The Partner Selection Modes for Knowledge-Based Innovation Networks: A Multiagent Simulation by Lvcheng Li, Jialong Xie, Ruyi Wang, Jiafu Su, Stavros Sindakis

“…With the advent of the knowledge economy, technological innovation has become more and more complex. Firms in innovation system need to cooperate with others to upgrade their knowledge level and market competitiveness. …”
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Integrating pollinators’ movements into pollination models by Juliane Mailly, Louise Riotte-Lambert, Mathieu Lihoreau

“…While most current models of pollination ecology assume random pollen movements, studies in animal behaviour show how pollinating insects, birds and bats rely on sensory cues, learning and memory to visit flowers, thereby producing complex movement patterns. …”
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Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation by Min Jeong, Hyeongmo Koo

“…Integrating spatio-temporal kriging with machine learning improves estimation accuracy by addressing complex spatial and temporal variations in spatio-temporal phenomena. …”
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Weather sensing with structured light by Zhaozhong Chen, Ultan Daly, Aleksandr Boldin, Martin P. J. Lavery

“…Abstract Environmental conditions, such as temperature and wind speed, heavily influence the complex and rapidly varying optical distortions propagating optical fields experience. …”
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Efficient Aero-Optical Degraded Image Restoration via Adaptive Frequency Selection by Yingjiao Huang, Qingpeng Zhang, Xiafei Ma, Haotong Ma

“…During high-speed flight, the aircraft causes rapid compression of the surrounding air, creating a complex turbulent flow field. This high-speed flow field interferes with the optical transmission of optical imaging systems, resulting in high-frequency random displacement, blurring, intensity attenuation, or saturation of the target scene. …”
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Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning by Yusuf Alaca

“…The images of the pancreatic CT were transformed into graph structures using the Harris Corner Detection algorithm, which enables the capture of complex spatial relationships. Subsequently, the graph representations were processed using MobileViT models that had been optimised with Differentiable Architecture Search (DARTS), thereby enabling dynamic architectural adaptation. …”
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Tomato yellow leaf curl virus detection based on cross-domain shared attention and enhanced BiFPN by Henghui Mo, Linjing Wei

“…Existing models face challenges such as diverse lesion characteristics, detection of small targets, uneven illumination, and occlusions in complex orchard backgrounds, which lead to low recognition accuracy and poor adaptability. …”
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Quantifying Extreme Values in Geomagnetic Perturbations Using Ground Magnetic Records by J. Rodríguez‐Zuluaga, J. Gjerloev, S. Ohtani, Y. Zou, B. Anderson

“…Additionally, we observed dusk‐dawn asymmetries in the magnitude of perturbations related to the auroral electrojets, indicating complex interactions between the magnetosphere and ionosphere. …”
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GIRH-Unet: Improved Residual Tobacco Segmentation Algorithm Based on GhostNetV3-Unet by Jianhua Ye, Yunda Zhang, Pan Li, Ze Guo

“…Furthermore, we enhance the semantic representation of fine residue features amidst complex backgrounds by incorporating a hybrid self-attention mechanism that integrates spatial information and channels attention within the decoding phase. …”
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Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints by Ryong-Gyu Lee, Yong-Hoon Kim

“…The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. …”
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An Elite Wolf Pack Algorithm Based on the Probability Threshold for a Multi-UAV Cooperative Reconnaissance Mission by Hanrui Zhang, Xiao Lv, Chao Ma, Liangzhong Cui

“…In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. …”
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Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci by Michael J. Betti, Phillip Lin, Melinda C. Aldrich, Eric R. Gamazon

“…Notably, the use of genetically regulated expression of enhancer RNAs provides substantial tissue-specific predictive power, supporting a role for these transcripts in modulating spatial chromatin organization. We identify schizophrenia-associated enhancer RNAs independent of GWAS loci using enhancer RNA-based TWAS and determine the causal effects of these enhancer RNAs using Mendelian randomization. …”
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The impact of family farming on Afrotropical flower fly communities (Diptera, Syrphidae): A case study in Tanzania. by Sija Kabota, Jacqueline Bakengesa, Jenipher Tairo, Abdul Kudra, Ramadhani Majubwa, Marc De Meyer, Maulid Mwatawala, Kurt Jordaens, Massimiliano Virgilio

“…Landscape provided a comparably much stronger effect than farming practices, and the percentage of variation explained by landscape, as a standalone factor, was approximately five times higher than for farming practices. Spatial heterogeneity and seasonality also provided a large and significant proportion of random variability. …”
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Comparative analysis of the performance of regression machine learning models for indoor visible light positioning systems by Mohamed Hussien Moharam

“…XGBoost's exceptional error correction capabilities and consistent performance across all evaluation metrics make it particularly suitable for high-precision indoor VLP systems, demonstrating superior ability in handling complex spatial relationships and the inherent variability of indoor environments. …”
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Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge by Shuntaro Aotake, Takuya Otani, Masatoshi Funabashi, Atsuo Takanishi

“…Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments.…”
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Dental Identification Based on Single Tooth Feature Concatenation by Li Yuan, Luoji Zhu, Kang-Hyun Jo, Yanfeng Li

“…To verify the effectiveness of our method in various dental conditions, we apply random spatial transformations to the teeth of the same individual and simulate different cases of missing teeth on our self-constructed dataset. …”
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Digital mapping of soil erodibility factor in response to land use change using machine learning models by Wudu Abiye, Orhan Dengiz

“…Abstract Understanding the spatial variability of soil erodibility and its associated indices across different land uses is critical for sustainable land use planning and management. …”
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Study on the mechanisms associating community outdoor public spaces with elderly behavior by Lei Wang, Wenqi He, Shan Wu, Bo Zhang, Xiaorui Zhang, Hu Yin

“…Subsequently, a set of quantifiable indicators for community outdoor public spaces is established, and nonlinear machine learning models (Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting) are employed to reveal the association mechanisms between these six behaviors and the four categories of spatial metrics. …”
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Interaction of Growth Pole and Shannon’s Entropy in Banda Aceh City by Noer Fadhly, Muhammad Rusdi, Alfa Taras Bulba

“…The development of growth poles, urban activity centers, and service centers in Banda Aceh City has impacted the spread, a complex urban phenomenon challenging to measure due to urban sprawl. …”
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Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva, Lourenço Lázaro Magaia

“…Four components were analyzed: atmospheric, temporal, geographic, and spatial components. These quantify dispersed, absorbed, and reflected solar energy, causing energy to fluctuate when it arrives at the surface of a PV plant. …”
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