MUFIN: Improving Neural Repair Models with Back-Translation

Automated program repair is the task of automatically repairing software bugs. A promising direction in this field is self-supervised learning, a learning paradigm in which repair models are trained without commits representing pairs of bug/fix. In self-supervised neural program repair, those bug/fix pairs are generated in some ways. The main problem is to generate interesting and diverse pairs that maximize the effectiveness of training. As a contribution to this problem, we propose to use back-translation, a technique coming from neural machine translation. We devise and implement MUFIN, a back-translation training technique for program repair, with specifically designed code critics to select high-quality training samples. Our results show that MUFIN's back-translation loop generates valuable training samples in a fully automated, self-supervised manner, generating more than half-a-million pairs of bug/fix. The code critic design is key because of a fundamental trade-off between how restrictive a critic is and how many samples are available for optimization during back-translation

The impact of revolutionary aircraft designs on global aviation emissions

The discussion about the environmental impact caused by aviation has gained greater prominence due to the increased demand for this sector and, consequently, the increase in the number of flights. Environmental concerns have stimulated the development of novel approaches to reduce pollutants and CO2 emissions. This study aims to assess the impact of disruptive concepts on commercial aircraft by reducing CO2 emissions by 50% by 2050. In this regard the fleet system dynamics model is used to assess the effects of technological progress on future air transport systems. It accounts for the manufacturer’s production capabilities and current projections and forecasts on the needs and evolution of global air transport, as well as their expected entry into service. The main factors reported were production capacity, year of entry of the technology/concept, and the transport capacity and range of aircraft. The sensitivity study on the production capacity of new aircraft/concepts showed that with a 15% increase, emissions can be reduced between 1 and 2.6%, depending on the case and scenario. On the other hand, increasing the aircraft production capacity could lead to a problem of overcapacity.Fundação para a Ciência e a Tecnologiainfo:eu-repo/semantics/publishedVersio

Ga[NO2A-N-(alfa-amino)propionate] chelates: synthesis and evaluation as potential tracers for 68Ga3+ PET

The availability of commercial 68Ge/68Ga cyclotron-independent 68Ga3+ generators is making Positron Emission Tomography (PET) accessible to most hospitals, which is generating a surge of interest in the design and synthesis of bi-functional chelators for Ga3+. In this work we introduce the NO2A-N-(alfa-amino)propionic acid family of chelators based on the triazacyclononane scaffold. Complexation of the parent NO2A-N-(alfa-amino)propionic acid chelator and of a low molecular weight (model) amide conjugate with Ga3+ was studied by 1H and 71Ga NMR. The Ga3+ chelate of the amide conjugate shows pH-independent N3O3 coordination in the pH range 3-10 involving the carboxylate group of the pendant propionate arm in a 6 member chelate. For the Ga[NO2A-N-(alfa-amino)propionate] chelate, a reversible pH-triggered switch from Ga3+ coordination to the carboxylate group to coordination to the amine group of the propionate arm, was observed upon pH increase/decrease in the pH range 4-6. This phenomenon can conceivably constitute the basis of a physiological pH sensor. Both complexes are stable in the physiological range. The [67Ga][NO2A-N-(alfa-benzoylamido)propionate] chelate was found to be stable in human serum. Biodistribution studies of the 67Ga3+-labeled pyrene butyric acid conjugate NO2A-N-(alfa-pyrenebutanamido)propionic acid revealed that, despite its high lipophilicity and concentration-dependent aggregation properties, the chelate follows mainly renal elimination with very low liver/spleen accumulation and no activity deposition in bones after 24 hours. Facile synthesis of amide conjugates of the NO2A-N-(alfa-amino)propionic acid chelator, serum stability of the Ga3+chelates and fast renal elimination warrant further evaluation of this novel class of chelators for PET applications.This work was financially supported by Fundação para a Ciência e Tecnologia, Portugal: PEst-C/QUI/UI0686/2013; FCOMP-01-0124-FEDER-037302; PTDC/QUI/70063/2006; grant SFRH/BD/63994/2009 to Miguel Ferreira and sabbatical grant SFRH/BSAB/1328/2013 to J. A. Martins; Rede Nacional de RMN (REDE/1517/RMN/2005) for the acquisition of the Varian VNMRS 600 NMR spectrometer at the University of Coimbra and the Bruker Avance-3 400 Plus at the University of Minho in Braga. We also acknowledge the COST Action TD1004 “Theragnostics Imaging and Therapy”

Lignin-enriched tricalcium phosphate/sodium alginate 3D scaffolds for application in bone tissue regeneration

The bone is a connective, vascularized, and mineralized tissue that confers protection to organs, and participates in the support and locomotion of the human body, maintenance of homeostasis, as well as in hematopoiesis. However, throughout the lifetime, bone defects may arise due to traumas (mechanical fractures), diseases, and/or aging, which when too extensive compromise the ability of the bone to self-regenerate. To surpass such clinical situation, different therapeutic approaches have been pursued. Rapid prototyping techniques using composite materials (consisting of ceramics and polymers) have been used to produce customized 3D structures with osteoinductive and osteoconductive properties. In order to reinforce the mechanical and osteogenic properties of these 3D structures, herein, a new 3D scaffold was produced through the layer-by-layer deposition of a tricalcium phosphate (TCP), sodium alginate (SA), and lignin (LG) mixture using the Fab@Home 3D-Plotter. Three different TCP/LG/SA formulations, LG/SA ratio 1:3, 1:2, or 1:1, were produced and subsequently evaluated to determine their suitability for bone regeneration. The physicochemical assays demonstrated that the LG inclusion improved the mechanical resistance of the scaffolds, particularly in the 1:2 ratio, since a 15 % increase in the mechanical strength was observed. Moreover, all TCP/LG/SA formulations showed an enhanced wettability and maintained their capacity to promote the osteoblasts' adhesion and proliferation as well as their bioactivity (formation of hydroxyapatite crystals). Such results support the LG inclusion and application in the development of 3D scaffolds aimed for bone regeneration.info:eu-repo/semantics/publishedVersio

Polarization Control of the Non-linear Emission on Semiconductor Microcavities

The degree of circular polarization ($\wp$) of the non-linear emission in semiconductor microcavities is controlled by changing the exciton-cavity detuning. The polariton relaxation towards \textbf{K} $\sim 0$ cavity-like states is governed by final-state stimulated scattering. The helicity of the emission is selected due to the lifting of the degeneracy of the $\pm 1$ spin levels at \textbf{K} $\sim 0$. At short times after a pulsed excitation $\wp$ reaches very large values, either positive or negative, as a result of stimulated scattering to the spin level of lowest energy ($+1/-1$ spin for positive/negative detuning).Comment: 8 pages, 3 eps figures, RevTeX, Physical Review Letters (accepted

Using an Artificial Neural Network Approach to Predict Machining Time

One of the most critical factors in producing plastic injection molds is the cost estimation of machining services, which significantly affects the final mold price. These services’ costs are determined according to the machining time, which is usually a long and expensive operation. If it is considered that the injection mold parts are all different, it can be understood that the correct and quick estimation of machining times is of great importance for a company’s success. This article presents a proposal to apply artificial neural networks in machining time estimation for standard injection mold parts. For this purpose, a large set of parts was considered to shape the artificial intelligence model, and machining times were calculated to collect enough data for training the neural networks. The influences of the network architecture, input data, and the variables used in the network’s training were studied to find the neural network with greatest prediction accuracy. The application of neural networks in this work proved to be a quick and efficient way to predict cutting times with a percent error of 2.52% in the best case. The present work can strongly contribute to the research in this and similar sectors, as recent research does not usually focus on the direct prediction of machining times relating to overall production cost. This tool can be used in a quick and efficient manner to obtain information on the total machining cost of mold parts, with the possibility of being applied to other industry sectorsinfo:eu-repo/semantics/publishedVersio