Development of the Regional Carbon Cycle Model in the Central Pacific Sector of the Southern Ocean by Takamitsu Ito

“…The model is further validated by comparing it with biogeochemical float observations from the Southern Ocean Carbon and Climate Observations and Modeling project, revealing the model performance and challenges to accurately represent physical and biogeochemical properties in frontal regions.…”
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A Hybrid Deep Learning Model for UAV Path Planning in Dynamic Environments by Junchi Zhang, Yanning Xian, Xun Zhu, Hongtao Deng

“…In this work, we introduce a UAV path planning model that integrates diverse sensor data through a neural network architecture featuring CNN, LSTM, and an attention mechanism. …”
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Computer-Aided Supporting Models of Customized Crack Propagation Sensors for Analysis and Prototyping by Paulina Kurnyta-Mazurek, Rafał Wrąbel, Artur Kurnyta

“…The main advantage of the developed GUI is the capacity for personnel inexperienced in using the Abaqus environment to perform analysis. Some results of simulation tests carried out in both COMSOL Multiphysics as well as Abaqus software are delivered in this paper, using a predefined parametric sensor model. …”
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ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. by Brett A McKinney, Bill C White, Diane E Grill, Peter W Li, Richard B Kennedy, Gregory A Poland, Ann L Oberg

“…For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. …”
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CF-YOLO for small target detection in drone imagery based on YOLOv11 algorithm by Chengcheng Wang, Yuqi Han, Chenggui Yang, Mingjie Wu, Zaiqing Chen, Lijun Yun, Xuesong Jin

“…Experiments show that on the public VisDrone dataset, TinyPerson dataset, and HIT-UAV dataset, CF-YOLO improves the mAP50 by 12.7%, 10.1%, and 3.5%, respectively, compared to the baseline model. Compared to other methods, CF-YOLO demonstrates superior performance.…”
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Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields by Xiajun Zheng, Xinyi AI, Hao Qin, Jiacheng Rong, Zhiqin Zhang, Yan Yang, Ting Yuan, Wei Li

“…By using hash encoding to map coordinates to trainable feature vectors, we balance quality, memory usage, and performance in NeRF training. …”
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TBM Net Advance Rate Prediction Model Based on Ridge Regression Analysis by SHI Jian, ZHANG Shilin, FAN Zuosong, KONG Desen

“…The correlations between the TBM net advance rate and various input variables are analyzed, and collinearity diagnostics are performed on the input variables. A TBM net advance rate prediction model based on ridge regression analysis (hereinafter referred to as the ′ridge regression prediction model′) is established, and its predictive performance is validated. …”
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GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios by Shanshan Li, Zhe Zhang, Shijun Li

“…Experimental results demonstrate the model’s superior performance, achieving an AP@0.5 of 90.55%. …”
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Menstrual cycle inspired latent diffusion model for image augmentation in energy production by Gamal M. Mahmoud, Mostafa Elbaz, Wael Said, Amira A. Elsonbaty

“…This paper introduces menstrual cycle-inspired latent diffusion model (MCI-LDM), a novel framework that addresses these challenges with three key modifications. …”
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Intelligent method for supporting decision-making on software security using hybrid models by Оксана Сітнікова, Маргарита Мельник, Олена Сирота, Сергій Семенов

“…The research is aimed at developing an intelligent decision support method for software security assessment using a hybrid model based on deep learning and gradient boosting. …”
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A Cognitive Topic Modeling Approach for Detailed Learner Analysis in Asynchronous Discussions by Ruofei Ding, Xianfei Luo, Shumin Chen, Xiaomin Wu, Chi Zhang, Lixiang Liu, Yanyu Huang, Ling Chen, Jingxiu Huang, Yunxiang Zheng, Ke Zhu

“…This approach consists of two parts: 1) SLPoK, a topic modeling technique that uses sentence embedding to improve latent Dirichlet allocation (LDA); and 2) the integration of SLPoK and epistemic network analysis (ENA) to perform cognitive topic modeling. …”
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iPSC-modelling reveals genetic associations and morphological alterations of oligodendrocytes in schizophrenia by Man-Hsin Chang, Jan Benedikt Waldeck, Marius Stephan, Nirmal Kannaiyan, Valéria de Almeida, Emanuel Boudriot, Temmuz Karali, Lukas Röll, Laura Fischer, Damianos Demetriou, Nadia Gabellini, Sabrina Galinski, Andrea Schmitt, Sergi Papiol, Daniel Keeser, Peter Falkai, Moritz J. Rossner, Florian J. Raabe

“…Moreover, transcriptomic profiling revealed a dysregulation in oligodendroglial cell signaling and proliferation. In sum, hiPSC-modelling shows an impact of SCZ genetics on dedicated features of the oligodendroglial lineage.…”
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Dynamic Modeling and Control Characteristics of the Two-Modular HTR-PM Nuclear Plant by Zhe Dong, Yifei Pan, Maoxuan Song, Xiaojing Huang, Yujie Dong, Zuoyi Zhang

“…Since the plant power control technique of traditional single-modular nuclear plants cannot be directly applied to the multimodular plants, it is necessary to develop the power control method of multimodular plants, where dynamical modeling, control design, and performance verification are three main aspects of developing plant control method. …”
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Accuracy and robustness evaluation of deep learning algorithms in facial recognition systems by Jing Zhang, Ningyu Hu

“…Second, a feature extraction model based on the loss function of the improved FaceNet is constructed. …”
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ProBoost: Reducing Uncertainty Using a Boosting Method for Probabilistic Models by Fabio Mendonca, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-Garcia, Mario A. T. Figueiredo

“…Uncertainty analysis of classification or regression models is a key feature of probabilistic approaches to supervised learning, allowing the assessment of how trustworthy predictions are. …”
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Using convolutional neural networks for image semantic segmentation and object detection by Shuangmei Li, Chengning Huang

“…This model combines multi-scale perceptual aggregation and feature alignment (MPAFA) mechanisms. …”
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Predictive Modeling for Diabetes Subtype Classification in India: A Machine Learning Approach by Ulagamadesan Venkatesan, Anandakumar Amutha, Ranjit Mohan Anjana, Ranjit Unnikrishnan, Bagavandas Mappillairaju, Viswanathan Mohan

“…Model performance was high, with the area under the receiver operating characteristic curve (ROC AUC) ranging from 0.92 (using only BMI and age at diagnosis) to 0.97 (utilizing all predictors). …”
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Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping by Yue‐Lin Dong, Zhen‐Jie Zhang

“…Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. …”
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Strategy for Construction and Improvement of 4R Resilience Evaluation Model for Coastal Cities by Qixiang QIN, Weiping ZHANG, Yasen YIN

“…On the contrary, due to the large land scale and low degree of development and construction, city samples with low development intensity are typically featured by relatively poor spatial redundancy and relatively sparse distribution of emergency rescue facilities. 3) There is no significant correlation between the overall performance of 4R resilience and development intensity in coastal cities, and cities with different development intensity can enhance the overall resilience level by strengthening their own advantages or appropriately compensating for weaknesses. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…A comprehensive dataset covering the entire strawberry growth cycle is constructed to serve as the foundation for model training and testing. An innovative lightweight convolutional neural network, named GrowthNet, is designed as the backbone of SGSNet, facilitating efficient feature extraction while significantly reducing model parameters and computational complexity. …”
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