1,725,882 research outputs found
Bacteriophages as a model for studying carbon regulation in aquatic system
Full text linkThe interconversion of carbon in organic, inorganic and refractory carbon is still beyond the grasp of present environmentalists. The bacteria and their phages, being the most abundant constituents of the aquatic environment, represent an ideal model for studing carbon regulation in the aquatic system. The refractory dissolved organic carbon (DOC), a recently coined terminology from the microbe-driven conversion of bioavailable organic carbon into difficult-to-digest refractory DOC by microbial carbon pump (MCP), is suggested to have the potential to revolutionize our view of carbon sequestration. It is estimated that about 95% of organic carbon is in the form of refractory DOC, which is the largest pool of organic matter in the ocean. The refractory DOC is supposed to be the major factor in the global carbon cycle whose source is not yet well understood. A key element of the carbon cycle is the microbial conversion of dissolved organic carbon into inedible forms. The time studies of phage-host interaction under control conditions reveal their impact on the total carbon content of the source and their interconversion among organic, inorganic and other forms of carbon with respect to control source. The TOC- analysis statistics stipulate an increase in inorganic carbon content by 15-25 percent in the sample with phage as compared to the sample without phage. The results signify a 60-70 fold increase in inorganic carbon content in sample with phage, whereas, 50-55 fold in the case of sample without phages as compared with control. This increase in inorganic carbon content may be due to lysis of the host cell releasing its cellular constituents and utilization of carbon constituent for phage assembly and development. It also proves the role of phages in regulating the carbon flow in aquatic systems like oceans, where their concentration outnumbered other species
Antipowers in Uniform Morphic Words and the Fibonacci Word
No full textFici, Restivo, Silva, and Zamboni define a -antipower to be a word
composed of pairwise distinct, concatenated words of equal length. Berger
and Defant conjecture that for any sufficiently well-behaved aperiodic morphic
word , there exists a constant such that for any and any index ,
a -antipower with block length at most starts at the th position of
. They prove their conjecture in the case of binary words, and we extend
their result to alphabets of arbitrary finite size and characterize those words
for which the result does not hold. We also prove their conjecture in the
specific case of the Fibonacci word
Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges
Full text linkModern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of the most important in modern cities, introducing several challenges to the Smart City paradigm. In order to satisfy the stringent requirements of new vehicular applications and services, Edge Computing (EC) is one of the most promising technologies when integrated into the Vehicular Networks (VNs). EC-enabled VNs can facilitate new latency-critical and data-intensive applications and services. However, ground-based EC platforms (i.e., Road Side Units—RSUs, 5G Base Stations—5G BS) can only serve a reduced number of Vehicular Users (VUs), due to short coverage ranges and resource shortage. In the recent past, several new aerial platforms with integrated EC facilities have been deployed for achieving global connectivity. Such air-based EC platforms can complement the ground-based EC facilities for creating a futuristic VN able to deploy several new applications and services. The goal of this work is to explore the possibility of creating a novel joint air-ground EC platform within a VN architecture for helping VUs with new intelligent applications and services. By exploiting most modern technologies, with particular attention towards network softwarization, vehicular edge computing, and machine learning, we propose here three possible layered air-ground EC-enabled VN scenarios. For each of the discussed scenarios, a list of the possible challenges is considered, as well possible solutions allowing to overcome all or some of the considered challenges. A proper comparison is also done, through the use of tables, where all the proposed scenarios, and the proposed solutions, are discussed
Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems
Full text linkSupported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance
A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks
Full text linkVehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy
Pervasive Health Systems: Convergence through Artificial Intelligence and Blockchain Technologies
No full textIntegrated Aerial-Ground Computation Offloading for Dependency-Aware IoV Multitask Services
No full textThe Internet of Vehicles (IoV) is a fundamental paradigm for enabling intelligent transportation systems and promoting high-quality services and applications that require a tremendous amount of data processing resources. In this paper, we consider a computational offloading problem on an Integrated Aerial-Ground (IAG) Edge Computing (EC) architecture, where each task is modeled as a chain of dependent subtasks. To solve the problem, a V2X-based Computation and Communication-efficient Multitask Offloading Approach (CCMTOA) is proposed in which mutual information is exchanged between users, allowing one to effectively solve the multitask multilayer network selection problem. The parameters of vehicle mobility are estimated using a realistic intelligent mobility model. The numerical results with varying VU density show the effectiveness of the proposed method over the benchmark approaches
Redescription of Ploiaria mellea (Heteroptera: Reduviidae: Emesinae) and its first report from India
No full textBoyane, Swapnil S., Ghate, Hemant V. (2020): Redescription of Ploiaria mellea (Heteroptera: Reduviidae: Emesinae) and its first report from India. Zootaxa 4729 (4): 595-600, DOI: 10.11646/zootaxa.4729.4.1
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