Novel approximate distribution of the sum of lognormal-Rician turbulence channels with pointing errors and applications in MIMO FSO links

<p>The performance analysis for the MIMO-FSO systems employing EGC technology over Lognormal-Rician turbulence channels with pointing errors is important. However, the results so far are greatly limited since the PDF of Lognormal-Rician turbulence channels is not analytic, even for the SISO systems. In this paper, we propose a new method to approximate the sum of lognormal-Rician turbulence channels with Rayleigh pointing errors. Based on the developed formula, the approximate closed-form expressions of ergodic capacity, outage probability, and bit-error rate are derived. Numerical results demonstrate the accuracy of the proposed approach.</p>

Novel approximate distribution of the sum of lognormal-Rician turbulence channels with pointing errors and applications in MIMO FSO links

<p>The performance analysis for the MIMO-FSO systems employing EGC technology over Lognormal-Rician turbulence channels with pointing errors is important. However, the results so far are greatly limited since the PDF of Lognormal-Rician turbulence channels is not analytic, even for the SISO systems. In this paper, we propose a new method to approximate the sum of lognormal-Rician turbulence channels with Rayleigh pointing errors. Based on the developed formula, the approximate closed-form expressions of ergodic capacity, outage probability, and bit-error rate are derived. Numerical results demonstrate the accuracy of the proposed approach.</p>

Hierarchical Missing Data and Multivariate Behrens–Fisher Problem

This article firstly defines hierarchical data missing pattern, which is a generalization of monotone data missing pattern. Then multivariate Behrens–Fisher problem with hierarchical missing data is considered to illustrate that how ideas in dealing with monotone missing data can be extended to deal with hierarchical missing pattern. A pivotal quantity similar to the Hotelling T 2 is presented, and the moment matching method is used to derive its approximate distribution which is for testing and interval estimation. The precision of the approximation is illustrated through Monte Carlo data simulation. The results indicate that the approximate method is very satisfactory even for moderately small samples.

The food insecurity and the young generations' perception: A systematic review

Food insecurity is a global problem mainly generated by financial issues, critical geopolitical situations and constantly changing weather conditions that have direct effects on availability and prices of food products. These issues reduce capacity to manage the available resources with the consequence of obtaining an approximate distribution of food all over the world. Food insecurity involves multiple population groups and different generations, including University students. In order to evaluate the relationship between food insecurity and University students investigated from different points of view, this article provides a systematic literature review dedicated to this topic with the aim of identifying any research gaps. For this purpose, a selection of 29 articles was created and the subsequent analysis highlighted the main objectives dedicated to this topic i.e. "Food safety, nutrition and health", "Food safety and determinants", "Food security linked to financial issues", "Food security linked to school performance" and "Food security and socio-demographic variables". In particular, food insecurity exists in campuses mainly due to living costs, income and budget, dietary priority; it affects physical health, mental health and ultimately impactson students' academic performance. All surveys mainly concern individual University campuses in countries developed or in development and therefore a lack of studies dedicated to the comparison of campuses belonging to countries with different socio-economic conditions is highlighted. In light of the results obtained, the authors propose further comparative studies on the perception of food insecurity among University campuses in different geographical areas in order to provide new knowledge on the subject.

An Analytical Framework for the IEEE 802.15.4 MAC Layer Protocol under Periodic Traffic

As the reference communication standard of wireless sensor networks (WSNs), the IEEE 802.15.4 standard has been adopted in various WSN-based applications. In many of these applications, one of the most common traffic pattern types is a periodic traffic patterns, however, the majority of existing analytical models target either saturated or unsaturated network traffic patterns. Furthermore, few of them can be directly extended to the periodic traffic scenario, since periodic traffic brings unstable load status to sensor nodes. To better characterize the WSNs with periodic traffic, we propose an accurate and scalable analytical framework for the IEEE 802.15.4 MAC protocol. By formulating the relationship between clear channel assessment (CCA) and its successful probability from the perspective of channel state and node state, single node’s behavior and whole network’s performance under different network scales and traffic loads can be derived. Extensive simulations are conducted to validate the proposed framework in terms of both local statistics and overall statistics, and the results show that the model can represent the actual behavior and the real performance of both single node and whole network. Besides, as the simplified version of double CCAs mode (DS mode), single CCA mode (SS mode), is also analyzed with simple modifications on the proposed analytical framework. Combining the analytical framework with simulation results, the applicable network scenarios of two modes are also demonstrated respectively. Finally, an approximate distribution of one data packet’s backoff duration is proposed. With this approximate distribution, a conservative estimation of data packet’s average transmission latency in networks with given configurations can be easily carried out.

African Language Types

The goal of this chapter is to describe major salient features in the structures of African languages and their approximate distribution. A typological classification is not aimed at. The chapter begins with a sketch and discussion of typological subject areas generally, followed by a review of previous studies in African comparative typology that highlights the broad spectrum of objectives and methodological operations, as well as the basic principles of typological classification. The presentation of salient typological features of African languages is divided into phonological and morphosyntactic characteristics. Special emphasis is laid on noun class systems, which are widely found in Africa, case marking, verbal extensions, and serial verb constructions.

scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks

Single cell RNA sequencing (scRNA-seq) provides a rich view into the heterogeneity underlying a cell population. However single-cell data are usually noisy and very sparse due to the presence of dropout genes. In this work we propose an approach to impute missing gene expressions in single cell data using generative adversarial networks (GANs). By learning an approximate distribution of the data, our approach, scGAIN, can impute dropouts in simulated and real single cell data. The work in this paper discusses how to adopt GAIN training model into the domain of imputing single cell data. Experiments show that scGAIN gives competitive results compared to the state-of-the-art approaches while showing superiority in various aspects in simulation and real data. Imputation by scGAIN successfully recovers the underlying clustering of different subpopulations, provides sharp estimates around true mean expressions and increase the correspondence with matched bulk RNAseq experiments.

Polynomial-based density inversion of gravity anomalies for concealed iron-deposit exploration in North China

Inversion of residual gravity anomalies is an important geophysical technique for depicting subsurface density contrasts, for example, for mineral deposits. We have expressed subsurface density variations using depth-variable polynomial functions and developed the polynomial coefficient inversion (PCI) method, which is an alternative method for mapping subsurface density distributions by inverting the coefficients of density-contrast functions. PCI enables the linear inversion of density variations without vertically subdividing the subsurface. Synthetic tests indicate that PCI combines polynomial functions and multiple constraints to highlight the anomalous masses through an iterative process with appropriate weighting parameters. We apply our method to a local investigation of banded iron formation (BIF) deposits in the Hebei Province, North China. The inversion results depict the approximate distribution of the subsurface density contrasts to identify the stratigraphic boundaries of different lithologies and BIF-favorable zones, thus implying that local iron-rich ore bodies may be located at the syncline axis or dip along the faults. The successful application of PCI for the BIF deposits indicates that this method is a promising strategy for density mapping.