Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study by Yanli Guo, Hongxia Zhang, Yang Guang, Wen He, Bin Ning, Chen Yin, Mingchang Zhao, Fang Nie, Pintong Huang, Rui-Fang Zhang, Qiang Yong, Jianjun Yuan, Yicheng Wang, Lijun Yuan, Litao Ruan, Tengfei Yu, Haiman Song, Yukang Zhang

“…Data from 547 potentially eligible patients were prospectively enrolled from 10 hospitals, and 205 patients with CEUS video were finally enrolled for analysis. …”
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Distinct effects of global signal regression on brain activity during propofol and sevoflurane anesthesia by Fa Lu, Fa Lu, Lunxu Li, Juan Wang, Juan Wang, Xuanling Chen, Ho-Ching Yang, Xiaoli Li, Lan Yao, Zhenhu Liang, Zhenhu Liang

“…IntroductionGlobal signal regression (GSR) is widely used in functional magnetic resonance imaging (fMRI) analysis, yet its effects on anesthetic-related brain activity are not well understood.MethodsUsing fMRI data from patients under general anesthesia, we analyzed temporal variability indices, amplitude of low-frequency fluctuations, functional connectivity, and graph theoretical measures with and without GSR.ResultsHere we show that GSR differentially affects brain activity patterns during propofol- and sevoflurane-induced unconsciousness. …”
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Observing Effects of Calcium/Magnesium Ions and pH Value on the Self-Assembly of Extracted Swine Tendon Collagen by Atomic Force Microscopy by Xuan Song, Zhiwei Wang, Shiyu Tao, Guixia Li, Jie Zhu

“…Self-assembly of extracted collagen from swine trotter tendon under different conditions was firstly observed using atomic force microscopy; then the effects of collagen concentration, pH value, and metal ions to the topography of the collagen assembly were analyzed with the height images and section analysis data. Collagen assembly under 0.1 M, 0.2 M, 0.3 M CaCl2, and MgCl2 solutions in different pH values showed significant differences (P < 0.05) in the topographical properties including height, width, and roughness. …”
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Structural and transport properties of newly synthesized ZSM-5 sourcing silica from coconut shell ash by Aparna Sarker, Abu Sufian Rabbi, Nasrin Akter Nadi, A. K. M. Lutfor Rahman, A.A. Momin, Khondoker Shahin Ahmed, Hasina Akhter Simol

“…The XRD pattern revealed the presence of a pure crystalline MFI phase in the ZSM-5, and the FTIR findings corroborated its pentasil structure. Microscopic images (SEM and TEM) confirmed that the zeolite was polycrystalline, with agglomerated rod-shaped particles. …”
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Advancements in Semi-Supervised Deep Learning for Brain Tumor Segmentation in MRI: A Literature Review by Chengcheng Jin, Theam Foo Ng, Haidi Ibrahim

“…This literature review explores key semi-supervised learning techniques for medical image segmentation, including pseudo-labeling, consistency regularization, generative adversarial networks, contrastive learning, and holistic methods. …”
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Cancer Narratives in Instagram: Self-presentation of Cancer Patients by V. G. Silantieva, A. V. Kozhokina

“…When narrating about their life with the cancer diagnosis, bloggers broadcast a positive media image of a happygo-lucky person. In the narratives chosen for this study, there is hardly an example of the CANCER-WAR metaphor. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

“…The ASCL framework enhances interpretability by incorporating attention mechanisms, such as squeeze-excitation and convolutional block attention module, which highlight key regions in plant images, aiding in transparent decision-making. In the present work, a pre-trained squeeze-excitation ResNet50 Siamese backbone network on the unlabeled PlantVillage dataset was used to validate the generalizability of the learned representations. …”
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Letter from editor by Carlos A. Vargas

“…From Turkey, the second manuscript presents a site response analysis and estimation of S-wave velocity depending on acceleration data. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. Although porescale imaging and network modeling techniques can compute capillary pressure from micro-CT rock images (Øren and Bakke, 2003; Valvatne and Blunt, 2004), these approaches are time-consuming, limited to small sample volumes, and not yet practical for routine reservoir evaluation. …”
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Explainable AI for Healthcare 5.0: Opportunities and Challenges by Deepti Saraswat, Pronaya Bhattacharya, Ashwin Verma, Vivek Kumar Prasad, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma

“…Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. …”
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Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning. by Huu Hoang, Shinichiro Tsutsumi, Masanori Matsuzaki, Masanobu Kano, Keisuke Toyama, Kazuo Kitamura, Mitsuo Kawato

“…Through tensor component analysis of two-photon Ca2+ imaging data from more than 6,000 Purkinje cells, we found that climbing fiber inputs of the two distinct components, which were specifically activated during Go and No-go cues in the learning process, showed an inverse relationship with predictive reward-prediction errors. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Notable approaches like YOLO (You Only Look Once) and its variants have shown exceptional performance in detecting defects with varying scales and complexities, leveraging innovations such as feature pyramid networks and efficient loss functions. Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. …”
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Transcriptional Patterns of Nodal Entropy Abnormalities in Major Depressive Disorder Patients with and without Suicidal Ideation by Minxin Guo, Heng Zhang, Yuanyuan Huang, Yunheng Diao, Wei Wang, Zhaobo Li, Shixuan Feng, Jing Zhou, Yuping Ning, Fengchun Wu, Kai Wu

“…We applied the methodology of edge-centric network analysis to construct the functional brain networks and calculate the nodal entropy. …”
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Factors creating favorable conditions for activating innovative entrepreneurship and startups in post-war Ukraine by Oksana Khymych, Enno Masurel

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Precision Target Spraying System Integrated with Remote Deep Learning Recognition Model for Cabbage Plant Centers by ZHANG Hui, HU Jun, SHI Hang, LIU Changxi, WU Miao

“…By meticulously recording the response times of the three primary components of the system, the valuable data were gathered. The average time required for image processing was determined to be 29.50 ms, while the transmission of decision signals took an average of 6.40 ms. …”
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Genetic neurocognitive profile of autism unveiled with gene transcription by Yingxing Zhang, Fangfang Li, Hongli Wu, Wei Du, Jianhua Shu, Anqing Wang, Chunsheng Xu, Chuanfu Li, Ya Wang, Sheng Hu

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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity by Abdulrahman Alzahrani, Patty Kostkova, Hamoud Alshammari, Safa Habibullah, Ahmed Alzahrani

“…CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. …”
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Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery by Junfei Xia, Roland Romeiser, Wei Zhang, Tamay Ozgokmen

“…The rapid advancement of satellite technology has led to a substantial increase in the volume of remote sensing data, particularly synthetic aperture radar (SAR) imagery, demanding efficient processing and analysis solutions. …”
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Adversarial domain adaptation for deforestation detection in remote sensing imagery by José Matheus Fonseca dos Santos, Pedro Juan Soto Vega, Guilherme Lucio Abelha Mota, Gilson Alexandre Ostwald Pedro da Costa

“…Additionally, the accuracy delivered by those networks is directly impacted by the quality and volume of training data. …”
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Logging Evaluation of Mixed-sediments Reservoirs of the Lower Cambrian Canglangpu Formation in Penglai Gas Area, Central Sichuan Basin by WANG Haiqing, WANG Zeyu, LAI Qiang, LI Shurong, WU Yuyu, ZHANG Mingyong, WANG Yaqing

“…From an overall perspective, the regularity of porosity to permeability is poor, the electrical differentiation of gas abundance is fuzzy, so the traditional parameter calculation model is not applicable, and it is urgent to study an effective logging evaluation method. Using the core analysis data such as cast thin section, XRD, physical property, logging data and gas testing data, the paper studied a systematic evaluation method for calculating the mineral component by dimensionality reduction, neural network analysis and element content to mineral content inversion method, calculating porosity by variable matrix parameters on the basis of tri-porosity logging curves, calculating permeability by lithologic subdivision model, and quantitative evaluation of reservoir property, connectivity and permeability by array acoustic wave combined with electrical imaging logging, then determined the lower limit of effective reservoir and the classification standard of reservoir. …”
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