Recombinant expression and functional analysis of transgelin-like protein from the shell of Mytilus coruscus by JIANG Yuting, SUN Qi, XU Huanzhi, SHEN Wang, ZHANG Xiaolin, FAN Meihua, LIAO Zhi

“…Sequence analysis showed that the TLP contained a calponin homology (CH) domain, and the spatial structure of TLP predicted by SWISS-MODEL presented a conformation formed predominately by α-helices. …”
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Advances in tumor subclone formation and mechanisms of growth and invasion by Yuhong Zhang, Weidong Wang

“…Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. …”
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Detecting forest and linear woody feature change between 1954 and 2019 in Southeastern Canadian agroecosystems for regional biodiversity assessment by Darren Pouliot, Niloofar Alavi, Mao Mao, Jon Pasher, Jason Duffe

“…The deep learning model developed here successfully mapped forests and LWFs between 1954 and 2019 using just a single image band, enabling reliable change estimation. …”
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Disaster recovery and business continuity: A case of MSMEs in Dar es Salaam by Tumpale Sakijege

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How the hadron gas affects X(3872) and ψ(2S) production in heavy ion collisions by Abreu Luciano M., Navarra Fernando S., Vieira Hildeson P.L.

“…The initial conditions for this evolution are given by the coalescence model, which depends on the spatial configuration of the states and yields quite different numbers for compact tetraquarks and extended meson molecules. …”
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A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content by Feiyu Lu, Boming Zhang, Yifei Hou, Xiao Xiong, Chaoran Dong, Wenbo Lu, Liangxue Li, Chunli Lv

“…A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. …”
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Automated Detection of Center-Pivot Irrigation Systems from Remote Sensing Imagery Using Deep Learning by Aliasghar Bazrafkan, James Kim, Rob Proulx, Zhulu Lin

“…YOLOv11, using Landsat 8 panchromatic data, achieved the highest detection accuracy (precision: 0.98; recall: 0.91; and F1-score: 0.94) among all tested datasets and models. Spatial autocorrelation and hotspot analysis revealed systematic prediction errors, suggesting a need to adjust training data regionally. …”
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Mapping soil organic carbon stocks of different land use types in the Southern Moscow region by applying machine learning to legacy data by Yury A. Dvornikov, Lukyan A. Mirniy, Ekaterina S. Mukvich, Kristina V. Ivashchenko

“…The root mean square error of cross-validation (RMSEcv = 0.67 kg/m2) was chosen to describe the uncertainty of soil organic carbon stock prediction. According to the model, the total soil organic carbon stocks in the upper 10 cm soil layer of the Podolsky District were 2.65 0.72 Tg, for the Serpukhovsky District 2.77 0.73 Tg.…”
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Urban tree species benchmark dataset for time series classificationEasyData - Data Terra by Clément Bressant, Romain Wenger, David Michéa, Anne Puissant

“…Model outputs—predicted species, confidence scores, and correctness flags—are provided, along with an interactive t-SNE visualization of the latent feature space for interpretability and error analysis. …”
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Transcending the urban–rural dichotomy: inequality in urban green space availability among urban neighbourhoods, urban villages and rural villages in Guangzhou, China by Fubin Luo, Yunzheng Zhang, Luyang Zhang

“…This study unveils the spatial disparity of UGS inequality among different types of neighbourhoods, providing insights into equal green space planning and contributing to environmental justice.…”
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Discriminative Cross-Modal Attention Approach for RGB-D Semantic Segmentation by emad mousavian, Danial Qashqai, Shahriar B. Shokouhi

“…By integrating the DCMAF module with efficient channel- and spatial-wise attention modules, we introduce the Discriminative Cross-modal Network (DCMNet), a scale- and appearance-invariant model. …”
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Snow drought to hydrologic drought progression using machine learning and probabilistic analysis by Pouya Moghaddasi, Keyhan Gavahi, Hamid Moradkhani

“…This study explores the relationship between Snow Water Equivalent (SWE) and streamflow in snow-dominated watersheds using the Long Short-Term Memory (LSTM) model and probabilistic analysis. While LSTM model is typically used for prediction, we employed it primarily to understand how snow affects streamflow. …”
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Cosmological Constraints from Combining Photometric Galaxy Surveys and Gravitational Wave Observatories by E.L. Gagnon, D. Anbajagane, J. Prat, C. Chang, J. Frieman

“…More interestingly, the optical-GW data combination provides tight constraints on models with primordial non-Gaussianity (PNG), due to the predicted scale-dependent bias in PNG models on large scales. …”
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The mean and extreme tropical cyclone wave climate throughout the Coral Sea, from 1979 to 2020 by John G. Miller, Guilherme Vieira da Silva, Darrell Strauss

“…An available WAVEWATCH III® hindcast model dataset with surface wind forcing from the National Centres for Environmental Prediction supplied Climate Forecasts System Version 2 was used. …”
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DHS-YOLO: Enhanced Detection of Slender Wheat Seedlings Under Dynamic Illumination Conditions by Xuhua Dong, Jingbang Pan

“…Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…Overall, this study reveals that the inclusion of PlanetScope and the application of more complex CNN models is not necessary for canopy height predictions in tropical forests, as open-access optical data combined with simpler tree-based models, such as RF, demonstrate comparable performance.…”
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Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Approach with Integration of Reservoir Mineralogy and Porosity by Papa Owusu, Abdelmoneam Raef, Essam Sharaf

“…This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. …”
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Global and regional patterns of soil metal(loid) mobility and associated risks by Chongchong Qi, Tao Hu, Yi Zheng, Mengting Wu, Fiona H. M. Tang, Min Liu, Bintian Zhang, Sybil Derrible, Qiusong Chen, Gongren Hu, Liyuan Chai, Zhang Lin

“…Here we show a theory-guided machine learning model that predicts soil metal(loid) fractionation across the globe. …”
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The hydrological impact of greening and climate change on the Mu Us Sandy land of China under the background of declining ecological efficiency by Jiazheng Li, Rong Wu, Mengjing Li, Zijun Wang, Chenfeng Cui, Junrong Liu

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Research on the Error Estimation Method for Electric Energy Meters of Electric Vehicle Charging Piles based on Deep Learning by Wang Juan, Liu Wei, Zhang Yong, Liu Zhi, Zheng Xiaolei, Wang Yuxin, Hao Jianshu, Dai Xuanding

“…First, the convolutional module is combined with the highway network to extract spatial features from smart meter data for charging facilities while retaining some original information to improve model prediction performance. …”
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