Structuring, Integrating and Innovating: The Training Mode of Master of Public Administration (MPA)—Based on the Example of Chongqing

Master of Public Administration (MPA) is a specialized degree especially for governments and non-governmental public sectors to train high-level, high quality, professional public administration talents. This paper analyses the background of the training mode of MPA and determines the implication and consisting factors, and concludes the current situation of the training mode of MPA. Through the SWOT analysis of samples in universities in Chongqing, it confirms the major factors and interaction pattern and constructs the SPAC systematic framework. Then it analysis the systematic mode and dynamic innovation of MPA and verifies the feasibility of them based on the example of Southwest University

Innovation and Development in Basic Level Party Construction for Universities With “Four Building” System

Basic level party organizations in universities are thefoundation and guarantee for educational principlesand policies of the party. This paper expounds therole of basic level party organizations in collegesand universities, including the guidance of educationmanagement, coordinating and balancing the interestsand providing service guarantee. Current situation andproblems of basic level party construction in collegesand universities are analyzed with the actual surveydata. Main problems are that team construction isnot harmonious, scientific and effective managementsystem is scarce, which reasons include the influenceof social environment, the backward of strategicplanning, and the faultiness of incentive mechanism.Accordingly, on the basis of systematic analysis, weput forward the “four building”, namely “to build theright strategic concept and values, to build the partymembers’ long-term education training mode, to build ascientific performance evaluation model and build openand clear communication platform” so as to promotethe innovation and development of basic level partyconstruction in colleges and universities. Finally, baseon the perspective of strategic management, we shape“1+4” strategic framework of the basic level partyconstruction in colleges and universities and stressthe “four construction” system integration to realizethe innovation and development of basic level partyconstruction

Rationale and Methodology of Reprogramming for Generation of Induced Pluripotent Stem Cells and Induced Neural Progenitor Cells.

Great progress has been made regarding the capabilities to modify somatic cell fate ever since the technology for generation of induced pluripotent stem cells (iPSCs) was discovered in 2006. Later, induced neural progenitor cells (iNPCs) were generated from mouse and human cells, bypassing some of the concerns and risks of using iPSCs in neuroscience applications. To overcome the limitation of viral vector induced reprogramming, bioactive small molecules (SM) have been explored to enhance the efficiency of reprogramming or even replace transcription factors (TFs), making the reprogrammed cells more amenable to clinical application. The chemical induced reprogramming process is a simple process from a technical perspective, but the choice of SM at each step is vital during the procedure. The mechanisms underlying cell transdifferentiation are still poorly understood, although, several experimental data and insights have indicated the rationale of cell reprogramming. The process begins with the forced expression of specific TFs or activation/inhibition of cell signaling pathways by bioactive chemicals in defined culture condition, which initiates the further reactivation of endogenous gene program and an optimal stoichiometric expression of the endogenous pluri- or multi-potency genes, and finally leads to the birth of reprogrammed cells such as iPSCs and iNPCs. In this review, we first outline the rationale and discuss the methodology of iPSCs and iNPCs in a stepwise manner; and then we also discuss the chemical-based reprogramming of iPSCs and iNPCs

Non-linear dynamic data reconciliation for industrial processes

This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDDR) for a real industrial process. NDDRS is a technique for data reconciliation that requires an objective function to be minimised subject to both algebraic and differential, equality and inequality constraints. These constraints are obtained from the mathematical description of the process and ensure that the measurement data can be optimised to conform as closely as possible to the true behaviour of the process. One of the difficulties of using the original NDDR is that a rigorous process dynamic model is required as a constraint. Unfortunately it is very hard to establish a rigorous dynamic model for a complex industrial process, particularly for data reconciliation purpose. A transfer function matrix model has been introduced in this new NDDR method. Therefore the rigorous dynamic model is avoided. The real industrial data from FCCU is used to illustrate the efficiency of the new NDDR method

Non-linear Dynamic Data Reconciliation For Industrial Processes

This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDDR) for a real industrial process. NDDRS is a technique for data reconciliation that requires an objective function to be minimised subject to both algebraic and differential, equality and inequality constraints. These constraints are obtained from the mathematical description of the process and ensure that the measurement data can be optimised to conform as closely as possible to the true behaviour of the process. One of the difficulties of using the original NDDR is that a rigorous process dynamic model is required as a constraint. Unfortunately it is very hard to establish a rigorous dynamic model for a complex industrial process, particularly for data reconciliation purpose. A transfer function matrix model has been introduced in this new NDDR method. Therefore the rigorous dynamic model is avoided. The real industrial data from FCCU is used to illustrate the efficiency of the new NDDR method

Rationale and Methodology of Reprogramming for Generation of Induced Pluripotent Stem Cells and Induced Neural Progenitor Cells.

Great progress has been made regarding the capabilities to modify somatic cell fate ever since the technology for generation of induced pluripotent stem cells (iPSCs) was discovered in 2006. Later, induced neural progenitor cells (iNPCs) were generated from mouse and human cells, bypassing some of the concerns and risks of using iPSCs in neuroscience applications. To overcome the limitation of viral vector induced reprogramming, bioactive small molecules (SM) have been explored to enhance the efficiency of reprogramming or even replace transcription factors (TFs), making the reprogrammed cells more amenable to clinical application. The chemical induced reprogramming process is a simple process from a technical perspective, but the choice of SM at each step is vital during the procedure. The mechanisms underlying cell transdifferentiation are still poorly understood, although, several experimental data and insights have indicated the rationale of cell reprogramming. The process begins with the forced expression of specific TFs or activation/inhibition of cell signaling pathways by bioactive chemicals in defined culture condition, which initiates the further reactivation of endogenous gene program and an optimal stoichiometric expression of the endogenous pluri- or multi-potency genes, and finally leads to the birth of reprogrammed cells such as iPSCs and iNPCs. In this review, we first outline the rationale and discuss the methodology of iPSCs and iNPCs in a stepwise manner; and then we also discuss the chemical-based reprogramming of iPSCs and iNPCs

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies

Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method

Sensor-based human activity recognition can benefit a variety of applications such as health care, fitness, smart homes, rehabilitation training, and so forth. In this paper, we propose a novel two-layer diversity-enhanced multiclassifier recognition method for single wearable accelerometer-based human activity recognition, which contains data-based and classifier-based diversity enhancement. Firstly, we introduce the kernel Fisher discriminant analysis (KFDA) technique to spatially transform the training samples and enhance the discrimination between activities. In addition, bootstrap resampling is utilized to increase the diversities of the dataset for training the base classifiers in the multiclassifier system. Secondly, a combined diversity measure for selecting the base classifiers with excellent performance and large diversity is proposed to optimize the performance of the multiclassifier system. Lastly, majority voting is utilized to combine the preferred base classifiers. Experiments showed that the data-based diversity enhancement can improve the discriminance of different activity samples and promote the generation of base classifiers with different structures and performances. Compared with random selection and traditional ensemble methods, including Bagging and Adaboost, the proposed method achieved 92.3% accuracy and 90.7% recall, which demonstrates better performance in activity recognition