Motivational and information aspects of the reward systems applied to Chinese state enterprises.

This thesis analyses the motivational and information aspects of reward systems applied to Chinese state enterprises since 1949. It attempts to apply relevant concepts and analytical tools developed utilising the framework of agency and contracting theory in the analysis of the relationship between the State and enterprises in both planning and control. The research is comprised of three parts. The first part critically reviews research in the area of managerial motivation in a centrally planned economy with particular reference to the New Soviet Incentive Model ("bonus literature"). It also presents systematically the relevant concepts and models of agency research. The second part describes and evaluates the reward systems applied to Chinese State enterprises during the period 1949-1989. The systems considered include the prereform system (1949-1978), the profit incentive systems (1979-1986), and the contract system (1987-1989). This description presents both documentary and empirical surveys concerning system design, operational models, and problems of application. The third part sets up the analytical framework, models the Chinese systems, and analyses these models. Firstly, it attempts to establish the feasibility and suitability of using agency tools to analyse the State-firm relationship in central planning environments. It does this by comparing the bonus literature and agency research. Second, theoretical models are presented in a specific setting. A number of assumptions with regard to the elements of the theoretical models relevant to Chinese context are made. Models of various reward systems are then presented and analysed using an agency perspective and some suggestions for reform are made. The analysis also reveals some limitations of agency research and its power as an analytical tool in a Chinese context

A Service Chain Discovery and Recommendation Scheme Using Complex Network Theory

Service chain discovery and recommendation are significant in services composition. A complex network module based algorithm using services invocable relations is proposed to search useful service chains on the network. Furthermore, a new scheme for discovering composite services processes automatically and recommending service chains by ranking their QoS is provided. Simulations are carried out and the results indicate that some useful service chains in the dataset provided by the WSC2009 can be found by the new algorithm

Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach

Author's accepted manuscript.Available from 16/05/2022.acceptedVersio

The Genetic Mechanism of Inertinite in the Middle Jurassic Inertinite-Rich Coal Seams of the Southern Ordos Basin

Inertinite is an important type of organic maceral in coal deposits, andalso an important geological information carrier of coal forming environments. In the southern section of the Ordos Basin, the No. 4 inertinite-richcoal seam of the Middle Jurassic Yan’an Formation in the Binchang Coalfield was selected as an example to study the genetic mechanism of theinertinite. In this study, the results obtained from experimental tests ofcoal rock, including principal and trace elements, stable carbon isotopes,scanning electron microscopy, inertinite reflectance, sporopollen andfree radical retorting methods, were analyzed. Then, the findings werecombined with the previous understanding of the oxygen content in theatmosphere and ground fire characteristics, in order to discuss the genesismechanism of inertinite in the No. 4 coal seam. The obtained researchresults were as follows: (1) During the coal forming period of the No. 4coal seam, the overall climate had been relatively dry. There were fourrelatively dry-wet climate cycles in the No.4 coal seam, which werecontrolled by the eccentricity astronomical period. The inertinite contentwere relatively high during the dry periods; (2) The temperature rangesuitable for microorganism activities during the oxidation processes wasbetween 0 and 80℃ . The simulation results of the free radical concentrations showed that the maximum temperature of fusain in the No. 4 coalseam during the process of coalification had not exceeded 300℃ , whichwas significantly higher than the temperature range of microorganismactivities. Therefore, these were not conducive to the activities of microorganism and formation of inertinite during the coal-forming period;(3) The genesis temperature of the inertinite in the No. 4 coal seam wascalculated according to the reflectance of the inertinite, which was lowerthan 400 ℃ . This result supported the cause of wildfire of the inertiniteand reflected that the type of wildfire was mainly ground fire, along withpartially surface fire. Moreover, the paleogeographic location, climaticconditions, atmospheric oxygen concentration, etc. of the study areashowed that the conditions for wildfire events were in fact available; (4)There were dense and scattered fusinite observed in the No. 4 coal seam,and the thickness of cell walls were found to differ. It was speculated thatthis was related to the type of wildfire, combustion temperatures, combustion timeframes, and different initial conditions of the burned objectsduring the coal forming periods

MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network

Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristics. However, the event stream output from event cameras has asynchronous, sparse characteristics that existing computer vision algorithms cannot handle. Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks. However, direct training of deep SNNs suffers from degradation problems. This work addresses these problems by proposing a spiking neural network architecture with a novel residual block designed and multi-dimension attention modules combined, focusing on the problem of depth prediction. In addition, a novel event stream representation method is explicitly proposed for SNNs. This model outperforms previous ANN networks of the same size on the MVSEC dataset and shows great computational efficiency