Pathway planning study based on regional dual-carbon impact analysis and future projections by Shou-Yu Wei, Xiao-Qing Lu, Xin Song, Ze-Hua He

“…This paper takes the southeast coastal region of China as the scope from 2010 to 2020, and first establishes the Kuznets model and the factor decomposition model to analyse the relationship between regional carbon emissions and economic and energy consumption, respectively; then chooses the BP-LSTM model as the basis of predicting future regional carbon emissions; and finally sets up a regional dual-carbon target using scenario analysis to solve the path planning method. …”
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Infrared Small Target Detection Based on Approximate Background Regularization and Bimodal Slice Based Graph Constraints by Xiaoling Ge, Zipeng Fu, Xuelian Yu, Weixian Qian, Kan Ren, Minjie Wan, Guohua Gu

“…Low-rank decomposition models excel in small target detection due to their strong background separation. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Traditional statistical methods typically rely on unrealistic assumptions or oversimplified models, neglecting the nonlinear and high-dimensional characteristics of market data. …”
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Effective management of science development as a whole: approaches to the topic by I. E. Selezneva, Yu. V. Sidel`nikov

“…A statement of the research goal is proposed and, based on its decomposition, five tasks are set. Some shortcomings of modern methods and techniques of monitoring, evaluating the progress of scientific research and determining the level of development of science, based on scientometric indicators, are indicated. …”
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How does academic self-efficacy influence learning anxiety and academic burnout in Chinese characters learning among international students in China? by Yang Lin

“…The dynamics of self-efficacy, anxiety, and burnout in Chinese character acquisition remain under-explored, creating a critical gap in understanding how these constructs operate in non-alphabetic language contexts.ObjectiveThe purpose of this study was to investigate how learning anxiety (LA) affects academic burnout (AB) and explores the role of academic self-efficacy (ASE) in the relationship between the two.MethodsA study of 537 international students (50.4% males, mean age = 20.96 years, SD = 1.36) was conducted using the Academic Self-Efficacy Scale (ASES), Foreign Language Learning Anxiety Scale (FLLAS), Academic Burnout Scale (ABS).Results① LA was significantly and positively correlated with AB, and significantly and negatively correlated with ASE. ② ASE mediated the relationship between LA and AB. ③ Grade level, sleep quality, and parental education level have a significant effect on ASE, LA and AB; and ④ Extroversion has a significant effect on ASE, but not on LA and AB.ConclusionThe chain mediation model validated by this study provides valuable insights into the effects of international students’ learning anxiety (LA) on academic burnout (AB) in China, alongside practical implications for preventing and intervening in LA and AB among other current students.…”
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PRECISION, STABILITY ANALYSIS OF THE CRANKSHAFT GRINDING TECHNOLOGICAL PROCESS by S. A. Kornilovich, B. S. Trofimov

“…The structural and functional representation of the object is also used for research, and the model for the analysis of the grinding process serves as the basis for the  decomposition.Results. …”
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Studying the Photocatalytic Degradation of Tri-n-Butyl Phosphate Using Nano Nd-Doped TiO2 by H. Ghasemi Mobtaker, A. Malekinejad, T. Yousefi, H. Aghayan

“…Results showed that the method has high efficiency and is a promising procedure for decomposition of TBP.…”
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Robust fault detection and severity classification in rotating machinery using VMD-LSTM for limited data scenarios by Ammar Mrabti, Ramdane Younes, Nouredine Ouelaa, Tarek Kebabsa, Zakarya Ouelaa

“…However, it faces difficulties due to complex, noisy fault signatures, non-stationary behavior, and the impracticality of obtaining large labeled datasets, limiting the effectiveness of both traditional and deep learning-based methods in real-world applications. This paper introduces a novel approach that combines Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks to improve gear and bearing defect detection, filling a gap in fault diagnostics by effectively handling limited training data. …”
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Synthesis of silver nanoparticles from neem and cow dung, their characterization, and their application as a nano pesticide and nano fertilizer by Ardesana Yash N., Ajudia Ravi, Patel Dhruvi, Srinivas N.

“…The polyol method, microemulsions, thermal decomposition, electrochemical synthesis, chemical vapor deposition, microwave irradiation, pulsed laser method, sonochemical reduction, and gamma radiation are just a few of the many techniques used in the synthesis and characterization of silver nanoparticles (Ag NPs). …”
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Remaining Useful Life Interval Prediction for Lithium-Ion Batteries via Periodic Time Series and Trend Filtering Segmentation-Based Fuzzy Information Granulation by Chunsheng Cui, Guangshu Xia, Chenyu Jia, Jie Wen

“…The construction method for periodic time series is used to form a new dataset from the original data, based on which the fusion model, by combining the variational mode decomposition (VMD) and gated recurrent unit (GRU), is used as the RUL interval prediction model of LiBs. …”
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基于PolyMAX法的齿轮箱试验模态分析 by 王佳, 潘宏侠, 杨晓波

“…Based on the method of PolyMAX,a system of modal analysis is built,the model of JZQ-250 gear-box is set up under the LMS modal analysis module.The relating modal datum are obtained by hammering test of single-input multi-output analysis method,then the transfer function is established after the analysis of modal datum.The various modal parameters of the gearbox are obtained by order selection and fitting of the datum which extracted through the method of PolyMAX Modal Parameter Identification.Through the comparison with calculating modal,the validity and reliability are proved.The theoritical basis for the improvement of the gear box structure characteristics and the fault diagnosis of gearbox are provided.The natural frequency is extracted from the modal analysis,the wavelet decomposition of the corresponding frequency band component is removed,and the noise reduction processing on the fault signal is achieved.…”
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Scientific and strategic foresighting: The trajectory of sustainable development (on the example of Ukraine's energy security) by Yurii Kharazishvili, Yuriy Bilan, Oleksandr Sukhodolia, Olena Grishnova, Halyna Mishchuk

“…The goal of the article is the further development of the model for analyzing a specific area of management in the security dimension and the methods of strategizing the development of such an area – scientific and strategic foresight for the development of strategic scenarios for post-war recovery using the example of Ukraine's energy security. …”
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Effects of different pre-conditioning exercise on leptin synthesis and its downstream signalling pathway in T2DM rats by Sen Lin, Yuzhi Hu, Shuqiao Ding, Yazhe Hu

“…Objective(s): This study aimed to evaluate the effects of pre-conditioning exercise on body lipid metabolism, leptin secretion, and the downstream pathways at the early stage of type 2 diabetes mellitus (T2DM).Materials and Methods: The T2DM model was established using an 8-week high-sugar, high-fat diet combined. …”
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PatchTSFL: Patch Fourier Enhanced Linear for Long-Term Time-Series Forecasting by Ling Li, Xianyun Wen, Weibang Li, Chengjie Li, Chengfang Zhang

“…Recently, various transformer-based models have been employed for this task; however, these methods face two key challenges: difficulty in retaining local series information and failure to fully capture the overall trend of time series. …”
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Large-Eddy Simulation of Unsteady Flow in a Mixed-Flow Pump by Chisachi Kato, Hiroshi Mukai, Akira Manabe

“…The method is implemented as a parallel program by applying a domain-decomposition programming model.…”
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Elevator fault precursor prediction based on improved LSTM-AE algorithm and TSO-VMD denoising technique. by Hao Cao, Xiaoyan Du

“…This study proposes an advanced elevator fault precursor prediction method integrating Variational Mode Decomposition (VMD), Bidirectional Long Short-Term Memory (BILSTM), and an Autoencoder with an Attention Mechanism (AEAM), collectively referred to as the VMD-BILSTM-AEAM algorithm. …”
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FAULT DIAGNOSIS OF ROLLING BEARING BASED ON LEARNING SAMPLE SELECTION VIA CORRELATION ENERGY FLUCTUATION EVALUATION AND DEEP BELIEF NEURAL NETWORK (MT) by QIN Bo, LUO QuanYi, FENG WeiWei, ZHANG Peng, ZHAO ZhenHua, LI ZiXian, WANG Zhuo

“…The data-driven intelligent diagnosis of rolling bearing status suffers from low recognition rate due to the poor quality of learning samples in the process of identification model construction. To address this problem, a method is proposed to improve the recognition rate of the rolling bearing intelligent diagnosis model by selecting learning samples using the deep belief neural network. …”
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Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM by Qin Bo, Sun Guodong, Zhang Liqiang, Liu Yongliang, Zhang Chao, Wang Jianguo

“…And by using the bearing data of Case Western Reserve University,the validity of the method is verified. The experimental results show that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value compared with the fault classification model based on BP,SVM has higher precision and stronger generalization ability.…”
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Nonlinear Numerical Investigation on Higher Harmonics at Lee Side of a Submerged Bar by D. Ning, X. Zhuo, L. Chen, B. Teng

“…The decomposition of a monochromatic wave over a submerged object is investigated numerically in a flume, based on a fully nonlinear HOBEM (higher-order boundary element method) model. …”
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Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN by Nadikatla Chandrasekhar, Sujatha Canavoy Narahari, Sreedhar Kollem, Samineni Peddakrishna, Archana Penchala, Babji Prasad Chapa

“…Hence, this study proposes a new HD classification accuracy prediction approach using the Polynomial Jacobian Matrix-based Deep Jordan Recurrent Neural Network (PJM-DJRNN). The proposed method involves noise removal from ECG and PCG signals separately using the Brownian Functional-based BesseL Filter (BrF-BLF) and Frequency Ratio-based Butterworth Filter (FR-BWF), decomposition of the signals using Hamming-based Ensemble Empirical Mode Decomposition (HEEMD), and clustering of the signals as normal and abnormal using Root Farthest First Clustering (RFFC). …”
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