Optimization on theBuried Depth of Subsurface Drainage under Greenhouse Condition Based on Entropy Evaluation Method

Optimization and Evaluation of Tourism Mascot Design Based on Analytic Hierarchy Process-Entropy Weight Method

With the tourism industry continuing to boom, the importance of tourism mascots in promoting and publicizing tourism destinations is becoming increasingly prominent. Three core dimensions, market trend, appearance design, and audience feedback, are numerically investigated for deeply iterating tourism mascot design. Further, a subjective and objective evaluation weighting model based on the hierarchical analysis method (AHP) and entropy weighting method is proposed, aiming to utilize the advantages of these methods and ensure the entireness and correctness of results. Taking the mascots of six famous tourist attractions in Xi'an as an example, the feasibility and effectiveness of the evaluation model are verified. Data analysis and modeling results confirm that the three core evaluation indexes of scalability, innovation, and recommendation should be focused on in the design of tourism mascots in the three dimensions of market trends, appearance design, and audience feedback. The evaluation index scores are 0.1235, 0.1170, and 0.1123, respectively, which further illustrates the priority of mascot design. The evaluation model constructed by the research provides decision-makers with a comprehensive evaluation tool from the perspective of tourist experience, and also effectively assists the optimization process of mascot design. In addition, the model has good versatility and adaptability in structural design and evaluation logic and can be widely used in the optimization and evaluation research of brand mascots.

Assessing the impact of biochar and nitrogen application on yield, water-nitrogen use efficiency and quality of intercropped maize and soybean

Introduction

Biochar (BC) and nitrogen (N) application have the potential to increase grain yield and resource use efficiency in intercropping systems. However, the effects of different levels of BC and N application in these systems remain unclear. To address this gap, the study is intended to ascertain the impact of various combinations of BC and N fertilizer on the performance of maize-soybean intercropping and determine the optimum application of BC and N for maximizing the effect of the intercropping system.

Methods

A two-year (2021-2022) field experiment was conducted in Northeast China to assess the impact of BC (0, 15, and 30 t ha^-1) and N application (135, 180, and 225 kg ha^-1) on plant growth, yield, water use efficiency (WUE), N recovery efficiency (NRE) and quality in an intercropping system. Maize and soybean were selected as materials in the experiment, where every 2 rows of maize were intercropped with 2 rows of soybean.

Results and discussion

The results showed that the combination of BC and N significantly affected the yield, WUE, NRE and quality of intercropped maize and soybean. The treatment of 15 t ha^-1 BC and 180 kg ha^-1 N increased grain yield and WUE, while that of 15 t ha^-1 BC and 135 kg ha^-1 N enhanced NRE in both years. Nitrogen promoted the protein and oil content of intercropped maize, but decreased the protein and oil content of intercropped soybean. BC did not enhance the protein and oil content of intercropped maize, especially in the first year, but increased maize starch content. BC was found to have no positive impact on soybean protein, but it unexpectedly increased soybean oil content. The TOPSIS method revealed that the comprehensive assessment value first increased and then declined with increasing BC and N application. BC improved the performance of maize-soybean intercropping system in terms of yield, WUE, NRE, and quality while N fertilizer input was reduced. The highest grain yield in two years was achieved for BC of 17.1-23.0 t ha^-1 and N of 156-213 kg ha^-1 in 2021, and 12.0-18.8 t ha^-1 BC and 161-202 kg ha^-1 N in 2022. These findings provide a comprehensive understanding of the growth of maize-soybean intercropping system and its potential to enhance the production in northeast China.

Can collaborative innovation constrain ecological footprint? Empirical evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China

Collaborative innovation can promote scientific productivity and the development of clean technology and thus has a great potential in constraining the ecological footprint. However, current studies on the impact of collaborative innovation on ecological footprint are insufficient, and results remain controversial. To better understand these impacts, this paper took Guangdong-Hong Kong-Macao Greater Bay Area of China as a case, estimated the ecological footprint at the municipal level from 2008 to 2018, measured collaborative innovation both from four dimensions and from a composite approach, then applied threshold regression models to compare the impact of collaborative innovation on the ecological footprint across different economic intervals. The findings showed that: the ecological footprint of the Greater Bay Area displayed an overall upward trend with prominent spatial heterogeneity. The impact of collaborative innovation on the ecological footprint presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted the ecological footprint, which intensified with economic development, while collaboration in technology exerted significant inhibitory effects on ecological footprint, and the influence of inter-city knowledge collaboration was limited. Overall, collaborative innovation inhibited ecological footprint when measured by a composite index. This might inspire policymakers to adopt sustainable strategies depending on the type of collaborative innovation and the economic status of the city to constrain growth of the ecological footprint, thus minimizing the pressures of human activities on the environment and moving towards a more carbon neutral society.

Analysis of Driving Control Characteristics in Typical Road Types

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.

An Optimum Irrigation Schedule with Aeration for Greenhouse Tomato Cultivations Based on Entropy Evaluation Method

To balance multiple objectives of high yield by farmers, high quality by customers, and high irrigation water use efficiency (IWUE) for sustainable development of agriculture, a two-growing-season study was performed to test the effect of aeration at three irrigation levels (60%, 80%, and 100% of full irrigation) on crop growth, nutrient uptake, yield, IWUE, and fruit quality of tomato. The results showed that compared to the control, aeration significantly increased total dry weight at harvest, total N and K accumulation, which increased tomato yield by 23% and IWUE by 23% (p < 0.05). Yield and IWUE were significantly affected by irrigation with the increasing rate of 20.5% and 14.3% for yield, and with the decreasing rate of 27.7% and 8.6% for IWUE under 100% of full irrigation than that under 60% and 80% of full irrigation, respectively. Aeration positively impacted fruit quality while irrigation had a negative impact (p > 0.05). Based on these indicators, full irrigation with aeration could be an appropriate schedule for greenhouse tomato cultivations by the entropy evaluation method with the comprehensive score of 0.879 and 0.77 for the spring-summer and autumn-winter season, respectively. The result is of great significance to the farmers’ or researchers’ management of aerated irrigation in greenhouse tomatoes.

Optimization and Evaluation of Ventilation Mode in Marine Data Center Based on AHP-Entropy Weight

The ventilation mode affects the cooling efficiency of the air conditioners significantly in marine data centers. Three different ventilation modes, namely, underfloor ventilation, overhead ventilation, side ventilation, are numerically investigated for a typical marine data center. Four independent parameters, including temperature, velocity, air age, and uniformity index, are applied to evaluate the performances of the three ventilation modes. Further, the analytic hierarchy process (AHP) entropy weight model is established and further analysis is conducted to find the optimal ventilation mode of the marine data center. The results indicate that the underfloor ventilation mode has the best performance in the airflow patterns and temperature distribution evaluation projects, with the highest scores of 91.84 and 90.60. If low energy consumption is required, it is recommended to select the overhead ventilation mode with a maximum score of 93.50. The current evaluation results agree fairly well with the three dimensional simulation results, which further proves that the AHP entropy weight method is reasonable and has a high adaptability for the evaluation of air conditioning ventilation modes.