Free Convection Heat Transfer in Composite Enclosures with Porous and Nanofluid Layers by Abeer Alhashash

“…Numerical solutions are obtained using an iterative finite difference method. Key parameters studied include the porous thickness (0.0≤S≤1.0), the nanoparticle volume fraction (0.0≤ϕ≤0.05), the thermal conductivity ratio (0.5≤Rk≤10), and the Darcy number (10−5≤Da≤10−2). …”
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Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data. by Qihai Liu, Kevin H Lee, Hyun Bin Kang

“…We further design an estimation method for MFGM using an iterative Expectation-Maximization (EM) algorithm and functional graphical lasso (fglasso). …”
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Lightweight Design of Drive Axle Housing based on OptiStruct by Xiaofei Song, Ronghui Lin, Shuaichao Li, Yunfa Qiao, Yulong Ji, Juan Deng, Zhitian Guo

“…Then the optimization analysis is conducted in the OptiStruct solver， and several iterative calculations are used to get the analysis result. …”
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Distributed K-Means algorithm based on a Spark optimization sample. by Yongan Feng, Jiapeng Zou, Wanjun Liu, Fu Lv

“…This ensures that the initial centers better represent the distribution and structure of the dataset, leading to improved clustering performance. During the iteration process, a novel distance comparison method is employed to reduce computation time, optimizing the overall efficiency of the algorithm. …”
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Ekyeyo Mobile Application by Nkuba, Blair, Kansiime, Daniel

“…By implementing the Iterative Model for development, the researchers designed and tested a platform that empowers job seekers to access opportunities and simplifies the hiring process for employers. …”
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Improved CenterNet for Accurate and Fast Fitting Object Detection by Huimin He, Qionglan Na, Dan Su, Kai Zhao, Jing Lou, Yixi Yang

“…Finally, the high-resolution feature fusion network with iterative aggregation is designed to reduce the loss of spatial semantic information in subsampling and further improve the accuracy of small and occlusion objects. …”
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Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror by Xu Chang, Yao Hu, Jintao Wang, Xiang Liu, Qun Hao

“…Compared with the traditional phase modulation scheme that relies on iteration, the scheme proposed in this paper is more efficient and is conducive to meeting dynamic requirements. …”
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Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group by Lin Ding, Weihong Xu, Yuantao Chen

“…Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. …”
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UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring by Carmen Maria Giordano, Valentina Alena Girelli, Alessandro Lambertini, Maria Alessandra Tini, Antonio Zanutta

“…For this reason, the goal of this work is to evaluate the performance of the iterative closest point (ICP) method for registering point clouds in coastal environments, using a single-epoch and multi-sensor survey of a coastal area (near the Bevano river mouth, Ravenna, Italy). …”
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Nonlinear forced vibration of the FGM piezoelectric microbeam with flexoelectric effect by Lichang Shan, Guangchun Xiao, Anqing Li, Shasha Zhou, Li Wang, Weiguang Su, Yonglong Liu, Lei Yang, Xiaoyue Song

“…The governing equations, initial conditions and boundary conditions are obtained by applying Hamilton’s principle and then solved by combining the differential quadrature method (DQM) and iteration method. The innovation of this paper is to construct a nonlinear forced vibration model of piezoelectric microbeams. …”
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Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features by Wei Peng, Dong Wang, Changqing Shen, Dongni Liu

“…Secondly, to reduce the in-band noises existing in the sparse wavelet coefficients, an adaptive morphological analysis with an iterative local maximum detection method is developed to extract sparse envelope coefficients. …”
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A Decentralised Task Mapping Approach for Homogeneous Multiprocessor Network-On-Chips by Peter Zipf, Gilles Sassatelli, Nurten Utlu, Nicolas Saint-Jean, Pascal Benoit, Manfred Glesner

“…Based on local information on processor workload, task size, communication requirements, and link contention, iterative decisions on task migrations to other processors are made. …”
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Research on autonomous navigation of mobile robots based on IA-DWA algorithm by Quanling He, Zongyan Wang, Kun Li, Yuting Zhang, Menglong Li

“…In terms of path planning time, the algorithm reduces by 23.3% compared to A*-DWA; in terms of path length, the algorithm reduces by 1.8% compared to A*-DWA, and the optimization iterations converge faster. Finally, the reliability of the improved algorithm is verified by conducting autonomous navigation experiments using a ROS (Robot Operating System) mobile robot as an experimental platform.…”
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An Adaptive Source-Channel Coding with Feedback for Progressive Transmission of Medical Images by Jen-Lung Lo, Saeid Sanei, Kianoush Nazarpour

“…The parity length in the channel code varies with respect to both the proximity of the image subblock to the RoI and the channel noise, which is iteratively estimated in the receiver. The overall transmitted data can be controlled by the user (clinician). …”
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Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation by Rui Fan, Huiying Jing, Zhixin Jing

“…When the model has completed around 70 iterations, it converges and maintains a fitness value in the range of 600 to 800. …”
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An optimal strategy for energy storage allocation in active distribution networks considering new energy consumption rates by PENG Gang, KOU Qilong, FANG Tao, GENG Xin, KONG Xiangwen, XU Yaoyao

“…The investment costs for energy storage are incorporated as a fitness function， providing feedback to the upper layer to guide the iterative optimization. Case studies using the modified IEEE 33-bus system demonstrate that the optimal energy storage allocation derived from the proposed strategy effectively increases the consumption rates of wind and solar energy while enhancing the overall economy of the distribution networks.…”
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Transmitter and receiver design for integrated full‐duplex multiple‐input‐multiple‐output communication and multiple‐input‐multiple‐output radar system by Shengnan Shi, Zishu He, Ziyang Cheng

“…Then, for one of the sub‐problems that is still intractable, its solution is iteratively obtained based on the fractional programing technique and the consensus alternating direction method of multipliers algorithm. …”
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Evaluating the impacts of large-scale opencast copper mining on vegetation productivity change in recent thirty years: a case study of Dexing Copper Mine by Duan Huang, Zhiping Chen, Hengkai Li, Hong Chi, Ming Liao

“…Using the simple non-iterative clustering (SNIC) and random forest (RF) methods, we mapped the Dexing Copper Mine (DCM) at a 30 m spatial resolution with Landsat data from 1991 to 2022. …”
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Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field by Rasif Nahari, Utama Widya, Ardhya Garini Sherly, Fitri Indriani Rista, Pratama Novian Putra Dhea

“…Gradient boosting employs an ensemble of decision trees, iteratively correcting errors to model complex data patterns. …”
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Improved Low-Complexity Demodulation: Integrating Minimum-Mean-Square-Error and Maximum-Likelihood Detection for Image-Sensor-Based Visible Light Communication by Yuki Ohira, Shintaro Arai, Kengo Fujii, Tomohiro Yendo

“…To address these limitations, this study introduces an improved error signal candidate selection algorithm that focuses on residual signals and channel matrix characteristics to refine the signal estimation result. Through iterative refinement using low-complexity detectors such as those based on zero-forcing (ZF), and minimum-mean-square error (MMSE), our method substantially narrows the search space for the MLD, thus striking a balance between demodulation performance and computational efficiency. …”
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