Nitrogen addition enhances soil carbon and nutrient dynamics in Chinese croplands: a machine learning and nationwide synthesis by Yu Li, Yuan Li

“…We further developed the high-resolution (5 km) national-scale dataset that predicts the spatial responses of SOC and nutrient dynamics to nitrogen addition across China. …”
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LSL-SS-Net: level set loss-guided semantic segmentation networks for landslide extraction by Yueheng Yang, Zelang Miao, Xiaojing Li, Hua Zhang, Shuai Chen

“…However, typical loss functions have several challenges and shortcomings, including difficulty in handling objects of different scales and neglecting spatial correlation. These drawbacks hinder semantic segmentation networks from solely concentrating on accurately predicting landslide categories with rich semantic information. …”
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A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments by Yanchen Zheng, Gemma Coxon, Ross Woods, Jianzhu Li, Ping Feng

“…The results indicate that similar pre‐event catchment conditions may cause distinct runoff response in different catchments, thus predicting the correct spatial cluster is crucial to the estimation accuracy. …”
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Land subsidence in Beijing: response to the joint influence of the South-to-North Water Diversion Project and ecological water replenishment, observed by satellite radar interferom... by Min Shi, Mingliang Gao, Zheng Chen, Mingyuan Lyu, Huili Gong, Yuanzheng Zhai, Yun Pan

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Spatio-temporal evolution and driving mechanism of ecosystem services in typical hilly and gully areas of the loess Plateau: A case study in Yan’an Region, Shaanxi Province by Qiaoyang Yang, Hui Qian, Yanyan Gao, Yicheng Duan, Zhiming Cao, Panpan Tian, Kang Li, Shuhan Yang, Weijie Zhao, Qi Long

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PLIN2 promotes colorectal cancer progression through CD36-mediated epithelial-mesenchymal transition by Fan Yang, Ying Li, Xue Shang, Yun Zhu, Wenting Hou, Yi Liu, Qing Hua, Zhirong Sun

“…The challenge remains to construct reliable prognostic prediction models and to further elucidate the key molecular mechanisms of tumor progression. …”
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Multi-label Text Classification by Fusing Pseudo-label Generation and Data Augmentation by WANG Shuitao, ZHANG Sijia, SUN Xufei, YANG Jingjie, BI Tiantian, ZHANG Zhenglong

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Pre- and post-COVID-19 pandemic identification of dengue hotspots and exploration of population and environmental determinants of dengue in Quezon City, Philippines by John Robert Carabeo Medina, Shin’ya Kawamura, Rie Takeuchi, Rolando V. Cruz, Johnedel Mendoza, Paul Michael R. Hernandez, Fernando B. Garcia, Ernesto R. Gregorio, Jun Kobayashi

“…The association of dengue cases with greenness, surrounding greenness, transportation network hubs, small building ratios, and population density was determined through a generalized linear model (GLM). Results revealed that incidence rates of dengue across barangays were spatially heterogeneous, and the dengue hotspots were unstable as they varied quarterly each year. …”
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Cross-scale study of heat transfer performance in metal rubber with complex topological structures by Kequan Tang, Liangliang Shen, Linwei Shi, Weidong Yan, Qiang Song, Zhiying Ren

“…This approach comprehensively considers critical factors such as porosity, temperature, and interlayer thermal resistance, culminating in the development of a predictive numerical model for thermal conductivity in porous metal-based materials. …”
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CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO₂ fertilization by Y. Kang, Y. Kang, Y. Kang, M. Bassiouni, M. Bassiouni, M. Gaber, M. Gaber, X. Lu, X. Lu, T. F. Keenan, T. F. Keenan

“…Our machine learning models effectively predict monthly GPP (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">∼</span> 0.72), the mean seasonal cycles (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">∼</span> 0.77), and spatial variabilities (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">∼</span> 0.63) based on cross-validation at flux sites. …”
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Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States by Meredith M. Brehob, Michael J. Pennino, Amalia M. Handler, Jana E. Compton, Sylvia S. Lee, Robert D. Sabo

“…We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. …”
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A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

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Intersectionality in residental solar adoption: Examining socio-demographics, racial diversity, geography, solar suitability, and policy in the United States by Gerald Jones, Chien-Fei Chen, Wei-An Chen, Lillian Lovingood, Xueping Li

“…The interplay of income and education significantly contributes to a higher adoption rate. …”
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Real-Time Facial Expression Recognition Based on Image Processing in Virtual Reality by Qingzhen Gong, Xuefang Liu, Yongqiang Ma

“…Hence, this paper suggests the Deep Automatic Facial Expression Recognition Model (DAFERM) for interactive virtual reality (VR) applications such as intelligent education, social networks, and virtual training. …”
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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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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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