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Evaluating, ranking, and clustering (ERC) stand as fundamental tasks in scientific research, each requiring a mathematical foundation. This study presents an ERC system anchored in the CCHZ-DISO (Chen, Chen, Hu, and Zhou-Distance between Indices of Simulation and Observation) system. Previous research underscores the optimality achieved by the CCHZ-DISO system (Hu et al., 2022). Since the inception of CCHZ- DISO-series research by Hu et al. (2019), DISO has found extensive applications across various domains including geography, hydrology, and economics. Analogous to the CCHZ-DISO system's construction, the ERC system employs the Euclidean distance to perform evaluating, ranking, and clustering tasks. Furthermore, illustrative examples are provided to elucidate the application of the ERC system. In fact, the ERC system unified the evaluating, ranking, and clustering tasks in one simple equation which is more flexible and simpler than the present system. It will have a more widely application than CCHZ-DISO in diverse scientific domains. [ABSTRACT FROM AUTHOR]

This paper is concerned with the existence and nonexistence of traveling waves of a virus infection model with humoral immunity, cell‐to‐cell transmission, and general nonlinear incidence. Our results show that the existence of traveling wave solutions is determined not only by the basic reproduction number R0$$ {\mathcal{R}}_0 $$ of virus infection and the antibody response reproduction number R1$$ {\mathcal{R}}_1 $$ but also by the critical wave speed c∗$$ {c}^{\ast } $$. More precisely, we obtain the existence of traveling wave solution connecting infection‐free equilibrium and antibody‐free infection equilibrium for R0>1,R1<1$$ {\mathcal{R}}_0>1,{\mathcal{R}}_1<1 $$, and c>c∗$$ c>{c}^{\ast } $$ and connecting infection‐free equilibrium and antibody‐present infection equilibrium for R0>1,R1>1$$ {\mathcal{R}}_0>1,{\mathcal{R}}_1>1 $$, and c>c∗$$ c>{c}^{\ast } $$. The existence of traveling wave solution connecting infection‐free equilibrium and antibody‐present infection equilibrium is discussed. Some numerical simulations are carried out to illustrate our analytical results. [ABSTRACT FROM AUTHOR]

The terrestrial water cycle is important for the arid regions of central Asia (CA). In this study, the spatiotemporal variations in the three climate variables [temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET)] and terrestrial water cycle components [soil moisture (SM), snow water equivalent (SWE), runoff, terrestrial water storage (TWS), and groundwater storage (GWS)] of CA are comprehensively analyzed based on multiple datasets from 2003 to 2020. The major results are as follows: (1) Significant decreasing trends were observed for the TWS anomaly (TWSA) and GWS anomaly (GWSA) during 2003–2020, indicating serious water resource depletion. The annual linear trend values of TWSA and GWSA are −0.31 and −0.27 mm/a, respectively. The depletion centers are distributed over most areas of western and southern Kazakhstan (KAZ) and nearly all areas of Uzbekistan (UZB), Kyrgyzstan (KGZ), and Tajikistan (TJK). (2) TMP and PET have the largest significant negative impacts on SM and SWE. The PRE has a positive impact on terrestrial water variations. (3) During 1999–2019, water withdrawal did not significantly increase, whereas TWS showed a significant decreasing trend. Our results provide a comprehensive analysis of the basic TWS variation that plays a significant role in the water resource management of CA. [ABSTRACT FROM AUTHOR]

Global climate changes, particularly extreme weather events, can directly or indirectly affect freshwater availability and food production, and cause disease outbreaks, floods and droughts. Using the advanced research version of the weather research and forecasting model (ARWv3) and a hydrostatic wind speed change equation, Liu et al. [[5]] assessed the effects of four CPSs on a 10 m wind speed simulation over mainland China in the summer of 2003. It was found that the IAP climate model creditably reproduced the spatial patterns of the first three dominant modes of summer rainfall in Thailand, and that the correlation between the observed rainfall anomalies and the Niño 3.4 index could be reproduced through the use of the IAP model. Advanced climate simulations and observations can improve the accuracy of climate change predictions and long-term trends, which can mitigate the impacts of climate events on social and economic development, as well as human lives. [Extracted from the article]

In this paper, we separately constructed ARIMA, ARIMAX, and RNN models to determine whether there exists an impact of the air pollutants (such as PM2.5, PM10, CO, O3, NO2, and SO2) on the number of pulmonary tuberculosis cases from January 2014 to December 2018 in Urumqi, Xinjiang. In addition, by using a new comprehensive evaluation index DISO to compare the performance of three models, it was demonstrated that ARIMAX (1,1,2) × (0,1,1)12 + PM2.5 (lag = 12) model was the optimal one, which was applied to predict the number of pulmonary tuberculosis cases in Urumqi from January 2019 to December 2019. The predicting results were in good agreement with the actual pulmonary tuberculosis cases and shown that pulmonary tuberculosis cases obviously declined, which indicated that the policies of environmental protection and universal health checkups in Urumqi have been very effective in recent years. [ABSTRACT FROM AUTHOR]

With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question. However, most existing lots of metrics focus on specific aspects of the assessment, and comprehensive assessment is rare. Therefore, it is very necessary to develop new assessment system. To address this problem, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ‐DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Due to its simplicity and flexibility, CCHZ‐DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple. Plain Language Summary: With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question and plays an essential and key role in any science subject. However, most existing lots of metrics focus on specific aspects of the assessment, and integrative and comprehensive assessment is rare. Therefore, it is very necessary and high urgency to develop new assessment system. To address this problem, in this study, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ‐DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Two examples are provided to illustrate the CCHZ‐DISO application. Moreover, we have a brief comparison between CCHZ‐DISO and other approaches to prove the advantage of our new system. Due to its simplicity and flexibility, CCHZ‐DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple. Key Points: A Timely New Assessment System for data quality or model performance is establishedChen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) has the advantage to evaluate the overall performance of different models for multiple variables and add the weights of metricsCCHZ‐DISO can be widely used to quality the model performance in any subjects [ABSTRACT FROM AUTHOR]

The accurate calculation of sustainable development indicators is essential for the accurate assessment of the Sustainable Development Goals. This study develops a methodology that combines nighttime light indices, population distribution data, and statistics in order to examine changes and key drivers of SDG7 in the Aral Sea Basin from 2000–2020. In this study, the best-performing combination of four light indices and five simulation methods (two linear regression methods and three machine learning methods) was selected to simulate the spatial distribution of GDP in the Aral Sea Basin. The results showed that: (1) The prediction using the XGBoost model with TNL had better performance than other models. (2) From 2000 to 2020, the GDP of the Aral Sea Basin shows an uneven development pattern while growing rapidly (+101.73 billion, +585.5%), with the GDP of the lower Aral Sea and the Amu Darya River gradually concentrating in the middle Aral Sea and Syr Darya River basins, respectively. At the same time, the GDP of the Aral Sea Basin shows a strong negative correlation with the area of water bodies. (3) Although there is a small increase in the score (+6.57) and ranking (+9) of SDG7 for the Aral Sea Basin from 2000 to 2020, it is difficult to achieve SDG7 in 2030. Deepening inter-basin energy cooperation, enhancing investment in renewable energy, and increasing energy intensity is key to achieving SDG7. [ABSTRACT FROM AUTHOR]

In April 2022, a COVID-19 outbreak caused by the Omicron variant emerged in Guangzhou. A case–control study was conducted to explore the relationship between vaccination intervals and SARS-CoV-2 infection in the real world. According to the vaccination dose and age information of the cases, a 1:4 matched case–control sample was established, finally including n = 242 for the case group and n = 968 for the control group. The results indicated that among the participants who received three vaccine doses, those with an interval of more than 300 days between the receipt of the first vaccine dose and infection (or the first contact with a confirmed case) were less likely to be infected with SARS-CoV-2 than those with an interval of less than 300 days (OR = 0.67, 95% CI = 0.46–0.99). After age-stratified analysis, among participants aged 18–40 years who received two doses of vaccine, those who received the second dose more than 30 days after the first dose were less likely to be infected with SARS-CoV-2 (OR = 0.53, 95% CI = 0.30–0.96). Our findings suggest that we need to extend the interval between the first dose and the second dose and further explore the optimal interval between the first and second and between the second and third doses in order to improve vaccine efficacy. [ABSTRACT FROM AUTHOR]

With global warming and socioeconomic developments, there is a tendency toward the emergence and spread of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in China. Timely identification of the transmission risk and spread of MT-ZVL is, therefore, of great significance for effectively interrupting the spread of MT-ZVL and eliminating the disease. In this study, 26 environmental variables—namely, climatic, geographical, and 2 socioeconomic indicators were collected from regions where MT-ZVL patients were detected during the period from 2019 to 2021, to create 10 ecological niche models. The performance of these ecological niche models was evaluated using the area under the receiver-operating characteristic curve (AUC) and true skill statistic (TSS), and ensemble models were created to predict the transmission risk of MT-ZVL in China. All ten ecological niche models were effective at predicting the transmission risk of MT-ZVL in China, and there were significant differences in the mean AUC (H = 33.311, p < 0.05) and TSS values among these ten models (H = 26.344, p < 0.05). The random forest, maximum entropy, generalized boosted, and multivariate adaptive regression splines showed high performance at predicting the transmission risk of MT-ZVL (AUC > 0.95, TSS > 0.85). Ensemble models predicted a transmission risk of MT-ZVL in the provinces of Shanxi, Shaanxi, Henan, Gansu, Sichuan, and Hebei, which was centered in Shanxi Province and presented high spatial clustering characteristics. Multiple ensemble ecological niche models created based on climatic and environmental variables are effective at predicting the transmission risk of MT-ZVL in China. This risk is centered in Shanxi Province and tends towards gradual radiation dispersion to surrounding regions. Our results provide insights into MT-ZVL surveillance in regions at high risk of MT-ZVL. [ABSTRACT FROM AUTHOR]

In this paper, the periodic solution and extinction in a periodic chemostat model with delay in microorganism growth are investigated. The positivity and ultimate boundedness of solutions are firstly obtained. Next, the necessary and sufficient conditions on the existence of positive -periodic solutions are established by constructing Poincaré map and using the Whyburn Lemma and Leray-Schauder degree theory. Furthermore, according to the implicit function theorem, the uniqueness of the positive periodic solution is obtained when delay is small enough. Finally, the necessary and sufficient conditions for the extinction of microorganism species are established. [ABSTRACT FROM AUTHOR]

Pulmonary tuberculosis (PTB) has been a major threat to global public health. The association between meteorological factors and the incidence of PTB has been widely investigated by the generalized additive model, auto-regressive integrated moving average model and the distributed lag model, etc. However, these models could not address a non-linear or lag correlation between them. In this paper, a penalized distributed lag non-linear model, as a generalized and improved one, was applied to explore the influence of meteorological factors (such as air temperature, relative humidity and wind speed) on the PTB incidence in Xinjiang from 2004 to 2019. Moreover, we firstly use a comprehensive index (apparent temperature, AT) to access the impact of multiple meteorological factors on the incidence of PTB. It was found that the relationships between air temperature, relative humidity, wind speed, AT and PTB incidence were nonlinear (showed "wave-type ", "invested U-type", "U-type" and "wave-type", respectively). When air temperature at the lowest value (−16.1 °C) could increase the risk of PTB incidence with the highest relative risk (RR = 1.63, 95% CI: 1.21–2.20). An assessment of relative humidity demonstrated an increased risk of PTB incidence between 44.5% and 71.8% with the largest relative risk (RR = 1.49, 95% CI: 1.32–1.67) occurring at 59.2%. Both high and low wind speeds increased the risk of PTB incidence, especially at the lowest wind speed 1.4 m/s (RR = 2.20, 95% CI: 1.95–2.51). In particular, the lag effects of low and high AT on PTB incidence were nonlinear. The lag effects of extreme cold AT (−18.5 °C, 1st percentile) on PTB incidence reached a relative risk peak (RR = 2.18, 95% CI: 2.06–2.31) at lag 1 month. Overall, it was indicated that the environment with low air temperature, suitable relative humidity and wind speed is more conducive to the transmission of PTB, and low AT is associated significantly with increased risk of PTB in Xinjiang. [ABSTRACT FROM AUTHOR]

Terrestrial water storage (TWS) is an essential element of the water cycle and a key state variable for land surface–atmosphere interaction. This study investigates the changes of TWS in Xinjiang of China in response to climate change, using Gravity Recovery and Climate Experiment (GRACE)-derived data and ground hydrological/meteorological observations in 2003–2015. The results show that the TWS has an overall significant decreasing trend of −2.8 mm per year. TWS is in a state of surplus in the first half year (from February to July), while it is in a state of deficit in the second half year (from August to next January). The change rates in TWS exhibit strong regional disparities, with profound decreasing trends in Tianshan Mountains and increasing in Kunlun Mountains. Further analyses suggest that the spatial-temporal change of TWS was closely related to the variations in seasonal snow melt and glacier retreat due to temperature and precipitation changes. [ABSTRACT FROM AUTHOR]

Taylor diagram has been frequently used to evaluate climate or hydrology models or data. A Taylor diagram summarizes three frequently used metrics including correlation coefficient (CC), standard deviation (STD), and centred root mean square error (RMSEc). Although these three metrics are relevant metrics for some applications, in some cases, additional indicators are needed, which calls for a new method. This study firstly addressed the short comments about the distance between indices of simulation and observation (DISO) described in a previous study. Secondly, the number of statistical metrics of DISO is extended from 3 to more than 3. The statistical metrics of the expanded DISO are more flexible than the Taylor diagram which uses the three fixed metrics. Thirdly, the current work compares the Taylor diagram with the expanded DISO in terms of their theoretical bases, revealing the advantage of DISO in terms of its flexibility in the selection of different types of metrics, and its suitability as an effective single metric to express a model's or dataset's overall quality. The power and flexibility of the expanded DISO are discussed. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID‐19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this study, global COVID‐19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 84 countries across the five climate regions: tropical, arid, temperate, and cold. A new approach named Yi Hua Jie Mu is proposed to obtain the transmission rates based on the COVID‐19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID‐19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, based on the simulated results by the COVID‐19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID‐19. The role of the climate on the COVID‐19 variations should be concluded with more data and more cautions. The non‐pharmaceutical interventions still play the key role in controlling and prevention this global pandemic. Plain Language Summary: In this work, global COVID‐19 data were used to examine the dynamical variations from the perspectives of immunity and contact over five climate regions: tropical, arid, temperate, cold, and polar. A new approach is proposed to obtain the infection rates based on the COVID‐19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID‐19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID‐19. Key Points: A new approached is proposed to predict the future COVID‐19 variations rather than relying on information on other corona virusesCOVID‐19 pandemic will persist in multiple periods of 1–2 yearsThe temperate and cold climate regions have higher infection rates than the tropical and arid climate regions [ABSTRACT FROM AUTHOR]

Climate models use quantitative methods to simulate the interactions of the important drivers of climate system, to reveal the corresponding physical mechanisms, and to project the future climate dynamics among atmosphere, oceans, land surface and ice, such as regional climate models and global climate models. A comprehensive assessment of these climate models is important to identify their different overall performances, such as the accuracy of the simulated temperature and precipitation against the observed field. However, until now, the comprehensive performances of these models have not been quantified by a comprehensive index except the existed single statistical index, such as correlation coefficient (r), absolute error (AE), and the root‐mean‐square error (RMSE). To address this issue, therefore, in this study, a new comprehensive index Distance between Indices of Simulation and Observation (DISO) is developed to describe the overall performances of different models against the observed field quantitatively. This new index DISO is a merge of different statistical metrics including r, AE, and RMSE according to the distance between the simulated model and observed field in a three‐dimension space coordinate system. From the relationship between AE, RMSE, and RMS difference (RMSD) (i.e., standard deviation [SD] of bias time series), the new index also has the information of RMSD which is the statistical index in Taylor diagram. An example is applied objectively to display the applications of DISO and Taylor diagram in identifying the overall performances of different simulated models. Overall, with the strong physical characteristic of the distance in three dimensional space and the strict mathematical proof, the new comprehensive index DISO can convey the performances among different models. It can be applied in the comparison between different model data and in tracking changes in their performances. [ABSTRACT FROM AUTHOR]

The "dry gets drier, wet gets wetter" (DGDWGW) paradigm well describes the pattern of precipitation changes over the oceans. However, it has also been usually considered as a simplified pattern of regional changes in wet/dry under global warming, although GCMs mostly do not agree this pattern over land. To examine the validity of this paradigm over land and evaluate how usage of drought indices estimated from different hydrological variables affects detection of regional wet/dry trends, we take the arid regions of central Asia as a case study area and estimate the drying and wetting trends during the period of 1950–2015 based on multiple drought indices. These indices include the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the Palmer drought severity index (PDSI) and self‐calibrating PDSI (sc_PDSI) with both the Thornthwaite (th) and Penman–Monteith (pm) equations in PDSI calculation (namely, PDSI_th, PDSI_pm, sc_PDSI_th and sc_PDSI_pm). The results show that there is an overall agreement among the indices in terms of inter‐annual variation, especially for the PDSIs. All drought indices except SPI show a drying trend over the five states of central Asia (CAS5: including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan). The four PDSIs and SPEI reveal a wetting tendency over the northwestern China (NW; including Xinjiang Uygur Autonomous Region and Hexi Corridor). The contrasting trends between CAS5 and NW can also be revealed in soil moisture (SM) variations. The nonlinear wet and dry variations are dominated by the 3–7 years oscillations for the indices. Relationships between the six indices and climate variables show the major drought drivers have regional features: with mean temperature (TMP), precipitation total (PRE) and potential evapotranspiration (PET) for CAS5, and PRE and PET for NW. Finally, our analyses indicate that the dry and wet variations are strongly correlated with the El Niño/Southern Oscillation (ENSO). Study area and its topography, including the major rivers: Syr Darya and Amu Darya, the major mountains: the Tianshan Mountain, Kunlun Mountain and Altai Mountains. [ABSTRACT FROM AUTHOR]

Existing evidence suggested that the risk of tuberculosis (TB) infection was associated to the variations in temperature and PM2.5. A total of 9,111 cases of TB were reported in Ningxia Hui Autonomous Region, China from 2013 to 2015 on a daily basis, and 57.2% of them were male. The TB risk was more prominent for a lower temperature in males (RR of 1.724, 95% CI: 1.241, 2.394), the aged over 64 years (RR of 2.241, 95% CI: 1.554, 3.231), and the high mobility occupation subpopulation (RR of 2.758, 95% CI: 1.745, 4.359). High concentration of PM2.5 showed a short-term effect and was only associated with an increased risk in the early stages of exposure for the female, and aged 36–64 years group. There were 15.06% (1370 cases) of cases of TB may be attributable to the temperature, and 2.94% (268 cases) may be attributable to the increase of PM2.5 exposures. Low temperatures may be associated with significantly increase in the risk of TB, and high PM2.5 concentrations have a short-term association on increasing the risk of TB. Strengthening the monitoring and regular prevention and control of high risk groups will provide scientific guidance to reduce the incidence of TB. [ABSTRACT FROM AUTHOR]

The accuracies of gridded precipitation data sets are important for regional climate studies and hydrological models. In this study, the performances of Global Precipitation Climatology Centre (GPCC) V7, Climatic Research Unit (CRU) TS 3.22 and Willmott and Matsuura (WM) precipitation data sets were examined over central Asia by comparing them against observed precipitation records (OBS) from 586 meteorological stations during 1901–2010. The results show that all the three gridded data sets underestimated the observed precipitation at annual and monthly scales, especially in mountainous areas. Both GPCC and WM underestimated seasonal precipitation, especially for spring precipitation. Among the three gridded data sets, GPCC had the highest correlation and lowest bias compared with CRU and WM when against the OBS. WM had a higher correlation than that of CRU, and its bias was larger than that of CRU. In terms of the drought and heavy rainfall events, CRU had the best performance in capturing drought events, and GPCC was best at representing heavy rainfall events. These differences in the performances between the three gridded data sets were primarily induced by their different interpolation methods and the numbers of available meteorological stations used in the interpolations of the three gridded data sets. Therefore, compared to the other two data sets, GPCC is more suitable for studies of long‐term precipitation variations over central Asia. [ABSTRACT FROM AUTHOR]

Abstract: Evaluation of drought and its spatial distribution is essential to develop mitigation measures. In this study, we employed the entropy index to investigate the spatiotemporal variability of meteorological droughts over China. Entropy values, with a reliable hydrological and geographical basis, are closely related to the months of precipitation deficit and its mean magnitude and can thus represent the physical formation of droughts. The value of entropy index can be roughly classified as <0.35, 0.36–0.90, and >0.90, reflecting high, middle, and low occurrence probabilities of droughts. The accumulated precipitation deficits, based on the standardized precipitation‐evapotranspiration index at the 1, 3, 6, and 12 month scales, consistently increase with entropy decrease, no matter considering the moderately, severely, or extremely dry conditions. Therefore, Northwest China and North China, with smaller entropy values, have higher occurrence probability of droughts than South China, with a break at 38°N latitude. The aggravating droughts in North China and Southwest China over recent decades are represented by the increase in both the occurrence frequency and the magnitude. The entropy, determined by absolute magnitude of the difference between precipitation and potential evapotranspiration, as well as its scatter and skewness characteristics, is easily calculated and can be an effective index for evaluating drought and its spatial distribution. We therefore identified dominant thresholds for entropy values and statistical characteristics of precipitation deficit, which would help evaluate the occurrence probability of droughts worldwide. [ABSTRACT FROM AUTHOR]

ABSTRACT This study examines the temporal variations and spatial distributions of annual precipitation over Central Asia during the periods of 1901-2013, 1951-2013, and 1979-2013 using the latest version of Global Precipitation Climatology Centre (GPCC) full data reanalysis version 7 ( GPCC V7) data set. The linear trend and multiperiods of the precipitation over the entire region and plain and mountainous area separately are analysed by linear least square method and ensemble empirical mode decomposition method. An overall increasing trend [0.66 mm (10 years)−1] is found for the entire region during 1901-2013, which is smaller than that of 1951-2013. The regional annual precipitation exhibits multi-decadal variations, with a sharp decline during 1901-1944, followed by an increase until 1980s, and a fluctuation thereafter. During 1979-2013, the mountainous area shows a greater increasing trend than the entire region. Furthermore, the regional annual precipitation has exhibited high-frequency variations with 3-year and 6-year quasiperiods and a low-frequency variation with 28-year quasiperiods. In terms of the spatial distribution, increasing trend in the annual precipitation is found in Xinjiang and decreasing trends appear over the five countries of Central Asia during 1951-2013. Empirical orthogonal function results show that the mountainous area is the large variability centre of the annual precipitation. The dominant mode of interannual variability in Central Asia annual precipitation is related to El Niño-Southern Oscillation, which explains about 17% of the interannual variance during 1951-2013. The results of this study describe the long-term variation in the annual precipitation over Central Asia as well as its relationship with some key climate indices in great detail, which will benefit the understanding and the prediction of the climate variations in this region. [ABSTRACT FROM AUTHOR]

Discrete epidemic models are popularly used to detect the pathogenesis, spreading, and controlling of the diseases. The three-dimensional discrete ${SIRS}$ epidemic models are more suitable than the two-dimensional discrete models to describe the spreading characters of the diseases. In this paper, the complex dynamical behaviors of a three-dimensional discrete ${SIRS}$ epidemic model with standard incidence rate are discussed. We choose the time step size parameter as a bifurcation parameter, the existence, stability, and direction of Hopf bifurcation are proved by using the normal form theorem and bifurcation theory. Moreover, the numerical simulations not only illustrate our results, but they also exhibit the complex dynamical behaviors, such as the invariant cycle, period-7 orbits and period-12 orbits with more than one attractors and chaotic sets. The flip bifurcation caused by the step size parameter is also obtained by a numerical simulation. Most importantly, when the adequate contact rate and the death rate of the infective individuals are chosen as the bifurcation parameters, there also exist a Hopf bifurcation, a flip bifurcation, chaos, and strange attractors. These results provide significant information for the disease controlling when there appear complex dynamical behaviors in the epidemic model. [ABSTRACT FROM AUTHOR]

The relationship between the large-scale circulation dynamics and regional precipitation regime in the Tibetan Plateau (TP) has so far not been well understood. In this study, we classify the circulation types using the self-organizing maps based on the daily field of 500 hPa geopotential height and link them to the precipitation climatology in the eastern and central TP. By virtue of an objective determining method, 18 circulation types are quantified. The results show that the large amount of precipitation in summer is closely related to the circulation types in which the enhanced and northward shifted subtropical high (SH) over the northwest Pacific and the obvious cyclconic circulation anomaly over the Bay of Bengal are helpful for the Indian summer monsoon and East Asian summer monsoon to take abundant low-latitude moisture to the eastern and southern TP. On the contrary, the dry winter in the central and eastern Tibet corresponds to the circulation types with divergence over the central and eastern TP and the water vapor transportations of East Asian winter monsoon and mid-latitude westerly are very weak. Some circulation types are associated with some well-known circulation patterns/monsoons influencing the TP (e.g. East Atlantic Pattern, El Niño Southern Oscillation, Indian Summer Monsoon and the mid-latitude westerly), and exhibit an overall good potential for explaining the variability of regional seasonal precipitation. Moreover, the climate shift signals in the late 1970s over the eastern Pacific/North Pacific Oceans could also be reflected by both the variability of some circulation types and their correspondingly composite precipitations. This study extends our understandings for the large-scale atmospheric dynamics and their linkages with regional precipitation and is beneficial for the climate change projection and related adaptation activities in the highest and largest plateau in the world. [ABSTRACT FROM AUTHOR]

Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the dynamical behaviors of a class of discrete-time SIRS epidemic models are discussed. The conditions for the existence and local stability of the disease-free equilibrium and endemic equilibrium are obtained. The numerical simulations not only illustrate the validity of our results, but also exhibit more complex dynamical behaviors, such as flip bifurcation, Hopf bifurcation and chaos phenomenon. These results reveal far richer dynamical behaviors of the discrete epidemic model compared with the continuous epidemic models. [Copyright &y& Elsevier]

Abstract: The paper studies the dynamical behaviors of a discrete predator–prey system with nonmonotonic functional response. The local stability of equilibria of the model is obtained. The model undergoes flip bifurcation and Hopf bifurcation by using the center manifold theorem and the bifurcation theory. Numerical simulations not only illustrate our results, but also exhibit the complex dynamical behaviors of the model, such as the period-doubling bifurcation in periods 2, 4 and 8, and quasi-periodic orbits and chaotic sets. The most interesting aspect is choosing the same parameters and the initial value of the model; then we vary the parameter , and obtain series bifurcations, such as flip bifurcation and Hopf bifurcation. [Copyright &y& Elsevier]

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations. [ABSTRACT FROM AUTHOR]