Specificity, propagation, and memory of pericentric heterochromatin by Katharina Müller‐Ott, Fabian Erdel, Anna Matveeva, Jan‐Philipp Mallm, Anne Rademacher, Matthias Hahn, Caroline Bauer, Qin Zhang, Sabine Kaltofen, Gunnar Schotta, Thomas Höfer, Karsten Rippe

“…From the experimental data, we developed a predictive mathematical model that explains how chromatin‐bound SUV39H1/2 complexes act as nucleation sites and propagate a spatially confined PCH domain with elevated histone H3 lysine 9 trimethylation levels via chromatin dynamics. …”
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Size Effect on Energy Characteristics of Axial Flow Pump Based on Entropy Production Theory by Hongliang Wang, Xiaofeng Wu, Xiao Xu, Suhao Bian, Fan Meng

“…By solving the unsteady Reynolds-averaged Navier–Stokes (URANS) equations with the Shear Stress Transport (SST) k-omega turbulence model, the external characteristic parameters and internal flow field structures were predicted. …”
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The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program by Michael P. Harms, Kang-Ik K. Cho, Alan Anticevic, Nicolas R. Bolo, Sylvain Bouix, Dylan Campbell, Tyrone D. Cannon, Guillermo Cecchi, Mathias Goncalves, Anastasia Haidar, Dylan E. Hughes, Igor Izyurov, Omar John, Tina Kapur, Nicholas Kim, Elana Kotler, Marek Kubicki, Joshua M. Kuperman, Kristen Laulette, Ulrich Lindberg, Christopher Markiewicz, Lipeng Ning, Russell A. Poldrack, Yogesh Rathi, Paul A. Romo, Zailyn Tamayo, Cassandra Wannan, Alana Wickham, Walid Yassin, Juan Helen Zhou, Jean Addington, Luis Alameda, Celso Arango, Nicholas J. K. Breitborde, Matthew R. Broome, Kristin S. Cadenhead, Monica E. Calkins, Eric Yu Hai Chen, Jimmy Choi, Philippe Conus, Cheryl M. Corcoran, Barbara A. Cornblatt, Covadonga M. Diaz-Caneja, Lauren M. Ellman, Paolo Fusar-Poli, Pablo A. Gaspar, Carla Gerber, Louise Birkedal Glenthøj, Leslie E. Horton, Christy Lai Ming Hui, Joseph Kambeitz, Lana Kambeitz-Ilankovic, Matcheri S. Keshavan, Sung-Wan Kim, Nikolaos Koutsouleris, Jun Soo Kwon, Kerstin Langbein, Daniel Mamah, Daniel H. Mathalon, Vijay A. Mittal, Merete Nordentoft, Godfrey D. Pearlson, Jesus Perez, Diana O. Perkins, Albert R. Powers, Jack Rogers, Fred W. Sabb, Jason Schiffman, Jai L. Shah, Steven M. Silverstein, Stefan Smesny, William S. Stone, Gregory P. Strauss, Judy L. Thompson, Rachel Upthegrove, Swapna K. Verma, Jijun Wang, Daniel H. Wolf, Rene S. Kahn, John M. Kane, Patrick D. McGorry, Barnaby Nelson, Scott W. Woods, Martha E. Shenton, Stephen J. Wood, Carrie E. Bearden, Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ), Ofer Pasternak

“…We also present preliminary variance component analyses of several measures, such as signal- and contrast-to-noise ratio (SNR/CNR) and spatial smoothness, to provide quantitative data on the relative percentages of participant, site, and platform (i.e., scanner model) variance. …”
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Inter-annual cascade effect on marine food web: A benthic pathway lagging nutrient supply to pelagic fish stock. by Lohengrin Dias de Almeida Fernandes, Eduardo Barros Fagundes Netto, Ricardo Coutinho, PELD-RECA

“…These sequential events can explain major changes in ecosystem food web that were not predicted in previous short-term models.…”
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Advances on the Links Between Turbulent and Submeso‐ to Mesoscales During EUREC4A by E. Gauvrit, M.‐N. Bouin, J.‐M. Delouis, F. Boulanger

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Ring-like Bright Monster Waves in Variable-Coefficient Partially Nonlocal Coupled NLS Equations with Directional Diffraction and External Perturbations in (3+1)D by Emmanuel Yomba

“…Partially nonlocal (PNL) variable-coefficient nonlinear Schrödinger equations (NLSEs) represent a significant area of study in mathematical physics and quantum mechanics, particularly in scenarios where potential and coefficients vary spatially or temporally. The (3+1)-dimensional partially nonlocal (PNL) coupled nonlinear Schrödinger (NLS) model, enriched with different values of two transverse diffraction profiles and subjected to gain or loss phenomena, undergoes dimensional reduction to a (2+1)-dimensional counterpart model, facilitated by a conversion relation. …”
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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms by Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq

“…Both models demonstrated high accuracy in predicting flood-prone areas, with RF achieving an accuracy of 92% and XGBoost slightly outperforming it with an accuracy of 94%. …”
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Pathways to Turbulent Dissipation in a Submarine Canyon by Charlotte Bellerjeau, Matthew H. Alford, Arnaud Le Boyer, Giovanni Dematteis, Alberto Naveira Garabato, Gunnar Voet, Nicole Couto, Bethan L. Wynne‐Cattanach

“…Fluxes from both methods agree within a factor of 3 with dissipation estimates from a finescale parameterization which is often used in climate‐scale ocean models. Coarse graining predicts 68% of energy fluxing to dissipation from frequencies lower than 8cpd, while the weakly nonlinear method predicts 34%. …”
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MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition by S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, Jayashree Shetty, Praveen Gurunath Bharathi

“…This study introduces an innovative MTL framework designed to incorporate Multi-Person Attribute Recognition (MPAR) within a single-model architecture. Named MPAR-RCNN, this framework unifies object detection and attribute recognition tasks through a spatially aware, shared backbone, facilitating efficient and accurate multi-label prediction. …”
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Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil by Reginald B. Silva, Piero Iori, Cecilia Armesto, Hugo N. Bendini

“…Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE) are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. …”
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A novel method for grading urban socio-economic development levels based on NTL data and Landsat data by Xiang Hua, Jiehai Cheng, Yuke Meng, Rongji Luo

“…Experimental results demonstrate that the proposed hybrid model achieves strong performance in predicting urban socio-economic development levels, with an accuracy of 86.2%. …”
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Numerical Simulation of Storm Surge-Induced Water Level Rise in the Bohai Sea with Adjoint Data Assimilation by Liqun Jiao, Youqi Wang, Dong Jiang, Qingrong Liu, Jing Gao, Xianqing Lv

“…Based on in situ water level measurements from five tide gauge stations, the model simulated the spatial distributions of water levels under different wind stress drag coefficients (<i>C_D</i>) schemes driven by reanalysis wind fields and interpolated wind fields. …”
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Convolution Representation of Traveling Pulses in Reaction-Diffusion Systems by Satoshi Kawaguchi

“…Additionally, we consider the traveling pulses in the kernel-based Turing model. The stability of the spatially homogeneous state and most unstable wave number are examined. …”
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TourismNeuro xLSTM: neuro-inspired xLSTM for rural tourism planning and innovation by Jing Jiang, You Li, You Li

“…Our model integrates an extended Long Short-Term Memory (xLSTM) framework with spatial and temporal attention mechanisms and a memory module, enabling it to capture both short-term fluctuations and long-term trends in tourism data. …”
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Neutrino Follow-Up Analysis of GRB 221009A with KM3NeT by Palacios González Juan

“…Despite no neutrinos have been detected so far in coincidence with these violent phenomena, numerous models predict neutrino emissions by different mechanisms. …”
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Human attention-guided visual perception is governed by rhythmic oscillations and aperiodic timescales. by Isabel Raposo, Ian C Fiebelkorn, Jack J Lin, Josef Parvizi, Sabine Kastner, Robert T Knight, Assaf Breska, Randolph F Helfrich

“…Our results extend the rhythmic sampling framework of attention by demonstrating that aperiodic neural timescales predict behavior in a spatially-, context-, and demand-dependent manner. …”
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Tooth segmentation in panoramic dental radiographs using deep convolution neural network -Insights from subjective analysis by Suvarna Bhat, Gajanan K. Birajdar, Mukesh D. Patil

“…Also, a subjective analysis of the model’s predicted mask output from the practitioners is carried out. …”
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X-FuseRLSTM: A Cross-Domain Explainable Intrusion Detection Framework in IoT Using the Attention-Guided Dual-Path Feature Fusion and Residual LSTM by Adel Alabbadi, Fuad Bajaber

“…The model predictions are explained using the XAI techniques. …”
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AI Painting Effect Evaluation of Artistic Improvement with Cross-Entropy and Attention by Yihuan Tian, Shiwen Lai, Zuling Cheng, Tao Yu

“…Among other things, the probabilistic calibration network adjusts the model output so that its predicted probabilities are closer to the probability that the sample truly belongs to the classification. …”
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Determinants of variation in home range of wild pigs by Peter E. Schlichting, Sarah R. Fritts, John J. Mayer, Philip S. Gipson, C. Brad Dabbert

“…We used mixed‐effects linear‐regression models to assess how allometric effects of body size and environmental variables (temp, elevation, latitude, and rainfall) could be used to predict home range size at a local scale. …”
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