Secure D2D Communication in Large-Scale Cognitive Cellular Networks with Wireless Power Transfer

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multi-antenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the cellular base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, we propose a new power transfer policy, namely, best power beacon (BPB) power transfer. To characterize the power transfer reliability of the proposed policy, we derive new closed-form expressions for the exact power outage probability and the asymptotic power outage probability with large antenna arrays at PBs. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), and 2) nearest receiver selection (NRS). To assess the secrecy performance, we derive new expressions for the secrecy throughput considering the two receiver selection schemes using the BPB power transfer policies. We show that secrecy performance improves with increasing densities of PBs and D2D receivers because of a larger multiuser diversity gain. A pivotal conclusion is reached that BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead.

Synthesis by extrusion: continuous, large-scale preparation of MOFs using little or no solvent

Grinding solid reagents under solvent-free or low-solvent conditions (mechanochemistry) is emerging as a general synthetic technique which is an alternative to conventional solvent-intensive methods. However, it is essential to find ways to scale-up this type of synthesis if its promise of cleaner manufacturing is to be realised. Here, we demonstrate the use of twin screw and single screw extruders for the continuous synthesis of various metal complexes, including Ni(salen), Ni(NCS)(2)(PPh3)(2) as well as the commercially important metal organic frameworks (MOFs) Cu-3(BTC)(2) (HKUST-1), Zn(2-methylimidazolate)(2) (ZIF-8, MAF-4) and Al(fumarate)(OH). Notably, Al(fumarate)(OH) has not previously been synthesised mechanochemically. Quantitative conversions occur to give products at kg h(-1) rates which, after activation, exhibit surface areas and pore volumes equivalent to those of materials produced by conventional solvent-based methods. Some reactions can be performed either under completely solvent-free conditions whereas others require the addition of small amounts of solvent (typically 3-4 mol equivalents). Continuous neat melt phase synthesis is also successfully demonstrated by both twin screw and single screw extrusion for ZIF-8. The latter technique provided ZIF-8 at 4 kg h(-1). The space time yields (STYs) for these methods of up to 144 x 10(3) kg per m(3) per day are orders of magnitude greater than STYs for other methods of making MOFs. Extrusion methods clearly enable scaling of mechanochemical and melt phase synthesis under solvent-free or low-solvent conditions, and may also be applied in synthesis more generally.

The Application of Semiconductor Photocatalysis for the Removal of Cyanotoxins from Water and Design Concepts for Solar Photocatalytic Reactors for Large Scale Water Treatment

There has been a significant increase in the occurrence of cyanobacterial blooms in freshwaters over the past few decades due to escalating nutrient levels. These cyanobacteria release a range of toxins, for example microcystins which are chemically very stable. Many cyanotoxins are consequently very difficult to remove from water using existing treatment technologies. Semiconductor photocatalysis, however, has proven to be a very effective process for the removal of these compounds from water. In this chapter we consider the application of this highly versatile and exciting technology for the decomposition of cyanotoxins. Furthermore design concepts for solar photocatalytic reactors that could be utilized for the removal of these toxins are also considered

BOB CAT: a Large-Scale Review and Delphi Consensus for Management of Barrett’s Esophagus With No Dysplasia, Indefinite for, or Low-Grade Dysplasia

OBJECTIVES: Barrett’s esophagus (BE) is a common premalignant lesion for which surveillance is recommended. This strategy is limited by considerable variations in clinical practice. We conducted an international, multidisciplinary, systematic search and evidence-based review of BE and provided consensus recommendations for clinical use in patients with nondysplastic, indefinite, and low-grade dysplasia (LGD). METHODS: We defined the scope, proposed statements, and searched electronic databases, yielding 20,558 publications that were screened, selected online, and formed the evidence base. We used a Delphi consensus process, with an 80% agreement threshold, using GRADE (Grading of Recommendations Assessment, Development and Evaluation) to categorize the quality of evidence and strength of recommendations. RESULTS: In total, 80% of respondents agreed with 55 of 127 statements in the final voting rounds. Population endoscopic screening is not recommended and screening should target only very high-risk cases of males aged over 60 years with chronic uncontrolled reflux. A new international definition of BE was agreed upon. For any degree of dysplasia, at least two specialist gastrointestinal (GI) pathologists are required. Risk factors for cancer include male gender, length of BE, and central obesity. Endoscopic resection should be used for visible, nodular areas. Surveillance is not recommended for <5 years of life expectancy. Management strategies for indefinite dysplasia (IND) and LGD were identified, including a de-escalation strategy for lower-risk patients and escalation to intervention with follow-up for higher-risk patients. CONCLUSIONS: In this uniquely large consensus process in gastroenterology, we made key clinical recommendations for the escalation/de-escalation of BE in clinical practice. We made strong recommendations for the prioritization of future research.

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.

METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.

RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.

CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.

Large scale test of a novel back-pass non-perforated unglazed solar air collector

This paper describes large scale tests conducted on a novel unglazed solar air collector system. The proposed system, referred to as a back-pass solar collector (BPSC), has on-site installation and aesthetic advantages over conventional unglazed transpired solar collectors (UTSC) as it is fully integrated within a standard insulated wall panel. This paper presents the results obtained from monitoring a BPSC wall panel over one year. Measurements of temperature, wind velocity and solar irradiance were taken at multiple air mass flow rates. It is shown that the length of the collector cavities has a direct impact on the efficiency of the system. It is also shown that beyond a height-to-flow ratio of 0.023m/m³/hr/m², no additional heat output is obtained by increasing the collector height for the experimental setup in this study, but these numbers would obviously be different if the experimental setup or test environment (e.g. location and climate) change. An equation for predicting the temperature rise of the BPSC is proposed.

A large scale model experimental study of a tidal turbine in uniform steady flow

An experimental study measuring the performance and wake characteristics of a 1:10th scale horizontal axis turbine in steady uniform flow conditions is presented in this paper.
Large scale towing tests conducted in a lake were devised to model the performance of the tidal turbine and measure the wake produced. As a simplification of the marine environment, towing the turbine in a lake provides approximately steady, uniform inflow conditions. A 16m long x 6m wide catamaran was constructed for the test programme. This doubled as a towing rig and flow measurement platform, providing a fixed frame of reference for measurements in the wake of a horizontal axis tidal turbine. Velocity mapping was conducted using Acoustic Doppler Velocimeters.
The results indicate varying the inflow speed yielded little difference in the efficiency of the turbine or the wake velocity deficit characteristics provided the same tip speed ratio is used. Increasing the inflow velocity from 0.9 m/s to 1.2 m/s influenced the turbulent wake characteristics more markedly. The results also demonstrate that the flow field in the wake of a horizontal axis tidal turbine is strongly affected by the turbine support structure

CSFinder: A Cold-Start Friend Finder in Large-Scale Social Networks

Recommending users for a new social network user to follow is a topic of interest at present. The existing approaches rely on using various types of information about the new user to determine recommended users who have similar interests to the new user. However, this presents a problem when a new user joins a social network, who is yet to have any interaction on the social network. In this paper we present a particular type of conversational recommendation approach, critiquing-based recommendation, to solve the cold start problem. We present a critiquing-based recommendation system, called CSFinder, to recommend users for a new user to follow. A traditional critiquing-based recommendation system allows a user to critique a feature of a recommended item at a time and gradually leads the user to the target recommendation. However this may require a lengthy recommendation session. CSFinder aims to reduce the session length by taking a case-based reasoning approach. It selects relevant recommendation sessions of past users that match the recommendation session of the current user to shortcut the current recommendation session. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains recommended items and critiques that sufficiently overlap with the ones in the current session. Our experimental results show that CSFinder has significantly shorter sessions than the ones of an Incremental Critiquing system, which is a baseline critiquing-based recommendation system.

Explaining the Coexistence of Large-scale and Small-scale Magnetic Fields in Fully Convective Stars

Despite the lack of a shear-rich tachocline region, low-mass fully convective (FC) stars are capable of generating strong magnetic fields, indicating that a dynamo mechanism fundamentally different from the solar dynamo is at work in these objects. We present a self-consistent three-dimensional model of magnetic field generation in low-mass FC stars. The model utilizes the anelastic magnetohydrodynamic equations to simulate compressible convection in a rotating sphere. A distributed dynamo working in the model spontaneously produces a dipole-dominated surface magnetic field of the observed strength. The interaction of this field with the turbulent convection in outer layers shreds it, producing small-scale fields that carry most of the magnetic flux. The Zeeman–Doppler-Imaging technique applied to synthetic spectropolarimetric data based on our model recovers most of the large-scale field. Our model simultaneously reproduces the morphology and magnitude of the large-scale field as well as the magnitude of the small-scale field observed on low-mass FC stars.

Accelerating Large Scale Centroid-based Clustering with Locality Sensitive Hashing

Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing clustering algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing (LSH) to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.

FPGA-based Tabu Search for Detection in Large-Scale MIMO Systems

The increasing scale of Multiple-Input Multiple- Output (MIMO) topologies employed in forthcoming wireless communications standards presents a substantial implementation challenge to designers of embedded baseband signal processing architectures for MIMO transceivers. Specifically the increased scale of such systems has a substantial impact on the perfor- mance/cost balance of detection algorithms for these systems. Whilst in small-scale systems Sphere Decoding (SD) algorithms offer the best quasi-ML performance/cost balance, in larger systems heuristic detectors, such Tabu-Search (TS) detectors are superior. This paper addresses a dearth of research in architectures for TS-based MIMO detection, presenting the first known realisations of TS detectors for 4 × 4 and 10 × 10 MIMO systems. To the best of the authors’ knowledge, these are the largest single-chip detectors on record.