A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics by Jianrong Huang, Yanlong Ji, Haiquan Yu

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SGCL-LncLoc: An Interpretable Deep Learning Model for Improving lncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning by Min Li, Baoying Zhao, Yiming Li, Pingjian Ding, Rui Yin, Shichao Kan, Min Zeng

“…Extensive experimental results demonstrate that SGCL-LncLoc outperforms both deep learning baseline models and existing predictors, showing its capability for accurate lncRNA subcellular localization prediction. …”
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SpatConv Enables the Accurate Prediction of Protein Binding Sites by a Pretrained Protein Language Model and an Interpretable Bio-spatial Convolution by Mingming Guan, Jiyun Han, Shizhuo Zhang, Hongyu Zheng, Juntao Liu

“…Traditional protein binding site prediction models usually extract residue features manually and then employ a graph or point-cloud-based architecture borrowed from other fields. …”
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Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and <i>K</i>-Means Clustering by Zhenzhu Meng, Yating Hu, Shunqiang Jiang, Sen Zheng, Jinxin Zhang, Zhenxia Yuan, Shaofeng Yao

“…Overall, the proposed model offers a robust and data-efficient solution for slope deformation prediction, providing substantial potential for early warning systems and risk management.…”
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LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research by Yintao Zhang, Lingyan Zheng, Nanxin You, Wei Hu, Wanghao Jiang, Mingkun Lu, Hangwei Xu, Haibin Dai, Tingting Fu, Ying Zhou

“…All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.…”
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Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

“…The top-ranked features (52 tested) from recursive feature elimination (RFE) were short-wave infrared 2, normalized difference moisture index, and average turf soil temperature in the machine learning (ML) model developed. The random forest (RF) model produced the highest accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.83</mn></mrow></semantics></math></inline-formula>) among others tested for biomass yield prediction. …”
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The Prediction of the In Vitro Release Curves for PLGA-Based Drug Delivery Systems with Neural Networks by Zheng Zhang, Bolun Zhang, Ren Chen, Qian Zhang, Kangjun Wang

“…We establish a dataset through a literature review, and the model is trained and validated to determine its effectiveness in predicting different drug release curves. …”
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Wavelet Multiresolution Analysis-Based Takagi–Sugeno–Kang Model, with a Projection Step and Surrogate Feature Selection for Spectral Wave Height Prediction by Panagiotis Korkidis, Anastasios Dounis

“…We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. …”
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Two-Stage Global Biomass Pyrolysis Model for Combustion Applications: Predicting Product Composition with a Focus on Kinetics, Energy, and Mass Balances Consistency by Germán Navarrete Cereijo, Pedro Galione Klot, Pedro Curto-Risso

“…Secondly, kinetic parameters for primary and secondary reactions are determined following a Shafizadeh and Chin scheme but with a modified Arrhenius form dependent on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>T</mi><mi>n</mi></msup></semantics></math></inline-formula>, significantly enhancing the accuracy of product composition prediction. …”
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A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models by Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili, Mohammad Lutfi Othman, Mohammad Effendy Ya’acob, Noor Izzri Bin Abdul Wahab, Masoud Ahmadipour

“…The proposed IMGOMFFNN model is ultimately combined with Polynomial regression model to improve the predictability of the PV system. …”
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Comparative Study of Sphere Decoding Algorithm and FCS-MPC for PMSMs in Aircraft Application by Joseph O. Akinwumi, Yuan Gao, Xin Yuan, Sergio Vazquez, Harold S. Ruiz

“…In this study, we propose a long prediction horizon finite control set model predictive control (FCS-MPC) framework for PMSMs. …”
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IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions by Mert Can Turkmen, Yee Hui Lee, Eng Leong Tan

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Enhanced Forecasting of Equity Fund Returns Using Machine Learning by Fabiano Fernandes Bargos, Estaner Claro Romão

“…In addition, models trained on 1 year of data maintained predictive reliability for up to 2 months into the future, achieving precision above 90% in forecasting funds with 3-month returns above the average. …”
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HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors by Duong Thanh Tran, Nhat Truong Pham, Nguyen Doan Hieu Nguyen, Leyi Wei, Balachandran Manavalan

“…Moreover, we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse, highlighting its effectiveness in enhancing model performance. Furthermore, the HyPepTox-Fuse server is freely accessible at https://balalab-skku.org/HyPepTox-Fuse/ and the source code is publicly available at https://github.com/cbbl-skku-org/HyPepTox-Fuse/. …”
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An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD) by Hephaes Chuen Chau, Julia Yuen Hang Liu, John Anthony Rudd

“…This study used a state-of-the-art deep-learning model with time-series classification to explore the feasibility of using raw electrophysiological recordings from tissues to predict ADRs. …”
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A Comprehensive Ensemble Model for Marine Atmospheric Boundary-Layer Prediction in Meteorologically Sparse and Complex Regions: A Case Study in the South China Sea by Yehui Chen, Tao Luo, Gang Sun, Wenyue Zhu, Qing Liu, Ying Liu, Xiaomei Jin, Ningquan Weng

“…The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. …”
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MED-Prompt: A novel prompt engineering framework for medicine prediction on free-text clinical notes by Awais Ahmed, Xiaoyang Zeng, Rui Xi, Mengshu Hou, Syed Attique Shah

“…To bridge these gaps, this study introduces a novel MED-Prompt framework that employs pretrained models such as BERT, BioBERT, and ClinicalBERT. The core of our framework lies in developing specialized prompts, which act as guiding instructions for the models during the prediction process. …”
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ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized by Vinicius Lins Costa Ok Melo, Pedro Emmanuel Alvarenga Americano do Brasil, PhD

“…The model's bias-corrected intercept and slope were − 0.0004 and 1.079 respectively, the average prediction error was 0.028. …”
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Tuned Generalised <i>k</i>-<i>ω</i> (GEKO) Turbulence Model Parameters for Predicting Transitional Flow Through Stenosis Geometries of Various Degrees by Jake Emmerling, Sara Vahaji, David A. V. Morton, Svetlana Stevanovic, David F. Fletcher, Kiao Inthavong

“…The GEKO model was also able to closely match the axial velocity results predicted by previously published large-eddy simulation models under the same flow conditions. …”
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A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems by Jonathan G. M. Conn, Abdullah Ahmad, David S. Palmer

“…Solvation free energy (SFE) is an important thermodynamic property in characterising molecular solvation and so accurate prediction of this property is sought after. The One-Dimensional Reference Interaction Site Model (RISM) is a well-established method for modelling solvation, but it is known to yield large errors in the calculation of SFE. …”
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