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Objectives The doctor–patient relationship is usually measured in line with patient needs and demands. This study aimed to develop a scale measuring such a relationship from the perspective of doctors. Methods A draft scale was developed and adapted to the hospital context of China based on several existing scales, with an intention to measure how medical doctors view and manage their relationship with patients beyond episodic clinical encounters. Two rounds of Delphi consultations involving 14 experts were conducted to seek their consensus on the inclusion and descriptions of items. This resulted in a 19-item scale measuring four domains of the relationship. The scale was validated through a survey of 1,712 medical doctors selected from 27 public hospitals in Heilongjiang province of China. The internal consistency of the scale was assessed using Cronbach’s α coefficients of the four domains. Confirmatory factor analyses were performed to test the construct validity of the scale. Linear regression analyses were performed to assess the known-group validity of the scale. Results The scale measures four domains. The Cronbach’s α of the scale reached an acceptable level, ranging from 0.61 to 0.78 for its four domains. Good fitness of data into the four-domain structure of the scale was confirmed by the confirmatory factor analysis. Known-group differences were demonstrated in the regression analyses. Conclusion The doctor–patient relationship scale developed in this study is a psychometrically valid tool assessing how medical doctors view and manage their relationship with patients in the hospital setting in China.

This paper introduces the exergy efficiency that takes into account both the quantity and quality of energy, and constructs a bi-level planning optimization model of the regional integrated energy system. Factors such as equipment exergy efficiency are considered by the upper-level planning model. Through matter-element information theory and Frequent Patterm-growth algorithm, the energy structure of the regional integrated energy system planning is determined by quantitative means. The lower-level planning model is intended for economy and exergy efficiency, and the tabu search algorithm is embedded in the solving algorithm of the multi-objective genetic algorithm for solving, determining the capacity of each equipment in the energy structure. In the case study, three comparison schemes are used to verify the proposed model and method through the regional integrated energy system in a resort town in northern China. Among them, Scheme 1 is devised for economy, Scheme 2 is proposed for economy and energy efficiency, and Scheme 3 is intended for economy and exergy efficiency. The results show that compared with the Scheme 1 and Scheme 2, the system exergy efficiency of Scheme 3 corresponding to this model has increased by 17.34% and 11.17%, respectively. Despite an increase of 10.61% in total annualized cost in Scheme 3 compared to Scheme 1, there is a decrease of 6.37% compared to Scheme 2. Despite the conflict in improving exergy efficiency and economy, there is still a balance to draw in the proposed model in this paper.

The economy of the Regional Integrated Energy System (RIES) scheduling scheme is affected by the parameter accuracy of different energy conversion equipment models. The traditional static energy hub (EH) model regards the equipment efficiency as a constant and ignores the variable condition characteristics of the equipment efficiency changing with the load rate. This paper presents an optimal scheduling method of RIES considering the characteristics of equipment under variable operating conditions. Firstly, the architecture of the integrated energy system is analyzed. Secondly, based on the DEH model, an optimal scheduling method of RIES was proposed considering the characteristics of equipment under variable operating conditions. Finally, an optimal scheduling analysis is performed for a typical RIs. The results show that the proposed method can improve the accuracy of the equipment model and reduce the cost prediction error by considering the variable operating conditions of the equipment, thus providing a more economical scheduling scheme for Ries.

Due to the limitations of a single power load forecasting model, the power load forecasting cannot be performed well. In order to obtain a greater closeness to predict results with actual data, this paper presents the power load forecasting model based on gray neural network combined return to Guangzhou, 2010 - 2019 on actual data for example, the results show that: As used herein, the combined model method has high accuracy and strong use value.