Low-light image enhancement method based on retinex theory and dual-tree complex wavelet transform by Yuqian Zhang, Jie Jiang, Zhan Wang, Qi Zhang, Yudi Jiang, Jun Liu, Zeyao Hou

“…To address these problems, various Low-light Image Enhancement (LIE) algorithms have been developed in recent years, including histogram-based methods, gamma correction, and approaches based on Retinex theory. …”
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Using de novo assembly to identify structural variation of eight complex immune system gene regions. by Jia-Yuan Zhang, Hannah Roberts, David S C Flores, Antony J Cutler, Andrew C Brown, Justin P Whalley, Olga Mielczarek, David Buck, Helen Lockstone, Barbara Xella, Karen Oliver, Craig Corton, Emma Betteridge, Rachael Bashford-Rogers, Julian C Knight, John A Todd, Gavin Band

“…Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. …”
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Model Development for Estimating Sub-Daily Urban Air Temperature Patterns in China Using Land Surface Temperature and Auxiliary Data from 2013 to 2023 by Yuchen Guo, János Unger, Tamás Gál

“…Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. …”
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A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…Experimental results validate the effectiveness of ORA-DL, achieving 93.38% energy demand prediction accuracy, improving grid stability to 96.25%, and reducing energy wastage to 12.96%. …”
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Evaluating Network Performance in a Web-Based Augmented Reality System for Lipmatte Color Recommendation by Zulfikri Paidi, Nurhanna Md Abd Wahid, Nurzaid Muhd Zain, Mahfudzah Othman

“…Similarly, resource loading times varied, with an average loading time of 3.76 ms for four devices, improving to 2.44 ms with eight devices, and peaking at 3.96 ms with sixteen devices before stabilizing at 2.80 ms with twenty devices. …”
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Advances in application of ultrasound in diagnosis of diabetic nephropathy by GUO Juan, YANG Zhifang, JI Ri

“…In the future, combining AI algorithms with ultrasound technology is expected to enable automatic learning and identification of renal structures and pathological features from large volumes of ultrasound images, automatic quantification of key parameters such as RRI and SWV, and dynamic analysis of changes in renal microcirculation, thereby significantly improving the accuracy and efficiency of DN diagnosis.…”
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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

“…By integrating quantum annealing algorithms, the framework efficiently addresses the combinatorial complexity of large-scale ESS placement and dispatch, outperforming traditional heuristic and classical optimization techniques in both computational speed and solution quality. …”
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Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, Najmus Saqib Bin Rafi

“…Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. …”
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Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador by Mohammad Amin Khalili, Silvio Coda, Domenico Calcaterra, Diego Di Martire

“…To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. …”
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Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems by Prashant Nene, Dolly Thankachan

“…The results validate its capability when compared against traditional methods such as Genetic Algorithms and Particle Swarm Optimization. With this, we now have a scalable and real-time energy-efficient solution for future smart grid systems. • Integrated Intelligence Stack: Combines RL-ENN, T-STFREP, FL-DEO, GNNHSCO, and Q-GAN-ESO into a unified architecture for real-time control, forecasting, decentralized optimization, network routing, and synthetic scenario generation. • Real-Time, Scalable, and Privacy-Preserving: Enables adaptive energy dispatch, federated optimization without compromising data privacy, and graph-based power routing, making it suitable for large-scale, smart grid deployments. • Proven Long-Term Performance: Achieved significant improvements over traditional methods (GA, PSO) with 27.5 % lower NPC, 18.2 % reduction in COE, and 30.2 % increase in battery life, validated using 30 years of meteorological data.…”
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Wellcounter: Automated high‐throughput phenotyping for aquatic microinvertebrates by Claus‐Peter Stelzer, Dominique Groffman

“…For those interested in developing image analysis algorithms, we provide large annotated datasets, including high‐resolution movies and images with known quantities and positions of specimens. …”
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Towards an Efficient Remote Sensing Image Compression Network with Visual State Space Model by Yongqiang Wang, Feng Liang, Shang Wang, Hang Chen, Qi Cao, Haisheng Fu, Zhenjiao Chen

“…As the network’s effective receptive field (ERF) expands, it can capture more feature information across the remote sensing images, thereby reducing spatial redundancy and improving compression efficiency. However, the majority of these learned image compression (LIC) techniques are primarily CNN-based and transformer-based, often failing to balance the global ERF and computational complexity optimally. …”
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YouTube and Bilibili as sources of information on oral cancer: cross-sectional content analysis study by Qilei zhang, Zhe Li, Huiping Zhang, Ling Han, Shugang Zhao, Siyu Jia

“…In conclusion, YouTube videos exhibited higher audience engagement and video quality, yet improvements are needed on both platforms. In order to promote high-quality health information, it is essential to encourage the development of more professional content creators and to optimize platform algorithms.…”
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Urban Heat Island Effect: Remote Sensing Monitoring and Assessment—Methods, Applications, and Future Directions by Lili Zhao, Xuncheng Fan, Tao Hong

“…The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. …”
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Guidelines for the management of emergencies and critical illness in pediatric and adult patients with sickle cell disease by Armand Mekontso Dessap, Stephane Dauger, Mehdi Khellaf, Maite Agbakou, Sophie Agut, François Angoulvant, Jean-Benoît Arlet, Cécile Aubron, Florent Baudin, Florence Boissier, Nicolas Bounaud, Pierre Catoire, Jérôme Cecchini, Djamila Chaiba, Anthony Chauvin, Richard Chocron, Benedicte Douay, Delphine Douillet, Narcisse Elenga, Olivier Flechelle, Ségolène Gendreau, Sybille Goddet, Jeremy Guenezan, Anoosha Habibi, Claire Heilbronner, Bérengère Koehl, Pierrick Le Borgne, Philippe Le Conte, Annick Legras, Michael Levy, Bernard Maitre, Mathieu Oberlin, Mehdi Oualha, Nicolas Peschanski, France Pirenne, Corinne Pondarre, Jérôme Rambaud, Keyvan Razazi, Geoffroy Rousseau, Aurélie Schirmann, Isabelle Thuret, Ruddy Valentino, Guillaume Voiriot, Barbara Villoing, Marion Grimaud, Sandrine Jean

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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study by Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

“…Its use can optimize preoperative evaluations, minimize unnecessary testing, and streamline perioperative care, significantly improving patient outcomes and resource use. We anticipate that applying this model to actual electronic health records will benefit clinical practice.…”
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Development of a machine learning-derived dendritic cell signature for prognostic stratification in lung adenocarcinoma by Fang Zhou, Fang Zhou, Meng Wang, Meng Wang, Zheng Wang, Zheng Wang, Wei Li, Wei Li, Xike Lu, Xike Lu

“…This work highlights the pivotal role of dendritic cell programs in shaping the tumor microenvironment and provides potential targets for improving precision immunotherapy.…”
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A self-configuration framework for balancing services in the fog of things by Edson Mota, Jurandir Barbosa, Gustavo B. Figueiredo, Maycon Peixoto, Cássio Prazeres

“…The experimental results demonstrate significant improvements in network performance, response time, and load balancing. …”
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Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH by Qing-Yu Chen, Shu-Min Yin, Ming-Ming Shao, Feng-Shuang Yi, Huan-Zhong Shi

“…However, machine learning (ML) offers a novel approach based on artificial intelligence to improving diagnostic accuracy and capture the non-linear relationships. …”
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