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Phenological changes play a central role in regulating seasonal variation in the ecological processes, exerting significant impacts on hydrologic and nutrient cycles, and ultimately influencing ecosystem functioning such as carbon uptake. However, the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated. These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity. Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019, we found that an advancement in spring phenology increased spring gross (GPP) and net primary productivity (NPP) in subtropical vegetation of China by 2.1 gC m−2 yr−1 and 1.4 gC m−2 yr−1, respectively. A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m−2 yr−1 and 0.2 gC m−2 yr−1, respectively. Temporally, the contribution of the spring phenology to spring carbon uptake increased significantly during the study period, while this positive contribution showed a nonsignificant trend in summer. In comparison, the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake; however, this contributing effect was weakened. Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology, respectively. Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change. [ABSTRACT FROM AUTHOR]

The chloroplast (cp) genome sequence of Mussaenda pubescens, a promising resource that is used as a traditional medicine and drink, is important for understanding the phylogenetic relationships among the Mussaenda family and genetic improvement and reservation. This research represented the first comprehensive description of the morphological characteristics of M. pubescens, as well as an analysis of the complete cp genome and phylogenetic relationship. The results indicated a close relationship between M. pubescens and M. hirsutula based on the morphological characteristics of the flower and leaves. The cp was sequenced using the Illumina NovaSeq 6000 platform. The results indicated the cp genome of M. pubescens spanned a total length of 155,122 bp, including a pair of inverted repeats (IRA and IRB) with a length of 25,871 bp for each region, as well as a large single-copy (LSC) region and a small single-copy (SSC) region with lengths of 85,370 bp and 18,010 bp, respectively. The results of phylogenetic analyses demonstrated that species within the same genus displayed a tendency to group closely together. It was suggested that Antirhea, Cinchona, Mitragyna, Neolamarckia, and Uncaria might have experienced an early divergence. Furthermore, M. hirsutula showed a close genetic connection to M. pubescens, with the two species having partially overlapping distributions in China. This study presents crucial findings regarding the identification, evolution, and phylogenetic research on Mussaenda plants, specifically targeting M. pubescens. [ABSTRACT FROM AUTHOR]

Pure organic phosphorescence resonance energy transfer is a research hotspot. Herein, a single-molecule phosphorescence resonance energy transfer system with a large Stokes shift of 367 nm and near-infrared emission is constructed by guest molecule alkyl-bridged methoxy-tetraphenylethylene-phenylpyridines derivative, cucurbit[n]uril (n = 7, 8) and β-cyclodextrin modified hyaluronic acid. The high binding affinity of cucurbituril to guest molecules in various stoichiometric ratios not only regulates the topological morphology of supramolecular assembly but also induces different phosphorescence emissions. Varying from the spherical nanoparticles and nanorods for binary assemblies, three-dimensional nanoplate is obtained by the ternary co-assembly of guest with cucurbit[7]uril/cucurbit[8]uril, accompanying enhanced phosphorescence at 540 nm. Uncommonly, the secondary assembly of β-cyclodextrin modified hyaluronic acid and ternary assembly activates a single intramolecular phosphorescence resonance energy transfer process derived from phenyl pyridines unit to methoxy-tetraphenylethylene function group, enabling a near-infrared delayed fluorescence at 700 nm, which ultimately applied to mitochondrial targeted imaging for cancer cells.Phosphorescence resonance energy transfer systems have potential in light-harvesting and bioimaging, but host-guest systems are rarely explored. Here, the authors report the development of a macrocyclic host-guest system for targeted cell imaging. [ABSTRACT FROM AUTHOR]

Coconut shell–activated carbon is a highly promising catalytic carrier due to its chemical stability and high porosity. Meanwhile, manganese-based oxidants are very effective catalysts in NH3-SCR, but their instability restricts their use. In an attempt to create catalysts that perform well at low temperatures, this study characterized nitric acid-modified coconut shell-activated carbon carriers at different concentrations using N2 adsorption-desorption, XRD, BET, XPS, DRIFTS, and other methods. By doping with Cu and Mn as the main active substance, optimal Mn-Cu composite catalysts were prepared. The study found that Mn7Cu3/AC-B catalyst had the best activity at low temperatures, achieving up to 99% denitrification efficiency at 225 °C. The catalyst had the highest content of Mn4+, Cu2+, and Oα, which favored the reaction and made the catalyst more resistant to water and sulfur. The use of the appropriate concentration of nitric acid helps to remove carrier impurities and improve carrier structural parameters, thus enabling uniform dispersion of active substances and facilitating the reaction. Moreover, copper and manganese have a synergistic effect, and the composite catalysts have higher redox capacity, catalyst acidic sites, and N2 selection compared to single manganese catalysts. This study aims to prepare better resistant catalysts for low-temperature reactions and contribute to the optimization of manganese-based catalysts. [ABSTRACT FROM AUTHOR]

Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate. However, the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood. Using a long-term solar-induced chlorophyll fluorescence dataset, we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau (TP) from 2000 to 2018. We found that the effect of maximum temperature exposure days (TxED) and minimum temperature exposure days (TnED) on the start of the growing season (SOS) was initially delayed and shifted to advance along the increasing temperature gradients. However, the response of the end of the growing season (EOS) to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached, above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end. The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54°C, respectively. In contrast, cumulative precipitation (CP) was more likely to advance SOS and delay EOS as the precipitation gradient increased, but the advance of SOS is gradually weakening. Four vegetation types (i.e., forest, shrubland, meadow, and steppe) showed similar trends in response to different climates, but the optimal climatic conditions varied between the vegetation types. Generally, meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland. These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities, implying that the SOS will continue to advance with warming, whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming. [ABSTRACT FROM AUTHOR]

We provide new causal evidence for the impact of equity financing incentive on firms' voluntary disclosure decisions by exploring the 2008 seasoned equity offering deregulation, which exogenously facilitates small firms' access to public equity financing and increases their equity issuance incentives without changing their business and information environments. We argue that the heightened equity financing incentive due to the deregulation can motivate a firm to increase disclosures even in the period without actual equity issuance, because such disclosures, by signaling a commitment to disclosure, could reduce the cost of equity in case the firm issues equity in the future. Consistent with this argument, we find that, benchmarking against control firms that are not affected by the deregulation, an average treatment firm that is affected by the deregulation but does not issue equity provides more management earnings forecasts in the post-deregulation period. The effect is mainly driven by repeated forecasters and is more pronounced for firms with greater equity financing needs and firms with higher information asymmetry in the equity market. [ABSTRACT FROM AUTHOR]

With the rapid popularization of smart phones and various kinds of apps, many college students are addicted to mobile phones and suffer from "mobile phone dependence", which has become the cause of low learning efficiency. It can be seen that the research on the causes of mobile phone dependence of college students is of great practical significance to improve the learning efficiency and effect of college students. This paper designs a questionnaire from five aspects, such as personal basic situation, entertainment, social interaction, learning and self-management ability. Taking college students as the research object, it conducts an empirical study, explores the causes of college students' mobile phone dependence, and puts forward suggestions and countermeasures to improve their learning efficiency. [ABSTRACT FROM AUTHOR]

Background: Forest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5). Results: The runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060. Conclusions: The obtained results lead to conclusion that proper management of China's subtropical secondary forests could be considered as one of the steps towards achieving China's target to become carbon neutral by 2060. [ABSTRACT FROM AUTHOR]

The rental housing market plays a critical role in the United States real estate market. Prior studies have used various approaches to model housing rent, such as interpolation, hedonic modeling, and machine learning. However, only a few studies have modeled rental prices based on textual data, which provides rich and contextual information about rental properties. In addition, many models, especially deep learning models, use an end-to-end black box for prediction, which hides the decision process. Such models are difficult to interpret and explain the driving factors. This study builds on our previous work, aiming to develop and evaluate rental market spatial dynamics models combining Long Short-Term Memory (LSTM) networks and self-attention mechanism. We compare the performance of the proposed model with our previous models on predicting rental prices in Atlanta, Georgia, USA. We also use techniques from saliency maps to explain the generated model. Results show that the self-attention-based model outperforms our previous models. The saliency map techniques reveal how the model attends to a different part of the textual information. The predicted results reflect the spatial variation of textual information. Such a model offers practical pricing references for homeowners and renters, and spatial patterns for urban planners and stakeholders. [ABSTRACT FROM AUTHOR]

Background: Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results: Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions: Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget. [ABSTRACT FROM AUTHOR]

An efficient and effective spatiotemporal prediction algorithm for PM2.5 (i.e. particulate matter with a diameter of less than 2.5 micrometers) is urgently needed to study the distribution of PM2.5 over a continuous spatiotemporal domain, which not only helps to make scientific decisions on the prevention and control of PM2.5 pollution but also promotes meaningful assessment of the quantitative relationship between adverse health effects and PM2.5 concentrations over time. Existing spatiotemporal interpolation algorithms are usually based on the assumption that interpolation models follow explicit and simple mathematical descriptions. Unfortunately, the real world does not really follow these perfect mathematical models. Combining data fusion techniques and a Long Short-Term Memory (LSTM) recurrent neural network (RNN), we present a novel spatiotemporal interpolation model, which is able to achieve high estimation accuracies over a long time period and a large area. By fusing the daily PM2.5 data, meteorological data, elevation data, and land-use data collected from China in 2016, four experiments were conducted in this study to evaluate the efficiency and effectiveness of the proposed approach. Results showed that applying LSTM RNN on the fused dataset can achieve consistent and high accuracy in different geographies. [ABSTRACT FROM AUTHOR]

Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above- and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results: Based on the presented two-step process, the estimation of China's FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6–6.1 PgC for China's forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t∙m− 3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates. Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales. [ABSTRACT FROM AUTHOR]

A better understanding of spatiotemporal distribution of PM2.5 (particulate matter with diameter less than 2.5 micrometer) concentrations in a continuous space-time domain is critical for risk assessment and epidemiologic studies. Existing spatiotemporal interpolation algorithms are usually based on strong assumptions by restricting the interpolation models to the ones with explicit and simple mathematical descriptions, thus neglecting plenty of hidden yet critical influencing factors. In this study, we developed a novel deep-learning-based spatiotemporal interpolation model, which includes the bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) as the main ingredient. Our model is able to take into account both spatial and temporal hidden influencing factors automatically. To the best of our knowledge, it is the first time of applying the bidirectional LSTM RNN in the spatiotemporal interpolation of air pollutants concentrations. We evaluated our novel method using a dataset that contains daily PM2.5 measurements in 2009 over the contiguous southeast region of the USA. Results demonstrate a good performance of our model. We also conducted simulations to explore the properties of spatiotemporal correlations. In particular, we found the temporal correlation is stronger than the spatial correlation. [ABSTRACT FROM AUTHOR]

To better assess the relationships between environmental exposures and health outcomes, an appropriate spatiotemporal interpolation is critical. Traditional spatiotemporal interpolation methods either consider the spatial and temporal dimensions separately or incorporate both dimensions simultaneously by simply treating time as another dimension in space. Such interpolation results suffer from relatively low accuracy as the true space-time domain is skewed inappropriately and the distance calculation in such domain is not accurate. We employ the efficient k-d tree structure to store spatiotemporal data and adopt several machine learning methods to learn optimal parameters. To overcome the computational difficulty with large data sets, we implement our method on an efficient cluster computing framework - Apache Spark. Real world PM2.5 data sets are utilized to test our implementation and the experimental results demonstrate the computational power of our method, which significantly outperforms the previous work in terms of both speed and accuracy. [ABSTRACT FROM AUTHOR]

The relationships between impervious surface area (ISA) patterns and characteristics have long been studied to examine the impacts of hydrological cycles, water management, surface energy balances, and biodiversity. This study assessed the Dianchi Lake watershed as a case study area to illustrate ISA change characteristics and their impacts on Dianchi Lake water quality. More than 200 Landsat images (from 1988 to 2017) were first downloaded through the US Geological Survey (USGS) online portal. Then, a series of ISA maps of the Dianchi Lake watershed at an annual resolution were built using a robust method we proposed. Then, the change characteristics of main water quality parameters were analyzed from spatial-temporal viewpoints. Finally, correlations between ISA and Lake water quality were made. The research results indicated that the ISA proportion of the Dianchi Lake watershed rapidly increased (from 1.84% in 1988 to 34.32% in 2017), the annual ISA total growth rate exceeded 21 km2/year, and the urban area encircled Dianchi Lake. The Dianchi Lake watershed has begun to adopt the “Rashly Advancing” and “Great Leap Forward” strategies of urbanization. Chlorophyll a (Chla), dissolved oxygen (DO), and transparency (Trans) showed significantly changing tendencies, whereas the values of pH, total nitrogen, and total phosphorus stayed relatively constant. The polynomial regression models and corresponding R2 values indicated strong correlations between ISA and three water quality parameters (Chla, DO, and Trans). The development of better strategies for the control and reduction of water pollution without optimizing ISA spatial distribution is essential for Kunming’s continued urbanization. [ABSTRACT FROM AUTHOR]

A growing number of studies have used standalone GPS or smartphones to track where people move and the environment to which they expose. In the same time, questionnaire, survey, and travel diaries have been used to capture people’s perception, emotions, and interactions with the nearby environment. The growing volume of qualitative data together with locational information calls for new ways to conveniently, intuitively, and visually convey georeferenced narratives. In this study, we developed a web-based tool that automatically processes GPS trajectories and narratives, displays information regarding travel routes and georeferenced environmental perception. Our georeferenced word-cloud approach provides an innovative way to visualize qualitative information. The developed platform can be widely used in community engagement and participatory planning processes to empower the local communities. [ABSTRACT FROM AUTHOR]

Water crises have been among the most serious environmental problems worldwide since the twenty-first century. A water crisis is marked by a severe shortage of water resources and deteriorating water quality. As an important component of water resources, lake water quality has deteriorated rapidly in the context of fast urbanization and climate change. This deterioration has altered the water ecosystem structure and influenced lake functionality. To curb these trends, various strategies and procedures have been used in many urban lakes. Among these procedures, accurate and responsive water environment monitoring is the basis of the forecasting and prevention of large-scale cyanobacteria outbreaks and improvement of water quality. To dynamically monitor and predict the outbreak of cyanobacteria in Dianchi Lake, in this study, wireless sensors networks (WSNs) and the geographic information system (GIS) are used to monitor water quality at the macro-scale and meso-scale. Historical, real-time water quality and weather condition data were collected, and a combination prediction model (adaptive grey model (AGM) and back propagation artificial neural network (BPANN)) was proposed. The correlation coefficient ( R) of the simulation experiment reached 0.995. Moreover, we conducted an empirical experiment in Dianchi Lake, Yunnan, China using the proposed method. R was 0.93, and the predicting error was 4.77. The results of the experiment suggest that our model has good performance for water quality prediction and can forecast cyanobacteria outbreaks. This system provides responsive forewarning and data support for lake protection and pollution control. [ABSTRACT FROM AUTHOR]

A key feature of positive education is its focus on factors that can help students flourish. In the current study, we examined the role of resilience in promoting optimal mental health of Chinese secondary school students who suffered the 2008 Wenchuan earthquake, China. In the earthquake central region, 518 participants recruited in November 2008 about 6 months and 473 participants recruited 1 year after the earthquake, were assumed to have the highest and mid-level disaster stress levels, respectively. 602 participants from areas unaffected by the earthquake in December 2008 were assumed to have the lowest stress level. Participants' mental health and resilience were assessed by the clinical scales of the Cross-cultural (Chinese) Personality Assessment Inventory for Adolescents and the Resilience Scale for Chinese Adolescents, respectively. Higher stress level participants reported significantly lower resilience and higher mental health problems. Negative correlations between resilience and mental health problems were confirmed across the three groups. Only supportive power resilience showed significant predictions on mental health problems in the two earthquake groups. The implications on mental health counseling and positive education in Chinese secondary school students were discussed. [ABSTRACT FROM AUTHOR]

All related astronomical records of King Wu ’ s Conquest have been searched and analysed comprehensively. Constrained by the newest conclusions of archeology, philology and history in the Xia-ShangZhou Chronology Project and based mainly on dates in Wucheng, Jupiter’s position in Guoyu and information on the season, our first choice of the date of King Wu’s Conquest is Jun. 20, BC104.6. This conclusion explains properly most relevant literature. [ABSTRACT FROM AUTHOR]

The ancient record, “During the first year of King Yi, the day dawned twice at Zheng”, has provided important clues to early Chinese chronicles. The astronomical conditions and visible area distributions related to such a “double dawn” event are discussed, and the precision and current problems in the calculations of ancient astronomical phenomena are shown. On such a basis, all the solar eclipses from 1000 BC to 840 BC are calculated and their associated “double dawn” features investigated. The conclusion that the “double dawn” was a solar eclipse occurring on April 21st, 899 BC is corfirmed to be the most reasonable. [ABSTRACT FROM AUTHOR]

Background: The nonstructural carbohydrates (NSCs) of plants are posited to be crucial traits for the resistance and resilience of plants to climate change-induced drought and flooding. However, the potential effects of persistent drought and waterlogging on the dynamics of the NSCs and the underlying mechanisms are still poorly understood. Methods: We measured the NSCs concentrations and pool size, photosynthetic rate and biomass of Robinia pseudoacacia L. seedlings for both 2015 and 2016 under five soil water treatments: 3%, 8%, 17%, 24% and 34% soil water content, representing extreme drought (ED), moderate drought (MD), the control group (CG), field capacity (FC) and waterlogging (WL) stresses, respectively. We observed the relationship between the pool size of NSCs and the survival of seedlings under water stress (drought and waterlogging) for 30 days in greenhouse. Results: Compared with CG, the net photosynthetic rate decreased 91%, 67%, 34% and 71%, and the biomass decreased by 37%, 15%, 16% and 33% under ED, MD, FC and WL, respectively. The total NSC (TNSC) concentration was significantly increased by 154% under ED after 10 days and sharply decreased by 50% under ED after 30 days. The concentrations of soluble sugars (SS) were significantly increased by 100% under MD after 10 days and sharply decreased by 60% under ED after 30 days. Compared with GC, the response of NSCs, photosynthetic rate and biomass under ED were more dramatic than that under WL. The pool sizes of fructose and sucrose were larger under ED and WL, but the maximum pool size of starch occurred under the CG. The depletion of NSCs was not observed under ED at the end of the experiments in both 2015 and 2016. Conclusions: Our results indicate that the dynamics of NSCs is an important physiological feature of plant adaptation and resistance to drought and waterlogging. In addition, high sugars concentrations are beneficial for the plants during the short-term extreme drought and the longer term mild drought or waterlogging. [ABSTRACT FROM AUTHOR]