Deep learning based model predictive control of active filter inverter as interface for photovoltaic generation by Amin Rasoulian, Hadi Saghafi, Mohammadali Abbasian, Majid Delshad

“…While 1D‐CNN benefits from the inherent strong feature extraction/selection capability and lower computational complexity than other deep methods, it still cannot properly track the dynamic changes due to fixed weights during the training process. …”
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CSANet: Context–Spatial Awareness Network for RGB-T Urban Scene Understanding by Ruixiang Li, Zhen Wang, Jianxin Guo, Chuanlei Zhang

“…To address these limitations, we propose CSANet (Context Spatial Awareness Network), a novel framework that effectively integrates RGB and thermal infrared (TIR) modalities. CSANet employs an efficient encoder to extract complementary local and global features, while a hierarchical fusion strategy is adopted to selectively integrate visual and semantic information. …”
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Dynamic 3D-Network Coating Composite Enables Global Isolation of Phosphopeptides, Stepwise Separation of Mono- and Multi-Phosphopeptides, and Phosphoproteomics of Human Lung Cells by Linlin Liu, Zhenhua Chen, Danni Wang, Weida Liang, Binbin Wang, Chenglong Xia, Yinghua Yan, Chuanfan Ding, Xiaodan Meng, Hongze Liang

“…These results demonstrate the material’s superior enrichment efficiency in complex biological samples.…”
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Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…Study region: The watersheds of the four flux sites in the United States were selected as the study areas for this research. Study focus: This study validates the efficiency of Convolutional Long Short-Term Memory Network (ConvLSTM) models for site-scale ETa prediction. …”
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ATLAS-seq: a microfluidic single-cell TCR screen for antigen-reactive TCRs by Siwei Luo, Amber Notaro, Lan Lin

“…ATLAS-seq identifies distinct TCR clonotype populations with higher T cell activation levels compared to TCRs recovered by MHC multimer staining. Select TCR clonotypes from ATLAS-seq are more efficient in target cell killing than those from MHC multimer staining. …”
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Radioactive pollution of the Chernobyl cooling pond bottom sediments. II. Distribution of 137Cs, 241Am, 90Sr in a solid phase by L. S. Pirnach

“…Distribution coefficients 137Cs and 90Sr, selectivity coefficients of their exchange with similar cations and physical-chemical forms are defined. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…However, accurately and efficiently detecting citrus ripeness in complex orchard environments for selective robotic harvesting remains a challenge. …”
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YAREN: Humanoid Torso Robot Platform for Research, Social Interaction, and Educational Applications by Daniela Sanchez-Orozco, Rhandall Valdez, Yandri Uchuari, Marcelo Fajardo-Pruna, Luis Cavazos Quero, Redhwan Algabri, Emiliano Quinones Yumbla, Francisco Yumbla

“…In the pursuit of maximum efficiency and performance, robots are often designed with complex functionalities and mechanisms customized for specific applications. …”
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Physiological responses in Neolitsea sericea seedlings to drought stress by WANG Yihang, ZHAO Luyao, WANG Guoming, ZHU Aiyi

“…However, few studies on drought tolerance of N. sericea have been reported yet.Plant response to drought stress at a molecular level is a complex biological process, involving consideration of the stress effects and regulation events. …”
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STVMamba: precipitation nowcasting with spatiotemporal prediction model by Maoyang Zou, Longrui Wen, Yuanyuan Huang, Yuan He, Jingzhong Xiao

“…STVMamba achieves high parallel computational efficiency, operates with linear time complexity, and excels at modeling long-range dependencies, overcoming the shortcomings of previous deep learning methods. …”
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Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI by H. Kim, H. Kim, X. Chen, X. Chen, J. Wang, J. Wang, J. Wang, J. Wang, Z. Lu, Z. Lu, Z. Lu, M. Zhou, G. R. Carmichael, G. R. Carmichael, S. S. Park, J. Kim

“…Although aerosol vertical structures can be complex, assuming a certain shape for the aerosol vertical profile allows for the retrieval of a single parameter – aerosol layer height (ALH) – from passive remote sensing measurements. …”
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Investigating the psychometric properties of the Iranian version of the Post- Secondary Student Stressors Index by Majid Yousefi Afrashteh, Seyedeh Mohadeseh Sadidi Shal

“…Nowadays, due to the complexity of societies, many factors cause stress. …”
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SPBO-ANFIS Model of Combined Monthly Runoff Forecasting Based on Singular Spectrum Analysis by ZHANG Yajie, CUI Dongwen

“…In view of the multi-scale non-stationarity and other characteristics of monthly runoff in hydrological time series,this paper proposes a singular spectrum decomposition (SSD)-based model of combined monthly runoff forecasting that integrates the student psychology based optimization (SPBO) algorithm with the adaptive network based fuzzy inference system (ANFIS),namely the SSD-SPBO-ANFIS model.This model is then applied to the monthly runoff forecasting at a hydrological station in Yunnan Province.Specifically,time series data of sample monthly runoff are decomposed into various independent sub-series components through SSD to reduce the complexity of the time series data;then,the principle of the SPBO algorithm is outlined,and eight standard functions are selected for simulation verification and comparison of the SPBO algorithm;finally,the SPBO algorithm is employed to optimize the ANFIS condition and conclusion parameters.The SSD-SPBO-ANFIS model is built to forecast each sub-series,which is then superimposed to obtain the final monthly runoff forecasting result.In addition,the results of the proposed model are compared with those of the ensemble empirical mode decomposition (EEMD)-based EEMD-SPBO-ANFIS model and the SPBO-ANFIS model without decomposition.The following observations can be made from the results:The SPBO algorithm has favorable optimization accuracy;with a mean absolute percentage error of 5.57%,a mean absolute error of 0.20 m³/s,a Nash coefficient of 0.994 8,and a pass rate of 96.7%,the SSD-SPBO-ANFIS model has an effect better than that of the EEMD-SPBO-ANFIS model and far better than that of the SPBO-ANFIS model in forecasting sample monthly runoff.The proposed model and method can provide references for related research on hydrological time series forecasting.…”
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Effect of Enzyme on Chromed Leather Dyeing With Acidic Dyes by Renata Biškauskaitė-Ulinskė, Virgilijus Valeika

“…Leather dyeing is a difficult task due to its heterogeneity and complexity. Several studies are aimed at improving the effectiveness of dyeing, including process optimization, use of dye auxiliaries, ultrasound, liposomes, and enzymes. …”
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PAPR Reduction in DCO-OFDM Visible Light Communication Systems Using Optimized Odd and Even Sequences Combination by Wei-Wen Hu

“…Simulation results show that the proposed scheme can achieve comparable PAPR reduction performance with the conventional selected mapping (CSLM) scheme with random and chaotic sequences, but at a considerably low computational complexity. …”
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2D nonlinear seismic response characteristics of a megacity-scale site under Ricker wavelets by Wei Ren, Yan-Zhen Wang, Jiao Zhu, Ding-Feng Zhao, Qi Wu, Guo-Xing Chen

“…Ricker wavelets with varying peak times, peak frequencies (f p), and amplitudes were selected as input bedrock motions. The analysis revealed the spatial distribution characteristics of 2D nonlinear seismic effects on the surface of deep and complex sedimentary layers. …”
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Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression by Daniel S. Nicolau, Lucas A. Thomaz, Luis M. N. Tavora, Sergio M. M. Faria

“…Additionally, this approach allows to reduce the computational complexity by almost half in comparison to selecting the most compression-efficient after testing both schemes.…”
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Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests. by Mattias Frånberg, Karl Gertow, Anders Hamsten, PROCARDIS consortium, Jens Lagergren, Bengt Sennblad

“…Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. …”
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Optimization of Sustainable Seismic Retrofit by Developing an Artificial Neural Network by Hafiz Asfandyar Ahmed, Waqas Arshad Tanoli

“…The study found that networks with one or two hidden layers provided more reliable and efficient results compared to more complex architectures, achieving a total regression value of 0.911. …”
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The Bayesian mixture expert recognition model for tobacco leaf curing stages based on feature fusion by Panzhen Zhao, Shijiang Duan, Songfeng Wang, Aihua Wang, Lingfeng Meng, Zhicheng Wang, Yingpeng Dai

“…The proposed model achieves a recognition accuracy of 93.96% on the test set, with other performance metrics surpassing those of the base models. This research efficiently captures and robustly recognizes the complex dynamic visual features of the tobacco curing process through the integration of diverse features, adaptive adjustments, and expert collaboration mechanisms, thereby enhancing the system’s adaptability and interpretability in complex environments. …”
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