How were capital Inflows stimulated under the dollar peg system?

노트 : Volume Title: Regional and global capital flows: Macroeconomic causes and consequences Chapter Title: How were capital Inflows stimulated under the dollar peg system

Ancillary Service Capacity Optimization for Both Electric Power Suppliers and Independent System Operator

Ancillary Services (AS) in electric power industry are critical to support the transmission of energy from generators to load demands while maintaining reliable operation of transmission systems in accordance with good utility practice. The ancillary services are procured by the independent system operator (ISO) through a process called the market clearing process which can be modeled by the partial equilibrium from the ends of ISO. There are two capacity optimization problems for both Market participants (MP) and Independent System Operator (ISO). For a market participant, the firm needs to determine the capacity allocation plan for various AS to pursue operating revenue under various uncertainties which can never be accurately estimated. We thereby employ a heuristic named “resource reservation” to suggest two types of bids, the regular and the must-win for a market participant to pursue higher expected revenue and satisfactory performance in terms of revenue under the worst case scenario. Meanwhile, the ISO, needs to determine the total amount of capacity required to guarantee the overall reliability of the transmission system. Our numerical experiment is based on our industrial partner’s operational data and the simulation result suggests that our proposed methods would greatly outperform the deterministic methods in terms of the profitability for a market participant and the ISO’s entire system’s reliability

Development of a Systems Engineering Model for Chemical Separation Process

This thesis is concerned with the efforts to develop a general-purpose systems engineering model software TRPSEMPro1 that can be used to improve productivity in the design process. Different features of TRPSEMPro will be presented in this thesis. First, Systems Engineering technology is presented, followed by the exposition of different numerical optimization technologies and DOE (Design of Experiments) study technologies. Second, the detailed software process, Object-Oriented Analysis and Design (OOA&D) for the TRPSEMPro is presented. All the design data models are expressed by using Unified Modeling Language (UML). AMUSESimulator is another software package which has been designed and implemented in order to serve as a bridge between AMUSE Macro, developed by ANL, and systems engineering model, TRPSEMPro. The design process for AMUSESimulator is elaborated in this thesis. The topics in this thesis also include SQL Server Database, XML, DOE techniques and optimization techniques. Several study cases which apply the developed systems engineering model to solve typical design problems are demonstrated

Prediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches

PurposeThis study aimed to develop prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches and evaluate their performance.MethodsThe study was a secondary analysis based on a high-quality dataset from a randomized controlled trial (NCT00418457), including patients with primary breast cancer undergoing mastectomy. The primary outcome was CPSP at 12 months after surgery, defined as modified Brief Pain Inventory > 0. The dataset was randomly split into a training dataset (90%) and a testing dataset (10%). Variables were selected using recursive feature elimination combined with clinical experience, and potential predictors were then incorporated into three machine learning models, including random forest, gradient boosting decision tree and extreme gradient boosting models for outcome prediction, as well as logistic regression. The performances of these four models were tested and compared.Results1152 patients were finally included, of which 22.1% developed CPSP at 12 months after breast cancer surgery. The 6 leading predictors were higher numerical rating scale within 2 days after surgery, post-menopausal status, urban medical insurance, history of at least one operation, under fentanyl with sevoflurane general anesthesia, and received axillary lymph node dissection. Compared with the multivariable logistic regression model, machine learning models showed better specificity, positive likelihood ratio and positive predictive value, helping to identify high-risk patients more accurately and create opportunities for early clinical intervention.ConclusionsOur study developed prediction models for CPSP after breast cancer surgery based on machine learning approaches, which may help to identify high-risk patients and improve patients’ management after breast cancer

DNA Checkpoint and Repair Factors Are Nuclear Sensors for Intracellular Organelle Stresses-Inflammations and Cancers Can Have High Genomic Risks.

Under inflammatory conditions, inflammatory cells release reactive oxygen species (ROS) and reactive nitrogen species (RNS) which cause DNA damage. If not appropriately repaired, DNA damage leads to gene mutations and genomic instability. DNA damage checkpoint factors (DDCF) and DNA damage repair factors (DDRF) play a vital role in maintaining genomic integrity. However, how DDCFs and DDRFs are modulated under physiological and pathological conditions are not fully known. We took an experimental database analysis to determine the expression of 26 DNA D

Characteristics, risk management and GMP standards of pharmaceutical companies in China

The Good Manufacturing Practice (GMP) is one of the gold standards by which governments worldwide judge modern pharmaceutical companies’ production processes and product-safety standards. However, in all the nations, it is di cult to obtain real data about GMP inspection results, so conducting the related research is impossible. Taking advantage of a rare chance to obtain the on-site GMP inspection results in China, we have been able to initiate an empirical analysis of how company characteristics and risk management aWeb of Science11art. no. 110355

Development of a Systems Engineering Model of the Chemical Separations Process

Project Overview: • Two Components – Refine AMUSE Code – Develop Systems Engineering Model • Research Objectives – Develop a framework and environment for a systems engineering analysis of the chemical separations system for the AAA program. – Establish a baseline systems engineering model from which modifications and improvements can be made. – Refine the existing AMUSE program that gives a detailed examination of the UREX process, a critical component of the overall separation scheme