Energy Efficiency in Measurement and Image Reconstruction Processes in Electrical Impedance Tomography by Barbara Stefaniak, Tomasz Rymarczyk, Dariusz Wójcik, Marta Cholewa-Wiktor, Tomasz Cieplak, Zbigniew Orzeł, Janusz Gudowski, Ewa Golec, Michał Oleszek, Marcin Kowalski

“…Research studies confirm the device’s high energy efficiency while maintaining the accuracy of the classification of lung disease using the LightGBM algorithm. …”
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Clinical and imaging diagnosis of pediatric testicular microlithiasis: a physician’s dilemma by Ghada Habachi, Ghada Habachi, Ghada Habachi, Yosra Kerkeni, Yosra Kerkeni, Jouini Riadh, Jouini Riadh

“…Its clinical significance and optimal diagnostic and therapeutic management remain subjects of ongoing debate. …”
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I-YOLOv11n: A Lightweight and Efficient Small Target Detection Framework for UAV Aerial Images by Yukai Ma, Caiping Xi, Ting Ma, Han Sun, Huiyang Lu, Xiang Xu, Chen Xu

“…However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. …”
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Implementing Blockchain Technology for Secure Data Transactions in Cloud Computing Environments Challenges and Solutions by Dandu Varalakshmi, N Shirisha, K Suresh, Saidhbi Shaik, V Venkatesh, C F Theresa Cenate

“…It also includes quantum-resilient cryptographic algorithms, which provide protection against potential future quantum attacks. …”
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Wavenumber-Domain Joint Estimation of Rotation Parameters and Scene Center Offset for Large-Angle ISAR Cross-Range Scaling by Bakun Zhu, Weigang Zhu, Hongfeng Pang, Chenxuan Li, Lei Qui, Jinhai Yan, Fanyin Ma, Yijia Liu

“…Utilizing this model and the sensitivity of wavenumber-domain imaging to SCO, a joint estimation algorithm that combines particle swarm optimization (PSO) and image entropy evaluation is proposed, achieving accurate parameter estimation. …”
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Generalized BER Performance Analysis for SIC-Based Uplink NOMA by Mahmoud AlaaEldin, Emad Alsusa, Mohammad Al-Jarrah, Karim G. Seddik, Michail Matthaiou

“…It is shown that these expressions are essential for designing optimal uplink power allocation (PA) strategies and mitigating SIC error floors, as evidenced by the proposed PA algorithm which achieves significant performance gains. …”
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Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations by Hai Cao, Shaoming Hu, Junju Du, Xu Chen, Shuqi Liu, Shuai Feng, Bo Zhang, Yuchen Jiang

“…To support the training of the deep neural network, we propose a scoring mechanism to evaluate the effectiveness of the observations, which is optimized through a refined REINFORCE algorithm with a baseline. …”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…This review highlights the significant potential of AI-enhanced qEEG as a non-invasive, cost-effective tool for the diagnosis of AD in its prodromal and dementia stages, while also identifying areas requiring further research to optimize its clinical application. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. …”
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Ubiquitous memory augmentation via mobile multimodal embedding system by Dongqi Cai, Shangguang Wang, Chen Peng, Zeling Zhang, Zhenyan Lu, Tao Qi, Nicholas D. Lane, Mengwei Xu

“…The core design draws inspiration from the memory functions of the human brain, utilizing coarse-grained embeddings to identify likely candidates, which are then refined through query-driven fine-grained retrieval. A series of algorithm-hardware orchestrated optimizations automatically navigates this process and strengthen the embedding quality. …”
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Double Deep Q-Network-Based Solution to a Dynamic, Energy-Efficient Hybrid Flow Shop Scheduling System with the Transport Process by Qinglei Zhang, Huaqiang Si, Jiyun Qin, Jianguo Duan, Ying Zhou, Huaixia Shi, Liang Nie

“…In addition, 36 test instances of varying sizes, simplified from the excavator boom manufacturing process, are designed for comparative experiments with traditional algorithms. The experimental results demonstrate that DDQCE achieves 40% more Pareto-optimal solutions compared to NSGA-II and MOEA/D while requiring 10% less computational time, confirming that this algorithm efficiently solves the TDEHFSP problem.…”
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The potential & limitations of monoplotting in cross-view geo-localization conditions by Bradley J. Koskowich, Michael J. Starek, Scott A. King

“…Classical keypoint matching methods find the extreme pose transitions between cameras present in a CVGL configuration challenging to operate in, while deep neural networks demonstrate superb capacity in this area. …”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side by Ali Sharida, Abdullah Berkay Bayindir, Sertac Bayhan, Haitham Abu-Rub

“…Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. …”
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Model‐Free Deep Reinforcement Learning with Multiple Line‐of‐Sight Guidance Laws for Autonomous Underwater Vehicles Full‐Attitude and Velocity Control by Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Jianguo Ma, Yuan Fang, YuChen Sun

“…This framework enables seamless switching among guidance modes, such as waypoint following, path following, and trajectory tracking, to achieve optimal attitude control. For comprehensive control of roll, pitch, yaw, and longitudinal velocity, an augmented‐twin delayed deep deterministic policy gradient (A‐TD3) algorithm streamlines the training of the control agent. …”
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Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections by Ziyi Lu, Tianxiong Wu, Jinshan Su, Yunting Xu, Bo Qian, Tianqi Zhang, Haibo Zhou

“…Second, this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time (EI-OET) algorithm based on edge-cloud computing power support. …”
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Deep Reinforcement Learning-Based Active Disturbance Rejection Control for Trajectory Tracking of Autonomous Ground Electric Vehicles by Xianjian Jin, Huaizhen Lv, Yinchen Tao, Jianning Lu, Jianbo Lv, Nonsly Valerienne Opinat Ikiela

“…The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm for dynamic optimization of controller parameters to improve tracking accuracy and robustness. …”
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Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition by Libang Chen, Jinyan Lin, Qihang Bian, Yikun Liu, Jianying Zhou

“…The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. …”
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Unveiling nature's secrets: Deep learning for enhanced biogenic emission resolution by Antonio Giganti

“…For instance, understanding gas emissions from crops can help farmers optimize their activities, reducing fertilizer and pesticide use while improving yields. …”
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Deep learning-based phase demodulation for distributed acoustic sensor by Yiming Tang, Kewei Liu, Chen Liu, Haiyong Wu, Rugang Wang, Mengmeng Chen, Fei Xu

“…Experimental results demonstrate that the proposed method significantly enhances processing speed while maintaining accuracy comparable to traditional demodulation algorithms. …”
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