SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing by Gilberto Alvarado-Robles, Isac Andres Espinosa-Vizcaino, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes

“…SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. …”
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Changes in AXL and/or MITF melanoma subpopulations in patients receiving immunotherapy by M. Willemsen, J. Bulgarelli, S.K. Chauhan, R.R. Lereim, D. Angeli, G. Grisendi, G. Krebbers, I. Davidson, J.A. Kyte, M. Guidoboni, R.M. Luiten, W.J. Bakker

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Field Testing of a Smart Geomat Based on Fiber Optic Sensors and Sensor Fusion for Levee Monitoring by Manuel Bertulessi, Francesca Ceccato, Francesco Ioli, Federico Barbieri, Livio Pinto, Paolo Simonini, Giovanni Menduni

“…ABSTRACT Real‐time monitoring of the functionality of river embankments, especially during flood events, is an increasingly important topic in the new scenario outlined by climate change. …”
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Cumulative Distribution-Based Method for Pavement Performance Modeling by Chenhao Tu, Wenya Ye, Rui Zhang, Xudong Qian, Qun Yang

“…A continuous distribution function is created from a limited set of discrete observed field pavement condition data using the sampling theorem. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. The coordinates of the control points of the NURBS surface at the bulbous bow are taken as the design variables. …”
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Bottom-up Biomaterial strategies for creating tailored stem cells in regenerative medicine by Brenda Cruz-Gonzalez, Ellie Johandes, Dominique Gramm, Donny Hanjaya-Putra, Donny Hanjaya-Putra, Donny Hanjaya-Putra, Donny Hanjaya-Putra

“…Unlike conventional methods that adapt cells to pre-existing materials, this strategy prioritizes designing biomaterials from the molecular level upward to address key challenges, including differentiation variability, incomplete matching of iPSCs to somatic counterparts, functional maturity of derived cells, and survival of ECFCs/MSCs in therapeutic niches. …”
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Evaluation of the Characteristics of Short Acquisition Times Using the Clear Adaptive Low-Noise Method and Advanced Intelligent Clear-IQ Engine by Ryosuke Ogasawara, Akiko Irikawa, Yuya Watanabe, Tomoya Harada, Shota Hosokawa, Kazuya Koyama, Keisuke Tsuda, Toru Kimura, Koichi Okuda, Yasuyuki Takahashi

“…The images were evaluated based on the coefficient of variation, recovery coefficient, % background variability (N_10mm), % contrast-to-% background variability ratio (Q_H10mm/N_10mm), and contrast-to-noise ratio. …”
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Multi-Scale Multi-Domain Hybrid Finite Element Modeling of Light Propagation by Jingwei Wang, Zhanwen Wang, Lida Liu, Yuntian Chen

“…We revisit finite element method of modeling multi-scale photonic/electromagnetic devices via the proposed beam basis function, in combination with domain decompositions. …”
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Optimization of fermentation conditions for enhanced acetylcholine and biomass production of Lactiplantibacillus plantarum AM2 using the Taguchi approach by Walid A. Lotfy, Amira M. Ali, Heba M. Abdou, Khaled M. Ghanem

“…Abstract This study aimed to optimize the fermentation conditions and medium composition for maximum acetylcholine (ACh) and biomass production by Lactiplantibacillus plantarum AM2 using the Taguchi array design, which enables efficient identification of influential variables through minimal experimental runs. …”
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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…Statistical metrics confirmed that the proposed OMVMD-GRKR model, concerning the best efficiency in the Ahvaz (R = 0.987, RMSE = 0.761, and U95% = 2.108) and Molasani (R = 0.963, RMSE = 1.379, and U95% = 3.828) stations, outperformed the OMVMD and simple-based methods such as ridge regression (Ridge), least squares support vector machine (LSSVM), deep random vector functional link (DRVFL), and deep extreme learning machine (DELM). …”
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Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. These features are combined through a fusion layer, and a customized loss function is employed to accurately predict fitness scores.ResultsExtensive experimental evaluation demonstrates that the proposed model consistently outperforms competitive baseline models. …”
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Optimizing the Production of LNG and NGL from Arab Crudes and Wet Gases by Adel M. Hemeida, Mohammed S. Al-Blehed, Saad El-Din Desouky

“…The developed model should be utilized as a useful tool to help the design of an efficient processing of natural gases. A great deal of the unlimited what if questions can be answered using this model. …”
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Flow-based cytometric analysis of cell cycle via simulated cell populations. by M Rowan Brown, Huw D Summers, Paul Rees, Paul J Smith, Sally C Chappell, Rachel J Errington

“…We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. …”
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HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation. by Yong Wang, Seri Mastura Mustaza, Mohammad Syuhaimi Ab-Rahman, Siti Salasiah Mokri

“…We also improved the loss function to better capture edge-level details and enhance segmentation precision in edge regions. …”
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A Knowledge-Driven Smart System Based on Reinforcement Learning for Pork Supply-Demand Regulation by Haohao Song, Jiquan Wang

“…Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. …”
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A LINEAR SIMULATION MODEL FOR OPTIMIZING CROP STRUCTURE IN ORDER TO MAXIMIZE INCOME IN A VEGETAL AGRICULTURAL FARM by Sorin IONITESCU

“…The model included: the 8 unknown variables for the cultivated area with 8 crops: wheat, rye, barley, peas, rape, soybean, maize and sunflower, 14 restrictions regarding Diesel fuel, fertilizers, herbicides, total surface, expenditures, income, and area per each crop, and objective - function f(Max) Income. …”
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Ionospheric Correction Based on Ingestion of Global Ionospheric Maps into the NeQuick 2 Model by Xiao Yu, Chengli She, Weimin Zhen, Nava Bruno, Dun Liu, Xinan Yue, Ming Ou, Jisheng Xu

“…The accuracy of TEC prediction can be improved further through performing a four-coefficient function expression of Az versus MODIP. …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

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Optimized Temporal Interference Stimulation Based on Convex Optimization: A Computational Study by Chao Geng, Yang Li, Long Li, Xiaoqi Zhu, Xiaohan Hou, Tian Liu

“…CVXTI accommodates various optimization objectives by incorporating different objective functions, thereby enhancing the focality of the stimulation field. …”
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Elucidating Genotypic Variation in Quinoa via Multidimensional Agronomic, Physiological, and Biochemical Assessments by Samreen Nazeer, Muhammad Zubair Akram

“…The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. …”
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