Highly stable two-level current fluctuation in complex oxide heterostructures by Doyeop Kim, Jung-Woo Lee, Jihyun Lim, Sungjun Choi, Khimananda Acharya, Seobin Oh, Jaewhan Oh, Tula R. Paudel, Yongsoo Yang, Kitae Eom, Sunwoo Lee, Hyungwoo Lee

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Diagnostics of leaks with unknown amplitudes against the background of interference caused by accidental consumption in the hydraulic system for the forest complex by Sazonova Svetlana, Zolnikov Konstantin, Skvortsova Tatyana, Kravchenko Andrey, Zarevich Anton

“…Mathematical models and methods for diagnosing leaks in hydraulic systems are considered, including identifying the facts of leakage based on the use of mathematical models to determine the location and size of such leaks. In this paper the research focuses on the detection of leaks with unknown amplitudes based on the verification of a two-alternative hypothesis for a hydraulic system, taking into account interference from stochastic consumption. …”
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Minimizing Delay and Power Consumption at the Edge by Erol Gelenbe

“…Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. …”
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Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data by Dev Dinesh, Shashi Kumar, Sameer Saran

“…This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. …”
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Quantum-enabled topological optimization of distributed energy storage for resilient black-start operations by Yinchi Shao, Yu Gong, Xiaoyu Wan, Xianmiao Huang, Shanna Luo, Yuntao Cao, Tao Zhang

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Probabilistic Topology Optimization Framework for Geometrically Nonlinear Structures Considering Load Position Uncertainty and Imperfections by Muayad Habashneh, Oveys Ghodousian, Hamed Fathnejat, Majid Movahedi Rad

“…Extending the conventional framework to encompass imperfect geometrically nonlinear analyses, this research discovers the intricate interplay between nonlinearity and uncertainty, shedding light on their combined effects on probabilistic analysis. …”
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Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach by Vasileios Xezonakis, Olusegun David Samuel, Christopher Chintua Enweremadu

“…Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. …”
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Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? by Mohamed Hassan, Aleksandar Vakanski, Boyu Zhang, Min Xian

“…In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. …”
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Selective Reviews of Bandit Problems in AI via a Statistical View by Pengjie Zhou, Haoyu Wei, Huiming Zhang

“…Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. …”
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Comparative Analysis of Control Strategies for Microgrid Energy Management with a Focus on Reinforcement Learning by Parisa Mohammadi, Razieh Darshi, Saeed Shamaghdari, Pierluigi Siano

“…This review aims to enhance understanding in this field and offer insights for future research on smart energy management systems.…”
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The Reactor Monte Carlo code RMC: The state-of-the-art technologies, advancements, applications, and next by Wang Kan, Liu Zhaoyuan, An Nan, Luo Hao, Jia Conglong, Shen Pengfei, Jiang Shihang, Hu Yingzhe, Gou Yuanhao, Wang Wu, Feng Zhiyuan, Liu Guodong, Zhao Xingyu, Chan Kok Yue, Su Zilin, Tan Zhe Chuan, Liu Guanyang, Li Zeguang, Yu Ganglin, Yu Jiyang, Huang Shanfang

“…Parallel acceleration on heterogeneous architecture supercomputers and machine learning algorithms would be incorporated in ongoing research and future development plans.…”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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A comprehensive approach to Queue Waiting Time Prediction using Tree-Based Ensembles with Data Balancing and Explainable AI by Tapodhir Karmakar Taton, Bipin Saha, Md. Johirul Islam, Shaikh Khaled Mostaque

“…It is possible to introduce a robust approach to the prediction of waiting times based on previous queuing data and artificial intelligence (AI) algorithms. This paper contributes to the field by offering a robust approach to waiting time prediction and suggests potential directions for further research. …”
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Providing a Robust Dynamic Pricing Model and Comparing It with Static Pricing in Multi-level Supply Chains Using a Game Theory Approach by Sara Mehrjoo, Hanan Amoozad Mahdirji, Jalil Heidary Dahoei, Seyyed Hossein Razavi Haji Agha, Mahnaz Hosseinzadeh

“…Results This article introduces the development of a stochastic demand function utilizing genetic algorithms and particle optimization. …”
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The Comprehensive Effect of Depression, Anxiety, and Headache on Pain Intensity and Painkiller Use in Patients with Headache Analyzed by Unsupervised Clustering Using Machine Learn... by Jong-Ho Kim, Minha Ahn, Jong-Hee Sohn, Sung-Mi Hwang, Jae-Jun Lee, Young-Suk Kwon

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Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations by Nguyen Ky Phat, Yoonah Lee, Dinh Hoa Vu, Nguyen Phuoc Long, Seongoh Park

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Next Generation Power System Planning and Operation With Quantum Computation by Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara, Giorgio Cortiana, Jan Schiefelbein-Lach, Antonello Monti

“…Additionally, we provide an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. …”
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Drone Navigation and Target Interception Using Deep Reinforcement Learning: A Cascade Reward Approach by Ali A. Darwish, Arie Nakhmani

“…First, we tackle the challenge of partial observability when employing nonlinear function approximators for learning stochastic policies. Second, we optimize the task of maximizing the overall expected reward. …”
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Digitalization of Spectral Measurements in the Fourier Basis – Development Trends and Problems by O. V. Ponomareva, A. V. Ponomarev, N. V. Smirnova

“…It has been shown that the disadvantages of digital technologies for measuring spectra arise both from the nature of digital methods and from the analytical and stochastic properties of the bases of the applied transformations in measuring the spectra. …”
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Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematolo... by Vinod Kumar, Chander prabha, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…A multi-model machine learning approach compares nine algorithms: KNN, AdaBoost (AB), logistic regression (LR), random forest (RF), SVM, naive Bayes (NB), decision tree (DT), gradient boosting (GB), and stochastic gradient descent (SGD). …”
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