Abstract Cloud computing environments encounter significant challenges in resource management through queueing and scheduling systems, as traditional methods struggle with dynamic workload optimization. This research introduces an innovative AI-enhanced framework combining deep prediction reinforcement learning for scheduling. The features a dual-layer neural network architecture hybrid decision engine that merges conventional metrics learned policies. Experimental results simulated cloud...

<title>Abstract</title> Wireless Sensor Networks (WSNs) face numerous security challenges due to their limited resources, unsupervised operation, and reliance on broadcast transmission. Traditional systems often struggle detect mitigate complex threats effectively. This study introduces an innovative methodology leveraging artificial intelligence enhance the of WSNs. By employing machine learning algorithms such as neural networks, support vector machines, random forests, deep we develop...

The fast expansion of mobile networks has sparked worries regarding base station EM radiation's health impacts. Traffic load is commonly ignored when evaluating radiation levels using maximum power output. This study proposes utilizing AI and ML on real network traffic data to optimize GSM estimations. We obtained measurements from selecting stations by location configuration. To predict levels, patterns were used train linear regression, random forests, neural networks. Base clustered...

Exorbitant energy expenses can supersede data center profits. Electricity prices often vary across the geographic regions, caused by gaps in supply-demand, time of use, and production cost factors. Geo-distributed cloud centers facilitated a smart grid enabled computing potentially utilize spatiotemporal diversity to reduce operational expenditure maximize profit. In this article, we solve profit formulating it as constrained multi-objective optimization problem. The proposed solution...

Overwhelming energy-related costs mar data center profits. In a smart grid, the price of electricity may change with real-time demand, geographic area, and time-of-use. Data centers flexible request dispatch resource allocation capabilities can cooperatively avail these variations to reduce expenditures maximize profit. this paper, we model profit maximization as constrained multi-objective optimization problem. Our proposed scheme optimizes revenue expense objectives simultaneously best our...

Challenge of efficient protocol design for energy constrained wireless sensor networks is addressed through application specific cross-layer designs. This approach along with strong assumptions limits protocols in universal scenarios and affects their practicality. With proliferation embedded mobile sensors consumer devices, a changed paradigm requires generic capable managing greater device heterogeneousness mobility. In this paper, we propose novel lifetime maximization uncontrolled...

Extreme weather events cause widespread power outages that affect critical infrastructures and other communities alike. Microgrids donate or trade their surplus capacity the grid can use to up load do not have alternatives arrangements. Service restoration based on energy donation during a crisis is viable highly effective option. We propose novel service framework using in an islanded distribution system involving multi microgrids. The proposed algorithm uses priority users' historical...

<title>Abstract</title> The fast expansion of mobile networks has sparked worries regarding base station EM radiation's health impacts. Traffic load is commonly ignored when evaluating radiation levels using maximum power output. This study proposes utilising AI and ML on real network traffic data to optimise GSM estimations. We obtained measurements from a selection stations by location configuration. patterns were used train linear regression, random forests, neural predict levels. Base...

<title>Abstract</title> Background Various radiobiological models aim to estimate crucial tumor cell-killing effects for radiotherapy and radiation risk assessment, each with unique applications. This paper presents a specific probabilistic model predicting control probability (TCP) introduces user-friendly standalone simulation app tailored this purpose. Methods A pragmatic is suggested estimating by incorporating fractionated treatment approach. Within model, ionizing induces the formation...

Natural hazards disrupt power networks hampering societal services and individual lives. Microgrids donate or trade their surplus capacity that the grid can use to up critical load communities do not have alternative arrangements. This article proposes a blockchain approach for service restoration in distribution network using energy donation aftermath of disaster. The proposed approach's key element is novel consensus mechanism called Proof Welfare (PoWel). protocol enables prioritized...

This paper presents a novel and highly miniaturized single-layer frequency selective surface (FSS) for RF shielding. The FSS unit cell is comprised of meandered crossed dipole structure designed over low-profile laminate. achieves shielding effectiveness 43 dB at the 10 GHz frequency. Moreover, it provides fractional bandwidth 34% TE 46% TM mode normal incidence. In addition, exhibits stable spectral responses wide range oblique angles vertical horizontal polarization states. compact has...

Natural hazards and technical malfunctions can cause widespread outages of power networks, adversely affecting communities infrastructures. Microgrids with distributed generation storage help mitigate some these devastating effects. However, not many infrastructures have alternative mechanisms. When needed, microgrids may needy neighbors or critical communities, such as hospitals, by donating trading surplus capacity. Energy donation in a smart grid is viable highly effective restoration...