Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…The research addresses these challenges by developing a green, energy-aware video transcoding system that predicts energy availability from renewable sources (solar and wind) using machine learning techniques and optimizes tasks allocation. …”
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Resource allocation mechanism in the TWDM-PON and C-RAN joint architecture with hybrid energy supply by Ruyan WANG, Ningning XU

“…Aiming at the problems of low resource utilization rate,high energy consumption and poor user service quality in the existing virtualized Cloud Radio Access Network,an energy-aware virtualized resource allocation mechanism with hybrid energy supply was proposed.According to the energy sources and energy consumption of different network devices,energy arrival and energy consumption models were established.Furthermore,under the premise of guaranteeing the quality of user services,considering proportional fairness and energy consumption optimization,distributed algorithms based on asynchronous update were used to allocate resources and harvested energy for different types of virtual cloud radio access networks and user virtual base stations to effectively improve the energy efficiency of network.The simulation results show that the proposed resource allocation mechanism can reduce energy consumption while effectively reducing the latency and improving the throughput.…”
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Memory Monitoring Recognition Test (MMRT), a new measurement of stimular source monitoring: Software and comprehension. by Pedro C Martínez-Suárez, José Alejandro Valdevila Figueira, Joselyn M Luna-Cambi, Carlos E Guerrero-Granda, Rocío Valdevila Santiesteban

“…<h4>Background</h4>Reality monitoring allows the evaluation and monitoring of reality through the assignment of information to internal or external sources, which is crucial to differentiate real events from imaginary ones. …”
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Phenology-Aware Transformer for Semantic Segmentation of Non-Food Crops from Multi-Source Remote Sensing Time Series by Xiongwei Guan, Meiling Liu, Shi Cao, Jiale Jiang

“…PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. …”
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Speculation resource provisioning in high-performance computing by Leena Sri, Balaji Narayanan

“…We have conveyed our proposed work in an open source cloud structure and contrasted our outcomes and other machine learning approaches. …”
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Processing streams in a monitoring cloud cluster by Alexey N. Nazarov

“…Based on the poly-burst nature of the bit rate model, a model of group content traffic of any sources of different services processed in the cloud cluster was created. …”
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Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability by Attuluri R. Vijay Babu, N. Bharath Kumar, Rajanand Patnaik Narasipuram, Soundhar Periyannan, Alireza Hosseinpour, Aymen Flah

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Application Placement in the Cloud Continuum With Resource Overbooking by Ivan Mongiardo, Luisa Massari, Maria Carla Calzarossa, Belen Bermejo, Daniele Tessera

“…For this purpose, our heuristic tries to allocate application modules as close as possible to their data sources. …”
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Cloud-edge MQTT messaging for latency mitigation and broker memory footprint reduction by Yi-Hsuan Tseng, Chao Wang, Yu-Tse Wei, Yu-Ting Chiang

“…The article introduces three message scheduling policies for subscribers deployed at network edge, and a memory allocation scheme for MQTT broker deployed at network cloud. …”
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Cloud information system «Redactor.Online» for the create of periodic scientific-educational editions by E. V. Golubev, A. G. Marakhtanov, O. Yu. Nasadkina

“…As a means of implementation chosen freelydistributed products, such as PostgreSQL, PHP, Yii Framework.Logically, the cloud structure of the system is a set of components, based on common data sources and interacting with each other. …”
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Large Language Model-Guided SARSA Algorithm for Dynamic Task Scheduling in Cloud Computing by Bhargavi Krishnamurthy, Sajjan G. Shiva

“…The implementation of the LLM_SARSA is carried out using the CloudSim express open-source simulator by considering the Google cloud dataset composed of eight different types of clusters. …”
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Research on Key Technology of Cloud-edge Coordinated Digital Twin Manufacturing Platform Towards Customized Production by HU Tianliang, ZHOU Shuaichang, MENG Qi, DONG Lili, ZHOU Tingting, LIU Xiaojun

“…First, a virtual-physical synchronous digital-twin model is constructed within a hierarchical cloud-edge framework to tackle the dual challenges of dynamic sensing in complex production systems and the fusion of heterogeneous data sources. …”
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Efficient Task Scheduling and Load Balancing in Fog Computing for Crucial Healthcare Through Deep Reinforcement Learning by Prashanth Choppara, Bommareddy Lokesh

“…This architecture reduces transmission latency by placing processing nodes close to the source of data generation, namely IoT-enabled healthcare devices. …”
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The Development and Construction of City Information Modeling (CIM): A Survey from Data Perspective by Wenya Yu, Xiaowei Zhou, Dongsheng Wang, Junyu Dong

“…With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. …”
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Research on Intelligent Hierarchical Energy Management for Connected Automated Range-Extended Electric Vehicles Based on Speed Prediction by Xixu Lai, Hanwu Liu, Yulong Lei, Wencai Sun, Song Wang, Jinmiao Xiang, Ziyu Wang

“…A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through <i>EMS_MPC-P</i>, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). …”
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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML) by Abu S.M. Mohsin, Shadab H. Choudhury, Munyem Ahammad Muyeed

“…The system is designed to process and analyze diverse data streams from multiple sources, such as IoT sensors, enabling rapid and informed responses to emergencies prioritizing emergency responder (Police, fire brigade, hospital or emergency contacts etc.). …”
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