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The process of urbanization promotes the development of economy and society, and also brings great pressure to the environment. In order to better understand the harmonious and interactive relationship between environment and urbanization, by selecting 13 cities in Hunan province of China as cases, this paper establishes a correlation model and a comprehensive evaluation system, uses entropy weight method to weight the index, the coupling coordination model to analyze the coupling coordination relationships empirically, and gray prediction model to predict the trend and make corresponding decision recommendations. The results show with novelty that the overall performances of environment and urbanization for the 13 cities in Hunan province have similarities, the coupling coordination degrees are mild with slight fluctuations, and the next years will keep the similar trends. However, the coupling coordination development is unbalanced with the coupling degree of the east higher than that of the west; therefore, corresponding measures for better environmental governance and urban planning need to be taken in different cities.

The results show that a comprehensive evaluation system consisting of natural resources, pollution response, government management, and legal involvement can systematically assess the coupling coordination degree (CCD) of environmental governance. The information entropy weight (IEW) method and technique for order preference by similarity to an ideal solution (TOPSIS) are jointly used to analyze the CCD of environmental governance in China. The combination of the IEW method and TOPSIS can provide effective references for the sustainable development of environmental governance and are helpful for effectively providing systematic and specific measures. From 2008 to 2017, the CCD of China’s environmental governance fluctuated and generally showed a benign trend. At the same time, the range of CCD among regions was more aggregated and balanced. In addition, the CCD of China’s environmental governance showed spatial relationships and formed a clear spatial cluster in which the coastal areas had higher CCD than did the inland areas.

With the rapid development of the tourism industry, the water resource consumption in tourism has largely increased and gets more complicated, making water resource management in tourism more difficult. To achieve sustainable water utilization in tourism, water resource management has to take full account of the local natural, social, and industrial conditions, both satisfying the demands of water resource protection and tourism development. To analyze this coupling relationship, an integrated index system comprised of 15 indices is designed, and a coupling degree model between tourism-related water resource management and local conditions is introduced. The result revealed that tourism-related water resource management is generally congruent with the local conditions in China, and provinces at the very high/low coupling stage presented four clusters. A discussion combining the change of water policies and the water use efficiency of hotels in Beijing revealed that water-saving policies are proven to be necessary for the tourism development. Furthermore, a discussion of the four clusters revealed the advanced experience and deficiency of water policies in substantial tourism areas. The results could provide references for the improvement of water policies in the tourism industry in China.

Emergency events in the industrial sector have been increasingly reported during the past decade. However, studies that focus on emergency evacuation to improve industrial safety are still scarce. Existing evacuation-related studies also lack a perspective of fire assembly point’s analysis. In this research, location of assembly points is analyzed using the multi-criteria decision analysis (MCDA) technique based on the integrated information entropy weight (IEW) and techniques for order preference by similarity to ideal solution (TOPSIS) to support the fire evacuation plan. Next, we propose a novel simulation model that integrates fire dynamics simulation coupled with agent-based evacuation simulation to evaluate the impact of smoke and visibility from fire on evacuee behavior. Factors related to agent and building characteristics are examined for fire perception of evacuees, evacuees with physical disabilities, escape door width, fire location, and occupancy density. Then, the proposed model is applied to a case study of a home appliance factory in Chachoengsao, Thailand. Finally, results for the total evacuation time and the number of remaining occupants are statistically examined to suggest proper evacuation planning.

Urban competitiveness is an indispensable topic for urban management. The purpose of this work was to study the status quo of urban system competitiveness in any region and explore the internal factors that affect urban competitiveness. In this study, 30 indicators were selected from six dimensions: population, economic strength, infrastructure, technology and culture, open exchange, and quality of life, and a two-level evaluation index system was constructed. The entropy weight method was used to calculate the weight, and 12 prefecture-level cities in Hubei Province were taken as the evaluation object. This study found that in Hubei province, (1) science, technology, and culture are the first driving forces of urban competitiveness; (2) the impact of the quality of life on urban competitiveness is deepening and obvious, especially the impact of residents’ consumption; and (3) Wuhan, the provincial capital city, is far ahead in terms of its competition and its position is unshakable, followed by Yichang and Xiangyang. Overall, the competitiveness gap between cities in the region is gradually narrowing.

An energy efficiency monitoring method of the steam turbine system is studied in this paper. Multivariate state estimation technique (MSET) is utilized to compare the actual monitoring parameters and the healthy data of the equipment in normal working condition with a multi parameter estimation model. Due to the limitation of a single heat rate index in evaluating energy efficiency variation, the energy efficiency deviation degree combined with improved information entropy weight is proposed to judge the steam turbine’s operation condition levels. The index value in the modified weight method has been searched for more steady weight values calculated by information entropy values with small variation. Taking a 600 MW unit as an example, the energy efficiency levels of the unit under a 550 MW normal working condition are clustered into four groups, testifying the MSET model correctness and calculating the deviation degree value. Then, the energy efficiency status monitoring model is utilized to record residuals of actual data and estimated data during abnormal energy efficiency period. The residuals over deviation degree are then marked and judged as related with the abnormal data. The results show that the MSET model can timely and accurately judge the change of unit operation state, and the deviation degree calculated by the modified information entropy weight method can provide earlier warnings for the abnormal energy efficiency working conditions.

[Purpose / Significance] Through the research on the personal informatization indicators of rural residents,this article constructs a measurement model of digital rural personal information,which can reveal the internal information behavior of individuals more comprehensively and profoundly, and understand the demands and opinions of rural residents on informatized public services. [Method / Process] According to the current situation of digital rural informatization construction in China, this paper firstly uses the entropy weight method to calculate and select the informatization micro-observation index, and determines the measurement index model. Then by combining with the questionnaire data, we study and evaluate the personal informatization level of rural residents in China. [Results / Conclusions] The entropy weight method determines the weight of indicators, which can fully reflect the status of rural residents' information development level. The results show that we should raise the awareness of information and expand the channels for villagers to obtain information.

The main objective of the present study was to produce a landslide susceptibility map by implementing a novel methodology that combines Information Theory and GIS-based methods for the Nancheng County, China, an area with numerous reported landslide events. Specifically, the information coefficient that is estimated from Shannon’s entropy index was used to determine the number of classes of each landslide-related variable that maximizes the information coefficient, while three methods, logistic regression, weight of evidence, and random forest algorithm, were implemented to produce the landslide susceptibility map. The comparison of the various models was based on the assessment of a database of 112 past landslide events, which were divided randomly into a training dataset (70 %) and a validation dataset (30 %). The identification of the areas affected was established by analyzing airborne imagery, extensive field investigation, and the examination of previous research studies, while the morphometric variables were derived using remote sensing technology. The geo-environmental conditions in those locations were analyzed regarding their susceptibility to slide. In particular, 11 variables were analyzed: lithology, altitude, slope, aspect, topographic wetness index, sediment transport index, profile curvature, plan curvature, distance to rivers, distance to faults, and distance to roads. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic, and the area under the success and predictive rate curves. Each model gave similar outcomes; however, the random forest model had a slightly higher predictive performance in terms of area under the curve (0.9220) against the ones estimated for the weight of evidence (0.9090) and the logistic regression model (0.8940). The same pattern of performance was reported when the success power of the models was calculated. Random forest was slightly better than the other two models in terms of area under the curve (0.9350) in comparison with the weight of evidence (0.9255) and logistic regression (0.9097). The predictive performance was estimated by using the validation dataset, while the success power of the models was estimated by using the training dataset. From the visual inspection of the produced landslide susceptibility maps, the most susceptible areas are located at the west and east mountainous areas, while moderate to low susceptibility values characterize the central area.

The main purpose of this paper is to find new estimations for the Shannon and Zipf–Mandelbrot entropies. We apply some refinements of the Jensen inequality to obtain different bounds for these entropies. Initially, we use a precise convex function in the refinement of the Jensen inequality and then tamper the weight and domain of the function to obtain general bounds for the Shannon entropy (SE). As particular cases of these general bounds, we derive some bounds for the Shannon entropy (SE) which are, in fact, the applications of some other well-known refinements of the Jensen inequality. Finally, we derive different estimations for the Zipf–Mandelbrot entropy (ZME) by using the new bounds of the Shannon entropy for the Zipf–Mandelbrot law (ZML). We also discuss particular cases and the bounds related to two different parametrics of the Zipf–Mandelbrot entropy. At the end of the paper we give some applications in linguistics.

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig’s method. To compare the influence of various normalization methods in both the EWM and Hellwig’s method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries’ realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max–min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig’s method. The results are compared with the outcome obtained by Hellwig’s method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig’s method results.