Pavement performance model for road maintenance and repair planning: a review of predictive techniques

This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.Machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users.

The Use of Imitation Models at Developing and Introducing Information-Control Systems

Imitation models for computing the environmental water pollution level depending on the intensity of pollution sources created by the author over the years are presented. For this purpose, an additive model of a non-stationary random process is considered. For the modeling of its components, models that consider only dilution and self-purification processes are proposed for waste water and three-dimensional turbulent diffusion equations for river waters, and multidimensional Gaussian Markov series are proposed for modeling the random component. The purpose, the capabilities and the peculiarities of such imitation models are discussed taking into account the peculiarities of the water objects. The modular principle of creating imitation models is proposed to facilitate their development and use.

The Assessment of the Arising of Food Allergy among Antiacid Users Using Mathematical Model

The first case of a new food allergy, an anaphylactic reaction to Manioc (Manihot esculenta or Manihot utilissima), also called cassava and tapioca, was described in 2001. Cassava is a tuber widely consumed in Brazil, which has been consumed by native Indians, i.e., more than 500 years ago, so why are the first cases just emerging now? We address this question by developing a mathematical model considering the fact that proton pump inhibitors (PPIs) for digestive disorders maintain the cassava allergen intact due to the elevation in pH of gastric juice, thereby facilitating its presentation to the immune system. The model assumed the mass action law including saturation to describe the recruitment of PPIs users, and Hill function to describe the sensitization of immune system by the allergens and the acquisition of full food allergy. Analytical results were obtained from the model, and numerical simulations were done. The estimated period of time elapsed since the introduction of antiacids before the diagnosis of food allergy was 15 years. The food allergy may become a public health problem, if PPIs are being used indiscriminately and irregularly. The results obtained from the analysis of the model suggest that the use of nonprescription antiacids, like PPIs, may be recommended or approved by the physician in order to avoid the rising of food allergy.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method

In this context,two different approaches of soil liquefaction evaluation using a soft computing technique based on the worldwide standard penetration test(SPT) databases have been studied.Gene expression programming(GEP) as a gray-box modeling approach is used to develop different deterministic models in order to evaluate the occurrence of soil liquefaction in terms of liquefaction field performance indicator(LI) and factor of safety(FS) in logistic regression and classification concepts.The comparative plots illustrate that the classification concept-based models show a better performance than those based on logistic regression.In the probabilistic approach,a calibrated mapping function is developed in the context of Bayes’ theorem in order to capture the failure probabilities(PL) in the absence of the knowledge of parameter uncertainty.Consistent results obtained from the proposed probabilistic models,compared to the most well-known models,indicate the robustness of the methodology used in this study.The probability models provide a simple,but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction triggering thresholds.

Uncertainty analysis of seawater intrusion forecasting

In order to describe the importance of uncertainty analysis in seawater intrusion forecasting and identify the main factors that might cause great differences in prediction results, we analyzed the influence of sea level rise, tidal effect, the seasonal variance of influx, and the annual variance of the pumping rate, as well as combinations of different parameters. The results show that the most important factors that might cause great differences in seawater intrusion distance are the variance of pumping rate and combinations of different parameters. The influence of sea level rise can be neglected in a short-time simulation （ten years, for instance）. Retardation of seawater intrusion caused by tidal effects is obviously important in aquifers near the coastline, but the influence decreases with distance away from the coastline and depth away from the seabed. The intrusion distance can reach a dynamic equilibrium with the application of the sine function for seasonal effects of influx. As a conclusion, we suggest that uncertainty analysis should be considered in seawater intrusion forecasting, if possible.

Probabilistic Quantile Regression-Based Scour Estimation Considering Foundation Widths and Flood Conditions

Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flow.The carrying capacity of a deep-water foundation is influenced by the formation of a scour hole,which means that a severe scour can lead to a bridge failure without warning.Most of the current scour predictions are based on deterministic models,while other loads at bridges are usually provided as probabilistic values.To integrate scour factors with other loads in bridge design and research,a quantile regression model was utilized to estimate scour depth.Field data and experimental data from previous studies were collected to build the model.Moreover,scour estimations using the HEC-18 equation and the proposed method were compared.By using the“CCC(Calculate,Confirm,and Check)”procedure,the probabilistic concept could be used to calculate various scour depths with the targeted likelihood according to a specified chance of bridge failure.The study shows that with a sufficiently large and continuously updated database,the proposed model could present reasonable results and provide guidance for scour mitigation.

The comparison of ensemble or deterministic dispersion modeling on global dispersion during Fukushima Dai-ichi nuclear accident

Ensemble forcasting,originally developed for weather prediction,is lately being extended to atmospheric dispersion applications,which is a new,effective methodology for improving the atmospheric dispersion numerical modeling.In March 2011,due to the massive 9.0 earthquakes and ensuing tsunami that struck off the northern coast of the island of Honshu,the Fukushima Nuclear Plant I had the substantial leak of radioactive materials into surrounding environment and atmosphere.To aim at the global dispersion modeling of atmospheric radionuclides from Fukushima Nuclear Accident,this paper presents two approaches of atmospheric dispersion forecasting:ensemble dispersion modeling(EDM) and deterministic dispersion modeling(DDM),conducts the globally dispersion modeling cases for Fukushima nuclear accident,and analyzes and evaluates the simulation results using observation data.In this paper,EDM includes three different perturbation methods:meteorological perturbation method,turbulence perturbation method,and physical parameterization ensemble forecasting method.The simulation results show that the trajectories from EDM have a better performance,which is in better agreement with the atmospheric circulation and observation data; the spread from DDM is slower and not as far as EDM.Additionally,the results from EDM display a better performance in the modeling of transport from Japan to China East Sea on April 4.The reasons for these results are:the techniques of MET and TUR are performed by adding perturbations on mean wind and turbulent velocity,respectively; the various different flow fields will result in far spreading in horizontal and the simulation results closer to observation; PHY is performed by using different diffusion physical parameterizations and produces the perturbations on vertical wind,which results the spreading in smaller range and discontinuous in horizontal.Finally,the comparative analysis between modeling results and observation data shows that all cases results are in good agreement with trends of observed radionuclides surface concentration; however,the modeling surface concentration is smaller than observation,especially in DDM and PHY.Furthermore,the EDM results show that MET and TUR are of more evolutionary advantage than PHY in modeling of average and maximum concentration.Therefore,this study can serve as a reference to atmospheric dispersion and environmental emergency response(EER).

New Deterministic and Stochastic Simulation Models for UAV-MIMO Ricean Fading Channels

For the practical simulation and performance evaluation of unmanned aerial vehicle(UAV)multiple-input multiple-output(MIMO)Ricean fading channels,it is desirable to develop accurate UAV-MIMO channel simulation models for more realistic scenarios of non-isotropic scattering.In this study,using a twocylinder reference model to describe the distribution of scatterers,we propose new deterministic and stochastic simulation models.Analytical and numerical results indicate that both simulation models provide good approximations to the desired statistical properties of the reference model,and the stochastic simulation model results in a better performance under comparable computational complexity.

Advances on Microstructure Modeling of Solidification Process of Shape Casting

Simulation technology for shape casting at macro-scale has been successfully put into engineer- ing application in a number of casting plants and as a result the quality of castings is assured, the research and development time is shortened, and the manufacturing cost is greatly saved as well. In this paper, mod- eling and simulation technologies of solidification process of shape casting at microstructure-scale, espe- cially deterministic, cellular automaton, and phase field models are studied and reviewed.

Modeling and simulation of pinhole channels

This article presents a general pinhole channel reference model based on the cause of pinhole effect. On the base of this reference model, a deterministic simulation model is developed by keeping the difference of correlation properties between reference and simulation model as smaller as possible. The correlation properties include temporal autocorrelation fimction （ACF）, two-dimensional （2-D） space cross-correlation function （CCF） and frequency correlation function （FCF）. The results show that although pinhole channel has good correlation properties, the channel capacity is very low because of the low rank of channel transform matrix.

Modelling the impact of antimalarial quality on the transmission of sulfadoxine-pyrimethamine resistance in Plasmodium falciparum

Background:The use of poor quality antimalarial medicines,including the use of nonrecommended medicines for treatment such as sulfadoxine-pyrimethamine(SP)monotherapy,undermines malaria control and elimination efforts.Furthermore,the use of subtherapeutic doses of the active ingredient(s)can theoretically promote the emergence and transmission of drug resistant parasites.Methods:We developed a deterministic compartmental model to quantify the impact of antimalarial medicine quality on the transmission of SP resistance,and validated it using sensitivity analysis and a comparison with data from Kenya collected in 2006.We modelled human and mosquito population dynamics,incorporating two Plasmodium falciparum subtypes(SP-sensitive and SP-resistant)and both poor quality and good quality(artemether-lumefantrine)antimalarial use.Findings:The model predicted that an increase in human malaria cases,and among these,an increase in the proportion of SP-resistant infections,resulted from an increase in poor quality SP antimalarial use,whether it was full-or half-dose SP monotherapy.Interpretation:Our findings suggest that an increase in poor quality antimalarial use predicts an increase in the transmission of resistance.This highlights the need for stricter control and regulation on the availability and use of poor quality antimalarial medicines,in order to offer safe and effective treatments,and work towards the eradication of malaria.

Developing and studying the dynamical behavior of a nonlinear mathematical model for cancers with tumor by considering immune system role

We are constrained by widespread cancerous diseases to improve treatment methods which save patients and provide better living conditions during and after the treatment period.Because of the complexity of the treatment process,mathematical models need to be used in order to have a better understanding of the process.However,deriving an adequate complex model that can capture the disease pattern which could be confirmed by simulations and experiments has its own barriers.In this paper,a new mathematical model is developed concerning immune system effect on cancer.The model is introduced using nonlinear ordinary differential equations.Also,the qualitative behavior of the proposed system is studied in order to examine the extent of the model with respect to the nature of tumor evolution.Thus,number and status of equilibria points in line with the existence of limit cycles are obtained for sub-systems and the whole system.Meanwhile,possible bifurcations are mentioned,and the consequent evolutions are described.It is shown that the model conforms well to natural possibilities,cancer growth or remission.Thus,the model would be fit for further studies for prediction and contemplating treatment method,especially for immune stimulating drugs and immunotherapy.

Dense gas-particle flow in vertical channel by multi-lattice trajectory model

A multi-lattice deterministic trajectory(MLDT) model is developed to simulate dense gas-particle flow in a vertical channel.The actual inter-particle collision and particle motion are treated by a Lagrangian model with three sets of lattices to reduce computational time.Cluster formation and motion near the wall are successfully predicted with mean particle volume fraction and velocity,showing quantitatively agreement with experimental results.The mechanism of particles concentrated near the wall is investigated by considering effects of gravity,particle-wall collisions,inter-particle collisions and velocity profiles of the gas phase.It is shown that the inter-particle collision and gas-phase velocity distribution are the essential factors for cluster formation near the wall,while gravity and particle-wall collision only have minor effects on particle concentration near the wall.Particles are unable to remain in the high velocity region due to the strong inter-particle collisions,while they tend to stay in the low velocity region for weak inter-particle collisions.In addition,the effects of channel width and particle sizes on cluster formation are also investigated and it is found that particle concentration near the wall reduces with the decrease of channel width and increase of particle size.

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities.Kuwait reported its first imported cases of COVID-19 on February 24,2020.Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management.The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave.Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed(SEAIR)transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait.We fit case data from the first 96 days in the model to estimate the effective reproduction number and used Google mobility data to refine community contact matrices.The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness.The model can help inform future pandemic wave management,not only in Kuwait but for other countries as well.