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A general framework to investigate the interference and coverage probability is proposed in this paper for indoor terahertz (THz) communications with beamforming antennas. Due to the multipath effects of THz band (0.1–10 THz), the line of sight and non-line of sight interference from users and access points (APs) (both equipped with beamforming antennas) are separately analyzed based on distance-dependent probability functions. Moreover, to evaluate the effects of obstacles in real applications, a Poisson distribution blockage model is implemented. Moreover, the coverage probability is derived by means of signal to interference plus noise ratio (SINR). Numerical results are conducted to present the interference and coverage probability with different parameters, including the indoor area size, SINR threshold, numbers of interfering users and APs and half-power bandwidth of beamforming antenna. [ABSTRACT FROM AUTHOR]

Highlights • A succinct and efficient Opposition-Based Learning strategy is inherited in the algorithm. • A novel efficient strategy called Global Grid Ranking is proposed. • A charming multi-objective optimization algorithm called MOWOA is demonstrated. Abstract Nature-inspired computing has attracted a lot of research effort especially for addressing real-world multi-objective optimization problem (MOP). This paper proposes a new nature-inspired optimization algorithm which is named opposition-based multi-objective whale optimization algorithm with global grid ranking (MOWOA). The proposed approach utilizes several parts to enhance the performance in optimization. First, the efficient evolution process is inherited from the single objective whale optimization algorithm(WOA). Second, opposition-based learning(OBL) is applied into the algorithm. Meanwhile, a novel mechanism called global grid ranking(GGR) which is inspired by grid mechanism has been incorporated into the proposed algorithm. To show the significance of the proposed algorithm, MOWOA is tested on a diverse set of benchmark with a series of well-known evolutionary algorithms and the influence of each individual strategy is also verified through 14 benchmarks. Moreover, the new proposed algorithm is also applied to the simple data clustering problem and a real-world water optimization problem in China. The results demonstrate that MOWOA is not only an algorithm with well performance for bench-mark problems but also expected to have a more wide application in real-world engineering problems. [ABSTRACT FROM AUTHOR]

Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems. [ABSTRACT FROM AUTHOR]

Nano-networks are composed of interconnected nano-nodes and can enable unprecedented applications in various fields. Due to the peculiarities of nano-networks, such as high density, extremely limited energy and computational resources, traditional carrier-sensing based Media Access Control (MAC) protocols are not suitable for nano-networks. In this paper, a Slot Self-Allocation based MAC protocol (SSA-MAC) is proposed for energy harvesting nano-networks. Two transmission schemes for centralized and distributed nano-networks are designed, respectively. In centralized nano-networks, nano-nodes can only send packets to the nano-controller in their Self-Allocation Slots (SASs), while, in distributed nano-networks, nano-nodes can only receive packets from surrounding nano-nodes in their SASs. Extensive simulations were conducted to compare the proposed SSA-MAC with PHysical LAyer aware MAC (PHLAME), Receiver-Initiated Harvesting-aware MAC (RIH-MAC) and Energy Efficient Wireless NanoSensor Network MAC (EEWNSN). From the results, it can be concluded that the proposed SSA-MAC achieves better performance and can reduce the collision probability, while improving the energy efficiency of nano-networks. [ABSTRACT FROM AUTHOR]

A Gram-stain-negative, milky white, aerobic, rod-shaped bacterium named strain H3-26T was isolated from gills of Oncorhynchus mykiss in Lhasa, Tibet Autonomous Region, PR China. Strain H3-26T grew at 4–30 °C and pH 5.0–11.0 (optimum, 25 °C and pH 7.0) with 0–1% (w/v) NaCl (optimum, 0%). The 16S rRNA gene sequence of strain H3-26T showed the highest similarity to Deefgea rivuli WB 3.4-79T (98.42%), followed by Deefgea chitinilytica Nsw-4T (96.91%). Phylogenetic analysis based on 16S rRNA genes indicated that strain H3-26T was a new member of the genus Deefgea. The digital DNA–DNA hybridization and average nucleotide identity values between the genome sequence of strain H3-26T and Deefgea spp. were 21.2–21.9% and 76.3–77.4%, respectively. The genomic DNA G+C content of strain H3-26T was 48.74%. The predominant fatty acids were C12:0, C14:0, C16:0 and C16:1 ω7c. Based on phenotypic, phylogenetic, and genotypic data, strain H3-26T is considered to represent a novel species of the genus Deefgea, for which the name Deefgea salmonis sp. nov. is proposed. The type strain is H3-26T (= JCM 35050T = CICC 25103T). [ABSTRACT FROM AUTHOR]

Many real-world networks are embedded in spaces. Recent studies have found that spatial characteristics are closely related to network features. Bus transport networks (BTNs) are typical spatially embedded networks, but their spatial characteristics are commonly disregarded in previous researches. In this paper, we propose a new spatial representation model for BTNs with information on the geographical location of bus stations and routes, for which we named as the ES model. The new model aids in the study of real-world BTNs. By performing a statistical study with the new representation model on three typical BTNs in China, namely the Beijing, Shanghai and Hangzhou BTNs, we identify some network features that are consistent with those revealed by previous studies, as well as some new features such as high clustering of short-distance station pairs (SSPs) and small average number of bus routes in a path. The result shows that the existence of SSPs can significantly influence the characteristics of BTNs. Besides, with the help of the ES model, we designed a new transfer algorithm for BTNs. [ABSTRACT FROM AUTHOR]