Search

• The mathematical model is useful for analyzing the heat exchanger performance. • The most probable variant of heat exchanger operation is established. • There is a risk of frost accumulation within the fixed-bed regenerator. The paper describes an original mathematical model of heat and mass transfer for a fixed bed regenerator used to determine the effect of internal airflow leakage on heat exchanger efficiency. First, the build and operating principles of a fixed-bed heat exchanger were discussed. The presence of internal leakage was shown along with a proposed solution for its elimination. Then, a mathematical model based on a modified ε-NTU method for numerical simulation and analysis of coupled heat and mass transfer in a fixed-bed heat exchanger was presented. The model was supplemented with an additional detailed submodel designed to determine frost surface temperature and frost growth analysis. Initial numerical simulations of the operation of the model were performed for low outdoor air temperature conditions. Different zones of heat and mass transfer within the regenerator matrix were distinguished. The presence of accumulated frost area and its influence on the course of heat and mass transfer processes was revealed. In addition, the presence of a relative humidity limit curve associated with a mass balance deficiency conditioning the occurrence of frost accumulation was established. [ABSTRACT FROM AUTHOR]

Most soil-water characteristic curve (SWCC) models contain more than three parameters, but these parameters typically do not have a distinct geometric or physical meaning, or the parameters used are inaccurate because of their high reliance on inverse parameter estimation methods. This phenomenon greatly limits the application of SWCC in the study of mechanical and hydraulic properties of unsaturated soils. To explore the nature of SWCC, this paper reexamines the role of the parameters in the two most commonly used SWCC models from the perspective of the relationship between capillary and adsorbed water. A new and flexible SWCC model (named the MVF model) with four parameters is proposed, which can separate the two different regions by physical constraints on the residual saturation S r and residual suction ψ r. Ten published experimental data sets are compiled to evaluate the performance of the MVF model by comparing it with both the existing models and the experimental data. The MVF model shows excellent performance in terms of goodness of fit, and the fitting parameters are provided with a clear geometric meaning, which helps to establish the connection between the basic properties of the soil and the SWCC. This study consolidates the application and reliable determination of SWCC in estimating the mechanical and hydraulic properties of unsaturated soils. • The relationship of capillary and adsorbed water and the role of the fitting parameters for FX and VG models are reviewed. • A flexible modified SWCC model with four fitting parameters based on FX and VG model is proposed. • The physical constraints from residual suction and residual saturation are employed in this study to reduce the uncertainty of the parameters. • The parameters in the proposed model can better capture the features of the SWCC. • A flexible variation on the mathematical form of the model can be applied to bimodal soils. [ABSTRACT FROM AUTHOR]

• We address a complex single machine scheduling problem. • We consider flexible and variable maintenance activity to be scheduled. • We develop a novel self-adaptive harmony search algorithm. • We compare different heuristic and meta-heuristic algorithms. • We perform a sensitivity analysis involving several influencing factors. A common assumption in the scheduling literature provides that machines are always available, while often they have to be stopped because of preventive maintenance operations. Inspired by a real-life manufacturing context, this paper deals with the single machine scheduling problem with a flexible/variable maintenance to be executed within a specific time window. Differently from other studies in the literature, a set of non-resumable jobs with sequence dependent setup-times and non-zero release dates have to be scheduled along with a maintenance activity whose duration is a non-decreasing function of its starting time, with the objective of minimizing the total tardiness (T-problem). A mathematical model of the problem is proposed and, due to the high computational complexity, several heuristics have been implemented and tested with the aim of using some of them to make metaheuristic algorithms more effective and computationally efficient. Hence, a modified harmony search, powered by several reinforcement strategies and even equipped with a self-adaptive calibration mechanism, has been developed and compared with a set of additional algorithms proposed by the literature. Numerical results reveal that the modified harmony search outperforms the competing algorithms and no performance loss arises by adopting the self-adaptive mechanism. Finally, a proper sensitivity analysis allows assessing the impact of several influencing factors on the total tardiness minimization. [ABSTRACT FROM AUTHOR]

• We present a new variant of the two-level lot sizing scheduling problem. • The new variant problem was inspired by a real case in the cosmetic industry. • We present a mathematical model for the proposed problem. • We also present a hybrid exact method combining different local search procedures. • We provide computational results and some managerial insights based on real data. In this paper we address a novel variant of the two-level lot-sizing and scheduling problem inspired by a cosmetic industry in Brazil. In this problem, we seek to optimize production activities in an environment consisting of two dependent levels and an intermediate inventory between them. We consider sequence-dependent setup in the first level, non-sequence dependent setup in the second level, and a different number of machines by level. Furthermore, following the mandatory requirements in the cosmetic industry, we also consider that each job must be stored for a minimum time limit and cannot exceeds a maximum time limit in the intermediate inventory. These practical requirements ensure a certain level of quality of the items produced. To solve the problem, we propose Mixed Integer Linear Programming (MILP) formulations and a hybrid exact method combining the traditional Branch and Bound (B&B) algorithm with Local Search (LS) procedures using a sequential framework. Our procedures apply several strategies of the Fix and Optimize (F&O) heuristic and Variable Neighborhood Descent (VND) principles. All methods were evaluated using a set of instances based on a real case scenario. The computational experiments indicated that our hybrid method can achieve competitive solutions, outperforming the results provided by the optimization of the MILP model. [ABSTRACT FROM AUTHOR]

The topic of swept-volumes is well-studied. Yet, the high degree of underlying mathematical and computational complexity has prevented a general implementation which accepts freeform input, even for the planar case. Likewise for Minkowski sums. This paper proposes a unified approach to the two related problems, in 2D, via algebraic modeling of the underlying mathematical constraints. Handling self-intersections in the envelope has been the special focus of this work. Algebraic modeling results in a high degree of numerical precision and guarantee of topological correctness. A top-down computational strategy leads to a numerically robust algorithm. Further, an auxiliary point-cloud representation of the swept-volume yields a significant boost in computational efficiency. The robustness and numerical stability of our approach are demonstrated by tens of thousands of examples, generated from an implementation of our algorithm. • A novel, unified framework is proposed for swept volumes and Minkowski sums. • Algebraic modeling is employed to achieve high degree of numerical precision. • A top-down approach leads to a numerically robust algorithm. • Tens of thousands of examples demonstrate the robustness of our approach. [ABSTRACT FROM AUTHOR]

In the present research paper, a unique single feed-forward multilayer perceptron neural network (MLPNN) based on data transformation technique and mathematical models were proposed for forecasting density and dynamic viscosity of Alumina (Al 2 O 3)/distilled water (DW), multiwall carbon nanotube (MWCNT)/DW, and Graphene nanoparticle (GNP)/DW nanofluids. The density and dynamic viscosity of nanofluids (0.1%, 0.25%, 0.5%, 0.75%, and 1%) were experimentally measured in temperature range 30–80 °C. Nanofluids, volume concentration, and temperature were given as input and density and dynamic viscosity as output from the network. The statistical analysis was performed to predict the accuracy of both models. The experiments revealed that density and viscosity increase with an increase in nanofluid concentration, and decrease with an increase in temperature. The maximum increase in dynamic viscosity and density of 41.59% and 5.06% was noticed for Al 2 O 3 /DW nanofluid. The outcome shows that the MLPNN model is well trained at 12 perceptrons in the hidden layer using the Levenberg-Marquardt algorithm. The output obtained from MLPNN and the mathematical model were compared with experimental results. The maximum error < 0.2% in density and < 1% in viscosity measurement was noticed for both models. [Display omitted] • Dynamic viscosity and density of several nanofluids were experimentally measured. • Maximum increase in dynamic viscosity and density of 41.59% and 5.06% was observed for Al 2 O 3 nanofluid. • A unique artificial intelligence approach was adopted for modeling the network. • A non-linear mathematical model is proposed for estimating both viscosity and density. • The statistical analysis was performed to estimate performance of MLPNN and mathematical model. [ABSTRACT FROM AUTHOR]

• 151 sets of pull-out experimental data of FRP reinforced concrete at different temperatures in the literatures was established. • The BP-ANN model was established to predict the bond strength between FRP bars and concrete at high temperature. • The BP-ANN model was well-trained and tested by using 151 sets of experimental data. • The applicable scopes and computational errors of BP-ANN model and currently available mathematical models were compared. In view of the difficulty in establishing a mathematical model to characterize the interfacial performance of FRP reinforced concrete at high temperature, the back-propagation neural network (BP-ANN) paradigm is used as a computational tool for capturing the bond behavior in this paper. Through taking temperature, FRP type, surface form, FRP bar diameter, anchorage length, failure mode, concrete compressive strength and normalized concrete cover thickness as input variables and the interface bond strength as output objective, the BP-ANN model has been established, well-trained and tested by using 151 sets of pull-out experimental data of FRP reinforced concrete at different temperatures in the literatures. The prediction accuracy of BP-ANN model has been evaluated by comparing the training and testing values with experimental results. The more advantages in universality of BP-ANN model than the currently available mathematical models are demonstrated by comparing their applicable scopes and computational errors. [ABSTRACT FROM AUTHOR]

This paper presents a novel model-based control strategy for the curvature and the contact force at the contact point of a metal strip and a roll. Due to the plastic deformation of the strip, the bending history of the strip has to be considered in the controller design. A multivariable two-degrees-of-freedom (2-DOF) control structure is used to track user-defined periodic references for the outputs. The proposed multi-input/multi-output (MIMO) feedback control structure consists of a spatial iterative learning control (ILC) combined with a multivariable PI controller. The control strategy is implemented in a real-time system and verified by measurements on an experimental test rig. [ABSTRACT FROM AUTHOR]

A new model for laser cutting is presented. It is derived by an asymptotic expansion of a free boundary problem for the arising melt and describes the coupled movements of the melt boundaries by a system of two nonlinear partial differential equations. The paper extends former models by presenting a complete first-order expansion allowing to include arbitrary higher-order terms. A stationary solution is derived and investigated for stability since damped perturbations do not disturb the solidification process whereas growing perturbations lead to unwanted ripple structures at the cutting front. New higher order terms in the model are identified with physical phenomena which are shown to provide stabilization of the process. The stability analysis is evaluated in a parameter study with varying cutting velocities for 1 µm fiber laser cuttings and validated with published experimental and simulated data for the same study. Increasing the cutting speed provides larger stability areas leading to smoother cutting fronts. As a novelty, published ripple depth tendencies are shown to be met qualitatively by our model and typical ripple wavelengths are predicted in the order of magnitude. Hence, our model provides a purely analytical method to investigate the cutting quality. [Display omitted] • A new model for laser cutting is derived by first-order asymptotic expansion. • Ripple forming melt boundary perturbations are detected by stability analysis. • Convective and diffusive effects of the melt provide process stabilization. • Increasing cutting speed in 1 μ m fiber laser cutting increases cut quality. • Analytical results are validated with published experimental and simulated data. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel 1D singularly perturbed reaction–convection–diffusion mathematical model, with non-linear coefficients (SP-RCD model), for the physical modeling of a fuel cell. The model is a generalization of the macro-homogeneous model, revisited from the point of view of singularly perturbed differential equations. To solve the system of coupled second-order differential equations, we propose a numerical scheme based on vanishing the artificial diffusion of the finite element method within an iterative fixed-point algorithm. We also propose an adaptive Shishkin mesh, as a function of the derivative of the current density in the subdomain with a fast-growing slope. Results of the proposed SP-RCD model are comparable to those of the macro-homogeneous model. In addition, it describes the oxygen concentration profiles in the thickness of the cathode catalytic layer under different operating currents and represents, with enough precision, the experimental polarization curve reported in the literature. • We introduce a novel RCD mathematical model for the numerical simulation of PEM fuel cells. • We introduce a method for stabilizing the numerical solution, it introduces artificial diffusion (AD), until stabilization, then AD is removed. • We introduce a numerical strategy for adapting a Shishkin mesh, avoiding user-made tuning. • Mathematical arguments about convergence are provided. • We validate the algorithm with reported cases and other model. [ABSTRACT FROM AUTHOR]

The classical model of lipid membranes is expressed in terms of a curvature-dependent energy involving the mean and the Gaussian curvatures of membranes. It is customary to ignore the Gaussian energy as it takes a constant value for topologically equivalent closed membranes. In this work, we study the effect of the Gaussian energy on configurations of axisymmetric lipid membranes by using a phase field model. As the weak form of the governing equations involves the second-order derivatives of the phase field function, we employ C 1 Argyris finite element to approximate solutions. The numerical results of this paper suggest that including the Gaussian energy in the existing mathematical model may be necessary to capture the topology changes of membranes accurately. • Gaussian energy affects the phase transition of closed membranes. • Conforming C 1 finite element is effective to accurately model lipid membranes. • Gaussian curvature is necessary for accurately capturing the topology changes of closed membranes. [ABSTRACT FROM AUTHOR]

Spatially resolved environmental models are important tools to introduce and highlight the spatial variability of the real world into modeling. Although various spatial models have been developed so far, yet the development and evaluation of these models remain a challenging task due to several difficulties related to model setup, computational cost, and obtaining high-resolution input data (e.g., monitoring and emission data). For example, atmospheric transport models can be used when high resolution predicted concentrations in atmospheric compartments are required, while spatial multimedia fate models may be preferred for regulatory risk assessment, life cycle impact assessment of chemicals, or when the partitioning of chemical substances in a multimedia environment is considered. The goal of this paper is to review and compare different spatially resolved environmental models, according to their spatial, temporal and chemical domains, with a closer insight into spatial multimedia fate models, to achieve a better understanding of their strengths and limitations. This review also points out several requirements for further improvement of existing models as well as for their integration. [Display omitted] • Spatial atmospheric transport models and multimedia fate models were reviewed. • More than 50 spatial models were described including their applications. • Their spatial, temporal, and chemical domains were investigated for comparison. • Further developments especially their integration is suggested. [ABSTRACT FROM AUTHOR]

Laser ablation is an effective and advantageous dressing method for dressing diamond grinding wheels. Previously, research on laser dressing has focused on parallel and convex face diamond wheels. Studies of the laser dressing model analysis and the sources of shape error for concave diamond wheels are less numerous. There is no research to analyse the source of the diamond wheel error from the optics itself. In this paper, a novel deflectable dual-laser misaligned tangential (DDLMT) dressing method was proposed for processing concave rectangular bronze-bonded diamond wheels. Using the DDLMT process, the slope effect error in dressing concave rectangular diamond wheels was basically eliminated. To analyse the causes of the slope effect error during profiling, a theoretical study of the laser radial and tangential dressing methods was carried out, and a mathematical model was established to analyse the laser energy loss mechanism. Experiments on different positions dressings were conducted to validate the mathematical model. The DDLMT dressing method offered a significant improvement in contour accuracy and more than doubled the dressing efficiency compared to single beam profiling. As the laser deflection angle increased, the angle deviation of the dressed right-angle contour gradually decreased. The least deviation of right-angle profile obtained was 0.51° when the deflection angle was 5°. The final PV value of the bottom contour was 11.83 μm, which had a better contour accuracy. The surface quality of the grinding wheel by DDLMT dressing is far better than that of radial dressing. The experimental results demonstrated the potential of the DDLMT dressing method for precision profiling diamond wheels. [Display omitted] • A novel deflectable dual-laser misaligned tangential dressing method was proposed. • A mathematical model was established to analyse the laser energy loss mechanism. • The least angle deviation obtained was 0.51° when the deflection angle was 5°. • The study demonstrated potential of the DDLT method for dressing grinding wheels. [ABSTRACT FROM AUTHOR]

Microplastic pollution in marine and riverine environments is a threat not only for the aquatic ecosystem itself but also for human activity and life. Although there are reviews regarding microplastic debris in environments, most of them focus on the studies on their type, occurrence, and distribution. Only a limited number of these studies focus on the modeling methods, usually concentrating on particular aspects, such as settling or bioaccumulation. In this paper, physically-based existing microplastics modeling studies are classified and reviewed according to the environment, modeling methodology, and input-output relationships. Considering the strengths and weaknesses of all modeling methodologies, it is deduced that more reliable results are obtained using hybrid methods, especially the coupling of hydrodynamic and process-based models, and hydrodynamics and statistical models. The significance of having much more consideration and knowledge on the microplastics' physical properties and the environmental processes affecting their fate and transport in the aquatic environments is revealed for future research. It has also been recommended that a standardized method for data calibration, validation, and verification is necessary to be able to compare the modeling results with field investigations more efficiently than it is currently. [Display omitted] • Microplastics are a contaminant of increasing concern in aquatic environments. • Physically-based models of microplastics in water environments are classified and reviewed. • Hybrid methods in modeling are found to give more accurate results. • A standardization in modeling methods is essential to enable the comparison between studies. [ABSTRACT FROM AUTHOR]

To reduce the negative impact of the conventional vehicles, governments and authorities try to provide facilities in order to encourage people to use electric vehicles. For this purpose, determining the location and the number of charging stations is of paramount importance. It becomes more challenging when some charging stations cannot provide service due to the disruption or breakdown. In this paper, a bi-objective mixed-integer linear mathematical model for a charging station location problem is developed and solved by Lagrangian relaxation method. Two different chargers that could be installed in the charging stations and budget constraint is considered in the model. To evaluate the applicability of the proposed model on real-world problems, a case study on Tehran is conducted. Based on computational experiments and numerical results, sensitivity analysis on key parameters of the problem is carried out and some helpful suggestions are presented for decision-makers. • A bi-objective region-based model for optimizing CSLP is presented. • Disruption in charging stations is taken into account. • Two different types of chargers and tolerable distance for the users are considered. • The proposed model is implemented in a real-world case study to evaluate the applicability of the model. • Lagrangian relaxation algorithm is used to solve the model and some useful managerial insights are also presented. [ABSTRACT FROM AUTHOR]

Bio-drying of solid waste is a complex process that involves anoxic hydrolysis, aerobic oxidation, evaporation, absorption, and condensation. To understand the interaction between different variables (concentration of volatile matter, moisture, microbial biomass, and temperature) and to predict the system performance, a powerful tool is necessary for improved drying rates. Hence, a mathematical model of mass balances of the batch bio-drying of municipal solid waste (MSW), associated with rate equations of evaporation of moisture from the waste matrix and absorption of leachate on absorbents is developed which is the novelty of the present study. This model integrates 18 first-order differential equations based on process kinetics for mass balances of components in the solid, liquid, and gas phases, comprising the specific degradability of food, paper, and inert material of the MSW. The proposed model is simulated using MATLAB ode15s solver and validated with experimental data from the pilot-scale bio-drying reactor. The model predicted values are in good agreement with experimental values with a deviation of ±10%. • One dimensional mathematical model for biodrying of municipal solid waste with high moisture content. • Hydrolysis and biological oxidation of fast degradable material by integrating Contois and Monod models • Non-uniform biodrying of solid waste matrix by inclusion of absorption kinetics of leachate • Moisture evaporation based on the two-film theory of mass transfer. [ABSTRACT FROM AUTHOR]

Safe and stable operation of hydropower plants is the basis and prerequisite of great importance for clean energy production and integration of renewables. To better analyze the operating stability of hydropower generating systems (HGSs), a novel frequency-domain method (global matrix method) is proposed in this paper. First, the principle of the new method and the mathematical models of each common hydraulic component in HGSs are presented. Second, three general steps for deriving the global matrix of an arbitrary system and order-reduction and numerical calculation of the matrix are illustrated in detail. In two case studies of HGS stability analysis, the transfer function method (TFM) suitable for time-frequency domain analysis is applied to evaluate the global matrix method. For simple single-unit system, the new method is compared by the TFM from both frequency domain and time domain. For complicated multi-unit system, the TFM is not applicable to the frequency-domain stability analysis, and only the time-domain simulation is applied to verify the new method. The results indicate that the stability regions by the two methods are in good agreement, verifying the accuracy of the new approach. The proposed method can replace the TFM for stability analysis of HGSs in frequency domain, outperforming the latter in applicability, flexibility, and quantification function. • A global matrix method for stability analysis of hydropower systems is proposed. • Order-reduction and numerical calculation of the global matrix are illustrated. • The accuracy of the novel method is verified by transfer function method (TFM). • Applicability, flexibility and quantification function are the main features. [ABSTRACT FROM AUTHOR]

[Display omitted] • Generalised framework for modelling of swellable microneedles for drug delivery. • Rate of insulin release is faster in initial phase, followed by a steady rate of release. • Insulin diffusion rate from reservoir to skin increases with the increase in the polymer hygroscopic swelling. • Skin viscoelasticity pushes swelled microneedle upward, decreasing insulin diffusion rate. Recently, swellable microneedles (SMNs) have gained significant interest due to their potential applications in transdermal drug delivery (TDD). SMNs swell by absorbing interstitial fluid in the skin, and they facilitate drug transport into the skin without polymer dissolution. To establish the SMNs for practical usages, one requires an accurate understanding of the drug transport mechanisms from these MNs that would allow both controlled TDD as well as optimisation of the MN geometries. In addressing this issue, a mathematical model is formulated which incorporates the key parameters defining the skin properties and physicochemical properties of the MNs, such as the polymer swelling capacity, mechanical deformation, amount of drug loading, and penetration depth in the skin. Numerical simulations are performed using well-defined MN parameters which represent the MN constituent and drug, namely, polyvinyl alcohol and insulin, in this study. The proposed mathematical model predicts that insulin diffuses into the blood to reach a maximum concentration of 11.72 ng/ml in 1.5 h, and ~95% insulin is eliminated from the blood within 11 h. The rate of insulin diffusion to blood increases with an increase in MN swelling. However, not much difference is observed in the insulin profiles when MNs swell more than two times their mass as the skin tends to displace the MN out of the skin, as it increases the length of the diffusion pathway for the drug in the skin layers. The depth of MN penetration affects the overall insulin delivery, with insulin getting eliminated from the body in 8 h when MN is fully inserted into the skin. The rate of change of blood concentration of insulin is found to increase and decrease non-linearly with time for the cases studied in this paper. The developed model provides a platform for optimization of SMNs for the controlled delivery of different drug molecules. [ABSTRACT FROM AUTHOR]

This paper deals with a mathematical modeling of fully coupled reinforced magneto-electro-thermo-mechanical behaviors. The modeling is based on micro–macro transition inclusion problem using the localization and concentration tensors. Various micromechanical modelings are elaborated mainly the Mori–Tanaka, self-consistent, incremental self consistent and the differential approaches. The higher dispersion between the magneto-electro-elastic properties leads to ill-conditioned concentration tensors. This drawback is point out here and a remedy based on block matrix formulations leading to well-conditioned tensors is proposed. The effective magneto-electro-thermo-elastic moduli are derived as a function of the resulting well-conditioned magneto-electro-thermo-mechanical concentration tensors. These properties are derived for different inclusion's volume fraction, types, and shapes, and compared with the existing results. • Mathematical modeling of effective properties of fully coupled thermo-magneto-electro-mechanical heterogeneous materials. • General mathematical formulation using the thermal strain effect and elliptic integrals. • Elaboration of thermo-magneto-electro-elastic Green's functions and integral equations formulations. • Well conditioned localization tensors based on block decompositions for stable and accurate modeling. • Computation of effective anisotropic thermal conductivity and thermo-magneto-electro-elastic coefficients. [ABSTRACT FROM AUTHOR]

In the paper we investigate the effect of friction on the stress–strain state of a hyperelastic membrane in contact with a massive solid body. We consider the axisymmetric indentation of a thin circular rubber membrane by a cylindrical indenter for different contact conditions: dry contact, liquid lubrication, and gel lubrication. We present experimental results and a mathematical model of the indentation. We use the non-linear theory of elastic membranes and the Coulomb friction model in the simulation. We describe different contact conditions with different friction coefficients. We compare the experimental and theoretical dependences between indentation force and indenter displacement. • Mechanics of indentation process of circular membrane by cylindrical indenter is presented. • Experiments dry contact and contact with lubricates are presented. • Contact problem for different friction coefficient is numerical simulated. • Experimental and simulation results are compared. [ABSTRACT FROM AUTHOR]

The paper investigates peculiarities of infection evolution and spreading models in non-uniform biological systems of individuals (humans, animals, plants) using the approach of mathematical modeling. The effects of different characteristic features are revealed. A robust tool for prediction of infection evolution (growth and spreading) under different external conditions is developed accounting for spatial non-uniformity of governing parameters. • Peculiarities of infection evolution and spreading models in non-uniform systems studied. • Evolution of systems of individuals (humans, animals, plants) is subject for mathematical modeling. • The effects of different characteristic features are revealed. • A robust tool for prediction of infection evolution is developed accounting for spatial non-uniformity. [ABSTRACT FROM AUTHOR]

Coalitional games are mathematical models suited to study payoff distribution problems in cooperative scenarios. In abstract terms, a coalitional game can be specified by explicitly listing all possible—in fact, exponentially many—coalitions with their associated distributions. This naïve representation, however, quickly becomes infeasible over games involving many agents, thereby calling for suitable compact representations, that is, encoding mechanisms that (on some specific classes of games of interest) take an amount of space that grows polynomially with the number of agents. To date, a plethora of compact encodings have been already introduced and analyzed from the algorithm and computational viewpoints. Despite their specific technical differences, these encodings typically share the assumption that the utility associated with a coalition can be freely transferred among agents. Indeed, designing encoding mechanisms for the non-transferable utility (NTU) setting is a research issue that has been largely unexplored so far. The paper addresses this issue by proposing a compact encoding for representing and reasoning about the outcomes of NTU coalitional games founding on answer set programming. By exploiting the expressiveness of this well-known knowledge representation formalism, it is shown that the proposed representation can succinctly encode several games of interest within a wide range of application domains. Computational issues arising in the setting have been studied too, by addressing questions related to certain payoff distributions enjoying desirable stability properties. Eventually, a prototype system supporting the proposed framework has been implemented by leveraging a state-of-the-art answer set engine, and results of a thorough experimental activity conducted on top of it have been discussed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model to predict the relative permeability of ideal MMMs, which requires the specification of only two geometrical parameters: the relative projected area available for permeation and the relative thickness of the nanofiller. The model, which was initially developed for MMMs with impermeable nano cuboids, provides excellent predictions for nanofillers with a constant relative thickness (i.e., vertical cylindrical and elliptical rods and vertical tubes) with the average prediction error not exceeding 2.1%. For nanofillers with a variable relative thickness (i.e., spheres, horizontal cylindrical and elliptical rods, horizontal tubes), the model is still excellent using the average relative thickness, which is the ratio of the volume fraction and the projected area of the nanofiller. The corresponding average prediction error does not exceed 8%. Except for spherical nanofillers, the model is superior to seven theoretical models commonly used in the literature. After introducing a simple correction factor, the prediction error decreases to 2.3% or less for spheres and horizontal cylindrical and elliptical rods. [Display omitted] • A generalized mathematical model with only two geometrical parameters is proposed to predict the relative permeability of mixed-matrix membranes with diverse filler geometry; • The model applies to MMMs with both vertical and horizontally aligned fillers; • The new model is superior to predict the relative permeability of seven commonly used theoretical models. • The new model's average prediction error of MMMs with each type of vertically oriented nanofillers is less than 2.1%. • The modified new model's average prediction error of MMMs with each type of horizontally oriented nanofillers is less than 2.4%. [ABSTRACT FROM AUTHOR]

• Proposing an improved group teaching optimization algorithm for UAV route planning. • A mathematical model of UAV route planning with various obstacles is established. • Introducing a segment-based fitness strategy to deal with the constraints. • Performance of the method is assessed on 3 different UAV flight environment models. • The proposed algorithm is very competitive and superior to the compared algorithms. The complexity and diversity of the flight environment pose great challenges to unmanned aerial vehicle route planning, which demands feasible flight strategies and efficient route planning algorithms. To address the issue, this paper constructs a 3-D flight environment model with multiple obstacles, and designs a novel diversified group teaching optimization algorithm for the generation of flight routes of unmanned aerial vehicles. In the environment model, a variety of obstacles are taken into consideration to make the flying scenarios more realistic, including mountain, cuboid, cylinder and triangular prism, and corresponding strategies are presented for unmanned aerial vehicles to safely avoid these obstacles. In the proposed algorithm, three novel teaching methods are introduced to balance the exploitation and exploration phases. Besides, a novel constrained optimization strategy is adopted, in which constraints are incrementally added to the fitness function to avoid the premature phenomenon in the initial iteration stage of algorithm. The experimental results show that compared with several state-of-the-art optimization algorithms, the proposed algorithm is significantly superior and can always generate the optimal flight route in complicated environments. [ABSTRACT FROM AUTHOR]

• For the first time, pilot-plant scale Scheibel column was investigated for the extraction of zinc ions. • For the first time, the forward mixing model was used for the description of mass transfer coefficient in this column • Results showed the positive and negative effects of operating parameter on the backflow and axial dispersion coefficients. • The FMM model has good desirability to predict the volumetric overall mass transfer coefficients. An experimental and modeling study on the feasibility of the Scheibel extraction column is presented for heavy metal separation from chloride solution. In continuous experiments, the chemical reaction systems are considered zinc extraction by D2EHPA and stripping from the loaded organic phase by chloride solutions. The experimental results represent that the best extraction and stripping rates in the agitation speed of 155 rpm and the rate of inlet phases of 28 L/h are equal to 99.56% and 98.11%, respectively. The mass transfer modeling by applying the forward mixing is interpreted for solvent extraction systems in this column with the fitting technique. The calculated results from theoretical models compared with the experimental data, which good agreement was obtained using this model. The mathematical modeling on the reactive mass transfer data expresses that the axial dispersion coefficients based on the dispersed phase improve with increased agitation speed and dispersed phase rate. In contrast, these coefficients have an inverse relation with the continuous phase flow rate. This paper's experimental and numerical study provides beneficial information about the metallurgical industry to design solvent extraction columns. [Display omitted] [ABSTRACT FROM AUTHOR]

• Mathematical model of carousel for continuous slurry filtration, washing and drying. • Multi-component differential mass, energy and momentum balances. • Experimental model calibration to isolation of paracetamol crystals. • Identification of probabilistic design space with calibrated model. • Assessment of maximum throughput conditions in the design space. This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The simulator includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For a set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The filtration and drying model parameters were identified for the isolation of paracetamol from a crystallization slurry. The calibrated model was used for designing the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity, too. The probabilistic design space, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable, and the maximum throughput were identified. [ABSTRACT FROM AUTHOR]

• Detailed literature survey. • Mathematical modelling and problem formation. • Problem specific implementation of BO and GBMO algorithms. • Evaluation of outcomes and detailed comparison with existing algorithms. • Qualitative and statistical analysis of outcomes. Image processing is one of the major research regions in the most recent decades, in which, image segmentation has gained more popularity due to its enormous applications. Before segmenting an image, it has to be pre-processed using thresholding techniques. A properly thresholded image would give better segmentation results. Many researchers have contributed significant algorithms based on Otsu and Kapur based methods and reported outstanding results. These methods are suitable for bi-level as well as multilevel thresholding cases, however, the process of selecting the optimal thresholds in the case of multilevel thresholding is difficult and time consuming. To avoid this problem, this paper investigates the ability of two different algorithms namely: BOA (Butterfly Optimization Algorithm) and GBMO (Gases Brownian Motion Optimization) to determine the optimal threshold levels for image segmentation. The BOA is inspired from natural behaviour of butterflies which have special style of information propagation based on intensity of fragrance; whereas, GBMO is based on law of motion of gases which have greater ability to trade-off between exploration and exploitation. The candidate solutions were created using image histogram based on selected algorithms, and then they were updated according to the characteristics of each algorithm. The solutions are estimated using Otsu's between class variance as fitness function during the process of optimization. The performance of the proposed algorithms has been evaluated using various benchmark images and has been compared with seven different algorithms such as MFO (Moth Flame Optimization), WOA (Whale Optimization Algorithm), SSO (Social Spider Optimization), FASSO (Firefly Assisted Social Spider Optimization), FA (Firefly Algorithm), SCA (Sine Cosine Algorithm), and HS (Harmony Search). The outcomes have been analysed based on various metrics like fitness value, PSNR and SSIM, as well as time complexity and the Wilcoxon statistical test. The experimental results showed that the proposed methods outperformed the other algorithms. Among BOA and GBMO, BOA yields better results than GBMO for all images at all threshold levels. [ABSTRACT FROM AUTHOR]

The use of scaled vehicle prototyping is driven through costs, safety, and time effectiveness in evaluating state-of-the-art algorithms, particularly to demonstrate and facilitate application in advanced vehicle control and sensing systems. This paper illustrates the use of a car-like 1:5-scale vehicle as a valid and reliable test-bed platform whose lateral dynamics are similar to those of a full-sized vehicle. The validation scheme is proposed by way of three model-based essential concepts, comprising an identification process, a stability analysis step, and a control design. The identification process is proposed to determine the unknown model parameters necessary to correctly describe the vehicle behavior. The mathematical model, validated through a series of matching experiments, is then used for further analysis and control purposes. The analysis design is intended to identify the vehicle's operating conditions, according to its stability properties. Finally, a lateral control strategy that merges knowledge acquired from these analyses is introduced for a path-following system. For each presented characterization, a wide set of experiments were carried out using the scaled vehicle platform. A persistent agreement between expected (simulated) and achieved (measured) lateral response was obtained. The results demonstrate a systematic consistency regarding the platform test-bed and the behavior described by the mathematical model. [ABSTRACT FROM AUTHOR]

• A mathematical model to evaluate motion-induced forces on slender structures subjected to thunderstorm outflows is proposed. • Time-varying aerodynamic damping matrix and aerodynamic stiffness matrix of the system are defined. • Three levels of analysis are addressed, according to the characteristics of the wind event. • The classical solution for synoptic winds is found as a particular case. • Suitable wind fields compatible with single-point records are rebuilt and applied on two configuration of a structure, estimating the motion-induced forces. • The effects of the transient aeroelasticity are analysed and critically commented. • Motion-induced forces are evaluated on slender real structures. Literature is rich about studies which concern the insurgence of instability of structural systems under the action of steady flows, which have led to well-consolidated methods to assess the structural safety with regard to aeroelastic phenomena induced by synoptic winds. On the other hand, there are no clear references regarding aeroelasticity induced by the effect of thunderstorm outflows, events characterised by irregular variations of the wind speed and direction in a limited duration of time, which prevent the furnishing of a long time interval for the structure and the fluid to synchronise. This paper, based under the hypothesis of applicability of the classic strip and quasi-steady theory, illustrates a mathematical model able to consider the aeroelastic effects of thunderstorm outflows into the dynamic response of slender structures, from which the methodology for synoptic winds may be found as a particular case. After defining three different levels of analysis, applications regarding the Endless Column by Constantin Brâncuși under the action of ten thunderstorm outflows are proposed, whose outcomes are critically analysed and commented. [ABSTRACT FROM AUTHOR]

The paper is the first to propose the mathematical model of the process of intermetalliс composite synthesis in the mode of dynamic thermal explosion taking into account the stages of chemical reactions, the rate of which slows down with the increase of the refractory product content. The model takes into account the mutual influence of thermal and chemical processes, as well as the condition of conjugate heat exchange with the reactor walls. Examples of composition evolution in the synthesis process are given. According to the results of the study, diagrams have been compiled for the first time to illustrate changes in the phase composition of the product depending on changes in the composition of the initial pressed material. It is shown that as a result of bulk synthesis a composite of complex composition is formed. It was found that in the studied conditions in the general case there is an inhomogeneous distribution of concentrations. [ABSTRACT FROM AUTHOR]

• Mathematical models of ion transport through specific ion channels are discussed. • Various mathematical kinetic models for specific membrane carriers are presented. • Models of electrical fluxes carried by symporters, antiporters, and pumps are presented. • This work provides tools for simulation of ion transport across the cell membrane. Mathematical modeling of membrane transporters and ion channels helps researchers obtain a thorough understanding of the complex regulation process between membrane transporters, estimate the changes and uncertainties of their mechanisms, and study how disorders in these procedures may cause disease. A unified mathematical framework that includes most available models for ion channels and transporters can help scientists choose the appropriate model more conveniently. In this regard, this work presents a comprehensive, up-to-date set of currently available mathematical equations for modeling ion channel and membrane transporter mechanisms. This paper aims to provide a mathematical framework for modeling molecule transport, such as nutrients, inorganic ions, drugs, and toxins, across the cell through membrane transporters and ion channels. Our purpose is to substantially save the time and resources needed to find the mathematical models available for different classes of membrane transporters and improve communication between scientists with different backgrounds interested in this field. [ABSTRACT FROM AUTHOR]

The COVID-19 virus in a short time has caused a terrible crisis that has been spread around the world. This crisis has affected human life in several dimensions, one of which is a sharp increase in urban waste. This increase in waste volume during the pandemic, in addition to the intense increase in costs associated with the risks of virus contagion through infectious waste. In this study, a hybrid mathematical modelling approach including a Bi-level programming model for infectious waste management has been proposed. At the higher level of the model, government decisions regarding the total costs related to infectious waste must be minimized. At this level, the collected infectious waste is converted into energy, the revenue of which is returned to the system. The lower level relates to the risks of virus contagion through infectious waste, which can be catastrophic if ignored. This study has considered the low, medium, high and very high prevalence scenarios as key parameters for the production of waste. In addition, the uncertainty in citizens' demand for waste collection was also included in the proposed model. The results showed that by energy production from waste during the COVID-19 pandemic, 34% of the total cost of collecting and transporting waste can be compensated. Finally, this paper obtained useful managerial insights using the data of Kermanshah city as a real case. [Display omitted] [ABSTRACT FROM AUTHOR]

The high-angle-of-attack uncommanded roll oscillations are dangerous and cause significant challenges in flight control. This paper focuses on investigating the feasibility and performance of an artificial-intelligence method - "model-free reinforcement learning" (MFRL) on this issue, in both simulation and experiment. In simulation, two algorithms TD3 and SAC were used to learn the policies to suppress the roll oscillations of a widely used mathematical model. The agents only utilized current states as observation vector, and achieved perfect results. In the experiments, these two algorithms were used to learn the policies to suppress the roll oscillations of a flying-wing model which utilized spanwise blowing as its roll effectors. It is worth noting that unlike in simulation, the experiments investigated the influence of the observation vectors' memory size on training results. The results show that for both algorithms, the agents cannot learn good-enough policies when their observation spaces were constructed only by the current sensor data. This phenomenon, which is a big difference between experiments and simulations of MFRL, is due to the non-Markovian characteristic of the real-world dynamics caused by inevitable latencies. However, constructing the observation space using current and past sensor data help both the TD3 and SAC agents to learn great policies to suppress the oscillations using spanwise blowing in real world. Surprisingly, the trained agents showed some counterintuitive "smart" behaviors in tests. [ABSTRACT FROM AUTHOR]

Lattice-type structures can provide a combination of stiffness with light weight that is desirable in a variety of applications. Design optimization of these structures must rely on approximations of the governing physics to render solution of a mathematical model feasible. In this paper, we propose a topology optimization (TO) formulation that approximates the governing physics using component-wise reduced order modeling as introduced in Huynh et al. (2013); Eftang and Patera (2013), which can reduce solution time by multiple orders of magnitude over a full-order finite element model while providing a relative error in the solution of < 1%. In addition, the offline training data set from such component-wise models is reusable, allowing its application to many design problems for only the cost of a single offline training phase, and the component-wise method is nearly embarrassingly parallel. We also show how the parameterization chosen in our optimization allows a simplification of the component-wise reduced order model (CWROM) not noted in previous literature, for further speedup of the optimization process. The sensitivity of the compliance with respect to the particular parameterization is derived solely at the component level. In numerical examples, we demonstrate a 1000x speedup over a full-order FEM model with relative error of < 1% and show minimum compliance designs for two different cantilever beam examples, one smaller and one larger. Finally, error bounds for the displacement field, compliance, and compliance sensitivity of the CWROM are derived. • For the first time, CWROM is applied to lattice-type structure design to accelerate the whole design optimization process. • The compliance sensitivity for the CWROM is derived at the component level. • Error bounds for the displacement, compliance, and its sensitivity for the CWROM are derived. • A speedup of 1,000x and relative error of less than 1% are demonstrated in compliance minimization problems. • The reusability of the trained components for the lattice structure is demonstrated in the design optimization problems. [ABSTRACT FROM AUTHOR]

The question about how to choose the best recycling policy is particularly relevant in developing countries. On the one hand, many of these countries have no national waste management strategies, with recycling programs emerging as an independent initiative financed by municipalities, mostly in urban areas. On the other hand, the municipal budget tends to be linked to the wealth of their inhabitants. Consequently, an efficient municipal resources allocation policy to support recycling programs is especially crucial for the most vulnerable population, often located in rural areas. In this paper, we address the problem of optimal allocation of resources for the adoption of a recycling program by municipalities located in both urban and rural areas of a developing country, determining the best combination between two options: increasing and redistributing existing resources. Our research methods involve comprehensive data collection and mathematical modelling, adopting a logistic regression model proposed in the literature, formulating a non-linear optimisation model, and implementing a resolution method. In addition, recycling policies with equivalent results are described and analysed, discussing their political feasibility and economic efficiency. To illustrate our proposal, the case of Chilean municipalities is considered. Chile is a country from the Global South that belongs to the OECD and can be therefore compared to other documented cases. Although no official national data is available, we were able to collect figures by requesting information from every municipality in the country. The obtained results show an important lack of resources at the national level in MSWM, especially affecting rural areas. In this context, equivalent recycling policies with combined options could better favour rural municipalities. However, the average probability for the adoption of a recycling program by municipalities, although shows slight increases, is not enough for a substantial change towards a more sustainable waste management model. Our results illustrate the gap between urban and rural municipalities, the need to inject important funds into the system as a whole, and open the question of whether a more direct 'command and control' policy is required instead of just trusting the individual initiative of each municipality, even if more resources are provided. [ABSTRACT FROM AUTHOR]

In this paper, a methodological approach for the three-dimensional static behavior of functionally graded magnetoelectroelastic rectangular plates is elaborated. The state-space method is used to establish the set of state-space equations for arbitrary graded functionalities throughout the thickness direction of multilayered composite plates. To solve the resulting state-space equations, a numerical scheme that consists of dividing the heterogeneous plate into a number of virtual layers having homogeneous properties calculated in the thickness average sense within the corresponding layer is elaborated. In each virtual homogeneous layer the propagator matrix method is used and a recursive relationship is resulted. The proposed mathematical modeling has been validated and the convergence according to the number of virtual layers has been analyzed. The obtained results are in agreement with the available ones given by the pseudo-Stroh formalism, the finite elements method, the modified Pagano method and the three-dimensional asymptotic approach. Various types of graded properties can be easily considered. It has been revealed that the materials properties gradient index and the loading conditions remarkably influence the static response of functionally graded composites plates. [ABSTRACT FROM AUTHOR]

The objective in this paper is to determine analytically the maximally sustainable pro-rata density-dependent harvest rate of a hypothetical biological species on the spatial boundary of its habitat, which is otherwise a protected zone (i.e. no harvesting of the species is allowed in the interior of its habitat). This is achieved by analysing an abstract mathematical model for the spatio-temporal evolution of the species density over its habitat if it is subjected to a continuum of potential pro-rata density-dependent harvest rates on the spatial boundary. The model takes the form of an initial-boundary value problem involving a reaction-diffusion equation in which the reaction term is a concave function of the population density and Robin boundary conditions are prescribed. A long-time asymptotic analysis of the population density is undertaken by invoking classical results from the theory of eigenproblems. In this way, necessary and sufficient conditions on the pro-rata density-dependent harvest rate are established for the existence of a strictly positive equilibrium attractor of model solutions. Moreover, important necessary properties of this equilibrium attractor are established to guarantee the existence of a density pro-rata harvest rate which maximises the total harvest per unit time at equilibrium. • A model for describing the spatio-temporal evolution of a biological is analysed. • The existence of a unique, positive equilibrium attractor is established. • Important characteristics of the unique equilibrium attractor are established. • Monotonic properties of the unique positive equilibrium solution are established. • A guarantee of the existence of an optimal pro-rata harvest rate is established. [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose an architecture of a probabilistic KBS that meets the need of the consistency assurance and the compliance with probability rules. • Build a mathematical model for the merging problem that takes into account inconsistency levels, the structural diversity of probabilistic knowledge bases, and ensure the consistency for the joint probabilistic knowledge base. • Prove the reliability of the proposed merging model in both the theoretical and experimental aspect. • Investigate a family of merging operators with a large range of divergence distance functions between probability distributions. The knowledge-based systems (KBSs) in general and solving the knowledge merging problem in particular have seen a great surge of research activity in recent years. However, there still exist two main shortcomings that need to be addressed in the probabilistic framework. Firstly, the current methods only deal with the problems in which original probabilistic knowledge bases (PKBs) are required to be consistent and formed in the same structure. It is a very strong requirement and difficult to apply in practice. Secondly, only a few measures of distance between probability distributions have been studied to apply in existing models. To overcome these disadvantages, in this paper, we introduce a novel framework for merging PKBs. To this end, a theoretical model is introduced and several experiments are implemented. In theoretical model, some theorems are pointed out and proved to provide mathematical background to construct the merging model. Moreover, a deep survey on how to employ divergence distance functions (DDFs) between probability distributions to carry out the merging process are performed. In experimental aspect, a consistency recovery algorithm and some merging algorithms based on DDFs are proposed. Through the results of conducted experiments, issues about the time cost of merging process, the number of iterations, and CPU Time Elapsed parameter to solve the class of optimization problems in the merging process are analyzed, compared, and evaluated. [ABSTRACT FROM AUTHOR]

• Increasing the pressure in the hose results in a reduction in length. • Increasing the flow rate in the hose results in its elongation. • Changes due to internal pressure are immediate. • Changes due to the flow rate are gradual. Composite materials are gaining more and more popularity in mechanical engineering. It is no different in the area of hydrostatic drive, where they are mainly used in high-pressure hydraulic hoses. The paper presents experimental studies of dynamic changes in the length of a microhydraulic hose under the influence of step pressure and flow load. It has been shown that an increase in internal pressure leads to ever greater shortening of the hose, while an increase in the flow rate leads to its elongation. It has also been shown that these two changes do not occur simultaneously. Length changes due to pressure take place virtually immediately, while those due to flow rate are much slower. A mathematical model that describes these changes based on a continuous system was also presented. The Matlab Simulink package was used to solve the model numerically. [ABSTRACT FROM AUTHOR]

The paper presents the development of a complete analytical model of a thermoelectric generator (TEG). Based on data coming from a small number of easily obtainable measurements, the model makes it possible to determine basic parameters of the TEG. Owing to measurements performed in the steady state and immediately after a rapid change of the TEG electrical state, the following quantities can be established analytically: the Seebeck coefficient, thermal conductivity and thermal resistance between heat exchangers and the TEG. The applied rapid method is based on the large difference in inertia between the electric and the thermal field. A rapid change from the short-circuit to the open-circuit electrical state makes it possible to record the voltage for the system thermal state corresponding to the short circuit. Experimental tests were carried out on a commercially available thermoelectric generator and the results proved the developed model usefulness. The simulation results show that the thermal resistance of the contact layers between the heat exchangers and the thermoelectric generators caused a loss of the temperature difference of the junctions from 30% at the recommended mechanical load to even over 50% if the clamping force was too low. Due to that, the determined power ratio, being an indicator of the utilization of the theoretical power capacity of a system burdened with thermal resistance, reaches values lower than 40% for the required clamping force and is below 20% for improper contact conditions The impact of the accuracy of the measurements on the values of the system parameters obtained from the model is evaluated by means of sensitivity analysis. [ABSTRACT FROM AUTHOR]

Establishing mathematical models is a ubiquitous and effective method to understand the objective world. Due to the complex physiological structures and dynamic behaviors, the mathematical representation of the human face is an especially challenging task. In this paper, an explicit function called GmFace is proposed for face image representation in the form of a multi-Gaussian function. The model utilizes the advantages of two-dimensional Gaussian function which provides a symmetric bell surface with a controllable shape. The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet. Furthermore, using GmFace, several face image transformation operations can be realized mathematically through simple parameter computation. Experimental results demonstrate that GmFace has a superior representation ability for face images compared to convolutional autoencoder (CAE), principal component analysis (PCA) and discrete cosine transform (DCT) method. • A mathematical model (GmFace) for face image representation is proposed in the form of a multi-Gaussian function. • A neural network (GmNet) is designed for parameter solving of multi-Gaussian function. • A modeling method for a personal face image with GmFace is presented. • GmFace is the first mathematical face model in an explicit function form. [ABSTRACT FROM AUTHOR]

The wellbeing of modern societies is dependent upon the functioning of their infrastructure networks. This paper introduces the 3C concept, an integrative multi-system and multi-stakeholder optimization approach for managing infrastructure interventions (e.g., maintenance, renovation, etc.). The proposed approach takes advantage of the benefits achieved by grouping (i.e., optimizing) intervention activities. Intervention optimization leads to substantial savings on both direct intervention costs (operator) and indirect unavailability costs (society) by reducing the number of system interruptions. The proposed optimization approach is formalized into a structured mathematical model that can account for the interactions between multiple infrastructure networks and the impact on multiple stakeholders (e.g., society and infrastructure operators), and it can accommodate different types of intervention, such as maintenance, removal, and upgrading. The different types of interdependencies, within and across infrastructures, are modeled using a proposed interaction matrix (IM). The IM allows integrating the interventions of different infrastructure networks whose interventions are normally planned independently. Moreover, the introduced 3C concept accounts for central interventions, which are those that must occur at a pre-established time moment, where neither delay nor advance is permitted. To demonstrate the applicability of the proposed approach, an illustrative example of a multi-system and multi-actor intervention planning is introduced. Results show a substantial reduction in the operator and societal costs. In addition, the optimal intervention program obtained in the analysis shows no predictable patterns, which indicates it is a useful managerial decision support tool. • Multi-system optimization approach for infrastructure intervention planning; • Mathematical model and simulation approach for intervention scheduling that is simple and easily scalable. • Multiple infrastructure networks can be considered in the analysis as well as the societal impact; • Accounting for several types of intervention activities, such as upgrading and removal; • Accounting for central interventions that are usually implemented with a fixed time interval; • Interdependencies with and across infrastructure systems are tackled by introducing the interaction matrix; • Illustrative example to demonstrate its applicability. [ABSTRACT FROM AUTHOR]

• The energy-oriented activity splitting and recombining proactively. • Dynamic fuzzy clustering strategy for the time-varying resource consumption level. • Metaheuristic solution for project scheduling with different granularity levels. Energy-cost-aware scheduling in manufacturing process has recently been considered as an effective way to use energy. In addition, applying time-of-use is regarded as one of the optimal uses of energy which the governments extend to control energy consumption. Complex product system (CoPS) has more complex and huge amounts of multiple manufacturing activities with higher energy sensibility in project scheduling than commodity products. This paper presents a new variant of resource-constrained project scheduling problem (RCPSP), which is named energy-cost-aware resource-constrained project scheduling problem with activity splitting and recombining (eRCPSP-AS&R) for CoPS. In the proposed model, a dynamic activity splitting and recombining strategy for CoPS is used by considering energy consumption. Also, a bi-objective (project delay and energy consumption cost) mathematical model is established. The solution framework of eRCPSP-AS&R model consists of two parts: activity splitting and recombining, project scheduling. The dynamic strategy of activity splitting and recombining is used to perform energy-cost-aware based on hybrid intuitionistic fuzzy information entropy, TOPSIS and fuzzy clustering method. Moreover, an improved NSGA-II algorithm is employed to solve the Pareto-optimal solutions for the proposed bi-objective optimization problem. Series of computational experiments are conducted and verified the overall performance of proposed eRCPSP-AS&R model and approaches. [ABSTRACT FROM AUTHOR]

The problem of maximizing dispersion requires the selection of a specific number of elements from a given set, in such a way that the minimum distance between the pairs of selected elements is maximized. In recent years, this problem has received a lot of attention and has been solved with many complex heuristics. However, there is a recent variant in which the selected elements have to satisfy two realistic constraints, a minimum capacity limit and a maximum budget, which in spite of its practical significance in facility location, has received little attention. In this paper, we first propose mathematical models to obtain the optimal solution of small- and medium-size instances, and then present new metaheuristic procedures for finding approximate solutions to target large-size instances. Specifically, we develop a GRASP and a tabu search to obtain high quality solutions in short computational times. We perform extensive experimentation to compare our heuristic proposals with the optimal solutions obtained with the models applied to the Gurobi optimizer, as well as with a previous heuristic. Statistical tests confirm the superiority of our methods. [ABSTRACT FROM AUTHOR]

Road traffic collisions (RTC) analysis is almost a daily activity in many autopsy room. Especially when analyzing an RTC with multiple occupants in the car, it can be necessary to distinguish driver from front seat passenger in order to provide the judicial authority with elements useful to understand and to prove who was driving, considering the criminal and civil responsabilities that may derive from it. Despite this, it is beyond doubt that there is enormous difficulty in providing such information. The aim of this paper is then to evaluate whether it is possible to differentiate driver and front seat passenger in case of fatal collisions using a mathematical model based on injury pattern alone. Autopsy reports concerning 90 drivers and 60 front-seat passengers were analyzed. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger, considering skin, skeletal and visceral injuries. Results show that certain skin injuries, fractures and internal organ lesions are possibly associated with drivers and front seat passenger status and the overall injury pattern seems to be able to provide useful information. A mathematical model is presented. The process to distinguishing driver from front seat passenger following fatal motor vehicle collision may use multiple sources of information, including autopsy injury pattern analysis. [ABSTRACT FROM AUTHOR]