The ZTF Source Classification Project. III. A Catalog of Variable Sources by Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, Theophile Jegou du Laz, Andrew Drake, Matthew J. Graham, Lynne A. Hillenbrand, Jan van Roestel, Paula Szkody, LeighAnna Zielske, Mohammed Guiga, Muhammad Yusuf Hassan, Jill L. Hughes, Guy Nir, Saagar Parikh, Sungmin Park, Palak Purohit, Umaa Rebbapragada, Draco Reed, Daniel Warshofsky, Avery Wold, Joshua S. Bloom, Frank J. Masci, Reed Riddle, Roger Smith

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Global prevalence of post-COVID-19 condition: a systematic review and meta-analysis of prospective evidence by Mohamed Kadry Taher, Talia Salzman, Allyson Banal, Kate Morissette, Francesca R. Domingo, Angela M. Cheung, Curtis L. Cooper, Laura Boland, Alexandra M. Zuckermann, Muhammad A. Mullah, Claudie Laprise, Roberto Colonna, Ayan Hashi, Prinon Rahman, Erin Collins, Tricia Corrin, Lisa A. Waddell, Jason E. Pagaduan, Rukshanda Ahmad, Alejandra P. Jaramillo Garcia

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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. …”
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Global trends in the use of artificial intelligence for urological tumor histopathology: A 20-year bibliometric analysis by Fazhong Dai, Yifeng He, Juan Duan, Kangjian Lin, Qian Lv, Zhongxiang Zhao, Yesong Zou, Jianhong Jiang, Zongtai Zheng, Xiaofu Qiu

“…Conclusions The integration of AI into urological tumor pathology demonstrates transformative potential, significantly enhancing diagnostic accuracy and efficiency through automated analysis of whole-slide imaging and Gleason grading, comparable to pathologist-level performance. …”
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Transformer-based multimodal precision intervention model for enhancing diaphragm function in elderly patients by Ma Xinli, Zhao Jie, Yan Ming, Zhang Yanping, Li Fan, Jia Jing, Ding Lu

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Alterations in sulcal depth and associated functional connectivity in schizophrenia with auditory verbal hallucinations by Zhenru Guo, Zimo Zhou, Shuai Wang, Lianlian Yang, Xiaoshan Gao, Yu Xia, Yuanyuan Yang, Zhangyan Shan, Haixia Huang, Lin Tian, Lin Tian

“…However, the characterization of sulcal depth alterations and associated functional connectivity across the whole brain remains unclear.MethodWe recruited 38 schizophrenia patients with auditory verbal hallucinations and 31 schizophrenia patients without auditory verbal hallucinations. Magnetic resonance imaging data were collected on all participants, and clinical symptoms were assessed using standardized clinical scales. …”
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Digital health and mobile health: a bibliometric analysis of the 100 most cited papers and their contributing authors by Andy Wai Kan Yeung, Olena Litvinova, Nicola Luigi Bragazzi, Yousef Khader, Md. Mostafizur Rahman, Zafar Said, Robert S. H. Istepanian, Anastasios Koulaouzidis, Adeyemi Oladapo Aremu, James M. Flanagan, Navid Rabiee, Sheikh Mohammed Shariful Islam, Devesh Tewari, Ganesh Venkatachalam, Giustino Orlando, Josef Niebauer, Alexandros G. Georgakilas, Mohammad Reza Saeb, Dalibor Hrg, Yufei Yuan, Muhammad Ali Imran, Huanyu Cheng, Eliana B. Souto, Hari Prasad Devkota, Maurizio Angelo Leone, Jamballi G. Manjunatha, Nikolay T. Tzvetkov, Maima Matin, Olga Adamska, Sabine Völkl-Kernstock, Fabian Peter Hammerle, Farhan Bin Matin, Bodrun Naher Siddiquea, Dongdong Wang, Jivko Stoyanov, Jarosław Olav Horbańczuk, Magdalena Koszarska, Emil Parvanov, Iga Bartel, Artur Jóźwik, Natalia Ksepka, Bogumila Zima-Kulisiewicz, Björn Schuller, Gaurav Pandey, David Bates, Tien Yin Wong, Benjamin S. Glicksberg, Maciej Banach, Cyprian Tomasik, Seifedine Kadry, Stephen T. Wong, Ronan Lordan, Faisal A. Nawaz, Rajeev K. Singla, ArunSundar MohanaSundaram, Himel Mondal, Ayesha Juhi, Shaikat Mondal, Merisa Cenanovic, Aleksandra Zielińska, Christos Tsagkaris, Ronita De, Siva Sai Chandragiri, Robertas Damaševičius, Mugisha Nsengiyumva, Artur Stolarczyk, Okyaz Eminağa, Marco Cascella, Harald Willschke, Atanas G. Atanasov

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Intelligent rehabilitation in an aging population: empowering human-machine interaction for hand function rehabilitation through 3D deep learning and point cloud by Zhizhong Xing, Zhizhong Xing, Zhizhong Xing, Zhijun Meng, Gengfeng Zheng, Guolan Ma, Lin Yang, Lin Yang, Xiaojun Guo, Li Tan, Yuanqiu Jiang, Huidong Wu

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Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution by Vivaldi Rinaldi, Masoud Ghandehari

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Unveiling the dynamic effects of major depressive disorder and its rTMS interventions through energy landscape analysis by Chun-Wang Su, Chun-Wang Su, Yurui Tang, Nai-Long Tang, Nai-Long Tang, Nian Liu, Nian Liu, Jing-Wen Li, Shun Qi, Hua-Ning Wang, Zi-Gang Huang, Zi-Gang Huang

“…This study investigates the dynamics of functional brain networks in major depressive disorder (MDD) patients to decipher the mechanisms underlying brain dysfunction during depression and assess the impact of repetitive transcranial magnetic stimulation (rTMS) intervention.MethodsWe employed energy landscape analysis of functional magnetic resonance imaging (fMRI) data to examine the dynamics of functional brain networks in MDD patients. …”
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Intelligent Technologies and Robotic Machines for Garden Crops Cultivation by I. G. Smirnov, D. O. Khort, A. I. Kutyrev

“…The following programming languages were used: (C / C ++)-based  OpenCV library, Spyder Python Development Environment, PyTorch and Flask frameworks, and JavaScript. Image marking for training neural networks was carried out via VGG ImageAnnotator and in Labelbox. …”
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A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation by Chun Zhang, Yinjie Zhao, Guangyu Wu, Han Wu, Hongli Ding, Jian Yu, Ruoqing Wan

“…Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. …”
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Artificial intelligence aided microwave coagulation therapy: Analysis of heat transfer to tumor tissue via hybrid modeling by Zheng Yang, KeWei Dai, Wujun Zhang, Rui Zhou, QingBin Wu, Liang Liu, HuaiRong Qu

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Right inferior frontal cortex and preSMA in response inhibition: An investigation based on PTC model by Lili Wu, Mengjie Jiang, Min Zhao, Xin Hu, Jing Wang, Kaihua Zhang, Ke Jia, Fuxin Ren, Fei Gao

“…We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. …”
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Automated Identification and Analysis of Cracks and Damage in Historical Buildings Using Advanced YOLO-Based Machine Vision Technology by Kui Gao, Li Chen, Zhiyong Li, Zhifeng Wu

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Integrative multi-omic analysis of NLRP3 inflammasome dysregulation and subtyping for personalized treatment in acute myeloid leukemia by Hong Zhang, Linlin Yang, Yukexin Liu, Dongju Wang, Liming Shi, Changyu Tian

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Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
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An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection by Seong-O Shim, Lal Hussain, Eesa Alsolami, Monagi H. Alkinani

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Deep Learning for Automatic Detection of Volcanic and Earthquake-Related InSAR Deformation by Xu Liu, Yingfeng Zhang, Xinjian Shan, Zhenjie Wang, Wenyu Gong, Guohong Zhang

“…In this paper, we first introduce several representative deep learning architectures commonly used in InSAR data analysis, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and Transformer networks. …”
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Fusion of DS-InSAR and THPF-LSTM for monitoring and predicting surface deformation in closed mines by Jianyang ZHANG, Hongdong FAN, Xiangyang ZHU, Minghu SUN

“…To this end, we proposed a prediction model for surface deformation of closed mines combining distributed scatter interferometric synthetic aperture radar (DS-InSAR), temporal high pass filtering (THPF), and a long short term memory network (LSTM). Taking the 98-view Sentinel-1A uptrack image as the data source, firstly, the DS-InSAR method combined with persistent scatterer (PS) and DS points was used to obtain the time-series surface subsidence information of the closed mines in western Xuzhou for the period from November 2019 to December 2022; then the THPF was used to decompose the original subsidence sequences to obtain the high frequency and low frequency, and then, LSTM was used to complete the deformation prediction of the high and low frequency sub-sequence, and the predicted values of the high and low frequency sub-sequence were superimposed to obtain the final prediction result. …”
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