Clinical outcomes of limited open reduction and intramedullary nailing with steel cable cerclage in Seinsheimer III femoral subtrochanteric fractures by Hongru Cai, Hongru Cai, Hongru Cai, Hongru Cai, Hongru Cai, XueYi He, XueYi He, XueYi He, XueYi He, XueYi He, Zhengchao Zhang, Zhengchao Zhang, Zhengchao Zhang, Zhengchao Zhang, Zhengchao Zhang, Pinhua Chen, Pinhua Chen, Pinhua Chen, Pinhua Chen, Pinhua Chen, Ruoli Wang, Ruoli Wang, Ruoli Wang, Ruoli Wang, Ruoli Wang, Qi Fang, Qi Fang, Qi Fang, Qi Fang, Qi Fang, Zhixian Xu, Zhixian Xu, Zhixian Xu, Zhixian Xu, Zhixian Xu, Wubing He, Wubing He, Wubing He, Wubing He, Wubing He

“…It provides faster bone healing, reduced intraoperative blood loss, improved early functional recovery, and does not increase complication risks. …”
Get full text

A Review of Plant-Mediated and Fertilization-Induced Shifts in Ammonia Oxidizers: Implications for Nitrogen Cycling in Agroecosystems by Durga P. M. Chinthalapudi, William Kingery, Shankar Ganapathi Shanmugam

Get full text

BioImpedance Spectroscopy to maintain Renal Output: the BISTRO randomised controlled trial by Simon J Davies, David Coyle, Elizabeth Lindley, David Keane, John Belcher, Fergus Caskey, Indranil Dasgupta, Andrew Davenport, Ken Farrington, Sandip Mitra, Paula Ormandy, Martin Wilkie, Jamie MacDonald, Mandana Zanganeh, Lazaros Andronis, Ivonne Solis-Trapala, Julius Sim

“…Background Fluid removal is a key component of dialysis treatment but, if excessive, can result in a faster decline in residual kidney function. Prescribing the optimal removal of fluid on dialysis to avoid this is therefore important. …”
Get full text

High-temperature performance evaluation of sustainable date palm fiber concrete with activated carbon: An MCDM and Weibull analysis approach by Musa Adamu, Yasser E. Ibrahim, Ashwin Raut

“…The optimal mix demonstrated high compressive strength (54.13 MPa), residual strength at 800 °C (25.17 MPa), and low mass loss (9.84 %), making it suitable for high-temperature applications. …”
Get full text

Estimating Shape Parameters of Piecewise Linear-Quadratic Problems by Zheng, Peng, Ramamurthy, Karthikeyan Natesan, Aravkin, Aleksandr Y.

“…Properties of these estimators depend on the choice of penalty and its shape parameters, such as degree of asymmetry for the quantile loss, and transition point between linear and quadratic pieces for the Huber function.In this paper, we develop a statistical framework that can help the modeler to automatically tune shape parameters once the shape of the penalty has been chosen. …”
Get full text

Effectiveness of CAD-CAM Milled Versus DMLS Titanium Frameworks for Hybrid Denture Prosthesis: A Narrative Review by Yahya Deeban

Get full text

Solving Fractional Differential Equations by Using Triangle Neural Network by Feng Gao, Yumin Dong, Chunmei Chi

“…Then, based on the technique of minimizing the loss function of the neural network, the proposed numerical methods reduce the fractional differential equation into a gradient descent problem or the quadratic optimization problem. …”
Get full text

The Influence of Insertion Torque on Stress Distribution in Peri-Implant Bones Around Ultra-Short Implants: An FEA Study by Mario Ceddia, Lorenzo Montesani, Luca Comuzzi, Alessandro Cipollina, Douglas A. Deporter, Natalia Di Pietro, Bartolomeo Trentadue

Get full text

Emulsion-Based Encapsulation of Fibrinogen with Calcium Carbonate for Hemorrhage Control by Henry T. Peng, Tristan Bonnici, Yanyu Chen, Christian Kastrup, Andrew Beckett

Get full text

Evaluating the effect of printing parameters on the performance of resin occlusal splints for a sustainable dentistry by Ashish Kaushik, Ramesh Kumar Garg, Ravinder S. Saini, Francesco Bennardo, Artak Heboyan

Get full text

An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes by Juan Lu, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, Bing Huang

“…In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε-insensitive loss function, respectively. …”
Get full text

CV-YOLOv10-AR-M: Foreign Object Detection in Pu-Erh Tea Based on Five-Fold Cross-Validation by Wenxia Yuan, Chunhua Yang, Xinghua Wang, Qiaomei Wang, Lijiao Chen, Man Zou, Zongpei Fan, Miao Zhou, Baijuan Wang

“…By introducing the MPDIoU loss function, the YOLOv10 network is optimized to effectively enhance the positioning accuracy of the model in complex background and improve detection of small target foreign objects. …”
Get full text

Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope by Bozhou Li, Dajiang Ju, Xingwang Li, Yan Liu, Hongru He, Hao Wang

“…Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. …”
Get full text

Image acquisition technology for unmanned aerial vehicles based on YOLO - Illustrated by the case of wind turbine blade inspection by Zhenjun Dai

“…The research results indicated that the minimum loss function value of the improved model was 2.75, the average accuracy was 95 %, and the highest intersection over union was 91 %. …”
Get full text

Learning Multi-Attribute Differential Graphs With Non-Convex Penalties by Jitendra K. Tugnait

“…Existing methods for multi-attribute differential graph estimation are based on a group lasso penalized loss function. In this paper, we consider a penalized D-trace loss function with non-convex [log-sum and smoothly clipped absolute deviation (SCAD)] penalties. …”
Get full text

An Efficient and Cost-Effective Vehicle Detection and Tracking System for Collision Avoidance in Foggy Weather by Naeem Raza, Muhammad Asif Habib, H. M. Sajid Imran, Abdul Qayum, Sajida Perveen, Sohail Jabbar, Shahzad Sarwar, Mudassar Ahmad

“…These design changes significantly improved the baseline YOLO-V5s vehicle detection performance. The optimized deep-SORT algorithm is proposed by utilizing the SiLU activation function in the CNN network of the baseline deep-SORT algorithm, compared to the ReLU activation function. …”
Get full text

FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. …”
Get full text

PCB defect detection based on pseudo-inverse transformation and YOLOv5. by Xiaoli Wang, Siti Sarah Maidin, Malathy Batumalay

“…Secondly, Transformer is introduced to improve YOLOv5, and the batch normalization and network loss function are optimized. These methods improve the speed and accuracy of PCB defect detection. …”
Get full text

Physics-Informed Deep Neural Network for Low Sidelobe Time-Modulated Antenna Array Synthesis With Harmonic Suppression by Tarek Sallam, Qun Wang, Ahmed M. Attiya

“…Additionally, the approach is compared to genetic algorithm (GA) which corresponds to a representative evolutionary optimization algorithm. Numerical results indicate that the PIDNN surpasses the GA in both computational efficiency and loss function evaluation.…”
Get full text

GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention by Caixiong Li, Dali Wu, Xing Zhang, Peng Wu

“…Lastly, we proposed a Focal-DIoU Loss to replace the original loss function, optimizing bounding box regression and improving localization accuracy. …”
Get full text