The superior coupled model with innovative strategy for accurate imputation of missing hydrological monitoring data in water research-A case of groundwater level data by Xian-Qi Zhang, Zi-Yu Li, Xian-Liang Liu, Xiao-Yan Wu, Xin-Yong Xu, En-Kuan Li, Bo-Wen Wang, Su-Bo Han, Shao-Bo Liu

“…This paper proposes a strategy of “Decomposition-Classification-Feature Extraction-Imputation” and establishes a coupled model integrating Extreme-point Symmetric Mode Decomposition (ESMD), Permutation Entropy (PE), Singular Value Decomposition (SVD), and Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) for the hydrological monitoring data imputation. …”
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Design Optimization of Feedforward Equalization for Mobile Fronthaul Based on Delta-Sigma Modulation With High-Order QAM Signals by Jianghao Li, Yangsheng Yuan, Yangjian Cai

“…In this paper, we propose a novel feedforward equalization (FFE) scheme based on the least mean square (LMS) algorithm for MFH networks employing 4th-order delta-sigma modulation technology with 1 and 2 bit quantization to alleviate the performance degradation caused by ISI. …”
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Variable-Rate Nitrogen Application in Wheat Based on UAV-Derived Fertilizer Maps and Precision Agriculture Technologies by Alexandros Tsitouras, Christos Noulas, Vasilios Liakos, Stamatis Stamatiadis, Miltiadis Tziouvalekas, Ruijun Qin, Eleftherios Evangelou

“…A real-time VR-N application algorithm was used to calculate N rates based on easily obtainable near-real-time data from unmanned aerial vehicle (UAV) imagery, tailored to the crop’s actual needs. …”
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Identification of Dicrocoelium dendriticum isolated from small ruminants of east Azerbaijan province using ITS2 and Nad1 gene markers by Abbas Imani Baran, Salar Alidoost, Ahmad Nematollahi, Masoomeh Firouzamandi, Behzad Ghorbanzadeh

“…Results: The analysis of sequences showed that, among the 4 ITS2 sequences, there was only one additional nucleotide relevant to one of the sheep isolates, while no difference was found among the sequences of Nad1 fragments. …”
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Hyperspectral imaging combined with residual-attention-net for spectral-spatial feature fusion in liver disease diagnosis by Yunze Li, Jingjing Wang, Miaoqing Zhao, Jinlin Deng, Chongxuan Tian, Qize Lv, Yifei Liu, Kun Ru, Wei Li

“…Distinguishing well-differentiated hepatocellular carcinoma (HCC) from cirrhosis is critical for effective treatment. However, while pathological morphology remains the gold standard, it has limitations in differentiating these two conditions. …”
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Coal mining equipment lifecycle management system architecture and key technology by Yihui PANG, Jinglong BI, Pengzhe YUAN, Baofu ZHAO, Ziwei DING

“…The main influencing factors of coal mining equipment selection optimization design were analyzed, and a calculation method for comprehensive mining equipment selection optimization was studied. An optimization algorithm and software platform for comprehensive mining equipment selection were developed, and a method for constructing a series spectrum and coding system for coal mining equipment was proposed. …”
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Capacity Estimation of Lithium-Ion Battery Systems in Fuel Cell Ships Based on Deep Learning Model by Xiangguo Yang, Jia Tang, Qijia Song, Yifan Liu, Lin Liu, Xingwei Zhou, Yuelin Chen, Telu Tang

“…A TCN-BiGRU model is then developed, with hyperparameters determined by the Kepler optimization algorithm (KOA). Cells from a battery pack under consistent conditions are used for training, while other cells in the same pack serve as the test set. …”
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A Spatiotemporal Dataset of Soil Properties in Northeast China Based on Soil Sampling and Interpolation From 2009 to 2020 by Shuzhen Li, Jieyong Wang, Xu Lin, Yaqun Liu

“…By integrating a spatiotemporal covariance function with the Kriging interpolation algorithm, the study systematically resolves the challenge of spatiotemporal collaborative modelling for multi‐year discontinuous observational data. …”
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Unraveling regional variability in Dengue outbreaks in Brazil: leveraging the Moving Epidemics Method (MEM) and climate data to optimize vector control strategies. by Ayrton Sena Gouveia, Marcelo Ferreira da Costa Gomes, Iasmim Ferreira de Almeida, Raquel Martins Lana, Leonardo Soares Bastos, Lucas Monteiro Bianchi, Sara de Souza Oliveira, Eduardo Correa Araujo, Danielle Andreza da Cruz Ferreira, Dalila Machado Botelho Oliveira, Vinicius Barbosa Godinho, Luã Bida Vacaro, Thais Irene Souza Riback, Oswaldo Gonçalves Cruz, Flávio Codeço Coelho, Cláudia Torres Codeço

“…A multivariate cluster analysis using the k-means algorithm was applied to MEM outputs and climate data. …”
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Enhancing proximal and remote sensing of soil organic carbon: A local modelling approach guided by spectral and spatial similarities by Qi Sun, Pu Shi

“…As a result, the optimal modelling strategy, with partial least squares regression (PLSR) as the local fitting algorithm, consistently produced superior performances (R2: 0.66 to 0.82) than the conventional global modelling approach (R2: 0.59 to 0.77) for all three data types. …”
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The unresolved struggle of 16S rRNA amplicon sequencing: a benchmarking analysis of clustering and denoising methods by Mohamed Fares, Engy K. Tharwat, Ilse Cleenwerck, Pieter Monsieurs, Rob Van Houdt, Peter Vandamme, Mohamed El-Hadidi, Mohamed Mysara

“…Using unified preprocessing steps, we were able to compare DADA2, Deblur, MED, UNOISE3, UPARSE, DGC (Distance-based Greedy Clustering), AN (Average Neighborhood), and Opticlust objectively. Results ASV algorithms—led by DADA2— resulted in having a consistent output, yet suffered from over-splitting, while OTU algorithms—led by UPARSE—achieved clusters with lower errors, yet with more over-merging. …”
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Application of the LDA model to identify topics in telemedicine conversations on the X social network by Mario Sierra Martín, Fang-Wei Chen, Pilar Alarcón Urbistondo

“…In the present analysis, we focus on unstructured tweet texts written by Internet users and apply both machine learning and the Latent Dirichlet Allocation algorithm to model X databases and identify tweet topics. …”
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Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model by Guowei Li, Gang Tang, Jingyu Zhang, Qun Sun, Xiangjun Liu

“…However, the system’s efficiency and accuracy are compromised by the significant delay incurred while obtaining real-time motion signals and driving the actuator for motion compensation. …”
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A Novel Prognostic Nomogram Based on TIGIT and NKG2A Can Predict Relapse‐Free Survival of Hepatocellular Carcinoma After Hepatectomy by Junqi Wang, Yuqing Cao, Yu Tian, Chaoliu Dai, Tianqiang Jin, Feng Xu

“…Results TIGIT and NKG2A expression were identified as independent risk factors for RFS, while TIGIT expression alone significantly impacted OS. …”
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Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model by Kai Cheng, Xiaokang Zhang, Keping Zhou, Chenao Zhou, Jielin Li, Chun Yang, Yurong Guo, Ranfeng Wang

“…High-frequency noise-containing IMFs are selectively removed, while LSTM predicts retained components. SSA optimizes LSTM parameters (learning rate, hidden layers, epochs) to minimize prediction errors. …”
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Fractional-Order Swarming Intelligence Heuristics for Nonlinear Sliding-Mode Control System Design in Fuel Cell Hybrid Electric Vehicles by Nabeeha Qayyum, Laiq Khan, Mudasir Wahab, Sidra Mumtaz, Naghmash Ali, Babar Sattar Khan

“…The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. …”
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Computer Program for Primer Design for Loop-Mediated Isothermal Amplification (LAMP) by L. U. Akhmetzianov

“…More than 150 computer programs have been proposed for the design of PCR primers, while for LAMP-primers there are less than 10 of them, and each of them has a number of drawbacks, e.g., in terms of the length of the analyzed site. …”
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Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany by S. Kunz, A. Schulz, M. Wetzel, M. Nölscher, T. Chiaburu, F. Biessmann, S. Broda

“…Performance varied widely: 25 % of wells achieved an NSE <span class="inline-formula">&gt;0.68</span>, while 15 % had an NSE <span class="inline-formula">&lt;0</span> with the best N-HiTS model. …”
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An enhanced alpha evolution moss growth optimizer for prognostic prediction in spontaneous intracerebral hemorrhage by Lingxian Hou, Yongsheng Wang, Xiuqi Lin, Chengye Li, Huangrong Guo, Congcong Jin, Yi Chen, Huiling Chen, Jing Ji, Wenzong Zhu

“…This study aims to improve SICH outcome prediction by developing the Alpha Evolution Moss Growth Optimization (AEMGO) algorithm for feature selection in high-dimensional medical datasets. …”
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Use of RUSH protocol to diagnose the type of shock in children by K. Yu. Ermolenko, K. V. Pshenisnov, Yu. S. Aleksandrovich, A. I. Konev, T. M. Kalinichenko, D. D. Lopareva, D. R. Rybakova, I. E. Gorbunov, L. O. Kiseleva

“…In three patients, the RUSH protocol diagnosed a combined type of shock (sensitivity 100%), while the physical examination did not establish a diagnosis of shock depending on the leading link of pathogenesis.Conclusion. …”
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