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Carbon emissions are critical to climate change, and land-use change is an essential source of growth in carbon emissions. Research on land-use carbon emissions has become one of the hotspots in academic research. To explore the research hotspots and development trends of land-use carbon emissions in the last 20 years, CiteSpace software was used to conduct a quantitative analysis of relevant literature. This paper was based on the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) database literature on land-use carbon emissions from 2001 to 2020. The scientific research cooperation network CiteSpace software, with keyword co-occurrence, clustering, and burst word detection, was used to systematically analyze the main research strengths, hotspots and frontiers and clarify the research progress. The research results are as follows: (1) the amount of literature and the depth of research on land-use carbon emissions have increased yearly. However, there is little cooperation between research institutions and scholars, and there is still a lack of large-scale and stable research teams. (2) At the research hotspot level, the English literature focuses on building models and theoretical frameworks to study the internal mechanisms and driving factors of carbon emissions and climate change. The Chinese literature focuses on achieving regional carbon emissions reductions and carbon cycle goals and optimizing a low-carbon economy, transportation and land-use structure. (3) Research frontiers and trends show that the English literature first explored carbon sequestration, organic carbon, and carbon accounting. In China, the research frontiers are gradually becoming focused on influencing factors, decoupling analysis, and the built environment. The study will strengthen the intensity and depth of global carbon emission research and provide a reference for improving global climate change, protecting ecology and balancing economic development. [ABSTRACT FROM AUTHOR]

Mining user data and constructing web user profiles of older adults from the perspective of elderly services is conducive to understanding their behavioral habits, needs, and usage preferences on the web, which provides more targeted elderly care services. In this paper, IGA-SOMK + + , which is a novel clustering method for constructing web user profiles of older adults, is proposed based on the China Family Panel Studies (CFPS) survey data, which include 6596 older adults aged greater than 60 years. The selected data aspects include basic information, work situation, health situation, living habits, and web use services. To describe the web user profiles of older adults, a hybrid method based on improved genetic algorithm (IGA) feature selection, self-organizing feature maps (SOM), and K-means + + is proposed. Data on older adults' web use behaviors are first processed, and IGA is used for feature selection based on the adaptive crossover and mutation probabilities. SOM is then used to determine the initial center vectors of K-means + + for further clustering, which is referred to as SOMK + + (SOM-K-means + +). The results of IGA-SOMK + + are compared with those of the state-of-the-art methods, including the K-means, mini batch K-means, Agnes, K-modes, FCM, K-means + + , SOMK + + , and IHPSO-KM. In addition, the significance and robustness of IGA-SOMK + + are analyzed. The experimental results show that the IGA feature selection reduces the influence of the redundant feature factors and improves the performance of the clustering algorithm. SOMK + + overcomes the sensitivity of K-means to initial cluster centers. Moreover, IGA-SOMK + + has the best clustering effect among the compared algorithms in terms of silhouette coefficient (SC), calinski-harabaz (CH) index, and davies-bouldin (DB) metrics. For example, it increases the SC from 0.280 to 0.629. Finally, by analyzing the results, the user group of older adults is segmented to perform the deep mining of CFPS data, which verifies the feasibility of the user profile model. This paper summarizes the basic situation of the current web access of older adults in China in terms of web use services, as well as the importance of the web in their lives and in the information channels. It also provides suggestions for the current problems of older adults in accessing the web. [ABSTRACT FROM AUTHOR]

Recently, food safety and cold chain food have been closely related to the epidemic. The party and the state have intensified efforts to solve food safety problems and prevent possible epidemic risks. China has issued a series of policies and plans to strengthen food safety supervision to improve the food safety policy system. To our knowledge, little work has studied policy problems of food safety with in-depth quantitative analysis for an extended period. In accordance with the different national policies and regulations for food safety, this paper fills the gap by analyzing the policies and comparing the central and local policies issued in China from 2007–2022. In addition, the Latent Dirichlet Allocation (LDA) topic model and K-Means clustering model are constructed to analyze the content of food safety policies and identify hot topics. Finally, a quantitative analysis of China's food safety policies is made from four aspects: the number of policy release years, the distribution area, the range of action, and the affiliated institutions. The results show that: (a) there is a partial surge in food safety policies issued in 2007, 2011, and 2017; (b) the local food safety policy has a high inheritance to the central policy content, and the trends of annual publication number are highly consistent; (c) the innovation of different policy contents in the region have their own characteristics; (d) the proportion of compulsory and capacity policies is much more significant than that of other types of policies. This paper provides some novel insights into food safety policies. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper chooses the most representative second-tier coastal cities in the critical period of China's economic transformation as the research object. This paper innovatively constructs the evaluation system of manufacturing transformation and upgrading environmental measurement from the specific content contained in ''Made in China 2025'' which leads the transformation and upgrading of China's manufacturing industry. Using the combination of factor analysis and cluster analysis, combined with the entropy weight method of objective weighting, the measurement and difference of coastal environment in manufacturing transformation and upgrading are studied. The results show that the development environment of the transformation and upgrading of the manufacturing industries in different cities is quite different. The most prominent feature of the empirical analysis is that Hangzhou is a typical city, showing that the supporting factors of innovation capability play an important role in the transformation and upgrading of manufacturing. [ABSTRACT FROM AUTHOR]

The agglomeration of airports into multi-airport regions (MARs) has become one of the salient features of the worldwide air transport system in the last decades. Meanwhile, in China, the development of HSR is growing quickly, and is both competitive to and cooperative with the aviation network. To date some research has focused on the airport classification with aviation network properties within the specific MARs. However, little research comprehensively integrates complementary transport systems (such as civil aviation versus high-speed rail (HSR)) into the analytical framework for airport classification. The purpose of this paper is to identify the unique nature of component airports and their distribution in MARs in China from multiple perspectives by accounting for the influence of the HSR system. Airports are classified along multiple dimensions including competitive concentrations, the interaction between air transport and HSR, and airport community structure, among others. The results produce distinct partitions of the component airports in Chinese MARs, and provide insights into Chinese airport functionality and impacts of the HSR network on the distribution of different types of airports. The conclusions provide a more comprehensive assessment of airports' spatial arrangement in the unique Chinese MAR context. Although this paper did not derive the market share allocation and co-opetition relationship between a specific airport and HSR, it proposed a basic framework for future research. [ABSTRACT FROM AUTHOR]

Debris flows can damage infrastructure and threaten human life and property safety, especially in tourist attractions. Therefore, it is crucial to classify and evaluate the risk of debris flows. This article takes 14 debris flows in Longmenshan Town, Pengzhou, Sichuan, China, as the research object. Based on on-site geological surveys, combined with drone images and multiple remote sensing images, the essential characteristics of each debris flow are comprehensively determined. A total of nine factors are used as the primary indicators affecting the risk of debris flow: drainage density, roundness, the average gradient of the main channel, maximum elevation difference, bending coefficient of the main channel, the loose-material supply length ratio, vegetation area ratio, population density, and loose-material volume of unit area. The subjective weights of each indicator are obtained using the Analytic Hierarchy Process, while the objective weights are obtained using the CRITIC method. Based on this, the distance function is introduced to couple the subjective and objective weights, determine each indicator's combined weights, and obtain the integrated evaluation score values of different debris flow hazards. Considering the integrated evaluation score of debris flow, cluster analysis was used to classify 14 debris flows and cluster effectiveness indicators were introduced to determine the effectiveness of debris flow classification. A quantitative standard for the risk of debris flow within the study area was proposed, and finally, a risk assessment of debris flow in the study area was made. Comparing the results of this paper with the gray correlation method, the coupled synergistic method, and the geological field survey results, proves that the proposed method is feasible and provides a reasonable scientific basis for the study of the hazard assessment of regional debris flow clusters and other related issues within the scope of the Jianjiang River basin and other areas. [ABSTRACT FROM AUTHOR]

Low-carbon development of China's power sector is the key to achieving carbon peaking and carbon neutrality goals. Based on the logarithmic mean divisor index (LMDI) model, considering the carbon transfer caused by inter-provincial electricity trading, this paper analyzes the influencing factors of CO2 emissions in the provincial power sector and uses K-means clustering method to divide 30 provinces into four categories to analyze the differences in regional carbon emission characteristics. In addition, by establishing different development scenarios, the carbon emission trends and emission reduction potentials of each cluster under different emission reduction measures from 2020 to 2040 are studied, in order to explore the differentiated emission reduction paths of each cluster. The results show that the contribution of influencing factors shows great differences in different provinces. Trends in CO2 emissions vary widely across scenarios. In the reference scenario, the CO2 emissions of each cluster will continue to increase; in the existing policy scenario, the total power industry will peak at 6.1Gt in 2030; in the advance peak scenario that puts more emphasis on the development of advanced technologies and renewable energy under the clean development model, the carbon emission peak will be brought forward to 2025, and the peak will be reduced to 5.2Gt. Finally, differentiated emission reduction paths and measures are proposed for the future low-carbon development of different cluster power industries, providing theoretical reference for the deployment of provincial-level emission reduction work, which is of great significance to the global green and low-carbon transformation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water inrush disasters in mining areas are one of the most serious geological disasters in coal mining. The purpose of this study is to study the establishment of a water chemical database and water inrush source discrimination model in the Weibei coalfield to provide the basis for regional hydrogeological conditions for future mining under pressure in the Weibei area, as well as a basis for the rapid identification of water inrush sources in the Weibei coalfield. In this paper, a conventional hydrochemical and trace element discrimination model for mine water inrush was established, and the hydrochemical characteristic files of the entire mining area were integrated. Based on 10 indicators, three hydrochemical discrimination models of rock stratum aquifers were established. Through the Mahalanobis distance test, it was found that the six selected variables, K+ + Na+, Mg2+, NH4+, Cl−, SO42−, and pH, have significant discrimination ability and good effect and can effectively distinguish the three main water inrush aquifers in the Weibei mining area. Then, the clustering stepwise discriminant analysis method was used to select 24 water samples and 14 trace element indicators from the conventional water chemistry test results. Based on principal component analysis, a principal component analysis discriminant model of trace elements was established for the four main aquifers. The accuracy and misjudgment rate of the Bayes multi-class linear discriminant using conventional ions as explanatory variables were 64.3% and 35.7%, respectively, showing a poor discriminant effect. On this basis, seven characteristic trace elements were analyzed according to Bayes multi-class linear discriminant analysis, the mutual influence and restriction relationship regarding the migration of these seven trace elements in the groundwater system of the mining area was determined, and the modified Bayes multi-class linear discriminant analysis model of trace elements for the water inrush source was established, which was more accurate than the conventional ion Bayes multi-class linear discriminant analysis model. The accuracy rate reached 92.9%. This research is of great significance for mine water-source identification and water-inrush prevention guidance. [ABSTRACT FROM AUTHOR]

Several scholars have recently recommended a transition from focusing on Internet access (access divide) and Internet use (use divide) to the tangible outcomes of Internet use (outcome divide). This study evaluates the utility of this perspective in China for investigating digital inequalities across provinces. Despite having the world's largest ICT market, digital disparities persist at the provincial, city, and county levels. This paper establishes an explanatory conceptual model of the digital divide (DD) at the provincial level and applies mapping through ArcGIS software to investigate spatial agglomeration. Spatial autocorrelation (Moran-i) is used to reveal high significance and multivariate analysis is used to investigate the key correlates. Among the provinces, a significant DD was found, which shows that provinces that are administratively sophisticated (such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon, and Zhejiang) having a high digital development index score (IDI). Concomitantly, poverty-stricken regions and rural areas, mostly in southwest, central, and western China, have low IDI values. The factors found by this study to be most influential for improved ICT usage and outcomes are residential income (urban), e-government, literacy rate (secondary gross enrollment), and the working-age population. This indicates that socio-economic problems should be addressed first instead of institutional and innovational considerations. This study is the first to develop a conceptual model of the third-level digital divide (3-LDD) (ICT outcome) at the provincial level and utilize spatial analysis and mapping to supplement traditional methodologies. [ABSTRACT FROM AUTHOR]

Global climate crises are forcing the world to behave sustainably. The building energy service industry (BESI) emerged and promoted building energy conservation by providing a market-oriented mechanism for initial investment. However, the BESI lacks scientific and rational planning, lagging far behind the energy service in the industrial sector. This paper attempts to analyze BESI from the perspective of order parameter-based analysis by adopting the DEMATEL (Decision-Making and Trial Evaluation Laboratory) method, thereby revealing the characteristics of industrial evolution and ascertaining the main order parameters. Consequently, the results show that the evolution of the BESI industry relies on synergistic interaction in the corporate operation, industry-standard, production factor-related, and external environmental industry subsystems. The synergy of the BESI consists of synergy in subsystems, between subsystems, and between the system and the external environment. Three main order parameters dominating the evolution of the BESIs system are determined, namely, "financial support", "industry standard", and "technology innovation". Financial support and industry standard drove the development of the BESI in China over the last few years, while technological innovation will lead its evolution in the coming years. Based on these results, policy suggestions can be proposed to foster BESI development, especially regarding technological innovation. [ABSTRACT FROM AUTHOR]

Hemorrhagic fever with renal syndrome(HFRS) is a worldwide fulminant infectious disease. Since the first HFRS cases in Hubei Province were reported in 1957, the disease has spread across the province and Hubei has become one of seriously affected areas in China. However, the epidemic characteristics of HFRS are still not entirely clear. Therefore, a systematic investigation of spatial and temporal distribution pattern of HFRS system is needed. In order to facilitate better prevention and control of HFRS in Hubei Province, in this paper, a GIS spatiotemporal analysis and modeling tool was developed to analyze the spatiotemporal dynamics of the HFRS epidemic, as well as providinga comprehensive examination the dynamic pattern of HFRS in Hubei over the past 30 years (1980-2009), to determine spatiotemporal change trends and the causes of HFRS. This paper describes the experiments and their results. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the importance of the digital economy in restoring economic and social development, creating more jobs and improving people’s well-being. To inform policy makers about changes to digital strategies, measuring the digital economy is a prerequisite. This study aimed to compile an index of digital economy at the provincial (municipalities, autonomous regions, collectively referred to as “provinces”) level to present an accurate and in-depth depiction of how it has developed in China. Our sample covers 31 provinces in China, over the period 2010–2020. This paper firstly constructs the digital economy index system from the four dimensions of digital users, digital platforms, digital industries and digital innovation, and then adopts a combination of entropy weighting method and grey target theory to measure the digital economy index. This paper study revealed that China’s digital economy has been on an upward trend from 2010 to 2019 and has a decline in 2020, and the digital innovation is an important driving force for the growth of the digital economy index. The convergence of China’s digital economy is decreasing, indicating that the gap in digital economy development between provinces is increasing. The proposed index in this study can be used as a screening tool, decision making tool, benchmarking tool and guidance of high-quality digital economy development. [ABSTRACT FROM AUTHOR]

Statistical monitoring of the level of economic growth momentum transformation is the basis for implementing quantitative policies for economic growth transformation. Based on theoretical analysis and combined with the process of national economic operation, this paper designs an economic growth momentum transformation monitoring indicator system consisting of four dimensions, supply and demand, factors, consumption and trade, using a comprehensive evaluation method to compile the economic growth momentum transformation level index. Using mainland China's provincial data from 2002 to 2021, we calculated the economic growth momentum transformation level of each province and extracted corresponding characteristics. The research found that the economic growth momentum transformation level of provincial economies in China exhibits stage-wise and dimensional differentiation over time, and it has category agglomeration and tiered transfer characteristics in space. [ABSTRACT FROM AUTHOR]

Wu, Q. and Wang, W.-C., 2019. Environmental measurement and cluster analysis of manufacturing transformation and upgrading: An empirical study in Eastern Coastal cities in China. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 867–872. Coconut Creek (Florida), ISSN 0749-0208. This paper chooses the most representative second-tier coastal cities in the critical period of China's economic transformation as the research object. This paper innovatively constructs the evaluation system of manufacturing transformation and upgrading environmental measurement from the specific content contained in "Made in China 2025" which leads the transformation and upgrading of China's manufacturing industry. Using the combination of factor analysis and cluster analysis, combined with the entropy weight method of objective weighting, the measurement and difference of coastal environment in manufacturing transformation and upgrading are studied. The results show that the development environment of the transformation and upgrading of the manufacturing industries in different cities is quite different. The most prominent feature of the empirical analysis is that Hangzhou is a typical city, showing that the supporting factors of innovation capability play an important role in the transformation and upgrading of manufacturing. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis., Methods: Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network., Results: The results from experiments on the Pharmacopoeia of the People's Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas., Conclusions: We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision.

Cluster analysis is a method in data processing. It can be applied to marketing in many aspects in practice, providing a scientific and reasonable method for the formulation of marketing strategies. Based on the essence of the cluster analysis method, this study has a new exploration, and explores the some factors of Taiwan tourists' demands, involvement, motivation and Intention. The impact of travel purchase behavior. This study intends to explore the cluster analysis method from the mutual influence of each variable, and analyze the influence of each factor on the purchase behavior in the specific application of consumer behavior in marketing the goal is to make the tourism marketing theory of this paper more perfect. [ABSTRACT FROM AUTHOR]

The mitigation of CO2 emissions requires a global effort with common but differentiated responsibilities. In this paper, we identify clusters of CO2 emissions across 72 countries. First, using the stochastic version of the IPAT and employing the dynamic common correlated effects technique, we identify three key determinants affecting CO2 emissions (non-renewables, population, and real GDP). In the second step, both hierarchical and non-hierarchical clustering methods are considered to identify the optimal number of clusters. We identify two to four clusters with different member countries, and in particular establish that in most cases, a 2-cluster solution appears to be optimal. The contents of clusters vary slightly according to the clustering methods for each period. The clustering results from using only the overall CO2 emissions indicate that the countries we consider form three clusters, with China and the USA each within a single member cluster. The remaining 70 countries form the third cluster. Our findings reflect the prominent roles of China and the USA in overall CO2 emissions. Analyses with sub-period and largest emitters reflect a different clustering structure. Some policy recommendations in setting emission reductions are made, considering different clusters across countries. [ABSTRACT FROM AUTHOR]

Purpose: Remanufacturing is the only end-of-life (EOL) treatment process that results in as-new functional and aesthetic quality and warranty. However, applying mental model theory, the purpose of this paper is to argue that the conception of remanufacturing as an EOL process activates an operational mental model (OMM) that connects to resource reuse, environmental concern and cost savings and is thus opposed to a strategic mental model (SMM) that associates remanufacturing with quality improvements and potential price increases. Design/methodology/approach: The authors support the argument by empirically assessing consumers' multi-attribute decision process for cars with remanufactured or new engines among 202 car buyers in China. The authors conduct a conjoint analysis and use the results as input to simulate market shares for various markets on which these cars compete. Findings: The results suggest that consumers on average attribute reduced utility to remanufactured engines, thus in line with the OMM. However, the authors identify a segment accounting for about 30 per cent of the market with preference for remanufactured engines. The fact that this segment has reduced environmental concern supports the SMM idea that remanufactured products can be bought for their quality. Research limitations/implications: A single-country (China) single-brand (Volkswagen) study is used to support the conceptualised mental models. While this strengthens the internal validity of the results, future research could improve the external validity by using more representative sampling in a wider array of empirical contexts. Moreover, future work could test the theory more explicitly. Practical implications: By selling cars with remanufactured engines to customers with a SMM that values the at least equal performance of remanufactured products, firms can enhance their profit from remanufactured products. In addition, promoting SMM enables sustainable business models for the sharing economy. Originality/value: As a community, the authors need to more effectively reflect on shaping mental models that disconnect remanufacturing from analogies that convey inferior quality and performance associations. Firms can overcome reduced utility perceptions not only by providing discounts, i.e. sharing the economic benefits of remanufacturing, but even more by increasing the warranty, thus sharing remanufacturing's performance benefit and reducing consumers' risk, a mechanism widely acknowledged in product diffusion but neglected in remanufacturing so far. [ABSTRACT FROM AUTHOR]

This paper adopts both one-dimensional and multi-dimensional cluster analysis to analyze China’s HDI data for 1982, 1995, 1999, and 2003, and to classify China’s provinces into four tiers based on the three basic developmental aspects embedded in HDI. The classifications by cluster analysis depends on the observations’ similarities with respect to clustering variable(s), rather than subjectively predetermined threshold values adopted by major international organizations, like UNDP, to make their classification. By investigating the multi-dimensional clustering results, this paper describes the features of each of the four tiers based on their characteristics within the three developmental aspects, explores the patterns of cross-tier disparities, and tracks the evolutions from a historical perspective. Also found is that, in China, the overall regional disparities have been increasingly attributable to regional economic disparities. [ABSTRACT FROM AUTHOR]

Online customer segmentation is a significant research topic of customer relationship management. Previous literatures mainly studied the differences between non-purchasers and purchasers, lacking further segmentation of online purchasers. There is still existing significant heterogeneity within purchaser-groups. This paper focuses on Chinese online purchaser segmentation based on large volume of real transaction data on Taobao.com, we firstly extracted and investigated Chinese online purchaser behavior indicators and classified them into six types by cluster analysis, these six categories are: economical purchasers, active-star purchasers, direct purchasers, high-loyalty purchasers, risk-averse purchasers and credibility-first purchasers; then we built an empirical model to estimate the sensitivity of each type of online purchasers to three mainstream promotion strategies (discount, advertising and word-of-mouth), and found that economical purchasers are the most sensitive to discount promotion; direct purchasers are the most sensitive to advertising promotion; active-star purchasers are the most sensitive to word-of-mouth promotion; finally, the implications of online purchaser classification for marketing strategies were discussed. [ABSTRACT FROM AUTHOR]

An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole swarm were recorded at the nearest station, RSH, which is located about 12 km from the epicenter. We examine the statistical characteristics of the Rushan swarm in this paper using RSH station data to assess the arrival time difference, $$t_{{{\text{S}} \,-\, {\text{P}}}}$$ , of Pg and Sg phases. A temporary network comprising 18 seismometers was set up on May 6, 2014, within the area of the epicenter; based on the data from this network and use of the double difference method, we determine precise hypocenter locations. As the distribution of relocated sources reveals migration of seismic activity, we applied the mean-shift cluster method to perform clustering analysis on relocated catalogs. The results of this study show that there were at least 16 clusters of seismic activities between May 6, 2014, and June 30, 2014, and that each was characterized by a hypocenter spreading process. We estimated the hydraulic diffusivity, D, of each cluster using envelope curve fitting; the results show that D values range between 1.2 and 3.5 m/d and that approximate values for clusters on the edge of the source area are lower than those within the central area. We utilize an epidemic-type aftershock sequence (ETAS) model to separate external triggered events from self-excited aftershocks within the Rushan swarm. The estimated parameters for this model suggest that α = 1.156, equivalent to sequences induced by fluid-injection, and that the forcing rate ( μ) implies just 0.15 events per day. These estimates indicate that around 3% of the events within the swarm were externally triggered. The fact that variation in μ is synchronous with swarm activity implies that pulses in fluid pressure likely drove this series of earthquakes. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence and frequency of haze are constantly increasing, severely threatening people's daily lives and health and bringing enormous losses to the economy. To this end, we used cluster analysis and spatial autocorrelation methods to discuss the spatial and temporal distribution characteristics of severe haze in China and to classify regions of China. Furthermore, we analyzed the interaction between haze pollution and the influence of economy and energy structure in 31 provinces in China, providing references for the prevention and treatment of haze pollution. The processed data mainly include API, meteorological station data, and PM 2.5 concentration distribution vector graph. The results show the yearly haze pattern from 2008 to 2012, and present a strong pattern of pollution concentrated around Beijing–Tianjin, the Yangtze River Delta, southwest China, and central China. The overall spatial pattern of decreasing from north to south is relatively constant over the study period. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new concept of convergence which is based on the metric entropy measure recently proposed by Granger et al. (2004, Journal of Time Series Analysis 25, 649–69) to investigate economic convergence in China. This entropy measure compares whole distributions of growth rates across individual provinces. Separately, based on this same entropy measure, we also implement cluster analysis to identify any convergence clubs. Our four main conclusions are: (1) while we certainly reject the null hypothesis that there exists a nationwide convergence, we do find that there exist convergence clubs for both the pre- and post-reform periods, (2) we find a number of very small convergence clubs. In particular, there are eleven and six convergence clubs for the pre- and post-reform periods, respectively, (3) in comparing the number and size of convergence clubs for both the pre- and post-reform periods, it could be argued that the extent of convergence is more prevalent during the post-reform period than during the pre-reform period and (4) convergence groups cannot be simply characterized by such unique features as region or the extent of policy preference level that are commonly used in the literature. [ABSTRACT FROM AUTHOR]

Calligraphy is the treasure of traditional Chinese culture and it is ubiquitous in China. Because of the unique aesthetic characteristics of calligraphy, the difficulty of calligraphy teaching lies in the assessment of calligraphy works. The major problems concerning calligraphy assessment are the low time efficiency of calligraphy assessment and low engagement of students in the assessing process. As an active learning method, peer assessment (PA) has been widely applied to education. This research aims to explore the application of PA to the calligraphy class. With reasonable training, elementary school students can act rationally as an assessing group. On the basis of students' scores, this study further takes the most reasonable scores based on clustering analysis as the final score. Through the comparative analysis of the students' scores and the teachers' scores, it shows that the students' scores of calligraphy writing can obtain results that are close to the teachers' scores. The significance of the study lies in enhancing the time-efficiency of calligraphy assessment and maintaining the learners' high levels of engagement in the assessing process. The results show that the use of PA in the assessment of calligraphy writing is an effective and accurate method of calligraphy assessment, which contributes to students' command of the key points of calligraphy marking and plays an integral role in guiding students' future calligraphy practice. [ABSTRACT FROM AUTHOR]

PROBA-V is a new global vegetation monitoring satellite launched in the second quarter of 2013 that provides data with a 100 m to 1 km spatial resolution and a daily to 10-day temporal resolution in the visible and near infrared (VNIR) bands. A major mission of the PROBA-V satellite is global agriculture monitoring, in which the accuracy of crop mapping plays a key role. In countries such as China, crop fields are typically small, in assorted shapes and with various management approaches, which deem traditional methods of crop identification ineffective, and accuracy is highly dependent on image resolution and acquisition time. The five-day temporal and 100 m spatial resolution PROBA-V data make it possible to automatically identify crops using time series phenological information. This paper takes advantage of the improved spatial and temporal resolution of the PROBA-V data, to map crops at the Yucheng site in Shandong Province and the Hongxing farm in Heilongjiang province of China. First, the Swets filter algorithm was employed to eliminate noisy pixels and fill in data gaps on time series data during the growing season. Then, the crops are classified based on the Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering, the maximum likelihood method (MLC) and similarity analysis. The mapping results were validated using field-collected crop type polygons and high resolution crop maps based on GaoFen-1 satellite (GF-1) data in 16 m resolution. Our study showed that, for the Yucheng site, the cropping system is simple, mainly dominated by winter wheat-maize rotation. The overall accuracy of crop identification was 73.39% which was slightly better than the result derived from MODIS data. For the Hongxing farm, the cropping system is more complex (i.e., more than three types of crops were planted). The overall accuracy of the crop mapping by PROBA-V was 73.29% which was significantly higher than the MODIS product (46.81%). This study demonstrates that time series PROBA-V data can serve as a useful source for reliable crop identification and area estimation. The high revisiting frequency and global coverage of the PROBA-V data show good potential for future global crop mapping and agricultural monitoring. [ABSTRACT FROM AUTHOR]

Some researches on the relationship of China's regional industrial economy and the challenge to distinguish between the long-term and short-terms effects suffer because of the lack of data and adequate modelling techniques. In this paper, unit root test and Johansen co integration model are used to simulate statistical data from 1998 to 2011 in order to capture the industrial economic growth relationship among different regions in China. It is found there is a long-term stable relationship among some provinces. Based on the co integration result, a vector error correction (VEC) model is built to describe volatility relationship among them. When industrial output value of short-term fluctuations deviates from the long-term equilibrium, regional powers adjusts them from the non-equilibrium state to equilibrium. It is shown that there are interaction and similar development trend among regional economic growth in China. [ABSTRACT FROM AUTHOR]

As an inexhaustible clean energy, wind power is getting more attention by all over the world. This paper aims to study the factors affecting the development of wind energy in regional differences, selects the related data from 2003 to 2012 of 26 provincial electric power companies to study the regional differences of installed capacity of wind power in China. Firstly, with the cluster analysis result, we can figure out that there are regional differences in the development of installed capacity of China's wind power, but it is inconsistent with both standard geographic area and regional distribution of wind energy resources. Secondly, based on the factors of installed capacity of wind power, an empirical analysis is made to explore the reasons for the regional differences of the growth of installed capacity of China's wind power. It is drawn that the effects of wind power prices, the amount of wind power, wind power generation capacity in wind-rich region have a promoting effect on installed capacity of wind power. While in weak wind regions there is an inhibitory effect on installed capacity of China's wind power. The impacts of local economy on installed capacity of China's wind power in various regions are also different. [ABSTRACT FROM AUTHOR]

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer's body, thus enhancing both the fit and aesthetic value of the clothing. [ABSTRACT FROM AUTHOR]

(1) Background: Virtual agglomeration reshapes the organizational form and drives the sustainable development of the manufacturing industry. How to measure the virtual agglomeration level of the manufacturing industry is an important and difficult problem for current research. (2) Methods: In this study, we constructed a social network for the virtual agglomeration of the manufacturing industry, with each industry representing a node in the network. We also measured the virtual agglomeration level of the overall manufacturing industry using the network edge number and network density indicators in the social network analysis method. Each sub-industry virtual agglomeration level was measured using the point centrality index. Furthermore, the virtual agglomeration characteristics of the manufacturing industry were examined through cluster analysis and core–periphery analysis. The data sources include the supply chain statistics and virtual agglomeration text data of manufacturing enterprises. The virtual agglomeration text data were obtained with the help of Python crawler technology. Two types of data were matched, and the virtual agglomeration data of 29 manufacturing industries in China from 2012 to 2022 was obtained. (3) Results: The virtual agglomeration level of the overall manufacturing industry is constantly improving, but there are large differences among different industries. Moreover, the virtual agglomeration of the manufacturing industry has the characteristics of both specialization and diversification. The virtual agglomeration social network of the manufacturing industry is experiencing an evolution process from a "core–periphery" structure to a "core–semi-periphery–periphery" structure. (4) Conclusions: This study provides a theoretical basis and practical reference for improving the virtual agglomeration level of the manufacturing industry. [ABSTRACT FROM AUTHOR]

Abstract: This paper presented a multi-objective indicator system for the performance evaluation of crop production system based on emergy method. Eight emergy indicators were selected to analyze the crop production system in the 31 provinces of mainland China to compare intensive use, scale management, investment density, environmental pressure, output benefit, output density, output per capita, and economic benefit. This paper conducted a comparison of each indicator in different provinces, and divided the 31 provinces into 10 groups by cluster analysis. The results show that: the crop production system among provinces has a significant difference and it is summarized into 10 development modes; with the development of modern agriculture, Chinese crop production system still complies with the input–output balance; the majority of provinces cannot achieve win-win between economic investment and environmental health; most of the underdeveloped provinces with high productivity may not be able to obtain good economic benefit; there is a significant positive correlation between the scale management degree and emergy output per capita in the crop production system; 10 development modes and policy suggestions are deduced. It is concluded that emergy-based multi-objective indicators can serve as an effective method in the evaluation of crop production system. [Copyright &y& Elsevier]

Abstract: The study uses the carbon dioxide emission calculation model published by IPCC to measure carbon dioxide emissions and fuel inputs of the three types of multimodal transport (road-sea, railway-sea, and river-sea) in ports of China. Then, we make a case study on Shanghai port. Combined with carbon taxes launched around 2012 in China, this paper calculates the carbon taxes on the three types of multimodal transport and makes a pairwise comparison between roadway/railway, roadway/waterway and waterway/railway. The results show that increasing the proportion of railway-sea transportation and river-sea transportation to a reasonable level will achieve great energy saving, emission reduction, and economic benefits. According to different transportation network features, this paper applies Cluster analysis to raise separately suggestions for long-term development of coastal container ports in China based on low-carbon thinking. [Copyright &y& Elsevier]

Abstract: Using the methods of factor analysis and cluster analysis, the paper makes quantitative analysis and comprehensive assessment of our comprehensive analysis of all regions in China. The paper also gives the results verified with multivariate statistical analysis method, which can have a supplementary role for Chinese economic, environmental and the further development. [Copyright &y& Elsevier]

This paper addresses a kind of risk decision-making problem existing widely in public administration and business management, which is characterized by (1) occurrence probabilities of states of nature can be estimated by analysing historical observations, but historical observations of different objects are unhomogeneous, (2) the relation between observations and occurrence probabilities of states of nature are affected by some qualitative and quantitative indicators, (3) it is a real-time decision-making problem, that is, there are many decisions for different objects to be made in a limited time, (4) considering decision's execution, impact of resource constrains is an important issue in decision-making process. In this paper, we develop a rule-based approach to address the problem. In the proposed approach, a two-step clustering method is employed to classify objects into categories, and observations in each category can be approximately viewed as homogeneous. For objects in each category, occurrence probabilities of states of nature are estimated by logistic regression, and the decision rule is obtained through solving an optimization model, which is to minimize the total decision risks while satisfying resource constrains. Effect and efficacy of our approach are illustrated through its application to China's customs inspection decision. [ABSTRACT FROM AUTHOR]

Background: The aggregation of lifestyle behaviours and their association with metabolic-associated fatty liver disease (MAFLD) remain unclear. We identified lifestyle patterns and investigated their association with the risk of developing MAFLD in a sample of Chinese adults who underwent annual physical examinations. Methods: Annual physical examination data of Chinese adults from January 2016 to December 2020 were used in this study. We created a scoring system for lifestyle items combining a statistical method (multivariate analysis of variance) and clinical expertise (Delphi method). Subsequently, principal component analysis and two-step cluster analysis were implemented to derive the lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by sex. Results: A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included "healthy or unhealthy" patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviours. This study showed that subjects with an unhealthy or mixed pattern had a significantly higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men. Conclusions: Clusters of unfavourable behaviours are more prominent in men than in women. Lifestyle patterns, as important factors influencing the development of MAFLD, show significant sex differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioural clusters by sex stratification. [ABSTRACT FROM AUTHOR]

Persimmons (Diospyros kaki Thunb.) are a characteristic Chinese fruit and account for a large planting area in China. The evaluation and selection of persimmons that are astringent without softening play an important role in modern breeding programs. However, the phenotypic variability and diversity of some representative traits in the natural population of persimmon germplasm without softening are still unclear. In this study, 56 traits from 150 samples of D. kaki collected in East Asia were used to evaluate phenotypes and correlations using principal component analysis (PCA) and cluster analysis. The results show that the mean coefficient of variation for the persimmon germplasm traits was 26.19%, and significant variations in phenotypic traits were observed. The frequency distribution maps of most of the characteristics showed obvious normal distribution characteristics. The analysis of the correlations between the traits showed that the most significant positive correlations had correlation coefficients close to 0.7. For example, seed width was highly and significantly correlated with fruit longitudinal and transverse width (0.796 and 0.786), and pith height was highly and significantly correlated with seed length (0.816). Moreover, the correlations between fruit traits and support traits, such as those related to the fruit handle and sepals, were significant, and the coefficients of the correlations between sepal width and fruit traits were 0.671 to 0.739. Fruit water content was negatively correlated with the soluble solids content (−0.623). Principal component analysis reduced 38 traits to 8 principal components, explaining 82.459% of the total variations, which were related to fruit size, seed, pith, color, fruit hardness, and soluble solid content; thus, persimmon germplasm could be divided into three major categories according to the sizes and shapes of the traits, and the first cluster was divided into two subcategories. The first subclass of fruits is medium size and shape; the second subclass of fruits is small in all traits; the second cluster is of high-quality and large fruits, optimal in all traits; and the third cluster is of medium to high-quality and oblate fruits. The results of this study are important for genetic improvement, diversity conservation, and resource utilization regarding persimmons and further research in this regard. [ABSTRACT FROM AUTHOR]

Pinaceae plants are widely distributed in the world, and the resources of pine leaves are abundant. In the extensive literature concerning Pinus species, there is much data on the composition and the content of essential oil of leaves. Still, a detailed comparative analysis of volatile terpenes and terpenoids between different species is missing. In this paper, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was used to determine the volatile terpenes and terpenoids of typical Pinus species in China. A total of 46 volatile terpenes and terpenoids were identified, and 12 common compounds were found, which exhibited a great diversity in the leaves of Pinus species. According to the structures and properties of the compounds, all those compounds can be classified into four categories, namely monoterpenes, oxygenated terpenes, terpene esters, and sesquiterpenes. The results of principal component analysis and cluster analysis showed that the leaves of the six Pinus species could be divided into two groups. The species and contents of volatile terpenes and terpenoids in the leaves were quite different. The results not only provide a reference for the utilization of pine leaves resource, but also bring a broader vision on the biodiversity. [ABSTRACT FROM AUTHOR]

Accurate wind speed prediction improves the efficiency of electricity and also increases the economic benefits of wind farms. However, the previous studies do not fully consider the chronological feature of wind speed, and do not properly identify and use the characteristic decomposed components of wind speed series in different time periods. The phenomenon of insufficient and excessive decomposition of components weakens the prediction accuracy of the model and destroys the power generation plan of the wind farm. In this paper, a segmented multi-modal deep learning integrated model based on periodicity is proposed. Firstly, the time series is reconstructed into matrix according to date; the matrix is divided into different segment matrices according to chronological characteristics; each sub-matrix is spliced into sub-sequence according to chronological order. Secondly, the contribution extremum method is used to determine the optimal feature components of all sub-sequences. Thirdly, the optimized deep learning model integrates all sub-sequences and then obtains the prediction results of the current date. Finally, the uncertainty analysis of the predicted value further improves the reliability of wind speed prediction. The data of wind farm in Hexi corridor area of China are used to simulate the experiment, and the results show that the proposed model has good performance. • A time series segmentation method based on periodicity is proposed. • The contribution extremum is used to optimize the component decomposition method. • Optimized deep learning prediction model to achieve feature integration. • Uncertainty analysis enhances the persuasion of point prediction model. [ABSTRACT FROM AUTHOR]

Low-visibility air pollution frequently occurs in the major cities of China and affects residents' physical and mental health. This study, using Gaoyou City as a case study, selected 10 typical residential communities with different locations and types and conducted a questionnaire survey for two consecutive weeks to measure commuting behavior characteristics and health effects among residents in environments with different degrees of air pollution from November to December 2022. Concerning commuting distance, the average straight distance for short-distance commuting was 1.4 km, and the median commuting distance was 13.2 km. In air-polluted environments, residents' commuting times were mainly concentrated within 1.5 h, with a majority taking 20 min to 30 min. The working and living spaces presented a circular core distribution pattern, with low-visibility air pollution significantly affecting the choice of commuting mode and having an indirect impact on health. The proportion of people who chose bus commuting significantly increased as air pollution changed from mild to moderate, whereas the proportion of people choosing slower commuting, such as walking, significantly decreased. While no significant fluctuations in physical health levels occurred, psychological health levels significantly decreased. In high air pollution environments, residents' sleep quality, attitudes towards life, emotional states, and other assessed factors exhibited an inverted U-shaped correlation with commuting patterns. Low-visibility air pollution indirectly damaged the health of residents by affecting their commuting patterns, reducing their physical activity intensity and commuting options, and increasing their psychological stress and anxiety. [ABSTRACT FROM AUTHOR]

Background and aims: Gastrointestinal microbial metabolomics is closely related to the state of the organism and has significant interaction with the pathogenesis of many diseases. Based on the publications in Web of Science Core Collection (WoSCC) from 2004 to 2022, this study conducted a bibliometric analysis of this field, aiming to understand its development trend and frontier, and provide basic information and potential points for in-depth exploration of this field. Methods: All articles on gastrointestinal flora and metabolism published from 2004 to 2022 were collected and identified in WoCSS. CiteSpace v.6.1 and VOSviewer v.1.6.15.0 were used to calculate bibliometric indicators, including number of publications and citations, study categories, countries/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords. A map was drawn to visualize the data based on the analysis results for a more intuitive view. Results: There were 3811 articles in WoSCC that met our criteria. Analysis results show that the number of publications and citations in this field are increasing year by year. China is the country with the highest number of publications and USA owns the highest total link strength and citations. Chinese Acad Sci rank first for the number of institutional publications and total link strength. Journal of Proteome Research has the most publications. Nicholson, Jeremy K. is one of the most important scholars in this field. The most cited reference is "Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease". Burst detection indicates that Urine, spectroscopy, metabonomic and gut microflora are long-standing hot topics in this field, while autism spectrum disorder and omics are likely to be at the forefront of research. The study of related metabolic small molecules and the application of gastrointestinal microbiome metabolomics in various diseases are currently emerging research directions and frontier in this field. Conclusion: This study is the first to make a bibliometric analysis of the studies related to gastrointestinal microbial metabolomics and reveal the development trends and current research hotspots in this field. This can contribute to the development of the field by providing relevant scholars with valuable and effective information about the current state of the field. [ABSTRACT FROM AUTHOR]

Background: Carpal tunnel syndrome (CTS), as the most common compression neuropathy in the upper limb, can lead to upper limb dysfunction in patients. The effectiveness of acupuncture in treating CTS has been validated based on numerous clinical trials and meta-analyses, but questions remain, such as how to select the best acupoints. Our purpose is to conduct the first data mining analysis to identify the most effective acupoint selection and combinations for treating CTS. Methods: We will search 7 electronic bibliographic databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database and Chongqing VIP Database) from inception to March 2023. Clinical trials assessing the effectiveness of acupuncture therapy on the management of CTS will be selected. Reviews, protocols, animal trials, case reports, systematic reviews, and meta-analyses will be excluded. The primary outcome measure will be clinical result associated with CTS. Descriptive statistics will be performed in Excel 2019. Association rule analysis will be performed in SPSS Modeler 18.0. Exploratory factor analysis and cluster analysis will be performed in SPSS Statistics 26.0. Results: This study will investigate the most effective acupoint selection and combinations for patients with CTS. Conclusion: Our findings will provide evidence for the effectiveness and potential treatment prescriptions of acupoint application for patients with CTS, helping clinicians and patients make a more informed decision together. [ABSTRACT FROM AUTHOR]

Background: National and international child health surveys have indicated an increase in childhood obesity in China. The increase has been attributed to a rising standard of living, increasing availability of unhealthy foods, and a lack of knowledge about healthy diet. The objective of this study was to assess the effect of selected socio-demographic characteristics on the BMI, nutrition knowledge, and eating behavior of elementary school children.Methods: Multistage stratified cluster sampling was used. Information on demographics, nutrition knowledge, and eating behavior was gathered by means of questionnaires. The schools' doctors provided the height and weight data. The study was set in one economically advantaged and one economically disadvantaged province in China. The participants were Grade 3 students, ages 8-10 years (N = 3922).Results: A cluster analysis identified four socio-demographic variables distinguished by parental education and family living arrangement. A one-way ANOVA compared differences among the clusters in BMI, child nutrition knowledge, and child eating behavior. Students in the cluster with lowest parent education level had the lowest nutrition knowledge scores and eating behavior scores. There was no significant benefit from college education versus high school education of parents in the other three clusters. BMI was not affected by parent education level.Conclusion: The nutrition status of elementary school age children will benefit most by increasing the general level of education for those adults who are presently least educated. [ABSTRACT FROM AUTHOR]

The lack of diagnostic taxonomic characteristics in some species complexes leave the species delimitation of Oxytropis DC. unresolved. Seed morphological features have proved to be useful diagnostic and taxonomic characteristics in Fabaceae. However, there are few systematic studies on the seed characteristics of Oxytropis. Here, we used scanning electron and stereoscopic microscopy to investigate the seed characteristics of 35 samples obtained from 21 Oxytropis species from northwest China. Our examination showed two main types of hilum positions, terminal and central, and five different types of seed shapes: prolonged semielliptic, reniform, prolonged reniform, quadratic, and cardiform. Seven different sculpturing patterns were identified: scaled, regulated, lophate with stellated testa cells, simple reticulate, rough, compound reticulate, and lophate with rounded testa cells. The seeds ranged from 1.27 to 2.57 mm in length and from 1.18 to 2.02 mm in width, and the length-to-width ratio ranged from 0.89 to 1.55 mm. The seed shape was constant within species and was useful for species delimitation within the genus Oxytropis when combined with other macroscopic traits. In contrast, the sculpturing patterns were highly variable at the species level and could not be used for species identification. Results of the cluster analysis and principal component analysis (PCA) indicated that the seed traits of Oxytropis species are useful for taxa identification at the species level, but have low taxonomic value at the section level. [ABSTRACT FROM AUTHOR]

Lu, W.J. and Liu, X., 2020. Investment value and key influencing factors of listed marine fishery companies in coastal China. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 288-295. Coconut Creek (Florida), ISSN 0749-0208. In order to find out the key factors that influence the investment value of listed marine fishery companies and provide investors with investment suggestions, factor analysis and cluster analysis are adopted to conduct empirical analysis to the investment value of 23 listed marine fishery companies. 12 financial indexes from 2016 to 2018 are selected to construct an investment value evaluation system. The comprehensive scoring and ranking of 23 listed companies are calculated, and they are divided into four categories by cluster analysis. The research results show that the operating capability and equity expansion capability are the key factors that influence the investment value of listed marine fishery companies. Meanwhile, the profitability, growth capability and solvency, to some extent, have a role in evaluation of their investment value. [ABSTRACT FROM AUTHOR]

• This method integrates uncertainty analysis and cluster analysis methods. • Proposed novel zoning indicators are statistically significant. • Moves from traditional weather-based approaches to a performance-based approach. • Robust tool to tackle complex relation between climate and building energy efficiency. • Provide evidence for the rationality of decision making in energy policy. Climatic zoning is an important tool in building energy policy and regulations, smaller and more homogeneous climate zones could help enhance the rationality of energy policymaking. In most countries, climate zoning is based on simplified climate models, and it is difficult to accurately represent the energy demand characteristics of buildings. Therefore, this paper proposes a novel climate subdivision method that tackles the complex relations between climate and building energy-efficient, which move from traditional weather-based approaches to a performance-based approach. Integrate the uncertainty analysis method and the building dynamic performance simulation to extract a novel statistically significant index as the basis for classification. The K -means clustering method and discriminant analysis method are used to obtain and verify the clustering results, respectively. Furthermore, the comparison with clustering results based on degree days confirms the superiority of the new method proposed in this paper in identifying building energy demand. The method is applied to the cold climate zone of China as a showcase. The results show significant differences in building energy demand and the order of key features affecting building performance between each region in the climatic zones. The zone can be further sub-divided into four groups considering the climate factor and building feature uncertainty, and clustering results have been verified by several evaluation indicators. [ABSTRACT FROM AUTHOR]

In densely populated, highly urbanized regions such as Zhejiang Province (eastern China), species are under enormous environmental pressures resulting from human activities, land use changes, and habitat fragmentation, which affect species composition and species diversity. Although eastern China is one of the most concentrated distribution areas of endemic bryophytes in East Asia, these species are also faced with the threats of habitat inadequacy. In order to get more information on bryophyte habitat and diversity, we investigated species composition, habitat preference, as well as species diversity differences of bryophytes under different habitat conditions in Jiulongshan National Forest Park (JNFP). In particular, the results showed that 108 specimens of bryophyte were collected in total, including 46 species in 32 genera of 21 families. Furthermore, we had found 12 species of bryophytes that had never been recorded before in Zhejiang Province. Remarkably, 40.74% of bryophytes were found to grow on natural rocks, followed by understories and roadside habitats. In addition, the Patrick richness index (R) varied most significantly among different habitats (p < 0.001). In a conservation perspective, natural rocks, roadways and understories should be preserved to protect bryophyte diversity. [ABSTRACT FROM AUTHOR]

Flowering Chinese cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee, 2n=20, AA) is a vegetable species in southern parts of China that faces high temperatures in the summer and winter seasons. While heat stress adversely impacts plant productivity and survival, the underlying molecular and biochemical causes are poorly understood. This study investigated the gene expression profiles of heat-sensitive (HS) ‘3T-6’ and heat-tolerant (HT) ‘Youlu501’ varieties of flowering Chinese cabbage in response to heat stress using RNA sequencing. Among the 37,958 genes expressed in leaves, 20,680 were differentially expressed genes (DEGs) at 1, 6, and 12 h, with 1,078 simultaneously expressed at all time points in both varieties. Hierarchical clustering analysis identified three clusters comprising 1,958, 556, and 591 down-regulated, upregulated, and up- and/or down-regulated DEGs (3205 DEGs; 8.44%), which were significantly enriched in MAPK signaling, plant–pathogen interactions, plant hormone signal transduction, and brassinosteroid biosynthesis pathways and involved in stimulus, stress, growth, reproductive, and defense responses. Transcription factors, including MYB (12), NAC (13), WRKY (11), ERF (31), HSF (17), bHLH (16), and regulatory proteins such as PAL, CYP450, and photosystem II, played an essential role as effectors of homeostasis, kinases/phosphatases, and photosynthesis. Among 3205 DEGs, many previously reported genes underlying heat stress were also identified, e.g., BraWRKY25, BraHSP70, BraHSPB27, BraCYP71A23, BraPYL9, and BraA05g032350.3C. The genomewide comparison of HS and HT provides a solid foundation for understanding the molecular mechanisms of heat tolerance in flowering Chinese cabbage. [ABSTRACT FROM AUTHOR]

This paper applies for cluster analysis and factor analysis theory to statistically analyze environmental isotope (d18O,d2H, 3H, 14C) and water chemistry (K+, Na+, Ca2+, Mg2+, HCO3 -, SO4 2-, Cl-) test data from different water bodies in the coal-mining exploring district. The result shows that groundwater can be clustered into four categories, namely GA, GB, GC and GD classes. Deep karst groundwater and spring were grouped into GA class, and the contour map of the second-factor scores shows that karst water and spring of the GA group is in the same area, indicating the same recharging source from the northern mountainous area. Deep fissure water was clustered into GC class with the lowest second-factor scores, and cation exchange plays a central role, then did not detect tritium with 14C of lower levels, indicating the late Pleistocene rainfall recharging. Shallow pore water and surface water were clustered into GB class with the high third factors scores, indicating surface water leakage recharging. The water samples of GD class have the highest three factors score, pointing out that the shallow pore water and surface water were polluted. The results of this study provide a scientific basis for assessing groundwater circulation mechanism in the coal-mining exploring district. [ABSTRACT FROM AUTHOR]

The energy-efficiency promotion of new residential buildings in China has achieved great progress in the past three decades. However, the expected policy outcomes cannot be fully achieved due to the barriers in the policy-making and implementation process. Whereas governments play a critical role in policy formation, perceptions of stakeholders involved in the building life cycle and the industrial value chain (such as developers, design institutes and relevant material manufactures) are fundamental to the successful implementation. To collect and identify the barriers of significant influence on the industry from stakeholders' perspectives, this paper used a nationwide institutes' questionnaire to collect stakeholders' voices regarding the barriers, the corresponding popularity and the severity. All the barriers were categorized according to institutions' scorings through cluster analysis. The relationships between barriers were also analyzed according to the framework based on policy cycle and policy environment. Results indicated that the core problem is the ineffectiveness translating energy savings into benefits and profits. Accordingly, three major barriers were identified, namely the (1) unsatisfied policy design and implementation; (2) stakeholders' lack of implementation capacity in developing and constructing high-performance new residential building projects and adopting relevant technologies; and (3) insufficient legalization of relevant market. Other identified barriers either were those not agreed by all institutions, or those could be eliminated easily after the removal of the above three barriers. [ABSTRACT FROM AUTHOR]