global scale. Microneedle arrays are a rapidly evolving and promising technology for transdermal interstitial fluid extraction, which is used for many clinical diagnostic procedures. Hence, a developed mathematical predictive model used to optimize the design of microneedle patch for transdermal ISF extraction and subsequent diagnosis using dissolvable microneedle arrays was applied in this study. The model's solutions were obtained using the Differential Transform Method. The numerical Runge-Kutta method of fourth order was used to validate it. An experimental test result was also used to further validate the analytical results in the absence of the extracted velocity parameter. And there was a good agreement among them. Influence of dissolution rate constant, microneedle height, diffusion coefficient, velocity of ISF, microneedle ISF drug load, and density of the microneedle; were investigated. Increase in diffusion coefficient and density led to an increase in concentration of ISF extracted over time, an increase in dissolution rate led to a decrease in concentration extracted, while decrease in drug load and height, led to increase in ISF concentration extracted. A negligible effect was observed by varying the velocity of ISF extracted. The approximate analytical approach utilized in the current work has given us a more precise strategy for creating a mathematical model that predicts how ISF will be extracted from skin for use in transdermal and non-invasive rapid diagnostic tests.

Mechanical Behavior of Seamless Pipes Using Ring Expansion Technique and Novel Hoop Stress Correlation Factor (K)

This study investigated the stress–strain behavior of seamless pipes in the hoop direction using the ring expansion test, which is a non-standardized mechanical testing technique used for evaluating the mechanical properties of round tubes. However, this technique has limitations, such as unidentified specimen geometry, strain measurement, and the estimation of friction coefficients. The study employed experimental, numerical, and analytical methodologies to address these limitations and throughout the study, a novel hoop stress correlation factor (K) was identified to be multiplied by the hoop stress derived equation for reduced section ring specimens. The experimental strain was measured using a newly derived analytical equation, and a mathematical predictive model was developed to estimate the K-factor using the Design of Experiment (DoE) and Design-Expert statistical software. The study concluded that the ring expansion test is a promising technique for evaluating the mechanical properties of seamless pipes similar to the unified axial tensile stress–strain behavior. However, future research is needed to estimate the hoop stress correlation value (K) for all ring geometries. The study's finding of the novel hoop stress correlation factor (K) in the case of a reduced section ring specimen is particularly noteworthy, as it addresses a significant research gap in the field.

Implementation of analytical quality by design concepts for the optimization of quantitative 1H nuclear magnetic resonance (1H-qNMR) method for quantitation of novel anti-covid drugs (molnupiravir and favipiravir) in their pharmaceutical dosage forms

For the first time in the literature, the Analytical Quality by Design (AQbD) principles were applied to the development of a specific and simple quantitative proton nuclear magnetic resonance (1H-qNMR) method for quantitation of two antiviral drugs, molnupiravir (MOL) and favipiravir (FAV). This 1H-qNMR method was developed within the QbD framework, the new paradigm of quality recently outlined in the ICH Q14 guidelines. Basic principles of AQbD have been adapted starting with early risk assessment and identification of the analytical target profile (ATP), the analytical procedure attributes (APA) and the analytical procedure parameters (APPs). During the scouting phase, dimethyl sulfoxide-d6 (DMSO‑d6) was selected as the deuterated solvent and maleic acid as the internal standard. The quantitative determination was based on the selective proton signals from MOL's pyrimidine ring at 6.82 ppm (doublet) and FAV's hydroxyl group at 8.71 ppm (singlet), as well as a singlet signal from the IS at 6.24 ppm. A Box-Behnken Design was employed in response surface methodology to develop mathematical predictive models relating the APPs to the APAs followed by multicriteria- decision making with the aid of Derringer's desirability function. Finally, the method operable design regions (MODRs) were computed with the aid of Monte Carlo simulations and identified as the multidimensional combination of the APPs where the risk of failure to fulfill the requirements for the APAs was lower than 5 %. The most robust NMR conditions for MOL, with MODR intervals in brackets, were as follows: no. of scans of 72 (16–124.4), a relaxation delay of 19.8 s (1.2–40 s) and an acquisition time of 3.98 s (2.1–6 s). Similarly, for FAV NMR quantitation, the most robust conditions were found to be 72 scans (16–117.3), a relaxation delay of 19.8 s (1.4–40 s) and an acquisition time of 3.98 s (2.2–6 s). The developed method was validated in accordance with the recently endorsed ICH Q2R2 guidelines, and it exhibited detection limits of 1.04 and 0.88 mg mL−1, as well as quantitation limits of 3.45 and 2.95 mg mL−1 for MOL and FAV, respectively. The method was successfully applied to the analysis of the studied analytes in their pharmaceutical products, proving to be simple, fast, non-destructive, and accurate. Additionally, it can be considered an environmentally friendly alternative for quantitation and quality control analysis of pharmaceutical products.

Properties of alkali-activated slag mortar and prediction of its compressive strength

An investigation was carried out on alkali-activated slag AAS mortar mixtures cast with three different water-to-binder ratios W/B; 0.35, 0.40, and 0.45, eight different silica modulus Ms, wide range of Na2O%, activated using a mixture of two different activators; waterglass and sodium hydroxide, and cured with two different curing methodologies; sealing and full immersion in water. The standard 70.6 mm mortar cubes were tested under compression at the age of 3 days and 7 days. Furthermore, their flow, setting time, and longitudinal shrinkage were measured. The results showed that not only the percentage of Na2O is important; but, its source. Additionally, curing methodology affected the compressive strength at both ages where the full immersion in water may cause early development of strength, with no later strength gain. Also, flow test results showed that although increasing the W/B ratio can cause an increase in the flow, however, the strength may be affected negatively at the highest W/B ratio. Drying Shrinkage of AAS mortar mixtures was also found to be very sensitive to the W/B ratio. Furthermore, four mathematical models were used to generate prediction equations of the compressive strength of AAS mortar based on the experimental data, and for both curing methods. A comparison between predictions of different mathematical models was made based on the highest coefficient of determination R2, score, and standard error. The models were verified using four AAS mortar mixtures that were casted and tested at the age of 7 days under compression. The results showed that two models closely predicted the actual strengths, except at compressive strength higher than 60 MPa, where the models were significantly conservative.