Energy-Efficient Power Control: A Look at 5G Wireless Technologies

This work develops power control algorithms for energy efficiency (EE) maximization (measured in bit/Joule) in wireless networks. Unlike previous related works, minimum-rate constraints are imposed and the signal-to-interference-plus-noise ratio takes a more general expression, which allows one to encompass some of the most promising 5G candidate technologies. Both network-centric and user-centric EE maximizations are considered. In the network-centric scenario, the maximization of the global EE and the minimum EE of the network are performed. Unlike previous contributions, we develop centralized algorithms that are guaranteed to converge, with affordable computational complexity, to a Karush-Kuhn-Tucker point of the considered non-convex optimization problems. Moreover, closed-form feasibility conditions are derived. In the user-centric scenario, game theory is used to study the equilibria of the network and to derive convergent power control algorithms, which can be implemented in a fully decentralized fashion. Both scenarios above are studied under the assumption that single or multiple resource blocks are employed for data transmission. Numerical results assess the performance of the proposed solutions, analyzing the impact of minimum-rate constraints, and comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal Processin

Flat plate and turbine vane film-cooling performance with laid-back fan-shaped holes

Shaped holes are considered as an effective solution to enhance gas turbine film-cooling performance, as they allow to increase the coolant mass-flux, while limiting the detrimental lift-off phenomena. A great amount of work has been carried out in past years on basic flat plate configurations while a reduced number of experimental works deals with a quantitative assessment of the influence of curvature and vane pressure gradient. In the present work PSP (Pressure Sensitive Paint) technique is used to detail the adiabatic effectiveness generated by axial shaped holes with high value of Area Ratio close to 7, in three different configurations with the same 1:1 scale: first of all, a flat plate configuration is examined; after that, the film-cooled pressure and suction sides of a turbine vane model are investigated. Tests were performed varying the blowing ratio and imposing a density ratio of 2.5 . The experimental results are finally compared to the predictions of two different correlations, developed for flat plate configurations

Perspectives and Challenges in Microbial Communities Metabolic Modeling

Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These “super-organisms” play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g., metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as flux balance analysis, has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in the (near) future

Composition of supralittoral sediments bacterial communities in a Mediterranean island

Marine coasts represent highly dynamic ecosystems, with sandy beaches being one of the most heterogeneous. Despite the key importance of sandy beaches as transition ecosystems between sea and land, very few studies on the microbiological composition of beach sediments have been performed. To provide a first description of microbial composition of supralittoral sediments, we investigated the composition of bacterial communities of three sandy beaches, at Favignana Island, Italy, using metagenetic approaches (Terminal-Restriction Fragment Length Polymorphism, sequencing of 16S rRNA genes by Illumina-Solexa technology, functional genes detection, and quantitative Real-Time PCR). Results showed that the investigated beaches are harboring a rich bacterial diversity, mainly composed by members of classes Alphaproteobacteria, Gammaproteobacteria, Flavobacteria and Actinobacteria. The metagenetic analysis showed profiles of decreasing beta diversity and increasing richness, as well as a differentiation of communities, along the sea-to-land axis. In particular, members of Firmicutes and Proteobacteria displayed contrasting profiles of relative abundance (to decrease and to increase, respectively) along the sea-to-land axis of the beach. Finally, a search for the presence of genes related to the nitrogen and carbon biogeochemical cycle (nifH, nosZ, pmoA/amoA) detected the presence of ammonia monoxygenase sequences (amoA) only, suggesting the presence of bacterial ammonia oxidation to some extent, probably due to members of Nitrospira, but with the lack of nitrogen fixation and denitrification

Virtual multichannel SAR for ground moving target imaging

Slow moving ground targets are invisible within synthetic aperture radar (SAR) images since they appear defocused and their backscattered signal completely overlap the focused ground return. In order for this targets to be detected and refocused the availability of some spatial degrees of freedom is required. This allows for space/slow time processing to be applied to mitigate the ground clutter. However, multichannel SAR (M-SAR) systems are very expensive and the requirements in terms of baseline length can be very restrictive. In this study a processing scheme that exploits high PRF single channel SAR system to emulate a multichannel SAR is presented. The signal model for both target and clutter components are presented and the difference with respect to an actual M-SAR are highlighted. The effectiveness of the proposed processing is then demonstrated on simulated a measured dataset

Compressive sensing for interferometric inverse synthetic aperture radar applications

The applicability of interferometric inverse synthetic aperture radar (InISAR) techniques to images reconstructed via compressive sensing (CS)-based algorithms is investigated. Specifically, the three-dimensional (3D) reconstruction algorithm is applied after exploiting CS for data compression and image reconstruction. The InISAR signal model is derived and formalised in a CS framework. A comparison between conventional CS reconstruction and global sparsity constrained reconstruction techniques is performed for different compression rates and different signal-to-noise ratio conditions. Performances on the 2D and 3D reconstructions are evaluated. Results obtained on real data acquired during the NATO-SET 196 trial are shown

deciphering the ecology of cystic fibrosis bacterial communities towards systems level integration

Despite over a decade of cystic fibrosis (CF) microbiome research, much remains to be learned about the overall composition, metabolic activities, and pathogenicity of the microbes in CF airways, limiting our understanding of the respiratory microbiome's relation to disease. Systems-level integration and modeling of host–microbiome interactions may allow us to better define the relationships between microbiological characteristics, disease status, and treatment response. In this way, modeling could pave the way for microbiome-based development of predictive models, individualized treatment plans, and novel therapeutic approaches, potentially serving as a paradigm for approaching other chronic infections. In this review, we describe the challenges facing this effort and propose research priorities for a systems biology approach to CF lung disease