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This paper investigates the anti-windup synthesis problem for linear control systems subject to time-varying state delay and saturating actuators. To alleviate the redundant data transmission, the dynamic event-triggered mechanism is adopted. Moreover, to abate the inherent conservatism, novel delay-dependent sector conditions containing double integral terms are explored. Then, using augmented Lyapunov-Krasovskii functionals and several less conservative inequalities, delay-dependent anti-windup synthesis criteria are obtained in accordance with the feasibility of linear matrix inequalities. Subsequently, the optimization of the initial condition set is addressed. Finally, a simulation example illustrates the availability and technique advantages of the proposed results.

Metasurfaces of quarter-wave plate (QWP) meta-atoms have exhibited high flexibility and versatile functionalities in the manipulation of light fields. However, the generation of multi-channel vortex beams with the QWP meta-atom metasurfaces presents a significant challenge. In this study, we propose dielectric metasurfaces composed of QWP meta-atoms to manipulate multi-channel vortex beams. QWP meta-atoms, systematically arranged in concentric circular rings, are designed to introduce the modulations via the propagation phase and geometric phase, leading to the generation of co- and cross-polarized vortex beams in distinct channels. Theoretical investigations and simulations are employed to analyze the modulation process, confirming the capability of QWP meta-atom metasurfaces for generating the multi-channel vortex beams. This study presents prospective advancements for the compact, integrated, and multifunctional nanophotonic platforms, which have potential applications in classical physics and quantum domains.

Abstract Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum. The model consists of 366 occurrence records (10 repeats, 75% for calibration and 25% for verification) and 10 climate prediction variables. According to the model forecast, the distribution of X. italicum was expected to shrink in future climate scenarios with human intervention, which may be mainly caused by the rise in global average annual temperature. The ROC curve showed that the AUC values of the training set and the test set are 0.965 and 0.906, respectively, indicating that the prediction result of this model was excellent. The contribution rates of annual mean temperature, monthly mean diurnal temperature range, standard deviation of temperature seasonal change and annual average precipitation to the geographical distribution of X. italicum were 65.3%, 11.2%, 9.0%, and 7.7%, respectively, and the total contribution rate was 93.2%. These four variables are the dominant environmental factors affecting the potential distribution of X. italicum, and the influence of temperature is greater than that of precipitation. Through our study on the potential distribution prediction of X. italicum under the future climatic conditions, it has contribution for all countries to strengthen its monitoring, prevention and control, including early warning.

On the premise of ensuring profits, how to give a relatively dispersed portfolio selection result reasonably and rapidly is a challenging problem in both theory and practice. Although the use of optimization models to make decision has been shown to be an essential approach towards portfolio selection, there still has an acute need for developing a knowledge-based expert model for portfolio selection so that this model can achieve better performance in reliability and real time, especially in leading more distributed investments. In this paper, a knowledge-based expert model is proposed for portfolio selection with the aid of analytic hierarchy process (AHP) and fuzzy sets. In the proposed model, the expert knowledge which can reflect the investment attitude and experience of different investors is mainly integrated into the criterion layer and represented by a reciprocal matrix, and the scheme layer is abstracted to a strictly consistent matrix by comparing and analyzing the state characteristics of investment objects. In order to characterize the state characteristics of investment objects under fuzzy environment, their corresponding time series data are quantified as fuzzy variables in advance. Experiments involving synthetic and real-world data demonstrate that the proposed model produces better performance than other typical portfolio selection models and gives more distributed investments.

The emergence of Wireless Body Area Networks (WBAN) provides users with ubiquitous wireless communication services, such as continuous exchange of medical information in real time. Therefore, WBAN is considered to be one of the effective wireless sensor technologies for improving medical services. However, the characteristics of WBAN make it subject to multiple attacks, such as the leakage of private information of users, and WBAN is also inefficient. In this paper, a certificateless ring signature scheme CLRS is proposed. In addition, we propose a public auditing scheme with identity privacy protection, which combines the certificateless ring signature technology for cloud-assisted body area network. Through the analysis of security, it shows that the scheme can resist the existing attack methods such as forging attacks. Finally, the comparison between theoretical analysis and experimental simulation shows that the scheme has obvious efficiency advantages compared with the existing scheme.

Water use efficiency of vegetation (WUE), the ratio of carbon gain to water loss, is a valid indicator to describe the photosynthetic carbon–water coupling relationship. Understanding how and why WUE changes are essential for regional ecological conservation. However, the impacts of various factors and their interactions on the spatial variation of WUE remain uncertain in the arid land of Northwest China. Here, we selected the Qilian Mountains (QM) and Hexi Corridor (HC) as the study areas. Supported by the Google Earth Engine, we explored the spatio-temporal variations of WUE in QM and HC for 2002–2021 using STL decomposition (a seasonal-trend decomposition procedure), trend analysis, and the Hurst index. Then, the Geodetector method was applied to quantify impacts of geographical and eco-meteorological factors on the spatial variation of WUE. The WUE in HC was higher than that in QM. Interestingly, the opposite longitude zonality characteristics are shown in the QM and HC. In QM, the WUE showed an upward trend with longitude increasing, while a downward trend with longitude increasing in the oases of HC. The WUE of cropland was the highest (1.15 ± 0.35 gC kg−1 H2O), and that of alpine vegetation was the lowest (0.2 ± 0.15 gC kg−1 H2O). WUE showed a decreasing trend across the study area, almost certainly due to a drop from May to July during 2002–2021. The air temperature is the dominant factor influencing the spatial variation of WUE. In addition, the interaction of any two factors is greater than the independent influence of either factor alone. The Geodetector method proved to be effective for quantifying the impact of complex multi-factors on the spatial variation of WUE. This study provides a new technical scheme to analyze the spatio-temporal pattern and quantify the impact of multi-factors on the spatial variation of WUE. These findings aid in understanding underlying mechanisms of WUE variation and thereby will be beneficial for clarifying the response of vegetation to climate change.

The zero-dimensional Gröbner basis construction is a crucial step in Gröbner basis cryptanalysis on AES-256. In this paper, after performing an in-depth study on the linear transformation and the system of multivariate polynomial equations of AES-256, the zero-dimensional Gröbner basis construction method is proposed by choosing suitable term order and variable order. After giving a detailed construction process of the zero-dimensional Gröbner basis, the necessary theoretical proof is presented. Based on this, an algebraic cryptanalysis scheme of AES-256 using Gröbner basis is proposed. Analysis shows that the complexity of our scheme is lower than that of the exhaustive attack.

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

Rapid climate warming speeds up the solid-liquid water cycle and reduces the solid water storage in cold regions of the Earth. Snowfall is the most crucial input for the cryosphere. However, the potential snowfall phenology (PSP) variability has not been systematically and comprehensively studied. For this reason, we initially proposed three indicators, i.e., the start of potential snowfall season (SPSS), the end of potential snowfall season (EPSS), and the length of potential snowfall season (LPSS), to describe the characteristics of the PSP, then we explored the spatial-temporal variation of those three PSP indicators past, present, and future across the Chinese Tianshan mountainous region (CTMR) based on the observed daily air temperature from 26 meteorological stations during 1961-2017/2020 combined with 14 models data from CMIP6 (the Phase 6 of the Coupled Model Intercomparison Project) under four different scenarios (SSP126, SSP245, SSP370, and SSP585) during 2021-2100. It proved that the SPSS, EPSS, and LPSS could reproduce features of the PSP well across the study area. In the past and present, the potential snowfall season started on October 29th, ended on March 20th, and lasted for about four months and 23 days across the CTMR on average. The rate of advancing EPSS (-1.6 days/10a) was faster than that of postponing SPSS (1.1 days/10a) during 1961-2017/2020. It also found significant delaying by 2-13 days in the starting time, and advancing for 1-13 days in the ending time, respectively, which reduced 1-27 days for the LPSS. The potential snowfall season started later, ended later, and lasted longer in the north and center than in the south. Similar to the past and present, the SPSS, EPSS, and LPSS will vary under four emission scenarios during 2021-2100. Potential snowfall season will start later, end earlier, and last fewer days under the higher emission scenario. Under the highest emission scenario, SSP585, the starting time will be postponed by 41 days in 2100, while the ending time will be up to 23 days in advance in 2100, implying that it will cut down the length by 63 days (about two months), and the length of the potential snowfall season will only last two and a half months in 2100 under the SSP585 scenario. Spatially, the length of the potential snowfall season in the west and southwest of the CTMR will be compressed by more days because of the more delayed starting time and advanced ending time under all four scenarios. The results indicate that annual total snowfall will decrease, including amount and frequency, then reduce snow cover or mass, which finally feedback to the atmosphere in the form of more rapid warming for the lower reflectivity to solar radiation. Our research provides a new direction to capture the potential snowfall phenology in the alpine region and can be easily expanded to other snow-dominated areas worldwide. [ABSTRACT FROM AUTHOR]

Prediction of significant wave height (SWH) is considered an effective method in marine engineering and prevention of marine disasters. Support vector machine (SVM) model has limitations in processing nonlinear and non-stationary SWH time series. Fortunately, empirical mode decomposition (EMD) can effectively deal with the complicated series. So, the SWH prediction method based on EMD and SVM is proposed by combining the advantages of both methods. A statistical analysis was carried out to compare the results of two models i.e., between the hybrid EMD-SVM and SVM. In addition, two models are used for forecasting SWH with 3, 6, 12 and 24 hours lead times, respectively. A high R value of different prediction times for the hybrid model. Results indicate that SWH prediction of the hybrid EMD-SVM model is superior to the SVM model. [ABSTRACT FROM AUTHOR]