Alcohol withdrawal syndrome (AWS) is a complex set of symptoms that occur in alcohol-dependent individuals after sudden withdrawal or a significant reduction in alcohol consumption. AWS symptoms ...occur in about 50% of alcohol abusers. These symptoms may include restlessness, tremor, nausea, nervousness, tachycardia, elevated blood pressure, hyperhidrosis, insomnia, hyperactivity, and hallucinations. In some cases, seizures may occur and delirium tremens may develop, which is life-threatening and an absolute indication for hospitalization of the patient. Effective treatment of AWS is the key to prevent complications and to reduce the risk of death. Treatment of alcohol withdrawal syndrome requires a complex approach that combines properly performed diagnosis, careful monitoring of the patient's condition, pharmacotherapy, equalization of electrolyte disorders, adequate hydration of the patient, supplementation of thiamine deficiencies and, in the case of symptoms of alcoholic delirium, intensive medical care. Pharmacological treatment plays a key role, with the first line of treatment being benzodiazepines, which reduce the risk of epileptic seizures and delirium tremens, and reduce mortality in the course of AWS. Individualized therapy adjustment and patient monitoring are crucial to ensure effective and safe treatment.
Serverless computing has recently been presented as an effective technology for handling short-lived compute tasks in the cloud. It has the potential of becoming an attractive option also in the ...context of edge computing where resource-aware deployment, constrained by both limited edge computing resources and experienced latency, plays a vital role.
In this paper, we present and experimentally validate a framework that oversees serverless applications in an edge computing scenario. It completely automates serverless application deployment and provides hitless dynamic migration of application compute tasks between a pair of edge nodes, paving the way for handling significantly more complex cases. The framework relies on an integrated deployment, monitoring and offloading infrastructure that enhances AWS IoT Greengrass features and performance. Our implementation provides two separate options for relocating compute tasks by steering application traffic towards the most suitable node. One builds on an on-the-fly application component reconfiguration, while the other selects the suitable node through P4 in-network processing of resource metrics emitted by the nodes.
Our experimental demonstration evaluates the migration performance using a latency-sensitive application decomposed to serverless functions. Results reveal extremely fast dynamic reconfiguration and traffic rerouting operations. The used methods avoid congestion peaks at the edge and show no end-to-end latency increase upon migration between the nodes.
•Completely automated serverless application deployment in edge computing.•Hitless dynamic migration of application compute tasks.•On-the-fly application component reconfiguration.•Task offloading using P4 in-network processing of edge node resource metrics.•Avoiding congestion peaks, application latency increase upon migration at the edge.
Abstract
IoT concepts are heavily applicable in drone communication integrated with different network architecture for optimization. Distributing the burden allows more IoT devices to execute ...calculations, rather than everything being done on the cloud. There are numerous IoT designs that have emerged as a result of this. By relocating calculations away from the cloud, these designs make use of the enhanced processing capacity of the devices. Based on our needs, we’ve limited it down to four architectures, each of which we have discussed for optimized flow useful in drone technology. We have also applied the one live dataset for the test drone using raspberry pi processor system powered with for end-to-end drone communication establishment. The analysis of downlink and uplink were studied for time analysis for IoT architecture using drone cell characteristics. New technology makes it possible to implement drone cell (DC) connectivity, which is highly flexible and cost-effective for the gathering of Internet-of-things (IoT) data when terrestrial networks are not yet accessible. DC’s flight path has a substantial impact on data collecting systems.
The use of the Amazon Web Services cloud enables new functionalities that are not possible with traditional solutions: low latency, local data processing and storage, and direct connectivity to other ...cloud services. Reimagining the way IoT connectivity services are presented by combining AWS cloud technology with mobile connectivity offers rapid prototyping to help connect devices natively over Wi-Fi. For this, the MQTT commu-nication protocol is used to interact with the IoT device and exchange data, which allows controlling the basic functions of a sensor node. The installation is realized through a software development kit (SDK), which allows the creation of an application for Android devices. This solution gives the option to integrate together, improving the connectivity of the IoT system. The results enable board logging and network config-uration, and can also be used to control the IoT device. The embedded firmware provides the required security functions.
Abstract
Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid ...fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get real time stock prediction using GPU and Memory containers. Stock predictor is architected using GRPC based clean architecture which has the benefits of easy updates, addition of new services with reduced integration costs. Data archive features of the cloud will give benefit of reduced cost of the new stock predictor framework.
New architectural patterns (e.g. microservices), the massive adoption of Linux containers (e.g. Docker containers), and improvements in key features of Cloud computing such as auto-scaling, have ...helped developers to decouple complex and monolithic systems into smaller stateless services. In turn, Cloud providers have introduced serverless computing, where applications can be defined as a workflow of event-triggered functions. However, serverless services, such as AWS Lambda, impose serious restrictions for these applications (e.g. using a predefined set of programming languages or difficulting the installation and deployment of external libraries). This paper addresses such issues by introducing a framework and a methodology to create Serverless Container-aware ARchitectures (SCAR). The SCAR framework can be used to create highly-parallel event-driven serverless applications that run on customized runtime environments defined as Docker images on top of AWS Lambda. This paper describes the architecture of SCAR together with the cache-based optimizations applied to minimize cost, exemplified on a massive image processing use case. The results show that, by means of SCAR, AWS Lambda becomes a convenient platform for High Throughput Computing, specially for highly-parallel bursty workloads of short stateless jobs.
•A framework to run containerized applications in serverless computing is proposed.•Containers out of Docker images can now be run on AWS Lambda.•Highly-parallel event-driven file-processing serverless computing is introduced.•An analysis of the Freeze/Thaw cycle of AWS Lambda and caching is assessed.•Bursty workloads of short stateless jobs can benefit from serverless computing.
The goal of this work is to elaborate a lightweight solution to manage multiple Amazon Web Services (AWS) accounts in one place. Currently, no such system is available. In this paper, we present a ...system that solves this task and allows to visualise the usage of AWS services across a number of customer’s accounts. This system can be also used to enable optimisation cost and performance of the AWS services across the customer’s accounts.
Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data ...Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.
•Assimilating AWS data enhances the model performance with any assimilation setup.•Higher assimilation frequency does not guarantee better performance.•Winds play the most important role in improving the precipitation forecast.
Electromagnetic waves and atmospheric gravity waves (AGWs) attributed to thunderstorm/lightning events can alter the ionospheric conductivity and produce total electron content (TEC) fluctuations at ...acoustic and gravity wave frequencies. The present study investigates the thunderstorm-induced E-and F- region irregularities over the near-equatorial Indian sector on 16 th October 2019 and 9 th April 2022, based on the observations from a 205 MHz VHF radar at Cochin (10.04° N, 76.33° E), India and Global Navigation Satellite System (GNSS) receiver at a nearby station, Changanacherry (9.44° N, 76.55° E). Concurrent with the thunderstorm activity, E-region irregularities were observed on both the days following the lightning strikes. Lightning-induced sporadic E (Es) with a vertical width of 2 km was observed on 9 th April 2022 and lasted for two hours longer than the case observed on 16 th October 2019. Subsequent to the disappearance of Es, quasi-periodic irregularities spanning from 60 to 550 km altitude were observed. Lightning strikes induced wave like fluctuations in TEC with subsequent enhancements of amplitude scintillations. Vertically propagating AGW-induced ionospheric acoustic waves (AWs) of ~4 min periodicity on 16 th October 2019 and ~3 min periodicity on 9 th April 2022 were observed. Gravity wave (GW) periodicity of 12-13 min became prominent at ionospheric heights after 1-2 hours of the lightning strike. The mesoscale convective system with multiple cells developed over the Indian-near equatorial sector could have caused the early AWs and delayed GWs. This observation showcases the potential application of ground-based systems in exploring the lower atmosphere-ionosphere coupling process during thunderstorms.
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