Unified Pandemic Tracking System Based on Open Geospatial Consortium SensorThings API

With the current nations struggling to track the pandemic's trajectories. There has been a lack of transparency or real-live data streaming for pandemic cases and symptoms. This phenomenon has led to a rapid and uncontrolled spread of these deadly pandemics. One of the main issues in creating a global pandemic tracking system is the lack of standardization of communications protocols and the deployment of Internet-of-Things (IoT) device sensors. The Open Geospatial Consortium (OGC) has developed several sensor web Enablement standards that allow the expeditious deployment of communications protocols within IoT devices and other sensor devices like the OGC SensorThings application programming interface (API). In this paper, to address this issue, we outline the interoperability challenge and provide a qualitative and quantitative study of the OGC SensorThings API's deployment and its respective server. The OGC SensorThings API is developed to provide data exchange services between sensors and their observations. The OGC SensorThings API would play a primary and essential role in creating an automated pandemic tracking system. This API would reduce the deployment of any set of sensors and provide real-time data tracking. Accordingly, global health organizations would react expeditiously and concentrate their efforts on high infection rates

Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks

Autonomous Vehicles (AVs), furnished with sensors capable of capturing essential vehicle dynamics such as speed, acceleration, and precise location, possess the capacity to execute intelligent maneuvers, including lane changes, in anticipation of approaching roadblocks. Nevertheless, the sheer volume of sensory data and the processing necessary to derive informed decisions can often overwhelm the vehicles, rendering them unable to handle the task independently. Consequently, a common approach in traffic scenarios involves transmitting the data to servers for processing, a practice that introduces challenges, particularly in situations demanding real-time processing. In response to this challenge, we present a novel DL-based semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process. Specifically, our framework envisions scenarios where abrupt roadblocks materialize due to factors such as road maintenance, accidents, or vehicle repairs, necessitating vehicles to make determinations concerning lane-keeping or lane-changing actions to navigate past these obstacles. To formulate this scenario mathematically, we employ a Markov Decision Process (MDP) and harness the Deep Q Learning (DQN) algorithm to unearth viable solutions

Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques

A sudden roadblock on highways due to many reasons such as road maintenance, accidents, and car repair is a common situation we encounter almost daily. Autonomous Vehicles (AVs) equipped with sensors that can acquire vehicle dynamics such as speed, acceleration, and location can make intelligent decisions to change lanes before reaching a roadblock. A number of literature studies have examined car-following models and lane-changing models. However, only a few studies proposed an integrated car-following and lane-changing model, which has the potential to model practical driving maneuvers. Hence, in this paper, we present an integrated car-following and lane-changing decision-control system based on Deep Reinforcement Learning (DRL) to address this issue. Specifically, we consider a scenario where sudden construction work will be carried out along a highway. We model the scenario as a Markov Decision Process (MDP) and employ the well-known DQN algorithm to train the RL agent to make the appropriate decision accordingly (i.e., either stay in the same lane or change lanes). To overcome the delay and computational requirement of DRL algorithms, we adopt an MEC-assisted architecture where the RL agents are trained on MEC servers. We utilize the highly reputable SUMO simulator and OPENAI GYM to evaluate the performance of the proposed model under two policies; {\epsilon}-greedy policy and Boltzmann policy. The results unequivocally demonstrate that the DQN agent trained using the {\epsilon}-greedy policy significantly outperforms the one trained with the Boltzmann policy

Influence of Concrete Jacketing on the Performance of Steel Columns under Blast induced Progressive Collapse

This study investigates the influence of concrete jacketing on the performance of steel columns subjected to blast loading, which can lead to progressive collapse. Using Finite Element Method (FEM) simulations, the research evaluates concrete-encased steel columns with different concrete cover thicknesses to measure their resistance to lateral displacements induced by blasts. Findings reveal that increasing the thickness of the concrete cover markedly improves the column\u27s ability to withstand such loads. The study also highlights that steel has better energy dissipation properties than concrete. It examines how the combined challenges of progressive collapse and blast loading influence the overall structural response of a building, identifying potential failure points in structural members. This research emphasizes the need to integrate considerations of both blast resistance and progressive collapse potential into structural design, aiming to enhance the performance and resilience of steel columns and contribute to the development of more robust structural systems capable of surviving extreme events and reducing the risk of catastrophic failure

Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks

The vision of the upcoming 6G technologies, characterized by ultra-dense network, low latency, and fast data rate is to support Pervasive AI (PAI) using zero-touch solutions enabling self-X (e.g., self-configuration, self-monitoring, and self-healing) services. However, the research on 6G is still in its infancy, and only the first steps have been taken to conceptualize its design, investigate its implementation, and plan for use cases. Toward this end, academia and industry communities have gradually shifted from theoretical studies of AI distribution to real-world deployment and standardization. Still, designing an end-to-end framework that systematizes the AI distribution by allowing easier access to the service using a third-party application assisted by a zero-touch service provisioning has not been well explored. In this context, we introduce a novel platform architecture to deploy a zero-touch PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart system. This platform aims to standardize the pervasive AI at all levels of the architecture and unify the interfaces in order to facilitate the service deployment across application and infrastructure domains, relieve the users worries about cost, security, and resource allocation, and at the same time, respect the 6G stringent performance requirements. As a proof of concept, we present a Federated Learning-as-a-service use case where we evaluate the ability of our proposed system to self-optimize and self-adapt to the dynamics of 6G networks in addition to minimizing the users' perceived costs.Comment: IEEE Communications Magazin

Seminavis aegyptiaca sp. nov., a new amphoroid diatom species from estuary epilithon of the River-Nile Damietta Branch, Egypt

[EN] During a recent floristic–taxonomic study on the algal flora, including diatoms, from the estuary of the Damietta Branch of the Nile in Egypt, an interesting epilithic diatom species belonging to the genus Seminavis (Naviculaceae) was collected and investigated using both light and scanning electron microscopy. This new diatom species shares morphologically some taxonomic diagnostic features with other related taxa such as S. insignis, S. robusta, and S. ventricosa. However, it still differs by having ventral central striae that are shorter and more or less straight in the middle of the smaller frustules to be clearly radiate in the larger ones and then become geniculate and only radiate near the poles, the central raphe endings are externally more distantly spaced than in the similar species, the elongate central nodule is internally less prominent, and the areola density is much denser. Therefore, we here describe it as Seminavis aegyptiaca sp. nov. Hydrochemical analyses revealed that S. aegyptiaca commonly inhabits typical marine, with a weak tendency towards brackish water, habitats. It was found to be tolerant to meso–eutrophic, nutrient–enriched conditions, based on the data available on seasonal concentrations of N and P compounds. These findings not only contribute to the inventory of Egyptian diatoms, but also increase our understanding of the autecology and distribution of this relatively poorly–known diatom genusSIThis work was a part of the PhyBiO project funded by the Italian Ministry of Foreign Affairs and International Cooperation (MAECI) to the MUSE Post–Doc Abdullah A. Saber for the academic year 2018/201

A polishing the harmful effects of Broad Bean Mottle Virus infecting broad bean plants by enhancing the immunity using different potassium concentrations

Broad bean mottle virus (BBMV) infects a wide range of hosts, resulting in significant production reductions. The lack of adequate and effective control methods involves implementing novel BBMV control strategies. Herein, we demonstrate the effect of different potassium concentrations (20, 40, and 60 mM) against BBMV in broad bean plants. Potassium could control BBMV infection in broad bean by inhibiting the virus. In addition, infection with BBMV caused a significant decrease in morphological criteria, SPDA, photosynthetic characteristics, phytohormones, and mineral content in broad bean leaves compared to control plants. The levels of reactive oxygen species (ROS) (hydrogen peroxide, hydroxyl radical, and oxygen anion) and ROS scavenging enzymes (catalase, superoxide dismutase, peroxidase, phenylaniline ammonia-lyase, chitinase, and 1,3 - glucanase) increased significantly in plants inoculated with BBMV alone or in the presence of K+. In addition, proline and phenolic compounds increased significantly after being infected with BBMV. In conclusion, treatment with a high potassium concentration (60 mM) alleviates the adverse effect of BBMV on broad bean plants by boosting secondary metabolites, phytohormones, and enzymatic antioxidants