Mathematical Model For Prediction Research Articles

Harnessing computational techniques to analyze and enhance fandom engagement through interaction ritual chains

Abstract. This study explores the application of Interaction Ritual Chains (IRC) theory and computational techniques to analyze and enhance fandom engagement within the Chinese Australian community. By integrating sentiment analysis, social network analysis, and gender-based language analysis, this research provides a comprehensive understanding of how emotional energy, shared focus, and bodily synchronization contribute to fan interactions. We employ a predictive mathematical model to quantify these IRC components and their impact on engagement, leveraging data mining, natural language processing (NLP), and machine learning. The findings reveal the dynamics of fan behavior and offer insights into personalized content recommendations, gamification, and virtual reality experiences to foster deeper emotional bonds and sustained engagement. This study bridges the gap between theoretical frameworks and practical applications, providing a robust foundation for future research on digital fandoms.

Optimizing laser cladding process parameters for transformation-induced plasticity FeMnCoCr high entropy alloy: A multi-objective approach

A multi-objective optimization approach utilizing the response surface method (RSM) is proposed to determine the optimal processing parameters for laser cladding of Fe50Mn30Co10Cr10 coating. In this paper, a single-variable control method was employed to adjust the laser cladding parameters, and preliminary experiments were conducted to establish the optimization range for the RSM laser cladding parameters. A mathematical prediction model of input processing parameters (laser power, scanning speed and powder feed speed) and output response (dilution rate, W/H, microhardness and porosity) was established by the response surface method. The results indicated that both the dilution rate and microhardness had a positive correlation with laser power and scanning speed, while they were negatively correlated with the powder feed rate. The scanning speed is the most significant factor influencing the porosity of the coating, followed by the powder feed rate. The optimal processing parameters were identified as follows: laser power at 1750 W, scanning speed at 8.0 mm/s, and powder feed rate at 11.5 g/min. The model demonstrated strong agreement with the experimental results. The coating molding quality is exceptional, with an average porosity of 0.07 % and an average microhardness of 208.32 HV0.2. Meanwhile, the coating was composed of HCP phase and FCC phase, with the HCP phase comprising15.6 % and the FCC phase 84.4 %, respectively. Under the optimal processing parameters, the Fe50Mn30Co10Cr10 high-entropy alloy coating demonstrated impressive ductility and strength, with a deformation of 55.93 % and a compressive strength of 2153.7 MPa. The fracture mechanism of Fe50Mn30Co10Cr10 coatings is transcrystalline fracture and toughness fractures.

Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients.

Optimization of the Thermokinetic Method for the Control of Weld Decay in AISI 304L and AISI 316L Stainless Steel Weldment

This study investigates the control of weld decay in AISI 304L/316L alloy weldments for liquefied natural gas (LNG) and cryogenic environments using the Tungsten Inert Gas (TIG) welding technique. Weldment samples were thermokinetically treated at 1050, 1100, and 1150°C and cooled in five mediums: Water, Salt, Natural Air, Salt with annealing, and Water with annealing, to retain carbon and chromium in solid solution at approximately 30°C. Furthermore, evaluation methods based on metallography i.e. optical microscopy (OM), Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX), Wear Test (i.e. wear rate and wear track) and electrochemical corrosion potential measurements were adopted. An experimental design table was developed using the design expert software 13.0. This helped develop a predictive mathematical model for ascertaining the optimum operating service conditions of the material. From the results, the optical microscopy analysis revealed that the control sample exhibited a more irregular pattern than others. Results showed that the control sample had a more irregular structure, while air-cooled samples exhibited smoother surfaces, indicating better bonding. SEM revealed a coarse surface with uneven particle distribution post-heat application. The predominant elements were Iron (Fe), Chromium (Cr), and Nickel (Ni). Corrosion potential varied between -0.5 to 0.15 V, demonstrating wave-like behaviour over time. Wear analysis indicated that lower coefficients of friction correlate with better wear resistance. Finally, response surface methodology (RSM) revealed that increasing temperature proportionally increased yield and corrosion rates. While the identified optimal values for Temperature are (1112.68°C), Material (316L), and Quenching Medium (SQ+SA), resulting in specific values such as Wear Rate (5.76896E-06), Coefficient of friction (0.224254), Corrosion Rate (0.395566), and Weight of Heat-Treated Sample (14.7797). The study enhances understanding of the mechanisms affecting welding and contributes to optimizing welding procedures for improved mechanical properties and corrosion resistance.

Predictive Model for Cell Positioning during Periodic Lateral Migration in Spiral Microchannels.

The periodic lateral migration of submicrometer cells is the primary factor leading to low precision in a spiral microchannel during cell isolation. In this study, a mathematical predictive model (PM) is derived for the lateral position of cells during the periodic lateral migration process. We analyze the relationship of migration period, migration width, and starting point of lateral migration with microchannel structure and flow conditions and determine the empirical coefficients in PM. Results indicate that the aspect ratio of the microchannel and the Reynolds number (Re) are key factors that influence the periodicity of the cell lateral migration. The lateral migration width is jointly affected by Re, the cell blockage ratio, and the microchannel curvature radius. The inlet structure of the microchannel and the ratio of the cell sample to the sheath flow rate are critical parameters for regulating the initial position. Moreover, the structure of the pressure field at the inlet constrains the distribution range of the starting point of the lateral migration. Regardless of whether the particles/cells undergo 0.5, 1, or multiple lateral migration cycles, the lateral positions predicted by PM align well with the experimental observations, thus verifying the accuracy of PM. This research helps to elucidate the characteristics of periodic lateral migration of cells in spiral microchannels and can provide practical guidance for the development and optimization of miniature spiral microchannel chips for precise cell isolation.

Roll force prediction using hybrid genetic algorithm with semi-supervised support vector regression

Roll force prediction plays a significant role in rolling schedule and optimization. For a specific steel grade, the roll force can be determined by the aid of several factors: rolling speed, initial thickness, ratio of thickness reduction, the starting temperature of the strip, and the friction coefficient in the contact region. Roll force prediction mathematical models are sometimes rare and inaccurate. This paper presents a new approach to predict roll separating force using semi-supervised support vector regression (SSSVR). The parameters affecting the sensitivity of the SSSVR were optimized using the genetic algorithm to maximize the r-squared accuracy score. The intelligent system is evaluated using two quality metrics: the root mean square error (RMSE) and the mean absolute error calculated between the measured force from the industrial rolling field and the predicted force using the proposed system from one side, and the measured force and the calculated force from another side. Obtained results show the improvement while using the intelligent predictive system. The reduction in RMSE was achieved by the proposed system by 66.9% and 32.1% for oval and round shape passes, respectively in comparison to the conventional calculation method.

The bronchoalveolar lavage dilution conundrum: an updated view on a long-standing problem.

Bronchoalveolar lavage (BAL) is used by researchers to study molecular interactions within healthy and diseased human lungs. However, the utility of BAL fluid measurements may be limited by difficulties accounting for dilution of the epithelial lining fluid (ELF) sampled and inconsistent collection techniques. The use of endogenous markers to estimate ELF dilution has been proposed as a potential method to normalize acellular molecule measurements in BAL fluid, but these markers are also imperfect and prone to inaccuracy. The focus of this report is to review factors that affect the interpretation of acellular molecule measurements in lung ELF and present original data comparing the performance of several BAL dilution markers during health and in a human endobronchial endotoxin challenge model of acute inflammation. Our findings suggest that incomplete ELF and lavage fluid mixing, flux of markers across the alveolar barrier, and lung inflammation are all possible factors that can affect marker performance. Accounting for these factors, we show that commonly used markers including urea, total protein, albumin, and immunoglobulin M are likely unreliable BAL dilution markers. In contrast, surfactant protein D appears to be less affected by these factors and may be a more accurate and biologically plausible marker to improve the reproducibility of acellular BAL component measurements across individuals during health and inflammatory states.NEW & NOTEWORTHY In this report, mathematical prediction models and real-world measurements are used to compare the performance of molecular markers of dilution in bronchoalveolar lavage fluid samples. Effects of acute inflammation within individual subjects are highlighted. These findings inform recommendations for normalizing measurements across bronchoalveolar lavage samples and highlight the need for additional markers to improve the rigor of translational studies utilizing bronchoalveolar lavage measurements.

A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients

BackgroundIn recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer.MethodsIncluded in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results.ResultsAltogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = ex/ (1 + ex); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group.ConclusionIn this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients.

RSM-Based Optimization Analysis for Cold Plasma and Ultrasound-Assisted Drying of Caraway Seed.

In this study, the hot-air drying of caraway seeds was enhanced using two nonthermal physical field technologies: cold plasma (CP) and ultrasonic waves (US). Air drying temperatures of 35, 45, and 55 °C with CP pretreatment exposure times (CPt) of 25 and 50 s were used. When convective drying was accompanied by US, power levels (USp) of 60, 120, and 180 W were applied. Experimentally, the most effective contribution was found by using both CP pretreatment (25 s) and US (180 W), in which the maximum decreases of 31% and 39% were estimated for the drying period and specific energy consumption, respectively. The total color change, the rupture force, TPC, TFC, and antioxidant capacity were also estimated for evaluating the quality of dried products. In a CP-US-assisted drying program (25 s, 180 W), the minimum change in color and the rupture force were found to be 6.40 N and 20.21 N, respectively. Compared to the pure air drying, the combined application of CP and US resulted in a mean increase of 53.2, 43.6, and 24.01% in TPC, TFC, and antioxidant capacity of extracts at the temperature of 35 °C. Based on the response surface methodology (RSM) approach and obtained experimental data, accurate mathematical predictive models were developed for finding the optimal drying condition. The optimization process revealed that 39 °C, 180 W, and 23 s resulted in a desirability of 0.78 for drying caraway seeds.

Notches and Fatigue on Aircraft-Grade Aluminium Alloys.

The influence of notches and fatigue on the ultimate tensile strength and elongation at break of aluminium alloys (2024-T3, 6061-T4, 6061-T4 uncoated, 6061-T6 uncoated, 7075-T0, and 7076-T6) is presented in this study. A total of 120 specimens were used. On all specimens, notches were made using a CNC machine, with 60 of them subjected to low-cycle fatigue (LCF) before undergoing the tensile test. Based on the statistical examination of the measured data, mathematical prediction models have been established. Compared to their unscratched counterparts, the results indicate a significant decrease in the UTS and elongation at break for both notched and notched-fatigued specimens. The LCF pre-treatment contributes to the negative impacts of the notches, resulting in reduced values for the UTS and elongation at break, thus concluding that surface integrity is critical for maintaining the structural strength of aircraft components.

Numerical investigation of the geometric parameters effect of helical blades installed on horizontal geo heat exchanger

In this study, we investigate the enhancement of horizontal geothermal heat exchangers equipped with helical fins on the pipe's exterior and internally ribbed turbulators. Our approach focuses on the interplay between geometry and thermal efficiency through innovative design modifications. Utilizing the finite element method, three-dimensional numerical simulations assessed the effects of varying geometric parameters such as the diameter and thickness of the fins. Our findings indicate significant increases in heat transfer efficiency with the addition of helical fins; specifically, increasing the fin diameter from 5 mm to 10 mm results in a 15 % increase in the heat transfer rate, while doubling the fin thickness from 2 mm to 4 mm enhances the rate by 10 %. These improvements are due to the expanded surface area facilitating greater heat exchange. Optimization using the desirability function approach yielded models with high performance, achieving desirability scores of 0.9879 for outlet temperature and 0.9534 for the heat transfer coefficient. This reflects the effective tuning of geometric parameters to maximize thermal performance. The study also introduces two predictive mathematical models for the outlet temperature and convective heat transfer coefficient of the U-shaped pipe equipped with these enhancements. These models, derived from extensive numerical data, provide practical tools for future design and operational applications of geothermal heat exchangers. This research advances the design and operational efficiency of geothermal heat exchange systems, establishing new benchmarks for thermal efficiency in the field with actionable insights and robust mathematical tools.

Water temperature disturbance alters the conjugate transfer of antibiotic resistance genes via affecting ROS content and intercellular aggregation

Spread of antibiotic resistance genes (ARGs) in aquatic ecosystems poses a significant global challenge to public health. The potential effects of water temperature perturbation induced by specific water environment changes on ARGs transmission are still unclear. The conjugate transfer of plasmid-mediated ARGs under water temperature perturbation was investigated in this study. The conjugate transfer frequency (CTF) was only 7.16 × 10−7 at a constant water temperature of 5 °C, and it reached 2.18 × 10−5 at 30 °C. Interestingly, compared to the constant 5 °C, the water temperature perturbations (cooling and warming models between 5–30 °C) significantly promoted the CTF. Intracellular reactive oxygen species was a dominant factor, which not only directly affected the CTF of ARGs, but also functioned indirectly via influencing the cell membrane permeability and cell adhesion. Compared to the constant 5 °C, water temperature perturbations significantly elevated the gene expression associated with intercellular contact, cell membrane permeability, oxidative stress responses, and energy driven force for CTF. Furthermore, based on the mathematical model predictions, the stabilization times of acquiring plasmid maintenance were shortened to 184 h and 190 h under cooling and warming model, respectively, thus the water temperature perturbations promoted the ARGs transmission in natural conditions compared with the constant low temperature conditions.

Pore pressure prediction of hydrocarbon reservoirs with empirical models and artificial neural network: case study in the Doba basin, Chad

Accurate Pore pressure prediction is essential in drilling as it enables drilling hazards to be avoided. However, traditional methods of pore pressure prediction, especially empirical methods are field-specific. It is therefore important to evaluate the best empirical method which is to be applied in any basin or field according to its main characteristics. Besides, recently, artificial neural networks (ANNs) are the most used for pore pressure prediction because they can help to learn complex relationships between input and output parameters. They lead to accurate pore pressure prediction results in many cases. In light of this, the aim of this study carried out in the Doba basin is to analyse and compare the applicability of various sonic log-based empirical models of pore pressure prediction, including Eaton’s model, Raymer-Hunt’s model and Kozeny-Carman’s model and a ANN model of pore pressure prediction in order to find out the best predictive one to use in the studied area in order to achieve better prediction results. To achieve this objective, firstly, the mathematical models of pore pressure prediction from Eaton, Terzaghi and Raymer-Hunt equations and Kozeny-Carman’s model were used. The artificial neural network method uses 33 datasets of well log data including sonic transient time, vertical stress and porosity as input parameters to train the ANN model. The output parameters are the measured pore pressures produced by the Repeat Formation Tester. Using 70% of data set for training, 15% for test and 15% for validation, the trained model with the Bayesian Regularization Backpropagation function predicts pore pressure with an average correlation coefficient of 98%. The results show that pore pressure obtained from the Kozeny-Carman’s model, also has a 98% correlation coefficient, thus proving to be the most accurate of empirical methods. Indeed, pore pressures obtained from Raymer-Hunt’s model and Eaton’s model have 97% and 90% respectively as correlation coefficients. This study provides the best and most reliable empirical method of sonic log-based pore pressure prediction to use in the Doba basin, which can be used to achieve the best pore pressure prediction results and hence ensures drilling safety and wellbore stability in the studied area. The study also highlights a good application of the Bayesian function of the ANN for pore pressure prediction in the investigated area.

Photochromic webbing structures for monitoring UV-induced mechanical strength degradation

Abstract Webbing structures are critical load-bearing components in a wide array of applications from structural restraint layers in inflatable space habitats to safety harness belts used by construction workers. In the field, webbings are subjected to ultraviolet (UV) irradiation from sunlight, leading to material degradation and a loss of mechanical strength. To date, health monitoring of webbings has relied on empirically correlating UV-induced strength loss with variations in their inherent color, which often yields inconsistent and imprecise results. To fill this gap, we propose a novel class of photochromic webbing structures that afford noninvasive monitoring of UV-induced degradation of their mechanical strength. The webbings’ sensing capabilities are achieved by integrating a class of photochromic yarns, fabricated through a pressurized coating process. Under continuous UV irradiation, the proposed photochromic webbings exhibit a substantial color change, demonstrating a sensing lifetime equivalent to several months in field conditions. We establish a strong correlation between the webbings’ photochromic response and their strength loss, supporting the feasibility of the proposed webbings in monitoring their mechanical integrity. To elucidate the sensing mechanism, we propose a physics-based mathematical model that describes the underlying photochemical reactions. Through an asymptotic analysis, we demonstrate that the model accurately predicts the webbing’s long-term photochromic responses under extended UV irradiation. The proposed photochromic webbing structures and the predictive mathematical model could enhance the safety and integrity of webbing-based engineering systems.

Predictive models, as an idea, to advance the secondary to tertiary transition in science courses

Investigating the transition between the secondary and the tertiary levels is a main theme in mathematics and science education. More so, this paper considers the transition that intersects with the after-effects of COVID-19, or the transition together with an educational context dominated by sociocultural differences and educational disadvantages. With this knowledge in mind, we investigated the effects of predictive mathematical models (multiple regression, logistic regression, and decision trees) to predict <i>at-risk</i> students at three time intervals (weeks one, three, and seven) in the semester. The idea was implemented with a first-year life science class of 130 students. Variables from an academic readiness questionnaire along with early assessment grades were used to build these models. Through a Monte Carlo cross validation method, the performance of the executed predictive models was assessed, and limitations were reported. We argue that the results obtained from predictive models can support both lecturers and students in the transition phase. The idea can be expanded to other courses in STEM fields and other educational contexts.

Blasting safety criterion of existing high speed railway tunnel over tunnel

Drilling and blasting method is still an important method in the current tunnel excavation construction. Controlling the vibration effect of blasting during construction and its influence on the upper span tunnel is the key problem in tunnel construction. Based on Chongqing Science City tunnel excavation blasting project, combined with the tunnel blasting vibration monitoring and testing, this paper analyzes the propagation attenuation law of tunnel blasting vibration along the rock mass, and studies the load characteristics of the explosion stress wave propagating to the existing high-speed railway tunnel. Considering the influence of the buried depth of the blasting source, a mathematical prediction model of the attenuation law of the upper span existing high-speed railway tunnel caused by tunnel blasting is established. Based on the dynamic finite element numerical calculation method, the influence of blasting vibration on the structure of the existing high-speed railway tunnel under construction is analyzed, and the propagation and attenuation law of blasting vibration along the tunnel contour is studied. Based on the ultimate tensile stress criterion, the ultimate shear stress criterion and the Mohr criterion, and compared with the results obtained from the numerical simulation, the blasting safety criterion model of the existing high-speed railway tunnel over the tunnel is established.

The Optimization of Cut Surface for Computer Numerical Control Plasma Cutting Machine

This paper discusses a system designed to process parameter settings for achieving optimum cutting quality by a Computer Numerical Control (CNC) Plasma Machine, employing the Jaya algorithm. The aim is to obtain the most optimized parameter settings for the CNC Plasma Machine, focusing on the plasma cutting method applied to commonly used metals, 304 stainless steels, and S235. The investigation centres on understanding the impacts of these parameters on surface roughness (Ra) and tapper angle. The experiments, conducted using Full Factorial Design (FFD), involved mathematical prediction models developed through a regression method with three controllable levels for each parameter on 12mm S235 steel and 304 stainless steel plates. The Jaya algorithm, known for its efficiency in engineering optimization, operates on the principle of gravitating towards positive outcomes while avoiding unfavorable ones. What sets this study apart is the utilization of the Jaya algorithm, a sophisticated optimization tool that demonstrated exceptional efficiency by converging to the optimum solution within a mere 15 iterations. The Jaya algorithm’s robust performance was highlighted through a comparative analysis, positioning it as superior to alternative techniques for delivering the best optimum solution in a remarkably reduced number of iterations.

Research on dynamic bottleneck prediction methods in manufacturing workshops

Abstract To address the challenge of identifying bottleneck responsibilities when multiple bottlenecks arise in process workshops and predicting bottleneck trends before their occurrence, a novel dynamic prediction method based on the Markov chain is introduced. This approach effectively identifies all bottlenecks in a multi-bottleneck process shop and identifies the primary bottleneck unit with the highest level of responsibility, enabling anticipation of bottleneck shifts prior to their occurrence. Initially, the concept of bottleneck is elaborated, accounting for the intricate interplay among manufacturing processes, leading to the development of a static mathematical model for multi-bottleneck prediction incorporating parameters such as time, cost, and quality. This model evaluates the bottleneck severity of each manufacturing unit, facilitating the identification of all bottleneck units and the assignment of bottleneck responsibilities, thereby establishing primary and secondary bottlenecks. Subsequently, a bottleneck state transition matrix is formed based on historical bottleneck processes, initializing the state matrix through the process with the highest bottleneck responsibility in the static setting. Leveraging the Markov chain prediction technique, the method anticipates the location of bottleneck transitions preceding the occurrence of bottleneck drift. Finally, simulations were conducted in an authentic production line environment to validate the method’s efficacy. Results indicate a 9.1% increase in the main bottleneck coincidence rate compared to single-bottleneck prediction models. Moreover, the Markov chain prediction accurately forecasts bottleneck trends prior to drift occurrences, boasting an 86.4% accuracy rate. These findings underscore the applicability and guidance potential of the proposed method.

Mathematical modeling for prediction of physicochemical characteristics of cardiovascular drugs via modified reverse degree topological indices.

Global health concerns persist due to the multifaceted nature of heart diseases, which include lifestyle choices, genetic predispositions, and emerging post-COVID complications like myocarditis and pericarditis. This broadens the spectrum of cardiovascular ailments to encompass conditions such as coronary artery disease, heart failure, arrhythmias, and valvular disorders. Timely interventions, including lifestyle modifications and regular medications such as antiplatelets, beta-blockers, angiotensin-converting enzyme inhibitors, antiarrhythmics, and vasodilators, are pivotal in managing these conditions. In drug development, topological indices play a critical role, offering cost-effective computational and predictive tools. This study explores modified reverse degree topological indices, highlighting their adjustable parameters that actively shape the degree sequences of molecular drugs. This feature makes the approach suitable for datasets with unique physicochemical properties, distinguishing it from traditional methods that rely on fixed degree approaches. In our investigation, we examine a dataset of 30 drug compounds, including sotagliflozin, dapagliflozin, dobutamine, etc., which are used in the treatment of cardiovascular diseases. Through the structural analysis, we utilize modified reverse degree indices to develop quantitative structure-property relationship (QSPR) models, aiming to unveil essential understandings of their characteristics for drug development. Furthermore, we compare our QSPR models against the degree-based models, clearly demonstrating the superior effectiveness inherent in our proposed method.

Development, modelling and optimization of process parameters on the tensile strength of aluminum, reinforced with pumice and carbonated coal hybrid composites for brake disc application

This study focuses on optimizing double stir casting process parameters to enhance the tensile strength of hybrid composites comprising aluminum alloy, brown pumice, and coal ash, intended for brake disc applications. Analytical techniques including X-ray fluorescence, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy were employed to characterize the composite constituents. The Taguchi method was utilized for experimental design and optimization to determine the optimal weight compositions of brown pumice and coal ash, as well as stir casting parameters (stirrer speed, pouring temperature, and stirring duration). Regression analysis was employed to develop a predictive mathematical model for the tensile strength of the hybrid composites and to assess the significance of process parameters. The optimized composite achieved a predicted tensile strength of 186.81 MPa and an experimental strength of 190.67 MPa using 7.5 vol% brown pumice, 2.5 vol% coal ash, a pouring temperature of 700 °C, stirrer speed of 500 rpm, and stirring duration of 10 min. This represents a 52.23% improvement over the as-cast aluminum alloy’s tensile strength. Characterization results revealed that brown pumice and coal ash contain robust minerals (SiO2, Fe2O3, Al2O3) suitable for reinforcing metal matrices like aluminum, titanium, and magnesium. Thermogravimetric and differential thermal analyses demonstrated thermal stability up to 614.01 °C for the optimized composite, making it suitable for brake disc applications.