Remote sensing image fusion based on real time image smoothing and image similarity by Yanfang Hou, Kaixuan Guo, Xueyan Bi

“…In this study, image preprocessing algorithm is used to smooth remote sensing images for the fusion problem between different remote sensing images. Subsequently, a hybrid algorithm model based on non-negative matrix factorization and block term decomposition is used to fuse remote sensing images. …”
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Septic complications of surgical pathology of the mediastinum by A. M. Morozov, V. A. Kadykov, E. M. Askerov, E. V. Penyaz', M. O. Popova, M. A. Belyak

“…In the course of this work, the analysis of foreign and domestic literature on the problem of the occurrence of purulent-septic complications of mediastinal organs in surgery was carried out.Results. …”
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STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection by Xiaoyang Zhang, Kaihui Dong, Dapeng Cheng, Zhen Hua, Jinjiang Li

“…This network uses a pretrained Resnet18 to extract multiscale features to obtain features with sufficient spatial details and semantic information. …”
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Recommendation model based on separated embedding interaction networks by FENG Shulei, JIANG Zhongyun

“…Aiming at the problems that the existing feature interaction methods in deep learning recommendation models cannot fully utilize the embedding vector information and thus have the insufficient accuracy, we propose a deep learning recommendation model based on separated embedding interaction networks (SEIN). …”
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Optimization of deep learning architecture based on multi-path convolutional neural network algorithm by Chuan Zhou, Yan Liu, Xinghan An, Xiyao Xu, Hao Wang

“…At present, there are some problems in multi-path architecture, such as isolated information among paths, low efficiency of feature fusion mechanism, and high computational complexity. …”
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Symbolic Regression-Based Modeling for Aerodynamic Ground-to-Flight Deviation Laws of Aerospace Vehicles by Di Ding, Qing Wang, Qin Chen, Lei He

“…This paper proposes methods for modelling this correlation that combine feature extraction and symbolic regression. The neighborhood component analysis (NCA) method is utilized to extract features from the high-dimensional state space and then symbolic regression (SR) is applied to find the concise optimal expression. …”
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A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin by Haonan Jiang, Yanbo Zhao, Qiao Zhu, Yuanli Cai

“…Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. …”
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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization by Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Monagi Hassan Alkinani, Nasir Ayub, Elham Abdullah Alghamdi, Nourah Fahad Janbi

“…This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. …”
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Phases with layered (AB) and “defective” (A2B3) structures in AIII–BVI systems. Part 2. Phase diagrams and approaches to some problems of reproducible synthesis in AIII– BVI system... by Alexander Y. Zavrazhnov, Nikolay Y. Brezhnev, Ivan N. Nekrylov, Andrew V. Kosyakov

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Positive solutions to a coupled singular anisotropic system with nonstandard growth and singular nonlinearities by Seyedeh Atefeh Fallahshams, Abdolrahman Razani

“…By employing variational methods and an approximation problem, we prove the existence of positive solutions under suitable conditions on the nonlinearities.…”
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Spark: The First Choice for Novices by Judith Gal-Ezer, Smadar Szekely

“…Setting itself apart from its counterparts, Spark boasts an innovative formal language and a rich set of features. Unlike traditional platforms, Spark emphasizes computational problem solving over programming syntax, making it accessible to learners of all levels. …”
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Optimizing PHEV Routing with Hybrid Mode and Partial Charging via Labeling-Based Methods by Zhenhua Chen, Qiong Chen, Yiying Chao, Cheng Xue

“…This study investigates a variant of the shortest path problem (SPP) tailored for plug-in hybrid electric vehicles (PHEVs), incorporating two practical features: hybrid energy mode switching and partial charging. …”
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Introducing the ethical cycle model for resolving ethical conflicts in medical practice: addressing challenges in treating pandemic patients by Ensieh Madani, Ali Dizani, Saeedeh Saeedi Tehrani, Mansure Madani

“… Ethical dilemmas are among the most important ethical problems in medicine. With the advent of COVID-19, the moral problems of physicians have taken on new dimensions as the specific features of this disease pose additional ethical challenges that require particular solutions. …”
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A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling by Jian Li, Shifa Li, Pengbo He, Huankun Li

“…First, a mathematical model for flexible job shop scheduling optimization is established, with the makespan and total energy consumption of the shop as optimization objectives, and a disjunctive-graph is introduced to represent state features. Second, two intelligent agents are designed to address the simultaneous decision making problems of jobs and machines. …”
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THE EFFICIENCY IMPROVING OF PILOTS AND CADETS TRAINING TO SAFETY CONTROL USING THE MODIFICATIONS OF CLASSICAL METHOD OF "ROY" (PSO) by V. V. Yurasov, L. A. Yurasova

“…The increased accuracy and the introduction of additional variables in the optimization problem of security is solved based on the methodology PSO. …”
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Multiperiod Location–Allocation Optimization of Construction Logistics Centers for Large-Scale Projects in Complex Environmental Regions by Hao Shen, Jin Zhang, Wenjie Sun, Wenguang Yang, Guoqi Li

“…However, the CLC location–allocation problem, which considers periodic demand and transportation risk, has not been adequately solved. …”
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PIPNet: A Deep Convolutional Neural Network for Multibaseline InSAR Phase Unwrapping Based on Pure Integer Programming by Hui Liu, Ke Zheng, Changwei Miao, Xuemei Liu, Xianlin Liu, Lin Li, Yongguang Zhang, Longhai Xiong

“…This article transforms the MB PU problem into pure integer programming (PIP) problem and innovates a deep convolutional neural network named PIPNet to solve PU problem. …”
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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…Secondly, to solve the problem of ignoring shallow features in traditional feature extraction, CNN was used to extract the features of one-dimensional fusion parameters in layers, and KPCA was used to reduce the feature extraction results of different layers to one dimension. …”
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Remote sensing satellite big data high-recision integration processing technology by Xiaolan QIU, Yuxin HU, Songtao SHANGGUAN, Kun FU

“…With the rapid development of China’s aerospace information acquisition technology, aerospace data presents the characteristics of large data volume, large number of types, rapid growth and relatively low value density.Remote sensing satellite data is an important part of aerospace big data.How to make use of the scale effect and the complementary advantages of the data of different satellites, so as to improve the processing accuracy and efficiency, is the key problem to be solved in the remote sensing satellite big data processing system.The development history of our country’s remote sensing satellite ground data processing system was reviewed briefly.The core difficulties faced by the ground processing system were pointed out.A high-precision processing technology for remote sensing satellite big data based on stability feature mining was proposed.And a preliminary implementation was given.The method provides a useful reference for the development of our country’s aerospace big data processing system.…”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

“…Firstly, in the process of spatiotemporal graph modeling, this model introduces hop interaction instead of node interaction to update node features, which greatly reduces the times of graph convolution operations, alleviates the problem of feature smoothing, and greatly improves the accuracy of prediction. …”
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