Emerging role of generative AI in renewable energy forecasting and system optimization by Erdiwansyah, Rizalman Mamat, Syafrizal, Mohd Fairusham Ghazali, Firdaus Basrawi, S.M. Rosdi

“…Results indicate that GAN-based models reduce root mean square error (RMSE) by 15–20 % in solar irradiance forecasting and significantly enhance spatial-temporal wind simulations. …”
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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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Wireless Authentication Method Based on Near-Field Feature Fusion Network by QIU Jiefan, ZHOU Kezhong, ZHU Dongfu, ZHANG Jinhong, CHI Kaikai

“…Initially, in the dynamic feature extraction module, an Inception-based WiFi Convolutional Network (IWCN) was employed to capture multi-scale spatial features across subcarriers. The extracted spatial features were subsequently processed by a long short-term memory (LSTM) network to derive gait dynamic features. …”
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Combining Sentinel-2 Data and Risk Maps to Detect Trees Predisposed to and Attacked by European Spruce Bark Beetle by Per-Ola Olsson, Pengxiang Zhao, Mitro Müller, Ali Mansourian, Jonas Ardö

“…For single-date models, the accuracy ranged from 63 to 79% and 84 to 94% for the two tiles. …”
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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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Production, Transport, and Destruction of Dust in the Kuiper Belt: The Effects of Refractory and Volatile Grain Compositions by Thomas Corbett, Alex Doner, Mihály Horányi, Pontus Brandt, Will Grundy, Carey M. Lisse, Joel Parker, Lowell Peltier, Andrew R. Poppe, Kelsi N. Singer, S. Alan Stern, Anne J. Verbiscer

“…Models based on early SDC measurements predicted a peak dust number density at a heliocentric distance of ∼40 au, followed by a rapid decline. …”
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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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Using satellite-based Sun-induced chlorophyll fluorescence and spectral reflectance for improving terrestrial CO2 flux estimates of India by Aparnna Ravi, Dhanyalekshmi Pillai, Christoph Gerbig, Stephen Sitch, Sönke Zaehle, Vishnu Thilakan, Chandra Sekhar Jha, Thara Anna Mathew

“…We improve these model predictions by additionally using satellite-based solar-induced chlorophyll fluorescence (SIF), soil temperature , and soil moisture specific to the vegetation classes of the domain. …”
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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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Aerodynamic energy consumption analysis of divided evacuated tube transportation system by Ziwei Zhang, Yingxue Wang, Guanqing Wang, Feilong Li, Dengke Wang, Jianjun Luo

“…Theoretical discussions into reasonable parameter range including tube design and HST operation manners were conducted and then formed system-designing strategy. The spatial and temporal distribution law of tube gas circumstances were examined through model testing and numerical simulation. …”
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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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