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The development of machines explores the development of a manufacturing system. This perspective offers challenges adjacent to the materials, the product that wants to be manufactured, the technical considerations that must be covered during implementation and the manner of delivering the products to the user. This article addresses the design and development of machines, on the one hand the redesign of a multifunctional platform, and on the other hand a 3D food printer. The second is emphasised, since its main objective is to help the food problems that exist today. The design phases, aspects considered for its implementation such as food safety, the choice of the substrate, the functionality of the food, transportation and the pre- and post-processes required. In this manner, the selection and innovation of materials constitutes a primary source of innovation and research in the integral development of a 3D food printer. The main source of value of these both cases lie in customisation. The experiences of a multidisciplinary research team in the development processes are compiled and the design and creation process are shown, in order to contribute to deal with design and development to tackle fundamental problems of society.

ABSTRACTThis study presents the development of an event-driven hybrid control for position and force tracking applied on a mobile robotic manipulator for metal recycling tasks. The suggested controller operates in a sequenced strategy starting from a fixed spot, moving the mobile device towards a targeted zone ([Formula: see text]) from where the i-th piece-to-be-recycled is attainable (considering the arm manipulation). Once the event of entering the zone is completed, the mobile robot is fixed at a position, and the end-effector of the robotic arm is enforced towards the piece-to-be-recycled. When the end-effector touches the piece in a given spot ([Formula: see text]), the hybrid control changes to the force tracking intending to carry the piece towards the spot ([Formula: see text]) where it ill be processed. Each piece location is identified based on a vision-based system that applies deep learning tools using convolutional neural networks. A multi-physics numerical simulation illustrated the application of the developed controller in a realistic scenario, showing all the elements of the event-driven operation. To validate the suggested controller, the comparison with a robust control that works on a wide range of carrying mass confirms the operational improvement of the event-driven hybrid position and force design.

Vehicle safety relies on the capacity of vehicle systems to decrease the likelihood of biomechanical injuries to both vehicle occupants and pedestrians. This can take the form of active or passive measures applied to the materials and their geometry in the event of an impact. Nevertheless, due to the nonlinear behaviour of materials and deformation, a distinct collapse process emerges that requires analysis; this analysis can be virtual or through experimental tests. While observed performance can be ascertained through experimental designs, executing such a design for every parameter combination can be time-consuming and costly. This study employs a Long Short-Term Memory (LSTM) to predict the energy absorbed and crushing force of thin-walled aluminium 6063 T5 tubes subject to different collapse triggers and heat treatments. The LSTM model is constructed using experimental data derived from experiments that consider trigger shape, area, trigger position, furnace duration, cooling temperature, and heat treatment soaking method. The LSTM model achieved a Root Mean Square Error (RSME) of 0.56 and 0.0025 for crushing force and energy absorption, respectively. LSTM proves to be a valuable tool for predicting results in nonlinear analysis, particularly in the context of crush behaviour. Also, a comparison of the LSTM and finite element analysis (FEA) predictive performance is presented.

In this research, the decomposition of toxic organics from pulp and paper mill effluent by the sequential application of ozonation and biodegradation was studied. Ozonation, as a pre-treatment, was executed to transform the initial pollutants into less toxic compounds (such as organic acids of low molecular weights). Biodegradation was executed during three days with acclimated microorganisms that were able to complete the decomposition of the initial organic mixture (raw wastewater) and to achieve a higher degree of mineralization (85–90%). Experiments were performed under three different conditions: (a) only ozonation of the initial contaminants, (b) only biodegradation of residual water without previous treatment by ozone and (c) ozonation followed by biodegradation performed by acclimated microorganisms. In the case of 72 h of biodegradation, the mineralization efficiency reached 85% and 89% after 30 and 60 min of ozonation, respectively. The no significant difference in this parameter coincided with the calculated generalized microorganisms’ consortia specific growing rate μmax that was reduced from 2.08 × 10−3 h−1 to 6.05 × 10−4 h−1 when the ozonation time was longer. The identification of the organics composition by gas chromatography with mass detector (GC-MS) before and after treatments confirmed that the proposed combined process served as a more efficient alternative to secondary and tertiary treatments (mineralization degree between 60 and 80% in average) of the paper industry wastewater.

The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3−3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.

Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, which could lead to muscle fatigue or incomplete enforced therapeutic motion. This work proposes an FES system aided by machine learning strategies that could adjust the stimulating signal based on electromyography (EMG) information. The regulation of the stimulated signal according to the patient’s therapeutic requirements is proposed. The EMG signals were classified using Long Short-Term Memory (LSTM) and a least-squares boosting ensemble model with an accuracy of 91.87% and 84.7%, respectively, when a set of 1200 signals from six different patients were used. The classification outcomes were used as input to a second regression machine learning algorithm that produced the adjusted electrostimulation signal required by the user according to their own electrophysiological conditions. The output of the second network served as input to a digitally processed electrostimulator that generated the necessary signal to be injected into the extremity to be treated. The results were evaluated in both simulated and robotized human hand scenarios. These evaluations demonstrated a two percent error when replicating the required movement enforced by the collected EMG information.