MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions by Zili Chen, Yilong Peng, Jiadong Jiao, Aiguo Wang, Laigang Wang, Wei Lin, Yan Guo

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Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method by Jie Wu, Quansheng Yan, Shiping Huang, Chao Zou, Jintu Zhong, Weifeng Wang

“…For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum variance estimation to the known data in a region which have similar features. To predict the relationship between the structural parameters and responses, samples are preselected, and then Latin hypercube sampling (LHS) method is applied. …”
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Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation by Rio Arifando, Shinji Eto, Tibyani Tibyani, Chikamune Wada

“…The model also exhibits reduced computational complexity and storage requirements, highlighting its efficiency. …”
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Intelligent deep learning-based dual-task approach for robust power quality event classification by Lipsa Ray, Pampa Sinha, Siddhanta Pani, Anshuman Nayak, Kaushik Paul, Chitralekha Jena, Md. Minarul Islam, Taha Selim Ustun

“…The methodology integrates the tunable-Q wavelet transform (TQWT) for signal decomposition, optimizing Q-factor parameters to extract precise features, and morphological component analysis (MCA) with the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) for effective component separation. …”
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Differences in the Clinical and Hematological Characteristics of COVID-19 Patients with and without Type 2 Diabetes by Yujing Sun, Ruxing Zhao, Zhao Hu, Weili Wang, Shouyu Wang, Ling Gao, Jianchun Fei, Xiangdong Jian, Yu Li, Huizhen Zheng, Xinguo Hou, Li Chen

“…COVID-19 patients comorbid with T2D demonstrated distinguishing clinical features and hematological parameters during the infection. …”
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A Hybrid Framework for Referring Image Segmentation: Dual-Decoder Model with SAM Complementation by Haoyuan Chen, Sihang Zhou, Kuan Li, Jianping Yin, Jian Huang

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Sulfur-Fumigated Ginger Identification Method Based on Meta-Learning for Different Devices by Tianshu Wang, Jiawang He, Hui Yan, Kongfa Hu, Xichen Yang, Xia Zhang, Jinao Duan

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Understanding the Effect of Chiral NN Parametrization on Nuclear Shapes From an Ab Initio Perspective by Becker Kevin S., Launey Kristina D., Ekström Andreas, Sargsyan Grigor H., Mumma Darin C., Dytrych Tomáš, Langr Daniel, Draayer Jerry P.

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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

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Strong coupling theory of superconductivity and ferroelectric quantum criticality in metallic SrTiO3 by Sudip Kumar Saha, M. N. Gastiasoro, Jonathan Ruhman, Avraham Klein

“…We demonstrate that the linear coupling is sufficient to obtain a rough approximation of the experimentally measured phase diagram, but that nonlinear coupling terms are crucial in reproducing the finer features in the ordered phase. The primary role of nonlinear terms at the peak of the superconducting dome is to enhance the effective linear coupling induced by the broken order, shifting the dome’s maximum into the ordered phase. …”
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A Lightweight CNN for Multi-Class Classification of Handwritten Digits and Mathematical Symbols by Nicholas Abisha, Tita Putri Redytadevi, Sri Nurdiati, Elis Khatizah, Mohamad Khoirun Najib

“…The proposed model, implemented in Julia using the Flux.jl library, features a compact architecture with only two convolutional layers and approximately 55,000 trainable parameters significantly smaller than typical deep CNNs. …”
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First Estimations of Geomagnetically Induced Currents in the South of Portugal by Joana Alves Ribeiro, Fernando J. G. Pinheiro, Maria Alexandra Pais

“…Magnetotelluric impedance tensors used to compute the induced electric field are obtained using a conductivity model that takes into account general features of the local lithology and the proximity to the sea. …”
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OW-YOLO: An Improved YOLOv8s Lightweight Detection Method for Obstructed Walnuts by Haoyu Wang, Lijun Yun, Chenggui Yang, Mingjie Wu, Yansong Wang, Zaiqing Chen

“…Additionally, the model’s parameter count decreased by 49.2%, weight file size was reduced by 48.1%, and computational load dropped by 37.3%, effectively mitigating the impact of obstruction on detection accuracy. …”
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Modelling and Simulation of Surface Diffusion in Heterogeneous Porous Materials by Sean P. Rigby

“…It has been seen that the Galilean idealisation of atomistic models has rendered them sufficiently tractable in order to study the impact of certain surface features, such as traps or ruts, on surface diffusion. …”
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Thyroid-like follicular carcinoma of kidney by Chandranshu Tripathi, Naveen Kumar Gupta, Dilip Kumar Pal

“…In our case, a 62-year-male patient presented with flank pain with all blood parameters in normal range. Chest X-ray were normal, contrast enhanced computed tomography report showed well defined hyperdense lesion of size 26 mm × 21 mm showing homogenous enhancement (hu 46–126 from pre to post contrast) noted in lower pole of right kidney. …”
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Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM by Baoxian Chang, Xing Zhao, Dawei Guo, Siyu Zhao, Jiyou Fei, Hua Li, Xiaodong Liu

“…Subsequently, the KSD values of the IMFs are calculated to identify the optimal IMF components, with their Multi-scale Range Entropy (MRE) computed as a distinguishing feature. Lastly, the HKELM, enhanced through SSA optimization, is employed for the training and classification of rolling bearing faults, with the reliability and efficacy of the proposed methodology validated through simulation and empirical data.…”
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Steel Surface Defect Detection Technology Based on YOLOv8-MGVS by Kai Zeng, Zibo Xia, Junlei Qian, Xueqiang Du, Pengcheng Xiao, Liguang Zhu

“…Compared with YOLOv8n from experimental results, the average accuracy, recall rate, and frames per second of the improved model were improved by 5.2%, 10.5%, and 6.4%, respectively, while the number of parameters and computational costs were reduced by 5.8% and 14.8%, respectively. …”
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A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms by Dingyi Liao, Jun Sun, Zhiyong Deng, Yudong Zhao, Jiani Zhang, Dinghua Ou

“…The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. Additionally, a feature-based knowledge distillation strategy is employed to further improve the representational capacity of the lightweight model. …”
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Recursive-Parallel Algorithm for Solving the Maximum Common Subgraph Problem by Vladimir V. Vasilchikov

“…For the experiment, the author developed a special application in the C# language designed to generate various sets of initial data with specified parameters. The paper describes the features of the generated initial pairs of graphs, as well as the results obtained during the experiment.…”
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Texture analysis of CT colonography to develop a novel imaging biomarker for the management of colorectal cancer by Hisashi Mamiya, Toru Tochigi, Koichi Hayano, Gaku Ohira, Shunsuke Imanishi, Tetsuro Maruyama, Yoshihiro Kurata, Yumiko Takahashi, Atsushi Hirata, Hisahiro Matsubara

“…Abstract Background Recent studies have focused on evaluating the biomarker value of textural features in radiological images. Our study investigated whether or not a texture analysis of computed tomographic colonography (CTC) images could be a novel biomarker for colorectal cancer (CRC). …”
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