Benefiting from high flexibility and probability of line-of-sight, deploying unmanned aerial vehicles (UAV s) as aerial access points has emerged as a promising solution for ensuring reliable wireless connectivity in crowded events. This paper introduces a UAV scheduling process adaptive to dynamic mobile network demand, including three phases. In the sensing phase, the user distribution is sensed, and user number thresholds are set to determine whether UAV assistance is needed. The planning phase presents an enhanced mean shift algorithm to find suitable locations to deploy UAVs with a dynamic bandwidth derived from the user distribution, the UAV's maximum capacity, and the UAV's maximum throughput. The deploying phase dispatches and recalls UAV s based on planning results. Comprehensive simulation experiments are conducted on OMNeT ++ using real-world data. Results show that the proposed process shows great adaptivity, with an efficiency increase of 18.7% and a fairness increase of 28.9 % compared to the existing related works on average.

Industrial Internet of Things (IIoT), regarded as an important technology for Industry 4.0, has the capability to connect massive IoT devices anywhere and at anytime in manufacturing industry. Enabling such a huge network requires message delivering among sensors, actuators, controllers, and the remote control to be seamless and reliable. However, IIoT wireless environment typically faces challenges such as blockage caused by IoT obstacles. To tackle the above issue, the unmanned aerial vehicle (UAV) and the reconfigurable intelligent surface (RIS) are exploited in this paper, which can provide favorable air-to-ground links and further rebuild the wireless channels. Moreover, the device-to-device (D2D) communication technique is introduced to enable direct information exchange between IoT devices. Specifically, we consider both the communication between the UAV and the cellular users (e.g., fixed IoT infrastructures) as well as the communication between D2D users (e.g., mobile IoT devices). Instead of only considering throughput, we focus on energy efficiency optimization for D2D users while guaranteeing the quality of service for cellular users, since energy-efficient transmission or green communication is important for IIoT scenarios. The transmit power, channel allocation parameters, and RIS’s reflection coefficients are jointly optimized to maximize energy efficiency for D2D users. To solve the formulated optimization problem, both centralized and distributed optimization algorithms based on deep neural networks are provided. Simulation results show that the introduction of RIS can significantly improve system performance. Moreover, the proposed algorithms can approximate the optimal solutions without the need of exhaustive search.

With the advantages of high mobility and flexibility, unmanned aerial vehicles (UAVs) have recently deployed as aerial base stations (ABSs) to expand the network capacity [1, 2] and as relays to link users and nearby base stations [5], thus assisting wireless communication. In modern cities, where apparent peaks and valleys of travel exist, mobile networks demand change can be theatrical.

SUMMARY Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results. Copyright © 2011 John Wiley & Sons, Ltd.

To date, comparing and visualizing genome sequences remain challenging due to the large genome size. Existing approaches take advantage of the stable property of oligonucleotides and exhibit the main characteristics of the whole genome, yet they commonly fail to show progression patterns of the genome adjustably. This paper presents a novel visual encoding technique, which not only supports the binning process (phylogenetic analysis), but also allows the sequential analysis of the genome. The key idea is to regard the combination of each k -nucleotide and its reverse complement as a visual word, and to represent a long genome sequence with a list of local statistical feature vectors derived from the local frequency of the visual words. Experimental results on a variety of examples demonstrate that the presented approach has the ability to quickly and intuitively visualize DNA sequences, and to help the user identify regions of differences among multiple datasets.

Osteoporosis, which is characterized by reduced bone mass and debilitating fractures, may reach epidemic proportions with the aging of the US population. The intensity of research in this field of study is reflected by the facts that The American Society of Bone and Mineral Research has a membership of nearly 4,000 physicians, clinical investigators, and basic research scientists from over fifty countries and that NIH is expected to spend over 200 million dollars on osteoporosis research alone in 2010. Bone biologists may be overwhelmed by the amount of literature constantly being generated, thus the identification and extraction of existing and novel relationships among biological entities or terms appearing in the biological literature is an ongoing problem. The problem has become more pressing with the development of large online publicly available databases of biological literature. Extraction and visualization of relationships between biological entities appearing in these databases offers the opportunity of keeping researchers up-to-date in their research domain. This may be achieved through helping them visualize possible biological pathways and by generating likely new hypotheses concerning novel interactions through methods such as transitive closure network flow. All generated predictions can be verified against already existing data, and possible new relationships can be verified against experiment. This paper presents a method for the extraction and visualization of potentially meaningful relationships.