Allocation of Authority when a Person is not a Robot

We formalize a conception of authority, which is commonly defined as the right of controlling a person’s actions embedded in human assets in sociology. Due to the inalienable property of human assets, the contractible formal authority is hard to verify and enforce, while real authority usually diverges from formal authority. Inefficiency tends to arise when a task is not routine or can not be done by a robot. Using a framework of incomplete contract, we show that allocation of formal authority, as an instrument to mitigate the inefficiency, is determined by features of tasks and specificity of assets, and the relationship between the resources. Monitoring is then introduced to fine tune value of delegation

Allocation of Authority when a Person is not a Robot

We formalize a conception of authority, which is commonly defined as the right of controlling a person’s actions embedded in human assets in sociology. Due to the inalienable property of human assets, the contractible formal authority is hard to verify and enforce, while real authority usually diverges from formal authority. Inefficiency tends to arise when a task is not routine or can not be done by a robot. Using a framework of incomplete contract, we show that allocation of formal authority, as an instrument to mitigate the inefficiency, is determined by features of tasks and specificity of assets, and the relationship between the resources. Monitoring is then introduced to fine tune value of delegation.Transaction of human assets; real authority; formal authority; delegation; monitor

Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Supercritical Water Oxidation for Environmentally Friendly Treatment of Organic Wastes

Supercritical water oxidation is a promising, environment-friendly technology to efficiently deal with a wide variety of organic wastes such as wastewaters, industrial and municipal sludge, etc. As for the two key problems, i.e., corrosion and salt plugging, generally encountered in supercritical water oxidation plants, this chapter firstly reported the related mechanism analysis, solutions, research status, and development trends, respectively. From the perspectives of corrosion prevention and control, safety and automatic control, economic improvements, and development of novel reactors, a number of advanced technologies and equipment such as on-line desalination in supercritical water, new operation scheme assisted secondary traditional treatment, produced-gas recovery and oxygen reuse and novel TWM reactor, etc., were introduced systematically. Finally, this chapter summarizes the implementation status of industrial plants and the technological features of several firms being active in the construction of full-scale supercritical water oxidation plants. This chapter will provide very valuable information for the researchers and engineers who are interested in supercritical water oxidation for the harmless treatment of organic pollutants

System Reliability Evaluation Based on Convex Combination Considering Operation and Maintenance Strategy

The approaches to the system reliability evaluation with respect to the cases, where the components are independent or the components have interactive relationships within the system, were proposed in this paper. Starting from the higher requirements on system operational safety and economy, the reliability focused optimal models of multiobjective maintenance strategies were built. For safety-critical systems, the pessimistic maintenance strategies are usually taken, and, in these cases, the system reliability evaluation has also to be tackled pessimistically. For safety-uncritical systems, the optimistic maintenance strategies were usually taken, and, in these circumstances, the system reliability evaluation had also to be tackled optimistically, respectively. Besides, the reasonable maintenance strategies and their corresponding reliability evaluation can be obtained through the convex combination of the above two cases. With a high-speed train system as the example background, the proposed method is verified by combining the actual failure data with the maintenance data. Results demonstrate that the proposed study can provide a new system reliability calculation method and solution to select and optimize the multiobjective operational strategies with the considerations of system safety and economical requirements. The theoretical basis is also provided for scientifically estimating the reliability of a high-speed train system and formulating reasonable maintenance strategies

Model People Auscultation System Based on Capacitive Sensor

The medical teaching needs auscultation training, so a model people auscultation training system was designed based on capacitive sensing principle. The PIC32 CPU with charging time measuring unit was used as the system core. Capacitance sensors were set in different parts of the model, the sampled signal was digitalized and processed, the cancelling jitter algorithm and dynamic average filtering was used for improving signal, and then the simulation audio was played. At the same time, the acquisition data was sent to the workstation through Zigbee RF module for being processed. The experience results showed that the system could simulate the audio signal from the different model parts, and it’s useful for raising the training effect; the algorithms of dynamic average filtering and cancelling dithering played important role for keeping on the system stable