Estimating International Migration Flows for the Asia-Pacific Region: Application of a Generation-Distribution Model

This is a repository for our paper in Migration Studies. The paper estimates annual flows of international migration among 53 populations in the Asia-Pacific region and four macro world regions from 2000 to 2019 using a generation-distribution framework. This release contains: Simulated input data Code to produce the estimates Final estimated flows in the paper For questions with the code or request for all estimated flows with 1000 iterations, please email [email protected] or [email protected]

Prototyping and Production of Polymeric Microfluidic Chip

Microfluidic chips have found many advanced applications in the areas of life science, analytical chemistry, agro-food analysis, and environmental detection. This chapter focuses on investigating the commonly used manufacturing technologies and process chain for the prototyping and mass production of microfluidic chips. The rapid prototyping technologies comprising of PDMS casting, micro machining, and 3D-printing are firstly detailed with some important research findings. Scaling up the production process chain for microfluidic chips are discussed and summarized with the perspectives of tooling technology, replication, and bonding technologies, where the primary working mechanism, technical advantages and limitations of each process method are presented. Finally, conclusions and future perspectives are given. Overall, this chapter demonstrates how to select the processing materials and methods to meet practical requirements for microfluidic chip batch production. It can provide significant guidance for end-user of microfluidic chip applications

LMS-SM3 and HSS-SM3: Instantiating Hash-based Post-Quantum Signature Schemes with SM3

We instantiate the hash-based post-quantum stateful signature schemes LMS and HSS described in RFC 8554 and NIST SP 800-208 with SM3, and report on the results of the preliminary performance test

XMSS-SM3 and MT-XMSS-SM3: Instantiating Extended Merkle Signature Schemes with SM3

We instantiate the hash-based post-quantum stateful signature schemes XMSS and its multi-tree version described in RFC 8391 and NIST SP 800-208 with SM3, and report on the results of the preliminary performance test

Multiple-Decrement Compositional Forecasting with the Lee-Carter Model

Changes in cause of death patterns have a great impact on health and social care costs paid by government and insurance companies. Unfortunately an overwhelming majority of methods for mortality projections is based on overall mortality with only very few studies focusing on forecasting cause-specific mortality. In this project, our aim is to forecast cause-specific death density with a coherent model. Since cause-specific death density obeys a unit sum constraint, it can be considered as compositional data. The most popular overall mortality forecasting model, Lee-Carter model, is applied on compositional cause-specific death density. The predicted cause-specific death density is used to calculate life insurance and accidental death rider

Some new methods and models in functional data analysis

With new developments in modern technology, data are recorded continuously on a large scale over finer and finer grids. Such data push forward the development of functional data analysis (FDA), which analyzes information on curves or functions. Analyzing functional data is intrinsically an infinite-dimensional problem. Functional partial least squares method is a useful tool for dimension reduction. In this thesis, we propose a sparse version of the functional partial least squares method which is easy to interpret. Another problem of interest in FDA is the functional linear regression model, which extends the linear regression model to the functional context. We propose a new method to study the truncated functional linear regression model which assumes that the functional predictor does not influence the response when the time passes a certain cutoff point. Motivated by a recent study of the instantaneous in-game win probabilities for the National Rugby League, we develop novel FDA techniques to determine the distributions in a Bayesian model

Identifying the molecular targets and mechanisms of xuebijing injection for the treatment of COVID-19 via network parmacology and molecular docking

Xuebijing Injection have been found to improve the clinical symptoms of COVID-19 and alleviate disease severity, but the mechanisms are currently unclear. This study aimed to investigate the potential molecular targets and mechanisms of the Xuebijing injection in treating COVID-19 via network pharmacology and molecular docking analysis. The main active ingredients and therapeutic targets of the Xuebijing injection, and the pathogenic targets of COVID-19 were screened using the TCMSP, UniProt, and GeneCard databases. According to the ‘Drug-Ingredients-Targets-Disease’ network built by STRING and Cytoscape, AKT1 was identified as the core target, and baicalein, luteolin, and quercetin were identified as the active ingredients of the Xuebijing injection in connection with AKT1. R language was used for enrichment analysis that predict the mechanisms by which the Xuebijing injection may inhibit lipopolysaccharide-mediated inflammatory response, modulate NOS activity, and regulate the TNF signal pathway by affecting the role of AKT1. Based on the results of network pharmacology, a molecular docking was performed with AKT1 and the three active ingredients, the results indicated that all three active ingredients could stably bind with AKT1. These findings identify potential molecular mechanisms by which Xuebijing Injection inhibit COVID-19 by acting on AKT1

Applications of Gelatin in Biosensors: Recent Trends and Progress

Gelatin is a natural protein from animal tissue with excellent biocompatibility, biodegradability, biosafety, low cost, and sol–gel property. By taking advantage of these properties, gelatin is considered to be an ideal component for the fabrication of biosensors. In recent years, biosensors with gelatin have been widely used for detecting various analytes, such as glucose, hydrogen peroxide, urea, amino acids, and pesticides, in the fields of medical diagnosis, food testing, and environmental monitoring. This perspective is an overview of the most recent trends and progress in the development of gelatin-based biosensors, which are classified by the function of gelatin as a matrix for immobilized biorecognition materials or as a biorecognition material for detecting target analytes

Estimating Truncated Functional Linear Models With a Nested Group Bridge Approach

10.1080/10618600.2020.1713797Journal of Computational and Graphical Statistics1-