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The Blended-Wing-Body (BWB) is an unconventional configuration of aircraft and considered as a potential configuration for future commercial aircraft. One of the difficulties in conceptual design of a BWB aircraft is structural mass prediction due to its unique structural feature. This paper presents a structural mass prediction method for conceptual design of BWB aircraft using a structure analysis and optimization method combined with empirical calibrations. The total BWB structural mass is divided into the ideal load-carrying structural mass, non-ideal mass, and secondary structural mass. Structural finite element analysis and optimization are used to predict the ideal primary structural mass, while the non-ideal mass and secondary structural mass are estimated by empirical methods. A BWB commercial aircraft is used to demonstrate the procedure of the BWB structural mass prediction method. The predicted mass of structural components of the BWB aircraft is presented, and the ratios of the structural component mass to the Maximum TakeOff Mass (MTOM) are discussed. It is found that the ratio of the fuselage mass to the MTOM for the BWB aircraft is much higher than that for a conventional commercial aircraft, and the ratio of the wing mass to the MTOM for the BWB aircraft is slightly lower than that for a conventional aircraft. Keywords: Aircraft conceptual design, Blended-Wing-Body, Finite element method, Mass prediction, Structural optimization

Structural layout design of blended wing body (BWB) aircraft in the preliminary design phase is a challenging optimization problem due to large numbers of design variables and various constraints. A two-loop optimization strategy is proposed to solve the BWB aircraft structural layout design problem considering constraints of the displacement, stress, strain, and buckling. The two-loop optimization consists of an inner loop and an outer loop. The inner loop is to optimize each stiffened panel of the BWB aircraft structure, and outer loop is to find the best layout design. To improve computational efficiency, an equivalent finite element model is applied to BWB aircraft structure analysis, and an analytical method is used for buckling and static analysis of the stiffened panels. The proposed method can efficiently solve the structural layout optimization problem of a notional BWB aircraft with acceptable computational burden. The result indicates the mass of main load-carrying structure of the BWB aircraft is reduced by 9.28% compared to that of the initial structural layout.

The aim of this paper is to systematically evaluate a biased sampling issue associated with genome-wide association analysis (GWAS) of imaging phenotypes for most imaging genetic studies, including the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Specifically, the original sampling scheme of these imaging genetic studies is primarily the retrospective case-control design, whereas most existing statistical analyses of these studies ignore such sampling scheme by directly correlating imaging phenotypes (called the secondary traits) with genotype. Although it has been well documented in genetic epidemiology that ignoring the case-control sampling scheme can produce highly biased estimates, and subsequently lead to misleading results and suspicious associations, such findings are not well documented in imaging genetics. We use extensive simulations and a large-scale imaging genetic data analysis of the Alzheimer’s Disease Neuroimag-ing Initiative (ADNI) data to evaluate the effects of the case-control sampling scheme on GWAS results based on some standard statistical methods, such as linear regression methods, while comparing it with several advanced statistical methods that appropriately adjust for the case-control sampling scheme.

Summary In case-control studies, association analysis was designed to test whether genetic variants were associated with human diseases. To evaluate the association, analysing one genetic marker at a time suffered from weak power, because of the correction for multiple testing and possibly small genetic effects. An alternative strategy was to test simultaneous effects of multiple markers, which was believed to be more powerful. However, when the number of markers under investigation was large, they would be subjected to weak power as well, because of the greater degrees of freedom. To conquer these limitations in case-control studies, we proposed a novel method that could test joint association of several loci (i.e. haplotype), with only a single degree of freedom. In this research, we developed a nonparametric approach, which was based on U-statistics. We also introduced a new kernel for U-statistic, which could combine the haplotype structure information, and was expected to enhance the power. Simulations indicated that our proposed approach offered merits in identifying the associations between diseases and haplotypes. Application of our method to a study of candidate genes for internalising disorder illustrated its virtue in utility and interpretation, and provided an excellent result in detecting the associations.

This is a rejoinder to the discussions of “Why do we test multiple traits in genetic association studies”.

An efficient receiving status feedback in multicast communications can improve the system performance considerably. In this paper, we propose a novel compressed hybrid automatic repeat request (HARQ) mechanism for the reliable multicast services in the cellular network controlled Device-to-Device (D2D) communications. Closely located D2D cluster devices transmit the acknowledgement and negative acknowledgement (ACK/NACK) message to a cluster head through D2D links directly, after that the cluster head feeds back the whole D2D cluster receiving status using 2-bit ACK/NACK to the cellular network. Performance analysis and numerical simulation results reveal that the proposed HARQ feedback mechanism is better than the conventional D2D multicast mechanisms in terms of the error probability and signaling overhead.

Fast and reliable channel estimation is very important for wireless communication transmission. This paper addresses blind channel estimation for OFDM systems. A novel One-Cycle blind channel estimation algorithm based on cyclostationarity properties of OFDM signal is proposed in this paper, the new algorithm is formed from using the two related z-transform values of delay parameter . Furthermore, a lower complexity algorithm based on partial spectrum information can further reduce computational complexity. Computer simulation results verify that the performance of the proposed One-Cycle algorithm is superior to that of the Two-Cycle algorithm. Although the lower complexity One-Cycle algorithm has some performance loss in high SNR region, its computational complexity is dramatically reduced

The Framingham Heart Study is a well known longitudinal cohort study. In recent years, the community-based Framingham Heart Study has embarked on genome-wide association studies. In this paper, we present a Framingham Heart Study genome-wide analysis for fasting triglycerides trait in the Genetic Analysis Workshop16 Problem 2 using multivariate adaptive splines for the analysis of longitudinal data (MASAL). With MASAL, we are able to perform analysis of genome-wide data with longitudinal phenotypes and covariates, making it possible to identify genes, gene-gene, and gene-environment (including time) interactions associated with the trait of interest. We conducted a permutation test to assess the associations between MASAL selected markers and triglycerides trait and report significant gene-gene and gene-environment interaction effects on the trait of interest.

Genome-wide association is a powerful tool for the identification of genes that underlie common diseases. Genome-wide association studies generate billions of genotypes and pose significant computational challenges for most users including limited computer memory. We applied a recently developed memory management tool to two analyses of North American Rheumatoid Arthritis Consortium studies and measured the performance in terms of central processing unit and memory usage. We conclude that our memory management approach is simple, efficient, and effective for genome-wide association studies.

Random forest is an efficient approach for investigating not only the effects of individual markers on a trait but also the effect of the interactions among the markers in genetic association studies. This approach is especially appealing for the analysis of genome-wide data, such as those obtained from gene expression/single-nucleotide polymorphism (SNP) array experiments in which the number of candidate genes/SNPs is vast. We applied this approach to the Genetic Analysis Workshop 16 Problem 1 data to identify SNPs that contribute to rheumatoid arthritis. The random forest computed a raw importance score for each SNP marker, where higher importance score suggests higher level of association between the marker and the trait. The significance level of the association was determined empirically by repeatedly reapplying the random forest on randomly generated data under the null hypothesis that no association exists between the markers and the trait. Using random forest, we were able to identify 228 significant SNPs (at the genome-wide significant level of 0.05) across the whole genome, over two-thirds of which are located on chromosome 6, especially clustered in the region of 6p21 containing the human leukocyte antigen (HLA) genes, such as gene HLA-DRB1 and HLA-DRA. Further analysis of this region indicates a strong association to the rheumatoid arthritis status.