FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination by WenChang Zhang, LuZhi Yuan, Yun Sha, LingLin Yang, XueJun Liu, Yong Yan

“…The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra‐sequence and inter‐sequence correlations under the same period. …”
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Fault Detection of a Wheelset Bearing Based on Appropriately Sparse Impulse Extraction by Jianming Ding, Fenglin Li, Jianhui Lin, Bingrong Miao, Lu Liu

“…The type of atoms embedded in vibration signals is estimated by ENA. Aiming at the different types of atoms, the impulses with different sparse characteristic are spanned by CSR with different penalty parameters. …”
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Self-Healing Properties of Partially Coherent Schell-Model Beams by Gaofeng Wu, Xiaoyan Pang

“…The examples are showed to how the formula can be applied to study the self-healing for different beams and different obstructions. In particular, this approach can be used to conveniently deal with any partially coherent Schell-model beams partially blocked by any shape obstacle. …”
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Comment on S Memon, et al. (J Pak Med Assoc. 74: 1163-1166, June 2024) Osmolar gap in hyponatraemia: An exploratory study by Muhammad Ramish Irfan

“… Madam, Your paper about the osmolar gap in hyponatraemiawas much appreciated as it remains a subject shrouded inmisunderstanding.The observations reported in this paper are certainly thoughtprovoking, therefore I would extend a few conceptualclarifications that I believe your readership would benefit fromin gaining deeper insight about the findings reported in thisstudy.The difference between tonicity, osmolarity and osmolality isoften disregarded and appears convoluted however, it is crucialto delineate between these terms nevertheless. …”
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Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model by Li-Hsin Cheng, Xiaowu Sun, Charlie Elliot, Robin Condliffe, David G. Kiely, Samer Alabed, Andrew J. Swift, Rob J. van der Geest, David G Kiely, Lisa Watson, Iain Armstrong, Catherine Billings, Athanasios Charalampopoulos, Robin Condliffe, Charlie Elliot, Abdul Hameed, Neil Hamilton, Judith Hurdman, Allan Lawrie, Robert A Lewis, Smitha Rajaram, Alex Rothman, Andy J. Swift, Steven Wood, AA Roger Thompson, Jim Wild

“…Methods: We constructed an explainable convolutional neural network (CNN) taking cardiac MR cine series from four different views as input to predict mean pulmonary artery pressure (mPAP). …”
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Raman micro-spectroscopy reveals the metabolic alterations in primary prostate tumor tissues of patients with metastases by Xiaoguang Shao, Bo Liu, Hongyang Qian, Qihan Zhang, Yinjie Zhu, Shupeng Liu, Heng Zhang, Jiahua Pan, Wei Xue

“…The objective of this study was to investigate the metabolic differences in the primary tumor tissues between localized PC and metastatic PC using Raman micro-spectroscopy and metabolomics analysis, and then explore potential biomarkers for predicting metastasis and the potential metabolic pathways during the progression from localized prostate cancer to metastasis. …”
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Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome by Frederik M Zimmermann, Pim A L Tonino, Arjan Koks, Jesse P A Demandt, Marcel van ’t Veer, Pieter-Jan Vlaar, Thomas P Mast, Konrad A J van Beek, Marieke C V Bastiaansen

“…However, these studies were aimed at a study population with a high prevalence of occlusive myocardial infarction, which could explain the differing levels of diagnostic performance.Conclusion Integrating AI in prehospital ECG interpretation improves the identification of patients at low risk for having NSTE-ACS. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Although the performance differences among the models were subtle (within a range of 0.001), the SVM classifier outperformed the others, achieving a recall of 0.9979 and an AUC of 0.9998. …”
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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study by Seo Hyun Oh, Youngho Lee, Jeong-Heum Baek, Woongsang Sunwoo

“…Patients were stratified into colon and rectal cancer groups to account for biological and prognostic differences. Three models were developed and compared: a conventional artificial neural network (ANN), a basic convolutional neural network (CNN), and a transfer learning–based Visual Geometry Group (VGG)16 model. …”
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Instance Segmentation of Sugar Apple (<i>Annona squamosa</i>) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model by Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang, Wenlong Wang

“…An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. …”
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Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study by Alexander Brehmer, Constantin Seibold, Jan Egger, Khalid Majjouti, Michaela Tapp-Herrenbrück, Hannah Pinnekamp, Vanessa Priester, Michael Aleithe, Uli Fischer, Bernadette Hosters, Jens Kleesiek

“… Abstract BackgroundPressure ulcers (PUs) and incontinence-associated dermatitis (IAD) are prevalent conditions in clinical settings, posing significant challenges due to their similar presentations but differing treatment needs. Accurate differentiation between PUs and IAD is essential for appropriate patient care, yet it remains a burden for nursing staff and wound care experts. …”
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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy by Lindsay A. Edwards, Christina Yang, Surbhi Sharma, Zih-Hua Chen, Lahari Gorantla, Sanika A. Joshi, Nicolas J. Longhi, Nahom Worku, Jamie S. Yang, Brandy Martinez Di Pietro, Saro Armenian, Aarti Bhat, William Border, Sujatha Buddhe, Nancy Blythe, Kayla Stratton, Kasey J. Leger, Wendy M. Leisenring, Lillian R. Meacham, Paul C. Nathan, Shanti Narasimhan, Ritu Sachdeva, Karim Sadak, Eric J. Chow, Patrick M. Boyle

“…We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. …”
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HRNet Encoder and Dual-Branch Decoder Framework-Based Scene Text Recognition Model by Meiling Li, Xiumei Li, Junmei Sun, Yujin Dong

“…In the decoder module, the dual-branch structure is adopted, in which the super-resolution branch takes the feature maps with the highest resolution obtained in the encoder module as input and restores images by upsampling through transposed convolution. The four kinds of feature maps with different resolutions are fused through independent transposed convolution layers for multiscale fusion in the recognition branch and then inputted into the attention-based decoder for text recognition. …”
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Caste, Constitution, Court, Equality: The Social Justice Imbroglio in Contemporary India by Ishita Banerjee-Dube

“…This article addresses these issues by revisiting the convoluted trajectory of positive discrimination (termed “reservation”) in India as an illustrative and instructive example. …”
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Semantic-aware multi-task learning for image aesthetic quality assessment by Weiliang Yan, Yuqing Li, Huan Yang, Baoxiang Huang, Zhenkuan Pan

“…However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. …”
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Semantic Fusion-Oriented Bi-Typed Multi-Relational Heterogeneous Graph Neural Network by Yifan Sun, Jing Yan, Lilei Lu, Hongbo Zhang, Yanhong Shang

“…Additionally, it employs relational convolutions to capture relationship features within different types and fuses different relationship features through a relational-level attention mechanism. …”
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ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages by Yan Mo, Shaowei Bai, Wei Chen

“…Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. …”
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Evaluation of sports teaching quality in universities based on fuzzy decision support system by Kunjian Han, Jian Wan

“…The proposed model intakes different factors, such as training patterns, sessions, time, associated with the teaching sessions. …”
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Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy by Mingxi Zhang, Zefang Shen, Lewis Walden, Farid Sepanta, Zhongkui Luo, Lei Gao, Oscar Serrano, Raphael A. Viscarra Rossel

“…The SHAP values reflected the compositional modelling and identified important organic and inorganic functional groups that differed by fraction and land use. Our approach can complement conventional physical SOC fractionations and improve the cost-effectiveness of the measurements, especially when there are many samples to measure, thus enhancing our understanding of SOC dynamics.…”
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Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants by Xiandan Du, Zhongfa Zhou, Denghong Huang

“…The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. …”
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