Alternative Modeling Approach Research Articles

Artificial neural network modelling of green synthesis of silver nanoparticles by honey

Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with Levenberg–Marquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.

Effects of vertical anisotropy on optimization of multilateral well geometry

Multilateral wells have been increasingly used in recent years by different industries such as the oil- and gas industry (incl. coal-bed methane (CBM)) and geothermal energy production. The common purpose of these wells in the oil industry and partially in the geothermal industry is to achieve a higher production rate per well without increasing the hydraulic gradient significantly. In geothermal systems the ultimate objective is to improve heat extraction performance by enhancing productivity but also by increasing the heat transfer area. Optimal design of multilateral wells has been the focus of numerous publications in CBM and unconventional oil- and gas production, but as a result of cross-fertilization and technology-transfer among different sectors in the energy industry in the last few years, the focus of research on multilateral wells has turned to enhanced geothermal systems (EGS). Recently, several papers have been published on the use of multilateral wells in deep geothermal reservoirs. Coaxial closed-loop geothermal systems with multilateral wells utilize only the increased heat transfer area due to the laterals while EGS's with multilateral injection and production wells also benefit from the hydrodynamic advantages of such well configurations. However, substantial emphasis is – understandably – placed on the effect of artificial and natural fracture networks of EGS reservoirs and less focus is devoted to the impact of vertical permeability anisotropy which can seriously influence the flow pattern of a given configuration. Similarly, in CBM-related studies, the effect of horizontal permeability anisotropy along the face cleats to the butt cleats on multilateral well-design was investigated and vertical permeability anisotropy was not considered. In the current work we present a generalized approach for evaluating the effects of vertical permeability anisotropy on multilateral well geometry by numerical hydrodynamic modelling in an idealized, homogeneous hydrogeological setting. Current paper proposes an alternative modelling approach for representing the non-horizontal branches of multilateral wells by introducing a thin layer for the laterals with the same inclination as the well branches. This approach is then is used to investigate the effect of anisotropy on the flow pattern and near-wellbore drawdown and hydraulic gradient in the case of different branch deviations. Results suggest that the benefits of highly deviated or horizontal laterals emerge when vertical anisotropy is high. Evaluation of branch deviation vs. anisotropy indicates that above approx. 60° there is no significant benefit in increasing deviation which implies that very highly deviated or horizontal laterals might not necessarily pay off the associated technical challenges and extra costs.

Predicting the master curves of bituminous mastics with micromechanical modelling

The performance of asphalt mixtures is significantly affected by the viscoelastic properties of their mastic phase. The analytical approaches used to predict the properties of mastics from their constituents’ properties are limited in their accuracy and potential to handle non-linear material behaviour. An alternative micromechanical finite element modelling approach to calculate the master curves of mastics from the binder and filler phase properties is presented, where the representative volume elements of mastics consist of linear-viscoelastic bitumen matrices and elastic spherical filler particles. For validation, shear relaxation moduli of bitumen and bitumen-filler mastics are measured at °C°C. Additionally, the model is evaluated and compared with the existing analytical solutions. The results indicate that the proposed approach is advantageous as compared to the analytical solutions, as it allows predicting the mastics’ properties over wider temperature, frequency and material ranges at better agreement with the measurements while giving insight into the micromechanical behaviour.

A New Model Ice for Wave-Ice Interaction

The interaction of waves and ice is of significant relevance for engineers, oceanographers and climate scientists. In-situ measurements are costly and bear uncertainties due to unknown boundary conditions. Therefore, physical laboratory experiments in ice tanks are an important alternative to validate theories or investigate particular effects of interest. Ice tanks use model ice which has down-scaled sea ice properties. This model ice in ice tanks holds disadvantages due to its low stiffness and non-linear behavior which is not in scale to sea ice, but is of particular relevance in wave-ice interactions. With decreasing stiffness steeper waves are required to reach critical stresses for ice breaking, while the non-linear, respectively non-elastic, deformation behavior is associated with high wave damping. Both are scale effects and do not allow the direct transfer of model scale test results to scenarios with sea ice. Therefore, the alternative modeling approach of Model Ice of Virtual Equivalent Thickness (MIVET) is introduced. Its performance is tested in physical experiments and compared to conventional model ice. The results show that the excessive damping of conventional model ice can be reduced successfully, while the scaling of the wave induced ice break-up still requires research and testing. In conclusion, the results obtained are considered a proof of concept of MIVET for wave-ice interaction problems.

Network Models for Multiobjective Discrete Optimization

This paper provides a novel framework for solving multiobjective discrete optimization problems with an arbitrary number of objectives. Our framework represents these problems as network models, in that enumerating the Pareto frontier amounts to solving a multicriteria shortest-path problem in an auxiliary network. We design techniques for exploiting network models in order to accelerate the identification of the Pareto frontier, most notably a number of operations to simplify the network by removing nodes and arcs while preserving the set of nondominated solutions. We show that the proposed framework yields orders-of-magnitude performance improvements over existing state-of-the-art algorithms on five problem classes containing both linear and nonlinear objective functions. Summary of Contribution: Multiobjective optimization has a long history of research with applications in several domains. Our paper provides an alternative modeling and solution approach for multiobjective discrete optimization problems by leveraging graphical structures. Specifically, we encode the decision space of a problem as a layered network and propose graph reduction operators to preserve only solutions whose image are part of the Pareto frontier. The nondominated solutions can then be extracted through shortest-path algorithms on such a network. Numerical results comparing our method with state-of-the-art approaches on several problem classes, including the knapsack, set covering, and the traveling salesperson problem (TSP), suggest orders-of-magnitude runtime speed-ups for exactly enumerating the Pareto frontier, especially when the number of objective functions grows.

An examination of model fit and measurement invariance of general mental ability and personality measures used in the multilingual context of the Swiss Armed Forces: A Bayesian structural equation modeling approach

ABSTRACT Measurement invariance of psychological test batteries is an essential quality criterion when the test batteries are administered in different cultural and language contexts. The purpose of this study was to examine to what extent measurement model fit and measurement invariance across the two largest language groups in Switzerland (i.e., German and French speakers) can be assumed for selected general mental ability and personality tests used in the Swiss Armed Forces’ cadre selection process. For the model fit and invariance testing, we used Bayesian structural equation modeling (BSEM). Because the sizes of the language group samples were unbalanced, we reran the invariance testing with the subsampling procedure as a robustness check. The results showed that at least partial approximate scalar invariance can be assumed for the constructs. However, comparisons in the full sample and subsamples also showed that certain test items function differently across the language groups. The results are discussed regarding the three following issues: First, we critically discuss the applied criterion and alternative effect size measures for assessing the practical importance of non-invariances. Second, we highlight potential remedies and further testing options, that can be applied, once certain items have been detected to function differently. Third, we discuss alternative modeling and invariance testing approaches to BSEM and outline future research avenues.

Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation)

The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

Statistical implications of endogeneity induced by residential segregation in small-area modelling of health inequities

Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model’s offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as “offset endogeneity”. We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.

Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches

SummaryWe consider the analysis of count data in which the observed frequency of zero counts is unusually large, typically with respect to the Poisson distribution. We focus on two alternative modelling approaches: over‐dispersion (OD) models and zero‐inflation (ZI) models, both of which can be seen as generalisations of the Poisson distribution; we refer to these as implicit and explicit ZI models, respectively. Although sometimes seen as competing approaches, they can be complementary; OD is a consequence of ZI modelling, and ZI is a by‐product of OD modelling. The central objective in such analyses is often concerned with inference on the effect of covariates on the mean, in light of the apparent excess of zeros in the counts. Typically, the modelling of the excess zerosper seis a secondary objective, and there are choices to be made between, and within, the OD and ZI approaches. The contribution of this paper is primarily conceptual. We contrast, descriptively, the impact on zeros of the two approaches. We further offer a novel descriptive characterisation of alternative ZI models, including the classic hurdle and mixture models, by providing a unifying theoretical framework for their comparison. This in turn leads to a novel and technically simpler ZI model. We develop the underlying theory for univariate counts and touch on its implication for multivariate count data.

Deep decarbonisation of regional energy systems: A novel modelling approach and its application to the Italian energy transition

Deep decarbonisation – i.e. the transition towards net-zero emissions energy systems – will be enabled by a high penetration of intermittent renewables, storage and sector-coupling technologies. In this paper, we present a novel modelling approach to capture the increasing complexity of such future energy systems and help policy makers choose among the different possible transition scenarios. Salient features of our model, consisting of an extended and regionalised version of EnergyScope (Limpens et al., 2019 [1]), are a low computational time and a concise formulation which make it suitable for uncertainty and what-if analyses. As a case study, the model is applied to devise scenarios for the Italian energy transition. Specifically, we develop the first open-source whole-energy system model of Italy and assess the feasibility of its decarbonisation strategy with respect to uncertainties in the deployment of carbon capture and storage (CCS) and renewable technologies. Results show that emissions can be cut by 79%–97% vs. 1990 levels thanks to a radical electrification of the energy system coupled to a wide deployment of renewables and efficient energy conversion technologies. Finally, we discuss the synergies, advantages and disadvantages of our proposed approach with respect to alternative modelling approaches used across 88 recent deep decarbonisation studies. The analysis suggests that our model, thanks to its computational efficiency and a snapshot approach (i.e., modelling a target-year in the future), can complement more detailed and established energy models optimising the energy transition pathway (i.e., modelling the pathway from today to the target year).

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity

Mixed logit models with unobserved inter- and intra-individual heterogeneity hierarchically extend standard mixed logit models by allowing tastes to vary randomly both across individuals and across choice situations encountered by the same individual. Recent work advocates using these models in choice-based recommender systems under the premise that mixed logit models with unobserved inter- and intra-individual heterogeneity afford personalised preference estimation and prediction. In this study, we evaluate the ability of mixed logit with unobserved inter- and intra-individual heterogeneity to produce accurate individual-level predictions of choice behaviour. Using simulated and real data, we show that mixed logit models with unobserved inter- and intra-individual heterogeneity do not provide significant improvements in choice prediction accuracy over standard mixed logit models, which only account for inter-individual taste variation. We make these observations even in scenarios with high levels of intra-individual taste variation and when the number of choice situations per decision-maker is large. Also, the estimation of mixed logit with unobserved inter- and intra-individual heterogeneity requires at least seven times as much computation time as the estimation of standard mixed logit. Drawing from recent advances in machine learning and econometrics, we discuss alternative modelling approaches that can capture richer dependencies between decision-makers, alternatives and attributes.

Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation

This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation.

Neural Network Differential Equations For Ion Channel Modelling.

Mathematical models of cardiac ion channels have been widely used to study and predict the behaviour of ion currents. Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov state transitions. These models provide an abstract description of the underlying conformational changes of the ion channels. However, due to the abstracted conformation states and assumptions for the rates of transition between them, there are differences between the models and reality—termed model discrepancy or misspecification. In this paper, we demonstrate the feasibility of using a mechanistically-inspired neural network differential equation model, a hybrid non-parametric model, to model ion channel kinetics. We apply it to the hERG potassium ion channel as an example, with the aim of providing an alternative modelling approach that could alleviate certain limitations of the traditional approach. We compare and discuss multiple ways of using a neural network to approximate extra hidden states or alternative transition rates. In particular we assess their ability to learn the missing dynamics, and ask whether we can use these models to handle model discrepancy. Finally, we discuss the practicality and limitations of using neural networks and their potential applications.

Transaction activity and bitcoin realized volatility

We study the predictive value of transaction activity in the bitcoin network for the realized volatility of bitcoin returns constructed by high-frequency data. As an alternative modeling approach to the popular linear heterogeneous autoregressive model, we provide out-of-sample forecasts for realized volatility of bitcoin returns employing machine learning algorithms, and in particular by Random Forests. Our findings reveal that on-blockchain transaction activity does improve the out-of-sample forecast accuracy at all the forecast horizons considered.

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes.Objective: We aim to develop a data-driven workflow to extract, process, and develop deep learning (DL) methods to model the COVID-19 epidemic. We provide an alternative modeling approach to complement the current mechanistic modeling paradigm.Method: We extensively searched, extracted, and annotated relevant datasets from over 60 official press releases in Hubei, China, in 2020. Multivariate long short-term memory (LSTM) models were developed with different architectures to track and predict multivariate COVID-19 time series for 1, 2, and 3 days ahead. As a comparison, univariate LSTMs were also developed to track new cases, total cases, and new deaths.Results: A comprehensive dataset with 10 variables was retrieved and processed for 125 days in Hubei. Multivariate LSTM had reasonably good predictability on new deaths, hospitalization of both severe and critical patients, total discharges, and total monitored in hospital. Multivariate LSTM showed better results for new and total cases, and new deaths for 1-day-ahead prediction than univariate counterparts, but not for 2-day and 3-day-ahead predictions. Besides, more complex LSTM architecture seemed not to increase overall predictability in this study.Conclusion: This study demonstrates the feasibility of DL models to complement current mechanistic approaches when the exact epidemiological mechanisms are still under investigation.

The last-mile vehicle routing problem with delivery options

The ongoing rise in e-commerce comes along with an increasing number of first-time delivery failures due to the absence of the customer at the delivery location. Failed deliveries result in rework which in turn has a large impact on the carriers’ delivery cost. In the classical vehicle routing problem (VRP) with time windows, each customer request has only one location and one time window describing where and when shipments need to be delivered. In contrast, we introduce and analyze the vehicle routing problem with delivery options (VRPDO), in which some requests can be shipped to alternative locations with possibly different time windows. Furthermore, customers may prefer some delivery options. The carrier must then select, for each request, one delivery option such that the carriers’ overall cost is minimized and a given service level regarding customer preferences is achieved. Moreover, when delivery options share a common location, e.g., a locker, capacities must be respected when assigning shipments. To solve the VRPDO exactly, we present a new branch-price-and-cut algorithm. The associated pricing subproblem is a shortest-path problem with resource constraints that we solve with a bidirectional labeling algorithm on an auxiliary network. We focus on the comparison of two alternative modeling approaches for the auxiliary network and present optimal solutions for instances with up to 100 delivery options. Moreover, we provide 17 new optimal solutions for the benchmark set for the VRP with roaming delivery locations.

Prediction of branch growth using quantile regression and mixed-effects models: An example with planted Larix olgensis Henry trees in Northeast China

Branch is an essential component that supports foliage for photosynthesis, and its size appears as a major determinant for log grading. Forest managers have always been facing a challenge to maximize tree increment and timber quality; hence, linking the dynamic relationship between the branch and tree growth will be beneficial for crucial stand managerial decisions (pruning, thinning, and other silvicultural practices). In this study, branch growth models were developed using 77 destructively sampled Korean larch trees (Larix olgensis Henry) from several plantations in the Northeastern area of China. A modified Mitscherlich function that includes dynamic tree height and branch height was used to predict the growth of the branch’s diameter and length. The jackknifing technique was utilized to evaluate five alternative modeling approaches: (1) fixed-effects model; (2) calibrated mixed-effects model; (3) three-quantile regression method; (4) five-quantile regression method; and (5) nine-quantile regression method. The results showed that the prediction performance of both calibrated mixed-effects and quantile regression approaches outperformed the fixed-effects model, in which the calibrated mixed-effects provided better performance than quantile regression. In addition, three sampling strategies and various number of branches (1 to 8 branches per sample tree) were used for calibrating the mixed-effect and quantile regression model. The three sampling strategies brought relatively similar prediction performance, specifically for the larger sample size. Model performance improved as the sample size increased, but gains in performance decreased gradually. Overall, a combination of Type I sampling strategy and at least five sample branches per tree was recommended as a win–win solution to minimize the required measurement cost without sacrificing the model’s predictive accuracy.

An Alternative Modeling Approach to Stock Assessment of Central Philippine Waters

An Alternative Modeling Approach to Stock Assessment of Central Philippine Waters