Modeling Methods Research Articles

Experimental models for peri-implant diseases: a narrative review.

Peri-implant diseases, being the most common implant-related complications, significantly impact the normal functioning and longevity of implants. Experimental models play a crucial role in discovering potential therapeutic approaches and elucidating the mechanisms of disease progression in peri-implant diseases. This narrative review comprehensively examines animal models and common modeling methods employed in peri-implant disease research and innovatively summarizes the in vitro models of peri-implant diseases. Articles published between 2015 and 2023 were retrieved from PubMed/Medline, Web of Science, and Embase. All studies focusing on experimental models of peri-implant diseases were included and carefully evaluated. Various experimental models of peri-implantitis have different applications and advantages. The dog model is currently the most widely utilized animal model in peri-implant disease research, while rodent models have unique advantages in gene knockout and systemic disease induction. In vitro models of peri-implant diseases are also continuously evolving to meet different experimental purposes. The utilization of experimental models helps simplify experiments, save time and resources, and promote advances in peri-implant disease research. Animal models have been proven valuable in the early stages of drug development, while technological advancements have brought about more predictive and relevant in vitro models. This review provides clear and comprehensive model selection strategies for researchers in the field of peri-implant diseases, thereby enhancing understanding of disease pathogenesis and providing possibilities for developing new treatment strategies.

Employing generative adversarial neural networks as surrogate model for reactive transport modeling in the hyporheic zone

This study explores the application of advanced deep learning techniques, specifically conditional deep convolutional generative adversarial networks (cDC-GANs), to model and predict the complex dynamics of the hyporheic zone (HZ) driven by river stage fluctuations. The HZ is a critical transitional mixing zone between surface water and groundwater, where significant biogeochemical reactions occur. Traditional process-based reactive transport modeling methods are computationally intensive and require fine-scale parameterization. To overcome these challenges, we employ the cDC-GAN as a deep-learning-based surrogate model to predict zones of enhanced biogeochemical activity (i.e., hotspots) and reaction product concentrations in the HZ under varying bimodal sedimentary heterogeneity scenarios. The cDC-GAN model can efficiently capture nonlinear relationships between input data and output parameters without extensive parameterization, making it computationally less demanding. The model’s generative capabilities allow for the creation of new data instances, enabling the exploration of diverse scenarios and interpolation between observed data points. The model uses maps of liquid saturation and solute concentrations at different time steps to generate corresponding maps of reaction rates and reaction product concentrations. Results demonstrate the model’s qualitative and quantitative ability to capture complex relationships without relying on rigid assumptions about linearity or specific probability distribution functions. Notably, even amid variations in aquifer heterogeneity, the model consistently exhibits robust performance, validating its ability to adapt to dynamic geological settings. Our use of a simplified reaction scheme serves as a proof of concept for the cDC-GAN model’s ability to simulate solute transport in the HZ, opening avenues for future applications including more complex reaction networks within HZ system such as nutrient cycling, organic matter degradation, redox reactions, and beyond.

Dynamic System Modeling of Concentrated Electrical Energy Provision with Reducing CO2 Emissions in East Nusa Tenggara

Electric Power Generation (EPG) sourced from Fossil Energy (FE) has triggered an increase in CO_2 emissions in the atmosphere resulting in global warming which has a systemic impact on climate change. Whatever form of mechanism to reduce the increase in earth's temperature must be implemented. One way is to use a Solar Water Pump (SWP). This research aims to determine the use of EPG in East Nusa Tenggara (ENT) and find the amount of CO_2 emissions that can be reduced using SWP. This research uses dynamic modeling methods. The research results show that the production of electrical energy sourced from FE until 2030 is 2.270.656 MWh (87.77%), while the production of electrical energy sourced from RE is 316.441 MWh (12.23% ). EPG sourced from FE in ENT results in CO_2 emissions at 22.603.641 tons. According to survey results, conversion of FEPWP to SWP can reduce CO_2 emissions at 424.942 tons.

Geographic authentication of argentinian teas by combining one-class models and discriminant methods for modeling near infrared spectra

In this study, 110 tea samples from South American countries (Argentina, Brazil, and Paraguay) and Asian countries (India and China) were analyzed using near-infrared spectroscopy (NIRS) together with a two-step chemometric authentication strategy (class modeling techniques and discriminant analysis) to authenticate commercial teas from Argentina. In the first step, one-class models were built and validated to authenticate South American teas using preprocessed NIRS data. For this purpose, data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OC-PLS) were used. The DD-SIMCA model gave the best results, with a sensitivity of 93.10%, specificity of 100%, and efficiency of 95.00%. In the second step, a support vector machine (SVM) was used to build and validate a multiclass model to discriminate between tea samples from Argentina and neighboring countries of South America. The best model was the combination of nine variables selected by the fast correlation-based filter (FCBF) method, with an accuracy of 98.30%. Therefore, we conclude that the combination of NIRS and two-step chemometric tools can be used to authenticate the geographical origin of samples with high inter-class similarity.

Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative.

Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.

How can agent-based modelling provide new insights into the impact of minimum unit pricing in Scotland?

In recent years we have gained insight into the impact of minimum unit pricing (MUP)-a legal floor price below which a given volume of alcohol cannot be sold-on population-level reductions in alcohol sales, consumption and harm. However, several questions remain unanswered including how individual-level purchasing changes impact the local economy (e.g., balance between on-licence and off-licence outlets), lead to long-term population-level trends (e.g., youth drinking) and social harms (e.g., violence). Agent-based modelling captures heterogeneity, emergence, feedback loops and adaptive and dynamic features, which provides an opportunity to understand the nuanced effects of MUP. Agent-based models (ABM) simulate heterogeneous agents (e.g., individuals, organisations) often situated in space and time that interact with other agents and/or with their environment, allowing us to identify the mechanisms underlying social phenomena. ABMs are particularly useful for theory development, and testing and simulating the impacts of policies and interventions. We illustrate how ABMs could be applied to generate novel insights and provide best estimates of social network effects, and changes in purchasing behaviour and social harms, due to the implementation of MUP. ABMs like other modelling approaches can simulate alternative implementations of MUP (e.g., policy intensity [£0.50, £0.60] or spatial scales [local, national]) but can also provide an understanding of the potential impact of MUP on different population groups (e.g., alcohol exposure of young people who are not yet drinking). Using ABMs to understand the impact of MUP would provide new insights to complement those from traditional epidemiological and other modelling methods.

Comparison of modeling methods and mitigation measures for curve squealing

Predicting and mitigating curve squealing has always been difficult. This study employed various modeling methods to unify the existing models and establish three finite element models (FEMs) of wheelset–track systems to predict curve squealing. The FEMs of the wheelset–track systems, which incorporated solid fasteners, negative friction–velocity slopes, and multiple wheelsets, were examined using complex eigenvalue analysis to determine their relative capacity to predict curve squeal. The inhibitory effects of damping treatments on curve squeal in both wheels and rails were also studied. The results indicate that, in an improvement on the initial model, the FEMs developed in this study can effectively predict curve squeal with frequencies of 482, 1,205, and 2154 Hz. The wheelset–track system was more likely to produce frictional self-excited vibrations under negative damping and interference from multiple wheelsets, but the impact was small. In addition, the rubber block angle of the resilient wheel was set at 15°. Increasing the damping of the rail vibration absorbers and selecting a continuous–discrete under-rail vibration absorber effectively suppressed the generation of curve squeal.

Detection of Approaching Critical Transitions in Natural Systems Driven by Red Noise

Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past. Published by the American Physical Society 2024

Analysis of Female Employment Situation Based on Data from Jilin Province in China

In recent years, Chinas GDP has continued to grow, yet the labor share has not increased in a consistent manner. This paper employs an empirical analysis based on data from Jilin Province, China. It uses questionnaires and econometric modeling methods to investigate the role of gender in economic life and the role of other factors (such as employment pressure, the role of unions, etc.) in this process. The questionnaire survey examines the current state of gender employment discrimination in China, focusing on the existence, causes, and influencing factors of employment discrimination. In constructing the econometric model, this paper assumes a negative relationship between the proportion of female employees and the labor share, while there is a positive relationship between female education level and the labor income share. The two hypotheses, based on common sense and literature, are then tested by an econometric model. The results indicate that the proportion of female employees is indeed negatively correlated with the share of labor income, suggesting that women face more significant employment challenges.

Opportunity begets opportunity to drive macroevolutionary dynamics of a diverse lizard radiation

Abstract Evolution proceeds unevenly across the tree of life, with some lineages accumulating diversity more rapidly than others. Explaining this disparity is challenging as similar evolutionary triggers often do not result in analogous shifts across the tree, and similar shifts may reflect different evolutionary triggers. We used a combination of approaches to directly consider such context-dependency and untangle the complex network of processes that shape macroevolutionary dynamics, focusing on Pleurodonta, a diverse radiation of lizards. Our approach shows that some lineage-wide signatures are lost when conditioned on sublineages: while viviparity appears to accelerate diversification, its effect size is overestimated by its association with the Andean mountains. Conversely, some signals that erode at broader phylogenetic scales emerge at shallower ones. Mountains, in general, do not affect speciation rates; rather, the occurrence in the Andean mountains specifically promotes diversification. Likewise, the evolution of larger sizes catalyzes diversification rates, but only within certain ecological and geographical settings. We caution that conventional methods of fitting models to entire trees may mistakenly assign diversification heterogeneity to specific factors despite evidence against their plausibility. Our study takes a significant stride toward disentangling confounding factors and identifying plausible sources of ecological opportunities in the diversification of large evolutionary radiations.

Multi-criteria decision-making techniques for groundwater potentiality mapping in arid regions: A case study of Wadi Yiba, Kingdom of Saudi Arabia

Freshwater demand is further stressed due to the impacts of climate change and the rapid expansion of the population, particularly in dry and extremely dry regions where groundwater serves as a crucial water source. This study employed various multi-criteria decision making (MCDM) techniques to identify areas with potential for groundwater in Wadi Yiba, Kingdom of Saudi Arabia (KSA). A total of 14 parameters, including Geology, land use and land cover (LULC), Drainage Density (DD), Lineament density (LD), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rainfall, and others, were selected to create thematic layers for the delineation of groundwater potential zones (GWPZ). Using three MCDM techniques, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) methods, maps were generated to indicate the GWPZ in the study area. These maps were categorized into five categories: very low, low, medium, high, and very high. Results indicate that the GWPZ assessed by TOPSIS in Wadi Yiba categorizes 3% of the area as having very low groundwater potential, 9% as low, 29% as medium, 36% as high, and 23% as very high. In contrast, VIKOR classified the study region as 7%, 20%, 18%, 28%, and 26% for the five classes; while the EDAS study reveals a more balanced distribution, with 5.81%, 24.80%, 33.74%, 22.32%, and 13.33% for the five classes. We evaluated the accuracy of the final maps using the receiver operating characteristic-area under the curve (ROC-AUC) evaluation method. TOPSIS exhibited the highest accuracy (0.703), closely followed by VIKOR (0.701) and EDAS (0.557). TOPSIS and VIKOR were rated "excellent," while EDAS as "good". The findings are crucial for sustainable groundwater management in Wadi Yiba and underscore the importance of selecting appropriate methods in environmental modeling.

Managing Weak Signals In The Foreign Economic Activity of a Modern Enterprise: Preventing a Decrease in the Financial and Economic Security with a Strategic Approach to Solving the Problem

The main purpose of the article is to create a system for responding to weak signals in the foreign economic activity of a modern enterprise. The scientific task is to form models of response and management of weak signals in the foreign economic activity of a modern enterprise, in the context of preventing a decrease in the financial and economic security of the enterprise. The proposed approach is based on the use of methods of DFD and functional modeling. As a result, two key models were formed: the first is the main model of management decision-making in the system of responding to weak signals and the second is the management functions for responding to weak signals. Our research makes an innovative contribution by integrating DFD and functional modeling methodologies to develop comprehensive models that provide deeper and more effective understanding of managerial decision-making in the context of responding to weak signals in a dynamic foreign economic environment.

THEORETICAL DESCRIPTION OF PUCKER STATES OF CYCLIC GROUPS OF CAROTENOIDS

Carotenoids are one of the largest classes among biological pigments. They are found both in free form (dissolved in hydrophobic environments), and bound in stoichiometric complexes with protein (carotenoproteins). The interaction between the carotenoid and the protein environment is determined by the molecular conformation and dynamic behavior of the cofactor. In this work, using molecular modeling methods, we analyzed the entire set of alternative conformational states of cyclic carotenoids. The result of the theoretical study is an energysorted catalog of alternative conformational states for biogenic cyclic carotenoids and their optical isomers. The conformational states characterized in this study can be used to correctly select initial conditions when solving problems of molecular modeling of carotenoproteins

THEORETICAL DESCRIPTION OF PUCKER STATES OF CYCLIC GROUPS OF CAROTENOIDS

Carotenoids are one of the largest classes among biological pigments. They are found both in free form (dissolved in hydrophobic environments), and bound in stoichiometric complexes with protein (carotenoproteins). The interaction between the carotenoid and the protein environment is determined by the molecular conformation and dynamic behavior of the cofactor. In this work, using molecular modeling methods, we analyzed the entire set of alternative conformational states of cyclic carotenoids. The result of the theoretical study is an energysorted catalog of alternative conformational states for biogenic cyclic carotenoids and their optical isomers. The conformational states characterized in this study can be used to correctly select initial conditions when solving problems of molecular modeling of carotenoproteins

Seismic fragility assessment of embankment: A displacement-based approach using dynamic finite element model analysis

Abstract The assessment of seismic fragility of highway embankments is a key component of seismic risk modeling for traffic infrastructure. This study examines the seismic fragility of an embankment using advanced finite element dynamic analysis, contrasting findings with prior research to validate results. Based on established methodologies, an incremental dynamic analysis (IDA) was executed, with the Probabilistic Seismic Demand Analysis (PSDA) for the development of fragility curves. A comparative study was conducted between two modeling methods; one employing finite difference (FD) method and other employing finite element (FE)method. The analysis revealed that, selection of inputs like the nature of earthquake time history and number of records selected along with the analysis method influences the seismic fragility assessment of embankments. The choice between finite difference and finite element methods can yield differing results, with FD, being grid-based, can face challenges with complex geometries and boundary conditions, on the other hand, FE’s mesh-based approach offers flexibility in modeling intricate domains, handles complex boundary conditions more adeptly, and provides a more continuous representation of material behavior. A distinct dynamic stability test based on Newmark sliding block principles showed the embankment slope’s inherent resilience, with no permanent deformation, underscoring the requirement for further research.

Paradigmatic class as an element of the language system

The subject of the research in the article is the paradigmatic class (PC), considering as a set of variants of language units united by an invariant. The purpose of the research is to characterize the structural and functional properties of the PC as a compound unit of language that unit performing cognitive functions (problem formulation and choice of solution alternatives). The formulation of the problem involves the formulation of an invariant, which is a sign unit denoting a set of PC elements. Research methods are general linguistic methods of observation and description, methods of discursive and cognitive analysis, semiotic methods and methods of language modeling. The following results were obtained. The PC elements that are in interchangeability relation form a fuzzy set. The membership function of this fuzzy set is determined by the discourse in which the PC and its component parts function. The results of the study can be used to analyze the content of texts of various subjects when solving a number of communicative tasks (teaching, editing, writing creative works, etc.).The new results of the work are the consideration of the PC as an observable part of the universal set of language units. At the same time, there may be elements in the PC that have a small value of the membership function, and in the unobservable part of the universal set, there may be elements that have a large value of the membership function when they appear in the PC. The formation of a set of PC elements and the creation of an invariant are determined by the discourse of language functioning which is illustrated in the article by the text of A.P. Chekhov's short story "Horse surname". The realization of the discursive situation "difficulty in remembering the desired surname" leads to the creation of a paradigmatic class of "horse surnames", and the choice of the desired surname is non-trivial, creative and requires the involvement of elements of a universal set that at first were not included in the PC.

Neural Radiance Selector: Find the best 2D representations of 3D data for CLIP based 3D tasks

Representing the world in 3D space provides vivid texture and depth information. However, 3D datasets currently do not match the scale of 2D datasets. There is a growing trend in representing 3D data as multi-view 2D images and using large-scale 2D models, to solve 3D tasks. In this work, we present the Neural Radiance Selector, a method that automatically selects the optimal 2D representations of 3D data. Instead of indiscriminately sampling multi-view 2D images, we define the optimal 2D views as those capable of reconstructing the entire 3D scene with a conditional neural radiance field. We propose two distinct methods for 3D point cloud data and 3D implicit models to achieve faster inference. We demonstrate the efficacy of our methods in various 3D tasks, including zero-shot 3D point cloud classification, 3D implicit model classification, and language-guided NeRF editing.

A descriptive and prescriptive analysis of rail service subsidies in the China–Europe freight transportation market

This study delivers a descriptive and prescriptive analysis of rail service subsidies for China Railway Express (CRE) in the China-Europe freight transportation market. The analysis is conducted by advanced mathematical modeling and programming methods. Specifically, we implemented a multicommodity multimodal freight transportation network equilibrium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme. To properly quantify the impact of subsidies on individual shippers’ decision making, the model explicitly characterizes individual shippers’ mode-route choice behavior and takes into account shipping cost, transit time, capacity-induced congestion surcharge, and unobserved transportation impedances as shippers’ disutility. The solution of the network equilibrium model resorts to a disaggregate simplicial decomposition algorithm within the well-known Lagrangian relaxation framework. A bi-level network-based subsidy optimization model is constructed, in which the upper level aims at minimizing the sum of revenue loss and congestion charge, and the lower level is the aforementioned freight transportation network equilibrium model. A tabu search procedure is proposed and implemented to derive the solution of the bi-level model. The above models and algorithms are then applied to the China-Europe containerized freight transportation network, which comprises all China-Europe liner shipping lines, all CRE service lines, and the highway networks in China and Europe. The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield noteworthy improvements in the service utilization and economic efficiency of CRE.

87‐2: Modeling and Simulation for Mitigating Display Noise Caused by PMIC Ripple

The output ripple of the Power Management IC (PMIC) is one of the causes of display panel noise. This paper introduces modeling and simulation methods aimed at predicting and mitigating noise induced by PMIC ripple. If the display panel simulation is performed using an ideal voltage source, the ripple characteristics of the PMIC cannot be included. The improved method accurately predicted the ripple of the voltage input to the panel by modeling flicker noise and thermal noise, which are the main causes of PMIC noise, and modeling the Z‐parameter and decoupling capacitor of the PCB. In addition, it was possible to minimize ripples and finally reduce display noise by optimizing the design of the power supply circuit.

Investigation of bidirectional three-phase four-leg converter with LCL filter

A three-phase four-leg bidirectional power converter used as an input stage of an energy storage system using the inductance (L), capacitance (C), and inductance (L) or LCL filter on the grid side is considered. The article provides a variant of the three-phase four-leg bidirectional converter control system implementation based on 3D space vector pulse-width modulation and per-phase voltage control. A brief analysis of the applied control algorithms of the three-phase four-leg bidirectional converter in inverter and rectifier modes is given. A computer model has been built to investigate the operating modes of the converters of the designated class with both balanced and unbalanced load. Studies were carried out by computer modeling methods in order to optimize the parameters of the LCL filter on the grid side with bidirectional energy exchange. Optimal LCL filter parameters and modulation frequencies are determined for the developed converter, providing acceptable quality indicators when operating in bidirectional mode.