An Approach to Line Balancing on Virtual Supervisor Induction Method and Intelligent Agents

This approach develops a method for solving the line-balancing problem, which is based on two stages. The works in a first stage is to identify the task of workstation, the assignment of the tasks to stations on the line and the recognized balance delay. In this stage we propose the induction VS method, which allows further identify the exact position between pieces, machine into a workstation and also between extern workstation, as well as intracellular and intercellular part. This way each task is identified and measured. In the second stage is to carry out a macro-approach to choose the resource to perform each of them. The hybrid intelligent agent architecture is proposed for this second stage, which has consideration of machining sequence. The integration between both technologies allows us to develop new hybrid architecture capable to reduce the computational time in the deliberative layers fundamentally. Finally, a reconfigurable testbed has been proposed for future experiments and results to evaluate this new balancing method. Some previous computational experiments provide that the proposed approach is efficient to solve practical transfer line design for balancing problem

Manufacturing data for the implementation of data-driven remanufacturing for the rechargeable energy storage system in electric vehicles

Manufacturing industries are experiencing a data-driven paradigm shift that is changing how technical operations are run and changing present business models. Leveraging on manufacturing data from industries and digital intelligence platforms have become important in creating new forms of value. While extending the life of a product through the circular economy 3 R’s of reuse, re-manufacturing and recycling remains a technical and resource challenge for practitioners, optimizing the increasing forms and volumes of data presents a complementary and necessary challenge to the circular economy. This research aims to explore how the manufacturing data can inform remanufacturing parameters for implementing remanufacturing on the Rechargeable Energy Storage System

A Self-Organized ECM-Mimetic Model Based on an Amphiphilic Multiblock Silk-Elastin-Like co-Recombinamer with a Concomitant Dual Physical Gelation Process

Although significant progress has been made in the area of injectable hydrogels for biomedical applications and model cell niches, further improvements are still needed, especially in terms of mechanical performance, stability, and biomimicry of the native fibrillar architecture found in the extracellular matrix (ECM). This work focuses on the design and production of a silk-elastin-based injectable multiblock corecombinamer that spontaneously forms a stable physical nanofibrillar hydrogel under physiological conditions. That differs from previously reported silk-elastin-like polymers on a major content and predominance of the elastin-like part, as well as a more complex structure and behavior of such a part of the molecule, which is aimed to obtain well-defined hydrogels. Rheological and DSC experiments showed that this system displays a coordinated and concomitant dual gelation mechanism. In a first stage, a rapid, thermally driven gelation of the corecombinamer solution takes place once the system reaches body temperature due to the thermal responsiveness of the elastin-like (EL) parts and the amphiphilic multiblock design of the corecombinamer. A bridged micellar structure is the dominant microscopic feature of this stage, as demonstrated by AFM and TEM. Completion of the initial stage triggers the second, which is comprised of a stabilization, reinforcement, and microstructuring of the gel. FTIR analysis shows that these events involve the formation of β-sheets around the silk motifs. The emergence of such β-sheet structures leads to the spontaneous self-organization of the gel into the final fibrous structure. Despite the absence of biological cues, here we set the basis of the minimal structure that is able to display such a set of physical properties and undergo microscopic transformation from a solution to a fibrous hydrogel. The results point to the potential of this system as a basis for the development of injectable fibrillar biomaterial platforms toward a fully functional, biomimetic, artificial extracellular matrix, and cell niches.Este trabajo forma parte de Proyectos de Investigación financiados por la Comisión Europea a través del Fondo Europeo de Desarrollo Regional (ERDF), por el del MINECO (MAT2013-41723-R, MAT2013- 42473-R, PRI-PIBAR-2011-1403 y MAT2012-38043), la Junta de Castilla y León (VA049A11, VA152A12 y VA155A12) y el Instituto de Salud Carlos III bajo el Centro en Red de Medicina Regenerativa y Terapia Celular de Castilla y León

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe