Does the Birthplace Matter for Chinese Pop Music Talents? From the Perspective of Local Cultural Capital by Chen Yifei, He Jinliao

“…Using GIS methods, such as kernel density analysis, the study visualizes the spatial clustering characteristics of Chinese pop singers and explores the factors influencing the spatial distribution of their birthplaces through a negative binomial regression model. …”
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Highly Efficient Broadband Light Absorber Based on Nonuniform Hyperbolic Metamaterial Film by Nina A. Zharova, Alexander A. Zharov, Alexander A. Zharov

“…Using the model of nanowire medium (silver wires in silica host) we predict that 200 nm film of this hyperbolic metamaterial allows reaching almost total absorption of radiation throughout the visible band.…”
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Implications of Solar Radiation Modification on Rainfall and Temperature Patterns over Eastern Africa by Alex Nimusiima, Godwin Ayesiga, Ronald Ingula Odongo, Catherine Mulinde, Lawrence Aribo, Moses Ojara, Bob Alex Ogwang

“…However, G6sulfur shows some inconsistent results, with some models predicting increased temperatures under SRM compared to SSPs, especially for SSP245, while other models predict reduced temperatures in the same period.…”
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Kinematic Adaptive Frame Recognition (KAFR): A Novel Framework for Video Segmentation via Frame Similarity and Surgical Tool Tracking by Huu Phong Nguyen, Shekhar Madhav Khairnar, Sofia Garces Palacios, Amr Al-Abbas, Melissa E. Hogg, Amer H. Zureikat, Patricio M. Polanco, Herbert J. Zeh, Ganesh Sankaranarayanan

“…Our approach follows these steps:1) Tracking phase: a YOLOv8 model is utilized to detect tools presented in the scene, 2) Similarity phase: Similarities between consecutive frames are computed by estimating variation in the spatial positions and velocities of the tools, 3) Classification phase: An X3D CNN is trained to classify segmentation. …”
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HO 2 Generation Above Sprite‐Producing Thunderstorms Derived from Low‐Noise SMILES Observation Spectra by T. Yamada, T. O. Sato, T. Adachi, H. Winkler, K. Kuribayashi, R. Larsson, N. Yoshida, Y. Takahashi, M. Sato, A. B. Chen, R. R. Hsu, Y. Nakano, T. Fujinawa, S. Nara, Y. Uchiyama, Y. Kasai

“…A total of three areas was identified with enhanced HO 2 levels of approximately 10 25 molecules. A chemical sprite model indicates an increase in HO 2 in the considered altitude region; however, the predicted production due to a single sprite event is smaller than the observed enhancement. …”
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Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida by Inacio T. Bueno, Carlos A. Silva, Caio Hamamura, Victoria M. Donovan, Ajay Sharma, Jiangxiao Qiu, Jinyi Xia, Kody M. Brock, Monique B. Schlickmann, Jeff W. Atkins, Denis R. Valle, Jason Vogel, Andres Susaeta, Mauro A. Karasinski, Carine Klauberg

“…We combined Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with synthetic aperture radar (SAR) and passive optical satellite imagery to model GEDI AGBD as a function of image-derived data, enabling predictions across the study area and producing continuous AGBD maps. …”
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Investigating the Nonlinear Relationship Between the Built Environment and Urban Vitality Based on Multi-Source Data and Interpretable Machine Learning by Wenhao Liu, Zhen Yang, Chen Gui, Gen Li, Hongyi Xu

“…Additionally, we analyze the determinants of urban vitality across both micro and macro-scales using multi-source data, semantic segmentation models, and street view imagery. Our findings reveal the following key insights: (1) the distribution of urban vitality exhibits spatial heterogeneity within the main urban area of Shanghai, with high vitality areas concentrated in the Huangpu District and at intersections with neighboring districts, demonstrating a decline from the center to the periphery; (2) the XGBoost model outperforms other comparative models, showcasing superior capabilities in simulating and predicting urban vitality; (3) among the various built environment factors influencing urban vitality, building coverage, population density, and distance to the CBD exert the most significant effects, while the green view index and the number of bus stops contribute relatively less; (4) all built environment factors demonstrate nonlinear impacts and exhibit certain threshold effects on urban vitality. …”
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geodl: An R package for geospatial deep learning semantic segmentation using torch and terra. by Aaron E Maxwell, Sarah Farhadpour, Srinjoy Das, Yalin Yang

“…Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. …”
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Dissecting cirrus clouds: navigating effects of turbulence on homogeneous ice formation by Bernd Kärcher, Fabian Hoffmann, Adam B. Sokol, Blaž Gasparini, Milena Corcos, Eric Jensen, Rachel Atlas, Aurélien Podglajen, Hugh Morrison, Albert Hertzog, Riwal Plougonven, Kamal Kant Chandrakhar, Wojciech W. Grabowski

“…Here we shed first light on how turbulent fluctuations in temperature and supersaturation interact with probabilistic homogeneous freezing. We show that spatial model resolution substantially below 1–10 m is needed to properly simulate homogeneous freezing events. …”
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Uncovering the risks for driver injury severities for truck-trailer and passenger car crashes at highway-railroad crossings by Mouyid Islam, Asim Alogaili

“…The estimated model for truck-trailer and passenger car crashes at railroad crossings included factors, such as spatial variables, weather conditions, ambient lighting, vehicle maneuvers over the crossing, driver age and gender, gate operation characteristics, the location of warning signs, the type of crossing warning (including gates), the stopping position of the truck-trailer on the crossing, and the release of hazardous materials by rail equipment. …”
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Using traffic data to identify land-use characteristics based on ensemble learning approaches by Jiahui Zhao, Zhibin Li, Pan Liu

“…The result averages improved 12.63%, 12.84%, 11.05%, 5.44%, 12.84% for Area Under ROC Curve (AUC), Classification Accuracy (CA), F-Measure (F1), Precision, and Recall, respectively, in classification tasks and 56.81%, 21.20%, 47.29% for Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively, in regression tasks than other models. The Random Forest model performs better in labels with high regularity, such as education, residence, and work activities. …”
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The abundance and sett ecological characteristics of the Asian badger (Meles leucurus) in the Northeast Tiger and Leopard National Park (NE China) by Jiawei Li, Dongrui Li, Yanwen Fu, Yiwen Ma, Jianping Ge, Limin Feng

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Estimating structure of understory bamboo for giant panda habitat by developing an advanced vertical vegetation classification approach using UAS-LiDAR data by Xin Shen, Lin Cao, Yisheng Ma, Nicholas C. Coops, Evan R. Muise, Guibin Wang, Fuliang Cao

“…Three-dimensional understory bamboo point clouds were successfully isolated from the forest point cloud, then bamboo structure predictive models were developed through understory bamboo point cloud metrics and applied over the entire study area to generate spatially continuous maps of understory bamboo structure. …”
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Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection by J. Pérez-Aracil, C. Peláez-Rodríguez, Ronan McAdam, Antonello Squintu, Cosmin M. Marina, Eugenio Lorente-Ramos, Niklas Luther, Verónica Torralba, Enrico Scoccimarro, Leone Cavicchia, Matteo Giuliani, Eduardo Zorita, Felicitas Hansen, David Barriopedro, Ricardo García-Herrera, Pedro A. Gutiérrez, Jürg Luterbacher, Elena Xoplaki, Andrea Castelletti, S. Salcedo-Sanz

“…Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. …”
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The origin and dissemination of HIV-1 subtype B on Hainan Island, South China, 2007–2024 by Chunyun Lu, Kaokao Zhu, Nengde Zhang, Mu Li, Shanmei Zhong, Cai Qin, Tong Luo, Rongjing Zhang, Jing Zhang, Yi Ning, Hao Liang, Li Ye, Bingyu Liang, Dee Yu

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Projections of Extreme Precipitation Changes over the Eastern Tibetan Plateau: Exploring Thermodynamic and Dynamic Contributions by Xiaojiang Liu, Xi Liu, Chengxin Li, Xiaomin Ma, Kena Chen, Zhenhong Sun, Kangning Wang, Quanliang Chen, Hongke Cai

“…Both HR and LR models successfully reproduce the spatial distribution of annual precipitation, capturing the northwest-to-southeast increasing gradient. …”
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Tertiary lymphoid structure-related score as a predictor for survival prognosis and immunotherapy response in head and neck squamous cell carcinoma by Fan Wu, Fan Wu, Haotian Cao, Haotian Cao, Siqi Ren, Siqi Ren, Jiaying Wu, Jiaying Wu, Xin Liu, Xin Liu, Qunxing Li, Qiuping Xu, Qiuping Xu, Jiali Chen, Jiali Chen, Ruixin Wang, Ruixin Wang, Suling Chen, Suling Chen, Shijia Kuang, Shijia Kuang, Binbin Xia, Yanyan Li, Yanyan Li, Liansheng Wang, Jintao Li, Bowen Li, Bowen Li, Jinsong Li, Jinsong Li, Tianjun Lan, Tianjun Lan

“…TLSscore has proven to be a robust predictive model for predicting survival prognosis, immune cell infiltration, somatic mutation and immunotherapy response. …”
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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

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Changes in sex composition of births across regions and subgroups in India from an analysis of birth histories from National Family Health Surveys by Diwakar Mohan, Evan L. Eschliman, Anju Malhotra, Michelle R. Kaufman

“…We describe changes in the sex composition of live births across time through different phases of the PreNatal Diagnostics Technique Act by place of residence, caste, education, and wealth. Multilevel random effects logit regression models were used to estimate odds ratios and adjusted estimates for the different periods. …”
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Cognitive characteristics and ischemic prognosis of quantitative white matter hyperintensities in adult moyamoya disease by Ziqi Liu, Xiaokuan Hao, Qi Duan, Chaoran Shen, Haojin Lyu, Junze Zhang, Jing Gu, Shihao He, Yanru Wang, Xilong Wang, Zhenyu Zhou, Ning Ma, Ran Duan, Xinlin Zhou, Xin Lou, Rong Wang

“…The area under curve (AUC) of the quantitative PVWMH for the prediction of 2-year ischemic events is 0.701, which is better than that of the Fazekas grade (AUC = 0.561) (P = 0.000). …”
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