Plot-scale peanut yield estimation using a phenotyping robot and transformer-based image analysis by Zhengkun Li, Rui Xu, Nino Brown, Barry L. Tillman, Changying Li

“…Additionally, the Real-Time Detection Transformer (RT-DETR) was customized for pod detection by integrating partial convolution into a lightweight ResNet-18 backbone and refining the up-sampling and down-sampling modules in cross-scale feature fusion. …”
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Co-Infection of <i>Culex tarsalis</i> Mosquitoes with Rift Valley Fever Phlebovirus Strains Results in Efficient Viral Reassortment by Emma K. Harris, Velmurugan Balaraman, Cassidy C. Keating, Chester McDowell, J. Brian Kimble, Alina De La Mota-Peynado, Erin M. Borland, Barbara Graham, William C. Wilson, Juergen A. Richt, Rebekah C. Kading, Natasha N. Gaudreault

“…This can have severe implications in areas where RVFV is endemic and convolutes our ability to anticipate transmission and circulation in novel geographic regions. …”
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An Innovative Adaptive Threshold-Based BESS Controller Utilizing Deep Learning Forecast for Peak Demand Reductions by MD Mahmudul Hasan, Yun Seng Lim, Lee Cheun Hau, Xie Cherng Miow, Jianhui Wong, Dylon Hao Cheng Lam, Nor Shahida Hasan, Hamzah B. Adlan

“…This controller is developed on the free, open-source Node-RED platform using a deep learning-based one-dimensional convolution neural network (1D-CNN) to forecast the load profile for one day ahead. …”
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Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network by Chuang Lin, Chunxiao Zhao, Jianhua Zhang, Chen Chen, Ning Jiang, Dario Farina, Weiyu Guo

“…RNN series models, Convolution series models, and Transformer series models were used as reference models for comparison. …”
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LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection by Shijian Huang, Yunong Tian, Yong Tan, Zize Liang

“…Additionally, we propose the Aggregated Downsampling Convolution (ADown-Conv) module, a dual-path compression unit that enhances feature representation while efficiently reducing spatial dimensions. …”
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Severe hyperchloremic metabolic acidosis with SGLT2 inhibitors in patients with urinary diversion by Carolina Gomes, João Barbosa Barroso, Ana Isabel Oliveira, Sofia Guerreiro Cruz, Liliana Cunha, Ana Azevedo

“…The most frequent metabolic consequence is hyperchloremic metabolic acidosis, due to ammonium hydrogen and chloride ions absorption in exchange for the excretion of bicarbonate and sodium ions in the bowel conduit.Sodium-glucose co-transporter-2 (SGLT2) inhibitors are a class of antihyperglycemic agents that block the reabsorption of filtered glucose in the renal proximal convoluted tubules, promoting greater urinary glucose and sodium excretion.The authors describe two cases of patients with bowel conduit and mild/severe hyperchloremic metabolic acidosis, after starting SGLT2 inhibitors. …”
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High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China by Kangsan Yu, Shumin Wang, Yitong Wang, Ziying Gu

“…This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. …”
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Severe hyperchloremic metabolic acidosis with SGLT2 inhibitors in patients with urinary diversion by Carolina Gomes, João Barbosa Barroso, Ana Isabel Oliveira, Sofia Guerreiro Cruz, Liliana Cunha, Ana Azevedo

“…The most frequent metabolic consequence is hyperchloremic metabolic acidosis, due to ammonium hydrogen and chloride ions absorption in exchange for the excretion of bicarbonate and sodium ions in the bowel conduit.Sodium-glucose co-transporter-2 (SGLT2) inhibitors are a class of antihyperglycemic agents that block the reabsorption of filtered glucose in the renal proximal convoluted tubules, promoting greater urinary glucose and sodium excretion.The authors describe two cases of patients with bowel conduit and mild/severe hyperchloremic metabolic acidosis, after starting SGLT2 inhibitors. …”
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Application of a deep learning algorithm for the diagnosis of HCC by Philip Leung Ho Yu, Keith Wan-Hang Chiu, Jianliang Lu, Gilbert C.S. Lui, Jian Zhou, Ho-Ming Cheng, Xianhua Mao, Juan Wu, Xin-Ping Shen, King Ming Kwok, Wai Kuen Kan, Y.C. Ho, Hung Tat Chan, Peng Xiao, Lung-Yi Mak, Vivien W.M. Tsui, Cynthia Hui, Pui Mei Lam, Zijie Deng, Jiaqi Guo, Li Ni, Jinhua Huang, Sarah Yu, Chengzhi Peng, Wai Keung Li, Man-Fung Yuen, Wai-Kay Seto

“…Results: From 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under receiver-operating-characteristic curves (AUCs) of 0.919 (95% CI, 0.903–0.935) and 0.901 (95% CI, 0.879–0.924) at the observation (n = 1,077) and patient (n = 685) levels, respectively during internal validation, compared with 0.839 (95% CI, 0.814–0.864) and 0.822 (95% CI, 0.790–0.853), respectively for standard of care radiological interpretation. …”
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Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network by Qianxi Xu, Qianxi Xu, Qianxi Xu, Xue Wu, Xue Wu, Xue Wu, Xinya Chen, Ziyang Zhang, Jinrun Wang, Jinrun Wang, Zhengfang Li, Zhengfang Li, Zhengfang Li, Xiaomei Chen, Xiaomei Chen, Xiaomei Chen, Xin Lei, Xin Lei, Zhuoyu Li, Zhuoyu Li, Zhuoyu Li, Mengsi Ma, Mengsi Ma, Mengsi Ma, Chen Chen, Lijun Wu, Lijun Wu

“…DBayesNet is primarily composed of a two-branch structure, with features at different levels extracted by the Bayesian Convolution (BayConv) module, Attention module, and finally, feature fusion performed by Concate, which is performed by the Bayesian Linear Layer (BayLinear) output to obtain the result of the classification prediction.ResultsThe two sets of Raman spectral data were measured in the spectral wave number interval from 500 to 2000 cm-1. …”
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…These can be based on temporal correlations, convolutions, simple linear and nonlinear operations, wave interference patterns, and oscillatory interactions. …”
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Deteksi Covid-19 pada Citra Sinar-X Dada Menggunakan Deep Learning yang Efisien by Novanto Yudistira, Agus Wahyu Widodo, Bayu Rahayudi

“…In this study, we tested and compared the capabilities of ShuffleNet, EfficientNet, and ResNet because they have a smaller number of parameters than usual deep CNN, such as VGGNet or FullConv which uses a full convolution layers with a robust detection capability. …”
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基于<bold>KAN</bold>和<bold>MNv4</bold>的中子<bold>/</bold>伽马甄别方法 by 王 雨龙, 翁 秀峰, 刘 霄, 陈 翔, 盛 亮, 刘 彤

“…为实现在不同噪声水平下n/γ的有效甄别，本研究提出了使用小波散射网络（Scattering Convolution Networks，SCN）结合MobileNetV4（MNv4）和Kolmogorov–Arnold Networks（KAN）模型的n/γ甄别的算法。…”
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A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…Upon extracting local (within-procedure) spatiotemporal features, global (inter-procedure) feature fusion is performed through a combination of three-dimensional samples and convolution operations.Results and Discussions Experimental validation is conducted using an actual manufacturing process, specifically the float glass production process. …”
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Comparison of Surgical Outcomes between Endovenous Laser Ablation and Conventional Surgery in Patients with Lower Limb Varicose Veins: A Prospective Interventional Study by Mannam Viswateja, Deepak R Chavan, Vijaya Patil, Vikram U Sindagikar

“…Introduction: Dilated, convoluted, subcutaneous veins measuring more than 3 mm in diameter when measured while upright and exhibiting reflux are called varicose veins. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Firstly, a lightweight ADown module was incorporated to replace the conventional stride-2 convolution. The ADown module dynamically adapts its downsampling strategy according to the feature characteristics, effectively reducing the number of parameters and computational complexity, while enhancing the model's ability to capture crack edges and fine textural details. …”
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Short communication: Nanoscale heterogeneity of U and Pb in baddeleyite from atom probe tomography – ²³⁸U series alpha recoil effects and U atom clustering by S. Denyszyn, D. W. Davis, D. Fougerouse

“…Synthetic <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msup><mi/><mn mathvariant="normal">206</mn></msup><mi mathvariant="normal">Pb</mi><msup><mo>/</mo><mn mathvariant="normal">238</mn></msup><mi mathvariant="normal">U</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="57pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="976d5de522f5330a2ea037e20b88a23d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gchron-6-607-2024-ie00002.svg" width="57pt" height="15pt" src="gchron-6-607-2024-ie00002.png"/></svg:svg></span></span> profiles were determined from the convolution of the observed U profile with the redistribution functions for different alpha recoil distances. …”
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