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The purpose of this paper is to obtain knowledge about one of the issues that affects the ecosystem in Guanabara Bay, Rio de Janeiro State, Brazil. Studies on the environment are of fundamental importance if the quality of life of the inhabitants of certain regions is to be assured.

Physicochemical and biological analysis were performed of samples collected at four points along the river. Some of the parameters established by CONAMA Brazilian Regulation #357/05 were evaluated.

This paper did a physicochemical and biological characterization of a significant tributary river to the Guanabara Bay. This level of characterization has not been done before. The results achieved show that this river was in a worse condition than what was expected.

With its potential for tourism and fishing, and its importance to Rio de Janeiro City and the State as a whole, Guanabara Bay should be the object of more in‐depth studies and actions of a practical nature to bring about its ecological recovery.

This paper is a new source of information about this important tributary, as is Faria‐Timbó River to Guanabara Bay. This bay borders on a number of municipalities in the State and is of crucial importance not just for the neighboring towns and cities, but also for the entire State because of the fish stocks it harbors, which are consumed in the region.

The purpose of this study is to propose an approach for assessing sustainability aspect of facility layouts. Activity relationship chart (ARC) is a widely used assistive tool for facility layout design which assesses closeness requirement among all pairs. In a typical ARC, there are six closeness ratings which may be assigned to each pair of departments. Ratings are evaluated by considering a single factor or an overall rating is provided. On the other hand, there may be more than one criterion which has to be evaluated separately and then a fair decision should be taken by considering the relative weight of factors. With this purpose in mind, ARC alternatives are evaluated on the basis of three factors which are environmental aspect, safety concerns and manufacturing efficiency factors by using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.

In this paper, more than one ARC alternatives are evaluated by considering environmental, safety and manufacturing efficiency factors. Alternatives are then ranked via TOPSIS. An empirical case study is also presented to validate how effective the proposed model performs.

The TOPSIS method can be successfully used to choose the most sustainable ARC alternative appropriately by the help of experts. The proposed approach can be beneficial to researchers and practitioners for better understanding the ARC evaluation problem from the perspective of sustainability.

The obtained results confirm that the proposed model can assist practitioners in refining their decision processes, particularly in cases where there are many ARC alternatives to be considered.

This is the first time that this method is used in such a context. This study contributes to literature by presenting practical applications of TOPSIS in the ARC assessment field. It should be highlighted that there is no comparable research on the evaluation of ARCs using TOPSIS. Thus, this study fills the gap by providing an analytical approach in this area.

The principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.

In order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.

Brushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.

The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

The purpose of this paper is to address the case of toxic metal contamination of Sepetiba Bay caused by the Ingá Company. The paper reviews the history of the contamination and discusses the current presence of metals in the bay sediments, demonstrating that the toxic metals are clearly enriched. Sepetiba Bay is prone to significant dredging activities that make metals available in the food chain, affecting human populations, mainly fishermen communities.

The study presents the case of the Ingá Company based on international literature and data provided by previous studies.

Through the analysis and compilation of diverse data from the literature, this study demonstrates that the Ingá Company is a major source of Cd, Pb and Zn due to its calamine processing activities used to obtain high purity Zn.

This study highlights important research to complete the historical scenario of heavy metal contamination of the Sepetiba Bay by Ingá Company. The results indicate that the contaminants from the Ingá Company can indeed be traced in the sediments of Sepetiba Bay. These data have the utmost value for the environmental management of this coastal system, because such high concentrations of toxic metals in marine sediments have serious implications for the environmental quality of the bay and may negatively affect biota and human health. Therefore, this study suggests that it is now necessary to monitor this region for contamination continuously.

This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.

The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.

Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.

For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.

The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

Learning as the way in which labor acquire new knowledge and skills has important strategic implications for the competitive advantage of an organization. The purpose of this paper is to present an agent-based modeling (ABM) approach to investigate the learning behavior of workers. The effect of interactions among different workers as well as the factors affecting the workers’ learning behavior is assessed using the proposed ABM approach.

For this purpose, the processes through which the competency value of worker is changed are understood and the workers’ learning behavior is modeled, taking account of various influencing factors such as knowledge flow, social ability to teach and forgetting factor.

The proposed model is implemented on a real steel structure project to evaluate its applicability and performance. The variation in the competency value of different workers involved in the project is simulated over time taking account of all the influencing factors using the proposed ABM approach.

In order to assess the effect of interactions among welders as well as the welders’ characteristics on their learning behavior, the competence value of different welders is evaluated.

This research presents an ABM approach to investigate the workers’ learning behavior. To evaluate the performance of the proposed ABM approach, it was implemented on a real steel structure project. The learning behavior of different welders (agents) was simulated taking account of their interactions as well as the factors affecting the welders’ learning behavior. The project involved the welding of a 240-ton steel structure. The initial project duration was estimated as 100 days. In this project, it has been planned to execute the welding process using three different welders namely welder A, B and C.

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes.

A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented.

Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics.

This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.

The use of mobile devices by children and adolescents is increasing significantly; therefore, it is relevant to research the level of advertising literacy (AL) of parents who act as mediators between children and mobile advertising. This study aims to explore the conceptual, moral and attitudinal dimensions of AL and its relationship with different styles of parental control.

A cross-sectional survey was applied simultaneously to a sample of parents with children between 5 and 16 years old in three Spanish-speaking countries: Mexico, Spain and Colombia. Participants from the three countries were recruited via online social media networks and were asked to fill in an online survey. A questionnaire, which has been adapted from previous literature to suit the mobile advertising context and the population of interest, was designed. Cross-country samples of varying sizes, with a predetermined quota of 200 participants for each country, were used. The total sample consisted of 1,454 participants.

Four factors of mobile AL were found, which, to a greater extent, correspond to the dimensions of AL proposed in the literature. The following are the dimensions that were identified: cognitive, moral, attitudinal and an emerging factor is known as “children’s perceived mobile AL.” Differences in parents’ perceived knowledge of mobile advertising, parental control styles and AL levels in the three countries were identified. Parents with an authoritative style were identified to have more knowledge than those with an indulgent style. Differences were also identified between countries concerning the amount of exposure that children have to mobile advertising, while no significant differences were found in the moral dimension.

Marketing practitioners and public policymakers must consider that parents differ in some dimensions of AL. Parents also seem to lack adequate knowledge about the advertising tools available to announcers that affect children and adolescents in a mobile communication environment. Therefore, government agencies should consider developing mobile digital media literacy programs for parents.

This paper explores the dimensions of AL applied to the mobile context and identifies the level of parental mobile AL in three Spanish-speaking countries, as well as the differences between these sub-samples concerning parental mobile AL profiles and parental control styles, thus expanding the literature on AL with a cross-cultural approach.

The purpose of this paper is to propose a new scheme for obtaining acceptable solutions for problems of continuum topology optimization of structures, regarding the distribution and limitation of discretization errors by considering h-adaptivity.

The new scheme encompasses, simultaneously, the solution of the optimization problem considering a solid isotropic microstructure with penalization (SIMP) and the application of the h-adaptive finite element method. An analysis of discretization errors is carried out using an a posteriori error estimator based on both the recovery and the abrupt variation of material properties. The estimate of new element sizes is computed by a new h-adaptive technique named “Isotropic Error Density Recovery”, which is based on the construction of the strain energy error density function together with the analytical solution of an optimization problem at the element level.

Two-dimensional numerical examples, regarding minimization of the structure compliance and constraint over the material volume, demonstrate the capacity of the methodology in controlling and equidistributing discretization errors, as well as obtaining a great definition of the void–material interface, thanks to the h-adaptivity, when compared with results obtained by other methods based on microstructure.

This paper presents a new technique to design a mesh made with isotropic triangular finite elements. Furthermore, this technique is applied to continuum topology optimization problems using a new iterative scheme to obtain solutions with controlled discretization errors, measured in terms of the energy norm, and a great resolution of the material boundary. Regarding the computational cost in terms of degrees of freedom, the present scheme provides approximations with considerable less error if compared to the optimization process on fixed meshes.