Successive Refinement for Lossy Compression of Individual Sequences by Neri Merhav

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Research on load-balanced construction algorithm of logical carrying network by Hao-xue WANG, Ming JIANG, Ji FU

“…To improve the extensibility of network service,constructing algorithm of logical carrying network was pro-posed by mapping the demands of network service into substrate network.To solve the problem of construction efficiency,on-demand carrying strategy was introduced to provide different resources to meet different service requirements.Based on available resource and current link load,an improved multi-commodity flow model was proposed to compute the re-source allocation.Simulation results indicate this al ithm can improve network service capability from construction success rate and average construction profit.…”
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Initialization Methods for FPGA-Based EMT Simulations by Xin Ma, Xiao-Ping Zhang

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From Concept to Market: Ensemble Predictive Model for Research Project Crowdfunding Readiness by Andreea Cristina Ionica, Stanislav Cseminschi, Monica Leba

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Electricity pinch analysis method for flexibility supply-demand matching in power systems by Yaling Mao, Tiejiang Yuan, Xueqin Tian, Yue Teng

“…First, the net-load profile is decomposed by successive variational mode decomposition (SVMD) optimized with the Red-billed Blue Magpie Optimization (RBMO) algorithm to construct a flexibility demand model. …”
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SimpleMating: R‐package for prediction and optimization of breeding crosses using genomic selection by Marco Antônio Peixoto, Rodrigo Rampazo Amadeu, Leonardo Lopes Bhering, Luís Felipe V. Ferrão, Patrício R. Munoz, Márcio F. R. Resende Jr.

“…Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open‐source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. …”
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Deep reinforcement learning based resource provisioning for federated edge learning by Xingyun Chen, Junjie Pang, Tonghui Sun

“…The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. …”
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Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data by Tangxiao Yuan, Kossigan Roland Assilevi, Kondo Hloindo Adjallah, Ayité Sénah A. Ajavon, Huifen Wang

“…At the application level, this framework is successfully applied to two critical decision-making scenarios: the first is to optimize the power allocation strategy between solar energy and the auxiliary grid, aiming to maximize cost efficiency and minimize power outage losses; the second is to develop low-risk maintenance plans based on the predicted failure probabilities of microgrid components with uncertain information. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. …”
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Literature review for the topic of automation of scheduling classes and exams in higher education institutions by Wadhah M Al-Gabri

“…The analytical study results of a number of the Russian and foreign sources on methods and algorithms of the classes and exams timetabling automation are described. …”
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High-flow nasal cannula therapy versus continuous positive airway pressure for non-invasive respiratory support in paediatric critical care: the FIRST-ABC RCTs by Padmanabhan Ramnarayan, Alvin Richards-Belle, Karen Thomas, Laura Drikite, Zia Sadique, Silvia Moler Zapata, Robert Darnell, Carly Au, Peter J Davis, Izabella Orzechowska, Julie Lester, Kevin Morris, Millie Parke, Mark Peters, Sam Peters, Michelle Saull, Lyvonne Tume, Richard G Feltbower, Richard Grieve, Paul R Mouncey, David Harrison, Kathryn Rowan

“…Clinical management In both groups, most children who started any respiratory support were started with the allocated treatment (HFNC: 96.8%; CPAP: 92.6%). The starting HFNC gas flow rate and CPAP pressure were as per the trial algorithms. …”
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