Core Revelations: The Star Formation and Active Galactic Nucleus Connection at the Heart of NGC 7469 by Léa M. Feuillet, Steve Kraemer, Marcio B. Meléndez, Travis C. Fischer, Henrique R. Schmitt, James N. Reeves, Anna Trindade Falcão

“…We can separate the AGN- and SF-dominated regions using spatially resolved mid-infrared diagnostic diagrams, and we further investigate the ionization sources powering each region by constructing photoionization models. …”
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Direct Estimation of Forest Aboveground Biomass from UAV LiDAR and RGB Observations in Forest Stands with Various Tree Densities by Kangyu So, Jenny Chau, Sean Rudd, Derek T. Robinson, Jiaxin Chen, Dominic Cyr, Alemu Gonsamo

“…Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. …”
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Foreign object detection for mining conveyor belts based on YOLOv5n-CND by SUN Aoran, ZHAO Peipei, YANG Di, ZHANG Junyi, YU Hongjian

“…The mAP@0.5 and mAP@0.5:0.95 of YOLOv5n-CND were 2.6% and 3.4% higher than YOLOv5n, and 1.7% and 3.8% higher than YOLOv5s-CBAM, respectively. Although the model’s parameter count slightly increased compared to the YOLOv5n model, it was still lower than that of other models. …”
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Microenvironment-confined kinetic elucidation and implementation of a DNA nano-phage with a shielded internal computing layer by Decui Tang, Shuoyao He, Yani Yang, Yuqi Zeng, Mengyi Xiong, Ding Ding, Weijun Wei, Yifan Lyu, Xiao-Bing Zhang, Weihong Tan

“…However, it is quite difficult to involve nanobodies into molecular computation with programmed recognition order because of the “always-on” response mode and the inconvenient molecular programming. Here we propose a spatial segregation-based molecular computing strategy with a shielded internal computing layer termed DNA nano-phage (DNP) to program nanobody into DNA molecular computation and build a series of kinetic models to elucidate the mechanism of microenvironment-confinement. …”
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Estimating comprehensive growth index for drip-irrigated spring maize in junggar basin via satellite imagery and machine learning by Mingjie Ma, Jinghua Zhao, Tingrui Yang, Feng Liu, Yingying Yuan, Shijiao Ma, Zikang Chang

“…Shapley analysis scientifically demonstrated that RDVI was the most influential factor in the SSA-RF model's CGI predictions in total stage. Additionally, the study generated field-scale CGI spatiotemporal distribution maps, revealing the temporal and spatial variation patterns of crop growth. …”
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Major depressive disorder on a neuromorphic continuum by Jiao Li, Zhiliang Long, Gong-Jun Ji, Shaoqiang Han, Yuan Chen, Guanqun Yao, Yong Xu, Kerang Zhang, Yong Zhang, Jingliang Cheng, Kai Wang, Huafu Chen, Wei Liao

“…However, interindividual variations suggest that depression may be conceptualized as a “continuum,” rather than as a “category.” We use a Bayesian model to decompose structural MRI features of MDD patients from a multisite cross-sectional cohort into three latent disease factors (spatial pattern) and continuum factor compositions (individual expression). …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. …”
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Examining inter‐regional and intra‐seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways by Matthew J. Hardy, Christopher K. Williams, Brian S. Ladman, Maurice E. Pitesky, Cory T. Overton, Michael L. Casazza, Elliott L. Matchett, Diann J. Prosser, Jeffrey J. Buler

“…We used 9 years (2014–2023) of data from the US NEXRAD network to model winter waterfowl relative abundance in the CVC and MA as a function of weather, temporal period, environmental conditions, and landcover characteristics using boosted regression tree modelling. …”
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A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

“…The results demonstrate the CNN-LSTM model presents better prediction performance than the other eight previously proposed ML models. …”
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A systematic review of fishing impacts on the trophic level of fish populations and assemblages in the Mediterranean Sea by Audrey Marguin, Audrey Marguin, Simona Bussotti, Simona Bussotti, Paolo Guidetti, Paolo Guidetti, Francesca Rossi, Francesca Rossi

“…Interestingly, recent modelling studies used predictions from the model to explore the impact of different fishing pressure within global change scenarios. …”
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The Relative Constraining Power of the High-z 21 cm Dipole and Monopole Signals by Jordan Mirocha, Chris Anderson, Tzu-Ching Chang, Olivier Doré, Adam Lidz

“…This result holds for most of the available prior volume, which is set by constraints on galaxy luminosity functions, the reionization history, and upper limits from 21 cm power spectrum experiments. We also find that predictions for the monopole from a dipole measurement are robust to different choices of signal model. …”
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A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System by Dong Hyun Choi, Yoon Ha Joo, Ki Hong Kim, Jeong Ho Park, Hyunjin Joo, Hyoun-Joong Kong, Hyunju Lee, Kyoung Jun Song, Sungwan Kim

“…Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. …”
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Lessons learned from the co-development of operational climate forecast services for vineyards management by N. Pérez-Zanón, V. Agudetse, E. Baulenas, P.A. Bretonnière, C. Delgado-Torres, N. González-Reviriego, A. Manrique-Suñén, A. Nicodemou, M. Olid, Ll. Palma, M. Terrado, B. Basile, F. Carteni, A. Dente, C. Ezquerra, F. Oldani, M. Otero, F. Santos-Alves, M. Torres, J. Valente, A. Soret

“…The workflow includes the downscaling of model outputs to increase their spatial resolution, the storage of sub-seasonal climate forecasts at daily frequencies to feed phenological impact models, the provision of past climate simulations to train the impact models, the bias-adjust of climate forecasts to reduce systematic model errors, the assessment of the climate forecasts to aware users on their quality and the deployment of a server to allow access to impact modellers.The variables provided are mean, minimum and maximum temperature, accumulated precipitation and solar radiation. …”
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Extraction of typical oyster pile columns in the Maowei Sea, Beibu Gulf, based on unmanned aerial vehicle laser point cloud orthophotos by Jinze Du, Jinze Du, Meiqin Huang, Zhenjun Kang, Zhenjun Kang, Zhenjun Kang, Yichao Tian, Yichao Tian, Yichao Tian, Jin Tao, Jin Tao, Qiang Zhang, Qiang Zhang, Yutong Xie, Yutong Xie, Yutong Xie, Jinying Mo, Jinying Mo, LiYan Huang, LiYan Huang, Yusheng Feng, Yusheng Feng

“…By employing band features and texture indices extracted from UAV’s multi-spectral images as data sources and combining them with a classification and prediction model based on deep learning convolutional neural networks (CNN), we successfully extract the desired oyster pile columns. …”
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Lithologically constrained velocity–density relationships and vertical stress gradients in the North Alpine Foreland Basin, SE Germany by P. Obermeier, F. Duschl, M. C. Drews

“…The same is true for the distribution of vertical stress gradients, a key input parameter for geomechanical modelling and the prediction of natural and induced seismicity. …”
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Multi-Source Rainfall Data Assimilation based on Broad Learning System over Yunnan Province by Yuanyuan ZHOU, Xiaohui YANG, Tiangui XIAO

“…The accurate estimation of rainfall is always a topic of concern， given its pivotal role in accurately predicting rainfall-related disasters.This study proposed a multi-source rainfall assimilation technology based on a broad learning system （BLS） to improve the accuracy of rainfall estimation.Yunnan Province， located in China's low-latitude plateau， was chosen as the geographical area of interest to establish a multi-source rainfall assimilation model within this region.In particular， the model utilizes five satellite-derived rainfall datasets （3B42V7， IMERG， GSMaP， CMORPH， PERSIANN） and the latitude and longitude information as the source data， and the ground-based rainfall gauge data serves as the reference data.The time span of all the datasets is from April 2014 to December 2017.A leave-one-year-out cross-validation （LOYOCV） method was applied to verify the performance of the established assimilation model， where statistical indicators including Pearson’s correlation coefficient （CC）， root-mean square error （RMSE）， mean absolute error （MAE）， Nash efficiency coefficient （NSE） and Kling-Gupta efficiency （KGE） were used to quantify the accuracy of assimilation rainfall at different spatiotemporal scales.Concurrently， assimilation models based on support vector machine （SVM） and deep neural network （DNN） were established to highlight the accuracy and efficiency of the BLS， respectively.Additionally， the effectiveness of the latitude and longitude information within the proposed assimilation model was examined.The results show that the daily average statistical index of assimilation rainfall based on BLS is better than that of the other five satellite-based products in LOYOCV.At the temporal scale， the proposed assimilation technique effectively reflects the temporal variations observed in gauge-recorded rainfall.Moreover， it can accurately estimate the rainfall amounts during rainstorms in Yunnan Province throughout 2017.It is worth noting that the rainfall data generated through the BLS method outperforms the CMORPH product （the most accurate one among the five satellite-derived rainfall products） in both rainy and dry seasons （May to October and November to April of next year， respectively）.At the spatial scale， BLS-based rainfall results in most areas of Yunnan Province showed higher CC and NSE as well as smaller RMSE and MAE than the satellite-based products.The evaluation of the assimilation models based on BLS， SVM， and DNN highlights that the BLS exhibits superior functional mapping capabilities compared to SVM and demands fewer computational resources than DNN.It is reasonable to conclude that the multi-source rainfall assimilation approach utilizing the BLS while incorporating latitude and longitude information can enhance the precision of rainfall estimates in Yunnan Province.The proposed method presents practical significance in multi-source rainfall data assimilation.…”
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Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

“…Next, we design a lightweight channel-spatial hybrid attention mechanism to further refine multi-scale features, enhancing the model’s ability to extract global and local features with minimal computational cost. …”
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Relationship Between Landscape Character and Public Preferences in Urban Landscapes: A Case Study from the East–West Mountain Region in Wuhan, China by Xingyuan Li, Wenqing Pang, Lizhi Han, Yufan Yan, Xianjie Pan, Diechuan Yang

“…The results show that (1) public preferences hotspots are concentrated in three types: (a) urban construction-driven types, including areas dominated by commercial service functions and those characterized by mixed-function residential areas; (b) natural terrain-dominated types with well-developed supporting facilities; and (c) hybrid transition types predominated by educational and scientific research land uses. These areas generally feature a high degree of functional diversity and good transportation accessibility. (2) Landscapes eliciting stronger emotional responses integrate moderate slopes, multifunctional spaces, and robust public services, whereas areas with weaker responses are characterized by single-function use or excessive urbanization. (3) The emotional variations within categories could be influenced by (a) functional hybridity through enhanced environmental exploration; (b) spatial usage frequency through place attachment formation; and (c) visual harmony through cognitive overload prevention. …”
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Calculation of capacity and optimization design-composite slab wall soil solidification foundation based on neural network by Linggang ZHOU, Yiting HU, Xinwei CHEN, Feng TU, Zhaofeng WU, Yang YU, Yanbing WANG, Yin MAN, Weichao LI

“…Objective The soil solidification technique is widely used in soft foundation treatment. To exploit spatial plasticity of this technique, composite slab wall soil solidification foundations have gradually been applied in engineering projects. …”
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Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg, Asim Biswas

“…Furthermore, the comparison of the optimal maps to the reference maps showed little difference in the global statistics (concordance correlation coefficient and root mean square error) and spatial trends of the data, confirming that the optimal sample size was sufficient for creating predictions of similar accuracy to the full calibration dataset. …”
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