Market size, competition, and the product mix of exporters

We build a theoretical model of multi-product firms that highlights how market size and ge- ography (the market sizes of and bilateral economic distances to trading partners) affect both a firm's exported product range and its exported product mix across market destinations (the dis- tribution of sales across products for a given product range). We show how tougher competition in an export market induces a firm to skew its export sales towards its best performing products. We find very strong confirmation of this competitive effect for French exporters across export market destinations. Trade models based on exogenous markups cannot explain this strong sig- nificant link between destination market characteristics and the within-firm skewness of export sales (after controlling for bilateral trade costs). Theoretically, this within firm change in prod- uct mix driven by the trading environment has important repercussions on firm productivity and how it responds to changes in that trading environment

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Low temperature district heating system at the Uppsala university hospital : an analysis of energy, power, flow, costs and safety

Region Uppsala plans to build a new heating system called VS01 for the Uppsala University Hospital. VS01 will be a low temperature district heating system and connects all buildings in the hospital area. By building VS01, the existing 23 district heat subscriptions, one for every building, can be assembled into one. Today, one of Vattenfall’s main pipe extends through the hospital buildings. The high pressure and temperature entail a high safety risk for people in the vicinity if a leak should occur. By building VS01 Region Uppsala would be able to lower the temperature and pressure in the pipes, and by that lower safety risks. The energy consumption will stay the same. The power peak will be lower, however, no money will be saved from that aspect. The flux of district heat water from Vattenfall will be lower. By assembling all subscriptions into one, approximately 640 000 SEK per year can be saved due to volume discounts. Investment costs is generally not taken in consideration for this project, but the estimated cost for two new heat exchangers for VS01 is at least 510 000 SEK

Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models

The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study. This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration. This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment

Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models

The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study. This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration. This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment

Simulation of Hydronic Underfloor Heating With the Finite Element Method : Heat Release From Different Heating Pipe Patterns in Construction

This report formulates the boundary conditions and discretization method for conducting a simulation of heat with liquids and solids through the finite element method. It introduces the reader to the movement that is due today with optimization of heat transport and mitigation generally described as the fourth generation of district heating. It presents the scope: calculating the heat release from pipes in hydronic underfloor heating, and presents the belonging question: how does heat release from different heating pipe patterns affect the body’s heat transfer? Simulation of the work is conducted with the delimitations of using a single boundary slip condition addressing friction and only using water as pipe flow medium. It focuses on the pattern’s ability to affect the heat to the body, of which characteristically manifests a square concrete slab in the running simulations. By using different cases, it analyses how patterns using the same length of pipes emit their average heat to the covering top surface differently, both as the heating level alternates, and duration for response changes. This meanwhile they are affected by analog boundary temperature conditions.    A sensitivity analysis is done answering how the various patterns tested are affected by change of propagation speed for the flowing medium, showing that a spiral formed pattern with evenly spread piping is the least affected. The results show that the pattern with alternating pipe spacing gives the best average heat emission in the simulated cases. It also concludes that minor changes in the pattern area will have profound effect on the average transferred heat from the body’s top surface.