Exploring network-level performances of wireless nanonetworks utilizing gains of different types of nano-antennas with different materials

Wireless nanonetworks are not a simple extension of traditional communication networks at the nano-scale. Owing to being a completely new communication paradigm, existing research in this field is still at an embryonic stage. Furthermore, most of the existing studies focus on performance enhancement of nanonetworks via designing new channel models and routing protocols. However, the impacts of different types of nano-antennas on the network-level performances of the wireless nanonetworks remain still unexplored in the literature. Therefore, in this paper, we explore the impacts of different well-known types of antennas such as patch, dipole, and loop nano-antennas on the network-level performances of wireless nanonetworks. We also investigate the performances of nanonetworks for different types of traditional materials (e.g., copper) and for nanomaterials (e.g., carbon nanotubes and graphene). We perform rigorous simulation using our customized ns-2 simulation to evaluate the network-level performances of nanonetworks exploiting different types of nano-antennas using different materials. Our evaluation reveals a number of novel findings pertinent to finding an efficient nano-antenna from its several alternatives for enhancing network-level performances of nanonetworks. Our evaluation demonstrates that a dipole nano-antenna using copper material exhibits around 51% better throughput and about 33% better end-to-end delay compared to other alternatives for large-size nanonetworks. Furthermore, our results are expected to exhibit high impacts on the future design of wireless nanonetworks through facilitating the process of finding the suitable type of nano-antenna and suitable material for the nano-antennas.

     

Social learning for adaptive delta management: Tidal River Management in the Bangladesh Delta

ABSTRACT

The article analyzes Tidal River Management in Bangladesh from a social learning perspective. Four cases were investigated using participatory assessment. Knowledge acquisition through transformations in the Tidal River Management process was explored as an intended learning outcome. The study finds that social learning occurred more prominently at the individual stakeholder level and less at the collective level. For Tidal River Management to be responsive and sustainable, especially in times of increased uncertainty and climate vulnerability, more attention needs to be paid to coordination and facilitation of multi-level learning that includes all stakeholders.     

Graph bandit for diverse user coverage in online recommendation

We study a recommendation system problem, in which the system must be able to cover as many users’ preferences as possible while these preferences change over time. This problem can be formulated as a variation of the maximum coverage problem; specifically we introduced a novel problem of Online k-Hitting Set, where the number of sets and elements within the sets can change dynamically. When the number of distinctive elements is large, an exhaustive search for even a fixed number of elements is known to be computationally expensive. Even the static problem is known to be NP-hard (Hochba, ACM SIGACT News 28(2):40–52, 1997) and many known algorithms tend to have exponential growth in complexity. We propose a novel graph based UCB1 algorithm that effectively minimizes the number of elements to consider, thereby reducing the search space greatly. The algorithm utilizes a new rewarding scheme to choose items that satisfy more users by balancing coverage and diversity as it construct a relational graph between items to recommend. Experiments show that the new graph based algorithm performs better than existing techniques such as Ranked Bandit (Radlinski et al. 2008) and Independent Bandits (Kohli et al. 2013) in terms of satisfying diverse types of users while minimizing computational complexity.     

<Emphasis Type="Italic">Brassica rapa</Emphasis> var. <Emphasis Type="Italic">japonica</Emphasis> Leaf Extract Mediated Green Synthesis of Crystalline Silver Nanoparticles and Evaluation of Their Stability,Cytotoxicity and Antibacterial Activity

Silver nanoparticles (AgNPs) were successfully synthesized from the reduction of Ag⁺ using AgNO₃ solution as a precursor and Brassica rapa var. japonica leaf extract as a reducing and capping agent. This study was aimed at synthesis of AgNPs, exhibiting less toxicity with high antibacterial activity. The characterization of AgNPs was carried out using UV–Vis spectrometry, energy dispersive X-ray spectrometry, fourier transform infrared spectrometry, field emission scanning electron microscopy, X-ray diffraction, atomic absorption spectrometry, and transmission electron microscopy analyses. The analyses data revealed the successful synthesis of nano-crystalline Ag possessing more stability than commercial AgNPs. The cytotoxicity of Brassica AgNPs was compared with commercial AgNPs using in vitro PC12 cell model. Commercial AgNPs reduced cell viability to 23% (control 97%) and increased lactate dehydrogenase activity at a concentration of 3 ppm, whereas, Brassica AgNPs did not show any effects on both of the cytotoxicity parameters up to a concentration level of 10 ppm in PC12 cells. Moreover, Brassica AgNPs exhibited antibacterial activity in terms of zone of inhibition against E. coli (11.1?±?0.5 mm) and Enterobacter sp. (15?±?0.5 mm) which was higher than some previously reported green-synthesised AgNPs. Thus, this finding can be a matter of interest for the production and safe use of green-AgNPs in consumer products.     

GUISE: a uniform sampler for constructing frequency histogram of graphlets

Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose Guise, which uses a Markov Chain Monte Carlo sampling method for constructing the approximate GFD of a large network. Our experiments on networks with millions of nodes show that Guise obtains the GFD with very low rate of error within few minutes, whereas the exhaustive counting-based approach takes several days.     

Polymer nanocomposites based on graphite nanoplatelets (GNPs): a review on thermal-electrical conductivity,mechanical and barrier properties

Journal of Materials Science - In this paper, graphite nanoplatelets (GNPs)-based polymer nanocomposites were reviewed. This review mainly discusses various synthesis techniques for making graphite...     

Ultraviolet curing of epoxy coating on wood surface

Different formulations were developed with EB-600 (Ebcryl-600), an epoxy acrylate oligomer in the presence of N-vinylpyrrolidone and trimethylol propane triacrylate. Thin films were prepared with these formulated solutions under ultraviolet (UV) radiation. These solutions were coated on a low-grade wood substrate (simul) and cured under UV light. Both UV-cured thin films and surface coatings were characterized, and the best formulations for coating wood surface were evaluated. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 66: 1997–2004, 1997     

A Study of Single Nucleotide Polymorphisms of the SLC19A1/RFC1 Gene in Subjects with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders with complex genetic etiology. Recent studies have indicated that children with ASD may have altered folate or methionine metabolism, suggesting that the folate–methionine cycle may play a key role in the etiology of ASD. SLC19A1, also referred to as reduced folate carrier 1 (RFC1), is a member of the solute carrier group of transporters and is one of the key enzymes in the folate metabolism pathway. Findings from multiple genomic screens suggest the presence of an autism susceptibility locus on chromosome 21q22.3, which includes SLC19A1. Therefore, we performed a case-control study in a Japanese population. In this study, DNA samples obtained from 147 ASD patients at the Kanazawa University Hospital in Japan and 150 unrelated healthy Japanese volunteers were examined by the sequence-specific primer-polymerase chain reaction method pooled with fluorescence correlation spectroscopy. p < 0.05 was considered to represent a statistically significant outcome. Of 13 single nucleotide polymorphisms (SNPs) examined, a significant p-value was obtained for AA genotype of one SNP (rs1023159, OR = 0.39, 95% CI = 0.16–0.91, p = 0.0394; Fisher’s exact test). Despite some conflicting results, our findings supported a role for the polymorphism rs1023159 of the SLC19A1 gene, alone or in combination, as a risk factor for ASD. However, the findings were not consistent after multiple testing corrections. In conclusion, although our results supported a role of the SLC19A1 gene in the etiology of ASD, it was not a significant risk factor for the ASD samples analyzed in this study.