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This study uses a data-driven approach to mine the distribution of personality traits among Chinese people in the Chinese social context. Based on the hypothesis of personality lexicology, word embedding technology was employed in machine learning to mine personality vocabulary from Tencent's word embedding database. More than 10,000 Chinese personality descriptors were extracted and analyzed using Gaussian Mixture Model Cluster and Hierarchical clustering analysis. The data was collected from 658 Chinese people randomly from all parts of China through an online questionnaire method. The results reveal six personality traits in the Chinese context, expanding the personality thesaurus and providing examples to illustrate each trait. The findings coincide with previous research on the five-factor model, which partially describes the personality traits of Chinese people, but does not offer a complete explanation of their typical social behavior patterns. Additionally, the study supports the notion of cultural particularity in personality traits. The approach used in this study offers a richer personality vocabulary than traditional personality mining methods, and word embedding technology captures richer semantic information in Chinese. The six Chinese personality traits identified in this study will also be used to explore how to quantify and evaluate personality traits based on word embedding and personality descriptors. [ABSTRACT FROM AUTHOR]

Woodpeckers exhibit selectivity when choosing tree cavities for nest development in forest ecosystems, and fungi play a significant and important role in this ecological process. Therefore, there is a complex and intricate relationship between the various behaviors of woodpeckers and the occurrence of fungal species. Research into the complex bond between fungi and woodpeckers was undertaken to provide more information about this remarkable ecological relationship. Through the process of line transect sampling, woodpecker traces were searched for, and mist nets were set up to capture them. A total of 21 woodpeckers belonging to four species were captured. High-throughput sequencing of the ITS region was performed on fungal-conserved samples to enable an in-depth analysis of the fungal communities linked to the woodpeckers' nests. Members of Ascomycota were the most abundant in the samples, accounting for 91.96% of the total, demonstrating the importance of this group in the forest ecosystem of this station. The statistical results indicate significant differences in the fungal diversity carried by woodpeckers among the different groups. Species of Cladosporium were found to be the most prevalent of all the detected fungal genera, accounting for 49.3%. The top 15 most abundant genera were Cladosporium, Trichoderma, Beauveria, Epicococcum, Hypoxylon, Penicillium, Nigrospora, Aspergillus, Oidiodendron, Cercospora, Talaromyces, Phialemo-nium, Petriella, Cordyceps, and Sistotrema. The standard Bray–Curtis statistical technique was used in a hierarchical clustering analysis to compute inter-sample distances, allowing for the identification of patterns and correlations within the dataset. We discovered that in the grouped samples from woodpeckers, there were differences in the diversity of fungal communities carried by four woodpecker species, but the less dominant fungal species were still similar. The findings highlight the need to consider these diverse ecological linkages in woodpecker research and conservation efforts. [ABSTRACT FROM AUTHOR]

Heilongdong Spring Basin (HSB) is an ecologically and economically important area in northern China, with widespread and intensive anthropogenic activities (industrialization, mining, agriculture). To understand the hydrogeochemical characteristics and water quality status of groundwater in various aquifers, 33 groundwater samples were collected and analyzed by Piper diagram, Gibbs diagram, Entropy-Weighted Quality Index (EWQI) and Hierarchical cluster analysis (HCA) in the area. The results indicated that the groundwater was mainly derived from meteoric water, and rock weathering was the dominant source of ions in the area. The majority of the karst groundwater samples (KGWs) fell in the category of HCO3-Ca·Mg and HCO3·SO4-Ca·Mg, the hydrochemical faces of pore groundwater (PGWs) and fissure groundwater (FGWs) samples were mainly SO4·HCO3-Ca type with a few of the HCO3·SO4-Ca·type. According to the EWQI, all the KGWs were within the EWQI of below 100 and were suitable for drinking, while the central and eastern parts of regions have elevated EWQI values, especially 3 samples of KGWs were within the EWQI value in the range of 83–95, closing to the "poor" bracket. In addition, 66.7% of FGWs and all the PGWs were unsuitable for drinking. The HCA results showed that local groundwater quality was mainly controlled by geogenic processes and anthropogenic factors. Especially, the shallow groundwater was more affected by anthropogenic activities. Moreover, it was worth noticing that a few karst groundwater samples were close to shallow groundwater samples, which might be related to coal mining and agricultural activities. The present results provide a scientific basis for the protection and utilization of groundwater in different aquifers. [ABSTRACT FROM AUTHOR]

Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the "decomposition–reconstruction–integration" approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning. [ABSTRACT FROM AUTHOR]

Zizania latifolia (Z. latifolia) is a popular aquatic vegetable with various nutrients in south China, but little is known about its cultivars and growing seasons in terms of the nutritional components. This work aims to characterize the nutrients of five Z. latifolia cultivars in different growing seasons. The results showed that Z. latifolia samples differed in terms of chemical parameters, which were significantly affected by variety, growing season, and their interaction. Zhejiao No. 8, harvested in the autumn, stood out with the highest levels of vitamin C. Tangxiajiao and Zhejiao No. 1 contained the highest values of total soluble solids, reducing sugar, soluble proteins, and amino acids. Significant differences were observed between the autumn Z. latifolia and spring samples; the former were of higher quality than the latter based on hierarchical clustering analysis and principal component analysis. Moreover, total amino acids (TAA) and glutamic acid (GLU) were selected as the key indicators for Z. latifolia comprehensive quality by multiple linear regression analysis. This study provides essential information on Z. latifolia quality characteristics corresponding to cultivars and growing seasons, which lays the foundation for promoting the quality improvement of Z. latifolia scientifically. [ABSTRACT FROM AUTHOR]

The low-carbon development of the transportation sector is crucial for China to achieve its national goals of carbon peaking and carbon neutrality. Since China is a vast country with unbalanced regional development, there are considerable differences in the levels of carbon dioxide emissions from the transportation sector across regions. Therefore, revealing the influencing factors that shape the characteristics of transportation carbon dioxide emissions (TCO2) can inform tailored sub-national carbon reduction strategies based on local conditions, which is an important technical approach for achieving national goals. Based on an extended Kaya identity, we derived indicators of the impacts on provincial TCO2 from factors such as economic development, population density, energy structure, transportation efficiency, technology research and development (R&D), infrastructure construction, transportation operation conditions, and residents' transportation behavior. Using a multi-indicator joint characterization method, we explored the characteristics of provincial TCO2 in China in 2019. By applying Ward's method to hierarchical clustering, the thirty provinces of China were classified into six characteristic types (Types I to VI). Based on the total TCO2 (TC), the intensity of TCO2 (TI), and the per capita TCO2 (TP) calculated for each province in 2019, the priority control directions and indicators for carbon reduction in each type were obtained through relative relationships with provincial averages and correlation analysis with the indicators. Specifically, Type I and Type IV can be categorized as TP-controlled, Type II and Type III as TC-controlled, and Type V and Type VI as TI-controlled. Finally, we provided typological strategies and key performance indicators (KPIs) relevant to local governments to better achieve carbon reduction goals in each provincial type. It can promote cooperative development and collaborative governance in carbon reduction across regions and the unified implementation of China's dual-carbon goals. [ABSTRACT FROM AUTHOR]

• Applied entropy weight & LMDI to trace CO 2 disparity sources. • Used hierarchical clustering on 30 provinces' emission data. • Found 3 growth-transport decarbonization patterns in china. • Detected 27 provinces with growth-emission misalignment. As the world's largest CO 2 emitter, China's decarbonization in the transportation sector is vital for achieving global sustainable goals. This study develops an in-depth framework to elucidate and tailor decarbonization strategies across regions effectively. Through the application of the Logarithmic Mean Divisia Index (LMDI) approach, this analysis spans transportation, energy, economic, and demographic factors influencing CO 2 emissions in China's 30 provinces over the decade 2010–2019. The Entropy Weight Method (EWM), applied innovatively, pinpoints regional disparities in emission drivers. A dual-dimensional hierarchical clustering analysis evaluates the emission characteristics at provincial-level, laying the groundwork for customized mitigation strategies. The study delineates three principal discoveries: (1) robust economic development has spurred transportation demand over the past decade, escalating emissions. While initiatives like the "Beautiful China" campaign and decarbonization policies have fostered weak decoupling between economic growth and emissions, economic activity remains a dominant driver of emissions at the provincial level. (2) The energy intensity, freight intensity, and industrial structure are the primary sources of regional disparities in the driving mechanism of CO 2 emissions in the transportation sector. In economically developed areas, freight intensity is a considerable emissions accelerator, marking a departure from its national-level emissions reduction role. Given the starkly distinct regional disparity, a "one-size-fits-all" approach for China would be imprudent. (3) The novel application of hierarchical clustering reveals three synergistic relationships between regional development and transportation decarbonization, highlighting the misalignment between emissions reduction potential and driving forces in 27 provinces. This misalignment underscores the inefficiency of current management practices, necessitating the development of precise tailored strategies. By pioneering in the methodological analysis of emissions dynamics and stressing the imperative of customized mitigation strategies, this research significantly enriches the discourse on sustainable transportation and regional policy innovation. [ABSTRACT FROM AUTHOR]

Chlorinated paraffins (CPs) can accumulate in sediment and pose risks to ecological systems and human health. The Haihe River Basin is one of the seven main river basins in China and is mainly in the Beijing–Tianjin–Hebei region, which is densely populated and very urbanized. There is therefore a high probability of CP pollution in the Haihe River Basin. However, CP pollution and the environmental risks posed by CPs in the Haihe River are not well understood. In this study, the concentrations of short-chain CPs (SCCPs) and medium-chain CPs (MCCPs) in sediment from six rivers in the Haihe River Basin system were determined using two-dimensional gas chromatography electron-capture negative ionization mass spectrometry. The total SCCP and MCCP concentrations in the sediment samples ranged from 131.83 to 1767.71 and from 89.72 to 1442.82 ng/g dry weight, respectively. The total organic carbon content did not significantly correlate with the CP concentrations. The dominant SCCP congener groups were C 10 Cl 6–7 and the dominant MCCP congener groups were C 14 Cl 7–8. Significant relationships (R = 0.700, p < 0.05) were found between the SCCP and MCCP concentrations, indicating that SCCPs and MCCPs may have similar sources. Hierarchical cluster analysis and principal component analysis indicated that sediment in the study area was contaminated with CPs through the use of the CP-42 and CP-52 commercial products in industrial processes and human activities. The ecological risks posed by CPs were assessed and SCCPs were found to pose high risks in the Yongding New River but moderate risks in the other rivers. MCCPs were found to pose minimal risks to the aquatic environment at most of the sampling points. [Display omitted] • CPs in the sediment in the Haihe River Basin were investigated. • The dominant SCCP and MCCP congener groups were C 10 Cl 6–7 and C 14 Cl 7–8 , respectively. • SCCPs and MCCPs possibly derived from the production and use of CP-42 and CP-52. • SCCPs were high ecological risks in the Yongding New River. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-scale pore structure and shale components at medium-to-low maturity studied. • A complementary set of experimental tools, including SANS, utilized for a holistic view. • Statistical approaches of HCA and PCA utilized to interpret experimental data. • Three groups of shales classified with various pore structure and component contribution. An important issue concerning the sustainable production of shale oil is the characterization of pore structure at various scales, but there is an insufficient understanding of the coupled relationship between pore structure and shale components. Integrated analyses using field emission-scanning electron microscopy, mercury intrusion porosimetry, nitrogen physisorption, and small angle neutron scattering were conducted on nine shale samples from the Upper Triassic Chang 7 Member in Ordos Basin to investigate the relationship between multi-scale pore structure and shale minerals and organic matter (OM). The results show that the clays minerals (mainly mixed illite–smectite) are usually combined with OM to form complex OM-hosted pores, and inorganic pores consist of both inter- and intra-particle pores (mostly related to clay platelet clusters). Three groups of shales classified by hierarchical cluster analyses (HCA) vary widely in pore structure, with the highest pore volume and largest pore diameter observed in a decreasing order of Group 1, Group 2, and Group 3. Principal component analysis (PCA) shows that brittle minerals (mainly quartz) have preserved interP pores that contribute to the development of high pore volumes and large pore-throat diameters, and ductile clays minerals and OM tend to fill pores and reduce pore diameter and volume. The complex nature of shale porosity is influenced by multiple factors, and hence accurate and comprehensive characterization requires the use of diverse methods including both experimental and theoretical analyses. An integrated approach to elucidating the factors controlling pore structure in medium-to-low maturity shale will help with efficient shale oil production. [ABSTRACT FROM AUTHOR]

Sea buckthorn (Hippophaë rhamnoides L.) is well-known for its high content of flavonol glycosides (FGs), yet accurately determining the quantity of these nutrients present in the berries remains a challenge due to the significant variability of these nutrients and lack of reference standards. In this study, flavonol glycosides in sea buckthorn berries were identified and quantified using ultra-performance liquid chromatography Q-exactive high resolution mass spectrometry (UPLC-QE HRMS). The results showed that the total content of flavonol glycosides ranged from 39.5 to 240.2 mg/100 g fresh berries. The wild berries from Sichuan had remarkably high contents and unique profiles of flavonol glycosides. The main flavonol glycosides in all of the samples were isorhamnetin glycosides and quercetin glycosides, the predominant flavonol glycosides were different between different samples. The sPLS-DA statistical analysis indicated that cultivated and wild sea buckthorn can be distinguished based on their distinct composition and content of flavonol glycosides. The HCA clustered 16 high expression levels of FGs, which were revealed in a wild sample obtained from Sichuan. • High resolution and high sensitivity analysis was carried out on UPLC-QE HRMS. • 33 flavonol glycosides identified and quantified by UPLC-QE HRMS in sea buckthorn. • Differences of the flavonol glycosides between cultivated and wild samples were analyzed. • sPLS-DA analysis revealed clear distinction between cultivated and wild sea buckthorn. • High expression flavonol glycosides identified by hierarchical cluster analysis. [ABSTRACT FROM AUTHOR]

• Nonparametric kernel density estimator was used to calculate SGI. • Significant spatial variation in groundwater drought was observed across the region. • Groundwater drought is weakly connected to meteorological and agricultural droughts. • Agricultural irrigation and groundwater extraction influence groundwater drought. Groundwater drought is a significant type of hydrological drought and manifests as abnormal groundwater shortages as a result of prolonged drought propagation. This type of drought typically exhibits a longer duration than meteorological and agricultural droughts and can be severe in arid and semiarid regions. Human activities like pumping can further complicate the detection and characterization of groundwater drought. This study used long-term groundwater level monitoring data from 1991 to 2018 for the Yinchuan region to calculate a standardized groundwater level index and applied hierarchical clustering and the run theory to assess groundwater drought. Additionally, the relationship between groundwater and meteorological/agricultural droughts were investigated, and the factors driving groundwater drought were analyzed. Based on the results, regional groundwater droughts are becoming increasingly severe. In this study, there are regional differences in the trends of the standardized groundwater level index, which could be divided into three clusters (C1, C2, and C3). Wells in C2 are located near the Yellow River and show a shorter groundwater drought duration (mean value: 3.16 months), which means that these wells can recover from drought more quickly than those in the other clusters. Meteorological and agricultural droughts show a weak correlation with groundwater drought (with average cross-correlation coefficients of 0.03 and 0.25, respectively), and human activities most likely were the causes of regional groundwater droughts. Thus, a comprehensive understanding of regional groundwater droughts should consider human activities, in addition to the geographical location and natural environments. This research establishes a scientific foundation for managing regional groundwater resources and offers novel insights for future groundwater drought studies in the context of human activities. [ABSTRACT FROM AUTHOR]

The popularity of electric vehicles (EVs) brings environmental benefits, but their hard-to-estimate stochastic charging behaviors places additional diversity on grid load management. This paper proposes a procedure to identify typical charging load profiles (CLPs) via large scale charging session data from charging stations (CSs). The daily CLPs are computed from charging sessions, and a comprehensive similarity metric based on the weighting of Euclidean and Pearson correlation coefficient is proposed to achieve better clustering. The clustering is performed using the Clustering LARge Applications (CLARA) algorithm to accommodate large sample scenarios. Subsequently, hierarchical clustering of CSs is performed based on possible CLPs，and their CLPs are estimated by Monte Carlo simulation. The performance of the proposed method is tested and evaluated with over 340,000 charging sessions from 109 CSs in Wuhan at central China, and seasonal differences in CLPs are explored. The results show that the method of mining typical CLPs from charging sessions is effective, 17 typical CLPs are identified in different seasons, which provide effective information on the fluctuation and magnitude of daily power demand, the charging power demand also shows significant seasonal differences, and good accuracy is achieved by dividing the CSs into different groups for load estimation. • A method for identifying typical charging load profiles from charging session data is proposed. • 17 typical charging load profiles for charging stations in Wuhan are reported. • Monte Carlo simulation combined with typical charging load profiles enables good estimation of charging loads. • Seasonal differences in charging loads are compared. [ABSTRACT FROM AUTHOR]

• Development of a novel registration method combining ULS and TLS LiDAR data. • Hierarchical clustering of trunk point clouds generates accurate tree maps. • Validated in 40 sample plots in subtropical forests. • Achieved registration accuracy is better compared to existing methods. • Significantly reduced registration time to approximately 50 s per 30 × 20 m plot. Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) provide complementary, non-destructive approaches to acquire three-dimensional forest structure information. Registration of their point clouds enables the reconstruction of complete vertical structure of forests. Current registration methods are primarily designed to register different TLS scans and thus are not applicable to ULS-TLS registration directly. In this study, the proposed method first generated multi-layer tree maps from ULS and TLS data using hierarchical clustering, then extracted Fast Point Feature Histograms (FPFH) features for each cluster point based on spatial relationships in the tree maps. After that, a point-to-point matching strategy was used to obtain the transformation matrix of each layer between ULS and TLS trunk point clouds and the best matrix from all layers was finally selected for fine registration. The algorithm was validated in 40 sample plots in Guangxi province of China. Our findings indicated that both high-density ULS and TLS data generate accurate tree maps compared to manually counted tree number, with a Concordance Correlation Coefficient (CCC) of 0.961 and 0.973, respectively. The proposed method performed well in registration accuracy and time efficiency, and achieved a higher matching score (0.945 > 0.928) and lower RMSE (0.144 < 0.151) than manual registration. The average registration time per sample plot of 600 m2 was 48.9 s, with 19.4 s dedicated to coarse registration. This research highlights the potential of clustering-based registration methods for effectively aligning ULS-TLS point cloud data in forests, laying the foundation for further technological advancements in forest vertical structure reconstruction. [ABSTRACT FROM AUTHOR]