Article
Environmental Sciences
Stephanie Figary, Naomi Detenbeck, Cara O'Donnell
Summary: The United States Environmental Protection Agency and the Houlton Band of Maliseet Indians collaborated to build a stream temperature model for the Meduxnekeag Watershed, using a high-resolution hydrology dataset. The model predicted stream temperatures at different time periods, guiding riparian restoration projects to expand habitat for cold water fishes.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Biodiversity Conservation
Chaogui Lei, Paul D. Wagner, Nicola Fohrer
Summary: The influence of catchment characteristics on water quality varies with space and time, with soil properties, land use composition, and configuration being key factors. Spatial variation of water quality is better explained at larger scales and in summer, with different important variables at different scales and for different water quality variables.
ECOLOGICAL INDICATORS
(2021)
Article
Environmental Sciences
J. Eli Asarian, Crystal Robinson, Laurel Genzoli
Summary: Low streamflows can increase vulnerability to warming, impacting coldwater fish. Water managers need tools to quantify these impacts and predict future water temperatures. Contrary to most statistical models' assumptions, many seasonally changing factors (e.g., water sources and solar radiation) cause relationships between flow and water temperature to vary throughout the year.
WATER RESOURCES RESEARCH
(2023)
Article
Mathematics, Applied
L. N. Wang, M. Li, C. R. Zang
Summary: The problem of synchronicity quantification based on event occurrence time is currently heavily researched in various fields. Synchrony measurement methods provide an effective approach to explore the spatial propagation characteristics of extreme events. By using event coincidence analysis, a directed weighted network is constructed to investigate the direction of correlations between event sequences. The synchrony of traffic extreme events of base stations is measured based on trigger event coincidence. The study analyzes the topology characteristics of the network and the spatial propagation features of traffic extreme events, including the propagation area, influence, and spatial aggregation. This research establishes a network modeling framework for quantifying the propagation characteristics of extreme events, which is beneficial for further studies on extreme event prediction. Additionally, it examines the differences between precursor event coincidence and trigger event coincidence, as well as the impact of event aggregation on synchrony measurement methods. The study can serve as a reference for analyzing extreme climatic events such as rainstorms and droughts in the field of climate.
Review
Chemistry, Multidisciplinary
Giacomo Muntoni, Giorgio Montisci, Tonino Pisanu, Pietro Andronico, Giuseppe Valente
Summary: This paper focuses on the importance of space debris monitoring for worldwide space agencies, and the efforts to expand radar systems and analyze data for tracking and monitoring debris. The information gathered is organized and classified based on the volume of data and geographical location of facilities.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Ahmed Al-Baghdadi, Gokarna Sharma, Xiang Lian
Summary: In this paper, the authors focus on the integration of social networks and spatial road networks to form a spatial-social network. They propose an important and novel query type called GP-SSN, which retrieves a group of friends with common interests on social networks and a number of spatially close points of interest on spatial road networks that best match the group's preferences. The authors design effective pruning methods and indexing mechanisms to solve the GP-SSN problem and develop efficient query answering algorithms.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Engineering, Civil
Guiyang Luo, Hui Zhang, Quan Yuan, Jinglin Li, Fei-Yue Wang
Summary: This paper proposes an embedded spatial-temporal network (ESTNet) for accurate spatial-temporal prediction. The ESTNet extracts static features from fine-grained road networks using multi-scale graph convolution networks, and dynamic features from real-time traffic using gated recurrent unit networks. It simultaneously models the spatial-temporal dependencies using a three-dimension convolution unit. Experimental results demonstrate the effectiveness and superiority of the ESTNet over existing techniques.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Leixin Liu, Wenwei Liu, Fei Wang, Hua Cheng, Duk-Yong Choi, Jianguo Tian, Yangjian Cai, Shuqi Chen
Summary: This study successfully manipulates the spatial coherence of light fields by loading different random phase distributions onto the wavefront, thereby generating partially coherent light with a predefined degree of coherence. This design strategy can easily be applied to manipulate arbitrary phase-only special beams with the same degree of coherence.
Article
Neurosciences
Vladislav Ayzenberg, Marlene Behrmann
Summary: This study reveals the specific functional contributions of the dorsal visual pathway to object recognition. The dorsal cortex computes the spatial relations among an object's parts and transmits this information to the ventral pathway to support object categorization. The dorsal cortex is a crucial source of input to the ventral pathway and may support the ability to categorize objects based on global shape.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Statistics & Probability
Francesco Sanna Passino, Nicholas A. Heard
Summary: A new class of models, called Mutual Exciting Point Process Graphs (MEG), is proposed for dynamic networks. MEG is a scalable statistical model for point processes with dyadic marks, which can be used for anomaly detection. The model combines mutually exciting point processes to estimate dependencies between events and latent space models to infer relationships between the nodes. This model has important applications in real-world scenarios, such as computer networks.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2023)
Article
Environmental Sciences
Chao Qin, Baosheng Wu, Guangqian Wang, Ge Wang
Summary: At-many-stations hydraulic geometry (AMHG) provides a novel way to analyze river network development and water flow simulation in data-scarce regions. The study on rivers originating from the Qinghai-Tibet Plateau shows strong AMHG relations with increasing stream order and contributing area, indicating the coherence and maturity of river networks associated with discharge.
WATER RESOURCES RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Pier Luigi Conti, Daniela Marella, Paola Vicard, Vincenzina Vitale
Summary: The use of Bayesian networks in statistical matching allows for introducing additional sample information on the dependence structure between variables and simplifying parameter estimation, leading to improved matching quality in a multivariate context.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Engineering, Electrical & Electronic
Ke Feng, Martin Haenggi
Summary: This study introduces and analyzes a joint spatial-propagation model that considers the correlation between cell radii and large-scale signal propagation, demonstrating its practical importance in network performance analysis.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Wei Zeng, Chengqiao Lin, Kang Liu, Juncong Lin, Anthony K. H. Tung
Summary: Deep neural networks, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), are widely used for short-term traffic flow prediction. CNNs are suitable for region-wise prediction, while GNNs perform better on graph-structured traffic data. DeFlow-Net, a deep deformable convolutional residual network, is proposed to model global spatial dependence, local spatial nonstationarity, and temporal periodicity of traffic flows. The use of pre-conceived regions or self-organized regions for traffic flow aggregation and network input also improves the performance of DeFlow-Net.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Physics, Fluids & Plasmas
Antoine Bret
Summary: This article investigates the problem of two-stream instability and the impact of QED corrections on it, extending the results of previous work. The results indicate that even when considering QED effects, the two-stream instability remains fundamentally 1D, and the filamentation instability with wave vectors normal to the flow is weakly affected by QED corrections. It is worth noting that the unstable modes with oblique wave vectors respond differently to QED corrections.
