Prediction of Power System Ramping Demand Using Meteorological Features by Kuan Lu, Song Gao, Jun Li, Kang Chen, Chunhao Yu

“…A Gaussian negative log-likelihood loss function is employed for training to optimize uncertainty prediction performance. …”
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JCN: Joint Constraint-Based Human Pose Refinement Networks by Yuru Zhang, Jiayuan Zhao, Xiaodong Su, Hongyan Xu, Meijian Jin

“…Meanwhile, to make the model pay more attention to the hard-to-identify vital points, this paper adopts the focal loss function to optimize the model and improve the data’s long-tailed distribution. …”
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YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring by Lingrui Lei, Ying Tang, Weidong Zhang, Quan Tang, Haichi Hao

“…In fact, we designed a multilevel attention fusion block (LGFB) that enhances perception in complex scenarios, to optimize the accuracy of the detected frames, the Wise-ShapeIoU loss function was used, and in order to reduce the parameters and FLOPs of the model, a lightweight convolution method called PConv was introduced. …”
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Mielomeningocele and nutrition: a proposal of care protocol by Fernanda Graciano Bronzeri, Tatiana dos Santos Faria, Fernanda Simões de Andrade Silva, Patrícia Carla Falcão Cruz Coimbra, Vera Silvia Frangella

“…Excess weight may be explained by the loss of the function of the great inferior muscular groups, reducing therefore the corporal power cost. …”
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Improved Multi-Size, Multi-Target and 3D Position Detection Network for Flowering Chinese Cabbage Based on YOLOv8 by Yuanqing Shui, Kai Yuan, Mengcheng Wu, Zuoxi Zhao

“…Wise-IoU in combination with Inner-IoU is adopted as a new loss function to optimize the network for different quality samples and different size bounding boxes. …”
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YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images by Yin Pan, Zhenpeng Zhang, Xueyang Zhang, Zhi Zeng, Yibin Tian

“…By tokenizing feature maps and enhancing local focusing, it enables the model to pay closer attention to small targets. Additionally, to optimize the training process, a bounding box loss function is adopted to achieve faster and more accurate bounding box predictions. …”
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A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

“…Finally, the minimum-point-distance-based IoU (MPDIoU) loss function is utilized to optimize the boundary return mechanism, which speeds up model convergence and reduces the redundancy of bounding box regression. …”
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Robotic ileal ureter replacement for panureteral stricture disease: a step-by-step guide by Emily Ji, Devin Boehm, Jonathan Rosenfeld, Rebecca Arteaga, Jaewoo Kim, Aidan Raikar, Ziho Lee

“…The median operative time was 305 min (IQR 274–356) and estimated blood loss was 100 cc (IQR 100–200). There were no intraoperative complications. …”
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Multi-Objective Intelligent Routing Algorithm for LEO Satellite Networks Based on DQN by LUO Zongyi, JIN Shichao, DONG Tao, YIN Jie

“…With the designed DQN multi-objective reward function, it realized the optimization of delay, packet loss, load balancing. …”
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Cell biomechanics on muscle atrophy: from intricate mechanisms to therapeutic frontiers by Yilin Wang, Jingyuan Meng, Jiechao Zhang, Lichao Tian, Wenrui Wei, Xiaoye Tang, Qian Zhang, Daofang Ding, Xuepeng Wang, Zicheng Guo, Yong He

“…Background Muscle atrophy—the decline of skeletal muscle volume and function—is pervasive in chronic disease, aging, and inactivity. …”
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A COMPOSITION PATCH ANTENNA by S. A. Pogarsky, D. V. Mayboroda, S. M. Mykhaliuk

“…The form of the amplitude-frequency response was optimized according to the required values of the return loss level. …”
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GYS-RT-DETR: A Lightweight Citrus Disease Detection Model Based on Integrated Adaptive Pruning and Dynamic Knowledge Distillation by Linlin Yang, Zhonghao Huang, Yi Huangfu, Rui Liu, Xuerui Wang, Zhiwei Pan, Jie Shi

“…First, this paper introduces the following innovations in model structure: (1) A Gather-and-Distribute Mechanism is introduced in the Neck section, which effectively enhances the model’s ability to detect medium to large targets through global feature fusion and high-level information injection.(2) Scale Sequence Feature Fusion (SSFF) is used to optimize the Neck structure to improve the detection performance of the model for small targets in complex environments. (3) The Focaler-ShapeIoU loss function is used to solve the problems of unbalanced training samples and inaccurate positioning. …”
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Rule-Based Multi-Task Deep Learning for Highly Efficient Rice Lodging Segmentation by Ming-Der Yang, Hsin-Hung Tseng

“…Rule-based and multi-task learning optimizes the integration of rule-based and deep learning networks and dynamically adjusts the loss function model. …”
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Leveraging logit uncertainty for better knowledge distillation by Zhen Guo, Dong Wang, Qiang He, Pengzhou Zhang

“…These loss functions measure the discrepancy between the models’ outputs at the category and sample levels. …”
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Mechanosignaling and 3D morphological adaptation of MSCs in response to hydrogel rigidity underpin angiogenic and immunomodulatory efficacy for ischemic injury regeneration by Yeo Gyun Yun, Soon Chul Heo, Ji-Won Jung, Donghyeon Yeo, Seong-Jin Shin, Khaliunsarnai Tsogtbaatar, Yongsung Hwang, Jung-Hwan Lee, Jun Hee Lee, Hae-Won Kim

“…These findings underscore the interplay between cell mechanophenotype, morphology, and function, providing a strategy to optimize hydrogel-based MSC therapies. …”
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Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara by Michael Stephen Lui, Fitra Abdurrachman Bachtiar, Novanto Yudistira

“…Generator will receive real image with added noise as input of unsupervised training process, creating synthetic image, while discriminator will receive real image and synthetic image as input and calculate the realness of those image which will be used as loss value with Binary Cross Entropy loss function. …”
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A Data-Driven and Personalized Stance Symmetry Controller for Robotic Ankle-Foot Prostheses: A Preliminary Investigation by Christopher Prasanna, Jonathan Realmuto, Anthony Anderson, Eric Rombokas, Glenn Klute

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SECrackSeg: A High-Accuracy Crack Segmentation Network Based on Proposed UNet with SAM2 S-Adapter and Edge-Aware Attention by Xiyin Chen, Yonghua Shi, Junjie Pang

“…Additionally, a custom loss function incorporating weighted binary cross-entropy and weighted IoU loss is utilized to emphasize challenging pixels. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…A key contribution of this study is the evaluation of loss functions, including Binary Cross-Entropy (BCE) Loss, Dice Loss, and Binary Focal Loss. …”
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Physics-Informed Neural Network for Solving 2D Steady Incompressible Navier-Stokes Equations: Application to Poiseuille Flow by Peter Anthony, Philibus M Gyuk, Isaac H Daniel

“…The composite loss function, comprising seven components (three for momentum and continuity equations, four for boundary conditions), decreased significantly, achieving a total test loss of 8.71 × 10ିହ at step 9000. …”
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