Molecular Modelling in Bioactive Peptide Discovery and Characterisation by Clement Agoni, Raúl Fernández-Díaz, Patrick Brendan Timmons, Alessandro Adelfio, Hansel Gómez, Denis C. Shields

“…AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. …”
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DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications by Rabindra Khadka, Pedro G. Lind, Anis Yazidi, Asma Belhadi

“…However, most existing frameworks for EEG data analysis are either focused on preprocessing techniques or deep learning model development, often overlooking the crucial need for structured documentation and model interpretability. …”
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A model for estimating economic loss attributable to incorrect microbiological diagnosis of antimicrobial resistance and irrational antimicrobial use by Kuzmenkov A.Yu., Vinogradova A.G., Gultyaeva N.A., Svyato O.P.

“…The model's fundamental structure rests upon the relationship between the accuracy of microbiological diagnoses, the likelihood of irrational antimicrobial therapy, and the consequent economic losses. …”
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Complex concept of rudoneftegasogenesis (on the example of the Caspian, Crimean and Black sea regions and Arctic) by V. M. Kharchenko, D. V. Lapta

“…Discussions and conclusion: the basis of the work is the interpretation of the CST, the construction of a geological-tectonic and fluid-dynamic model. …”
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Soft Classification in a Composite Source Model by Yuefeng Cao, Jiakun Liu, Wenyi Zhang

“…A composite source model consists of an intrinsic state and an extrinsic observation. …”
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A classical model for semiclassical state-counting by Jonathan Sorce

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Deep learning model for gastrointestinal polyp segmentation by Zitong Wang, Zeyi Wang, Pengyu Sun

“…Our method employs an encoder-decoder structure with a pre-trained ConvNeXt model as the encoder to learn multi-scale feature representations. …”
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A model for measuring the Quality of Work-Life by R. Forouzandeh Joonaghani, I Raeesi Vanani, S. A. Hosseini

“…Additionally, it is found that Organizational - Physical Conditions (SM = 0.300, p < 0.05), Organizational Work-Life Balance (SM = 0.281, p < 0.05), Social aspect - Communication with Coworkers (SM = 0.291, p < 0.05), and social aspect - Communication with Managers (SM = 0.209, p < 0.05) significantly affect Organizational Psychological Atmosphere and indirect effect on Quality of Work-Life.CONCLUSION: Findings from the Partial Least Squares - Structural Equation Modeling analysis reveal no direct effect of physical conditions, job factors, or communication with managers on the Quality of Work-Life, although indirect relationships were supported. …”
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Bond-slip analysis via a cohesive-zone model simulating damage, friction and interlocking by R. Serpieri, L. Varricchio, E. Sacco, G. Alfano

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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. …”
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Geoelectrical Tomography Investigating and Modeling of Fractures Network around Bittit Spring (Middle Atlas, Morocco) by Kh. Qarqori, M. Rouai, F. Moreau, G. Saracco, O. Dauteuil, D. Hermitte, M. Boualoul, C. Le Carlier de Veslud

“…Joint interpretation of geophysical, geological, structural, and synthetic simulation data allowed identifying a conductive horizontal shallow layer overlying two subvertical families of fractures of NE-SW and NW-SE directions. …”
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On Minimizing Influences Under Multi-Attribute Models by Bo-Yao Wang

“…The proposed framework enables structured and interpretable evaluation of influence in complex cooperative systems with heterogeneous participation and conflicting objectives.…”
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XGBoost algorithm optimized by simulated annealing genetic algrithm for permeability prediction modeling of carbonate reservoirs by Changbing Huang, Xinyu Zhu, Mingyu Lu, Yuling Zhang, Shengbo Yang

“…Abstract Carbonate reservoir has strong heterogeneity, complex pore structure and poor correlation between porosity and permeability, so the traditional permeability model can not meet the needs of logging interpretation. …”
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Community density patterns estimated by species distribution modeling: The case study of an insect virus interaction. by Stéphane Dupas, Jean-Louis Zeddam, Katherine Orbe, Gloria Patricia Barrera Cubillos, Laura Fernanda Villamizar, Patricia Mora, Jovanni Suquillo, Olivier Dangles, Aristóbulo Lopez-Avilla, Alba-Marina Cotes-Prado, Jean-Francois Silvain

“…Infection status was analyzed for spatial structure at different scales: storage bag, storage room, field, locality, country. …”
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Monte Carlo optimization based QSAR modeling of the cytotoxicity of acrylic acid-based dental monomers by Bošković Mirjana, Stanković Saša, Živković Jelena V., Veselinović Aleksandar M.

“…Given that the majority of molecular databases adhere to this molecular structure conformation, the featured QSAR models can serve as a rapid and precise screening tool for novel dental monomers.…”
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Increasing uptake of self-management education programmes for type 2 diabetes in primary care: the Embedding research programme including an RCT by Melanie J Davies, Shona Agarwal, Danielle H Bodicoat, Alan Brennan, Simon Dixon, Helen Eborall, Agnieszka Glab, Laura J Gray, Michelle Hadjiconstantinou, Lisa Huddlestone, Nicky Hudson, Anju Keetharuth, Kamlesh Khunti, Caroline Kristunas, Graham Martin, Alison Northern, Mike Patterson, Daniel Pollard, Rebecca Pritchard, Sally Schreder, Jane Speight, Bernie Stribling, Jackie Sturt, Jess Turner, Christina Weis

“…Both patient-level referral to, and attendance at, structured self-management education programmes were lower or similar during the intervention than control conditions. …”
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Research on Personalized Course Resource Recommendation Method Based on GEMRec by Enliang Wang, Zhixin Sun

“…Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. …”
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Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

“…These results highlight the importance of combining visual and structural data for thorough risk assessment and further the use of interpretable machine learning in urban safety research. …”
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Explaining drivers of housing prices with nonlinear hedonic regressions by Heng Wan, Pranab K. Roy Chowdhury, Jim Yoon, Parin Bhaduri, Vivek Srikrishnan, David Judi, Brent Daniel

“…For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.…”
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DIA-TSK: A Dynamic Incremental Adaptive Takagi–Sugeno–Kang Fuzzy Classifier by Hao Chen, Chenhui Sha, Mingqing Jiao, Changbin Shao, Shang Gao, Hualong Yu, Bin Qin

“…To solve these issues, this study proposes a novel training method consisting of a single dynamic classifier—named the dynamic incremental adaptive Takagi–Sugeno–Kang fuzzy classifier (DIA-TSK)—which leverages the superior non-linear modeling capabilities and interpretability of the TSK fuzzy system. …”
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