Spatial-temporal Evolution and Its Influencing Factors of Tourism Eco-efficiency in China

Eco-efficiency is an invaluable indicator for the measurement of the relationship between production activities and environmental depletion. This study measures the tourism eco-efficiency of 30 provinces in China from 2005 to 2020 based on the super-efficiency SBM model, and explores its spatial-temporal evolution characteristics using the kernel density function, standard deviation ellipse, and center of gravity model. Then, the influencing factors of the tourism eco-efficiency in China are analyzed by Tobit regression model. The results show that the tourism eco-efficiency of China is generally fluctuating upwards, but has not yet reached the maximum production possibility frontier. The kernel density curve shows a unimodal-bimodal-unimodal pattern, while the inter-provincial differences have been decreasing and becoming more balanced. The center of gravity of tourism eco-efficiency is located at the junction of Henan and Hubei province and generally moves to the south (slightly to the southwest). Meanwhile, it is revealed that the level of economic development and the tourism eco-efficiency has a significant inverted U-shaped relationship. The level of economic openness, traffic conditions, and tourism eco-efficiency is positively correlated. The environmental regulations and industrial structure have a negative but limited impact on tourism eco-efficiency. Finally, recommendations and suggestions for policy formulation to promote quality and sustainable development of the tourism industry are put forward, such as increasing investment in ecological protection and governance in tourism development, improving capacity-building in allocating green and low-carbon technologies and resources, strengthening tourism infrastructure construction, and enhancing environmental governance systems and mechanisms

Prediction of the anti-inflammatory effects of bioactive components of a Hippocampus species-based TCM formulation on chronic kidney disease using network pharmacology

Purpose: To systematically study and predict the therapeutic targets and signaling pathways of Hippocampus (HPC) against chronic kidney disease (CKD) using network pharmacology.Methods: By combining database mining, literature searching, screening of disease targets, and network construction, the effects of various components of HPC on several proteins related to CKD were predicted and the active compounds were screened. Genes related to the selected compounds were linked using the SEA database. The correlation between CKD and genes was determined using OMIM, DisGenNet, and GeneCards databases. Pathway-enrichment analyses of overlapping genes were undertaken using online databases.Results: A total of 144 compounds in HPC were identified. Analyses of clusters suggest that the active components of HPC and the target genes against the inflammation caused by CKD were due to 10 compounds and 25 genes. Metascape results showed that these HPC targets are related to CKD inflammation.Conclusion: The active components of HPC and the target genes against CKD inflammation are involved in multiple signaling pathways, such as AGE-RAGE, TLR, TNF, and NF-κB. This work provides scientific evidence to support the clinical use of HPC against CKD

Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that pre-training loss is a better indicator of the model's performance than the model's parameter count. We apply supervised fine-tuning (SFT) with different amounts of supervised data and empirically find a log-linear relation between data amount and model performance, and we find better models improve less with enlarged supervised datasets. To augment more data samples for improving model performances without any human effort, we propose to apply Rejection sampling Fine-Tuning (RFT). RFT uses supervised models to generate and collect correct reasoning paths as augmented fine-tuning datasets. We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs. We also find RFT brings more improvement for less performant LLMs. Furthermore, we combine rejection samples from multiple models which push LLaMA-7B to an accuracy of 49.3\% on GSM8K which outperforms the supervised fine-tuning (SFT) accuracy of 35.9\% significantly.Comment: Working in Progres

Better-Fitted Probability of Hydraulic Conductivity for a Silty Clay Site and Its Effects on Solute Transport

The heterogeneous hydraulic conductivity of a subsurface medium is vital to the groundwater flow and solute transport. Probability is efficient for characterizing and quantifying the field characterization of hydraulic conductivity. Compared with sandy mediums, silty clay is paid less attention to due to its low hydraulic conductivity. For long-term solute transport and seawater intrusion, the low-permeable medium is considered as a remarkably permeable medium. This study reports on a comprehensive investigation on the hydraulic conductivity field of the Ningchegu site, located east of Tianjin City of China. Four layers recognized by 52 boreholes, plain fill, continental silty clay, mud–silt clay and marine silty clay, were deposited from the top to the bottom. The hydraulic conductivities measured via permeameter tests ranged from 2 × 10−6 m/d to 1.6 × 10−1 m/d, which corresponded to the lithology of silty clay. The magnitude and the range of the hydraulic conductivity increased with the depth. Five probability distribution models were tested with the experimental probability, indicating that a Levy stable distribution was more matched than the log-normal, normal, Weibull or gamma distributions. A simple analytical model and a Monte Carlo technique were used to inspect the effect of the silty clay hydraulic conductivity field on the statistical behavior of the solute transport. The Levy stable distribution likely generates higher peak concentrations and lower peak times compared with the widely-used log-normal distribution. This consequently guides us in describing the transport of contaminations in subsurface mediums

Sustainability Investigation of Resource-Based Cities in Northeastern China

Improving the sustainability of traditional resource-based cities in China has been a core issue and policy-priority for Chinese government to establish long-term ecological civilization, particularly for northeastern China which is recognized as a typical agglomeration area of resources cities. In this study, we establish a three-layer index system consisting of a comprehensive layer, systemic layer, and variable layer, and including 22 indicators which are grouped into economic, social and environmental subsystems. After that, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was applied to measure and rank the sustainability of the selected 15 typical resource-based cities in northeast China, and then a GIS (Geographical Information System) technique based on the software of SuperMap was applied to map the sustainability in terms of the spatial effects among these cities. The results reveal that a unilateral improvement of a subsystem did not mean an improvement or contribution to whole system. In detail, during the past 15 years from 2000 to 2015, the comprehensive sustainability of resource-based cities in Northeastern China shows a declining trend in the mass, and the sustainability of the economic subsystem shows increase; the sustainability of the social system remains stable, while the environmental subsystem shows decrease. These situations might result from policy interventions during the past 15 years, therefore, promoting the sustainability of resource-based cities needs a historical approach, which should focus on the coordinated development of its economic, social, and environmental subsystems

Effects of Urban Producer Service Industry Agglomeration on Export Technological Complexity of Manufacturing in China

Based on the measurement of producer service industry agglomeration and export technological complexity of manufactured products in 288 Chinese cities from 2000 to 2015, this paper illustrates the evolvement and spatial characteristics of the two factors through visualization figures, and discusses the effects of producer services agglomeration on export technological complexity of manufacturing through robust panel data models. The findings are as follows: as with the influence of industrial connection, empirical outcomes indicate that urban producer service agglomeration can promote technological complexity of export manufacturing on the full-sample level. Visualization analysis shows that the scale of producer service industry agglomeration and the export technological complexity of manufactured products around Chinese cities kept rising constantly during the study period. However, although the export technological complexity displayed a trickle-down effect, the producer service industry agglomeration experienced continuous polarization both on the national and the regional levels. Accordingly, as is shown in the empirical analysis by areas, regions with strong support from producer service industry saw a remarkable promotion in the export manufacturing technology, while the northwest and the northeast gradually lagged behind. Such results sufficiently prove that heterogeneity does exist in the performances of industrial connection between producer service industry and export manufacturing in cities of different regions in China

A New Method for Evaluating Riverside Well Locations Based on Allowable Withdrawal

This study aims to derive the optimal solution for well locations based on the allowable withdrawal. To demonstrate the proposed technique, a numerical model of a typical well field at the Qinbei Power Plant was constructed and 20 possible drawdown scenarios were simulated for each of three different arrangements of pumping wells. The concept of the Unit Increased Drawdown Value (UIDV) was used as a basis to select the location of pumping wells, where the UIDV is defined as the increase in drawdown associated with the addition of a unit of extraction. Results showed that for modeled well fields with the same number of wells and rates of exploitation, drawdown will reach the maximum and minimum when the well field is located in the recharge zone and discharge zone, respectively, because of the specific relationships between groundwater and surface water. This paper considered a pumping program with maximum exploitation and minimum costs corresponding to allowable withdrawals of 2.44 m3/s and 1.07 m3/s, respectively, and the relationship between groundwater and surface water was elucidated. The study results provide a theoretical basis for the layout of wells. The solution takes economic factors into consideration and describes the best solution for well locations to meet drawdown limitations during pumping applications

Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west