Suchergebnisse - parameterized resource code patterns~

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

Autoren: Joyanta Jyoti Mondal Md. Farhadul Islam Raima Islam et al.

Quelle: Sci Rep
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Environmental Engineering, Computer Science - Artificial Intelligence, Science, Computer Vision and Pattern Recognition (cs.CV), Health, Toxicology and Mutagenesis, Population, Resource (disambiguation), Computer Science - Computer Vision and Pattern Recognition, Convolutional neural network, 02 engineering and technology, Health Effects of Air Pollution, Impact of COVID-19 on Global Environment, Pattern recognition (psychology), 01 natural sciences, Article, Air Quality, Low-Cost Air Quality Monitoring Systems, Meteorology, Sociology, 11. Sustainability, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Data mining, 0105 earth and related environmental sciences, Demography, Global and Planetary Change, Computer network, Geography, FOS: Environmental engineering, Air Quality Monitoring, Deep learning, Computer science, Air quality index, FOS: Sociology, Algorithm, World Wide Web, Parameterized complexity, Artificial Intelligence (cs.AI), 13. Climate action, Environmental Science, Physical Sciences, Medicine, Index (typography)

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38238391
http://arxiv.org/abs/2308.03200
https://doaj.org/article/2865da1bea1a4d108dbaa669d1c6ff6b

Technical note: An illustrative introduction to the domain dependence of spatial principal component patterns.

Autoren: Lehr, Christian Hohenbrink, Tobias L.

Quelle: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 22, p6735-6760, 26p

Field-scale modelling reveals dynamic groundwater flow and transport patterns in a high-energy subterranean estuary.

Autoren: Greskowiak, Janek Meyer, Rena Cueto, Jairo et al.

Quelle: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 23, p7127-7147, 21p

Coupled SWAT–MODFLOW Model for the Interaction Between Groundwater and Surface Water in an Alpine Inland River Basin.

Autoren: Zhao, Zhen Cao, Xianghui Qin, Guangxiong et al.

Quelle: Water (20734441); Jan2026, Vol. 18 Issue 1, p85, 18p

Schlagwörter: GROUNDWATER, HYDROLOGIC models, MOUNTAIN watersheds, WATER management, WATER table, CLIMATE change, BODIES of water

Emulating human-like adaptive vision for efficient and flexible machine visual perception.

Autoren: Wang, Yulin Yue, Yang Wang, Huanqian et al.

Quelle: Nature Machine Intelligence; Nov2025, Vol. 7 Issue 11, p1804-1822, 19p

Image-Processing-Driven Modeling and Reconstruction of Traditional Patterns via Dual-Channel Detection and B-Spline Analysis.

Autoren: He, Xuemei Chen, Siyi Kuang, Yin et al.

Quelle: Journal of Imaging; Oct2025, Vol. 11 Issue 10, p349, 16p

Wolverines use spatial memory to plan efficient routes through rugged terrain.

Autoren: Glass, Thomas W. Copeland, Jeffery P. Olson, Lucretia E. et al.

Quelle: Movement Ecology. 6/21/2025, Vol. 13 Issue 1, p1-10. 10p.

Assessing the Dominant Impact of Climate and Land Use Change on Runoff Through Multi-Model Simulation in the Karst Headwater Region of the Wujiang River.

Autoren: Zhang, Qian Zhou, Yilin Ma, Yaoming et al.

Quelle: Water (20734441); Dec2025, Vol. 17 Issue 23, p3412, 22p

Schlagwörter: CLIMATE change, LAND use, HYDROLOGICAL research, RUNOFF, HYDROLOGIC models, KARST, HYBRID computer simulation, WATER management

The Impact of Climate and Land Cover Change on the Cryosphere and Hydrology of the Mackenzie River Basin, Canada.

Autoren: Elshamy, Mohamed E. Pomeroy, John W. Pietroniro, Alain et al.

Quelle: Water Resources Research; Dec2025, Vol. 61 Issue 12, p1-28, 28p

Schlagwörter: CLIMATE change, HYDROLOGY, WATERSHEDS, GLACIATION, FROZEN ground, WATER supply, ENVIRONMENTAL history

Geografische Kategorien: ARCTIC regions, CANADA, MACKENZIE River (N.W.T.)

Movement patterns of a small-bodied minnow suggest nomadism in a fragmented, desert river.

Autoren: Chavez, Martinique J. Budy, Phaedra Pennock, Casey A. et al.

Quelle: Movement Ecology. 7/31/2024, Vol. 12 Issue 1, p1-16. 16p.

Machine Learning Analysis of Streamflow Recession Patterns Across Climates in the Contiguous United States.

Autoren: Haugen, Hannah Kim, Minseok Bauser, Hannes et al.

Quelle: Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-19, 19p

Schlagwörter: MACHINE learning, CLIMATE change, EMPIRICAL research, PHASE space, WATERSHEDS, GROUNDWATER flow, HYDROLOGIC models

Geografische Kategorien: UNITED States

The Lost World: Characterizing and Detecting Undiscovered Test Smells.

Autoren: Yang, Yanming Hu, Xing Xia, Xin et al.

Quelle: ACM Transactions on Software Engineering & Methodology; Mar2024, Vol. 33 Issue 3, p1-32, 32p

Schlagwörter: OLFACTOMETRY, SMELL, QUESTION & answer websites, COMPUTER software developers, COMPREHENSION testing, SOFTWARE engineering

NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules.

Autoren: Linssen, Charl Babu, Pooja N. Eppler, Jochen M. et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-21, 21p

Schlagwörter: ARTIFICIAL neural networks, MODELING languages (Computer science), SYNAPSES, C++, SYNTAX (Grammar)

ILENET-LINKNET ARCHITECTURE TRAINED ON PATTERN AND COLOR FEATURES FOR SKIN LESION CLASSIFICATION: SEGMENTATION WITH IMPROVED ATTENTION-BASED RCNN MODEL.

Autoren: Howal, Sadanand S. Wagh, S. J.

Quelle: Biomedical Engineering: Applications, Basis & Communications; Oct2025, Vol. 37 Issue 5, p1-28, 28p

Schlagwörter: MACHINE learning, CONVOLUTIONAL neural networks, EXTREME learning machines, LONG short-term memory, INTERNET of medical things, DEEP learning, HUMAN skin color, NEVUS

Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms.

Autoren: Farmani, Mohammad A. Tavakoly, Ahmad Behrangi, Ali et al.

Quelle: Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-26, 26p

Schlagwörter: HYDROLOGIC models, GROUNDWATER flow, MACHINE learning, METEOROLOGICAL precipitation analysis, SOIL permeability, WATER management, ARID regions, STEADY-state flow

Geografische Kategorien: UNITED States, WEST (U.S.)

'Where the Creeks Run Dry or Ten Feet High': A Probabilistic Approach to Stage Height Forecasting in Australia.

Autoren: Athukorala, Rajitha Mohasel Afshar, Hadi Cripps, Sally et al.

Quelle: Water Resources Research; Sep2025, Vol. 61 Issue 9, p1-23, 23p

Schlagwörter: BAYESIAN analysis, HYDROLOGIC models, PRECIPITATION variability, WATER management, STREAMFLOW, UNCERTAINTY (Information theory), STOCHASTIC models

Geografische Kategorien: AUSTRALIA

Wind‐Induced Versus Plume‐Induced Inter‐Basin Exchange—Resolving Causal Influences in Plume‐Lake Modeling.

Autoren: Toledo, J. C. Rueda, F. J. Little, J. C. et al.

Quelle: Water Resources Research; Aug2025, Vol. 61 Issue 8, p1-21, 21p

Schlagwörter: PLUMES (Fluid dynamics), HYDRODYNAMICS, LAKES, HYPOXIA (Water), GAS flow, LAKE restoration

Timing Side-Channel Mitigation via Automated Program Repair.

Autoren: Ruan, Haifeng Noller, Yannic Tizpaz-Niari, Saeid et al.

Quelle: ACM Transactions on Software Engineering & Methodology; Nov2024, Vol. 33 Issue 8, p1-27, 27p

Schlagwörter: COMPUTER security vulnerabilities, SOFTWARE engineering, SOURCE code, PENDULUMS, POSSIBILITY

Learning to Learn Parameterized Classification Networks for Scalable Input Images

Autoren: Duo Li Anbang Yao Qifeng Chen

Quelle: Lecture Notes in Computer Science ISBN: 9783030585259

Schlagwörter: FOS: Computer and information sciences, 03 medical and health sciences, 0302 clinical medicine, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://arxiv.org/pdf/2007.06181
http://arxiv.org/abs/2007.06181
https://rd.springer.com/chapter/10.1007/978-3-030-58526-6_2
https://arxiv.org/abs/2007.06181
http://repository.ust.hk/ir/Record/1783.1-104716
https://dblp.uni-trier.de/db/journals/corr/corr2007.html#abs-2007-06181

Model‐Based Decomposition of Spatially Varying Temporal Shifts in Seasonal Streamflow Across North Temperate US Rivers.

Autoren: Collins, Kevin M. Schliep, Erin M. Wagner, Tyler et al.

Quelle: Water Resources Research; Sep2025, Vol. 61 Issue 9, p1-18, 18p

Schlagwörter: STREAMFLOW, HYDROLOGY, SEASONS, RIVER ecology, CLIMATE change, BAYESIAN field theory, HYDROLOGIC models

Geografische Kategorien: PACIFIC Northwest, UNITED States