Second-order Model Research Articles

Oily wastewater treatment using low-cost and highly efficient natural and activated Iraqi bentonite

One of the most dangerous organic compounds to the environment is gasoline emulsions, discharged without prior treatment by most oil refining industries. This study focuses on the modification of bentonite clays by acid and thermal activation to maximize their adsorption capacity for treating refinery wastewater. The surface properties and porous structure of the adsorbents were investigated with the use of BET, SEM, FTIR, XRD, XRF, EDX, and MAP methods. Based on the results of BET analysis, the surface area of the raw bentonite (RB) and activated bentonite (AB) was (17.57, 69.72) m2 /g. The four independent variables, namely the initial gasoline concentration (200–1000 mg/L), solution pH (3−9), contact time (30–120 min), and adsorbent dosage (ranging from 0.05 to 0.15 g/L). According to the results, the maximal adsorption capacity (74.6.38, 138 mg/g) by RB and AB was achieved using the following optimized conditions: initial COD gasoline concentration of 1000 mg/L, pH= 6 for RB and pH= 3 for AB, contact time 120 min, and adsorbent dosage 0.15 g for RB and AB. Additionally, the kinetic and equilibrium studies demonstrated that the present adsorption process's kinetics and equilibrium behavior were adequately represented by the pseudo second-order kinetic model and the Langmuir isotherm. The data by Langmuir isotherm showed that the maximum adsorption capacity for RB and AB was equal to 83 and 140.84 mg/g, respectively. Furthermore, thermodynamic studies demonstrated that the present adsorption procedures were both spontaneous and endothermic. The mechanism of gasoline adsorption onto RB and AB was effectively investigated with the assistance of characterization techniques. The current study demonstrates that RB and AB can be utilized as adsorbents for the elimination of gasoline with high efficiency.

Impact of stroke on cognition in old age: Comparison of two population-based cohorts, born up to 30 years apart and followed from age 70 to 85.

Little is known about birth cohort differences in the impact of stroke on cognitive aging. Given improved poststroke rehabilitation and better treatments for vascular health risk, we may expect a reduction in the stroke impact in later-born cohorts. We tested this prediction using data from two cohorts, born in 1901-1907 (n = 1,155) and 1930 (n = 919), identified from the same city population at the same age of 70 and subsequently measured on the same cognitive outcomes (i.e., spatial ability, perceptual-motor speed, and reasoning) at ages 70, 75, 79, and 85. We fitted multiple-group second-order latent growth curve models to the data, regressing the first-order cognitive factor on the time-varying stroke variable and controlling for relevant covariates. Findings revealed moderate to large average cognitive decline (d = -.45) following stroke, and the impact was relatively similar across cohorts (1901-1907: d = -.52; 1930: d = -.39). However, there was a stroke by age by cohort interaction, implying that the stroke impact increased with age in the 1901-1907 cohort (dage ≤ 75 = -.42; dage ≥ 79 = -.70) but decreased in the 1930 cohort (dage ≤ 75 = -.53; dage ≥ 79 = -.17). We found no evidence for lagged effect of stroke beyond the impact on measures following the incidence. Our hypothesis was only partially supported, as the impact of stroke was reduced in the later-born cohort but solely at higher ages. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Modeling Dimensionality of Bilingual Kindergarteners' Language Knowledge in Spanish and English.

This study used structural equation modeling to investigate the dimensionality of language in Spanish-English bilingual kindergartners. Five theoretical models were compared, including (a) a unidimensional model; (b) a two-dimensional model by language (Spanish, English); (c) a three-dimensional model by domain of language (phonology, semantics, morphosyntax); (d) a second-order model organized first by language (Spanish, English), with each language dimension comprising three domain-specific dimensions (phonology, semantics, morphosyntax); and (e) a six-dimensional model with freely covarying language-specific domains. Participants included 238 Spanish-English bilingual kindergartens, as identified by parent report of current language exposure and direct language measures. All participants completed a battery of phonology, semantics, and morphosyntactic test items in English and in Spanish. The six-dimensional covarying model constrained by domain and language provided the best fit for the data, with six separate factors for phonology, morphosyntax, and semantics in English and Spanish. The excellent model fit is supported by findings of a χ2 to df (degrees of freedom) ratio < 2 with no significance, comparative fit index > .95, standardized root-mean-square residual < .08, and root-mean-square error of approximation values ≤ .05. Results support emergent theories of bilingual language development. Application of the results to the evaluation and intervention of oral language abilities in bilingual children entering the formal education setting are considered. https://doi.org/10.23641/asha.25843846.

Career-Related Teacher Support Scale: Validation Among Italian Adolescents and Assessment of the Effect on Career Decision Self-Efficacy Through Career Adaptability

Teachers have a key role in adolescents’ career development. Nevertheless, few studies examined their effect on career outcomes. The present study has two objectives. First, to test the factorial structure of the Italian version of the Career-related Teacher Support Scale (CRTSS) with Italian adolescents. Second, to test how career-related teacher support leads to career decision self-efficacy through career adaptability. In Study I, 469 adolescents were asked to complete the CRTSS. A first-order model, a second-order model (hierarchical), a bi-factor model (hierarchical), and a single-factor model were compared to check the best model solution. Additionally, the measurement invariance was examined across male and female. Results reveal the superiority of the second-order (hierarchical) model and the invariance between males and females. In the Study II took part 228 adolescents. A structural equation modeling approach assessed the relationship between career-related teacher support, career adaptability and career decision self-efficacy. A mediation model showed a significant indirect effect of career-related teacher support on decision self-efficacy through career adaptability. Practical implications for interventions were discussed.

Agmatine ligand functionalized magnetic alginate beads for removal of U(VI) ions from solution

In the presented work, magnetic alginate (MA) beads were prepared and activated with epichlorohydrin and then functionalized with agmatine ligands (MA-A). The materials were analyzed by FTIR, SEM, XRD, and BET methods. The adsorption capacity of the MA-A for U(VI) was 451.4 mg/g. The Langmuir isotherm model well described the experimental data for the adsorption of U(VI) ions. The second-order kinetic model data proposed that the adsorption of U(VI) ions preferred the chemisorption mechanism. The adsorption enthalpy of the MA-A beads for U(VI) ions was 35.4 kJ/mol. After seven cycles of use, the adsorption capacity of MA-A beads was not significantly changed for U(VI) ions. Finally, the functionalization of MA beads with agmatine ligand can be a good candidate for applications for environmental technologies to remove metal ions.

Hybrid Evaluation Model to assess the overall barriers of public-private partnership contracts

Public-private partnerships are increasingly being used in many countries as a common management technique to enhance the delivery of public services and major public infrastructure projects. Understanding the project characteristics and obstacles to a public-private partnership is key for those involved to improve implementation. This study focuses on barriers that arise from public-private partnership contact in field wastewater treatment plants and offers a methodology for evaluating the overall barrier for public-private partnership projects by analyzing the relationships between the barrier elements. A primary list of barrier factors and their eight latent project characteristics were collected through literature reviews, then using the Delphi technique to filter the barrier insights concerning project characteristics and develop the final list using field interviews. 207 valid questionnaires were obtained. The data was deconvoluted into crisp numbers using fuzzy set theory, and a second-order structural equation model was used to validate the conceptual model. The results of the study indicate that the availability payment capability group has the highest score of all the groups in this study. This study proposed a model that enables the private sector to measure and evaluate aggregate barriers in projects so that it is easier for decision-makers to determine whether they can choose the project or not.

Ultrasound-Assisted Extraction of Phenolic Compounds from Celtuce (Lactuca sativa var. augustana) Leaves Using Natural Deep Eutectic Solvents (NADES): Process Optimization and Extraction Mechanism Research.

Natural deep eutectic solvents (NADESs), as emerging green solvents, can efficiently extract natural products from natural resources. However, studies on the extraction of phenolic compounds from celtuce (Lactuca sativa var. augustana) leaves (CLs) by NADESs are still lacking. This study screened the NADES L-proline-lactic acid (Pr-LA), combined it with ultrasound-assisted extraction (UAE) to extract phenolic compounds from CLs, and conducted a comparative study on the extraction effect with traditional extraction solvents. Both SEM and FT-IR confirmed that Pr-LA can enhance the degree of fragmentation of cell structures and improve the extraction rate of phenolic compounds. Molecular dynamics simulation results show that Pr-LA can improve the solubility of phenolic compounds and has stronger hydrogen bonds and van der Waals interactions with phenolic compounds. Single-factor and Box-Behnken experiments optimized the process parameters for the extraction of phenolic compounds from CLs. The second-order kinetic model describes the extraction process of phenolic compounds from CLs under optimal process parameters and provides theoretical guidance for actual industrial production. This study not only provides an efficient and green method for extracting phenolic compounds from CLs but also clarifies the mechanism of improved extraction efficiency, which provides a basis for research on the NADES extraction mechanism.

Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.

Young children with intellectual disabilities and their mathematical attainments: Do parents' attitudes toward mathematics, home numeracy, and literacy practices matter?

This study explored potential factors of numeracy acquisition in young children with intellectual disabilities (IDs). Those factors are determined to assess parents' academic expectations and home practices that they use with their children daily. The sample consisted of 187 Greek-speaking couples, comprising a total number of 374 parents. All of them had a child with mild IDs aged between 4 and 7 years old. Since Greece is placed among European countries with the lowest median disposable income it is significant to assess the potential effects of each family's socioeconomic status. We employed structural equation modeling to identify potential factors affecting a child's with IDs numeracy outcomes. Analysis of moments structures (AMOS) revealed that there is a good fit for the suggested second-order structural equation model. Results are discussed regarding their practical implications.

Mathematical modelling for predicting performance and emissions of a diesel engine fuelled with palm kernel biodiesel–diesel-diethyl carbonate fuel blends

The search for environmentally friendly and sustainable alternatives to conventional diesel fuels has prompted investigations into blends of biodiesel and oxygenated additives like diethyl carbonate. This study focuses on developing mathematical models to predict the performance and emissions of a single-cylinder diesel engine running on palm kernel biodiesel blended with varying proportions of diethyl carbonate. Response Surface Methodology with a general factorial design was employed to create mathematical models for brake thermal efficiency, brake-specific fuel consumption, hydrocarbon, carbon monoxide, and oxides of nitrogen emissions. The two input variables were engine load (five levels) and diethyl carbonate percentage in the fuel blend (five levels). Second-order models were developed, and model fit was evaluated using analysis of variance (ANOVA) at 95% confidence. The developed RSM models showed excellent fit with high R-squared values close to unity, satisfying the model adequacy criteria. ANOVA revealed the models to be statistically significant predictors of the response variables based on load and diethyl carbonate percentage. The optimum operating condition for best performance and emissions balance was found to be 71% load and 10% diethyl carbonate inclusion. The study emphasises the importance of alternative fuels in mitigating environmental pollution and reducing dependence on fossil fuels.

A Fault Restoration Method for DC-interconnected Distribution Systems with Renewable Resources Considering Multiple Fault Scenes

Background:: The distribution systems are gradually transformed into active systems with the large-scale integration of renewable resources, which brings greater potential to the fault recovery of the distribution networks and higher requirements for the power scheduling capability under fault conditions. Power electronic devices such as soft open points (SOPs) can break the radial constraints of the traditional distribution network, provide cross-station power support in fault scenarios, and enhance the resilience of the distribution networks. Methods:: This paper focuses on the distribution systems with multi-transformer flexible interconnection, and a multi-device coordinated fault restoration method is proposed. Firstly, the characteristics of different transformer fault scenes and the fault restoration process coordinated by the bus-bar switches and SOPs in each scene are sorted out. Secondly, an optimization model of the fault restoration for DC-interconnected distribution system is established considering the network operation and the equipment constraints in the fault restoration process. Result:: The proposed model aims at the maximization of the weighted load restoration amount, and is transformed into a second-order cone programming model to be solved quickly. Lastly, the effectiveness of the proposed method is verified in a typical flexible interconnection distribution system. Conclusion:: The simulation results prove that it can effectively improve the capability of preserving power supply for important loads after transformer failure.

Translation and psychometric validation of the Chinese version of the metacognitive awareness scale among nursing students.

This study endeavors to translate and psycho-metrically validate the metacognitive awareness inventory scale (MAS) for nursing students in China. A total of 592 nursing students were enlisted from four universities situated in the eastern, southern, western, and northern regions of China. Content validity and reliability were evaluated using the content validity index and item-total correlation coefficient, and Cronbach's alpha coefficients, respectively. Convergent validity examined the goodness of fit among sub-scales through the average extracted variance and composite reliability. Exploratory factor analysis confirmed the first-order and second-order factor models, contributing to a cumulative variance of 89.4 and 59.5%, respectively. The Cronbach's alpha values were 0.963 and 0.801, respectively. Confirmatory factor analysis outcomes indicated an excellent overall fit index for the model, satisfying the convergent validity criteria and achieving a target coefficient of 96.0%, which is consistent with the original scale structure. The Chinese version of the MAS (C-MAS) is a reliable and valid instrument for assessing metacognitive awareness among Chinese nursing students. Further research should consider a broader sample of nursing students across China to reinforce the scale's applicability.

Sludge deep dewatering by liquefied dimethyl ether: selection of operating conditions based on response surface methodology

ABSTRACT Sludge is an inevitable by-product of the sewage treatment process and its high moisture content poses significant challenges for its treatment and disposal. This study focuses on the technology of sludge deep dewatering using liquefied dimethyl ether (DME) and explores the relationship between operating parameters (DME/sludge ratio, extraction time and stirring speed) and the water content of the sludge after deep dewatering. After deep dewatering, the sludge's lower heating value (LHV) was significantly increased. The dehydrated filtrate is highly biodegradable and could be treated together with sewage. Based on the response surface method of central composite design, a second-order regression model of the above three variables and sludge water content as the response was established. Finally, the operating conditions diagram was drawn by target water content (36.96 wt.%) which meets the requirement of self-sustained incineration and model equation. This study provides a valuable perspective on sludge drying and fuelisation.

An Initial Validation of Transformative Social and Emotional Learning (SEL) Competencies Scale Among Asian American Pacific Islander (AAPI) Teachers

Although transformative social and emotional learning (SEL) has been recognized as a compelling concept to advance SEL practices toward equitable outcomes for both students and adults in schools, psychometrically and theoretically sound measurement tools facilitating the implementation of transformative SEL practices are still lacking. To address this research gap, we conducted the initial validation of a teacher self-reflection scale initially developed by Collaborative for Academic, Social, and Emotional Learning (CASEL) to assess teachers’ perception of their social and emotional competencies in transformative SEL practices among a sample of 249 Asian American and Pacific Islander (AAPI) teachers in the U.S. Results of exploratory confirmatory factor analyses suggested that a 22-item version of the Transformative Social and Emotional Learning Competencies Scale (TSELCS-22) was best supported by a second-order model with one second-order factor of overall social and emotional competencies and five first-order factors. The measurement invariance test indicated that the factor structure of the current scale was consistent across genders. Correlation analyses showed that teachers’ self-reported social and emotional competencies based on TSELCS-22 generally had positive correlations with teachers’ subjective well-being. TSELCS-22’s practical implications in supporting the transformative SEL practices for AAPI teachers and teachers and students from other diverse backgrounds were discussed. Impact Statement This study is the first validation study of the Transformative Social-Emotional Learning Competencies Scale among Asian American and Pacific Islanders (AAPI) teachers. In alignment with CASEL’s transformative social and emotional learning (TSEL) framework, it provides the initial empirical evidence for a self-reported assessment tool to understand and support transformative SEL practices among teachers and to improve teacher well-being and workforce, particularly for those from racial/ethnic minoritized backgrounds.

A Li-Ion Battery State of Charge Estimation Strategy Based on the Suboptimal Multiple Fading Factor Extended Kalman Filter Algorithm

The state of charge (SOC) is an important indicator for evaluating a battery management system (BMS), which is crucial for the reliability, performance, and life management of a battery. In this paper, the characteristics of a Li-ion battery are deeply studied to explore the charge/discharge curve under different environments. Meanwhile, a second-order RC equivalent circuit model is constructed. The function identification of the EMF and SOC is performed based on the least squares method. The model estimation error is verified by simulation to be less than 0.05 V. Based on the Suboptimal Multiple Fading Factor Extended Kalman Filter (SMFEKF) algorithm, the SOC under constant current and UDDS conditions are estimated. Matlab/simulink simulations illustrate that the estimated accuracy of the proposed algorithm is improved by 79.36% compared with the EKF algorithm. Finally, the validity of the algorithm is verified jointly with the BMS. The results show that the estimation error is within 4% in both constant current condition as well as UDDS conditions, and it can still be predicted quickly and accurately under the uncertainty in the initial value of the SOC.

Optimizing the performance of TPCB/SCA composite-modified asphalt using improved response surface methodology

Abstract A composite-modified asphalt was developed by incorporating tire pyrolytic carbon black (TPCB) with a silane coupling agent (SCA) to enhance its ductility and storage stability. The functional binding between asphalt and TPCB was improved by the addition of SCA. The gray correlation comprehensive evaluation method is incorporated into the response surface methodology (RSM), leading to an improved RSM. The improved RSM was employed to optimize the content of the modifier and the preparation process to enhance overall performance. A central composite design-based experiment was conducted to optimize the penetration, softening point, 10°C ductility, Brookfield rotational viscosity, and storage stability indicators of modified asphalt. The response surface of the gray comprehensive evaluation value of the evaluation indicators was calculated using the gray correlation analysis method, and a second-order regression model was established between independent variables such as TPCB, SCA content, and shear time factor and the gray comprehensive evaluation value. This model analyzed the impact of a single variable and variable interaction on the performance of the TPCB/SCA composite-modified asphalt. The optimized model results showed that the preparation parameters that optimize the expected comprehensive performance of the composite-modified asphalt are 9.2% TPCB content, 2.1% SCA content, and a shear time of 56.3 min. Finally, the TPCB/SCA composite-modified asphalt prepared with the improved RSM exhibited the highest gray comprehensive evaluation value and the best overall performance, indicating the feasibility of the optimized preparation parameters with the improved RSM.

Characterization and application of synthesized calcium alginate-graphene oxide for the removal of Cr3+, Cu2+ and Cd2+ ions from tannery effluents

Environmental sustainability has gained acceptance to achieving the goal of a secure ecosystem with a reliable management system. Heavy metal remediation of aqueous streams is of special concern due to the intractability and persistence in the environment. Adsorption is a potential alternative to the existing inefficient conventional technologies for the removal and recovery of metal ions from aqueous solutions and becomes vital to align with the Sustainable Development Goals (SDGs) and mitigate the adverse environmental and social impacts. Calcium Alginate-Graphene oxide (CA-GO) composite has been synthesized for the adsorption of heavy metals including Cr3+, Cu2+, and Cd2+ ions from tannery effluents. Graphene oxide is prepared from commercial graphite powder and reacted with sodium alginate and calcium chloride to form the beads of CA-GO composite. The developed composite was characterized by FTIR, elemental analysis, SEM, XRD analysis, and Raman spectroscopy. Moreover, the effect of pH, adsorbent dosage, contact time, and initial concentration of metal ions on the adsorption capacity were investigated through batch experiments. At a pH>3.0 (pHzpc), the carboxyl group of CA-GO was deprotonated to make the surface negatively charged and facilitate metal adsorption. The optimum pH and maximum adsorption capacity of CA-GO for removal of Cr(III), Cu(II), and Cd(II) were 4.5, 6.0, and 7.0, and 90.58, 108.57, and 134.77 mg g−1, respectively. The kinetics, adsorption isotherms, and thermodynamics were studied to determine the adsorption mechanism. The kinetic of adsorption adopted the second-order model. Thermodynamic parameter were calculated and the adsorption process was determined to be exothermic and spontaneous at room temperature. The developed composite has been efficaciously applied for the removal of metal ions and pollution from real tannery effluents.

Lead (Pb) Sorption Kinetics by Clay Minerals: Bentonite and Zeolite

Background: Heavy metal pollution chiefly lead (Pb) causes various environmental disequilibrium and health hazards. Immobilization of lead (Pb) usingclay minerals is cost effective for metal remediation due to their higher surface area and negative charges. Methods: This study was taken up to assess the Pb removal potentials of bentonite and zeolite from contaminated water and to study the effect of sorbent dosage, initial Pb2+ concentrations and incubation time intervals on Pb adsorption and desorption was studied. Result: Zeolite was effective in immobilising Pb (78.0%) than bentonite (70.9%) which increased with increasing sorbent dosage and time intervals. The pseudo second-order kinetic model described the Pb adsorption precisely. Chemisorption was the dominant mechanism operating in aqueous solution system, hence, it could be concluded that zeolite can be utilized as an efficient sorbent for wastewater treatment.

The Determining Role of Covariances in Large Networks of Stochastic Neurons.

Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.

Evaluating hydrothermal liquefaction hydrochar from sewage sludge as a phosphorus resource through struvite production

Hydrothermal liquefaction (HTL) has attracted considerable attention for valorizing sewage sludge to biofuels while the solid residue, hydrochar, has received little attention for its final application. In the current work hydrochar recovered from hot separation in a pilot-scale HTL plant is evaluated for its nutrient value, in particular as a sustainable Phosphorus (P) resource. Following thorough characterization of the hydrochar, P recovery as struvite (NH4MgPO4·6H2O) is investigated to produce a fertilizer with a lower heavy metal content that could be used in current agricultural practices. P extraction using inorganic (hydrochloric and sulphuric) and organic (citric) acids is evaluated by applying 0.1–1 mol/L of acids for 1–12 h with a liquid/solid ratio of 10 mL/g. A second-order polynomial model was established by response surface methodology (RSM) to predict the P extraction with different acids. The optimum operating conditions for maximum P extraction (94 %) are suggested as 0.7 M of acid strength (H2SO4) and 6.5 h of extraction time. Addition of citric acid (CA) during the struvite precipitation from acid-leached P solution was found to improve the struvite purity from 36.6 % to 54 % with a reduction in heavy metal content. Furthermore, morphological analysis validated the formation of struvite. Upon comparison with the regulations, hydrochar was found to exceed some of the heavy metal limits but was low in polyaromatic hydrocarbons (PAHs), per- and polyfluorinated substances (PFAS) and showed low toxicity to Aliivibrio fischeri. P extraction and struvite precipitation are shown to reduce heavy metal contents below regulatory limits with P recoveries from 40 % to 67 %. Thus, as an alternative to direct land application of hydrochar, harvested struvite from hydrochar could be applied as a fertilizer with no apparent environmental risk.