Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.…”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. by Shang-Ming Zhou, Fabiola Fernandez-Gutierrez, Jonathan Kennedy, Roxanne Cooksey, Mark Atkinson, Spiros Denaxas, Stefan Siebert, William G Dixon, Terence W O'Neill, Ernest Choy, Cathie Sudlow, UK Biobank Follow-up and Outcomes Group, Sinead Brophy

“…<h4>Objectives</h4>1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs.…”
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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization by Sumet Mehta, Fei Han, Muhammad Sohail, Bhekisipho Twala, Asad Ullah, Fasee Ullah, Arfat Ahmad Khan, Qinghua Ling

“…Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…Aiming at the problem of drought forecasting accuracy in a short time scale, this study proposed a drought forecasting model named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) and the JAVA optimization algorithm to improve the Informer model. This study conducted a comparative analysis of VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, and VMD-JAYA-Informer drought prediction models. …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…Directly applying off-the-shelf models to predict the optical field dynamics shows unsatisfying fidelity and efficiency since the model primitives are agnostic to the unique physical properties of Maxwell equations and lack algorithmic customization. …”
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Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China by Qingfeng Zhou, Chun Janice Wong, Xian Su

“…Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. …”
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Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators by Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, Sumanta Samal

“…The present investigation reports the design and development of an advanced material with a high figure of merit (FoM) for beampipe applications in particle accelerators by bringing synergy between computational and experimental approaches. Machine-learning algorithms have been used to predict the phase(s), low density, and high radiation length of the designed Al-Ti-V alloys. …”
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CPP: a path planning method taking into account obstacle shadow hiding by Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang

“…We also proposed a Minimum-Jerk Trajectory Optimization method with controllable path noise points, which enhanced path smoothness and reduced predictability. Comparative analysis showed that CPP significantly outperformed five other algorithms—RRT, Improved B-RRT, RRT*, Informed RRT*, and Potential Field-by reducing running time by 46.01% to 93.3%, increasing path safety by 10.42% to 83.44%, and improving path smoothness, making it particularly effective for path planning in tactical scenarios involving unmanned vehicles.…”
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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk by Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu

“…The results suggested that our novelty, SMOTEENN-ENC, integrated with the XGBoost algorithm demonstrated superiority and stability in the predictive performance when applied to skewed fat-tailed distributed datasets with inherent diverse features.…”
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Neural Network Approaches for Distributional Shifts in Environmental Sensors by Tobias Sukianto, Sebastian A. Schober, Cecilia Carbonelli, Simon Mittermaier, Robert Wille

“…Due to the distributional shift between the training and operational environment induced by sensor ageing and drift processes, the algorithms that predict air quality suffer from performance degradation during the products’ lifetime. …”
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Artificial intelligence models in corporate financial and accounting processes: systematic literature review by Deivi David Fuentes Doria, Aníbal Toscano Hernández, Johana Elisa Fajardo Pereira

“…The results suggest that supervised models are the most applied in the accounting and financial field, while the algorithms that have been most used are decision trees, support vector machines, random forests, neural networks, and logistic regressions, employed in specific areas of financial fraud, stock market predictions, and cash flow. …”
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Efficient Trans-Dimensional Bayesian Inversion of C-Response Data from Geomagnetic Observatory and Satellite Magnetic Data by Rongwen Guo, Shengqi Tian, Jianxin Liu, Yi-an Cui, Chuanghua Cao

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Modeling of erosion processes in open channels by M. R. Magomedova

“…A technique has been developed for predicting the general erosion of riverbeds and canals, composed of homogeneous and heterogeneous non-cohesive soils. …”
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Short-Term Traffic Flow Forecasting Model Based on GA-TCN by Rongji Zhang, Feng Sun, Ziwen Song, Xiaolin Wang, Yingcui Du, Shulong Dong

“…The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. …”
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Calibration Transfer of Near-Infrared Spectroscopic Model for Soluble Solid Content Predication of Apples by the Combined Use of Direct Standardization and Piecewise Direct Standar... by CHENG Ye, HUANG Haoran, WANG Ying, XIONG Zhixin

“…The results indicated that compared with DS and PDS, the combined algorithm not only significantly improved the prediction performance of the model for the slave instrument, but also mitigated the artifacts caused by PDS. …”
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Potential distribution of endemic lizards from Brazilian restingas: The present announcing the end by Hugo Andrade, Luisa Maria Diele‐Viegas Costa Silva, Carlos Frederico Duarte Rocha, Antônio Jorge Suzart Argôlo, Eduardo José dos Reis Dias

“…Here, we used an ensemble of three modeling algorithms (Bioclim, GLM, and SVM). In predicting the effects of climate change on their future distributions, we used intermediate and pessimistic socio‐economic pathway scenarios (SSP3 70 and SSP5 85, respectively) considering projections for 2081–2100. …”
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Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?—A Systematic Review by Sergiu-Ciprian Matei, Sorin Olariu, Ana-Maria Ungureanu, Daniel Malita, Flavia Medana Petrașcu

“…The future of AI in venous diagnostics is promising, and several areas of development were noted, including AI algorithms embedding directly into ultrasound devices to provide instantaneous diagnostic insights during patient evaluations; combining AI-processed Doppler data with other imaging modalities, such as computed tomography or MRI, for comprehensive assessments; AI usage in order to predict disease progression and tailor treatment strategies based on individual patient profiles; and constructing large-scale, multicenter datasets to improve the robustness and generalizability of AI algorithms.…”
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Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques by Manar Ahmed Hamza, Maha M. Althobaiti, Ola Abdelgney Omer Ali, Souad Larabi-Marie-Sainte, Majdy M. Eltahir, Anwer Mustafa Hilal, Mesfer Al Duhayyim, Ishfaq Yaseen

“…The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different K values produced the highest accuracy at K=9 and k=10 of 95.12% and 95.67%; the lowest accuracy is K=1 of 65.34%.…”
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Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study by Maryam Ramezani, Mohammadreza Mobinizadeh, Ahad Bakhtiari, Hamid R. Rabiee, Maryam Ramezani, Hakimeh Mostafavi, Alireza Olyaeemanesh, Ali Akbar Fazaeli, Alireza Atashi, Saharnaz Sazgarnejad, Efat Mohamadi, Amirhossein Takian

“…These models highlighted the importance of features like current health expenditure, domestic general government health expenditure, and GDP in predicting life expectancy. Conclusion The findings underscore the significance of employing innovative methods like CRISP-ML(Q) and ML algorithms to enhance health equity. …”
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