Mesenchymal Stem Cells and the Origin of Ewing's Sarcoma

The origin of Ewing's sarcoma is a subject of much debate. Once thought to be derived from primitive neuroectodermal cells, many now believe it to arise from a mesenchymal stem cell (MSC). Expression of the EWS-FLI1 fusion gene in MSCs changes cell morphology to resemble Ewing's sarcoma and induces expression of neuroectodermal markers. In murine cells, transformation to sarcomas can occur. In knockdown experiments, Ewing's sarcoma cells develop characteristics of MSCs and the ability to differentiate into mesodermal lineages. However, it cannot be concluded that MSCs are the cell of origin. The concept of an MSC still needs to be rigorously defined, and there may be different subpopulations of mesenchymal pluripotential cells. Furthermore, EWS-FLI1 by itself does not transform human cells, and cooperating mutations appear to be necessary. Therefore, while it is possible that Ewing's sarcoma may originate from a primitive mesenchymal cell, the idea needs to be refined further

A One-Field Fictitious Domain Method for Fluid-Structure Interactions

We present a one-field fictitious domain method (FDM) for simulation of general fluid-structure interactions (FSI). "One-field" means only one velocity field is solved in the whole (fluid and solid) domain based upon the finite element interpolation. The proposed method has the same generality and robustness as the FDM with a distributed Lagrange multiplier (DLM): both of them solve the fluid equations and solid equations as one system. However the one-field FDM only needs to solve for one velocity field while the FDM/DLM usually solves for fluid velocity, solid displacement and Lagrange multiplier. The proposed one-field FDM also has similar features with immersed finite element methods (IFEM): the explicit or implicit IFEM places all the solid information in a FSI force term which is arranged on the right-hand side of the fluid equations. The one-field FDM assembles the solid equations and implicitly includes them with the fluid equations. What we achieve is theoretically equivalent to an implicit IFEM but avoiding convergence problems, and a wide range of solid parameters can be considered in this scheme. In short, the one-field FDM combines the FDM/DLM advantage of robustness and the IFEM advantage of efficiency. In this thesis, we present a thorough review, summary and categorization of the existing finite element methods for FSI problems. The finite element weak formulation of the one-field FDM and discretization in time and space are introduced, followed by a stability analysis by energy estimate. The proposed scheme is first implemented in implicit form, followed by numerical validation for the property of non-increasing energy under the conditions of $\rho^f\le\rho^s$ (densities of the fluid and solid respectively) and $\nu^f\le\nu^s$ (viscosities of the fluid and solid respectively), and numerical tests for stability under the conditions of $\rho^f>\rho^s$ and/or $\nu^f>\nu^s$. The proposed scheme is then implemented based upon three explicit splitting schemes: 2-step splitting, 3-step splitting and 4-step splitting scheme. The fully coupled implicit FSI system is decoupled into subproblems step by step, which can be effectively solved. The pros and cons of these splitting schemes are analysed followed by a selection of numerical tests in order to illustrate the capabilities and range of applicability of the proposed one-field FDM scheme. The thesis concludes with a presentation of some topics and open problems that may be worthy of further investigation

A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles

It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model built by the conventional multiple linear regression method is feasible to predict the driving range, but the residual errors between -3.6975 km and 3.3865 km are relatively unfaithful for real-world driving. The study is innovative in its application of machine learning method, the gradient boosting decision tree algorithm, on the driving range prediction which includes a very large number of factors that cannot be considered by conventional regression methods. The result of the machine learning method shows that the maximum prediction error is 1.58 km, the minimum prediction error is -1.41 km, and the average prediction error is about 0.7 km. The predictive accuracy of the gradient boosting decision tree is compared against that of the conventional approaches. Document type: Articl

An optimal control method for time-dependent fluid-structure interaction problems

In this article, we derive an adjoint fluid-structure interaction (FSI) system in an arbitrary Lagrangian-Eulerian (ALE) framework, based upon a one-field finite element method. A key feature of this approach is that the interface condition is automatically satisfied and the problem size is reduced since we only solve for one velocity field for both the primary and adjoint system. A velocity (and/or displacement)-matching optimisation problem is considered by controlling a distributed force. The optimisation problem is solved using a gradient descent method, and a stabilised Barzilai-Borwein method is adopted to accelerate the convergence, which does not need additional evaluations of the objective functional. The proposed control method is validated and assessed against a series of static and dynamic benchmark FSI problems, before being applied successfully to solve a highly challenging FSI control problem

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed

On Two Factors Affecting the Efficiency of MILP Models in Automated Cryptanalyses

In recent years, mixed integer linear programming (MILP, in short) gradually becomes a popular tool of automated cryptanalyses in symmetric ciphers, which can be used to search differential characteristics and linear approximations with high probability/correlation. A key problem in the MILP method is how to build a proper model that can be solved efficiently in the MILP solvers like Gurobi or Cplex. It is known that a MILP problem is NP-hard, and the numbers of variables and inequalities are two important measures of its scale and time complexity. Whilst the solution space and the variables in many MILP models built for symmetric cryptanalyses are fixed without introducing dummy variables, the cardinality, i.e., the number of inequalities, is a main factor that might affect the runtime of MILP models. We notice that the norm of a MILP model, i.e., the maximal absolute value of all coefficients in its inequalities, is also an important factor affecting its runtime. In this work we will illustrate the effects of two parameters cardinality and norm of inequalities on the runtime of Gurobi by a large number of cryptanalysis experiments. Here we choose the popular MILP solver Gurobi and view it a black box, construct a large number of MILP models with different cardinalities or norms by means of differential analyses and impossible differential analyses for some classic block ciphers with SPN structure, and observe their runtimes in Gurobi. As a result, our experiments show that although minimizing the number of inequalities and the norm of coefficients might not always minimize the runtime, it is still a better choice in most situations

Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet.

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications