X-Raying CAMELS: Constraining Baryonic Feedback in the Circumgalactic Medium with the CAMELS Simulations and eRASS X-Ray Observations by Erwin T. Lau, Daisuke Nagai, Ákos Bogdán, Isabel Medlock, Benjamin D. Oppenheimer, Nicholas Battaglia, Daniel Anglés-Alcázar, Shy Genel, Yueying Ni, Francisco Villaescusa-Navarro

“…However, adopting these enhanced feedback parameters causes deviations in the stellar mass–halo mass relations from observational constraints below the group-mass scale. …”
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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…One of the primary goals of this study is to construct an optimized deep-learning model capable of accurately predicting lattice-physics parameters for future applications of machine learning in nuclear reactor analysis. …”
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The influence of frequency and temperature on the AC-conductivity in $$\text {TlInTe}_2$$ semiconductor single crystal by Mohamed M. Fangary, Muhammad A. O. Ahmed

“…$$\text {TlInTe}_2$$ at room temperature was found to be a tetragonal system with lattice parameters of $$a = 8.494$$ Å and $$c = 7.181$$ Å. The structural parameters, such as crystallite size D, micro strain $$\epsilon$$ , dislocation density $$\delta$$ , and unit cell parameters were determined from XRD spectra. …”
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Novel Insights into Non-Invasive Diagnostic Techniques for Cardiac Amyloidosis: A Critical Review by Marco Maria Dicorato, Paolo Basile, Giuseppe Muscogiuri, Maria Cristina Carella, Maria Ludovica Naccarati, Ilaria Dentamaro, Marco Guglielmo, Andrea Baggiano, Saima Mushtaq, Laura Fusini, Gianluca Pontone, Cinzia Forleo, Marco Matteo Ciccone, Andrea Igoren Guaricci

“…Echocardiography is the first-line imaging modality, although none of its parameters are pathognomonic. According to the 2023 ESC Guidelines, a left ventricular wall thickness ≥ 12 mm is mandatory for the suspicion of CA, making this technique crucial. …”
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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…In the absence of specific prior information, population-level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
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The Investigation of 84 TESS Totally Eclipsing Contact Binaries by Yani Guo, Kai Li, Liheng Wang, Qiqi Xia, Xiang Gao, Jingran Xu, Jingyi Wang

“…For the inconsistent targets, we conducted a detailed investigation and found that the main reasons are poor quality of historical data, or the fact that the machine learning methods used in historical studies might not accurately determine the physical parameters for individual targets.…”
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Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive by Elena Pastorelli, Alper Yegenoglu, Alper Yegenoglu, Nicole Kolodziej, Nicole Kolodziej, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz-Pier, Johan Frederik Storm, Pier Stanislao Paolucci

“…This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. …”
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Measuring lung mechanics in patients with COPD using the handheld portable rapid expiratory occlusion monitor (REOM): A cross‐sectional study by Felix‐Antoine Coutu, Dany Malaeb, Olivia C. Iorio, Seyedfakhreddin Nabavi, Tom McFarland, Lennart K. A. Lundblad, Ron J. Dandurand, Stewart B. Gottfried, Bryan A. Ross

“…Discrimination between GOLD 1 and 4 COPD (Wilcoxon rank sum test, Support Vector Machine (SVM) classifier) and patient user experience (System Utility Scale (SUS), Participant Satisfaction Survey (PSS)) served as secondary outcomes. …”
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Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels by Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey

“…While machine learning methods provide efficient paths toward emulating physics-based pipelines, they often do not properly account for uncertainties and have complex model structure, both of which can lead to biases and inaccurate label inference. …”
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Neuromorphic imaging cytometry on human blood cells by Ziyao Zhang, Haoxiang Yang, Jiayin Li, Shin Wei Chong, Jason K Eshraghian, Ken-Tye Yong, Daniele Vigolo, Helen M McGuire, Omid Kavehei

“…Imaging flow cytometry (IFC) is a powerful cell analytic tool that exploits multi-parameters in single-cell images to characterise cell phenotypes and fluorescence information. …”
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Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles by Zhaobin Chen, Yangpan Ou, Yudan Ding, Ying Wang, Huabing Li, Feng Liu, Ping Li, Dongsheng Lv, Yong Liu, Bing Lang, Jingping Zhao, Wenbin Guo

“…Abnormal ReHo alterations were found in orbitofrontal gyrus, temporal gyrus, and cingulum among three groups, associated with specific eye movement parameters. These differences in eye movement and ReHo allowed for high-accuracy discrimination between them. …”
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Formation drivers and photochemical effects of ClNO₂ in a coastal city of Southeast China by G. Chen, G. Chen, G. Chen, X. Fan, X. Fan, H. Wang, Y. J. Tham, Z. Lin, Z. Lin, Z. Lin, X. Ji, X. Ji, X. Ji, L. Xu, L. Xu, B. Hu, J. Chen, J. Chen

“…In this study, the field observations of <span class="inline-formula">ClNO₂</span> and related parameters were conducted in a coastal city of Southeast China during the autumn of 2022, combining with machine learning and model simulations to elucidate its key influencing factors and atmospheric impacts. …”
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Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval by Xiaodong Huang, Lorenzo Giuliano Papale, Marco Lavalle, Fabio Del Frate, Heresh Fattahi, Steven K. Chan, Rowena B. Lohman, Xiaolan Xu, Yunjin Kim

“…Soil moisture retrievals based on rigorous physical backscattering models require a comprehensive description of the vegetation structure and biophysical parameters, including the density of the scatterers, height, and vegetation water content. …”
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MLAR-Net: A Multilevel Attention-Based ResNet Module for the Automated Recognition of Emotions Using Single-Channel EEG Signals by M. Maithri, U. Raghavendra, Anjan Gudigar, Samir Kumar Praharaj, Karthikeyan Sriram, Massimo Salvi, Chai Hong Yeong, Filippo Molinari, U. Rajendra Acharya

“…Our study identifies channel number 24 (T7) as the most effective for emotion classification, achieving an average accuracy of 98.06% using a cubic support vector machine and a maximum accuracy of 99.51% using fine K-Nearest Neighbors. …”
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Development and validation of a CT-based multi-omics nomogram for predicting hospital discharge outcomes following mechanical thrombectomy by Feifan Liu, Jiayi Hong, Yuhan Chen, Huan Liu, Yue Wang, Lijian Su, Sheng Hu, Jingjing Fu

“…ObjectiveThis study aimed to develop a multi-omics nomogram that combines clinical parameters, radiomics, and deep transfer learning (DTL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict functional outcomes at discharge.MethodsThis study enrolled 246 patients with HIM who underwent MT. …”
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Fatigue trajectories by wearable remote monitoring of breast cancer patients during radiotherapy by Angela Barillaro, Chiara Feoli, Adriano Tramontano, Marco Comerci, Mara Caroprese, Renato Cuocolo, Oscar Tamburis, Mario Petrazzuoli, Maria Anna D’Arienzo, Antonio Farella, Caterina Oliviero, Stefania Clemente, Laura Cella, Mario Magliulo, Manuel Conson, Roberto Pacelli

“…A novel concept based on patient Repeated Activity Window (RAW) was introduced to evaluate HR and STP variations during RT. Several Machine Learning (ML) methods were trained to characterize RIF on the basis of HR and STP collected data. …”
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Cardiac computer tomography-derived radiomics in assessing myocardial characteristics at the connection between the left atrial appendage and the left atrium in atrial fibrillation... by Xiao-Xuan Wei, Cai-Ying Li, Hai-Qing Yang, Peng Song, Bai-Lin Wu, Fang-Hua Zhu, Jing Hu, Xiao-Yu Xu, Xin Tian

“…The radiomics model was built by extracting radiomic features of the myocardial tissue using Pyradiomics, and employing Least absolute shrinkage and selection operator (LASSO) method for feature selection, combining random forest with support vector machine (SVM) classifier.ResultsThere were 82 cases in the AF group [44 males, 65.00 (59, 70)], and 56 cases in the control group (21 males, 61.09 ± 7.18). …”
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Capturing constraints on boreal gross primary productivity using the remote sensing-based CAN-TG model. by Ramon Melser, Nicholas C. Coops, Michael A. Wulder, Chris Derksen, Sara H. Knox, Tongli Wang

“…Across highly heterogeneous landscapes like the Canadian boreal, these parameters are difficult to constrain and often site-specific. …”
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Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery by Anting Guo, Wenjiang Huang, Binxiang Qian, Kun Wang, Huanjun Liu, Kehui Ren

“…The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. The results indicated selected pigments and SIs effectively distinguished stripe rust-infected wheat from healthy wheat at 7, 16, and 23 DPI. …”
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LeafLaminaMap: Exploring Leaf Color Patterns Using RGB Color Indices by Péter Bodor-Pesti, Lien Le Phuong Nguyen, Thanh Ba Nguyen, Mai Sao Dam, Dóra Taranyi, László Baranyai

“…Linear discriminant analysis (LDA) and support vector machine (SVM) analysis provided a perfect recognition in calibration and confirmed that energy and entropy have the strongest discriminative power between the healthy and symptomatic samples. …”
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