Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…Five images featuring dense targets, minimal targets, dark scenes, occluded targets, and complex backgrounds are randomly selected from the Visdrone2021 test challenge set to evaluate detection performance in UAV aerial images. …”
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Introduction to Special Thematic Issue, part 2 "Microsaccades: Empirical Research and Methodological Advances“ by Rudolf Groner

“…In both stimulation conditions participants fixated a Gabor patch presented randomly in orientation of 45° or 135° over a wide range of spatial frequencies. …”
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Modeling actinic flux and photolysis frequencies in dense biomass burning plumes by J.-L. Tirpitz, S. F. Colosimo, N. Brockway, R. Spurr, M. Christi, S. Hall, K. Ullmann, J. Hair, T. Shingler, R. Weber, J. Dibb, R. Moore, E. Wiggins, V. Natraj, N. Theys, J. Stutz

“…VPC is designed for photochemical and remote sensing applications, particularly in BB plumes and other complex scenarios. To validate VPC and investigate photochemical conditions within BB plumes, the model was used to simulate spatial distributions of actinic fluxes and photolysis frequencies for the Shady wildfire (Idaho, US, 2019) based on plume composition data from the NOAA/NASA FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) campaign.…”
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Surface Defect Detection Based on Adaptive Multi-Scale Feature Fusion by Guochen Wen, Li Cheng, Haiwen Yuan, Xuan Li

“…However, the diversity of defects and the presence of complex backgrounds bring significant challenges to salient object detection. …”
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Stochastic spatiotemporal growth model reproducing the universal statistical laws of the gut microbiome by Rie Maskawa, Hideki Takayasu, Lena Takayasu, Wataru Suda, Misako Takayasu

“…The model is based on a microscopic model representing the dynamics of each species at local sites on the gut wall, modeled as a random multiplicative process. By introducing coarse-grained time and spatial scales that reflect real-world sampling processes, the model provides a physical interpretation of the dynamic properties of the empirical data, including timescales and temporal stochasticity. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). …”
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SODRS: Semisupervised Learning for One-Stage Small Object Detection in Remote Sensing Images by Mingquan Liu, Lei Kuang, Chengjun Li, Jing Tian, Zifang Chen, Xuewu Han

“…Finally, the conditional random fields-based label refinement is applied to postprocess the predicted labels, improving spatial relationships and dependencies among objects. …”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…Deep learning (DL) methods offer enhanced capabilities for CyanoHABs detection by learning complex patterns and adapting to varying conditions. …”
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Cross-dataset evaluation of deep learning models for crack classification in structural surfaces by Rashid Taha, Mokji Musa Mohd, Rasheed Mohammed

“…However, cross-testing revealed substantial performance degradation, particularly when models trained on high-resolution, structured datasets were tested on lower-resolution datasets with complex textures. ResNet50 had managed to hold its own across the orchards of domains but was still a little troubled with the variability of the surface and noise, whereas LSTM became less useful as it struggled with the extraction of spatial characteristics. …”
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Analysis of the Trends and Driving Factors of Cultivated Land Utilization Efficiency in Henan Province from 2000 to 2020 by Henggang Zhang, Chenhui Zhu, Tianyu Jiao, Kaiyue Luo, Xu Ma, Mingyu Wang

“…The results reveal the following: (1) From 2000 to 2020, the CLUE in Henan province showed an overall upward trend, with strong spatial heterogeneity across various regions: the central and eastern areas generally showed decline, the northern region remained stable with slight increases, the western region saw significant growth, while the southern area exhibited complex fluctuations. (2) Natural and economic factors had notable impacts on CLUE in Henan province. …”
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Revealing forest phenomenological heterogeneity in Yunnan using ICESat-2-derived canopy density and MODIS time series by Sunjie Ma, Jisheng Xia, Maolin Zhang, Guoyou Zhang, Yingying Pan, Pinliang Dong, Zhifang Zhao, Heng Liu

“…This study contributes a novel approach to understanding forest phenological heterogeneity in complex plateau terrain, emphasizing how variations in canopy density influence spatially variable phenological dynamics in forest ecosystems.…”
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A Low-Discrepancy and Latency-Aware, Scenario-Sensitive Resource Allocation Approach for Cloud Systems Using Latin Square-Based Improvised Genetic Optimization (LSBGO) by Aman Kumar Routh, Prabhat Ranjan, Asisa Kumar Panigrahy

“…The deterministic Latin-square initializer generates a mathematically uniform random initial distribution over the spatial and temporal task grid. …”
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Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu, Qinjun Qiu

“…The enhancement of remote sensing interpretation accuracy for rock strata in complex terrain areas has long been limited by challenges in field validation and the insufficient integration of geological knowledge in traditional spectral–spatial feature selection methods. …”
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A Dynamic Evaluation Method for Collaborative Search Efficiency of Multi-Sonar Systems Under Uncertain Situations by Shizhe Wang, Weiyi Chen, Zongji Li, Xu Chen

“…In sonar collaborative search tasks, effectively evaluating the collaborative search efficiency is an important way to measure whether a task can be successful, which can also provide strong support for optimizing search schemes. In complex marine environments, sonar collaboration search faces challenges such as uncertain task scenes and real-time changing situations. …”
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Time-dependent physical unclonable functions by long-lived triplet excitons in carbon dots by Yan-Wei Hu, Qing Cao, Shi-Yu Song, Yuan Sun, Ya-Chuan Liang, Wen-Bo Zhao, Chao-Fan Lv, Chong-Xin Shan, Kai-Kai Liu

“…Abstract Physical unclonable functions (PUFs), relying extensively on the random spatial distribution of block elements, are promising technology for generating unclonable cryptograph. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…Furthermore, although we train the semantic stress relationships that constitute each phase of the network using same-sized particles with a pre-specified inter-particle interaction, our algorithm demonstrates generalized predictive performance even for suspensions with randomly distributed particle sizes. Our results make it possible to predict the phase behavior of colloidal systems where traditional theoretical approaches have been challenging or impossible due to the inherent complexity of the colloidal system. …”
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Detachment of the Subducting Slab in Ancient Subduction Zones Discerned Using Machine Learning by Quan Ou, Dongyu Zheng, Alexander R. L. Nichols, Kun Wang, Sheng‐Ping Qian, Feng Zi, Anlin Ma, Yun‐Chuan Zeng, Zongying Huang, Jia Liao, Zixi Hou

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Machine Learning Recognizes Stages of Parkinson’s Disease Using Magnetic Resonance Imaging by Artur Chudzik

“…Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. …”
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A Multimodal Deep Learning Framework for Accurate Biomass and Carbon Sequestration Estimation from UAV Imagery by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Xusinov Ibragim Ismailovich, Young Im Cho

“…Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. …”
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A novel multi-modal rehabilitation monitoring over human motion intention recognition by Saleha Kamal, Saleha Kamal, Mohammed Alshehri, Yahya AlQahtani, Abdulmonem Alshahrani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…However, existing systems struggle with recognizing such subtle movements due to complex postural variations and environmental noise. …”
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