Deep Reinforcement Learning for Optimal Replenishment in Stochastic Assembly Systems by Lativa Sid Ahmed Abdellahi, Zeinebou Zoubeir, Yahya Mohamed, Ahmedou Haouba, Sidi Hmetty

“…The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. …”
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Inconsistency evaluation of the curriculum logical structure by Yu. D. Ageev, S. V. Fedoseev, Yu. A. Kavin, S. G. Vorona, I. S. Pavlovskiy

“…The overall approach to the assessment of structural integrity is adapted to assess the logical structure of the curriculum. Three models of curriculum structure are developed:Elementary model of interdisciplinary communication;Curriculum network model;Hierarchical curriculum model.Based on the parameters of the hierarchical curriculum model, using three adapted algorithms, the value of the inconsistency index of the curriculum structure in the direction of preparation “Applied Informatics” is calculated. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…ResNet18 and ResNet50 algorithms were employed to build deep learning models (deep learning radiomics (DLR) resnet18 / DLRresnet50), compared with a radiomics model (Rad) based on 17 optimized features. …”
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Development of PYMUS+ Code for Quantitative Evaluation of Nuclear Material Accounting (NMA) System by Byung Hee Won, Hee-Sung Shin, Se Hwan Park, Seong-Kyu Ahn

“…It is also important to quantitatively evaluate the performance of NMA system including NRTA from the standpoints of Safeguards and Security by Design (SSBD) prior to construction of nuclear-material-handling facilities. Such evaluation improves safeguards effectiveness and efficiency. Modeling and Simulation (M&S) work is a good way to evaluate performance for various NMA systems and to determine the optimal one among different options. …”
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Hybrid active-passive thermal management system for deep wells: Long-term stability and multi-objective optimization by Zongwei Yao, Haomin Li, Hongxu Xu, Hongfei Yang, Qiushi Bi, Tianshuang Xu

“…A multi-objective optimization framework combining Kriging surrogate modeling and NSGA-II algorithm is developed to maximize heat transfer coefficient (h) while minimizing pressure drop (P). …”
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Lessons from a Space Lab: An Image Acquisition Perspective by Leo Pauly, Michele Lynn Jamrozik, Miguel Ortiz del Castillo, Olivia Borgue, Inder Pal Singh, Mohatashem Reyaz Makhdoomi, Olga-Orsalia Christidi-Loumpasefski, Vincent Gaudillière, Carol Martinez, Arunkumar Rathinam, Andreas Hein, Miguel Olivares-Mendez, Djamila Aouada

“…The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. …”
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Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio by Feng Liu, Hao Zhou, Jiangtao Xie, Yue Tang, Shuyu Liu

“…This study analysed 53 schools, which are mainly located in urban areas of China, and variations in their design that were generated through computational modelling. It revealed that, while the architectural layout has a relatively limited direct impact on the safety of school public spaces, the shape and scale of these spaces can significantly enhance their safety performance by allowing for improved natural surveillance. …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…These improvements highlight the model’s ability to assimilate diverse climatic and topographical inputs for more accurate rainfall prediction, particularly in areas where conventional methods fall short due to sparse or irregular precipitation. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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Advancing Sika deer detection and distance estimation through comprehensive camera calibration and distortion analysis by Sandhya Sharma, Stefan Baar, Bishnu P. Gautam, Shinya Watanabe, Satoshi Kondo, Kazuhiko Sato

“…The calculated coefficients successfully corrected these distortions, improving image accuracy. The derived coefficients effectively corrected these distortions, improving image accuracy. …”
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Exploring a cost-effective way for nutrient management with machine learning for container plants by Ping Yu, Kuan Qin

“…Providing timely references of plant nutrient conditions is essential for container plant production with improved fertilizer use efficiency and reduced environmental waste. …”
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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, and the development of personalized treatment strategies. …”
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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

“…ABSTRACT Aims Accurately predicting recompensation in patients with decompensated hepatitis B‐related cirrhosis is crucial for guiding treatment strategies. As a reliable model for accurately predicting recompensation is currently lacking, this study aimed to develop a contrast‐enhanced computed tomography (CECT) radiomics model specifically for assessing recompensation outcomes in this patient population. …”
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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma by Ke Ma, Jie Xu, Jie Xu, Congyue Wang, Congyue Wang, Xu Cao, Xu Cao, Wenjie Yu, Jingjing Xi, Xuan Zhang, Jiamin Zhan, Yang Liu, Aoyang Yu, Aoyang Yu, Shuhan Liu, Yanhua Liu, Yanhua Liu, Chong Chen, Chong Chen, Xiaoli Mai, Xiaoli Mai

“…IntroductionThe development of high-throughput sequencing technologies and targeted therapeutic strategies has significantly improved the prognosis of lung adenocarcinoma (LUAD) patients with sensitive gene mutations. …”
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Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data by Gengbo Liu, Logan Brooks, John Canty, Dan Lu, Jin Y. Jin, James Lu

“…In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. …”
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Innovative Sandia Frequency Shift Islanding Detection for Microgrids Using Interval Type-II Fuzzy Logic and VP-PSO Optimization by Mehrdad Ahmadi Kamarposhti, Hassan Shokouhandeh, Yeonwoo Lee, Sun-Kyoung Kang, Ilhami Colak, El Manaa Barhoumi, Kei Eguchi

“…The proposed approach further integrates a variable-parameter PSO (VP-PSO) algorithm to optimize both fuzzy membership functions and controller parameters. …”
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Adaptive sliding mode control based on maximum power point tracking for boost converter of photovoltaic system under reference voltage optimizer by Borhen Torchani, Ahmad Taher Azar, Ahmad Taher Azar, Ahmad Taher Azar, Anis Sellami, Saim Ahmed, Saim Ahmed, Ibrahim A. Hameed, Ibraheem Kasim Ibraheem, Moamin Ibrahim Jameel Al-Obaidi

“…Simulations conducted demonstrate superior performance in terms of stability, speed, and control of the converter compared to traditional MPPT algorithms. The main contributions of this article include an improvement in system robustness against irradiance variations, thanks to the integration of an adaptive algorithm and a PI controller within the SMC. …”
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Design Optimization of Compliant Mechanisms for Vibration- Assisted Machining Applications Using a Hybrid Six Sigma, RSM-FEM, and NSGA-II Approach by Huy-Tuan Pham, Van-Khien Nguyen, Quang-Khoa Dang, Thi Van Anh Duong, Duc-Thong Nguyen, Thanh-Vu Phan

“…Vibration-assisted machining, a hybrid processing method, has been gaining considerable interest recently due to its advantages, such as increasing material removal rate, enhancing surface quality, reducing cutting forces and tool wear, improving tool life, or minimizing burr formation. …”
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Charting γ-secretase substrates by explainable AI by Stephan Breimann, Frits Kamp, Gabriele Basset, Claudia Abou-Ajram, Gökhan Güner, Kanta Yanagida, Masayasu Okochi, Stephan A. Müller, Stefan F. Lichtenthaler, Dieter Langosch, Dmitrij Frishman, Harald Steiner

“…Our approach outperforms state-of-the-art protein language models, improving prediction accuracy from 60% to 90%, and achieves an 88% success rate in experimental validation. …”
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