Modeling Approach Research Articles

Multi-parameter viscoelastic material model for denture adhesives based on time-temperature superposition and multiple linear regression analysis.

Restorative solutions designed for edentulous patients such as dentures and their accompanying denture adhesives operate in the complex and dynamic environment represented by human oral physiology. Developing material models accounting for the viscoelastic behavior of denture adhesives can facilitate their further optimization within that unique physiological environment. This study aims to statistically quantify the degree of significance of three physiological variables - namely: temperature, adhesive swelling, and pH - on denture adhesive mechanical behavior. Further, based on these statistical significance estimations, a previously-developed viscoelastic material modelling approach for such denture adhesives is further expanded and developed to capture these variables' effects on mechanical behavior. In this study a comparable version of Denture adhesive Corega Comfort was analysed rheologically using the steady state frequency sweep tests. The experimentally derived rheological storage and loss modulus values for the selected physiological variables were statistically analyzed using multi parameter linear regression analysis and the Pearson's coefficient technique to understand the significance of each individual parameter on the relaxation spectrum of the denture adhesive. Subsequently, the parameters are incorporated into a viscoelastic material model based on Prony series discretization and time-temperature superposition, and the mathematical relationship for the loss modulus is deduced. The results of this study clearly indicated that the variation in both the storage and loss modulus values can be accurately predicted using the oral cavity physiological parameters of temperature, swelling ratio, and pH with an adjusted R2 value of 0.85. The R2 value from the multi-parameter regression analysis indicated that the predictor variables can estimate the loss and storage modulus with a reasonable accuracy for at least 85% of the rheologically determined continuous relaxation spectrum with a confidence level of 98%. The Pearson's coefficient for the independent variables indicated that temperature and swelling have a strong influence on the loss modulus, whereas pH had a weak influence. Based on statistical analysis, these mathematical relationships were further developed in this study. This multi-parameter viscoelastic material model is intended to facilitate future detailed numerical investigations performed with implementation of denture adhesives using the finite element method.

Optimizing arsenic removal from groundwater using continuous flow electrocoagulation with iron and aluminum electrodes: An experimental and modeling approach

Arsenic contamination in groundwater is a global issue. This study examines arsenic removal using continuous flow electrocoagulation with iron and aluminum electrodes. Various parameters, such as electrode material, number, and applied voltage, were tested. Arsenic removal efficiencies ranged from 52 % to 89 % with aluminum electrodes and 46 % to 96 % with iron electrodes, depending on conditions. Surface pretreatment of electrodes significantly enhanced performance. Iron electrodes provided a faster and more cost-effective solution compared to aluminum. Statistical methods, including Pearson's correlation and Gradient Boosting Machine (GBM) modeling, were used to optimize operational parameters like current density, coagulant dose, pH, and conductivity. The GBM model achieved high predictive accuracy (R2 > 0.97), capturing complex relationships. Experiment duration was the most critical factor, contributing 70.2 % to arsenic removal efficiency, with a strong correlation (r = 0.75). Coagulant dose was also crucial, followed by current density and voltage. Cost analysis combined with GBM modeling optimized cost-efficiency for achieving arsenic levels below 10 μg/L. This research offers valuable insights for optimizing electrocoagulation processes, demonstrating the potential of machine learning in enhancing water treatment technologies.

National-level resilience: Innovative framework for Energy-Water-Food nexus evaluation

The escalating demand for energy, water, and food (EWF) resources, alongside external stressors such as climate change, is placing significant strain on national systems. Therefore, this paper introduces a novel framework for assessing the resilience of interconnected EWF systems at a national level. The framework utilizes a hybrid approach of network systems modeling and optimization models, integrating sectoral and dependency impacts to offer a comprehensive perspective on the interconnected resilience of EWF components. Applied to Qatar's EWF nexus, the framework revealed significant interdependencies, particularly between water and energy components, that led to cascading failures and challenged nexus resilience. Under single failure scenarios, unmet demands for energy and water ranged from 9 to 59 GWh and 97,000 to 359,000 m3, respectively, with resilience indices from 0.86 to 0.99. In double failure scenarios, significantly higher unmet demands caused the resilience index to drop to 0.43. Due to limited freshwater availability, the food sector is highly vulnerable to groundwater supply disruptions, resulting in an 85.6 % reduction in total crop production under extreme scenarios. Furthermore, the study highlighted resilience-enhancement strategies, such as utilizing treated wastewater for agricultural, industrial, and commercial activities and diversifying energy sources to reduce the risk of cascading failures, which increased the nexus's resilience by 17 %. The proposed framework serves as an essential tool for identifying critical components within the EWF nexus and planning strategies to enhance both operational and structural resilience.

A systematic review and BMD modeling approach to develop an AOP for humidifier disinfectant-induced pulmonary fibrosis and cell death

Dosimetry modeling and point of departure (POD) estimation using in vitro data are essential for mechanism-based hazard identification and risk assessment. This study aimed to develop a putative adverse outcome pathway (AOP) for humidifier disinfectant (HD) substances used in South Korea through a systematic review and benchmark dose (BMD) modeling. We collected in vitro toxicological studies on HD substances, including polyhexamethylene guanidine hydrochloride (PHMG-HCl), PHMG phosphate (PHMG-p), a mixture of 5-chloro-2-methyl-4-isothiazolin-3-one and 2-methyl-4-isothiazolin-3-one (CMIT/MIT), CMIT, and MIT from scientific databases. A total of 193 sets of dose-response data were extracted from 34 articles reporting in vitro experimental results of HD toxicity. The risk of bias (RoB) in each study was assessed following the office of health assessment and translation (OHAT) guideline. The BMD of each HD substance at different toxicity endpoints was estimated using the US Environmental Protection Agency (EPA) BMD software (BMDS). Interspecies- or interorgan differences or most critical effects in the toxicity of the HD substances were analyzed using a 95% lower confidence limit of the BMD (BMDL). We found a critical molecular event and cells susceptible to each HD substance and constructed an AOP of PHMG-p- or CMIT/MIT-induced damage. Notably, PHMG-p induced ATP depletion at the lowest in vitro concentration, endoplasmic reticulum (ER) stress, epithelial-to-mesenchymal transition (EMT), inflammation, leading to fibrosis. CMIT/MIT enhanced mitochondrial reactive oxygen species (ROS) production, oxidative stress, mitochondrial dysfunction, resulting in cell death. Our approach will increase the current understanding of the effects of HD substances on human health and contribute to evidence-based risk assessment of these compounds.

Theoretical investigation into saline optical properties for enhancing solar still performance: Mathematical modeling approach

This study investigates the dynamic optical properties of saline solutions, specifically transmissivity, absorptivity, and reflectivity, and their impact on the performance of solar stills. This research addresses the critical challenge of enhancing freshwater production through solar desalination, which is vital in water-scarce regions. Utilizing a validated mathematical model, the study examines how variations in saline depths (ranging from 5 mm to 40 mm), nanofluids, chemical additives, and dyes influence the optical properties and efficiency of solar stills. The results show that a depth of 20 mm emerges as optimal, providing a balance between high transmissivity, absorptivity, and low reflectivity, also a higher saline absorptivity, ranging from 0.014 to 0.021, significantly boosts solar still performance, while transmissivity, ranging from 0.26 to 0.95, affects instantaneous efficiency. The study reveals that the production of distilled water decreases from 6.77 to 4.87 L/m2 as the refractive index increases from 1.2 to 2.6, while higher extinction coefficients enhance production, reaching up to 6.96 L/m2 at 300 m−1. These findings demonstrate the importance of optimizing saline optical properties to improve solar still efficiency. The novelty of this work lies in its comprehensive analysis of the dynamic nature of saline optical properties and their practical application in enhancing solar desalination technology, going beyond previous efforts that assumed constant optical properties. This advanced understanding significantly contributes to the development of more efficient and effective solar desalination systems.

A Cognitive Appraisal Approach to Understanding Mountain Bikers' Post-Trip Trails Recommendation Intent

This paper presents findings from a study that examined how well mountain bikers' cognitive processes predicted their recommendation intent—the intention to share their recreation experience with others. Specifically, we examined relationships between different mountain biking recreation goal appraisals, emotions, and mountain bikers’ intentions to suggest mountain biking destinations to others. We tested hypothesized relationships using a structural equation modeling approach. Our results show that goal congruence and interest are the primary appraisal factors predicting recommendation intent among mountain bikers. While satisfaction and affective emotions are distinct factors that affect post-trip recommendation behavior, our results suggest that satisfaction emotions are significant secondary predictors of recommendation intent and have a stronger relationship with post-trip recommendation intentions than affective emotions. These study outcomes may be used to inform and develop effective management strategies for mountain biking destinations by designing based on user experiences.

Performance and Design Optimization of Solar Collectors

The objective of the present work was to conduct a comparative analysis of solar collectors. The experimental set-up consists of two flat plate solar collectors, the first collector was with a fixed surface tilted at 30о towards the South, and the second one was equipped with dual-axis rotation system. A programmable chronological tracker was used to control the motion of the moving collector. An experimental programme was performed to investigate the effect of the sun tracking system usage on the collector efficiency. The study includes development of a 3-D mathematical model of the system and extensive numerical simulation, based on the computational fluid dynamics (CFD) approach. The main aim of the modelling is to provide information on heat transfer, and to numerically simulate the heat transfer performances and the energy capture capabilities of the fixed and moving collectors in various operating modes. The comparative analysis shows that there is a good agreement between the experimental and numerically predicted results for different running conditions, and the presented modelling approach can be used for further investigations, including more complex problems.

Multi-compartment V/Q lung modeling: Log normal distributions of inspired or expired alveolar gas?

Using a 50-compartment Python-coded mathematical lung model, we compared mixed venous blood flow (Q) distributions and arterial oxygen tension/inspired oxygen fraction (PaO2/FiO2) relationships in lungs modeled with log normal distributions (LND) of inspired (VI) versus expired (VA) alveolar gas volumes. In lungs with normal V/Q heterogeneity, Q versus VA/Q and Q versus VI/Q distributions were similar with either approach, and PaO2/FiO2 sequences remained indistinguishable. In V/Q heterogeneous lungs at high FiO2, VILND generated low Q versus VA/Q shoulders and some negative VA units, while VALND preserved Q versus VA/Q log normality by blood flow diversion from low VI/Q units. We managed VILND-induced negative VA units either by shunt conversion (VI decreased to 0) or VI redistribution simulating collateral ventilation (VI increased till VA = 0). Comparing oxygen transfer: VALND > VILND (redistribution) > VILND (shunt). In V/Q heterogeneous lungs VALND and VILND (redistribution) regained near optimal oxygen transfer on 100% oxygen, while impairment persisted with VILND (shunt). Unlike VALND, VILND (redistribution) produced Q versus VA/Q distributions in V/Q heterogeneity compatible with multiple inert gas (MIGET) reports. VILND (redistribution) is a physiologically-based MIGET-compatible alternative to West's original VALND lung modeling approach.

The Impact of Economic Growth, Energy and Electricity Consumption Usage on CO2 Emissions: A Case Study of Morocco

Given the uncontrolled increase in environmental pollution and degradation of environmental quality worldwide, the environmental impact of economic growth has become a major focus in recent decades the most important question is: How does economic growth affect environmental quality? This study addressed this issue, examining the nature of the long-run relationship between economic growth per capita and pollutant emissions in Morocco. For this reason, the ARDL (Autoregressive Distributed Lag) modeling approach was used from 1990 to 2020. This approach mainly includes time series analysis and cointegration. The relationship tested introduces four variables: carbon dioxide (CO2), energy consumption per capita, electricity consumption per capita and GDP per capita. The main results indicate a long-term relationship between economic growth, energy consumption and CO2 emissions, indicating that a 1% increase in electricity consumption increases CO2 emissions by 0.36%. On the other hand, a 1% increase in GDP per capita increases CO2 emissions by 0.76%, and a 1% increase in energy consumption increases emissions by 2.06%. Thus, all variables show a significant effect. In conclusion, the strategy that Morocco needs to follow to solve our problems is to prioritize investment in research, development and artificial intelligence, while providing adequate training in energy-related fields.

Rainfall-runoff modeling using artificial neural networks in the Mono River basin (Benin, West Africa)

Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. However, the rainfall-runoff models mostly used in the Mono river basin struggle to better simulate high river flows. This paper presents a modeling approach based on Artificial Neural Networks (ANN) under different input meteorological parameters in the Mono River basin to better take into account the non-linearity of the relationship between rainfall and runoff. To this end, precipitation, potential evapotranspiration, and previously observed flow have been used for the daily flow simulation. The Levenberg-Marquardt algorithm is used to train the ANN rainfall-runoff models over the other optimization training algorithms mostly implemented in the study area. The analysis of the rainfall-runoff variability allowed us to show the strong correlation between rainfall and runoff and the impact of the Nangbéto dam on the flows at Athiémé. The results obtained after the training, validation, and testing of the ANN models are satisfactory (e.g., the coefficient of correlation varies between 0.93 and 0.99). The most efficient model has been identified and implemented in the Mono river basin at Nangbéto. The satisfactory results obtained show that ANN models can be considered good alternatives for traditional rainfall-runoff modeling approaches.

Is online shopping a trick or treat? Understanding the perceptions of generation Z towards purchasing through tourism e-commerce platforms

The emergence and popularity of e-commerce have led to a significant shift in the way consumers purchase products and services, and Generation Z (Gen Z) are playing a key role in driving this trend. In Malaysia, the tourism industry has benefited from the growth of e-commerce platforms such as Agoda, Expedia, Klook, and Airbnb. This study examines the factors that influence zoomers' behavioural intention towards tourism e-commerce platforms in Malaysia, with a focus on trust as a moderating variable. Questionnaire data was collected from a sample of 162 university students in Sarawak, Malaysia and analysed using a partial least square structural equation modelling (PLS-SEM) approach. The estimation procedure was conducted using the WarpPLS 8.0. The findings revealed the significance of brand recognition and perceived quality in influencing zoomers’ trust, attitude, and intention to purchase through tourism e-commerce platforms. The positive moderating role of trust was also identified. These findings are essential for tourism practitioners to better understand the needs and wants of young e-customers. The study's insights are particularly aligned with and meaningful for Sarawak's digital economy initiatives.

How ‘Ethical Leadership and Work Climate’ Drive Job Satisfaction: Insights from Banjarmasin’s Health Department

This research aims to analyze the influence of ethical leadership (X1) and ethical work climate (X2) on job satisfaction (Y) in employees of the Banjarmasin City Health Service, South Kalimantan Province, Indonesia. This quantitative research used a structural equation modeling (SEM) approach using SmartPLS. The population in this study was all employees of the Banjarmasin City Health Service, totaling 92 people. The total sampling technique of non-probability sampling was used. Findings showed that ethical leadership positively affects job satisfaction among Banjarmasin City Health Service employees. Furthermore, it showed that ethical work climate positively affects job satisfaction among Banjarmasin City Health Service employees.

MULTIVARIATE PROBIT MODELING OF CLIMATE-SMART ADAPTATION CHOICES AMONG FARMERS IN SUB-SAHARAN AFRICA: GHANAIAN STUDY OF COCOA FARMERS

Climate change and variability pose significant challenges to cocoa production in many sub-Saharan African countries, and Ghana is no exception. Therefore, knowledge of how farmers react to climate change and its variability effects is crucial in developing policies and programs to guide the design and implementation of effective climate-smart adaptation strategies at the farm level in Africa. This study aims to examine climate-smart adaptation choices cocoa farmers make as coping strategies for climate change and variability in Ghana using a multivariate probit modeling approach. Following a multistage sampling procedure, primary data was collected from 720 cocoa farmers across the major cocoa-growing regions in Ghana. The study found major adaptation choices for cocoa farmers to include improved and resistant varieties, optimal fertilizer application, optimal pesticide application, adjusting planting/harvesting dates, shade tree management, crop diversification, non-crop diversification, and off-farm economic diversification. We further observed that farmers’ adaptation choices were significantly influenced by climate change and adaptation-related variables such as rainfall variability index, temperature variability index, climate change impact perception index, and CSA awareness as well as agroclimatic zone effects. Concerning the influence of farmer and farm-specific variables, the study identifies that sex, age, education, farm size, quality of extension services delivery, access to credit, and membership in farmer groups were significant predictors of farmers’ CSA decision choices. This study offers beneficial insights for policymakers and stakeholders on how to promote climate-smart adaptation methods among cocoa farmers in Ghana.

Effects of Reynolds number on the unsteady performance and flow of a pre-swirl stator pump-jet propulsor

ABSTRACT Reynolds number is important when experimenting or numerical predicting an underwater propulsor. In order to assess the Reynolds effects on numerical prediction, this paper considers the performance and flow of a pre-swirl stator pump-jet propulsor (PJP) under different working points, where the Reynolds number is controlled by varying the oncoming flow velocity and rotating speed. Numerical predictions are conducted through a hybrid turbulence modelling approach, named the embedded large eddy simulation, whose predictions of performance and flow of the PJP show good agreements with experiments. Then, the performance, force components, flow, and vortical systems around the PJP are discussed and analysed. Results indicate that not only the propulsion performance but also the unsteady fluctuating forces and flow are considerably affected by the Reynolds number. The performance is slightly affected after a Reynolds number higher than two times the critical Reynolds number suggested by the International Towing Tank Conference (ITTC). More importantly, the stator wake is highly affected and causes high fluctuations of thrust and torque of the rotor when the Reynolds number is below two times the critical Reynolds number. This work provides some new insights into the numerical calculating of PJP hydrodynamics and contributes to exploring the unsteady characteristics and rotor-stator interaction.

Modularity and new product performance: the role of new product development speed and task uncertainty

Similar to the advantages of product modularity to a firm’s overall performance, new product modularity could provide advantages to the new product development process and result in its success in the marketplace. Accordingly, this study investigates the effect of new product modularity on its financial and market-related performance (new product performance). To identify processes underlying this effect, NPD speed and task uncertainty have been considered as its mediator and moderator, respectively. In this vein, survey data from 190 Iranian manufacturing firms have been gathered and research hypotheses have been examined using structural equation modelling and bootstrapping approach. The results of this study reveal that new product modularity improves NPD speed and new product performance. Additionally, NPD speed is found to have a partial mediating role between new product modularity and performance. Finally, this study empirically shows that the positive effect of new product modularity on new product performance amplifies in tasks marked by heightened uncertainty. The findings of the current study highlight the determinant role of modular strategy and NPD speed in developing successful new products.

Novel glucokinase activators: A structure-based pharmacophore modeling, QSAR analysis, and molecular dynamics approach

Novel glucokinase activators: A structure-based pharmacophore modeling, QSAR analysis, and molecular dynamics approach

Modelling the significance of entrepreneurial skills and entrepreneurial education for determining entrepreneurial intentions in university students: a structural equation modelling approach

Modelling the significance of entrepreneurial skills and entrepreneurial education for determining entrepreneurial intentions in university students: a structural equation modelling approach

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach

The mediating effects of basic psychological needs on the associations between teacher support and Chinese adolescents’ science and math self-efficacy

ABSTRACT Background Science and math self-efficacy in high school can encourage students to choose science and math-related majors and thus cultivate relevant talent. The importance of science and math self-efficacy in academic performance and science and math-related career choices is well-documented. Despite numerous studies on various factors related to adolescents’ science and math self-efficacy, little empirical research has examined the effects of perceived teacher support on science and math self-efficacy among adolescents. Purpose Based on self-determination theory (SDT), this study aimed to examine the parallel mediation effect of relatedness, autonomy, and competence between perceived teacher support and Chinese adolescents’ science and math self-efficacy. Sample: We carried out a cross-sectional study that collected data from 443 Chinese tenth-grade adolescents (209 males, 47.20%). The students completed measures of perceived support from teachers, basic psychological needs, and science and math self-efficacy through an online survey. Methods The model was performed with AMOS 23 using a structural equation modeling approach with bootstrapping techniques (bootstrap replications:5,000). Results The results indicate that the association between teacher support and science and math self-efficacy was fully mediated by autonomy. The indirect effects of teacher support and science and math self-efficacy on relatedness or competence were insignificant. Conclusion These findings highlight the importance of teacher support in adolescents’ science and math self-efficacy and provide a foundation for a novel approach to stimulate and motivate adolescents’ science and math self-efficacy.

Mitigating liquefaction risks for buildings on circular footings: a numerical modeling approach

Mitigating liquefaction risks for buildings on circular footings: a numerical modeling approach