STBNA-YOLOv5: An Improved YOLOv5 Network for Weed Detection in Rapeseed Field by Tao Tao, Xinhua Wei

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The effectiveness of solar radiation management using fine sea spray across multiple climatic regions by Z. Song, S. Yu, S. Yu, P. Li, N. Yao, N. Yao, L. Chen, L. Chen, Y. Sun, B. Jiang, D. Rosenfeld

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Spatiotemporal Distribution and Dispersal Pattern of Early Life Stages of the Small Yellow Croaker (<i>Larimichthys Polyactis</i>) in the Southern Yellow Sea by Xiaojing Song, Fen Hu, Min Xu, Yi Zhang, Yan Jin, Xiaodi Gao, Zunlei Liu, Jianzhong Ling, Shengfa Li, Jiahua Cheng

“…/m³), and the distribution areas varied between different months. The prediction of the model reveals the ecological adaptability of <i>L. polyactis</i> to temperature variations. …”
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Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework by Guo-Guang Li, Liang Sheng, Bao-Jun Duan, Yang Li, Yan Song, Zi-Jian Zhu, Wei-Peng Yan, Dong-Wei Hei, Qing-Zi Xing

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Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning by Vinu Sooriyaarachchi, David J. Lary, Lakitha O. H. Wijeratne, John Waczak

“…This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>PM</mi><mn>1</mn></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>PM</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. …”
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Attention-based hybrid contrastive learning for unsupervised person re-identification by Weihao Qin, Yongxia Li, Jianguang Zhang, Xianbin Wen, Jiajia Guo, Qi Guo

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Analyzing the environmental ideas of knowledgeable citizens toward the realization of a creative city in Tabriz by Heydar Beheshti Asl, Morteza Mirgholami, Karim Hosseinzadeh Dalir

“…Based on this analysis, a conceptual model of the environmental-creative city of Tabriz was developed. …”
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Assessment of the Level of Financial Inclusion of Women in Kazakhstan: An Empirical Analysis of Structural Factors by A. A. Kireyeva, M. T. Kurmasheva

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Knee Osteoarthritis Diagnosis: Future and Perspectives by Henri Favreau, Kirsley Chennen, Sylvain Feruglio, Elise Perennes, Nicolas Anton, Thierry Vandamme, Nadia Jessel, Olivier Poch, Guillaume Conzatti

“…Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. …”
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National Atlas of Arctic: structure and creation approaches by N. S. Kasimov, V. M. Kotlyakov, A. N. Chilingarov, D. M. Krasnikov, V. S. Tikunov

“…The National Atlas of Arctic is understood as a collection of knowledge of spatial-temporal information about geographical, ecological, economic, historical-ethnographic, cultural and social features of the Arctic. …”
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Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine le... by Jiajun Li, Nuo Xu, Leyin Hu, Jiayue Xu, Yifan Huang, Deqi Wang, Feng Chen, Yi Wang, Jiani Jiang, Yanggang Hong, Huajun Ye

“…Furthermore, CCT5-high tumors exhibited immune-cold phenotypes, with reduced TILs and CD8⁺ T cell activity. The CCT5.Sig model, based on genes co-expressed with CCT5, achieved superior predictive accuracy for ICB response (AUC = 0.82 in validation and 0.76 in independent testing), outperforming existing pan-cancer signatures. …”
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Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting by N. O. Edward‐Inatimi, M. J. Owens, L. Barnard, H. Turner, M. Marsh, S. Gonzi, M. Lang, P. Riley

“…Typically, forecasts combine coronal model outputs with heliospheric models to predict near‐Earth conditions. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. …”
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Reconstruction of U.S. Regional-Scale Soybean SIF Based on MODIS Data and BP Neural Network by YAO Jianen, LIU Haiqiu, YANG Man, FENG Jinying, CHEN Xiu, ZHANG Peipei

“…[Results and Discussions]The research findings confirmed the strong performance of the SIF reconstruction model in predicting soybean SIF. After simultaneously incorporating EVI, FPAR, and LST as explanatory variables to model, achieved a goodness of fit with an R2 value of 0.84, this statistical metric validated the model's capability in predicting SIF data, it also reflected that the reconstructed 8 d time resolution of SIF data's reliability of applying to small-scale agricultural crop photosynthesis research with 500 m×500 m spatial scale. …”
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Hyper Spectral Camera ANalyzer (HyperSCAN) by Wen-Qian Chang, Hsun-Ya Hou, Pei-Yuan Li, Michael W. Shen, Cheng-Ling Kuo, Tang-Huang Lin, Loren C. Chang, Chi-Kuang Chao, Jann-Yenq Liu

“…After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. …”
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Revealing the early ice flow patterns with historical Declassified Intelligence Satellite Photographs back to 1960s by Shujie Wang, Hongxing Liu, Bailang Yu, Guoqing Zhou, Xiao Cheng

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Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning by M. T. Widyastuti, J. Padarian, B. Minasny, M. Webb, M. Taufik, D. Kidd

“…Results showed that (1) models calibrated from the Australian dataset performed worse than Tasmanian models regardless of the type of DL approaches; (2) Tasmanian models, calibrated solely using local data, resulted in shortcomings in predicting soil moisture; and (3) transfer learning exhibited remarkable performance improvements (error reductions of up to 45 % and a 50 % increase in correlation) and resolved the drawbacks of the two previous models. …”
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Regional Pathways to Internationalization: The Role of Erasmus+ in European HEIs by Eleni Georgoudaki, Spyridon Stavropoulos, Dimitris Skuras

“…Employing hotspot analysis and a two-level random intercept model, this research analyses spatial patterns and the influences of regional characteristics and institutional variables on Erasmus mobility rates. …”
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Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods by Jiani Wang, Yunqi Wang, Manyi Li, Zihan Qi, Cheng Li, Haimei Qi, Xiaoming Zhang

“…In order to assess the impact of various negative sample collection strategies on the prediction accuracy of the landslide susceptibility assessment (LSA) model, and to investigate the effectiveness of landslide susceptibility zoning methods. …”
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Mapping potential water repellency of Danish topsoil by Lucas Carvalho Gomes, Peter Lystbæk Weber, Cecilie Hermansen, Anne-Cathrine Storgaard Danielsen, Sebastian Gutierrez, Deividas Mikstas, Charles Pesch, Mogens Humlekrog Greve, Per Moldrup, David A. Robinson, Lis Wollesen de Jonge

“…This study presents a comprehensive survey of SWR in Denmark and its predicted spatial distribution, using approximately 7,500 samples. …”
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