Discrimination of mercury, cadmium and lead polluted rice leaves based on near infrared spectroscopy technology by ZHANG Long, PAN Jiarong, ZHU Cheng

“…And the correct classification rates of rice in mercury, cadmium and lead polluted soil and control soil were 95.5%, 81.8%, 91.3% and 100.0% respectively.Our results indicated that it should be feasible to develop useful calibration models for the prediction of heavy metal in rice leaves. …”
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Preparation and Evaluation of Alpha-2-Macroglobulin Hydrogel for Drug Release Kinetics and Biocompatibility on Human Gingival Fibroblasts: An In-vitro Study by Poulami Chakraborty, ND Jayakumar, S Raghunandhakumar

“…Cytotoxicity tests revealed that the hydrogel was non toxic at concentrations up to 400 micrograms/mL, with cell viability rates exceeding 85%. Additionally, the hydrogel supported cell adhesion and proliferation, suggesting its suitability for periodontal regeneration. …”
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Climate change-induced shifts in landslide susceptibility in São Sebastião (southeastern Brazil) by Enner Alcântara, Cheila Flávia Baião, Yasmim Carvalho Guimarães, José Antonio Marengo, José Roberto Mantovani

“…Frequency Ratio (FR) analysis revealed varying levels of landslide susceptibility across scenarios, with RCP2.6 showing lower probabilities for moderate landslides (FR: 0.007946) compared to higher ratings for RCP4.5, RCP6.0, and RCP8.5 (FR: 1.663156 for high landslides). …”
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SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN by Shengfeng He, Wenhu Qin, Zhonghua Yun, Chao Wu, Chongbin Sun

“…Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. …”
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AEMS: Adaptive Ensemble GNNs for Multibehavior Stream Recommendation by Ritchie Natuan Caibigan, Punyaphol Horata, Pusadee Seresangtakul

“…The AEMS synergizes long-term preference patterns (derived from historical interactions) with real-time user intents and item attributes (captured through multibehavior signals), integrating them via an adaptive ensemble neural gating mechanism. …”
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Sleep Restriction and Weekend Sleep Compensation Relate to Eating Behavior in School-Aged Children by Chamorro R, Garrido-González M, Gutierrez M, Santos JL, Weisstaub G

“…Actigraphic recordings measured sleep patterns for 4 consecutive days, including a weekend day. …”
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Pedagogical university students’ ethical attitudes and competences regarding artificial intelligence: An empirical study by Alicja Baum, Maria Trzcińska-Król

“…Students expressed more positive attitudes towards the benefits of AI (M = 3.22) than levels of understanding of its disadvantages (M = 2.72). Respondents rated their competences (M = 2.65) and knowledge (M = 2.85) regarding AI as below average. …”
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Forecasting Sales in Live-Streaming Cross-Border E-Commerce in the UK Using the Temporal Fusion Transformer Model by Qi Zhang, Xue Li, Pengbin Gao

“…Our multimodal approach integrates diverse time series data, including historical sales, key opinion leader (KOL) influence, and seasonal patterns. The Temporal Fusion Transformer (TFT) model demonstrated consistently lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) across all forecasting horizons compared to other machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Unit(GPU)-accelerated architectures. …”
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A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence by Xinyi Yang, Juan Li

“…We develop tailored models that enhance prediction accuracy by clustering patients based on carbohydrate (CHO) intake patterns. Utilizing Simple Recurrent Neural Network (SimpleRNN) and Gated Recurrent Unit (GRU) methods, the study evaluates the performance of local patients who contribute to training the cluster and global (non-cluster) models. …”
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Neural signatures underlying the effect of social structure on empathy and altruistic behaviors by Xia Tian, Zixin Zheng, Renhui Li, Yue-Jia Luo, Chunliang Feng

“…Afterwards, participants rated the pain experienced by these innocents and shared money with other innocents. …”
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Development of a Quadruplex RT-qPCR Assay for Rapid Detection and Differentiation of PRRSV-2 and Its Predominant Genetic Sublineages in China by Guishan Ye, Siyu Xiong, Zhipeng Su, Guosheng Chen, Siyuan Liu, Zixuan Wang, Huanchun Chen, Anding Zhang

“…Field application using 938 samples from Guangxi A and B farms revealed NADC30-like PRRSV wild-type strains at positivity rates of 13.44% and 3.53%, respectively. Positive samples selected for sequencing were further confirmed using ORF5-based phylogenetic analysis and NSP2 deletion pattern comparison, which aligned with RT-qPCR detection results. …”
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Time Course of Changes in the Level of Procalcitonin in the Development of Nosocomial Pneumonia in Victims with Severe Concomitant Injury in an Intensive Care Unit by A. K. Shabanov, M. Sh. Khubutia, G. V. Bulava, N. V., Beloborodova, A. N. Kuzovlev, O. A. Grebenchikov, D. A. Kosolapov, M. I. Shpitonkov

“…The posttraumatic period in victims with severe concomitant injury is frequently complicated by nosocomial pneumonia, resulting in high mortality rates and longer time and increased cost of treatment in intensive care unit unit patients. …”
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Volumetric evolution of supraglacial lakes in southwestern Greenland using ICESat-2 and Sentinel-2 by T. Feng, T. Feng, X. Ma, X. Ma, X. Liu, X. Liu, X. Liu

“…Additionally, according to the evolution characteristics of SGLs at different elevations, SGLs above 800 m exhibit a similar evolution pattern. A temporal discrepancy in maximum values for both mean area and mean depth implies differential rates of SGL development in the horizontal and vertical dimensions. …”
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Does Seasonality Affect Peptic Ulcer Perforation? A Single-Center Retrospective Study by Iva Krajnović, Zenon Pogorelić, Iva Perić, Marija Ćavar, Matija Borić

“…Previous studies have suggested a seasonal pattern in the occurrence of symptomatic perforated peptic ulcers. …”
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CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING by Melina Melina, Sukono Sukono, Herlina Napitupulu, Norizan Mohamed, Yulison Herry Chrisnanto, Asep ID Hadiana, Valentina Adimurti Kusumaningtyas

“…The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. …”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. …”
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Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE) by Daixuan Wu, Yuecheng Shen, Zhongzheng Zhu, Tijian Li, Jiawei Luo, Zhengyang Wang, Jiaming Liang, Zhiling Zhang, Yunhua Yao, Dalong Qi, Lianzhong Deng, Zhenrong Sun, Meng Liu, Zhi-Chao Luo, Shian Zhang

“…However, limitations such as slow pattern projection rates and time-consuming reconstruction algorithms hinder its throughput for real-time imaging. …”
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Development technologies and models of different types of gas reservoirs in Ordos Basin, NW China by Ailin JIA, Dewei MENG, Guoting WANG, Guang JI, Zhi GUO, Naichao FENG, Ruohan LIU, Suqi HUANG, Shuai ZHENG, Tong XU

“…Four key directions of future research and technological breakthroughs are proposed: (1) Utilizing dual-porosity (fracture-matrix) modeling techniques in low-permeability carbonate reservoirs to delineate the volume and distribution of remaining gas in secondary pay zones, supporting well pattern optimization and production enhancement of existing wells. (2) Integrating well-log and seismic data to characterize reservoir spatial distribution of successive strata, enhancing drilling success rates in low-permeability sandstone reservoirs. (3) Utilizing the advantages of horizontal wells to penetrate effective reservoirs laterally, achieving meter-scale quantification of small-scale single sand bodies in tight gas reservoirs, and applying high-resolution 3D geological models to clarify the distribution of remaining gas and guide well placement optimization. (4) Further strengthening the evaluation of deep coal-rock gas in terms of resource potential, well type and pattern, reservoir stimulation, single-well performance, and economic viability.…”
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MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

“…On the other hand, the TMST module integrates a Gated Multi-stage Temporal Convolution (GMSTC) with a Temporal Multi-Head Self-Attention (TMHSA) to capture global temporal features and accommodate both long-term and short-term dependencies within action sequences. …”
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Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models by Ningchang Kang, Zhaocai Wang, Anbin Zhang, Hang Chen

“…These components are then modeled independently: long short-term memory (LSTM) and convolutional neural networks (CNN) handle trend and seasonal patterns, while gated recurrent units (GRU) and Transformer process residual fluctuations. …”
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