Health classification of pumps using transformer-based deep learning by Arunachalam Shivaa T V, Jayaprasanth Devakumar, Arunshankar Jayabalan

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Boosting Cognitive Focus via Attention Types Detection using Brain-Computer Interfaces: A Pilot Study by Mihai-Robert BEU, Tudor DURDUMAN-BURTESCU, David GHEORGHICĂ ISTRATE

“…Results demonstrated a 57% concentration increase in AR versus VR (where participants performed identical tasks in a non-adaptive virtual environment) with personality-tailored models boosting classification accuracy by 10%. High-performing classifiers (e.g., Deep Neural Networks, XGBoost) achieved 87% accuracy, underscoring BCIs’ potential for personalized cognitive interventions in education and therapy. …”
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Digital twin and contact analysis of ultra-long distance coiled tubing operation structures [version 2; peer review: 1 approved, 2 approved with reservations] by Wenlan Wei, Jiarui Cheng, Hao Qu, Maliang Wang, Wenyuan Wang

“…Results The research results show that the digital twin coiled tubing and contact algorithm can effectively predict the contact state of coiled tubing operations; the verification model shows that the contact algorithm can accurately analyze the contact state of different well trajectories. …”
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Multiscale Two-Stream Fusion Network for Benggang Classification in Multi-Source Images by Xuli Rao, Chen Feng, Jinshi Lin, Zhide Chen, Xiang Ji, Yanhe Huang, Renguang Chen

“…Experimental comparisons of different data inputs and network models revealed that the proposed method outperformed current state-of-the-art approaches in extracting spatial features and textures of Benggangs. …”
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AI-augmented pathology: the experience of transfer learning and intra-domain data diversity in breast cancer metastasis detection by Manuel Cossio, Nina Wiedemann, Esther Sanfeliu Torres, Esther Barnadas Sole, Laura Igual

“…To enhance interpretability, we developed a visualization tool that employs color-coded probability maps to highlight tumor regions alongside their prediction confidence. Our experiments demonstrated that integrating an external dataset (Camelyon16) during training significantly improved model performance, surpassing the benefits of data augmentation alone. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. …”
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Spatio-Temporal Coupling and Forecasting of Construction Industry High-Quality Development and Human Settlements Environmental Suitability in Southern China: Evidence from 15 Provi... by Keliang Chen, Bo Chen, Wanqing Chen

“…Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. …”
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Enhanced simulation of gross and net carbon fluxes in a managed Mediterranean forest by the use of multi-sensor data by Marta Chiesi, Nicola Arriga, Luca Fibbi, Lorenzo Bottai, Luigi D'Acqui, Alessandro Dell’Acqua, Sara Di Lonardo, Lorenzo Gardin, Maurizio Pieri, Fabio Maselli

“…The proposed advancements are aimed at improving the model performance in managed Mediterranean forests and concern: i) the calibration of C-Fix GPP sensitivity to water stress; ii) the quantification of the green, woody and soil C pools which regulate the prediction of NPP and NEP. …”
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Uncovering the importance of soil type, landscape connectivity, and pedodiversity for nature’s contributions to people in a typical agricultural region by Guangyi Deng, Haibo Jiang, Yang Wen, Chunguang He, Lianxi Sheng, Dehai Gu, Shuai Ma

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Mapping the invasive Spartina alterniflora in sub-meter level with improved phenological spectral features and deep learning method by Bingfeng Zhou, Meng Xu, Jinyan Tian, Yue Huang, Jie Song, Lin Zhu, Xiumin Zhu, Xinyuan Qu, Liyan Zhang, Xiaojuan Li, Huili Gong

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Evaluation of Pedestrian-Perceived Comfort on Urban Streets Using Multi-Source Data: A Case Study in Nanjing, China by Jiarui Qin, Yizhe Feng, Yehua Sheng, Yi Huang, Fengyuan Zhang, Kaixuan Zhang

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Study on the influence of complex fault geometry on earthquake risk assessment based on physical simulation: a case study of the Zhujiangkou Fault in the Guangdong–Hong Kong–Macao... by Houyun Yu, Yilong Li, Xiuwei Ye, Wenqiang Wang, Chunjing Wang, Zhenguo Zhang, Wei Zhang, Xiaofei Chen

“…Dynamic rupture simulations offer a physics-based approach to model earthquake processes and assess seismic hazards. …”
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Glacier meltwater has limited contributions to the total runoff in the major rivers draining the Tibetan Plateau by Yi Nan, Fuqiang Tian, Jeffrey McDonnell, Guangheng Ni, Lide Tian, Zongxing Li, Denghua Yan, Xinghui Xia, Ting Wang, Songjun Han, Kunbiao Li

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Challenges of COVID-19 Case Forecasting in the US, 2020-2021. by Velma K Lopez, Estee Y Cramer, Robert Pagano, John M Drake, Eamon B O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter P Mueller, Jade Xiao, Johannes Bracher, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Sen Pei, Jeffrey Shaman, Teresa K Yamana, Samuel R Tarasewicz, Daniel J Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L Hill, Joshua Kaminsky, Elizabeth C Lee, Joseph C Lemaitre, Justin Lessler, Claire P Smith, Shaun Truelove, Matt Kinsey, Luke C Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore Y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav Marathe, Akhil Sai Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V Prasad, Jo W Walker, Alexander E Webber, Rachel B Slayton, Matthew Biggerstaff, Nicholas G Reich, Michael A Johansson

“…We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. …”
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Enhancing seabed sediment classification with multibeam echo-sounding and self-training: a case study from the East Sea of South Korea by Changhoon Lee, Sujung Park, Daeung Yoon, Bo-Yeon Yi, Moonsoo Lim

“…To mitigate sample scarcity and class imbalance, a semi-supervised self-training loop iteratively added high-confidence pseudo-labels to the training set.ResultsField validation in the East Sea (Republic of Korea) showed that the Extreme Gradient Boosting model achieved the highest accuracy. Overall prediction accuracy increased from 60.81 % with the baseline workflow to 72.73 % after applying data interpolation, enhanced feature extraction, and self-training.DiscussionThe proposed combination of U-Net interpolation, multi-scale texture features, and semi-supervised learning significantly improves sediment classification where MBES data are incomplete and sediment samples are sparse. …”
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Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration by Jonathan E. Lee, Min Zhu, Ziqiao Xi, Kun Wang, Yanhua O. Yuan, Lu Lu

“…However, these simulations are often computationally expensive due to highly coupled physics and large spatial-temporal simulation domains. Surrogate modelling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. …”
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Assessment of relationship between sea surface temperature (SST) changes and precipitation types in Nigeria from 2000 to 2022 by Tertsea Igbawua, Fanan Ujoh, Solomon Kwaghfan Mkighirga, Grace Adagba

“…The analysis of NIF values indicates a varied but generally stronger relationship between WAf SST anomalies and precipitation types compared to nino3.4 SST versus precipitation types. As a signal for prediction of seasonal and spatial distribution of precipitation across Nigeria’s different climatic zones, this outcome can support planning for food security, water and biodiversity conservation, and climate change adaptation and mitigation.…”
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Mesoscale Numerical Simulation and Cloud Microphysical Characteristics of the Warm Zone Blizzard in Northern Xinjiang by Anbei LI, Chenxiang JU, Yaman ZHOU, Man LI, Ruqi LI

“…The warm zone blizzard are both infrequent and highly destructive， making their accurate prediction a challenging and crucial focus.This study utilized four distinct cloud microphysics schemes （Lin， Thompson， WDM6， and WSM6） within the WRF mesoscale model to conduct a numerical simulation of a typical warm zone blizzard process in the northern Xinjiang in the middle of November 2016.The research objectives encompassed the evaluation of the model's capacity to simulate the warm zone blizzard， the selection of an optimal parameterization scheme， an analysis of the vertical distribution and evolution of hydrometeors during the snowstorm， and an exploration of the developmental patterns of related mesoscale systems contributing to the snowstorm.The analysis yielded the following key findings： （1） Among the diverse cloud microphysics parameterization schemes tested， the Lin scheme demonstrated the most favorable performance， effectively simulating snowfall magnitudes， spatial distributions， and trends.（2） In the cloud， all kinds of water condensate particles are active in the lower and middle troposphere， with graupel and snow being the most.Ice crystals， snow， cloud water and graupel particles are distributed from the upper layer to the lower layer.Near the windward slope of Altai Mountain is the center of the large concentration of each water condensate particle.The vertical alignment of the high value center of the four kinds of cloud water condensate particles in the strong snowfall area is conducive to the transformation of each particle.（3） High-humidity systems upstream moved westward， with the intensification of low-level southward jet streams resulting in pronounced moisture convergence.The western foothills of the Altai Mountains acted as a barrier， promoting moisture convergence by blocking the windward side； The low-level southerly jet also provides a continuous updraft and unstable condition for the generation of the blizzard.Strong snowfall is located in a wide updraft area between two groups of secondary circulations.The explosive growth of vertical movement is conducive to triggering the release of unstable energy， providing strong dynamic lifting conditions for the development and maintenance of the blizzard.…”
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Assessing the declining trend in soil erodibility across China: A comparison of conventional and digital K-factor maps by Zhiyuan Tian, Yan Zhao, Longxi Cao, Yuan Zhao, Yin Liang

“…A digital K-factor map at the 250 m spatial resolution was generated by calculating the K values from soil survey points as training data and using environmental information as predictive variables. …”
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques by Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch

“…Including spatial distribution of spectral data further improves the results to 0.751. …”
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