Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models by Xuduo Lin, Ziang Qi 

Subjects: “…risk prediction…”
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Metabolic profiling and early prediction models for gestational diabetes mellitus in PCOS and non-PCOS pregnant women by Jin Wang, Can Cui, Fei Hou, Zhiyan Wu, Yingying Peng, Hua Jin

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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…While several experimental studies exist, there is a clear gap in utilizing data-driven strategies to efficiently predict and optimize the strength performance of such materials. …”
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Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance by Brittany Knight, Taylor Otwell, Michael P. Coryell, Jennifer Stone, Phillip Davis, Bryan Necciai, Paul E. Carlson, Shanmuga Sozhamannan, Alyxandria M. Schubert, Yi H. Yan

“…Our findings highlighted the potential of using machine learning models to predict the impact of emerging mutations on the performance of specific molecular test designs.…”
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Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models by Assaf Shmuel, Teddy Lazebnik, Oren Glickman, Eyal Heifetz, Colin Price

“…In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. …”
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Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors. by Toheeb Salahudeen, Maher Maalouf, Ibrahim Abe M Elfadel, Herbert F Jelinek

“…Notably, including mitochondrial peptides alongside clinical factors significantly enhances predictive capability, shedding light on the interplay between depression severity and mitochondrial oxidative stress pathways. …”
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StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models by Amanda Bleichrodt, Amelia Phan, Ruiyan Luo, Alexander Kirpich, Gerardo Chowell

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EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network by Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang

Subjects: “…GPCR-compound interaction prediction…”
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Development and validation of prediction models for special subtype of primary aldosteronism: patients with negative adrenal CT imaging by Hong Zhao, Pan Hu, Min Mao, Xin Li, Ling Wang, Jing Chang

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Artificial intelligence models development for profitability factor prediction in concentrated solar power with dual backup systems by Zaher Mundher Yaseen, Omer A. Alawi

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Short-Term Prediction of Bus Station Fleet Number Using a Combination of BiLSTM Models by Joko Siswanto, Ainun Rahmwati, Untung Rahardja, Nanda Dwi Putra, Muhammad Iman Nur Hakim, Tito Pinandita, Ilham Bagus Prasetyo

“… Predicting the number of bus station fleets requires a holistic approach, using sophisticated data analysis techniques and appropriate predictive modeling. …”
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Predicting Kinematic Viscosity and Cetane Number of Diesel- Biodiesel Blend Using Neural Network and Empirical Models by M. yari, Gh. Moradi, M. Abdolmaleki, Sh. Bashiri

“…Therefore, a reasonable approach is required for predicting the diesel-biodiesel blend properties. This study tries to estimate two substantial properties of blend, i.e. kinemattic viscosity (KV) and cetane number (CN), through neural network (NN) and empirical models which use pure properties of biodiesel (kinematic viscosity, boiling point, evaporation point, flash point, pour point, heat of combustion, cloud point, and specific gravity) as independent variables. …”
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Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids by Kaikai Li, Yuesong Zhu, Sensen Shi, Yongzheng Song, Haiyan Jiang, Xiaochun Zhang, Shaojuan Zeng, Xiangping Zhang

“…This work combined the Ionic Fragment Contribution (IFC) strategy with machine learning (ML) to develop four models (IFC-ML) to predict NH3 solubility in ILs. …”
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Development and evaluation of machine learning models for individualized prediction of myopia control efficacy treated with overnight orthokeratology by Lan Zhang, Mingjun Gao, Yiru Wang, Siqi Zhang, Huailin Zhu, Qi Zhao

“…PurposeThe primary objective of this study is to develop a predictive model utilizing fundamental clinical and ocular measurements to predict the effect of overnight orthokeratology on myopia control. …”
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Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models by P. Gooya, N. C. Swart, P. Landschützer

Subjects: “…decadal predictability…”
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Phy-X/PSD and NGCAL Models of Several Metal Sulphides: Theoretical Prediction of Gamma Shielding Efficiency by Nadher Ali Salman, Kafa Khalaf Hammud

“… Various metal sulphides were selected for gamma and neutron shielding prediction by (Phy-X and NGCAL). The gamma parameters calculated by both software programs in the studied energy range were found to be identical, with differences (Δ% less than 1%) between both models. …”
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Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers by Junxiao Wang, Shiying Zhang, Jia Li, Mei Deng, Zhi Zeng, Zehua Dong, Fangfang Chen, Wen Liu, Lianlian Wu, Honggang Yu

“…Recently, deep learning-based IHC biomarker prediction models have been widely developed, but few investigations have explored their clinical application effectiveness. …”
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Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next? by Juan Diaz-Colunga, Ramon Diaz-Uriarte

“…Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. …”
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New QSAR Models to Predict Human Transthyretin Disruption by Per- and Polyfluoroalkyl Substances (PFAS): Development and Application by Marco Evangelista, Nicola Chirico, Ester Papa

“…Uncertainty quantification for each model and prediction further enhanced the reliability assessment of predictions. …”
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Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars by Teerapun Saeheaw

“…Contrary to conventional approaches emphasizing electrochemical indicators, automatic relevance determination revealed supplementary cementitious materials (silica fume and fly ash) as dominant predictive factors. All advanced models exhibited excellent generalization (gaps < 0.02) and real-time efficiency (<0.006 s), with probabilistic uncertainty quantification enabling risk-informed infrastructure management. …”
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