Framework Of Model Research Articles

Mapping the interplay of work-arrangement, residential location, and activity engagement within an integrated model

Virtual activities, such as teleworking have been identified as major travel demand management strategies to tackle traffic congestion and emission. However, integrated models, which are capable of testing such strategies, have not yet been properly extended to capture emerging activity patterns. This study fills-up the literature gap by implementing individuals’ work-arrangement and introducing in-home (IH) virtual activities within an integrated Transport, Land-use, and Emission (iTLE) modeling framework. First, it conducts a household travel survey, which collected socio-economic, activity-travel, and work-related information through a questionnaire survey in the Halifax Regional Municipality (HRM), Nova Scotia, Canada. Next, it develops a work-arrangement choice model based on the mixed-logit modelling (MXL) approach and implements it within iTLE. After that, individuals’ daily activity programs are generated in a sequential manner utilizing a Markov Chain Monte Carlo (MCMC) modelling approach considering their work-arrangement, employment, and vehicle ownership. To demonstrate the application of the developed tool, individuals’ socio-demographics, residential-location, work-location, and activity-participation are longitudinally simulated up to 2031. The analysis of teleworking parameters revealed distinct clusters and individual preferences, emphasizing the significance of personalized approaches in formulating teleworking strategies. Most non-teleworkers stay closer to downtown than full-teleworkers while clustered behavior is observed among hybrid workers for residential location choice. Increase is observed for shopping, dining-out, and IH maintenance/discretionary activities while decrease is observed for out-of-home work. The outcomes of this paper will be helpful for policymakers and transport researchers to understand the evolution of work-arrangement and examine subsequent impacts on transportation and land-use systems.

Network intrusion detection in big datasets using Spark environment and incremental learning

<p class="Abstract">Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.</p>

Prediction of flow and dynamic recrystallization behavior based on three machine learning methods for a novel duplex-phase titanium alloy

In this work, the vacuum arc-melting was used to prepare the Ti-10V-5Al-2.5Fe-0.1B alloy. Single-pass isothermal compression experiments were carried out on the alloy in the temperature range of 770-920 °C at strain rates of 0.0005-0.5 s-1. The BP model optimized by the bald eagle search algorithm (BES-BP), the BP model optimized by the sparrow search algorithm (SSA-BP), and the BP model optimized by the gray wolf optimization algorithm (GWO-BP) were developed for high-precision prediction of flow stress. The above models were compared by using the mean square correlation coefficient, root mean square error, and average absolute relative error between the predicted and experimental flow stress. The three prediction accuracy parameters have indicated that the BES-BP model has a higher accuracy for flow stress prediction at the known and the new process parameters. A hot processing map based on the dynamic materials model was developed by using the flow stress predicted in the framework of the BES-BP model, and EBSD analysis was performed as well. The results show that the degree of dynamic recrystallization increases with an increase in the power dissipation factor, and the formation of deformation bands is the main cause of instability. The minimum critical stress for inducing dynamic recrystallization of the alloy was found to be 13.13 MPa at 890 °C/0.0005 s-1. Moreover, the power dissipation factor increases with a decrease in critical stress. In addition, microstructure validation data reveal that the dynamic recrystallization model has a high accuracy for critical stress prediction, confirming that the critical stress increases with a decrease in the dynamic recrystallization fraction.

The impact of social noise on the majority rule model across various network topologies

We explore the impact of social noise, characterized by nonconformist behavior, on the phase transition within the framework of the majority rule model. The order–disorder transition can reflect the consensus-polarization state in a social context. This study covers various network topologies, including complete graphs, two-dimensional (2-D) square lattices, three-dimensional (3-D) square lattices, and heterogeneous or complex networks such as Watts–Strogatz (W-S), Barabási-Albert (B-A), and Erdős-Rényi (E-R) networks, as well as their combinations (multilayer network). Social behavior is represented by the parameter p, which indicates the probability of agents exhibiting nonconformist behavior. Our results show that the model exhibits a continuous phase transition across all networks. Through finite-size scaling analysis and evaluation of critical exponents, our results suggest that the model falls into the same universality class as the Ising model.

Dielectric, elastic, and thermal characteristics of NH4HSO4 ferroelectric within the framework of a two-sublattice pseudospin model

A modified two-sublattice pseudospin model of NH4HSO4 ferroelectrics is proposed, which considers the displacement of sulfate groups in the two-minimum potential well as a pseudospin subsystem. The model takes into account the electrostrictive and piezoelectric coupling of the pseudospin and lattice subsystems. In the mean field approximation, the dielectric, piezoelectric, elastic and thermal characteristics of the crystal are calculated. A satisfactory quantitative description of the relevant experimental data has been obtained. Our model allowed us to describe the effect of hydrostatic pressure on the second order phase transition at the upper Curie point and the first order one at the lower Curie point. We have shown that the distinct first-order phase transition at the lower Curie point as well as the corresponding great changes in lattice strains can be caused by the strong electrostrictive coupling of the pseudospin and lattice subsystems.

Generative model-based framework for parameter estimation and uncertainty quantification applied to a compartmental model in epidemiology

We propose a new method in which a generative network (GN) set within a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations (PDE). The aim is to match available measurements and estimate the corresponding uncertainties associated with the states and parameters of a numerical physical simulation. We train the GN using only unconditional simulations of the discretized PDE model. A GN is used here to exploit the ability of these methods to generate realistic outputs from complex probability distributions, such as the ones that represent the possible states and parameters of a physical problem. We compare the proposed method with the gold standard Markov chain Monte Carlo (MCMC). Additionally, we suggest the use of a novel type of time-stepping regularization to improve the representativeness of the physical solution, and we present a new way of evaluating the GN training, taking advantage of the real/generated sample structure. We apply the proposed approaches to a spatio-temporal compartmental model in epidemiology. The results show that the proposed GN-based ROM can efficiently quantify uncertainty and accurately match the measurements and the gold standard. This is achieved using only a limited number of unconditional simulations from the full-order numerical PDE model (40 simulations). The GN-based ROM operates 60 times faster than the gold standard (MCMC), while producing uncertainties that closely match those generated by the MCMC approach. The proposed method is a general framework for quantifying uncertainties in numerical physical simulations and is not restricted to the specific physics of this application.

An agent-based socio-hydrological modeling to identify the feedbacks between agricultural irrigation and ecological water conveyance tradeoffs in Hotan River basin

Study regionHotan River basin in Northwest China Study focusThe conflict between agricultural irrigation and ecological water conveyance is particularly critical in arid inland river basins. Water resource systems involve multiple stakeholders and sectors, complicating the identification of dynamic feedback between agriculture and ecology. This study presents an agent-based socio-hydrological modeling (ABSHM) framework to address these interactions. New hydrological insights for the regionFeedback is quantified using dynamic state variable of oasis decision-making sensitivity in the Hotan River basin. When this sensitivity ranges from 0.03 to 0.06, water managers prioritize agricultural irrigation, increasing its usage from 8 to 47 million m3 at the expense of ecological water. Conversely, when sensitivity is from 0.015 to 0.03, ecological health requires increasing ecological water use from 27 to 66 million m³ by reducing irrigation. The ABSHM framework effectively captures these dynamic feedback processes, supporting basin water resources management and decision-making. The uniqueness of the ABSHM framework lies in its ability to capture the impact of individual decision-making behaviors on the overall water resource system. This offers new perspectives and approaches for addressing current and future water resource management challenges.

Barriers and enablers toward healthy eating and weight gain among pregnant women in Vietnam: A qualitative study with analysis informed by the theoretical domains framework and COM-B model

Undernutrition and insufficient gestational weight gain can negatively affect maternal and infant health short- and long-term. In Vietnam, 50% of pregnant women lack essential nutrients, and 75% do not gain enough weight. Current interventions have limited success due to a gap in understanding their determinants. This study aimed to identify barriers and facilitators to healthy eating and weight gain among pregnant Vietnamese women. This qualitative study collected data from 20 pregnant Vietnamese women via virtual focus groups. Discussions were audio-recorded and translated into English for thematic analysis. The study utilized the Capability, Opportunity, Motivation to Behaviour (COM-B) model and Theoretical Domains Framework (TDF) to map the identified themes. Fifteen themes mapped onto nine of the 14 theoretical TDF domains, providing a comprehensive understanding of barriers and enablers to healthy eating and gestational weight gain within the COM-B model. CapabilityWomen had limited knowledge about food sources and the implications of insufficient weight gain and micronutrient deficiencies, though they exhibited high self-care and digital literacy. OpportunityThey lacked reliable online sources, had limited healthcare provider communication, spousal support, and faced cultural food beliefs and taboos. MotivationThe women understood the need for healthier lifestyles during pregnancy, but often lacked confidence in managing gestational weight and misunderstood the role of prenatal supplements. Our research identified key factors to inform future interventions to promote healthy eating and recommended weight gain during pregnancy among Vietnamese women. To be effective, interventions should focus on increasing nutritional knowledge, enhancing communication with healthcare professionals, and improving husband supports. Addressing food taboos with culturally sensitive approaches is crucial. The potential of digits` al health interventions is enhanced by factors such as self-care and digital literacy among pregnant Vietnamese women.

A novel semi-analytical approach based on scaled boundary finite element method for fluid-structure coupling analysis of liquid sloshing in 3D containers

In this paper, a novel semi-analytical approach is proposed for the three-dimensional fluid-structure coupling analysis of liquid sloshing in elastic containers subjected to harmonic and seismic loading in the horizontal direction, based on the scaled boundary finite element method (SBFEM). A modified SBFEM model, referred to as the scaling surface-based SBFEM, is developed to simulate the container wall, which is treated as a thin shell structure. Within the framework of the scaling surface-based SBFEM, the geometry of the shell structure is entirely determined by scaling one surface of the structure. This approach differs significantly from the standard SBFEM, where approximation is achieved through coordinate mapping based on a scaling center, thereby enhancing the modeling accuracy and efficiency. Hydrodynamic pressure is treated as an independent nodal variables in the governing equations of the fluid domain, which is modeled using the standard scaling centre-based SBFEM. The coupled fluid-structure system is assembled by applying equilibrium and compatibility boundary conditions to ensure the balance of interaction forces. A synchronous solution algorithm, combined with the implicit-implicit scheme of the Newmark method, is used to determine the dynamic responses of the coupled system. The main advantage of this novel approach is that it meshes and discretizes the boundaries instead of the entire structural and fluid domains, thereby reducing computational costs. Additionally, analytical solutions can be obtained along the radial direction of the interior domain, enhancing the accuracy and convergence of the results. Another advantage is the approach's ability to provide a unified modeling framework for structures of any shape. Furthermore, the asymmetry issue of the coefficient matrix can be effectively avoided by using a synchronous solution algorithm. Benchmark examinations confirm the superior computational accuracy and robustness of the proposed approach. A comprehensive parametric study is conducted, focusing on the effects of liquid filling levels, as well as geometric and material parameters, on the transient vibration and distribution behaviors of the fluid-structure coupling system.

Deep one-class classification model assisted by radius constraint for anomaly detection of industrial control systems

Anomaly detection of industrial control systems (ICS) based on sensor data analytic is of utmost importance because ICS may suffer from various attacks leading to anomaly behaviors and even equipment failures. As an emerging deep learning technique, deep support vector data description (DeSVDD) has been successfully applied to ICS anomaly detection. Its advantage lies in only requiring normal data to train one-class classifier, which is suitable for actual industrial scenarios with extremely data imbalance of abundant normal data and scare abnormal data. However, this also results in its shortcoming of ignoring the valuable classification information hidden in the abnormal data. In order to overcome this issue, this paper proposes an improved DeSVDD method, called radius constraint DeSVDD (RC-DeSVDD). The proposed model constructs an anomaly detection model framework of abnormal data assisted DeSVDD, where a radius constraint is designed by considering the difference between normal and abnormal data. Further, considering the limited amount of abnormal data, a bi-directional generative adversarial network (BiGAN) is introduced to generate abnormal data. Experimental results on three benchmark datasets demonstrate the superiority of the proposed RC-DeSVDD anomaly detection method.

Applying a coupled model framework to assess global climate change impacts on the river-type algal blooms in the middle and lower reaches of the Hanjiang River, China

Global climate change (GCC), characterized by warming, affects the hydrological conditions at the basin scale and whether harmful algal blooms (HABs) occur at the scale of river ecological systems. Research on HABs mainly focuses on oceans and lakes, and there is still less research on the effects of GCC on river-type HABs that differ from oceans and lakes in hydrodynamic, water temperature, and nutrient conditions. This study constructed a coupled model framework that includes the GCC model, downscaling model, hydrological model, hydrodynamic model, and eutrophication model, analyzing and exploring the effect of changes in the aquatic ecological environment caused by GCC on river-type HABs in the middle and lower reaches of the Hanjiang River (MLHR). Firstly, based on the three GCC models and statistical downscaling model in CMIP6, high-precision meteorological factors such as future precipitation and temperature were obtained. Secondly, a coupled model based on SWAT and MIKE21-ECOLab was used with the digital elevation model (DEM), land use, soil, meteorological, pollution source, and measured terrain data in the MLHR Basin, which was validated by observed data. Thirdly, there has not been a significant increase in Chl-a, and the impact of GCC has not fundamentally changed the temporal and spatial distribution of HABs. Fourthly, this study proposes to use 0.2 m/s (Corresponding discharge 1160 m3/s) as the hydrodynamic condition for preventing and controlling HABs in the Shayang section.

Proactive cervical cancer risk assessment using data-driven analytics

This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.

A food-web assessment model for marine mammals, fish, and fisheries in the Norwegian and Barents Seas

The Norwegian and Barents Seas host large commercial fish populations that interact with each other, as well as marine mammal populations that feed on plankton and fish. Quantifying the past dynamics of these interacting species, and of the associated fisheries in the Norwegian and Barents Sea is of high relevance to support ecosystem-based management. The purpose of this work is to develop a food-web model of intermediate complexity and perform a quantitative assessment of the Norwegian and Barents Sea ecosystems in the period 1988–2021 in a manner that is consistent with existing data and expert knowledge, and that is internally coherent. For this purpose, we use the modelling framework of chance and necessity (CaN). The model construction follows an iterative process that allows to confront, discuss, and resolve multiple issues as well as to recognise uncertainties in expert knowledge, data, and input parameters. We show that it is possible to reconstruct the past dynamics of the food-web only if recognising that some data and assumptions are more uncertain than originally thought. According to this assessment, consumption by commercial fish and catch by fisheries jointly increased until the early 2010 s, after which consumption by fish declined and catches by fisheries stabilised. On an annual basis, fish have consumed an average of 135.5 million tonnes of resources (including 9.5 million tonnes of fish), marine mammals have consumed an average of 22 million tonnes of which 50 % (11 million tonnes) were fish, and fisheries and hunting have captured an average of 4.4 million tonnes and 7 thousand tonnes of fish and marine mammals respectively.

Bayesian modeling and mechanical simulations for fragility curve estimation of the mooring system of marine hydrokinetic devices

This work uses Bayesian modeling and mechanical model simulations through the Ansys-AQWA software to construct fragility curve estimates for marine hydrokinetic devices, more specifically, their mooring system. The fragility curves proposed here associate wind speed levels with the risk of damage to the equipment and could be used to better understand the susceptibility of these devices to damage from hurricanes. Our proposed modeling framework uses acoustic Doppler current profiler measurements from a site located off the North Carolina coast and the RM4 conversion device from the Sandia National Laboratory. By evaluating different scenarios with and without dynamic tension in mooring lines due to changes in current velocities caused by extreme wind speeds, our results indicate that the risks of damage may be significant depending not only on the average current velocity but also on the velocity variation.

Multiliteracies-translanguaging conceptual model framework for student agency in English as foreign language contexts

Multiliteracies-translanguaging conceptual model framework for student agency in English as foreign language contexts

Optimizing bioprocessing efficiency with OptFed: Dynamic nonlinear modeling improves product-to-biomass yield

Biotechnological production of recombinant molecules relies heavily on fed-batch processes. However, as the cells' growth, substrate uptake, and production kinetics are often unclear, the fed-batches are frequently operated under sub-optimal conditions. Process design is based on simple feed profiles (e.g., constant or exponential), operator experience, and basic statistical tools (e.g., response surface methodology), which are unable to harvest the full potential of production.To address this challenge, we propose a general modeling framework, OptFed, which utilizes experimental data from non-optimal fed-batch processes to predict an optimal one. In detail, we assume that cell-specific rates depend on several state variables and their derivatives.Using measurements of bioreactor volume, biomass, and product, we fit the kinetic constants of ordinary differential equations. A regression model avoids overfitting by reducing the number of parameters. Thereafter, OptFed predicts optimal process conditions by solving an optimal control problem using orthogonal collocation and nonlinear programming.In a case study, we apply OptFed to a recombinant protein L fed-batch production process. We determine optimal controls for feed rate and reactor temperature to maximize the product-to-biomass yield and successfully validate our predictions experimentally. Notably, our framework outperforms RSM in both simulation and experiments, capturing an optimum previously missed. We improve the experimental product-to-biomass ratio by 19% and showcase OptFed's potential for enhancing process optimization in biotechnology.

Impact of financial and internet support on SME performance: Moderating effect of technology adoption during COVID-19 pandemic

Small and medium-sized enterprises (SMEs) are widely recognised as significant catalysts for economic growth at a worldwide level. The benefits that are frequently highlighted for their promotion, particularly in developing nations like Bangladesh, include their relatively high labor intensity, reliance on local technology and skills, contributions to the growth of entrepreneurialism and innovativeness, and industrial linkage expansion. The objective of this study is to examine the influence of financial and internet support on the performance of small and medium enterprises (SMEs) in Bangladesh during the COVID-19 pandemic while also considering the moderating effect of technology adoption. By going through the literature, the study identified a number of factors namely financial support, ICT support, technological support played a strong role on SME performance during COVID-19. The present study utilises Partial Least Square (PLS) path modelling, a variance-based technique within the framework of Structural Equation Modelling (SEM), to investigate the correlations. A well-structured questionnaire is prepared for collecting primary data from the SME owners or managers and finally 378 data have been collected from the different types of SME entrepreneurs through using purposive sampling technique. Eight hypotheses are tested and found seven positive relationships with moderating interaction of technology adoption between influential factors and SME performance (financial and non-financial).

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

Identifying signals of memory from observations of animal movements

AbstractIncorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into step-selection analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data.

Performance analysis of position sensorless control of antagonistic shape memory alloy actuators

Abstract This paper presents a comprehensive performance analysis of position sensorless control of antagonistic shape memory alloy actuators (ASMAA), focusing on enhancing the sensorless position sensing capabilities and servo performance. The mechanisms and influencing factors of ASMAA self-sensing characteristics are comprehensively analyzed. A self-sensing model framework based on the constitutive model of ASMAA is proposed, and a self-sensing model based on differential resistance feedback is developed. An experimental platform based on the ASMAA motion mechanism is established to test and analyze SMA wires of two different materials under various pretension force conditions. The experimental results show that the antagonistic configuration mitigates hysteresis and improves linearity. A simple polynomial fitting is employed to establish the resistance-displacement relationship, achieving good accuracy with minimal residuals. The system identification is employed to avoid the parameter complexity and time-varying in the constitutive model. Based on the identified transfer function, a traditional PID controller is designed for position sensorless servo control. However, the inherent nonlinear hysteresis of ASMAA is difficult to suppress using a linear PID controller. Furthermore, a compound control strategy based on the Duhem model and PID controller is proposed, which utilizes the particle swarm optimization (PSO) algorithm to identify the Duhem inverse model controller. Experimental results demonstrate that the compound controller significantly enhances position tracking accuracy and response speed compared to a standalone PID controller.