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Multiple attributes group decision making (MAGDM) is regarded as the process of determining the best feasible solution by a group of experts or decision makers according to the attributes that represent different effects. In assessing the performance of each alternative with respect to each attribute and the relative importance of the selected attributes, quantitative/qualitative evaluations are often required to handle uncertainty, imprecise and inadequate information, which are well suited to represent with fuzzy values. This paper develops a VIKOR method based on intuitionistic fuzzy sets with multi-judges and multi-attributes in real-life situations. Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate individual judgments of experts to rate the importance of attributes and alternatives. Then, an intuitionistic ranking index is introduced to obtain a compromise solution to solve MAGDM problems. For application and validation, this paper presents two application examples and solves the practical portfolio selection and material handling selection problems to verify the proposed method. Finally, the intuitionistic fuzzy VIKOR method is compared with the existing intuitionistic fuzzy MAGDM method for two application examples, and their computational results are discussed. [ABSTRACT FROM AUTHOR]

Growth ofChlorella vulgarisand its lipid production were investigated under autotrophic, heterotrophic, and mixotrophic conditions. Cheap agricultural waste molasses and corn steep liquor from industries were used as carbon and nitrogen sources, respectively.Chlorella vulgarisgrew remarkably under this agricultural waste medium, which resulted in a reduction in the final cost of the biodiesel production. Maximum dry weight of 2.62 g L−1was obtained in mixotrophic growth with the highest lipid concentration of 0.86 g L−1. These biomass and lipid concentrations were, respectively, 140% and 170% higher than autotrophic growth and 300% and 1200% higher than heterotrophic growth. In mixotrophic growth, independent or simultaneous occurrence of autotrophic and heterotrophic metabolisms was investigated. The growth of the microalgae was observed to take place first heterotrophically to a minimum substrate concentration with a little fraction in growth under autotrophic metabolism, and then the cells grew more autotrophically. It was found that mixotrophic growth was not a simple combination of heterotrophic and autotrophic growth. [ABSTRACT FROM PUBLISHER]

In this research, physical, thermal and mechanical properties of polypropylene composites modified with natural polymers such as starch and corn are studied. Also, the effect of adding eggshell nano-particles as fillers is investigated. 4 weight percentage of maleic anhydride as a compatibilizer of polypropylene was used in order to improve mechanical properties. Polypropylene-wood composites have been introduced as new sources of raw materials in various industries and have found their way through many applications such as automotive, building and furniture's industries. This polymer by itself or in combination with various resistant has good heat resistance, great flexibility at low temperatures and resistance against oxidation and ultraviolet radiation In the current research, bending and tensile tests were utilized for physical and mechanical examinations and also in order to evaluate surface morphology, SEM test was used. Results show that adding maleic anhydride and eggshell nanoparticles instigates improvement in mechanical properties as well as better adhesion between polymer, starch and corn particles. [ABSTRACT FROM AUTHOR]

This paper proposes a model on the basis of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology as a compromised method to solve the Multi-Objective Large-Scale Nonlinear Programming (MOLSNLP) problems with block angular structure involving fuzzy coefficients. The proposed method is introduced for solving large scale nonlinear programming in fuzzy environment for first time. The problem involves fuzzy coefficients in both objective functions and constraints. In this method, an aggregating function developed from LP-metric is based on the particular measure of "closeness" to the "ideal" solution. The solution process is composed of two steps: First, the decomposition algorithm is utilized to reduce the q-dimensional objective space into a one-dimensional space. Then a multi-objective identical crisp non-linear programming is derived from each fuzzy non-linear model for solving the problem. Second, for finding the final solution, a single-objective large-scale nonlinear programming problem is solved. In order to justify the proposed method, an illustrative example is presented and followed by description of the sensitivity analysis. [ABSTRACT FROM AUTHOR]

In the present work, three-dimensional computational fluid dynamics analysis is employed to study the droplet dynamics of Newtonian and non-Newtonian droplets impinging on a hot surface under various impact conditions. The Navier-Stokes equations for unsteady, incompressible, and viscous fluid flow are solved using a control volume method. The volume-of-fluid (VOF) technique is also used to track the free-surface of the liquid. The effect of viscosity, density and surface tension on droplet dynamics is evaluated considering their dependence of temperature. The results indicate that the temperature dependence of the both Newtonian and non-Newtonian physicochemical liquid properties must be considered to obtain better agreement of the numerical results with experimental data. After ensuring the accuracy of the numerical methodology, the internal behavior of the droplets is examined, which is shown that the receding velocity of the non-Newtonian droplet is slower than the Newtonian one. [ABSTRACT FROM AUTHOR]

The use of third-party logistics (3PL) providers is regarded as new strategy in logistics management. The relationships by considering 3PL are sometimes more complicated than any classical logistics supplier relationships. These relationships have taken into account as a well-known way to highlight organizations' exibilities to regard rapidly uncertain market conditions, follow core competencies, and provide long-term growth strategies. Choosing service providers has been considered as a notable research area in the last two decades. The review of the literature represents that neural networks have proposed better performance than traditional methods in this area. Therefore, in this paper, a new enhanced artificial intelligence (AI) approach is taken into consideration to assist the decision making for the logistics management which can be successfully presented in cosmetics industry for long-term prediction of the real performance data. The presented AI approach is based on modern hybrid neural networks to improve the decision making for the 3PL selection. The model can predict the overall performance of the 3PL according to least squares support vector machine and cross validation technique. In addition, the proposed AI approach is given for the 3PL selection in a real case study for the cosmetics industry. The computational results indicate that the proposed AI approach provides high performance and accuracy through the real-life situations prediction along with comparing two other two well-known AI methods. [ABSTRACT FROM AUTHOR]

An analytical solution for bending of variable thickness annular sector plates is presented using the multiterm extended Kantorovich method (MTEKM). Utilizing the principle of minimum total potential energy the governing equations are derived based on the first-order shear deformation theory and converted into two sets of coupled ordinary differential equations (ODEs) using MTEKM. Next, the derived sets of ODEs are solved analytically by the application of state-space method. To show the applicability of the present method various examples are investigated. Moreover, solid sector plates as well as rectangular plates are studied as special cases. Results of the present method are compared to those of other methods whenever possible, as well as results obtained by the finite-element method (FEM). It is found that the method proposed here exhibits a high convergence rate as well as presenting accurate results in all cases. [ABSTRACT FROM AUTHOR]

This study proposes a strategy to make a quantitative correlation between the NMR log-derived free fluid porosity and seismic attributes using multiple linear regression and artificial neural network. At first the well logs tied to seismic data by creating a synthetic seismogram at each well using the sonic and density logs. Then the post-stack seismic data will be inverted to acoustic impedance by applying the created synthetic seismograms. The higher resolution of well logs than seismic data problem is dissolved by three-dimensional modeling and averaging all of the seismic and logs data in each model cell. Then the cell’s properties are processed as inputs and outputs for all operations. Stepwise regression is used to select the best attributes predicting the free fluid porosity. The multiple linear regression equation and its correlation at each step are shown. Finally, an artificial neural network of multi-layer neural network type with back propagation of errors is used for the prediction, and the better method between these two methods was selected to apply it to the whole volume of study. The proposed methodology was applied to the South Pars Gas Field in the Persian Gulf Basin. The seismic attributes were extracted from three separated seismic parts. The petrophysical logs from four wells in these seismic cubes were used for generating and evaluating the reliability of the neural network. [ABSTRACT FROM PUBLISHER]

This study was conducted to investigate the status of Lead (Pb) and Nickel (Ni) in greenhouse cucumber affected by fertigation and spraying as a factorial split plot arrangement based on a randomized complete block design (RCBD) with three replications at Soil and Water Research Institute, Karaj, Iran in 2023. The main and sub-plots were respectively fertigation and spraying that was applied in two levels [the minimum permissible concentration of these metals in granular triple super phosphate fertilizer (five ppm Pb and five ppm Ni) as the control treatment, and the maximum permissible concentration of Pb and Ni in granular triple super phosphate fertilizer (50 ppm Pb and 100 ppm Ni)]. Spraying was done in three modes (spraying leaves, leaves + fruit, and fruit). Based on the results, the highest concentration of Pb in the fruit (fruit peel + fruit flesh) (0.295 mg/kg DW) was recorded in the fertigation + spraying with the maximum permissible concentrations of Pb and Ni treatments. A general comparison between the two fertilization methods showed that the spraying method accumulated a higher concentration of Pb in the fruit (i.e., peel + flesh) than the fertigation method. The reverse of this status happened for Ni, so the fertigation method accumulated a higher concentration of Ni in the fruit (skin + flesh) compared to the spraying method. On average, the order of Pb concentration in different organs was as follows: leaf (0.765 mg/kg) > fruit peel (0.232 mg/kg) > fruit peel + flesh (0.174 mg/kg) > fruit flesh (0.129 mg/kg). This order for Ni was as follows: fruit flesh (0.597 mg/kg) > fruit peel + flesh (0.345 mg/kg) > leaf (0.3 mg/kg) > fruit peel (0.175 mg/kg). These orders show that the status of the heavy metals in plant tissues is related to the nature of the metal, the type of the organ, and the method of fertilization., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Human drivers and machine drivers (i.e., automated vehicles or AVs) will share roads and interact with each other, creating mixed traffic. In this perspective, we develop two mental models about them and their social interactions, aiming to understand the risk implications of AVs and mixed traffic. Based on Mental Model I (i.e., machine drivers are superior drivers without human weaknesses), many simulation-based safety assessments, which often overlook or oversimplify human-AV social interactions, have predicted significant safety benefits when machine drivers interact with or replace human drivers. In contrast, Mental Model II considers human and machine drivers as heterogeneous and incompatible, suggesting that their interactions may lead to unexpected and occasionally negative outcomes, particularly in imminent mixed traffic. This perspective gains support from recent comparative empirical studies that employ various methods such as survey experiments, driving simulators, test-tracks, on-road observations, and AV accident analysis. These studies provide initial evidence of emerging traffic risks arising from human-AV social interactions, including human drivers' aggression and road rage toward AVs, human drivers exploiting AVs, AVs exerting negative peer influences on human drivers, and their incompatibility increasing human drivers' challenges in joining mixed traffic and thus risky behaviors. We propose specific suggestions to mitigate problematic human-AV social interactions and the associated emerging risks., (© 2023 Society for Risk Analysis.)