Filter-enhanced adiabatic quantum computing on a digital quantum processor by Erenay Karacan, Conor Mc Keever, Michael Foss-Feig, David Hayes, Michael Lubasch

“…Eigenstate filters underpin near-optimal quantum algorithms for ground-state preparation. …”
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Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning researchMendeley Data by Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

“…The dataset aims to support the development of machine-learning models for precise disease detection and quality assessment in lentil cultivation. …”
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Navigating the landscape: Unveiling the reflection of AI in the sharing economy through bibliometric-based approach by Veljko Uskoković, Milica Maričić, Aleksandra Dacić-Pilčević, Veljko Jeremić

“…Furthermore, AI optimizes resource allocation and improves matching algorithms on online labour platforms, thereby boosting platform efficiency. …”
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Advancing cardiovascular care through actionable AI innovation by Giuseppe Biondi-Zoccai, Arjun Mahajan, Dylan Powell, Mariangela Peruzzi, Roberto Carnevale, Giacomo Frati

“…Indeed, offline RL refers to a class of ML algorithms that learn optimal decision-making policies from a fixed dataset of previously collected experiences—such as electronic health records or registries—without the need for active, real-time interaction with the clinical environment. …”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. …”
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Hole Cleaning during Drilling Oil and Gas Wells: A Review for Hole-Cleaning Chemistry and Engineering Parameters by Mohammed Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al-Shehri

“…The development of hole-cleaning models is important for use as hole-cleaning indicators to ensure optimized drilling efficiency. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. …”
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From Code to Life: The AI‐Driven Revolution in Genome Editing by Zhidong Li, Wasi Ullah Khan, Genxiang Bai, Chao Dong, Jungang Wang, Youpeng Zhang, Chong Wang, Hongbin Zhang, Wenyi Wang, Ming Luo, Fei Chen

“…Recent advancements in artificial intelligence (AI) have significantly enhanced genome editing by improving target selection, minimizing off‐target effects, and optimizing CRISPR‐associated systems. …”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

“…The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. …”
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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

“…In recent years, the safety of primary and secondary schools has garnered considerable attention from policymakers and architects, necessitating rational design methods to develop effective strategies that optimize the campus environment. This study utilizes algorithmic simulations, spatial analysis, and statistical methods to examine the relationships between the layouts of buildings, public space morphology, and campus safety. …”
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Impacts of noise and interference on the bit error rate of the FBMC-OQAM modulation scheme in 5G systems by A. Androon, O. V. Tikhonova

“…The graphical and numerical data obtained through computer modeling demonstrates improved BER in 5G networks using FBMC-OQAM. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion by Yice Cao, Chenchen Liu, Zhenhua Wu, Lei Zhang, Lixia Yang

“…Although segmentation algorithms based on convolutional neural networks (CNNs) or Transformers have achieved significant performance improvements, the trade-off between segmentation precision and computational complexity remains a key limitation for practical applications. …”
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Advancing Alzheimer's Therapy: Computational strategies and treatment innovations by Jibon Kumar Paul, Abbeha Malik, Mahir Azmal, Tooba Gulzar, Muhammad Talal Rahim Afghan, Omar Faruk Talukder, Samar Shahzadi, Ajit Ghosh

“…Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. …”
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The role of artificial intelligence in enhancing disaster preparedness and trauma care by Mohamad Sorani

“…Furthermore, ethical considerations such as data privacy, algorithmic bias, and equitable access are crucial to ensuring that AI-driven solutions are implemented responsibly and inclusively.This review also explores future directions for AI, including the development of sophisticated predictive models, autonomous systems for high-risk tasks, immersive training programs utilizing virtual and augmented reality, and global collaboration aimed at standardizing AI tools and practices. …”
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A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future Directions by Abdullah Al Noman, Aaron Heuermann, Stefan Wiesner, Klaus-Dieter Thoben

“…With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. …”
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Dynamic Dwell Time Adjustment in Wire Arc-Directed Energy Deposition: A Thermal Feedback Control Approach by Md Munim Rayhan, Abderrachid Hamrani, Fuad Hasan, Tyler Dolmetsch, Arvind Agarwal, Dwayne McDaniel

“…Future work should focus on integrating multi-axis thermal sensors, extending the framework to multi-material deposition scenarios and implementing machine learning algorithms for predictive thermal modeling.…”
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Workload Forecasting Methods in Cloud Environments: An Overview by Samah Aziz, Manar Kashmoola

“…We explore more sophisticated approaches like algorithms for deep learning (DL) and machine learning (ML) in addition to more conventional approaches like analysis of time series and models of regression. …”
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Intelligent gear decision method for vehicle automatic transmission system based on data mining by Yong Wang, Jianfeng Zeng, Pengfei Du, Huachao Xu

“…Subsequently, dynamic programming algorithms are employed to optimize intelligent gear decisions. …”
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