Diagnosis-related groups (DRGs) are gaining prominence in healthcare systems worldwide to standardize potential payments to hospitals. This study, conducted across public hospitals, investigates the impact of DRG implementation on human resource allocation and management practices. The research findings reveal significant changes in job roles and skill requirements based on a mixed-methods approach involving 70 healthcare professionals across various roles. 50% of respondents reported changes in daily responsibilities, and 42% noted the creation of new roles in their organizations. Significant challenges include inadequate training (46%), and coding complexity (38%). Factor analysis revealed a complex relationship between DRG familiarity, job satisfaction, and staff morale. The study also found a moderate negative correlation between the impact on morale and years of service in the current hospital, suggesting that longer-tenured staff may require additional support in adapting to DRG systems. This study addresses a knowledge gap in the human resource aspects of DRG implementation. It provides healthcare administrators and policymakers with evidence to inform strategies for effective DRG adoption and workforce management in public hospitals.

Cities play a key role in achieving the climate-neutral supply of heating and cooling. This paper compares the policy frameworks as well as practical implementation of smart heating and cooling in six cities: Munich, Dresden and Bad Nauheim in Germany; and Jinan, Chengdu and Haiyan in China, to explore strategies to enhance policy support, financial mechanisms, and consumer engagement, ultimately aiming to facilitate the transition to climate-neutral heating and cooling systems. The study is divided into three parts: (i) an examination of smart heating and cooling policy frameworks in Germany and China over the past few years; (ii) an analysis of heating and cooling strategies in the six case study cities within the context of smart energy systems; and (iii) an exploration of the practical solutions adopted by these cities as part of their smart energy transition initiatives. The findings reveal differences between the two countries in the strategies and regulations adopted by municipal governments as well as variations within each country. The policy frameworks and priorities set by city governments can greatly influence the development and implementation of smart heating and cooling systems. The study found that all six cities are actively engaged in pioneering innovative heating and cooling projects which utilise diverse energy sources such as geothermal, biomass, solar, waste heat and nuclear energy. Even the smaller cities were seen to be making considerable progress in the adoption of smart solutions.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

Papua, one of the provinces in Indonesia, is recognized for its limited infrastructure and high poverty rates. This limitation undoubtedly emphasizes the government’s special attention toward augmenting foreign and domestic investments by expanding industrial sectors to absorb more labor, thereby aiming to enhance the region’s economic performance. The focus of the study seeks to assess the extent to which foreign and domestic investments, industrial employment, and the proliferation of industries in Papua contribute to increasing the Gross Development Product (GDP) and reducing poverty. By employing secondary data from 2016 to 2022 and utilizing the Regression Data Panel method, it encompasses 29 districts. The findings reveal that domestic investment, employment in the industrial sector, and the number of industries significantly influence poverty rates. However, as conclusion, foreign investment, surprisingly, demonstrates no substantial impact on economic performance. This unexpected result might be attributed to issues linked with the inadequate quality of financial performance, which doesn’t align with the available investment funds. Utilizing the analytical network process (ANP), the study outlines two primary strategies. The first involves prioritizing investment expansion by focusing on both domestic and foreign investments. The second strategy emphasizes industrial revitalization through augmenting the number of industries and enhancing labor participation in the industrial sector.

The recent crisis-filled period has placed a significant burden on various businesses, including in the tourism sector. As a result, the concept of resilience, the flexible ability to resist, has become more and more tangible. This study aims to update the quantitative organizational resilience assessment scale of Orchiston, Prayag and Brown. The paper analyses a sample of 87 tourism service providers managing attractions, and factor analysis was carried out to identify the factors in order to be able to measure the resilience of tourism service providers. Four factors could be identified: Leadership and Organization, Strategy, Independence, and Internal Identity. These identified factors and the included 14 items mean the key contribution, as a new, updated assessment system.