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In recent years, vertical social communication among strangers characterized by anonymization and personalization is becoming a new approach to social communication in mobile Internet era. This paper developed a map vertical social platform based on LBS technology and Retrofit network framework in an attempt to solve such problems as the slow shift from online to offline social activities and barriers to communication between strangers . The platform implements real-time map display by connecting to the Golden Map SDK, uses C/S three-tier structure as the system architecture, SQL Server database to store and manage data, MVVM as the backend framework, and constructs four modules of map social, social activities and recommendation, group chat and user center based on the social needs of young people, which effectively meets the anonymous social needs of strangers.

Time series is a very common but important data type. A large number of time series data are generated in various professional research fields and daily life. Although there are many models being developed to deal with time series, the cluster methods for time series are insufficient and need to improve. This paper is focused on time series clustering, which uses deep learning approach to discover the shape characteristics of time series. We establish a new neural network model of time series clustering to jointly optimize the representation learning and clustering tasks of time series. Focusing on shape features with time series, we built the Soft-DTW layer into the neural network to learn the interpretable time series representation. Maximized regularization mutual information is used to jointly optimize representation learning and clustering tasks. Experiments show that this model can help obtain an excellent representation of time series. In comparison with the benchmark model, the best clustering effect is achieved in the proposed model on multiple data sets. This model has broad applicability in time series data.

Accurate precipitation prediction is very important for social life and economical activity. Prediction of the quantitative precipitation in semi-arid areas is difficult because of rain scarcity and volatility. In this study, the 3-h precipitation situation in the semi-arid region of Lanzhou is predicted, that is, the precipitation status after 3 h is forecasted on 5 levels: ‘no rain’, ‘light rain’, ‘moderate rain’, ‘heavy rain’ and ‘torrential rain’. We selected the meteorological data from 25 stations in and nearby Lanzhou, and processed the data with lag, difference and multiplication. Due to the large number of features, we use Mutual Information (MI) feature extraction method to reduce feature dimension, extract the features that are highly correlated with the target variable, and introduce spatio-temporal information in this way. Precipitation in semi-arid areas also has the problem of sample imbalance. We oversampled the data using Adaptive Synthetic (ADASYN) sampling approach and generated some minority class samples. Based on the MI feature extraction method and the ADASYN oversampling method, we constructed an Adaptive Synthesis and Mutual Information extraction Matrix (ASMI-M), which is the feature matrix used for model training. Then we proposed a Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) model based on deep learning to predict the 3-h precipitation in Lanzhou City, which has achieved better prediction performance than traditional machine learning methods.

Habitat loss and habitat fragmentations are the two main characteristics of habitat destruction and one of the main drivers of biological extinction. So far, many studies have made progress in theoretical studies of spatial food webs, but research on the effects of invasive species in omnivorous food webs is still lacking. Therefore, studying the impact of invasive species on omnivorous food webs in landscapes characterized by habitat loss and fragmentation is necessary. We first developed a simple model of an omnivorous food web. An invasive species with different roles was introduced into this simple omnivorous food web to construct different food web structures. In this different food web that we consider the predation relationship between species, competition, and different dispersal types. Our model simulation results show that the structure of the omnivorous food web formed after the invasion becomes significantly more complex and forms more food chains than before the invasion. There is competition between invasive and native species in the different omnivorous food web structures formed after the invasion. The results of our simulations show that this competition can lead to invasive species that will exclude native species or native species that will defeat invasive species. Overall, our study of this theoretical biological invasion model provides new insight into the actual biological invasion phenomenon.

It is extremely significant to construct a scientific and accurate forecasting model for energy consumption of the USA, because it could help to formulate energy policies and allocate energy resources. In recent years, more and more hybrid models based on divided-and-conquer method have been applied in energy consumption prediction to obtain satisfactory results. However, owning to the obvious enhancing effect of decomposition methods, the issue concerning seasonal fluctuation existed in time series is rarely considered before modeling. It is a fact that seasonality indeed influences the performance of prediction. This paper proposes a hybrid forecasting model for energy consumption, which combines seasonal adjustment method, ensemble empirical mode decomposition (EEMD), echo state network (ESN), and grasshopper optimization algorithm (GOA). The seasonal adjustment method that inherits the idea of divide and conquer is used to decompose original time series into only two parts including seasonal subseries and remainder subseries rather than regular three parts(seasonality, trend, and residual) for avoiding the complex modeling task of the residual subseries. Then models ESN and EEMD-GOA-ESN are utilized to model and predict the seasonal subseries and remainder subseries respectively. Finally, two parts are summed to generate the final predictive results. The empirical studies of fossil fuels, nuclear electric power, and renewable energy consumptions show that the proposed model outperforms other alternative benchmarks with regard to effectiveness and scalability. Besides, the sample extrapolation forecasting displays that the technique could limit the error of monthly energy consumption to 3.3%.

In the field of ecology, habitat loss and fragmentation are the two main characteristic forms of habitat destruction and the main drivers of species extinction, resulting in the gradual loss of biodiversity. So far, many scholars have made some progress in the theoretical research of the spatial food web, but research on the effect of introducing an invasive species in an omnivorous food web is very rare. In order to explore the impact of invader on the persistence of species in omnivorous food webs, we constructed a model framework to describe the patch occupation of each species in omnivorous systems. Our model results show that invasive species is a prey of species in omnivorous food webs is easier to invade than invasive species is a predator of species in original omnivorous food webs on habitat loss and fragmentation. One conclusion also can be drawn is that when an invasive species is a prey of species in omnivorous food webs, no matter what trophic level the invasive species is invade, it is more successful. But when invasive species is a predator of species in different trophic levels on omnivorous food webs, they show different coexistence patterns. The invasion of a species has little effect upon the stability of original omnivorous food web for habitat loss and fragmentation, and will only make the original omnivorous food web more stable and more complicated. Therefore, we have proved that the omnivorous food web is stable and is not easy to destroy this ecological fact. Some examples to illustrate the reliability of our model results are discussed.

Fruit fly optimization algorithm (FOA) is a kind of swarm intelligence optimization algorithm, which has been widely applied in science and engineering fields. The aim of this study is to design an improved FOA, namely evolution FOA (EFOA), which can overcome some shortcomings of basic FOA, including difficulty in local optimization, slow convergence speed, and lack of robustness. EFOA applies a few new strategies which adaptively control the search steps and swarm numbers of the fruit flies. The evolution mechanism used in EFOA can preserve dominant swarms and remove inferior swarms. Comprehensive comparison experiments are performed to compare EFOA with other swarm intelligence algorithms through 14 benchmark functions and a constrained engineering problem. Experimental results suggest that EFOA performs well both in global search ability and in robustness, and it can improve convergence speed.

The SLOSS (single large or several small reserves) debate is central to species conservation, and habitat loss is the primary cause of species extinction. As a result, habitat loss is crucial for exploring the debate. This study investigates the effect of nonrandom habitat loss and its interactions with individual migration and disturbance. The results suggest that (1) if the spatial distribution of lost habitats is dispersed, a few large reserves are optimal for the protected species; and if it is clustered, many small reserves are optimal. (2) The effects of the spatial distribution of lost habitats on the debate depends on individual migration, and if there are more individuals migrating among reserves, the spatial distribution of lost habitats has greater impacts. (3) If disturbance rate is low, many small reserves are optimal; and increasing disturbance rate tends to favor the implementation of few large reserves. And the spatial distribution of lost habitats exhibits a threshold above which the response of the optimal reserve number to disturbance rate is amplified. This study facilitates improving the nature reserve planning strategies.

Short-Term Electricity Load Forecasting (STLF) has become one of the hot topics of energy research as it plays a crucial role in electricity markets and power systems. Few researches aim at selecting optimal input features (Feature Selection, FS) when forecasting model is established, although more and more intelligent hybrid models are developed to forecast real-time electricity load. In fact, a good FS is a key factor that influence prediction accuracy. Based on the idea of selecting optimal input features, a hybrid model, AS-GCLSSVM, is developed to forecast electricity load in this research, which combines ACF (AutoCorrelation Function) and LSSVM (Least Squares Support Vector Machines). ACF is applied to select the informative input variables, and LSSVM is for prediction. The parameters in LSSVM are optimized by GWO (Grey Wolf Optimization Algorithm) and CV (Cross Validation). The proposed model is to forecast the half-hour electricity load of the following week. Experimental results show that it is an effective approach that can improve the forecasting accuracy remarkably, compared with the benchmark models.

Over the past 50 years, global land surface air temperature has been rising at a much higher rate at night than during the day. Understanding plant responses to the asymmetric daytime and nighttime warming in the context of climate change has been a hot topic in global change biology and global ecology. It has been debatable whether the asymmetric warming has opposite effects on vegetation activity (e.g., phenology, productivity). Here we settle the debate by scrutinizing the underpinnings of different statistical methods and revealing how the misuse or improper use of these methods could mischaracterize the effects of asymmetric warming with in situ and satellite observations. The use of the ordinary least square (OLS) methods including both daytime (T

Accuracy prediction of air quality is of crucial importance for people to take precautions and improve environmental conditions. By introducing adjacency matrix in Long Short-Term Memory (LSTM) cell, we propose in this research a Graph-based Long Short-Term Memory (GLSTM) model to predict PM2.5 concentration in Gansu Province of Northwest China. We regard all air quality monitoring stations as a graph, and construct a parameterized adjacency matrix on the basis of the adjacency matrix of the graph. Through the combination of parameterized adjacency matrix and LSTM, we introduce spatiotemporal information to achieve PM2.5 prediction. The advantage of GLSTM is that it can realize synchronous operation of all stations, making it unnecessary to train different model for each monitoring station to obtain the overall PM2.5 variation of a certain area. The parameterized adjacency matrix also enhances the interpretability of the model. By visualizing the parameterized adjacency matrix obtained from the end-to-end PM2.5 prediction task in training, the importance of introducing spatial information, i.e. the distribution importance of surrounding stations to a specific station is clearly demonstrated. We compared our model with several newly reported methods, and found that it achieved the best results on PM2.5 prediction tasks at almost all stations, which proved the effectiveness of the GLSTM model.

Glacier is an important component of the hydrological cycle at different temporal scales. A major mechanism of glacier formation is ″metamorphosis″, by which snow changes into ice. The process is affected by glacier temperature, velocity and mass balance. In recent decades, glaciers in the Western Kunlun Mountains manifested differently from those in other regions. Understanding the mechanism behind their abnormal response to climate change is pressing in order to predict their future trend. However, few simulation researches of Tibetan Plateau glacier change responding to climate warming have been done due to limitation of the availability of glaciological data in situ measurements. Knowledge of present-day ice temperature and velocity is important in order to determine how fast a glacier may respond to climate change. Guliya Ice Cap is located in the Western Kunlun Mountains which lies in the northwestern part of Tibetan Plateau. It is an extremely continental-type (cold) glacier and is characterized by low temperature and precipitation. Using the few observed data for ice temperature and surface velocity, we apply a two-dimensional higher-order thermomechanical flowline dynamical model to simulate the ice temperature and velocity in steady-state along the main flowline of Guliya Ice Cap. The rare but valuable observational data are used to validate the results of numerical simulation of the model in this study. Owing to the uncertainty of parameters′ values, some diagnostic experiments have been conducted to simulate the present-day spatial distributions of ice velocity and temperature along the main flowline of the Guliya Ice Cap. Based on the sensitivity experiments described above, the appropriate parameters on which the model can best simulate the present-day ice velocity and temperature distributions of Guliya are selected. Generally, modeled and observed ice surface velocities exhibit good agreement under the parameters we adopted. Our results show that significant spatial difference of velocity field of the ice cap exists. The ice flow velocities on the terminus and the ablation zone are lower compared to those in the accumulation area. Similar to other alpine land-terminating glaciers, the mean annual horizontal ice flow speeds of Guliya are relatively low (less than 20 m/a). Several velocity extremum regions inside the ice cap can be observed from the simulation results. Along the streamline profile, the glacier surface velocity is higher than that at the bottom, especially in the glacier accumulation area. Modeled ice temperatures agree well with observed values in a borehole drilled to bottom in 1992. Temperature distribution along the glacier′s streamline profile demonstrates an increasing trend from glacier surface to the bed rock, for example −16.2–−1.65°C at a site of 6200 m above sea level (asl). We extract several vertical profiles of temperature distribution at different altitudes. We find that ice temperature near the glacier terminus remains lower than the melting point, and the temperature gradients are various in different altitudes. This phenomenon indicates that physical property of basal ice frozen to the bed constrains the low amplitude response of the temperature field and the terminus dynamics. We speculate that subsequent simulation researches can possibly consider basal sliding contributes in the future by a sensitivity test of the basal temperature under different scenarios. For a large ice cap with few data in situ observations, it is difficult to construct a suitable glacier model with both reliability and simplicity, nevertheless, we can simulate the main glacier dynamic features with much accuracy. The model suggests that the relatively small change in strain rate and low ice temperature near the glacier terminus may be the reason why the Guliya Ice Cap is the most stable glacier found in China up to now.

The techniques for data analyses have been widely developed in past years, however, missing data still represent a ubiquitous problem in many scientific fields. In particular, dealing with missing spatiotemporal data presents an enormous challenge. Nonetheless, in recent years, a considerable amount of research has focused on spatiotemporal problems, making spatiotemporal missing data imputation methods increasingly indispensable. In this paper, a novel spatiotemporal hybrid method is proposed to verify and imputed spatiotemporal missing values. This new method, termed SOM-FLSSVM, flexibly combines three advanced techniques: self-organizing feature map (SOM) clustering, the fruit fly optimization algorithm (FOA) and the least squares support vector machine (LSSVM). We employ a cross-validation (CV) procedure and FOA swarm intelligence optimization strategy that can search available parameters and determine the optimal imputation model. The spatiotemporal underground water data for Minqin County, China, were selected to test the reliability and imputation ability of SOM-FLSSVM. We carried out a validation experiment and compared three well-studied models with SOM-FLSSVM using a different missing data ratio from 0.1 to 0.8 in the same data set. The results demonstrate that the new hybrid method performs well in terms of both robustness and accuracy for spatiotemporal missing data.

When designing nature reserves, it is very difficult to find an intact landscape because of the prevalence of habitat loss. The FLOMS debate – should we have Few Large Or Many Small reserves – is a focus of the design of nature reserves. Thus, habitat loss may greatly influences the FLOMS debate. Selecting the given area of suitable habitats from the center of a fragmented landscape (caused by habitat loss), we present a theoretical model and Cellular Automata simulation is used to investigate whether several large or many small reserves should be established in these suitable habitats. The results suggest that if the environmental carrying capacity is high, many small reserves are optimal; if it is low, several large reserves are optimal. When the carrying capacity is intermediate, the effects of both habitat loss and migration must be considered, and the following apply. (1) If migration is not allowed between reserves, several large reserves are better. (2) If migration is allowed between reserves and habitat loss is not severe, many small reserves are optimal. (3) If habitat loss is severe, few large reserves are beneficial, regardless of whether migration exists between reserves. (4) Increasing the migration mortality rate tends to favor the implementation of several large reserves.

In epidemic control, how the size of treatment blocks influences the disease is a very interesting problem. Here, we construct an SIS model with spatially clustered treatment blocks using cellular automata and pair approximation to investigate the disease control and optimal arrangement of treatment blocks. On a same treatment intensity, the treatment blocks are built randomly and the individuals in the treatment blocks are cured in the next interval. The migration and neighboring structures are also considered in this model. We find that the size of treatment blocks will influence the control of disease. The bigger size of treatment blocks is more effective in epidemic control. The results obtained by lattice simulations and pair approximation respectively are consistent. On the same treatment arrange, higher migration rate has completely different influence on the epidemic depending on different neighboring structures. Besides, in different neighboring structures, both infected and susceptible populations will get clustered under the treatment blocks. Meanwhile, the neighboring structures can influence the degree of aggregation and the bigger treatment blocks can promote the aggregation compared to the smaller ones. The results here are significant for the control of epidemic diseases, especially when available medical resources are limited.

As people pay more attention to the environment and health, P M 2.5 receives more and more consideration. Establishing a high-precision P M 2.5 concentration prediction model is of great significance for air pollutants monitoring and controlling. This paper proposed a hybrid model based on feature selection and whale optimization algorithm (WOA) for the prediction of P M 2.5 concentration. The proposed model included five modules: data preprocessing module, feature selection module, optimization module, forecasting module and evaluation module. Firstly, signal processing technology CEEMDAN-VMD (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition) is used to decompose, reconstruct, identify and select the main features of P M 2.5 concentration series in data preprocessing module. Then, AutoCorrelation Function (ACF) is used to extract the variables which have relatively large correlation with predictor, so as to select input variables according to the order of correlation coefficients. Finally, Least Squares Support Vector Machine (LSSVM) is applied to predict the hourly P M 2.5 concentration, and the parameters of LSSVM are optimized by WOA. Two experiment studies reveal that the performance of the proposed model is better than benchmark models, such as single LSSVM model with default parameters optimization, single BP neural networks (BPNN), general regression neural network (GRNN) and some other combined models recently reported.

Atmospheric environment has become the focus of global attention. Fine particulate matter has posed a serious threat to human respiratory system. In order to effectively control atmospheric environment and protect human health, pollutant prediction has become a necessary work for human survival and development. In order to improve the accuracy of PM2.5 prediction, this paper developed a new combined prediction model — SSA-LSTM. Firstly, the observed time series are decomposed into periodic component and noise component by SSA (Singular spectrum analysis). Then, LSTM (Long short-term memory) neural network was used to forecast the decomposed components. Finally, the predicted results of different components are integrated to generate the final predicted results. The results show that the proposed model has a significant improvement in the accuracy of prediction.

Whale Optimization Algorithm (WOA), as a newly developed meta-heuristic algorithm, performs well in solving optimization problems. A WOA with chaos mechanism based on quasi-opposition (OBCWOA) is proposed in this paper to overcome the slow convergence speed of the original WOA and to avoid being trapped in local optimal solutions when dealing with high-dimensional problems. We applied two strategies to the original WOA: using chaos mechanism to generate initial value to improve convergence speed of the algorithm and using the opposition-based learning method to balance exploration and development ability of the algorithm to help the algorithm jump out of local optimal solutions. The proposed algorithm is compared with other algorithms on unimodal functions, multimodal functions and fixed dimensional multimodal functions, and is applied to a famous engineering design problem. Results show that combination of the two strategies can improve convergence speed and enhance global search ability of the original WOA. OBCWOA proposed in this paper performs better than the other existing algorithms.

Ecological invasion is a common phenomenon and a critical driver of species’ coexistence in nature. We establish a pair approximation model to explore the possible invasive results of an exploitative competition food web formed by ecological invasion. Global and local hunting strategies of predators are considered. The influence of initial densities and the initial spatial structure of both native and invasive species for invasive results are analyzed. Generally, better local predation capacity and higher initial density of the invasive species favor successful invasion. Interestingly, the initial spatial structures of both native and invasive species can alter the invasive results. Whether higher aggregation of invasive species promotes invasion depends on the species’ predation strategies. Then two kinds of disturbance defined as destructive disturbance and targeted introductory disturbance are introduced. Three characteristics of disturbances are considered: the extent, spatial connectivity and periodicity. All the three characteristic have profound influence on the invasive results especially for the targeted introductory disturbance. Furthermore, the former kind of disturbance cannot eliminate the invasive species thoroughly while the latter not only can eliminate the invasive species but also can promote the coexistence of the native and invasive species depending on its three characteristics. Finally, several invasive events and hypotheses being reported are explained by our model. This study provides new insights into biodiversity conservation and invasion, and the results about disturbance can give us some inspirations on the control of invasion.

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

In this paper, an epidemic model with convalescent carrier was established, without considering the birth rate and natural mortality rate. The stabilities of the disease-free equilibrium and endemic equilibrium were analyzed, and the results of the analysis were verified by numerical solutions. https://doi.org/10.28919/cmbn/1998

Many investigations reveal that diseases and pathogens have a certain role in promoting the control of biological invasion. They are mostly based on mean-field assumption. Only few of them have considered the local spatial effect that is more close to the reality. In this research, we developed a local pair-dynamic model based on the SI (susceptible infected) framework among competitive interaction system in which invasive species is the superior competitor. By analyzing the values of several important parameters in steady state, we find that such a disease-introduction control measure does have a reversing effect on biological invasion, but this effect can only occur under specific conditions such as: (1) native species in the target invasion system should have a limited birth rate because too large colonization ability will correspond to a worse controlling result, (2) this kind of control measure will be more effective on the invaders with stronger competitive power and (3) in terms of chosen pathogens, we should select those with high infectiousness and moderately virulent diseases. These results are confirmed by cellular automata simulation, and the verdicts are different with cases under mean-field approximation. Our findings might provide meaningful guidance for invasion control before large-scale control programmes.

BackgroundObesity is highly prevalent worldwide. More and more studies have been conducted on the relationship between H. pylori infection and obesity or overweight. But the relationship between them is controversial in the literatures and there is no comprehensive evidence for the correlation.AimTo evaluate the prevalence of H. pylori infection in Chinese adult subjects who received routine physical examinations and the relationship between H. pylori and obesity.MethodsLiteratures on H. pylori infection and obesity in Chinese population were searched in online databases. Relevant data were extracted independently by two researchers and meta-analysis was performed by using Review manager 5.3 software.Results22 articles were selected with a total sample size of 178033. The pooled prevalence of H. pylori was 42% (95%CI: 37% to 47%) and mean difference of BMI between subjects with and without H. pylori infection was 0.94 (95%CI: -0.04 to 1.91). 9 eligible studies with 27111 subjects were used to calculated pooled OR value because they contained obesity groups. The OR value showed that H. pylori-positive subjects tended to be obese at a risk of 1.20 (95% CI: 1.13 to 1.28).ConclusionIn China, obesity has association with H. pylori infection. H. pylori infection may be one of the risk factors for obesity.

Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters. However, because of the complexity and non-stationary of rainfall data, it is difficult to forecast. In this paper, a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method. Firstly, SSA is used for extracting the trend components of the hydrological data. Then, SVR is utilized to deal with the volatility and irregularity of the precipitation series. Finally, the parameter of SVR is optimized by DA. The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai, Panshui, Lanma and Jiulongchi stations. To validate the efficiency of the method, four compared models, DA-SVR, SSA-GWO-SVR, SSA-PSO-SVR, SSA-CS-SVR are established. The result shows the proposed method has the best performance among all five models, and its prediction has high precision and accuracy.

Cellular Automata (CA) has shown to be a valuable approach in ecological modeling, in particular when dealing with local interactions between species and their environment. A stochastic cellular automata model, which included two competitors (the inferior species which is immune to a disease and the superior one which is sensitive to the disease), is constructed. Through time series analysis and spatial pattern analysis, the influence of competition effect upon the behavior of epidemic diseases has been investigated to know whether the competition effect is in favor of epidemics control. Then, some strategies for introducing competitors to the infectious system are explored. The result shows that introducing some right competitors into the infection region may be a considerable policy. The population with high colonization rate, low extinction rate and long colonization radius is introduced preferably. The result may give us some suggestions for epidemic control in conservation of wild populations.

A dimmable electrodeless fluorescent lamp (induction lamp) is introduced in this paper. The principles of the induction lamp are introduced in details. A dimming approach for the lamp is discussed in both theory and experiment. A continuous dimming range from 30%-100% can be realized with the application of the IC chip.

Strategies of regulated irrigation and fertilization of farmland in semi-arid regions of the Loess Plateau are analyzed in this paper. Theory and approach of niche-fitness are introduced into the research on field management of water use. Niche fitness of crop species describes the closeness degree between optimum niche point and actual resource state. Mathematical model is constructed to evaluate the adaptive extent of crop to different water–fertilizer conditions and the influence on yield. There are four water treatments in the research, in which rich water (RW), moderate water (MW) and low water (LW) indicate the irrigations of 520, 420 and 260 mm, respectively, while natural water (NW) the irrigation of precipitation (416.7 mm). Four treatments of fertilizers (i.e. high, moderate, low and non-fertilization) are disposed in RW, MW and LW while three types of plastic film coverings (i.e. 30, 60 days and non-cover) in NW. Field experiments with 15 dispositions repeated three times and there are 45 fields of 4 m ×4 m. Contrast to the result under LW, larger leaf area index (LAI) appear in RW, MW and NW, in which LAI of fertilization is even larger than that under non-fertilization, and the same result goes to the LAI influenced by plastic film covering. The mean values of niche-fitness of spring wheat under LW, NW, MW and RW are 0.4797, 0.5594, 0.5617 and 0.6508, and the grain yields are 1616, 2337, 2479 and 3190 kg ha−1, respectively. These results show that the value of niche-fitness and the yield of spring wheat will increase with the irrigation and the plastic film covering. The yield is significantly linear-correlated with niche-fitness. In addition, yield increase more significantly in MW and RW than in LW. The values of water use efficiency (WUE) calculated from grain yields of RW, MW, NW and LW are 6.504, 6.196, 5.609 and 6.217 kg ha−1 mm−1, in which the maximum WUE (8.660 kg ha−1 mm−1) and the maximum grain yield (4416.5 kg ha−1) are supplementing and in accordance with each other under rich water and high fertilizer, which shows rich water and high fertilizer as the best water–fertilizer condition. The above results might be helpful and support the farmland water–fertilizer management in semi-arid regions.

Controlled irrigation and fertilizing strategies under rainwater-harvesting technology in semi-arid areas were discussed. Effects of the amounts of applied water and fertilizer on water use and yield of spring wheat were determined. The experiment included four water treatments during the spring wheat growth period. The four treatments were (total water applied): rich water (RW), 400 mm; moderate water (MW), 300 mm; low water (LW), 100 mm, and natural water (NW), 212 mm. (In the first three situations, rainfall was excluded from irrigation plots while in the fourth only natural rainfall was utilized.) Four nutrition conditions were set up for each water treatment: high fertilizer (HF) 372 kg ha −1 , moderate fertilizer (MF) 248 kg ha −1 , low fertilizer (LF) 124 kg ha −1 and without fertilizer application (CK). Each water–fertilizer treatment was replicated three times. Both soil water content and water use efficiency (WUE) (in terms of grain yield) increased with increasing applied water. The mean WUE were 6.37, 5.61, 5.08 and 4.40 kg ha −1 mm −1 in RW, MW, NW and LW, respectively. WUE increased increasing applied N and P fertilizer. Compared with LW treatment, MW and RW resulted in stronger seedlings, larger and deeper root system, and higher leaf area index (LAI). For RW, MW and NW, the maximum of root biomass increased 96.4, 56.6 and 21.6%, respectively, compared with that for LW. The value of LAI increased 95.6, 66.9 and 40.9%, respectively. The values of leaf area duration (LAD) in RW, MW and NW were remarkably higher than that in LW. Under RW, MW, NW and LW condition, the mean grain yield for the four fertilizer treatments were 3290, 2347, 1665 and 964 kg ha −1 , respectively. The mean grain yield in RW, MW and NW increased 241, 143 and 73%, respectively, compared with that in LW. Yield components analysis indicates that the quality and quantity of spikelets and floscule played critical role in grain formation of spring wheat. Statistical analysis of experiment results indicates that the minimum coefficient of water-consumption (0.110 mm/(kg ha −1 )) occurred in RW, and the relevant optimal fertilizer application amount was 377 kg ha −1 . In various water–fertilizer treatments, WUE was the highest (8.733 kg ha −1 mm −1 ) under rich water with high fertilization, while grain yield was consistently the highest (4514 kg ha −1 ). This indicates that rich water with high fertilizer is the most efficient way in the experiment. These results may offer help to controlled irrigation and fertilization in agricultural water management in semi-arid regions.

Based on a field study on the semi-arid Loess Plateau of China, the strategies of limited irrigation in farmland in dry-period of normal-precipitation years are studied, and the effects on water use and grain yield of spring wheat of dry-period irrigation and fertilizer application when sowing are examined. The study includes four treatments: (1) with 90 mm dry-period irrigation but without fertilizer application (W); (2) with fertilizer application but without dry-period irrigation (F); (3) with 90 mm dry-period irrigation plus fertilizer application (WF); (4) without dry-period irrigation and fertilizer application (CK). The results indicate that dry-period irrigation resulted in larger and deeper root systems and larger leaf area index (LAI) compared with the non-irrigated treatments. The root/shoot ratio (R/S) in the irrigated treatments was significantly higher than in the non-irrigated treatments. The grain yields in F, W and WF are 1509, 2712 and 3291 kg ha −1 , respectively, which are 13.7, 104.3 and 147.9% higher than that (1328 kg ha −1 ) of CK, and at the same time the grain yields in W and WF are also significantly higher than in F. Water use efficiencies (WUE) in terms of grain yield are 5.70 and 6.91 kg ha −1 mm −1 in W and WF, respectively, being 65.7 and 101.1% higher than that (3.44 kg ha −1 mm −1 ) of CK. The highest WUE and grain yield consistently occurred in WF, suggesting that the combination of dry-period irrigation and fertilizer application has a beneficial effect on improving WUE and grain yield of spring wheat.

Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW) and Victorian State (VIC) in Australia). Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

This work deals with the necessary conditions of optimality for some optimal control problems governed by elliptic variational inequalities. Boundary control and state constrained problems are considered. The techniques used are based on those in Ref. 1 and a new penalty functional is defined in this paper.

In this work, we proposed a patchy predator–prey model with one patch as refuge and the other as open habitat, and incorporated prey refuge in the considered model explicitly. We applied an analytical approach to study the dynamic consequences of the simplest forms of refuge used by prey and the migration efficiency. The results have shown that the refuge used by prey and the migration efficiency play an important role in the dynamic consequences of the interacting populations and the equilibrium density of two interacting populations. This work also proposed a new approach which can incorporate prey refuge in predator–prey system explicitly.

Cellular automata (CA) have shown to be a valuable approach in ecological modeling, in particular when dealing with local interactions between species and their environment. In CA model, a global spatial pattern emerges through the evolution of interaction at a local scale. A cellular automata model for two competitive populations with overcrowding effect and Allee effect in a patchy environment has been developed. Having discussed the influence of the two effects on spatial pattern and dynamical complexity of two competitors, five primary results are given. First, overcrowding effect can produce the cricoids waves in space and chaos in time as well as single population model. And suitable increasing the colonization parameter of population produced the wave may shorten period of the spatial wave availably, also can vary its shape and is conducive to self-organization of system; Second, overcrowding effect of a competitor not only change complexity of two competitors but also influence their persistence in competitive system. But when its intensity exceeds some threshold, the complexity of two populations disappears suddenly and system will become stable. Third, the period of the wave caused by the overcrowding effect of the superior competitor is longer than the period of the wave caused by the overcrowding effect of the inferior. Fourth, Allee effect may not vary complexity of two populations until its intensity exceeds some threshold. In addition, extinction or invasion of some competitor may affect the dynamical complexity of original system significantly.

Ecosystem risk is a new concept in understanding environmental problems. It is important to study and develop quantitat ive methods for regional ecosystem risk analysis. In this study, some new indicators and methods for measuring oasis ecosystem risk were established using reliability theory. These indicators are linked to wa ter resource, which is the key restricting factor in arid are a oasis ecosystems. They have clear meanings and can also be compared in different arid area oases. A case study in the Liangzhou oasis of the Shiyang River Basin in China shows how to calculate these ecosystem risk indicators. The results of the case study are as follows: the reliability indicator, risk indicator, stability indicator, and integ rated loss indicator of the Liangzhou oasis are 0.686, 0.314, 0.743, and 0.301, respectively. This means that the reliability degree of the oasis’s ecosystem safety is 68.6%; the degree of risk that it is unsafe is 31.4%; the stability degree is 74.3%; and 30.1% of the oasis’s area i s supported by over-exploiting underground water and damaging the lower reaches of the ecosystem. This result can be used as a guide in controlling and managing ecosystem risk in the research area.