Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.
Background: Most studies on birth settings investigate the association between planned place of birth at the start of labor and birth outcomes and intervention rates. To optimize maternity care it also is important to pay attention to the entire process of pregnancy and childbirth. This study explores the association between the initial preferred place of birth and model of care, and the course of pregnancy and labor in low-risk nulliparous women in the Netherlands. Methods: As part of a Dutch prospective cohort study (2007–2011), we compared medical indications during pregnancy and birth outcomes of 576 women who initially preferred a home birth (n = 226), a midwife-led hospital birth (n = 168) or an obstetrician-led hospital birth (n = 182). Data were obtained by a questionnaire before 20 weeks of gestation and by medical records. Analyses were performed according to the initial preferred place of birth. Results: Low-risk nulliparous women who preferred a home birth with midwife-led care were less likely to be diagnosed with a medical indication during pregnancy compared to women who preferred a birth with obstetrician-led care (OR 0.41 95% CI 0.25-0.66). Preferring a birth with midwife-led care – both at home and in hospital - was associated with lower odds of induced labor (OR 0.51 95% CI 0.28-0.95 respectively OR 0.42 95% CI 0.21-0.85) and epidural analgesia (OR 0.32 95% CI 0.18-0.56 respectively OR 0.34 95% CI 0.19-0.62) compared to preferring a birth with obstetrician-led care. In addition, women who preferred a home birth were less likely to experience augmentation of labor (OR 0.54 95% CI 0.32-0.93) and narcotic analgesia (OR 0.41 95% CI 0.21-0.79) compared to women who preferred a birth with obstetrician-led care. We observed no significant association between preferred place of birth and mode of birth. Conclusions: Nulliparous women who initially preferred a home birth were less likely to be diagnosed with a medical indication during pregnancy. Women who initially preferred a birth with midwife-led care – both at home and in hospital – experienced lower rates of interventions during labor. Although some differences can be attributed to the model of care, we suggest that characteristics and attitudes of women themselves also play an important role.
MULTIFILE
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
Within the film and theater world, special effects make-up is used to adapt the appearance of actors for visual storytelling. Currently the creation of special effects makeup is a time-consuming process which creates a lot of waste that doesn’t fit in with the goals of a sustainable industry. Combine with the trend of the digitization of the movie and theater industry which require faster and more iterative workflows, the current ways of creating special effects makeup requires changing. Within this project we would like to explore if the traditional way of working can be converted to a digital production process. Our research consists of three parts. Firstly, we would like to explore if a mobile face scanning rig can be used to create digital copies of actors, and such eliminate the need to creates molds. Secondly, we would like to see if digital sculpting can replace the traditional methods of sculpting molds, casts and prosthetics. Here we would like to compare both methods in terms of creativity and time consumption. The third part of our project will be to explore the use of 3D printing for the creation of molds and prosthetics.
