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In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches.

A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper.

The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems.

The usage of the proposed approach can be valuable to industrial corporations, as it allows for improved workflows, maximization of available robotic operations and improvement of efficiency.

To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Streamlining automated processes is currently undertaken by developing optimization methods and algorithms for robotic manipulators. This paper aims to present a new approach to improve streamlining of automatic processes. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high-flexibility solution to automated processes.

The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path-planning algorithm. Gazebo was the simulation engine chosen, and the robotic manipulator used was the Universal Robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge.

The developed system was able to identify and describe the influence of each joint in the Cartesian space, and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene.

This new system was tested in both real and simulated environments, and data collected showed that this new system performed well in real-life scenarios, such as EuRoC and Amazon Picking Challenge.

The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulator path and motion planning.

The inappropriate lighting methods can have irreversible effects on artworks available in museums and exhibitions. Several factors affect the choice of the lighting system in the museums. By surveying all possible elements, this paper aims to propose a sustainability-based solution, as it relates to the development of artwork conservation, visual perception and energy efficiency during operation and maintenance (O&M).

The paper elicits optimal solutions out of the method presented based on functions and expert opinion to improve lighting quality in existing museums. To study the optimization, the energy consumption and life cycle cost (LCC) in both the proposed lighting and the existing lighting system are compared using HoneyBee and LadyBug plugins in GrassHopper, as well as BLCC5 energy cost estimation software.

The results indicated a practical method to select the most suitable solution for museum lighting. By applying the proposed solutions obtained from the case study, a significant reduction in energy consumption and LCC were achieved. Besides, greenhouse gases were remarkably decreased.

Providing the proper lighting systems for each museum is the issue that is given special attention during the facilities management. The quality of the lighting, energy consumption and costs are analyzed by the simulation software. It is recommended that the validity considerations of the practice are examined.

The present study tried to present an optimal method to choose the best lighting system by the simultaneous utilization of theoretical and practical aspects. The functional model is also introduced for performing the most effective method to enhance the lighting techniques in the art museums.

This study aims to achieve a “pseudo-ductile” behaviour in the response of hybrid fibre reinforced composites under uniaxial traction by solving properly formulated optimization problems.

The composite material model is based on the combination of different types of fibres (with different failure strains or strengths) embedded in a polymer matrix. The composite failure under tensile load is predicted by analytical models. An optimization problem formulation is proposed and a Genetic Algorithm is used. Multi-objective optimization problems balancing failure strength and ductility criteria are solved providing optimal mixtures of fibres whose properties may come either from a pre-defined list of materials, currently available in the market, or simply assuming their continuum variation within predefined bounds, in an attempt to attain unprecedented performance levels.

Optimal solutions of hybrid fibre reinforced composites exhibiting pseudo-ductile behaviour are presented. It is found that a fibre made from a material exhibiting relatively low stiffness combined with high strength is preferred for hybridization. Furthermore, the ratio of the average failure/critical strains between the low and high elongation fibres to be hybridized must be equal or greater than two.

Typically, a ductile failure is an inherent property of metals, that is, their typical response curve after the linear (elastic) region exhibits a yielding plateau still followed by an increase in stress till collapse. In stark contrast, composite materials exhibit (under some loading conditions) brittle failure that may limit their widespread usage. Therefore, a “pseudo-ductility” in composites is valued and targeted through optimization which is the main original contribution here.

Companion animals and social media are two important factors of social interaction and well-being among the older population. This study aims to compare social media use and/or having companion animals with respect to sociodemographic variables in conjunction with loneliness, social isolation, depression, satisfaction with life and satisfaction with social support.

This cross-sectional study involves a sample of 250 older community-dwelling adults. The questionnaire comprised sociodemographic, companion animals and social media questions and scales to assess social isolation, loneliness, satisfaction with life and social support and depression. Data were analyzed using descriptive and inferential statistics.

Four groups emerged: “companion animal/s, no social media” (37.6%); “none” (33.6%); “social media and companion animal/s” (14.4%); and “social media, no companion animal/s” (14.4%). Social media users (with or without companion animals) are the youngest and with higher levels of education; caregivers of companion animals (no social media use) are in-between in terms of age and level of education; and those without companion animals and no social media users are the oldest and with less formal education.

This research examines and compares two key influencers of older adults’ well-being and social interaction (social media and companion animals), that have been researched mostly separately. Findings underlined the cohort effect in the use of social media, suggesting that future older adult cohorts will use more social media whether they have or do not have companion animals.

The purpose of this paper is to present a new index of social vulnerability (SV), based on local level data [statistical blocks (SBs)]. This same methodology was applied before at the municipal level, which is a level of analysis that under-evaluates local spots of high SV, by one side, and generalizes the coverage of support capacity equipment and infrastructure. The geographical level of detail of the input data allows to overcome those limitations and better inform infra-municipal risk practitioners and planners.

The assessment of SV in this paper adopts an inductive approach. The research context of this conceptual and methodological proposal derived from the need to operationalize the concept of SV as a planning tool. This approach required to distinguish between the components of criticality and support capability, as their assessment provides knowledge with distinct applications in risk management. The statistical procedure is based on principal components analysis, using the SB as the unit of analysis.

Support capability acts as a counter-weight of criticality. This understanding is well illustrated in the mapping of each component and the final score of SV. The methodological approach allowed to identify the drivers of criticality and support capability in each SB, aiding decision-makers and risk practitioners in finding the vulnerability forcers that require more attention (public or private social equipment, housing policies, emergency anticipatory measures, etc.).

An original approach to SV assessments is the consideration of the components of criticality and support capability. The results allow for the definition of adapted and specific strategies of risk mitigation and civil protection measures to distinct types of risk groups and by different stakeholders and risk practitioners. By predicting the impact and the recovery capacity of communities, the results have applicability in several fields of risk governance as, for example, risk communication and involvement, social intervention (health, education and housing), emergency response, contingency planning, early warning and spatial planning.

Part of the runway at Madeira Airport is a platform above the sea at a 60 m height, supported by a series of frames. When aircraft land on this section, a load is exerted on the structure, resulting in bending of the beams which constitute the frames. A vision-based monitoring system was devised and implemented to measure the deflection of the runway's beams when a landing occurs.

An area on the midspan of two beams, located on the area where aircraft are most likely to land, was prepared with a speckle pattern, and a camera was assembled above a column on each of the adjacent frames, enabling the computation of displacements using digital image correlation (DIC). The camera continuously acquires images of the monitored area and compares them to a reference using DIC. If a displacement is detected, a number of frames before and after this event are saved for further DIC processing.

The installed systems successfully detected several events corresponding to landings and, for each of those events, measured the deflection of the beams over time and computed displacement fields for critical images, with strain values obtained up to this point being too small to measure using the current system.

This work provides novel insights into the behaviour of a unique structure and constitutes the first use of a vision system in its structural monitoring operations. It is also a valuable development in the implementation of automated DIC monitoring systems in locations of difficult access.

Hybrid methods, wherefore numerical and experimental data are used to calculate a critical parameter, have been used for several years with great success in Experimental Mechanics and, in particular, in fracture mechanics. The purpose of this paper is to report on the comparison of the strain field from numerical modelling forecasts against the experimental data obtained with the digital image correlation method under Mode II loading in fatigue testing. The numerical dual boundary element method has been established in the past as a very reliable method near singular regions where stresses tend to grow abruptly. The results obtained from the strain data near the crack tip were used in Williams expansion and agree fairly well with both the numerical results and the analytical solution proposed for pure Mode II testing.

The work presented in this note is experimental. The proposed methodology is of an hybrid experimental/numerical nature in that a numerical stress intensity factor calculation hinges upon a stress field obtained with an image method.

The obtained results are an important step towards the development of a practical tool for crack behaviour prediction in fatigue dominated events.

The results also stress the necessity of improving the experimental techniques to a point where the methods can be applied in real-life solicitations outside of laboratory premises.

Although several research teams around the globe are presently working in this field, the present research topic is original and the proposed methodology has been presented initially by the research team years ago.