ASSESSMENT OF ROMANIAN ALPINE HABITATS SPATIAL SHIFTS BASED ON CLIMATE CHANGE PREDICTION SCENARIOS by ADRIAN CONSTANTINESCU, JENICĂ HANGANU, ANTHONY LEHMANN, NICOLAS RAY

“…Under 1950–2000 climate scenario, both models exhibited high AUC values (> 0.9). The predicted geographical distribution of Mesophilous oligotrophic mountain pasture and Subalpine oligotrophic pastures coded as VNG and PON habitat modeled by Maxent and BIOCLIM shows differences between the modeling approaches, with Maxent predicting smaller areas (12% less) of suitable habitat than BIOCLIM. …”
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Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling by Hyunhye Song, Kiyeol Kim, Jihun Shin, Gitae Roh, Changsu Shim

“…Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. …”
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Analysis of the Full Spitzer Microlensing Sample. I. Dark Remnant Candidates and Gaia Predictions by Krzysztof A. Rybicki, Yossi Shvartzvald, Jennifer C. Yee, Sebastiano Calchi Novati, Eran O. Ofek, Ian A. Bond, Charles Beichman, Geoff Bryden, Sean Carey, Calen Henderson, Wei Zhu, Michael M. Fausnaugh, Benjamin Wibking, The Spitzer Team, Andrzej Udalski, Radek Poleski, Przemek Mróz, Michał K. Szymański, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Jan Skowron, Krzysztof Ulaczyk, Patryk Iwanek, Marcin Wrona, The OGLE Collaboration, Yoon-Hyun Ryu, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Kyu-Ha Hwang, Youn Kil Jung, In-Gu Shin, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Hyoun-Woo Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, The KMTNet Collaboration, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Akihiko Fukui, Ryusei Hamada, Shunya Hamada, Naoto Hamasaki, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Tutumi Nagai, Kansuke Nunota, Greg Olmschenk, Clement Ranc, Nicholas J. Rattenbury, Yuki K. Satoh, Takahiro Sumi, Daisuke Suzuki, Paul. J. Tristram, Aikaterini Vandorou, Hibiki Yama, MOA collaboration, Łukasz Wyrzykowski, Kornel Howil, Katarzyna Kruszyńska

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Development of Shear Capacity Prediction Model for FRP-RC Beam without Web Reinforcement by Md. Arman Chowdhury, Zubayer Ibna Zahid, Md. Mashfiqul Islam

“…Available codes and models generally use partially modified shear design equation, developed earlier for steel reinforced concrete, for predicting the shear capacity of FRP-RC members. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability. …”
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Does Attentional Bias Predict Relapse in Addiction? A Systematic Review of Longitudinal Studies by Zehra Su Topbaş, Eda Albayrak Günday, Nuray Şimşek, Emre Usta

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Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays by Cristina Fortuno, Inés Llinares-Burguet, Daffodil M. Canson, Miguel de la Hoya, Elena Bueno-Martínez, Lara Sanoguera-Miralles, Sonsoles Caldes, Paul A. James, Eladio A. Velasco-Sampedro, Amanda B. Spurdle

“…Data supported the use of SpliceAI ≥ 0.2 cutoff for predicted splicing impact of TP53 variants. Prediction of aberration types using SpliceAI-10k calculator generally aligned with the corresponding assay results, though maximum SpliceAI score did not accurately predict level of aberrant expression. …”
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DINOV2-FCS: a model for fruit leaf disease classification and severity prediction by Chunhui Bai, Chunhui Bai, Chunhui Bai, Lilian Zhang, Lilian Zhang, Lilian Zhang, Lutao Gao, Lutao Gao, Lutao Gao, Lin Peng, Lin Peng, Lin Peng, Peishan Li, Peishan Li, Peishan Li, Linnan Yang, Linnan Yang, Linnan Yang

“…The mIoU of the trained model reached 83.95%, and the accuracy of disease severity grading was 95.24%.DiscussionThe results demonstrate that the model exhibits superior performance compared to other state-of-the-art models and that the model has strong generalization capabilities. This study provides a new method for leaf disease classification and leaf disease severity prediction for a variety of fruits. …”
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The diagnostic and predictive value of LncRNA PANDAR in gastric cancer and its regulation of gastric cancer progression via miR-637 by Weikang Su, Zhiling Zhong, Huazhi Li, Lingjia Meng, Xinqiang Zhong

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A robust adaptive meta-sample generation method for few-shot time series prediction by Chao Zhang, Defu Jiang, Kanghui Jiang, Jialin Yang, Yan Han, Ling Zhu, Libo Tao

“…When using meta-learning techniques to process FTSP tasks, researchers set the meta-parameter in model-agnostic meta-learning (MAML) as a meta-sample and construct meta-sample generation methods based on advanced generative modeling theory to achieve better uncertainty coding. The existing meta-sample generation methods in FTSP scenes have an inherent limitation: With the increase of the complexity of prediction tasks, samples based on Gaussian distribution may be sensitive to noise and outliers in the meta-learning environment and lack of uncertainty expression, thus affecting the robustness and accuracy of prediction. …”
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Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach by Rafael Travincas, Maria Paula Mendes, Isabel Torres, Inês Flores-Colen

“…This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. …”
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Numerical and analytical study on the punching shear capacity prediction for eccentrically loaded flat slabs with openings by Bara'a R. Alnemrawi, Rajai Z. Al-Rousan

“…In addition, the NLFEA punching shear capacity results were compared with three international codes (American Concrete Institute (ACI318-119), EuroCode (EC2), and Model Code (MC2010)) to highlight their potential predictability and weakness points. …”
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Neural Network-Based Prediction of Amplification Factors for Nonlinear Soil Behaviour: Insights into Site Proxies by Ahmed Boudghene Stambouli, Lotfi Guizani

“…The identification of the most pertinent site parameters to classify soils in terms of their amplification of seismic ground motions is still of prime interest to earthquake engineering and codes. This study investigates many options for improving soil classifications in order to reduce the deviation between “exact” predictions using wave propagation and the method used in seismic codes based on amplification (site) factors. …”
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Improving drug-drug interaction prediction via in-context learning and judging with large language models by He Qi, He Qi, Xiaoqiang Li, Chengcheng Zhang, Tianyi Zhao, Tianyi Zhao

“…The proposed method outperforms existing LLM approaches, demonstrating the potential of LLMs for predicting DDIs. We introduce a novel in-context learning (ICL) prompt paradigm that selects high-similarity samples as positive and negative prompts, enabling the model to effectively learn and generalize knowledge. …”
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Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis by Alikhan Anuarbekov, Jiří Kléma

“…By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy. …”
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Alternative splicing analysis in a Spanish ASD (Autism Spectrum Disorders) cohort: in silico prediction and characterization by S. Dominguez-Alonso, M. Tubío-Fungueiriño, J. González-Peñas, M. Fernández-Prieto, M. Parellada, C. Arango, A. Carracedo, C. Rodriguez-Fontenla

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Ethical and social issues in prediction of risk of severe mental illness: a scoping review and thematic analysis by Ivars Neiders, Signe Mežinska, Neeltje E. M. van Haren

“…Abstract Background Over the last decade, there has been considerable development in precision psychiatry, especially in the development of novel prediction tools that can be used for early prediction of the risk of developing a severe mental disorder such as schizophrenia, depression, bipolar disorder. …”
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TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction by Kai Gao, Jiahao Zhou, Yunxian Chi, Yimin Wen

“…To address these existing problems, we propose a tourism named entity recognition model that jointly predicts entity boundaries, adopting a training strategy of data preprocessing to enhance the model’s ability for tourism named entity boundary recognition, while our model introduces a pre-trained Bert model as well as BiLSTM coding to enhance the representation of the model’s contexts, and uses a combined predictor of Biaffine and MLP to enhance the model’s recognition performance for boundaries, as well as introducing label smoothing cross entropy to smooth the target labels during the training process. …”
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Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters by Haining Wang, Jiang Geng, Bowen Feng, Di Xie, Peng Zhang, Liangliang Wang, Jigang Yao

“…This paper proposes a model predictive control (MPC)-based power quality optimization method designed to enhance the low-voltage ride-through (LVRT) capability of grid-connected inverters under various grid voltage sag conditions, while achieving multi-objective power quality optimization. …”
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Full radius integrated modelling of ohmic ramp-up at TCV including self consistent density prediction by M. Marin, Y. Camenen, C. Bourdelle, F.J. Casson, R. Coosemans, L. Garzotti, P. Maget, P. Manas, A. Najlaoui, O. Sauter, the TCV Team

“…The input and output are in machine and code generic IMAS data format. The HFPS predicts the evolution of the current, temperature, main ion density and impurity density up to the separatrix. …”
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