Healthcare services available these days deploy high technology to satisfy both internal and external customers by continuously improving various quality parameters. Quality improvement in healthcare services is a complex and multidimensional task. Although various quality management tools are routinely deployed for identifying quality issues in healthcare delivery, there is absence of an integ...

in the current age, the importance of using knowledge as a tool for creativity and innovation at national level is considerable. furthermore, it would be taken into consideration as strategic need in the competition among different countries. hence in this paper, a new knowledge strategic management framework was developed with emphasizing on knowledge based innovation, upon up studying knowled...

This paper describes the GF Widecoverage MT system submitted to WMT 2015 for translation from English to Finnish. Our system uses a interlingua based approach, in which the interlingua is a shared formal representation, that abstracts syntactic structures over multiple languages. Our final submission is a reranked system in which we combine this baseline MT system with a factored LM model.

To maximize the economic benefits of geothermal energy production, it is essential to optimize reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for well control reservoirs. construct hybrid convolution–recurrent neural network surrogate, combines convolution (CNN...

A central goal of vaccine research is to identify a vaccine-induced immune response that predicts protection from infection or disease. The term "correlate of protection" has been used to refer to at least 3 distinct concepts, which has resulted in confusion surrounding this topic. We propose precise definitions of these different concepts of immune correlates, using the nomenclature "correlate...

The set covering problem (SCP) is central in a wide variety of practical applications for which finding good feasible solutions quickly (often in real-time) is crucial. Surrogate constraints normalization is a classical technique used to derive appropriate weights for surrogate constraint relaxations in mathematical programming. This framework remains the core of the most effective one-pass con...

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption e...

This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite p...

For the design and optimization of multistage assembly processes, a computationally cheap mathematical model that links design parameters with the final product dimensional quality is highly desirable. We propose a systematic approach to building a surrogate model of simulations of multistage assembly processes. At the heart of this approach is a multiple-input-multiple-output surrogate modelin...