Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation by Yi LIU, Xin WU

“…Thermostatically controlled load is a flexible load that controls temperature regulation, such as air conditioning and electric water heaters.As a crucial demand side resource, flexible aggregation and regulation of load clusters can fully mobilize clean energy consumption capacity and ensure the balance between supply and demand of the power grid.Due to the common occurrence of thermostatically controlled loads in commercial office buildings and residential areas, a relatively stable control and transmission method can be adopted.Therefore, an efficient hierarchical transmission network is introduced to achieve data transmission and information interaction between loads and the power grid, and to flexibly, real-time, and accurately utilize the adjustable potential of load clusters.Firstly, an information interaction architecture of load IoT which structured “central cloud-edge cloud-regional load controller-thermostatically controlled load”was proposed.Then, for the “end edge”part, considering the requirements of different aggregation control tasks, an improved clustering algorithm was used to classify the tasks and reduce transmission overhead.For the “end-side” part, an improved clustering algorithm was used to optimize the transmission distance.For the edge-cloud collaboration part, a subchannel resource allocation algorithm was designed based on stable matching and water injection algorithms.The binary particle swarm optimization algorithm was used to solve the task upload decision problem.Finally, the effectiveness of the proposed model and algorithm is verified through simulation, and comparative experiments are also conducted.…”
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A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification by Amer Delilbasic, Bertrand Le Saux, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro

“…Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. …”
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Comparative Analysis of Automated vs. Expert-Designed Machine Learning Models in Age-Related Macular Degeneration Detection and Classification by Ceren Durmaz Engin, Ufuk Beşenk, Denizcan Özizmirliler, Mustafa Alper Selver

“…Results: The expert-designed model achieved an overall accuracy of 99.67% for classifying all images, with F1 scores of 0.99 or higher across all binary class comparisons. In contrast, the AutoML model achieved an overall accuracy of 89.00%, with F1 scores ranging from 0.86 to 0.90 in binary comparisons. …”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…The proliferation of the Internet of Things (IoT) has brought about unprecedented connectivity and automation, yet it has also introduced significant security vulnerabilities. …”
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Enhanced Extraction of Activation Time and Contractility From Myocardial Strain Data Using Parameter Space Features and Computational Simulations by Borut Kirn

“…These cohorts were represented as binary images—synthetic fingerprints—where the position and shape of each blob indicated extraction uniqueness. …”
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neura... by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

“…For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. …”
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Improved Quantum Query Upper Bounds Based on Classical Decision Trees by Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro

“…This answers a question posed by Lin and Lin, who asked whether the guessing complexity of a decision tree is related to any complexity-theoretic measure. We also show a polynomial separation between rank and randomized rank for the complete binary AND-OR tree. …”
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A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks by LiJuan Bai, Jiao Wu, Li Chen, Xin Jiang, ZhuYin Song

“…This research employs a weighted combination of deep neural networks and optimization techniques for MS disease diagnosis. This method uses slices of magnetic resonance imaging (MRI) images as input. …”
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The Electron Spin and Magnetic Properties of Cluster Fe3Ni3 by ZHENG Xinxi, FANG Zhigang, WANG Qian, XU You, JING Runtian

“…Based on density functional theory, full parameter optimization calculation is performed under B3LYP/Lanl2dz (level) , and finally five triplet optimized configurations are obtained. …”
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Predicting and Understanding Disorders of Consciousness of Sleep Patients Through Multimodal Data Fusion and Temporal Attention Mechanisms by Haixia Pan, Haotian Geng, Pingshu Zhang, Xiaodong Yuan

“…Employing a transformer-based architecture with task-aware guided masking and joint-optimization training, MTAMA-DoC integrates time series data (heart rate and breath rate) with structured clinical information, addressing the limitations of traditional assessment methods. …”
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Agrobacterium tume faciens-mediated transformation of Colletotrichum lagenarium, the causal agent of cucumber anthracnose by FANG Li, LIU Hai-qing, SONG Feng-ming, ZHENG Zhong

“…To elucidate the molecular mechanism of pathogenicity in Colletotrichum lagenarium, the casual agent of cucumber anthracnose, conditions for T-DNA insertional mutagenesis using Agrobacterium-mediated transformation approach were established and optimized. Binary vector BIG2RHPH2-GUS-GFP was used, which carried the bacterial hygromycin B phosphotransferase gene cassette under the control of Aspergillus nidulans trpC promoter as a selection marker and a GFP-GUS fusion cassette driven by the A. nidulans trpC promoter enabling detection of GFP microscopically and GUS stain in the resultant transformants. …”
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Meta-analytical approaches to ordinal outcome data in clinical interventional studies: A scoping review with reproducible research. by Ali Mulhem

“…Forward and backward citation searches were also performed. The screening was conducted in two phases using Covidence software. …”
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A Joint Knowledge Extraction Model for Tobacco Pest and Disease Prevention Based on BERT+BA+CASREL by Kehan Liu, Feng Zhang, Qiulan Wu, Xiang Sun, Huarui Wu, Ziruo Sun

“…The CASREL module, as the core component of the framework, employs a cascade binary tagging approach to achieve joint extraction of entities and relations, effectively improving the precision of complex relation recognition and end-to-end optimization. …”
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Enabling Power Systems With SQKD in Presence of Remote Nodes by Mariam Gado, Muhammad Ismail

“…The optimization problem is formulated as a binary program that upgrades a pre-existing classical cyber layer of power system to support SQKD. …”
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A study on the effective utilization of ultrafine fly ash and silica fume content in high-performance concrete through an experimental approach by Arava Lekhya, N. Senthil Kumar

“…The study includes the production of binary and ternary concrete mixes by replacing Portland cement with UFFA and SF at 0, 5, 10, and 15 %. …”
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Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis by Dang Li, Patrick Cheong-Iao Pang, Chi-Kin Lam

“…The results indicate that CIED is not only more practical in terms of visual effects but also robust in terms of contrast invariance. The comparison results with other methods also confirm the advantages of CIED. …”
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Contamination of fabric surface by the particles woven from the cotton waste open end rotor yarn by Gabriela Krupincová, Brigita Kolčavová Sirková

“…Surprisingly, dust and trash particles contribute to the binary portion equally. It leads to the recommendation connected with optimizing fabric quality, the final treatment should focus mainly on eliminating the visual impact of larger contaminants. …”
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Aircraft Engine Remaining Useful Life Prediction Using Machine Learning by Michael Kimollo, Xudong Liu

“…RUL helps anticipate engine failures beforehand so that airlines can proactively schedule maintenance, optimize resource allocation, and reduce the risk of downtime. …”
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Digital Art Feature Association Mining Based on the Machine Learning Algorithm by Zhiying Wu, Yuan Chen

“…It can be expanded to various binary numerical codes with computers as the center and can also be refined to various categories of creation with computers. …”
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Performance, Emissions, and Combustion Analysis of Gasoline-Ethanol-Methanol Blends in a Spark-Ignition Engine by Mokhtar, Danardono Agus Sumarsono, Askar Adika Agama, Ade Kurniawan, Hari Setiapraja

“…The fuel variations included three ternary fuel blends and two binary fuel blends, each comprising 80% gasoline and varying ratios of methanol and ethanol by volume. …”
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