Modeling Approach Research Articles

Seismic Performance of Precast Concrete Bridge Piers with Built-In Steel Tube Connection Key

Investigating the seismic behavior of precast concrete bridge piers is crucial in the design process due to the complex stress distribution in the connecting components. To demonstrate the seismic behavior of precast concrete bridge piers with hybrid joint connections, three bridge piers were designed with a scaling ratio of 1:8 and then tested under low cyclic loading conditions. The tests involved varying shapes of steel tube connection keys as parameters. This study involved examining failure modes and crack development, as well as analyzing the hysteretic performance, deformation capacity, energy dissipation, and stiffness degradation of the specimens. Furthermore, a finite element model was developed using ABAQUS, and the validity of the modeling approach suggested in this study was confirmed through tests. The results indicate that the precast piers exhibit reduced concrete damage at the joints. The enhanced strength of the joints is attributed to the incorporation of steel tube connection keys. The circular steel tube connection key integrated into the precast bridge pier offers a superior bearing capacity, energy dissipation, and stiffness degradation compared to the cross-shaped steel tube connection key. The presence of the built-in circular steel tube connection key in the precast bridge pier suggests that it complies with the seismic structural measures and is consistent with the design principle of “strong joint and weak member”.

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background settingTraditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.MethodsThe variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.ResultsCompared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.ConclusionsThe proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

Monitoring monthly mortality of maricultured Atlantic salmon (Salmo salar L.) in Scotland II. A hierarchical dynamic linear model

The sustainability of the salmon farming industry is being challenged by increased mortality rates. Scotland’s open-source salmon production data provides the possibility of developing an industry-wide mortality monitoring model, valuable for identifying and addressing unexpected increases in mortality without needing data sharing agreements across different companies. This study aimed to utilize these data to develop a hierarchical dynamic linear model (DLM) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. We evaluated whether considering the hierarchical structure present in the data (country, region, and site) would improve mortality predictions when compared to the production cycle level DLMs developed in a previous study. Our findings demonstrated that the hierarchical DLM outperformed the production cycle level DLMs, confirming the value of this more complex modelling approach. Nevertheless, the hierarchical model, like the production cycle level DLMs, exhibited some uncertainty in the mortality predictions. When mortality is higher than expected, site level warnings are generated, which can encourage producers and inspectors to further investigate the cause. Between 2015 and 2020, approximately 25% of the production cycles and 50% of the sites encountered at least one warning, with most warnings happening in the summer and autumn months. Additionally, the hierarchical model enabled monitoring mortality at multiple levels. This information is useful for various stakeholders as part of a monitoring system, offering insights into mortality trends at national, regional, and sites levels that may benefit from strategic resource management. Recommendations for model improvements include utilizing shorter data aggregation periods, such as weekly, which are not currently available as open-source data.

ANALYSIS OF THE FACTORS OF SOCIAL MEDIA ADVERTISING AND THEIR INFLUENCE ON CONSUMER’S BUYING INTENTION

In a social media setting, it is critical to explore the constructs that drive consumers’ reactions toward advertisements as the lack of understanding of these factors may lead to misplaced advertising for marketers online. This study creates a multifaceted advertising model that assesses the effect of informativeness, entertainment, irritability, and credibility on the valuableness of social media advertising and how these factors influence consumers’ buying intentions. It adopted a covariance-based structural equation modelling approach where primary data was collected by administering questionnaires to a sample study of 364 selected participants, who were university students, employees, and entrepreneurs. The data gathered were analyzed using confirmatory factor analysis and structural equation modelling method were used to examine the correlation between the identified predictors and purchase intention while validating the model constructed. The results established that a strong correlation exists between the perceived value of informativeness and credibility of social media advertising and customer buying intention. The findings, while frowning that advertisements with overly entertaining content may irritate potential buyers, posited that perceived value is critical in the conversion of advertising engagement into purchase habit. The study significantly highlighted that perceived value is the key mediator in the proposed model with a very strong correlation that impacts consumers’ buying intention; that is, consumers who perceive value in the ads are far more likely to express their buying intention. The study recommended that advertisers should focus on making their social media ads more valuable to positively affect customer perceptions.

Impact of Emotional Well-being on Job Performance: A Study Based on Service Sector Professionals

Emotional well-being is an important part of holistic wellness. Emotional well-being involves an individual's overall positive feelings and their general outlook on life. By investing in the emotional well-being of their employees, firms can get significant business benefits such as improved performance, productivity, and commitment from employees, which in turn lowers attrition rates. The study aims to empirically investigate the factors influencing emotional well-being and their impact on the job performance of service sector professionals. A survey method was employed to gather responses from professionals in the service sector across various industries. The collected data, comprising 318 responses, underwent analysis using the partial least squares structural equation modelling approach. The findings from the study establish personality traits, Mindfulness and Resilience as factors affecting emotional well-being and a positive impact on job performance is observed. This study helps organisations understand that increasing the emotional well-being of professionals is one of the important parameters for their job performance. Future researchers may try to explore the other factors affecting emotional well-being and their impact on job performance.

Potential novel HIV-1 reverse transcriptase inhibitors: a modeling and evaluation approach

Human immunodeficiency viru (HIV) is the causative agent of acquired immunodeficiency syndrome (AIDS), a disease that severely weakens the immune system and makes patients more susceptible to infections. Although there is no definitive cure for HIV, advances in drug development offer promising prospects. In this study, we targeted HIV-1 reverse transcriptase by performing virtual screening (VS) to identify novel candidate compounds. From a database of compounds similar to the inhibitor thymidine-5'-triphosphate (TTP), three compounds (CID441663, CID123650073, and CID123789980) were selected for their docking scores, which outperformed those of the reference compound TTP (-6.2302 kcal/mol). These compounds were then subjected to ADMET, PASS, and DFT analyses. Interestingly, all three ligands showed a broad spectrum of predicted antiviral activity, including targets related to human herpes virus and HIV. Specifically, while TTP primarily targets HIV-1 reverse transcriptase, the top three ligands were predicted to target HIV-1 integrase, with CID441663 and CID123789980 displaying higher confidence in this target compared to CID123650073. These findings suggest that the candidate ligands should undergo further in vitro validation to determine their precise roles as inhibitors or antagonists, and to confirm their selective targeting of HIV-related proteins.

A finite discrete element approach for modeling of desiccation fracturing around underground openings in Opalinus clay

Understanding and predicting potential failure mechanisms during the excavation and open drift stages of geological repository construction are among the crucial aspects of performance evaluation and safety assessment of nuclear waste storage facilities. The development of the Excavation Damage Zone (EDZ) and the generation of shrinkage-induced cracks during operational phases are prominent examples of failure mechanisms that can compromise the integrity of the repository systems. This study presents an integrated framework for investigating shrinkage-induced cracking of Opalinus Clay in niches and tunnels. To achieve this, the hybrid Finite Discrete Element Method (FDEM) is employed. The methodology incorporates a two-way staggered hydro-mechanical coupling scheme, where solid phase analysis relies on 2D FDEM and fluid flow is modeled using the nonlinear Richards’ equation and solved via Finite Volume discretization. To account for the effects of EDZ, characterized by a pronounced increase in hydraulic conductivity, a numerical simulation of tunnel excavation is first carried out. The resulting failure pattern around underground openings is then abstracted through the definition of an altered hydraulic conductivity field. Comparison of the numerical results with field observations demonstrates the framework’s ability to capture a wide range of failure mechanisms inherent in various stages of underground repository construction in Opalinus Clay.

Exploring South Australian households' perceptions towards various food waste policies

Increasing landfill and environmental costs are driving municipalities to search for effective policies to change household food waste disposal, balancing competing attributes such as effectiveness, fairness and feasibility. In order to better understand households' policy perspectives, we conducted an online survey with 1520 South Australians regarding four different food waste policies, namely: 1) frequency-based pricing for bin collection; 2) penalties for bin misuse; 3) changing bin collection timing; and 4) extension and education. A structural equation modelling approach was used to model overall policy preference, based on perceived policy attributes and household socio-demographics. Results highlighted that respondents' rated economic incentives (pricing and penalties) as the most effective in changing food waste behaviour, but rank these policies low in feasibility or acceptability. Conversely, education and information campaigns were rated as the lowest in effectively changing behaviour, but the most acceptable and fairest policy overall. Overall, respondents from households producing less food waste were more favourable towards introducing economic incentive food waste policies.

Advanced ERT techniques for methane potential evaluation in controlled dump sites: A forward modeling approach

Methane (CH4) emissions from large controlled dump sites play a significant role in global warming. However, when captured efficiently, CH4 is a valuable energy resource. This study optimizes electrical resistivity tomography (ERT) for improved CH4 detection in controlled dump sites, focusing on refining electrode array configurations. We assessed three configurations: dipole-dipole, Wenner-Schlumberger (WS), and Wenner arrays. The results were compared with synthetic data simulated using the forward modeling technique, and the model's accuracy was evaluated using the Nash-Sutcliffe model efficiency coefficient (NSE), model sensitivity, and root mean square error (RMSE). Additionally, we analyzed the model's correlation with CH4 flux measurements. The WS array demonstrated the best performance, achieving a lower RMSE (23.4 %), a higher NES (0.90), and model sensitivity (0.90), along with a moderate negative correlation (-0.50) with CH4 flux measurements. This configuration effectively identified subsurface features, such as organic waste layers and leachate zones, which are crucial for CH₄ capture and landfill gas collection. Forward modeling confirmed the WS array's ability to provide high-resolution subsurface imaging, enhancing CH₄ hotspot detection. By improving the spatial and temporal accuracy of CH₄ detection, this approach addresses limitations in traditional methods and supports the design of more efficient gas capture systems. Applied in Thailand, these findings highlight the potential of optimized ERT techniques for enhancing CH₄ recovery in waste-to-energy systems, contributing to more sustainable waste management practices and reducing greenhouse gas emissions.

Quantum Effects Induced by Defects in Thin-Film Structures: A Hybrid Modeling Approach to Conductance and Transmission Analysis

This study investigated the possibility of quantum effects arising from defects resulting from the use of textronic electroconductive thin films and evaluated their impact on control characteristics. A hybrid model, where the classical approach to determine stationary fields based on the boundary element method was combined with a quantum mechanical approach using nonequilibrium Green’s functions, was created. The results of conductance and transmission coefficient simulations for different types of defects in the studied structure and a wide range of temperatures assuming two different control modes are presented. Based on the results, the conditions for the occurrence of quantum effects on the surface of conducting paths containing defects were specified, and their impact on conductance in the quantum mechanical approach was estimated.

A modeling analysis on industrial radial-flow packed-bed reactors for the catalytic dehydrogenation of long-chain normal paraffins: Appraisal of the modeling approach

Catalytic dehydrogenation of long-chain normal paraffins is the most attractive route for producing of linear alkyl benzene. To make this happen, the radial-flow packed-bed reactors are employed as one of the most efficient currently available technologies. Simplifying assumptions that are sometimes imposed on reactor models to reduce the computational cost may also significantly decrease the accuracy of simulations. Here, it is decided to shed light on this matter by assessing the effect of typical model-simplifying assumptions on simulation results. To this end, one- and two-dimensional semi-homogeneous models are used to simulate an industrial-scale radial-flow packed-bed dehydrogenation reactor under isothermal and adiabatic conditions. Simulations are designed in four 1D isothermal, 1D adiabatic, 2D isothermal, and 2D adiabatic modes to compare different modeling strategies and investigate the effect of flow distribution on the reactor performance. An appropriate LHHW kinetics model is considered for paraffin dehydrogenation and the main associated side reactions over a commercial Pt-Sn-K-Mg/γ-Al2O3 catalyst. The model equations are solved numerically using the finite element method by COMSOL Multiphysics CFD software. The results show a 1–3 % discrepancy between the predictions of one- and two-dimensional models for feed conversion under isothermal and adiabatic conditions. In contrast, the comparison of isothermal and adiabatic results for each one- and two-dimensional models indicate a discrepancy of 33–36 %. Furthermore, the two-dimensional model shows a low non-uniformity in flow distribution under reaction conditions (∼ 0.175), which has a trivial negative effect on paraffin conversion.

Optimizing actual PID control for walking quadruped soft robots using genetic algorithms

The construction of soft robots’s models and controllers remains a significant challenge. In this paper, we propose a new walking control method for the quadruped soft robot named genetic algorithm-optimized PID. First, we construct the control model correlating valve voltage with leg bending based on the geometrical analysis. This modeling approach leverages the characteristics of novel leg structure and bend sensor, thereby streamlining the control model for locomotion of quadruped soft robotic. Moreover, We apply the genetic algorithm to automatically tune parameters and optimize PID controllers, aiming to enhance control performance. The application of the proposed method to the walking control has been uniquely demonstrated on a real 3D-printed quadruped soft robot. Experimental results indicate that the genetic algorithm-optimized PID controller significantly improves trajectory tracking compared to the Ziegler-Nichols tuning method. This optimization increases the robot’s walking speed from 5 mm/s to 8 mm/s, reduces the error rate by 2.4064%, decreases overshoot by 12.55%, and shortens response time by 0.5 s, substantially enhancing the controller’s overall performance. Additionally, compared to particle swarm optimization, the proposed method further improves performance by reducing the error rate by 0.4079%, overshoot by 8.4%, and response time by 1.0 s.

Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche

Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.

Research on Coordinated Relationship Between Deformation and Force in Shaft Foundation Pit Support Structures

In order to investigate the coordinated relationship between lateral deformation of the diaphragm wall and axial force of the internal strut, this paper first carried out a scaled model test on the mechanical features of a foundation pit support system based on a novel axial force servo device. Then, a finite element model was established to simulate the scaled model test, and the correctness of the finite element modeling approach was validated by comparing test results. After that, the same finite element modeling method was used to analyze the coordinated relationship between axial force and lateral deformation in the prototype foundation pit support structure. The results show that the axial force of the inner strut is negatively correlated with the lateral deformation in the diaphragm wall. The initial maximum lateral deformation in the diaphragm wall of the shaft foundation pit occurs at the bottom of the foundation pit, so changing the length of bottom strut simultaneously is the most effective way to adjust the mechanical behavior of the support structure. Under various support conditions, the maximum lateral deformation of the diaphragm wall in the prototype project is 0.59~0.66‰ of the total excavation depth of the foundation pit, and the maximum axial force of internal support is 11~30% of the yield load of a single steel strut.

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

AbstractModeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

Comprehensive Approach to Training English Communicative Competence in Chemistry

Abstract The development of foreign language professional communicative competence (FLPCC) is crucial for professionals, considering communicative skills as an integral part of their competence. In Kazakhstan, acquiring a foreign language is a prerequisite for advancing chemistry education, enabling students to access global scientific advancements and exchange knowledge. Content and language-integrated learning (CLIL) stands out as an effective approach for cultivating FLPCC in future chemistry teachers. This study addresses the scarcity of theoretical and practical tools in content-language integrated learning in pedagogical universities. It aims to enhance the training quality for pedagogy bachelor’s degree students studying chemistry in a foreign language. By identifying active teaching forms, methodologies, and approaches in integrated learning, students’ foreign language competence can be successfully developed within the professional domain. A meticulously designed training model and exercises were developed to achieve these goals. The proposed modeling approach was evaluated using the Quasi research method, involving questionnaire data analysis. The results confirm that implementing this approach significantly improves future chemistry professionals’ training quality, enabling confident communication in English within their professional domains and demonstrating well-developed speaking skills. The practical value of this research extends to university educators implementing CLIL practices and researchers studying students’ FLPCC formation.

Humans versus models: a comparative assessment of ecosystem services models and stakeholders’ perceptions

Mapping the production of Ecosystem Services (ES) is imperative for sustainable ecosystem management. Likewise, incorporating expert knowledge enhances ES research. Here, we calculate eight multi-temporal ES indicators for mainland Portugal using a spatial modelling approach. These indicators are then integrated into the novel ASEBIO index—Assessment of Ecosystem Services and Biodiversity—which depicts a combined ES potential based on CORINE Land Cover, using a multi-criteria evaluation method with weights defined by stakeholders through an Analytical Hierarchy Process (AHP). Outputs from the modelling show how ES have changed in Portugal in relation to land use changes, including trade-offs between 1990 and 2018. The composed ASEBIO index is compared against the stakeholders’ valuation of ES potential for the year 2018. The results reveal a significant mismatch between the ES potential perceived by stakeholders and the models, with stakeholder estimates being 32.8% higher on average. All the selected ES were overestimated by the stakeholders. Drought regulation and erosion prevention have the highest contrasts, while water purification, food production and recreation are the most closely aligned among both approaches. Providing the first national overview about the status of multiple ES over a 28 year-period, our findings highlight potential disparities between data-driven and stakeholder-based evaluations. Therefore, we suggest the need for integrative strategies that consider scientific models with expert knowledge for more effective ES assessments and land-use planning. This approach could help bridge the gap between data-driven models and human perspectives, resulting in more balanced and inclusive decision-making.

Modeling the occupancy rate and nights spent at tourism accommodations of Spain using autoregressive deep learning networks

Purpose The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these accommodations of Spain for the period of 1990–2023 using monthly data. Design/methodology/approach The monthly occupancy rate of hotels and the total number of hotel nights data of Spain for the 1990M01–2023M09 range is considered. An autoregressive deep learning network is developed for the modeling of both metrics. Moreover, the results of the proposed autoregressive deep learning method are compared to those of a classical artificial neural network. Findings The actual occupancy rate, total night data and the deep learning model results are compared showing the accuracy of the developed model. Moreover, the R2, mean absolute error, root mean square error and mean absolute percentage error of the models are calculated further demonstrating the high performance of the developed model. The R2 values higher than 0.9 are achieved for both occupancy rate and total number of hotel nights data. Practical implications The modeling results given in this paper demonstrate that the previous values of the net occupancy rate and the total number of nights can be used as inputs of a deep learning network model by which accurate forecasts can be made for the future values of the occupancy rate and the total number of hotel nights. This modeling approach possesses importance from the practical viewpoint considering that the accurate planning and forecast of the net occupancy rate and the total number of nights affect the tourism income. Originality/value This study differs from existing literature by attempting to model the occupancy rate and the total number of hotel nights data autoregressively using deep learning networks.

Effect of BMI on Caries Assessed by Structural Equation Modelling in an Adult Brazilian Population.

Compare different behavioural, environmental and socioeconomic factors for caries with transversal data to decompose the direct and indirect effects of body mass index (BMI) in relation to coronal and root caries. This cross-sectional study used a representative sample of 1002 individuals aged ≥ 35 years living in Porto Alegre. Questionnaires recorded age, sex, educational level, tooth brushing frequency and access to dental services. Oral examination assessed gingival bleeding and recession, coronal and root caries. Height and weight were collected to calculate the BMI. The structural equation modelling approach was used, and standardised coefficients (SC) to direct, indirect and total effects were calculated. The prevalence of excessive body weight was 71.17%, of which 40.09% were overweight and 31.07% were obese. The overall prevalence of coronal and root caries was 99.83 and 36.95, respectively. No direct link between BMI and coronal or root caries was observed. For coronal caries, positive SC was found for age (0.56; p < 0.01) and sex (female) (0.14; p < 0.01). For root caries, positive SC was detected for age (0.34; p < 0.01), smoking exposure (0.17; p < 0.01) and gingivitis (0.08; p < 0.01). Overweight and obese adults should not be regarded at higher risk for dental caries.

Exploring the Impact of Pharmacological Target-Mediated Low Plasma Exposure in Lead Compound Selection in Drug Discovery – A Modeling Approach

Exploring the Impact of Pharmacological Target-Mediated Low Plasma Exposure in Lead Compound Selection in Drug Discovery – A Modeling Approach