Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning by Daniel P. Cetnar, Ayaan Hossain, Grace E. Vezeau, Howard M. Salis

“…Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. …”
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Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance by Cagla Acun, Olfa Nasraoui

“…Post hoc explanations for black-box machine learning models have been criticized for potentially inaccurate surrogate models and computational burden at prediction time. …”
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Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps by Lei Liu, Y. Jade Morton, Yunxiang Liu

“…Abstract This study presents an image‐based convolutional long short‐term memory (convLSTM) machine learning algorithm to predict storm‐time ionospheric irregularities. …”
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RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot by Nattapong Ruenruedeepan, Sujin Bureerat, Natee Panagant, Nantiwat Pholdee

“…The proposed approach leverages the radial basis function (RBF)-learning operator during the reproduction phase of the MnMH to generate high-quality solutions. …”
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Parallel Simulation Multi-Sample Task Scheduling Approach Based on Deep Reinforcement Learning in Cloud Computing Environment by Yuhao Xiao, Yping Yao, Feng Zhu

“…Therefore, herein, a parallel simulation multi-sample task scheduling method based on deep reinforcement learning in a cloud computing environment is proposed. …”
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Cooperative Control of Intersection Traffic Signals Based on Multi-Agent Reinforcement Learning for Carbon Dioxide Emission Reduction by Hyemin Kim, Jinhyuk Park, Dongbeom Kim, Chulmin Jun

“…We consider not only the adjacent intersection’s last reward as a Q-function but also its state and action as state. This method has the advantage of considering only vehicles from adjacent intersections that enter an intersection. …”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. …”
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Exploiting full-duplex opportunities in WLANs via a reinforcement learning-based medium access control protocol by Song Liu, Peng Wei

“…This paper presents a reinforcement learning-based full-duplex (RLFD) medium access control (MAC) protocol for wireless local-area networks (WLANs) with full-duplex access points. …”
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Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach by Seung-Yeon Kim, Haneul Ko

“…Therefore, we propose a multi-agent Q-learning-based power control scheme in UDmN. To satisfy the quality of service (QoS) requirements of users and decrease the energy consumption of networks, we define a reward function while considering the outage and energy efficiency of each BS. …”
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A Novel Approach to Autonomous Driving Using Double Deep Q-Network-Bsed Deep Reinforcement Learning by Ahmed Khlifi, Mohamed Othmani, Monji Kherallah

“…Deep reinforcement learning (DRL) trains agents to make decisions by learning from rewards and penalties, using trial and error. …”
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Damping profile learning for human-robot collaboration using Bayesian optimization with a task success rate model by Liem Duc TRAN, Tasuku YAMAWAKI, Masahito YASHIMA

“…Furthermore, prior knowledge regarding the damping profile is incorporated as a constraint to improve the learning performance. Extensive simulations and real-world experimental evaluations with a 7-DOF robot arm demonstrate that the proposed method can generate appropriate impedance parameters, exhibiting a substantial advantage over conventional impedance-learning methods in terms of learning performance.…”
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Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model by Haotian Guo, Keng-Weng Lao, Junkun Hao, Xiaorui Hu

“…The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. …”
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Indonesian Sign Language (SIBI) Vocabulary Learning Media Design Based on Augmented Reality for Hearing-Impaired Children by Martianda Anggraeni, Widi Sarinastiti, Setiyo Wati

“…The rapid development of Information and Communication Technologies (ICT) such as the Augmented Reality (AR) should also be utilized to support the learning media for hearing-impaired children. In this study an application based on AR technology has been integrated with flashcards as a learning tool for children with hearing impairment in learning the vocabulary of the Indonesian sign language (SIBI) In designing the learning media, we use Luther's version of multimedia development method, include concept, design, material collecting, assembly, testing, and distribution. …”
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Research on operation optimization of heavy-haul combined trains in long and steep downhill sections based on reinforcement learning by WANG Jianhua, WANG Chunyi, ZENG Zhou, WANG Cong, WANG Qingyuan, YANG Hang

“…Then, an operation optimization algorithm was designed, establishing a reward function that prioritized speed following features, based on reinforcement learning. …”
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A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling by Peng Liang, Pengfei Xiao, Zeya Li, Min Luo, Chaoyong Zhang

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Forecasting monthly residential natural gas demand in two cities of Turkey using just-in-time-learning modeling. by Burak Alakent, Erkan Isikli, Cigdem Kadaifci, Tonguc S Taspinar

“…For each test point, a kernel function, tailored for the NGD predictions, is used in GPR to predict the query point. …”
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Model-Based Offline Reinforcement Learning for AUV Path-Following Under Unknown Ocean Currents with Limited Data by Xinmao Li, Lingbo Geng, Kaizhou Liu, Yifeng Zhao

“…Additionally, the carefully designed state space, action space, reward function, and domain randomization ensure strong generalization and disturbance rejection without extra compensation. …”
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Learning from the local: the variety and spatial pattern of vocal mimicry in songs of the invasive white-rumped shama in Taiwan by Bao-Sen Shieh, Shih-Hsiung Liang, Shuo-Chen Chang

“…Studying the model selection, especially in multiple heterospecific mimicry, is crucial for understanding the function of vocal mimicry. Invasive songbirds with large repertoires in novel auditory environments lacking conspecifics expand their repertoires by imitating heterospecifics, offering valuable insights into model selection. …”
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Physics‐Guided Deep Learning for Modeling Single‐Point Wave Spectra Using Wind Inputs of Two Resolutions by Tianxiang Gao, Haoyu Jiang

“…A tailored network architecture and loss function are developed to capture the relevant information from both wind scales. …”
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User Cost Minimization and Load Balancing for Multiple Electric Vehicle Charging Stations Based on Deep Reinforcement Learning by Yongxiang Xia, Zhongyi Cheng, Jiaqi Zhang, Xi Chen

“…In the DRL algorithm, we design a novel reward function that enables charging stations to meet user charging demands while minimizing user charging costs and reducing the load gap among distribution networks. …”
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