Research and practice on technologies for full stack deployment of autonomous networks by Fei XUE, Bin CHEN, Jing LIU, Xiaoyang LIANG, Lin ZHU, Feng WANG, Tian LI, Liang ZHANG, Zhenzhen CHEN, Xiao LI

“…Autonomous networks achieve network self management, self optimization, and self repair by building intelligent network infrastructure.Autonomous network was divided into two key stages: AI model building and AI model deployment.However, the industry paid less attention to AI model deployment.The deployment phase of autonomous networks was systematically studied.Firstly, it elaborated on the independent deployment mode and full stack deployment mode of autonomous networks, and pointed out that full stack deployment was the main direction.Secondly, a detailed introduction was given to the full stack architecture with “five layers, dual domains, and four closed-loops”, which achieved full life cycle intelligence through a layered closed-loop design of resources and processes.Then, three core technologies for independent innovation were proposed: AI model training and inference integration to achieve rapid iterative updates of models, AI fabric technology to achieve customized application by rapid construction, and AI model cloud-edge collaborative deployment technology to achieve efficient application.Finally, the effectiveness of these three core technologies was verified through cases such as anomaly detection, smart telecommunication rooms, and equipment inspections.The deployment of autonomous networks was systematically explored, especially in terms of architecture design and core technology innovation, which had important reference value for telecommunication operators’ network digital transformation.…”
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Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique by Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain

“…The proposed TensorFlow Lite model constructed by combining CNN layers and Adam in this study is called Optimised Light Weight Sequential Deep Learning Model (SDLM). …”
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Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…Enhancing the spatial resolution of gypsiferous soil detection, as a valuable baseline information layer, is beneficial for investigating agroecological processes and tackling land degradation in semi-arid environments. …”
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Life cycle assessment and sensitivity analysis of carbon emissions in full depth reclamation with portland cement and conventional pavement repair by Haiwei Zhang, Qingqing Zhang, Chuangdan Luo, Ning Liu, Ke Wang

“…Subsequently, carbon emission models were established based on the life cycle assessment (LCA) method and the construction processes, allowing for a comparison of carbon emissions and energy consumption among the three technologies. …”
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Effect of cellular protein kinases blockade on the s100 retina expression in experimental diabetic retinopathy by K. O. Usenko, S. O. Rykov, O. O. Dyadyk, S. V. Ziablitsev

“…With the development of experimental DR, the expression of S100 protein significantly increased in the bodies and processes of astrocytes and Müller cells. The latter were mainly located in the inner nuclear layer, significantly increased in size and had a process-like shape (reactive gliosis). …”
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Enhanced photovoltaic efficiency through 3D-Printed COC/Al₂O₃ anti-reflective coversheets by Naif Almakayeel, Gobinath Velu Kaliyannan, Raja Gunasekaran, Rajasekar Rathanasamy

“…An optical loss is a crucial factor that impacts the performance of the photovoltaic conversion process. This loss occurs when incoming sunlight is reflected back on the outer layer of the photovoltaic cells. …”
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Key technologies for intelligent, efficient, and safe mining of impact thick coal seams due to hard roof by Bin YU, Xiangbin MENG, Yang TAI, Ziyu SHANG, Mingxian PENG, Tiejun KUANG, Feilong GUO, Dongyin LI, Shibo WANG

“…A multi-source information database for intelligent top-caving longwall panels integrating human, machine, and environmental data was established, along with coordinated mining and caving decision-making models and intelligent decision-making technologies for caving processes. …”
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Dataset of SCAPS-1D simulated halide perovskite solar cells with SHAP and machine learning-based PCE optimizationZenodo by Ivan E. Novoselov, Alexander M. Gvozdev, Andrey A. Smirnov, Ivan S. Zhidkov

“…This data article describes a dataset generated by using SCAPS-1D simulation software to capture photovoltaic performance metrics for perovskite solar cells (PSC). The data collection process involved systematically modeling a range of device configurations by varying perovskite compositions, layer thicknesses, and combinations of charge transport layers. …”
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Long Short-Term Memory-Based Fall Detection by Frequency-Modulated Continuous Wave Millimeter-Wave Radar Sensor for Seniors Living Alone by Yun Seop Yu, Seongjo Wie, Hojin Lee, Jeongwoo Lee, Nam Ho Kim

“…The Raw-LSTM model processed raw mmWave radar images through LSTM layers and dense layers for classification. …”
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Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging by Paul J. Weiser, Georg Langs, Wolfgang Bogner, Stanislav Motyka, Bernhard Strasser, Polina Golland, Nalini Singh, Jorg Dietrich, Erik Uhlmann, Tracy Batchelor, Daniel Cahill, Malte Hoffmann, Antoine Klauser, Ovidiu C. Andronesi

“…Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps. …”
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Exploring the Role of Artificial Intelligence in Precision Photonics: A Case Study on Deep Neural Network‐Based fs Laser Pulsed Parameter Estimation for MoOx Formation by Jose R. Paredes‐Miguel, Miroslava Cano‐Lara, Andres A. Garcia‐Granada, Andres Espinal, Marcos J. Villaseñor‐Aguilar, Leonardo Martinez‐Jimenez, Horacio Rostro‐Gonzalez

“…Subsequently, eight different deep neural network models, each with varying hidden layers and numbers of neurons, are employed to characterize the laser behavior with different parameters. …”
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Industrial-Grade 3D Detection for Laser Cleaning of Metallic Components Based on Neural Radiance Fields and Temporal-Spatial Learning by Dandan Qi

“…Propose a NeRF optimization strategy based on Octree scene partitioning to achieve high-fidelity reconstruction of micro-structures such as rust layer edges and micro-cracks; 3. Improve the collaborative modeling ability between multi-temporal images through cross-scale attention fusion; 4. …”
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A novel approach towards the robustness of centrality measures in networks by Jens Dörpinghaus, Vera Weil, Robert Rockenfeller, Meetkumar Pravinbhai Mangroliya

“…For example, historical and narrative texts in ancient languages are usually challenging for natural language processing methods and artificial intelligence technologies developed for modern languages due to their complexity and missing models. …”
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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. …”
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Design and Implementation of Multilayer GIS Framework in Natural Resources Management: Red Sea Area by Thowiba E. Ahmed, K. M. Kheiralla, Fatima Rayan Awad Ahmed, Rashid A. Saeed, Hesham Alhumyani

“…This paper designs and implements GIS multiple database layers. The multiple layers were modelled to overcome the technical difficulties that result from processing of large quantities of tempospatial information. …”
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Current Sheet Broadening due to Turbulence in Three-dimensional Collisionless Reconnection by Keizo Fujimoto

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Joint neural denoising and consolidation for portable handheld laser scan by T. Zhang, S. Filin

“…These characteristics challenge such applications as feature extraction and 3D modeling when processing the raw pointset. To date, point cloud denoising and consolidation (address of distribution and void regions) have been treated independently despite their complementary nature and their mutual dependence on the underlying surface representation. …”
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Optimizing Convolutional Neural Network Architectures by Luis Balderas, Miguel Lastra, José M. Benítez

“…Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). …”
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Evaluation of Carbonate Reservoir Based on Electrical Imaging Floating Scale Porosity Spectrum by LIU Zhang, SUN Jianmeng, HE Juan, CHENG Xiaobo, DONG Huaimin, CHI Peng

“…The Mishrif formation in the X oilfield of Iraq is a thick layer of reef beach carbonate rock, which is influenced by multiple factors such as sedimentation, diagenesis, and tectonic processes. …”
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Thermodynamics, economy and environment analyses and optimization of series, parallel, dual-loop Kalina cycles for double-source heat recovery in cement industry. by Yali Wang, Yongjun Xu, Yongliang Qiu, Shengwang Ning

“…Three thermodynamic cycles including series Kalina Cycle (S-KC)、parallel Kalina Cycle (P-KC) and dual-loop Kalina Cycle (DL-KC) are introduced for converting dual-source heat resources into power to enhance the system efficiency for cement production process. Firstly, the multi-layer comprehensive evaluation models are established for the three thermodynamic cycles. …”
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