Order Model Research Articles

Efficacy Assessment of Molecularly Imprinted Polymer Incorporated with Starch and Macadamia Capped Silver Nanoparticles for the Removal of Multi‐Class Pharmaceuticals in Wastewater

AbstractThe presence of pharmaceuticals in water bodies has been reported to be mainly due to their incomplete removal by the conventional wastewater treatment processes, resulting to their release into the aquatic environment where they thratens to human health and aquatic life. Therefore, it is important to remove these pollutants from the aquatic environment using affordable highly efficient materials. The goal of this study was to incorporate silver nanoparticles (AgNPs) into MIPs for selective removal of sulfamethoxazole, nevirapine, and ibuprofen in wastewater. The AgNPs were synthesized using starch (St) and macadamia (MCD) as both reducing and stabilizing agents. Thermogravimetric analysis showed high stability for the St/MCD‐nanoMIPs. The ANOVA analysis revealed that the parameters affecting adsorption of St/MCD‐nanoMIPs have a significant difference (p<0.05). The St/MCD‐AgNPs experimental data fitted to both Langmuir and Freundlich adsorption isotherms, however, the non‐linear Langmuir isotherm model yielded a better fit based on the higher R2 value (0.999). The non‐linear Freundlich model best fitted for the St/MCD‐nanoMIPs. The linear pseudo‐second order model shown best fit for St/MCD‐nanoMIPs, the thermodynamic results revealed that the adsorption process was spontaneous and endothermic. The St/MCD‐nanoMIPs showed improved qualities and were highly selective (>70 %) and effective in removing the selected pharmaceuticals from wastewater.

Evaluation of Adsorption Ability of Lewatit® VP OC 1065 and Diaion™ CR20 Ion Exchangers for Heavy Metals with Particular Consideration of Palladium(II) and Copper(II).

The adsorption capacities of ion exchangers with the primary amine (Lewatit® VP OC 1065) and polyamine (Diaion™ CR20) functional groups relative to Pd(II) and Cu(II) ions were tested in a batch system, taking into account the influence of the acid concentration (HCl: 0.1-6 mol/L; HCl-HNO3: 0.9-0.1 mol/L HCl-0.1-0.9 mol/L HNO3), phase contact time (1-240 min), initial concentration (10-1000 mg/L), agitation speed (120-180 rpm), bead size (0.385-1.2 mm), and temperature (293-333 K), as well as in a column system where the variable operating parameters were HCl and HNO3 concentrations. There were used the pseudo-first order, pseudo-second order, and intraparticle diffusion models to describe the kinetic studies and the Langmuir and Freundlich isotherm models to describe the equilibrium data to obtain better knowledge about the adsorption mechanism. The physicochemical properties of the ion exchangers were characterized by the nitrogen adsorption/desorption analyses, CHNS analysis, Fourier transform infrared spectroscopy, the sieve analysis, and points of zero charge measurements. As it was found, Lewatit® VP OC 1065 exhibited a better ability to remove Pd(II) than Diaion™ CR20, and the adsorption ability series for heavy metals was as follows: Pd(II) >> Zn(II) ≈ Ni(II) >> Cu(II). The optimal experimental conditions for Pd(II) sorption were 0.1 mol/L HCl, agitation speed 180 rpm, temperature 293 K, and bead size fraction 0.43 mm ≤ f3 < 0.6 mm for Diaion™ CR20 and 0.315-1.25 mm for Lewatit® VP OC 1065. The maximum adsorption capacities were 289.68 mg/g for Lewatit® VP OC 1065 and 208.20 mg/g for Diaion™ CR20. The greatest adsorption ability of Lewatit® VP OC 1065 for Pd(II) was also demonstrated in the column studies. The working ion exchange in the 0.1 mol/L HCl system was 0.1050 g/mL, much higher compared to Diaion™ CR20 (0.0545 g/mL). The best desorption yields of %D1 = 23.77% for Diaion™ CR20 and 33.57% for Lewatit® VP OC 1065 were obtained using the 2 mol/L NH3·H2O solution.

Impact of wind turbine nominal power limitation over wind turbine blade remaining useful life and its economic consequences

The present paper investigates the effects of wind turbine nominal power limitation on the remaining useful life of turbine blades. It also looks at the economic impact of this limitation. In this context, the paper provides wind turbine owners and operators with an overview of how to potentially extend the remaining useful life of wind turbine blades and lays out the economic benefits that can be achieved via the modulation of nominal power. In investigating wind turbine blade damage, prior studies focused mainly on predictive models based on the 10 minutes SCADA data wind speed history, without however, trying to protract the remaining useful life of the blades. Only a handful of papers have explored the possibility of increasing the remaining useful life by adjusting the start-up and shutdown procedures with poor results. It would appear that wind turbine blade fatigue damage mainly increases when the wind turbine is in a power production regime, and the mechanical stresses associated with this regime are a function of the nominal power of the wind turbine. The present work therefore investigates the impacts of nominal power changes on both the remaining useful life of wind turbine blades and the economic value of the wind turbine in a bid to identify an optimal control mode. The wind turbine blade damage evaluation is based on 10 minutes SCADA data and the FAST simulation tool with the ultimate goal of providing wind turbine operators with an easy application. The damage evaluation is then applied considering different nominal power levels for the same wind turbine model in order to see the resulting impact on the remaining useful life. This project therefore takes a pioneering approach by proposing a remaining useful life optimization tool to wind turbine operators, in effect, a decision-making tool regarding which exploitation strategy to adopt.

The effect of phosphoric acid on the properties of activated carbons made from Myrtus communis leaves: Textural characteristics, surface chemistry, and capacity to adsorb methyl orange

In this work, chemical activation was employed to produce activated carbons and assess the impact of phosphoric acid (H3PO4) on their physicochemical properties. Using Myrtus communis Leaves (MC-Leaves) as the precursor and varying H3PO4 impregnation ratios (30 %, 60 %, 100 %, and 150 wt.%). The activation was conducted at 450 °C (10 °C/min) for 1 h in a room atmosphere. The effects of H3PO4 were evaluated through various techniques, including BET surface area analysis, FESEM-EDS imaging, XPS, FT-IR-ATR, Raman spectroscopy, CHNS-(O) elemental analysis, and Methyl orange (MO) adsorption studies. The specific surface area (SSA) and total pore volume increased from 642m2/g to 1237m2/g, total pore volume increased from 0.29cm3/g to 0.97cm3/g, and the mean pore diameter increased from 1.9 nm to 3.2 nm by increasing the impregnation ratio from 30 wt.% to 150 wt.%. The pH(pzc) of MC-ACs exhibited an acidic character, owing to the presence of oxygen-containing functional groups such as hydroxyl, carboxyl, metaphosphate (-PO3-), phosphates, and pyrophosphate groups, as indicated by XPS and FT-IR analysis. At a room temperature of 22 °C and an ideal pH of 2.06, the maximum adsorption capacity (qmax) rose from 46.02±4.63 mg g-1 to 326.54±37.67 mg g-1 as the impregnation ratio increased from 30 wt.% to 150 wt.%. Freundlich isotherm well explained the adsorption equilibrium at 22 °C for all MC-ACs. The effect of temperature illustrates that the adsorption of MO onto MC-AC30 % was endothermic, while the adsorption of MO onto MC-AC150 % was an exothermic. The kinetic study was conducted at 22 °C, when the equilibrium time was at 4hours, and it was a pseudo-second order (PSO) model.

A new proper orthogonal decomposition method with second difference quotients for the wave equation

Recently, researchers have investigated the relationship between proper orthogonal decomposition (POD), difference quotients (DQs), and pointwise in time error bounds for POD reduced order models of partial differential equations. In a recent work (Eskew and Singler, Adv. Comput. Math., 49, 2023, no. 2, Paper No. 13), a new approach to POD with DQs was developed that is more computationally efficient than the standard DQ POD approach and it also retains the guaranteed pointwise in time error bounds of the standard method. In this work, we extend this new DQ POD approach to the case of second difference quotients (DDQs). Specifically, a new POD method utilizing DDQs and only one snapshot and one DQ is developed and used to prove ROM error bounds for the damped wave equation. This new approach eliminates data redundancy in the standard DDQ POD approach that uses all of the snapshots, DQs, and DDQs. We show that this new DDQ approach also has pointwise in time data error bounds similar to DQ POD and use it to prove pointwise and energy ROM error bounds. We provide numerical results for the POD ROM errors to demonstrate the theoretical results. We also explore an application of POD to simulate ROMs past the training interval for collecting the snapshot data for the standard POD approach and the DDQ POD method.

CO2 capture using red mud & mechanically-activated red mud and its kinetics under ambient conditions

Greenhouse gas emissions and the growing stockpiles of red mud, an alkaline industrial waste with potential for CO2 sequestration, present significant environmental challenges. This article aims to enhance the CO2 sequestration capacity of red mud by mechanically activating its surface using a high-energy planetary ball mill for extended grinding periods. The novelty of the study lies in investigating the CO2 sequestration capacity and evaluating its kinetics includingadsorption and desorption on feed and mechanically-activated red mud under atmospheric temperature and pressure for the first time via a direct gas–solid carbonation route using a fixed bed column. The effect of ball size (10,8 and 6 mm) used in high-energy planetary ball mills while mechanical activation, CO2 flow rate (20–80 ml/min), and reaction time (1–5 h) on the CO2 capture has been studied. Apart from this, experimental data on CO2 sequestered (1–5 h) and the extent of CO2 desorption (7–35 days) are fitted to various kinetic models, including pseudo-first-order, pseudo-second-order, and Avrami’s fractional order models. Results showed that the CO2 sequestration capacity of feed red mud is 3.75 mg/g, while the capacity of mechanically-activated red mud increased to 18.28 mg/g, with an adsorption time of 5 h at ambient temperature and pressure. The most suitable fit is attained with Avrami’s fractional order model, suggesting that both physisorption and chemisorption contribute to enhancing the CO2 sequestration capacity of red mud. The mechanism for this process has also been proposed along with the suitable characterizations.

Effect of slip on MHD flow of fluid with heat and mass transfer through a plate

Fractional order derivatives are recognized as advanced mathematical tools with broad applications in physics and engineering for deriving real-world solutions. This study examines a fractional order model of a non-linear Casson fluid, highlighting the widespread utility of fractional derivatives. Additionally, the problem incorporates the Dufour and slip effects. Specifically, the constant proportional Caputo fractional model is formulated using generalized Fick's and Fourier's laws. Initially, the governing equations are transformed into a non-dimensional form and subsequently solved using the Laplace transform. Analysis of the figures indicates that the Casson parameter reduces fluid motion, while the diffusion thermo effect enhances it. Furthermore, the study includes a comparison between fractionalized and ordinary velocity fields. Highlight: Introduction of novel CPC fractional derivative model of Casson fluid flow. Semi-analytically solutions using Laplace transform method for accurate fluid behavior representation. comprehensive parametric analysis linking theoretical findings. Temperature contour affected by significant values of Du?

Improved a posteriori error bounds for reduced port-Hamiltonian systems

Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem, to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. The effectiveness of the described methods is demonstrated using a challenging three-dimensional port-Hamiltonian model of a classical guitar with fluid–structure interaction.

Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.

Exploration of a model driven by climate to simulate pond water temperature in aquaculture systems

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

Heterogeneous electro-Fenton oxidation of Congo red using carboxylic group activated carbon felt cathode: Electrokinetic mechanism and performance

In this study, the carboxyl functional group (–COOH) saturated-carbon-felt (ACF) as functional cathode was prepared and used in heterogenous (Fe2+) electro-Fenton (EF) catalytic degradation of Congo red (CR) in waster. As a heterogeneous catalyst, size-controlled magnetite (Fe3O4) nanoparticles (MNPs) were applied to the surface of the ACF cathode. The selectivity of ACF toward the two-electron oxygen-reduction reaction was estimated based on the proportion of H2O2 production; it was determined as >89 %. The removal efficiency of ACF for CR dye was 94 ± 2 % over 40 min reaction time, almost double that of a pristine CF cathode (48 ± 3 %). The kinetics data fitted to a pseudo-second order (PSO) model with K2 of 8.8 × 10−4 ± 1.72 × 10−5 mol−1 cm3 min−1 (0.88 ± 0.02 M−1 min−1). The mineralization current efficiency and TOC for CR were observed as 58 ± 2 %, and 83 ± 3 %, respectively. Overall, the results insight that the system could be a viable alternative method for treating dye-contaminated water.

High performance computational approach to study model describing reversible two-step enzymatic reaction with time fractional derivative

Enzyme reactions have numerous applications in diverse disciplines of science like chemistry, biology and biomechanics. In this study, we examine the role and act of enzymes in chemical reactions which is considered in the frame of fractional order model. The proposed model includes system of four equations which are studied via Caputo fractional operator. The systems of non-linear equations are evaluated by a semi-analytical approach called q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q$$\\end{document}-homotopy analysis transform method. The uniqueness and existence of the solutions has been investigated through fixed point theorem. The solutions of the proposed model are achieved through the considered method and the obtained outcomes are in the form of series which shows rapid convergence. The solutions are computed and graphs are plotted for the obtained results using mathematica software. The achieved results by the proposed method are unique and illustrate the significant dynamics of the considered model via 3D plots and graphs. The results of this study demonstrate the importance and effectiveness of projected derivative and technique in the analysis of time dependent fractional mathematical models. This study also gives an idea to extend the applications of enzymatic reactions in drug development, bio mechanics, and chemical reactions in various cellular metabolisms. Also, enzymatic reactions have a vital role in the fields of the food industry for processing food, in biotechnology for the manufacture of biofuels, and in metabolic engineering to design metabolic pathways.

Cu (II) adsorption in rice husk for water treatment: Batch and fixed column experiments

Cu (II) is a heavy metal found in water bodies and harmful to the environment and human health. Thus, this study evaluated Cu (II) adsorption in rice husk using batch and fixed bed tests, employing synthetic solutions and real water samples to simulate contaminated water treatment. Batch tests were performed using rice husk and synthetic Cu (II) solutions with concentrations varying from 1 to 128 mgL−1. The isothermal behavior of the adsorption process was evaluated using Langmuir and Freundlich nonlinear models, whereas mechanisms and adsorption rates were assessed using pseudo-first and pseudo-second order kinetic models. The fixed bed column was mounted on a 5-cm thick rice husk adsorbent bed and synthetic and real solutions were employed (concentration: 8 mg L−1; flux: 13 mL min−1). The equilibrium was reached after 20 min, with the best adjustment to the pseudo-second order model. The experimental data were better adjusted to the Langmuir model (estimated qmax: 7.19 mgg−1). The adsorption column exhibited adsorption capacities of 1.28 and 1.12 mgg−1 and saturation percentages of 83 % and 79 % for synthetic and real solutions, respectively, and were adjusted to Thomas model. The results indicate that rice husk can be an effective alternative for Cu (II) removal in contaminated water.

HIV/AIDS control owing to local and global awareness, diagnosis, treatment tactics and control theory: fractional order modeling

HIV/AIDS control owing to local and global awareness, diagnosis, treatment tactics and control theory: fractional order modeling

Trying to outrun causality with machine learning: Limitations of model explainability techniques for exploratory research.

Machine learning explainability techniques have been proposed as a means for psychologists to "explain" or interrogate a model in order to gain an understanding of a phenomenon of interest. Researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables that are associated with/predictive of an outcome of interest. However, and as we demonstrate, machine learning algorithms are highly sensitive to the underlying causal structure in the data. The consequences of this are that predictors which are deemed by the explainability technique to be unrelated/unimportant/unpredictive, may actually be highly associated with the outcome. Rather than this being a limitation of explainability techniques per se, we show that it is rather a consequence of the mathematical implications of regression, and the interaction of these implications with the associated conditional independencies of the underlying causal structure. We provide some alternative recommendations for psychologists wanting to explore the data for important variables. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Small signal analysis of DC voltage control based on a virtual resistance of DC/DC converter integrated in a multiterminal DC grid

AbstractThe future multi‐terminal direct‐current (MTDC) grid will require the interconnection of point‐to‐point high‐voltage (HV) DC links with different specifications such as DC voltage level, system grounding configuration and HVDC technology. To adapt these differences, it is obligatory for DC/DC converters to interconnect HVDC links. Additionally, they are capable of providing supplementary functionalities as they are highly controllable devices. In this article, a primary virtual resistance DC voltage controller associated with DC/DC converter is proposed for managing DC grid voltages of the interconnected HVDC grids, increasing the reliability of the system. The commonly known topology, Front‐to‐Front Modular Multilevel Converter (F2F‐MMC) is adopted for DC/DC converter. Time‐domain simulations are performed using EMTP software for validating the controller behaviour under power disturbances and large events of loss of one converter in a MMC‐based MTDC system. The converters are modelled using reduced order modelling (ROM) methodology. Apart from this, dynamic studies have been carried out using a linear state space model for small‐signal stability analysis of a HVDC system integrating DC/DC converter with a virtual resistance DC voltage controller. The results are examined through parametric sensitivity analysis.

Model order reduction and sensitivity analysis for complex heat transfer simulations inside the human eyeball.

Heat transfer in the human eyeball, a complex organ, is significantly influenced by various pathophysiological and external parameters. Particularly, heat transfer critically affects fluid behavior within the eye and ocular drug delivery processes. Overcoming the challenges of experimental analysis, this study introduces a comprehensive three-dimensional mathematical and computational model to simulate the heat transfer in a realistic geometry. Our work includes an extensive sensitivity analysis to address uncertainties and delineate the impact of different variables on heat distribution in ocular tissues. To manage the model's complexity, we employed a very fast model reduction technique with certified sharp error bounds, ensuring computational efficiency without compromising accuracy. Our results demonstrate remarkable consistency with experimental observations and align closely with existing numerical findings in the literature. Crucially, our findings underscore the significant role of blood flow and environmental conditions, particularly in the eye's internal tissues. Clinically, this model offers a promising tool for examining the temperature-related effects of various therapeutic interventions on the eye. Such insights are invaluable for optimizing treatment strategies in ophthalmology.

Stacking Ensemble Artificial Intelligence Model for Heart Disease Diagnosis

Personalized treatment plans, predictive analytics, and artificial intelligence (AI)-driven diagnostics are becoming more and more popular as a way to improve decision-making, expedite operations, and improve patient care. But there are still a number of substantial barriers to overcome, which includes but not limited to issues with user adoption, trust, prejudice, and fairness brought on by resistance from healthcare providers and a lack of confidence in the system's recommendations. To overcome these challenges and realize the full potential of AI-driven solutions, the system's accuracy and safety through meticulous testing and validation of AI algorithms becomes indispensable. This research provides a hybrid AI model that blends three base models with a Meta model in order to diagnose heart disease effectively. The essence is to revalidate the existing AI diagnostic models for cardiac diseases diagnostics and to tackle the concerns impeding the full utilization of the available AI diagnostic system. The study makes use of each model's advantages by merging these various and complimentary algorithms into a stacking ensemble model to create a diagnostic system that is more potent. Using publicly available heart disease data, the model performs remarkably well; it achieves 89% accuracy, 85% recall (sensitivity), 92% specificity, and 89% precision. This hybrid model's performance and proven efficacy are expected to boost trust in the system's recommendations and encourage broader implementation in clinical practice.

Current-Mode Control of a Distributed Buck Converter with a Lossy Transmission Line

This article presents a buck converter in which the inductor has been replaced by a transmission line. This approach would be practically conceivable if the power supply and load had a greater spatial distance. Alternatively, the model derived in this way could also be regarded as an intermediate model in order to replace a power coil via discretization with a larger number of smaller coils and capacitors. In the time domain, this new converter can be described by a system of coupled partial and ordinary differential equations. In the frequency domain, a transcendental transfer function is obtained. For comparison with an equivalently parameterized conventional converter, Padé approximants are derived. A linear controller is designed for the converter topology under consideration.

Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning

AbstractSign language serves as the primary means of communication utilized by individuals with hearing and speech disabilities. However, the comprehension of sign language by those without disabilities poses a significant challenge, resulting in a notable disparity in communication across society. Despite the utilization of numerous effective Machine learning techniques, there remains a minor compromise between accuracy rate and computing time when it comes to sign language recognition. A novel sign language recognition system is presented in this paper with an exceptionally accurate and expeditious, which is developed upon the recently devised metaheuristic WAR Strategy optimization algorithm. Following the preprocessing, both of spatial and temporal features has been extracted using the Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM) methods. Afterward, the WAR Strategy optimization algorithm has been adopted in two procedures, first in optimizing the extracted set of features, and second to fine-tune the hyperparameters of six standard machine learning models in order to achieve precise and efficient sign language recognition. The proposed system was assessed on sign language datasets of different languages (American, Arabic, and Malaysian) containing numerous variations. The proposed system attained a recognition accuracy ranging from 93.11% to 100% by employing multiple optimized machine learning classifiers and training time of 0.038–10.48 s. As demonstrated by the experimental outcomes, the proposed system is exceptionally efficient regarding time, complexity, generalization, and accuracy.