Development of a Multi‐Scale Meteorological Large‐Eddy Simulation Model for Urban Thermal Environmental Studies: The “City‐LES” Model Version 2.0 by Hiroyuki Kusaka, Ryosaku Ikeda, Takuto Sato, Satoru Iizuka, Taisuke Boku

“…Noteworthy features of this model include: (a) the calculation of long‐ and short‐wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three‐dimensional manner, and the model can also run on a graphics processing unit cluster. …”
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Assessing the performance and explainability of an avalanche danger forecast model by C. Pérez-Guillén, F. Techel, M. Volpi, A. van Herwijnen

“…SHapley Additive exPlanations (SHAP) were employed to make the model's decision process more transparent, reducing its “black-box” nature. …”
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Explainable Supervised Learning Models for Aviation Predictions in Australia by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…Artificial intelligence (AI) has demonstrated success across various industries; however, its adoption in aviation remains limited due to concerns regarding the interpretability of AI models, which often function as black box systems with opaque decision-making processes. …”
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Physics-Guided Memory Network for building energy modeling by Muhammad Umair Danish, Kashif Ali, Kamran Siddiqui, Katarina Grolinger

“…This paper introduces a Physics-Guided Memory Network (PgMN), a neural network that integrates predictions from deep learning and physics-based models to address their limitations. PgMN comprises a Parallel Projection Layers to process incomplete inputs, a Memory Unit to account for persistent biases, and a Memory Experience Module to optimally extend forecasts beyond their input range and produce output. …”
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Voice activity detection in noisy conditions using tiny convolutional neural network by R. S. Vashkevich, E. S. Azarov

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Protection Boundary Development in Historical–Cultural Built Environments Using Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) by Can Kara, Aminreza Iranmanesh

“…The proposed method systematically includes economic, architectural, environmental, social, and legal data layers, aiming to generate a more comprehensive model for developing protection boundaries tied to multidimensional and contextual complexities, as well as considering rapid urbanisation patterns. …”
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Numerical Study of the Effects of Heat Loss and Solid Thermal Conductivity on Syngas Production for Fuel Cells by Xiaolong Wang, Mengmeng Yu, Zunmin Li, Zhen Wang, Xiuxia Zhang, Junrui Shi, Xiangjin Kong, Jinsheng Lv

“…In the current paper, the conversion efficiency of methane to synthesis gas (H₂ and CO) within a two-layer porous media reactor is investigated by a one-dimensional two-temperature model. …”
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Comprehensive Effects of Operation of Pumped-Storage Hydropower Plants on Downstream Conventional Hydropower Plants by ZHU De-kang, QIN Rui, CHENG Xiang, GUO Xu-ye

“…[Methods] To systematically and quantitatively analyze this effect, this study proposed a water consumption rate-head curve fitting method based on a multi-layer perceptron (MLP). By integrating this method with the local grid’s time-of-use electricity pricing policy, a power generation scheduling model for conventional hydropower plants was established. …”
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Chinese Sequence Labeling Based on Stack Pre-training Model by LIU Yu-peng, LI Guo-dong

“…Sequence labeling is an important task in natural language processing. In this paper, according to the relevance of tasks, we use stacking pretraining model to extract features, segment words, and name entity recognition/chunk tagging.Through in-depth research on the internal structure of BERT, while ensuring the accuracy of the original model, the Bidirectional Encoder Representation from Transformers （BERT） is optimized, which reduces the complexity and the time cost of the model in the training and prediction process.In the upper layer structure, compared with the traditional long-short-term memory network (LSTM), this paper uses a two-layer bidirectional LSTM structure, the bottom layer uses a bidirectional long-short-term memory network (Bi-LSTM) for word segmentation, and the top layer is used for sequence labeling tasks.On the New Semi-Conditional Random Field (NSCRF), the traditional semi-Markov Conditional Random Field (Semi-CRF) and Conditional Random Field (CRF) are combined while considering the segmentation.The labeling of words improves accuracy in training and decoding. …”
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Construction exploration and application prospect of the large model in mining industry by Haijun WANG

“…This paper deeply analyzes the challenges of high R&D investment cost, difficulty in collecting high-quality data, and high difficulty in multimodal data fusion technology in the application of large model technology in the coal industry, and summarizes in detail the construction path and phased results achieved by SolStone Mine Large Model to cope with the above challenges from six aspects: infrastructure layer, data resource layer, algorithm model layer, application service layer, security and trustworthiness and testing layer, and industry ecological layer, and finally looks forward to the production and technological changes brought by the development of large model technology to the coal industry. …”
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Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN-LSTM Model by C. Ruth Vinutha, M. S. P. Subathra, S. Thomas George, Geno Peter, Albert Alexander Stonier, N. J. Sairamya, J. Prasanna, Vivekananda Ganji

“…This study proposes an efficient model to classify and provide insights into focal and nonfocal stages. …”
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Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing by Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu

“…The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. …”
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Artificial noise-aided robust secure information and power transmission scheme in heterogeneous networks with simultaneous wireless information and power transfer by Bo ZHANG, Kaizhi HUANG, Zhou ZHONG, Yajun CHEN

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Artificial noise-aided robust secure information and power transmission scheme in heterogeneous networks with simultaneous wireless information and power transfer by Bo ZHANG, Kaizhi HUANG, Zhou ZHONG, Yajun CHEN

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Turbulent Heat Transfer over roughness: a comprehensive review of theories and turbulent flow structure by Mohammadreza Kadivar, Himani Garg

“…By comparing with smooth-wall flows, the paper highlights how surface roughness affects turbulence structures and the thermal boundary layer. Existing models and scaling laws for heat transfer are critically evaluated, with attention to their applicability across different roughness types and flow conditions. …”
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Hierarchical Motion Field Alignment for Robust Optical Flow Estimation by Dianbo Ma, Kousuke Imamura, Ziyan Gao, Xiangjie Wang, Satoshi Yamane

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Multi-Node Small Radar Network Deployment Optimization in 3D Terrain by Zhiyi Wang, Min Wang, Xinghui Wu, Shuyuan Yang

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Unsupervised intrusion detection model based on temporal convolutional network by LIAO Jinju, DING Jiawei, FENG Guanghui

“…Most existing intrusion detection models rely on long short-term memory (LSTM) networks to consider time-dependencies among data. …”
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Effects of 2010–2045 climate change on ozone levels in China under a carbon neutrality scenario: key meteorological parameters and processes by L. Kang, H. Liao, K. Li, X. Yue, Y. Yang, Y. Wang

“…Analysis showed net chemical production was the most important process that increases O<span class="inline-formula">₃</span>, accounting for 34.0 %–62.5 % of the sum of all processes within the boundary layer. …”
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JAX‐CanVeg: A Differentiable Land Surface Model by Peishi Jiang, Patrick Kidger, Toshiyuki Bandai, Dennis Baldocchi, Heping Liu, Yi Xiao, Qianyu Zhang, Carlos Tianxin Wang, Carl Steefel, Xingyuan Chen

“…Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process‐based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process‐based models and the learning power of DNNs. …”
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