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CONDIMENT is the source code used in MELODIE, the overall French computer code for risk assessment of nuclear waste repositories in geological formations. This code models the diffusion-convection of elements released by a waste package, up to a distance of a few meters from the package. Two versions have been developed simultaneously:- CONDIMENT2 deals with the case of a single element. This version is more specifically designed for vitrified high-level wastes. The boundary conditions are furnished by studies on aqueous corrosion of French nuclear glass R7T7.- CONDIMENT3, deals with two ions that are liable to precipitate. This version is more specifically designed for wastes immobilized in cement.CONDIMENT3 is verified in a configuration for which an analytical solution exists.

Currently many National Metrology Institute (NMIs) as well as advanced calibration laboratories are using piston gas flow standards with mercury sealing method for gas flow calibration in the low pressure range. The flow in the small range is about cc/min. In this work, the low pressure gas flow calibration system at VMI is presented, designed and manufactured by the Taiwan National Metrology Institute, CMS/ITRI. The flow range is within (0.002 ÷ 24) L/min. The uncertainty of the reference system is assessed against the ISO/IEC Guide 98-3:2008 document, Uncertainty is evaluated from individual influence sources such as category A and B assessments. The standard uncertainty/relative standard uncertainty and degrees of freedom of the sources can be evaluated individually and then combined to produce a composite standard uncertainty/combined relative standard uncertainty and an effective degree of freedom. Finally, the relative expanded uncertainty is obtained by multiplying the relative standard uncertainty associated with a coverage factor at the 95% confidence level of the measurement result.

Security researchers have used Natural Language Processing (NLP) and Deep Learning techniques for programming code analysis tasks such as automated bug detection and vulnerability prediction or classification. These studies mainly generate the input vectors for the deep learning models based on the NLP embedding methods. Nevertheless, while there are many existing embedding methods, the structures of neural networks are diverse and usually heuristic. This makes it difficult to select effective combinations of neural models and the embedding techniques for training the code vulnerability detectors. To address this challenge, we extended a benchmark system to analyze the compatibility of four popular word embedding techniques with four different neural networks, including the standard Bidirectional Long Short-Term Memory (Bi-LSTM), the Bi-LSTM applied attention mechanism, the Convolutional Neural Network (CNN), and the classic Deep Neural Network (DNN). We trained and tested the models by using two types of vulnerable function datasets written in C code. Our results revealed that the Bi-LSTM model combined with the FastText embedding technique showed the most efficient detection rate on a real-world but not on an artificially constructed dataset. Further comparisons with the other combinations are also discussed in detail in our result. CCS CONCEPTS• Computing methodologies → Natural language processing; Information extraction; Neural networks; • Security and privacy → Software security engineering.