Species composition and minimum sampling area of a riparian mixed broadleaved-Korean pine forest in Changbai Mountain Nature Reserve

Riparian areas are unique although often small component of the overall watershed landscape. The structure of riparian forests along Erdaobai River on the north slope of Changbai Mountain were investigated by using field data collected from eight sampling transects perpendicular to the Erdaobai River channel. Two kinds of species-area saturation curves were used to examine the relationship between species number and minimum sampling area. The results showed that riparian gymnosperms accounted for a high proportion of all gymnosperms in the Changbai Mountain Nature Reserve while riparian ferns and angiosperms accounted for a relatively low proportion. The average minimum sampling areas of riparian forest that included 60%, 80%, and 90% of the community species pool were about 85, 185, and 328 m 2 , respectively; while those for nonriparian forest were about 275, 390, and 514 m 2 , correspondingly.

Cognitive MAC Protocol with Minimum Sampling Time and Cross-Layer Cooperation for Low Signal-to-Noise Ratio Environments

The previous Decentralised Cognitive Medium Access Control(DC-MAC) protocol allows Secondary Users(SUs) to independently search for spectrum access opportunities without the need for a central coordinator.DC-MAC assumes that the detection scheme is ideal at the Physical(PHY) layer.In fact,a more complex detection algorithm is impractical in distributed spectrum sharing scenarios.Energy Detection(ED) at the PHY layer has become the most common method because of its low computational and implementation complexities.Thus,it is essential to integrate the DC-MAC with ED at the PHY layer.However,ED requires the Minimum Sampling Time(MST)duration to achieve the target detection probability in low Signal-to-Noise Ratio(SNR)environments.Otherwise,it cannot achieve the expected detection performance.In this paper,we derive an accurate expression of MST for ED in low SNR environments.Then,we propose an Optimised DC-MAC(ODC-MAC) protocol which is based on MST,and which amends the aforementioned problems of DC-MAC with ED.Moreover,the closed-form expressions for the unreliable data transmission probability are derived for both DC-MAC and ODC-MAC.We show that the simulation results agree well with the theoretical analyses.The proposed ODC-MAC can improve the data transmission reliability and enhance the throughput compared to the performance of the traditional DC-MAC.

Modeling of Date Palm （Phoenix dactylifera L.） Vegetative Aerial Architecture, Example of Two Tunisian Cultivars

The present study was carried out to verify the statistical relationships between the characteristic parameters in terms of vegetative aerial architecture of the date palms for simulating realistic 3D models. The vegetal material was composed of two Tunisian varieties of Phoenix dactylifera L., ＂Barhi＂ and ＂Rochdi＂. The observations are taken place in Gabes and on one pair of palms per main stem and offshoot for each cultivar. The analysis of the characteristic dimensions of the pinnae and rachis allowed the determination of a minimum sample. The geometrical analysis confirmed the existence of a strong correlation between rotation angles and radial angles. The architectural analysis of the two Tunisian cultivars revealed that the distribution of characteristic parameter values of pinnae was the outcome ofa regionalized variable along the rachis. This statistical study of relationships between the characteristic parameters in terms of vegetative aerial architecture of the two varieties allows executing a new measurement protocol for computing and simulating realistic 3D models.

Sequential Shrinkage Estimate for COX Regression Models with Uncertain Number of Effective Variables

In the applications of COX regression models, we always encounter data sets that contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “noneffective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Fixed design is considered for numerical simulation. The strong consistency, asymptotic distributions and convergence rates of estimates under the fixed design are obtained. In addition, the sequential procedure is shown to be asymptotically optimal in the sense of Chow and Robbins (1965).

A Sequential Shrinkage Estimating Method for Tobit Regression Model

In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.