When heat is conducted across an interface between two different materials, the interaction between thermoelastic distortion and thermal contact resistance can cause the system to be unstable. This paper investigates the influence of material properties on the stability criterion for an interface between two half planes. It is found that most material combinations exhibit one or other of two kinds of stability behavior. In one of these, the stability criterion is closely related to that for uniqueness of the steady-state solution and instability is only possible for one direction of heat flow. In the other, instability can occur for either direction of heat flow and in one case is characterized by the oscillatory growth of a pressure perturbation.

The paper investigates the transient behaviour and stability of a system consisting of two thermally conducting elastic rods in contact on their end faces, the other ends of the rods being built-in to two rigid walls which are maintained at different temperatures. It is assumed that there is a thermal resistance at the interface between the rods which is a known function of contact pressure or gap. A perturbation method is used to analyse the stability of the system and it is shown that there is a range of conditions under which the steady-state solution is unique but unstable. A finite difference method is then used to model the transient behaviour; it shows that under such conditions the system always tends to a steady oscillatory state in which the contact pressure varies periodically with time, possibly with periods of separation.

In the face of severe water pollution, all provinces and cities in China have actively invested in water environment management funds driven by the goals of national energy conservation and emissions reduction. However, due to differences in natural environment, economic and technological levels, industrial structure, and other aspects in provinces and cities, their water environment management effects are also different across time and space. Under economic development and environmental regulation policies, it can be seen that the change in industrial GDP is not completely consistent with that of industrial wastewater discharge. How to improve desirable outputs and reduce undesirable outputs under the limited investment in water pollution control are key issues when investigating the efficiency of industrial water pollution control. This study uses the Dynamic SBM (Slacks-Based Measure) model to assess wastewater resources for research samples covering the 30 regions of China. There are two output variables, two input variables, and one carry-over variable. The output variables are industrial wastewater treatment and industrial output, the two input variables are industrial water consumption and facility operation cost, and the carry-over variable is industrial waste. This study concludes with implications for theory research, as these variables may lead to a better understanding and merging with the input variables, output variables, and carry-over variable of recent studies. The empirical results show that from the efficiency rank changes of the 30 regions for 2011–2015, regions with higher industrial output do not appear to have improved versus other regions, such as for Shandong, Guangdong, Jiangsu, Qinghai, and Zhejiang. The 30 regions’ efficiency scores show some volatility, with 13 regions’ efficiency score volatility clustering close to 0, like Beijing, Chongqing, Shandong, Guangdong, and Sichuan. In contrast, for Anhui, Inner Mongolia, Zhejiang, and Xinjiang, their efficiency scores fell more than other regions in this period and thus should adjust their input/output variables to increase their efficiency scores. This study further presents that many lower-/middle-/high-industrial output regions do not achieve a balance between industrial output and industrial wastewater treatment. How to find a balance between these two factors for any region is a vitally important issue for industrial wastewater treatment policy makers. Under such a circumstance, an industrial output region may not actually be highly efficient at doing this.

This article provides a fuzzy expert system for efficient energy smart home management systems (FES-EESHM), demand management, renewable energy management, energy storage, and microgrids. The suggested fuzzy expert framework is utilized to simplify designing smart microgrids with storage systems, renewable sources, and controllable loads on resources. Further, the fuzzy expert framework enhances energy and storage to utilize renewable energy and maximize the microgrid’s financial gain. Moreover, the fuzzy expert system utilizes insolation, electricity price, wind speed, and load energy controllably and unregulated as input variables to enable energy management. It uses input variables including insolation, electrical quality, wind, and the power of uncontrollable and controllable loads to allow energy management. Furthermore, these input data can be calculated, imported, or predicted directly via grid measurement using any prediction process. In this paper, the input variables are fuzzified, a series of rules are specified by the expert system, and the output is de-fuzzified. The findings of the expert program are discussed to explain how to handle microgrid power consumption and production. However, the decisions on energy generated, controllable loads, and own consumption are based on three outputs. The first production is for processing, selling, or consuming the energy produced. The second output is used for controlling the load. The third result shows how to produce for prosumer’s use. The expert method can be checked via the hourly input of variable values. Finally, to confirm the findings, the method suggested is compared to other available approaches.

This paper aims to gain more insights into risk-based decision-making of the brain. For this purpose, the author decided to adopt such methods as electroencephalography (EEG) experiment, literature review and hypothesis testing. Based on the neuroscience for decision-making, the mechanism of management risk-based decision-making was investigated under different economic risks. Then, the "economic management risk-based decision-making" hypothesis was presented in light of the mathematical model for risk-based decision-making and the neuroscience for decision-making. After that, the author prepared the detailed design of an experiment and conducted preexperiments. The high-time resolution event-related potential (ERP) technique was adopted to capture the signal processing procedure and neural action pattern in the brain in different risk scenarios. Through the analysis of the experimental data, it is discovered that the brain's decision-making under economic management risk demonstrates the decision-maker's adaptive choice. The research findings provide valuable reference for the decision-making of enterprise managers.