Generation of Spatial Structure of Urban Parks Based on Spatial Analysis of Agent-Based Models by Huizi KONG, Tianming LIU, Jiajie LIAO, Liu CUI

“…ObjectiveThis research aims to leverage the advances of computer algorithms to provide scientific analytical methods for spatial simulation and offer a more objective approach to reflect the natural operational laws of interdependencies between elements in the real physical world for landscape design. …”
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Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers by Devotha G. Nyambo, Edith T. Luhanga, Zaipuna O. Yonah, Fidalis D. N. Mujibi

“…Results indicated that, for the Ethiopian dataset, clusters derived from the fuzzy algorithm had the highest predictive power (77% for milk yield and 48% for milk sales), while for the Tanzania data, clusters derived from Self-Organizing Maps were the best performing. …”
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DAF-Net: Dual-Aperture Feature Fusion Network for Aircraft Detection on Complex-Valued SAR Image by Qingbiao Meng, Youming Wu, Yuxi Suo, Tian Miao, Qingyang Ke, Xin Gao, Xian Sun

“…Finally, a region-aware cross-attention fusion module is developed, which adaptively associates and fuses full-aperture and subaperture features based on spatial mapping relationships. …”
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Energy-Accuracy Trade-Offs for Resistive In-Memory Computing Architectures by Saion K. Roy, Naresh R. Shanbhag

“…This result clearly implies the need for other approaches, e.g., algorithmic- and learning-based methods, to improve the inference accuracy of resistive IMC architectures.…”
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Deciphering Socio-Spatial Integration Governance of Community Regeneration: A Multi-Dimensional Evaluation Using GBDT and MGWR to Address Non-Linear Dynamics and Spatial Heterogene... by Hong Ni, Jiana Liu, Haoran Li, Jinliu Chen, Pengcheng Li, Nan Li

“…By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. …”
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AdaSpline-Net for Improved Short-Term Solar Irradiance Forecasting by Ngiap Tiam Koh, Anurag Sharma, Jianfang Xiao, Cheng Siong Chin, Chin Jun Xing, Wai Lok Woo

“…Furthermore, its robustness across various neural network architectures demonstrates its adaptability and compatibility with backpropagation. …”
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Ensemble modelling reveals spiny monkey orange (Strychnos spinosa Lam.) as a vulnerable wild edible fruit tree in West Africa by Hospice Gérard Gracias Avakoudjo, Mahunan Eric José Vodounnon, Rodrigue Idohou, Aly Coulibaly, Achille Ephrem Assogbadjo

“…The Models’ performance was evaluated using the Area under the Curve (AUC), True Skill Statistic (TSS), Correlation coefficient (COR), and the Deviance. The most efficient algorithm (RF) was employed for the modelling, and the resulting maps were overlaid on the existing protected area network. …”
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Enhanced Defect Detection in Additive Manufacturing via Virtual Polarization Filtering and Deep Learning Optimization by Xu Su, Xing Peng, Xingyu Zhou, Hongbing Cao, Chong Shan, Shiqing Li, Shuo Qiao, Feng Shi

“…The YOLO V5-W model, integrated with CBAM attention, DenseNet connections, and an EIoU loss function, demonstrates superior performance in defect identification across various lighting conditions. Experiments show a 40.3% reduction in loss, a 10.8% improvement in precision, a 10.3% improvement in recall, and a 13.7% improvement in mAP compared to the original YOLO V5 model. …”
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The Coronal Plasma Density Structure over a Solar Cycle: A Comparison of Tomography and Magnetohydrodynamic Simulations by Liam T. Edwards, Huw Morgan, Pete Riley, Jon A. Linker

“…This study compares densities from tomography with the Magnetohydrodynamic Algorithm outside a Sphere (MAS) model across the whole corona over a solar cycle from 2007 to late 2023. …”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…This approach provides an efficient solution for UAV real-time perception tasks and offers novel insights for multi-scale object detection algorithm design.…”
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An Investigation of Infrared Small Target Detection by Using the SPT–YOLO Technique by Yongjun Qi, Shaohua Yang, Zhengzheng Jia, Yuanmeng Song, Jie Zhu, Xin Liu, Hongxing Zheng

“…Then, the pyramid level 2 feature map with minimum receptive field and maximum resolution is added to the neck, which reduces the loss of positional information during feature sampling. …”
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Defect intelligent recognition of membrane product based on deep learning by Maonian Wu, Ling Li, Wei Peng, Tao Wu, Jinwei Yu, Bo Zheng, Shaojun Zhu

“…The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.…”
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Effect of Droplet Contamination on Camera Lens Surfaces: Degradation of Image Quality and Object Detection Performance by Hyunwoo Kim, Yoseph Yang, Youngkwang Kim, Dong-Won Jang, Dongil Choi, Kang Park, Sangkug Chung, Daegeun Kim

“…To assess the effect on object detection, performance changes were evaluated across different detection algorithms. The findings reveal that droplet contamination can reduce the detection performance of small objects by up to 90%, as measured by the mean average precision (mAP) metric. …”
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Matching and Control Optimisation of Variable-Geometry Turbochargers for Hydrogen Fuel Cell Systems by Matt L. Smith, Alexander Fritot, Davide Di Blasio, Richard Burke, Tom Fletcher

“…A MATLAB-Simulink reduced-order model is used first to identify the most efficient components that match the fuel cell air path requirements. Maps representing the compressor and turbines are then evaluated in a 1D flow model to optimise cathode pressure and stoichiometry operating targets for net system efficiency, using an accelerated genetic algorithm (A-GA). …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

“…Chemical parameters from BG analysis and clinical parameters such as mean arterial pressure (MAP) and saturation (SpO2) were utilized as features. …”
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PFARN: Pyramid Fusion Attention and Refinement Network for Multiscale Ship Detection in SAR Images by Ke Li, Hang Yu, Suqi Li, Shanhu Chen, Bailu Wang

“…First, a shape scale convolution is used to improve the focus and extraction of mutiscale-ship features. Second, the feature maps are fused with pyramid fusion attention, which is based on self-attention and Gaussian cross-attention, ensuring alignment of both the semantic and spatial information. …”
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Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 by Peidong Jiang, Lai Jiang, Fengyan Wu, Tengteng Che, Ming Wang, Chuandong Zheng

“…This study developed an improved YOLOv8 model, named MCD-YOLOv8, for identifying traces of soil-dwelling termite activity, based on the Monte Carlo random sampling algorithm and a lightweight module. The Monte Carlo attention (MCA) module was introduced in the backbone part to generate attention maps through random sampling pooling operations, addressing cross-scale issues and improving the recognition accuracy of small targets. …”
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Neural Network-Based Coordinated Virtual Inertia Allocation Method for Multi-Region Distribution Systems by Heng Liu, Jingtao Zhao, Zhi Wu, Shu Zheng

“…Following this, a genetic algorithm-based solving strategy is designed to achieve the global optimal allocation of virtual inertia. …”
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Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. …”
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Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators by Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen

“…Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. …”
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