Suitable Modeling Method Research Articles

Study on the Engineering Application of LNG Accident Consequence Model in the Design of Terminal Sump

In order to provide a theoretical basis for the design of the LNG terminal sump, this paper proposes a suitable mathematical model and solution method by analyzing the physical process of evaporation diffusion and thermal radiation of the sump. Combined with the engineering practice, a lot of calculations have been carried out on the vapor diffusion concentration and pool fire thermal radiation intensity of the terminal sump, and the correlations between the vapor concentration and thermal radiation intensity and their influencing factors have been quantitatively calculated. Based on the analysis of the calculation results, it is concluded that the vapor concentration and thermal radiation value at the fire control room of the wharf are strongly related to the equivalent diameter of the sump and the distance from the fire control room to the sump. Among them, the distance from the fire control room to the sump is more closely related to the vapor concentration, and the equivalent diameter of the sump is more closely related to the thermal radiation; Keeping 19.2~33.8meters between the fire control room and the sump can basically ensure that the vapor concentration at the fire control room does not exceed the standard; In order to ensure that the thermal radiation at the fire control room does not exceed the standard, the distance between the fire control room and the sump should not be less than 15meters, and the equivalent diameter of the sump should not be greater than 7meters. The research methods and conclusions proposed in this paper can provide useful reference for engineering designers, and also build a bridge for mutual reference and application for theoretical research and engineering practice.

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis.

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.

Classification of Converter-Driven Stability and Suitable Modelling and Analysis Methods

Classification of Converter-Driven Stability and Suitable Modelling and Analysis Methods

Study on cuttings transport behavior in annulus and bottom-hole using CFD-DEM

The increased demand for energy has led to the development of innovative drilling methods and technologies in the oil and gas industry. Inefficient transportation of cuttings causes downhole cleanliness issues, which have drawn widespread attention. One way to overcome cuttings transport inefficiency is to use suitable modeling methods to determine capable drilling parameters that will ensure the effective removal of cuttings particles. In this paper, Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) are combined to deeply investigate the impact of various drill parameters on cutting transport. The effects of cuttings particle size, eccentricity, fluid velocity, drill pipe/bit rotation speed, and bit offset angle are analyzed by studying the velocity of cuttings particles and the spatial distribution of particles in the annulus and bottom-hole. The results show that an increase in eccentricity and particle size reduces the velocity of cuttings particles, with the velocity decreasing more significantly when the eccentricity is greater than 0.5 and the particle size exceeds 2 mm. An increase in fluid velocity and speed of drill pipe/bit significantly increases the velocity of cuttings particles, which has a positive feedback effect on the migration of cuttings particles in the downhole. Bit offset angle leads to changes in the bottom-hole spatial structure and reduces the cuttings transport efficiency. These findings provide a useful strategy for improving cuttings migration and downhole cleaning.

Performance Evaluation of Different Devices and Algorithms for Modelling Small Artefact

3D reconstruction and modelling play important roles in various applications, specifically in heritage preservation. With the aid of suitable hardware like the 3D sensors as well as respective data processing methods, the work has become more feasible in realizing the aim to conserve and preserve more artefacts. However, too many choices and alternatives might lead to different results which may affecting the preservation purpose. The objective of this work is to analyze and evaluate the performance of different devices and algorithms for small artefact modelling. Two 3D sensors, iPhone 13 Pro Max LiDAR and Structure sensor were selected to collect data of small artefact to be reconstructed and modelled. Two main, important surface reconstruction algorithms which are Poisson and Ball-Pivoting methods were also selected to be tested. Specifications of the sensors’ capabilities as well as modelling results of the artefact are examined. Different parameters of the algorithms were selected to study their effect. These findings will help to learn more about 3D sensors and the suitable modelling methods in making them better for usage in a variety of areas, including archaeology, architecture, and the protection of cultural heritage.

A systematic review of the modelling of patient arrivals in emergency departments.

Accident and Emergency Department (AED) is the frontline of providing emergency care in a hospital and research focusing on improving decision-makings and service level around AED has been driving a rising number of attentions in recent years. A retrospective review among the published papers shows that related research can be classified according to six planning modules: demand forecasting, days-off scheduling, shift scheduling, line-of-work construction, task assignment and staff assignment. As patient arrivals demand forecasts enable smooth AED operational planning and help decision-making, this article conducted a systematic review on the statistical modelling approaches aimed at predicting the volume of AED patients' arrival. We carried out a systematic review of AED patient arrivals prediction studies from 2004 to 2021. The Medline, ScienceDirect, and Scopus databases were searched. A two-step screening process was carried out based on the title and abstract or full text, and 35 of 1,677 articles were selected. Our methods and results follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We categorise AED methods for modelling patient arrivals into four main classes: regression, time series, artificial intelligence and time series regression. Choice of prediction model, selection of factors and model performance are compared. Finally, we discuss the advantages and limitations of the models and suggest future research directions. A total of 1,677 papers that fulfilled the initial searching criteria was obtained from the three databases. Based on the first exclusion criteria, 1,603 articles were eliminated. The remaining 74 full text articles were evaluated based on the second exclusion criteria. Finally, 35 articles were selected for full review. We find that the use of artificial intelligence-based model has risen in recent years, from the view of predictive model selection. The calendar-based factors are most commonly used compared with other types of dependent variables, from the view of dependent variable selection. All AEDs are inherently different and different covariables may have different effects on patient arrivals. Certain factors may play a key role in one AED but not others. Based on results of meta-analysis, when modelling patient arrivals, it is essential to understand the actual AED situation and carefully select relevant dominating factors and the most suitable modelling method. Local calibration is also important to ensure good estimates.

Modeling Insights Into the Assembly Challenges of Focal Plane Arrays

Ongoing technological advances in photodetector material growth and processing, readout integrated circuits, and robust hybridization (packaging) methods for assembling high-resolution and small-pitch size pixel arrays are the main enabling factors for pushing the frontiers of high-performance Focal Plane Array (FPA) technologies for imaging systems. This paper details the development of analytical and numerical models and demonstrates their use to generate insights into the feasibility of two flip-chip assembly processes for packaging infrared (IR) detector chips. The modelling studies focus on the challenges of forming the indium interconnection arrays in the case of the FPA technologies using Group III-V compound semiconductor materials and ultra-fine pitch pixel array layouts. The accurate alignment of the IR detector chip onto the readout chip in the case of high-density pixel architectures is a critical requirement for the packaging process. To gain better understanding of this requirement, which has a clear implication for the quality and subsequent reliability performance of the FPA, compression and reflow bonding process models are developed using suitable modeling approaches and methods and then demonstrated for two distinctive focal plane array design configurations. The novelty of this work is in the developed modeling capabilities utilizing different computational methods, from large deformation and contact analysis finite element to energy-based and harmonic motion mechanics, to characterize and optimize the mechanical and dynamic non-linear behavior of the indium solder joints and their formation during FPA packaging. The feasibility of bonding techniques for different resolution FPAs and under flip-chip misalignment conditions is assessed. The modeling results pointed to a very strict, sub-micrometer flip-chip placement accuracy requirement for the assembly of FPAs with ultra-fine indium bump array resolution.

Greywater Flow Characteristics for Closed Channel Maintenance

Knowing the characteristics of wastewater and its interaction with the channel is crucial to finding a suitable model and maintenance method to solve the closed channel problem. The purpose of this study is to find the relationship and how much it influences the characteristics of wastewater in closed channels and analyze the limit deposit velocity (LDV) of wastewater so that there is no deposition. The parameters used to analyze wastewater characteristics are density, oil and fat, specific gravity, total suspended solids, total dissolved solids, and kinematic viscosity. The parameters used to analyze the flow characteristics in closed channels are velocity, discharge, Reynolds number, friction coefficient, energy loss, and hydraulic gradient. The method used is experimental research by simulating a closed-channel model prototype. The closed channel model is made from an acrylic pipe with a length of 6 m and a pipe diameter of 0.064 m. Simulations on each wastewater sample and the discharge variations used were 0.005, 0.004, 0.003, and 0.0015 m3/s. Velocity measurements at a 0.5 pipe water level height and distances of 0, 2, 4, and 6 m. The results showed that the nature and composition of the wastewater the flow velocity. The large value of wastewater parameters shows that the flow velocity is small. The wastewater content is considered a load that must be transported to the end of the closed channel. When the discharge increases, the velocity will increase, Reynolds number will increase, and the energy loss will be large, while the friction coefficient is inversely proportional to Reynolds number. The velocities of clean water samples are 2.90 - 1.07 m/s, tofu - making is 2.83 - 1.07 m/s, household is 2.74 - 0.85 m/s, laundry is 2.84 - 1.03 m/s, and the workshop is 2.54 - 0.66 m/s. The limit deposit velocity (LDV) for household wastewater is 1.49 m/s to prevent deposition in closed channels. Doi: 10.28991/CEJ-2023-09-01-03 Full Text: PDF

A Bayesian analysis based on multivariate stochastic volatility model: evidence from green stocks.

Green stocks are companies environmental protective and friendly. We test Green stock index in Shanghai Stock Exchange and China Securities Index as safe-havens for global investors. Suitable multivariate-SV model and Bayesian method are used to estimate the spillover effect between different assets among local and global markets. We choose multivariate volatility model because it can efficiently simulate the spillover effect by using machine learning MCMC method. The results show that the Environmental Protection Index (EPI) of Shanghai Stock Exchange (SSE) and China Securities Index (CSI) have no significant volatility spillover from Shanghai Stock index, S &P index, gold price, oil future prices of USA and China. During COVID-19 pandemic, we find Green stock index is a suitable safe-haven with low volatility spillover. Green stock indexes has a strongly one-way spillover to the crude oil future price. Environmentally friendly investor can use diversity green assets to provide a low risk investment portfolio in EPI stock market. The DCGCt-MSV model using machine learning of MCMC method is accurate and outperform others in Bayes parameter estimation.

A novel comparative model proposal for hospital pneumatic systems

Hospital pneumatic systems are of great importance in terms of efficient use of resources and early diagnosis of diseases. Each sample is transported at a constant and specific speed/pressure in existing pneumatic systems. This study aims to provide pressure/speed control according to the type of material being transported, prevent deterioration during transportation, and make a positive contribution to system users. We have proposed a new control model for hospital pneumatic systems. This hospital pneumatic system is modeled using fuzzy logic, adaptive neural network-based fuzzy inference system (ANFIS), and artificial neural network (ANN) methods. The parameters affecting the performance of the system are determined by experimental trials carried out. Fuzzy logic, ANFIS, and ANN models are compared using the regression coefficient (R2), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE) metrics to determine the most suitable modeling method for the system. As a result of this study, carrying the pressure value (velocity) ideal for the sample type will increase the energy efficiency of the system.

Modeling and Advanced Control of Dual-Active-Bridge DC–DC Converters: A Review

This article classifies, describes, and critically compares different modeling techniques and control methods for dual-active-bridge (DAB) dc–dc converters and provides explicit guidance about the DAB controller design to practicing engineers and researchers. First, available modeling methods for DAB including reduced-order model, generalized average model, and discrete-time model are classified and quantitatively compared using simulation results. Based on this comparison, recommendations for suitable DAB modeling method are given. Then, we comprehensively review the available control methods including feedback-only control, linearization control, feedforward plus feedback, disturbance-observer-based control, feedforward current control, model predictive current control, sliding mode control, and moving discretized control set model predictive control. Frequency responses of the closed-loop control-to-output and output impedance are selected as the metrics of the ability in voltage tracking and the load disturbance rejection performance. The frequency response plots of the closed-loop control-to-output transfer function and output impedance of each control method are theoretically derived or swept using simulation software PLECS and MATLAB. Based on these plots, remarks on each control method are drawn. Some practical control issues for DAB including dead-time effect, phase drift, and dc magnetic flux bias are also reviewed. This article is accompanied by PLECS simulation files of the reviewed control methods.

Numerical study of modeling methods and evaluation indexes for jet fans

With the rapid development of turbulent jet technology, jet fans have been widely used in building ventilation system, especially for underground buildings. Researchers often use CFD to design the impulse ventilation system, but the study of modeling methods for jet fans have not been studied comprehensively. Because of the characteristic of jet fan, different kind of inlet boundary condition types coupled with different turbulence models lead to different flow fields. In order to find the most suitable modeling method, this paper studied the effect of the combination of 8 turbulence models and 3 flow boundary conditions on modeling accuracy of jet fans. In addition to comparing velocity and turbulence intensity, the research adopted jet core area length and diffusion angle to evaluate the pros and cons of the simulation results. Results showed that the Fan Boundary Method (FBM) integrated with the k-w SST or RSM (Stress-BSL) turbulence model; the Velocity Boundary Method (VBM) integrated with the k-w SST turbulence model; the Momentum Source Method (MSM) integrated with the standard k-ε turbulence model can represent most jet fans' performance with free jet. When the main task is to verify jet fan nozzles performance or optimize nozzles design, the RSM (Stress-BSL) and k-w SST turbulence models are recommended. When the main focus is on the fan range or the performance of the impulse ventilation system, the standard k-ε turbulence model is recommended. The choice of the final model depends on the available data and the application scenario.

Numerical modeling of masonry wall under underground waves

The dynamic behavior of structures has always received considerable attention. The dynamic behavior of structures requires the suitable numerical modeling method in order for the behavior of structures under dynamic loads to be illustrated. In this study, the response of two identical unreinforced masonry walls to the underground blast was examined. The experimental variables were the horizontal distance from the explosion point and depth in which the explosives were located. After examining the behavior of the masonry walls under high-frequency dynamic loads, different numerical models were applied to simulate the dynamic behavior of these two walls against the underground blast experiments. Thus, a number of different factors, including yield criterion, types of meso and macro modeling for the masonry wall, and topography of the site were investigated. Finally, due to the degree of accuracy required, it was concluded that each of the methods can be used; however, the most appropriate and accurate modeling method for the unreinforced masonry wall is the frictional-cohesive zone material and modified Mohr-Coulomb model, which provided accurate and precise responses.

Integrating ecoclimatic niche modelling methods into classical biological control programmes

Much of the success of a classical biological control programme hinges on identifying effective candidate agents, and once approved for release deploying them in the range invaded by the target organism at site-specific times of the year when they have the best chance of establishing. While suitable ecoclimatic modelling methods are available to guide decision-making, they have not been well integrated into biological control praxis. We present a framework that shows how ecoclimatic modelling techniques can be usefully and cost-effectively integrated into biological control programmes. The framework consists of a range of modelling methods within the CLIMEX software toolbox, differing in their information demands and outputs. To demonstrate the framework, we use different types of invasive plants in Australia: the annual forbs Conyza bonariensis (syn. Erigeron bonariensis) and Sonchus oleraceus, the woody perennial shrub Lycium ferocissimum, and the submerged aquatic Cabomba caroliniana. Simple climate-matching techniques, which only require data on the distribution of the target invasive species, are shown to be useful to identify the regions to search for candidate agents in the native range that are climatically adapted to those in the invaded range where biological control is most wanted. More sophisticated niche models can inform both where and when to search for candidate agents in the native range and when and where to release approved agents in the invaded range so that their hosts are actively growing and at the appropriate life stage at the selected sites. We demonstrate how simple manipulative experiments on the temperature response of the target invasive species can be used to parameterise these more complicated niche models. While the modelling framework has been demonstrated using invasive plants as targets, it is equally applicable to arthropod pests. The modelling has been a valuable component of the current biological control programmes, guiding agent prospecting and future deployment efforts in time and space.

Evaluation of clustering and topic modeling methods over health-related tweets and emails

BackgroundInternet provides different tools for communicating with patients, such as social media (e.g., Twitter) and email platforms. These platforms provided new data sources to shed lights on patient experiences with health care and improve our understanding of patient-provider communication. Several existing topic modeling and document clustering methods have been adapted to analyze these new free-text data automatically. However, both tweets and emails are often composed of short texts; and existing topic modeling and clustering approaches have suboptimal performance on these short texts. Moreover, research over health-related short texts using these methods has become difficult to reproduce and benchmark, partially due to the absence of a detailed comparison of state-of-the-art topic modeling and clustering methods on these short texts. MethodsWe trained eight state-of- the-art topic modeling and clustering algorithms on short texts from two health-related datasets (tweets and emails): Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), LDA with Gibbs Sampling (GibbsLDA), Online LDA, Biterm Model (BTM), Online Twitter LDA, and Gibbs Sampling for Dirichlet Multinomial Mixture (GSDMM), as well as the k-means clustering algorithm with two different feature representations: TF-IDF and Doc2Vec. We used cluster validity indices to evaluate the performance of topic modeling and clustering: two internal indices (i.e. assessing the goodness of a clustering structure without external information) and five external indices (i.e. comparing the results of a cluster analysis to an externally known provided class labels). ResultsIn overall, for number of clusters (k) from 2 to 50, Online Twitter LDA and GSDMM achieved the best performance in terms of internal indices, while LSI and k-means with TF-IDF had the highest external indices. Also, of all tweets (N = 286, 971; HPV represents 94.6% of tweets and lynch syndrome represents 5.4%), for k = 2, most of the methods could respect this initial clustering distribution. However, we found model performance varies with the source of data and hyper-parameters such as the number of topics and the number of iterations used to train the models. We also conducted an error analysis using the Hamming loss metric, for which the poorest value was obtained by GSDMM on both datasets. ConclusionsResearchers hoping to group or classify health related short-text data can expect to select the most suitable topic modeling and clustering methods for their specific research questions. Therefore, we presented a comparison of the most common used topic modeling and clustering algorithms over two health-related, short-text datasets using both internal and external clustering validation indices. Internal indices suggested Online Twitter LDA and GSDMM as the best, while external indices suggested LSI and k-means with TF-IDF as the best. In summary, our work suggested researchers can improve their analysis of model performance by using a variety of metrics, since there is not a single best metric.

Dynamic market risk of green stocks across regions: Where does the devil lie?

We examine the nature of the time-varying market risk of investment in green stocks across US, Europe, and the Asia-Pacific region, particularly during the periods of two recent global crises, 2007–08’s great recession and the recent SARS-CoV-2 crisis. We further explore the transmission mechanism of such risks, through both exogenous and endogenous channels. Time varying beta (TVB) is used to capture the risk. Suitable multivariate-GARCH model and CMAX method are used to locate exogenous channels in local and global markets. Finally, we explore the endogenous volatility channel examining deterministic chaos in the data. Results show that the US and European green stocks have significant volatility spillover from the local market. They are also strongly integrated with global market. For the Asia-Pacific green stocks, there is no spillover from the local market. However, they are weakly integrated with the global market. In US and Europe, global crisis weakens financial integration, insulating from global market fluctuations. But in Asia-Pacific market, global crises strengthen financial integration. The defensive green stocks exhibited remarkable resilience during both crises. The apparent resilience may attract investment in green stocks. Green stocks may be safe, but only apparently. The risk of green stocks lies in its chaotic nature that makes it endogenous to the system. With such chaotic market risks, an unfavorable reversal in investment optimism is possible. This calls for more caution for investors before investing in green stocks.

Determination of Cutoff Values for the Screening of Osteosarcopenia in Obese Postmenopausal Women

Osteosarcopenic obesity (OSO) describes the concurrent presence of obesity, low bone mass, and low muscle mass in an individual. Currently, no established criteria exist to diagnose OSO. We hypothesized that obese individuals require different cut-points from standard cut-points to define low bone mass and low muscle mass due to their higher weight load. In this study, we determined cutoff values for the screening of osteosarcopenia (OS) in obese postmenopausal Malaysian women based on the measurements of quantitative ultrasound (QUS), bioelectrical impedance analysis (BIA), and functional performance test. Then, we compared the cutoff values derived by 3 different statistical modeling methods, (1) receiver operating characteristic (ROC) curve, (2) lowest quintile of the study population, and (3) 2 standard deviations (SD) below the mean value of a young reference group, and discussed the most suitable method to screen for the presence of OS in obese population. One hundred and forty-one (n = 141) postmenopausal Malaysian women participated in the study. Bone density was assessed using calcaneal quantitative ultrasound. Body composition was assessed using bioelectrical impedance analyzer. Handgrip strength was assessed using a handgrip dynamometer, and physical performance was assessed using a modified Short Physical Performance Battery test. ROC curve was determined to be the most suitable statistical modeling method to derive the cutoffs for the presence of OS in obese population. From the ROC curve method, the final model to estimate the probability of OS in obese postmenopausal women is comprised of five variables: handgrip strength (HGS, with area under the curve (AUC) = 0.698 and threshold ≤ 16.5 kg), skeletal muscle mass index (SMMI, AUC = 0.966 and threshold ≤ 8.2 kg/m2), fat-free mass index (FFMI, AUC = 0.946 and threshold ≤ 15.2 kg/m2), broadband ultrasonic attenuation (BUA, AUC = 0.987 and threshold ≤ 52.85 dB/MHz), and speed of sound (SOS, AUC = 0.991 and threshold ≤ 1492.15 m/s). Portable equipment may be used to screen for OS in obese women. Early identification of OS can help lower the risk of advanced functional impairment that can lead to physical disability in obese postmenopausal women.

Numerical Modeling of Freestream Turbulence Decay Using Different Commercial Computational Fluid Dynamics Codes

Abstract This work models the spatial decay of freestream turbulence using three different commercial computational fluid dynamics (CFD) codes: Fluent, star-ccm+, and cfx. The two-equation shear stress transport k–ω (SST-k–ω) steady Reynolds-averaged-Navier–Stokes (RANS) model was used, within each of these three different commercial codes, and the modeling variations were analyzed. Comparison of the results from the SST-k–ω model with experiments and large eddy simulation (LES) (carried out using star-ccm+) were also made, which reveal that all the commercial CFD codes demonstrate either a higher or slower rate of spatial turbulent kinetic energy (TKE) decay. Attempts were then made to unify the resultant modeling approach between these three CFD tools, by careful manipulation of the inlet boundary conditions and subsequent fine-tuning of the SST-k–ω model constant (β∞∗). The results obtained not only displayed uniformity among the three CFD codes but also demonstrated a much better agreement to the experiments and the LES results. Thereafter, the optimized model coefficient (β∞∗) was integrated with the three-equation k–kl–ω transition model to examine its applicability in modeling a turbulent boundary layer flow over a flat plate with low incoming turbulence. The results showed good agreement with the theoretical boundary layer correlations, with correct prediction of the transition location. The findings from this study can be used as a suitable modeling method to accurately model the effects of freestream turbulence on bluff-body and boundary layer flows.

АНАЛИЗ СУЩЕСТВУЮЩИХ ЧИСЛЕННЫХ МЕТОДОВ МОДЕЛИРОВАНИЯ ВЗАИМОДЕЙСТВИЯ РАБОЧИХ ОРГАНОВ ЗЕМЛЕРОЙНЫХ И ПОЧВООБРАБАТЫВАЮЩИХ МАШИН С ПОЧВЕННЫМИ СРЕДАМИ

Carrying out theoretical studies of the working processes of various machines and units today has ample opportunities when using modern computer technologies. The use of programs greatly speeds up the study of complex systems under study. The use of simulation methods is advisable when the cost of carrying out real experimental research is high or, due to certain circumstances, making research on a real system impossible, and the calculation of the analytical model will allow many assumptions and approximations that will affect the entire system and change it. In the article, special attention is paid to the selection of the most suitable modeling method for studying the process, liquidation of a forest fire with a flow of soil, using serial software products. The analysis of existing numerical methods for modeling the interaction of working bodies of earth-moving and tillage machines with soil media is carried out. The studies performed by the discrete element method (DEM), the finite element method (FEM), the computational fluid dynamics (CFD) method, and the smooth particle hydrodynamics (SPH) method are analyzed. Conclusions are drawn on the prospects for the applicability of each of the methods for modeling the system of processes of processing and throwing soil.

Development of freight travel demand model with characteristics of vehicle tour activities

The actual movements of freight vehicles demonstrate comprehensive tour activities instead of a single trip. Freight vehicle tours activity represent trip chaining relationship between shippers, receivers, and carriers as a journey of tours. In India, identifying and modeling tour activity is complex because disaggregated freight tour activity data are generally not available to access. Hence, there is a need to identify the most suitable and sufficient modeling framework, which explains the non-linear behavior and complexity of tour-based freight travel demand. Thus, the objectives of this study are to identify the driver, vehicle, and journey characteristics of freight vehicle movement and their tour activities using the conventional non-linear and soft computing methods to estimate the number of tours and to identify the most suitable and effective modeling method. The questionnaire form designed, and study locations were selected in Tiruchirappalli, India. The selected survey locations were identified based on the availability of fright drivers who were making tours. An extensive face to face data collection process was performed, and a total of 600 drivers' data were collected from survey. A detailed description of the collected data was obtained through descriptive analysis. Next, significant relationship between characteristics of freight vehicle drivers and their tour activities was identified based on various correlation tests. Further, a non-linear regression method was adopted for explaining non-linear behavior and complexity of tour-based freight travel demand. Followed by soft computing models using Support-vector machines (SVM), k-Nearest Neighbors (k-NN), and Artificial Neuron Network (ANN) algorithms were developed to predict tour-based freight travel demand, which performed better than conventional non-linear model. Among all, ANN model outperformed and explained the variations with better accuracy. Finally, the outcome of this study will be helpful to transport planners and researchers for understanding the characteristics of freight vehicle tour activity and estimating tour-based freight travel demand.