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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]

Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field. [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]

The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. [ABSTRACT FROM AUTHOR]

Abstract: We abstract bus transport networks (BTNs) to complex networks using the Space P approach. First, we select three actual BTNs in three major cities in China, namely, Beijing, Shanghai and Hangzhou. Using the SIS model, we simulate and study the epidemic spreading in the three BTNs. We obtain the density of infected vertices varying with time and the stationary density of infected vertices varying with infection rate. Second, we simulate and study the epidemic spreading in a recently introduced BTN evolution model, the network properties of which correspond well with those of actual BTNs. Third, we use mean-field theory to analyze the epidemic dynamics behavior of the BTN evolution model and obtain the theoretical epidemic threshold of this model. The theoretical value agrees well with the simulation results. Based on the work in this paper, we provide the following possible forecasts for epidemic dynamics in actual BTNs. An actual BTN should have a finite positive epidemic threshold. If the effective infection rate is above this threshold, the epidemic spread in the network and the density of infected vertices finally stabilizes in a balanced state. Below this threshold, the number of infected vertices decays exponentially fast and the epidemic cannot spread on a large scale. [Copyright &y& Elsevier]

Abstract: We propose an ideal -depth clique network model. In this model, the original network is composed of cliques (maximal complete subgraphs) that overlap with each other. The network expands continuously by the addition of new cliques. The final diameter of the network can be set in advance, namely, it is controllable. Assuming that the diameter of the network is , the network exhibits a logistic structure with layers. In this structure, the 0th layer represents the original network and each node of the th layer corresponds to a clique in the th layer. In the growth process of the network, we ensure that any th layer network is composed of overlapping cliques. Any node in an th layer network corresponds to an -depth community in the original network, and the diameter of an -depth community is . Therefore, the th layer network will contain only one clique, the th layer network will contain only one node, and the diameter of the corresponding original network is . Then an ideal -depth clique network will be obtained. Based on the ideal -depth clique network model, we construct a bus transport network model with an ideal -depth clique network topology (ICNBTN). Moreover, our study compares this model with the real bus transport network (RealBTN) of three major cities in China and a recently introduced bus transport network model (BTN) whose network properties correspond well with those of real BTNs. The network properties of the ICNBTN are much closer to those of the RealBTN than those of the BTN are. At the same time, the ICNBTN has higher clustering extent of bus routes, smaller network diameter, which corresponds to shorter maximum transfer times in a bus network, and lower average shortest path time coefficient than the BTN and the RealBTN. Therefore, the ICNBTN can achieve higher transfer efficiency for a bus transport system. [Copyright &y& Elsevier]

The permutation flow-shop scheduling problem (PFSSP) is a typical combinational optimization problem, which is of wide engineering background and has been proved to be strongly NP-hard. In this paper, a hybrid algorithm based on differential evolution (DE), named HDE, is proposed for the single-objective PFSSPs. Firstly, to make DE suitable for solving PFSSPs, a largest-order-value (LOV) rule is presented to convert the continuous values of individuals in DE to job permutations. Secondly, after the DE-based exploration, a simple but efficient local search, which is designed according to the PFSSPs’ landscape, is applied to emphasize exploitation. Thus, not only does the HDE apply the parallel evolution mechanism of DE to perform effective exploration (global search), but it also adopts problem-dependent local search methodology to adequately perform exploitation (local search). Based on the theory of finite Markov chains, the convergence property of the HDE is analyzed. Then, the HDE is extended to a multi-objective HDE (MHDE) to solve the multi-objective PFSSPs. Simulations and comparisons based on benchmarks for both single-objective and multi-objective PFSSPs are carried out, which show the effectiveness, efficiency, and robustness of the proposed HDE and MHDE. [ABSTRACT FROM AUTHOR]