Climate Change And Land Use/cover Change Impacts On Watershed Hydrology, Nutrient Dynamics – A Case Study In Missisquoi River Watershed

Watershed regulation of water, carbon and nutrient dynamics support food, drinking water and human development. Projected climate changes and land use/cover change (LUCC) have been identified as drivers of watershed nutrient and hydrological processes and are likely to happen jointly in the future decades. Studying climate change and LUCC impacts on watersheds\u27 streamflow and nutrients dynamics is therefore essential for future watershed management. This research aimed to unveil how climate change and LUCC affect water and nutrient dynamics in the Missisquoi River watershed, Vermont. We used 12 scenarios of future climate data (2021 – 2050) generated by three GCMs (ccsm4, mri-cgcm3, and gfdl-esm2m) under four Representative Concentration Pathways (RCPs). For LUCC, we used three different scenarios generated by the Interactive Land Use Transition Agent-Based Model (ILUTABM). The three LUCC scenarios were Business As Usual (BAU), Prefer Forest (proForest), and Prefer Agriculture (proAg). New land use maps were generated every 10 years for the period of 2021 – 2050. Combining each climate change and LUCC scenario resulted in 36 scenarios that were used to drive Regional Hydro-Ecologic Simulation System (RHESSys) ecohydrological model. In chapter 3, we used RHESSys to study streamflow. We found climate was the main driver for streamflow because climate change directly controlled the system water input. For streamflow, climate change scenarios had larger impacts than LUCC, different LUCCs under the same climate change scenario had similar annual flow patterns. In chapter 4, we used RHESSys to study streamflow NO3-N and NH4-N load. Because fertilizer application is the major source for nitrogen export, LUCC had larger impacts; watersheds with more agricultural land had larger nitrogen loads. In chapter 5, we developed RHESSys-P by coupling the DayCent phosphorus module with RHESSys to study climate change and LUCC impacts on Dissolved Phosphorus (DP) load. RHESSys-P was calibrated with observed DP data for 2002 – 2004 and validated with data for 2009 - 2010. In both calibration and validation periods, simulated DP basically captured patterns of observed DP. In the validation period, the R2 of simulated vs observed DP was 0.788. Future projection results indicated BAU and proForest annual loads were around 4.0 × 104 kg under all climate change scenarios; proAg annual loads increased from around 4.0 × 104 kg in 2021 to 1.6 × 105 kg in 2050 under all climate change scenarios. The results showed LUCC was the dominant factor for dissolved phosphorus loading. Overall, our results suggest that, while climate drives streamflow, N and P fluxes are largely driven by land use and management decisions. To balance human development and environmental quality, BAU is a feasible future development strategy

Similarity-Based Classification in Partially Labeled Networks

We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.Comment: 13 pages,3 figures,1 tabl

In vitro biofilm formation of Gardnerella vaginalis and Escherichia coli associated with bacterial vaginosis and aerobic vaginitis

ObjectiveTo determine the optimum biofilm formation ratio of Gardnerella vaginalis (G. vaginalis) in a mixed culture with Escherichia coli (E. coli).MethodsG. vaginalis ATCC14018, E. coli ATCC25922, as well as five strains of G. vaginalis were selected from the vaginal sources of patients whose biofilm forming capacity was determined by the Crystal Violet method. The biofilm forming capacity of E. coli in anaerobic and non-anaerobic environments were compared using the identical assay. The Crystal Violet method was also used to determine the biofilm forming capacity of a co-culture of G. vaginalis and E. coli in different ratios. After Live/Dead staining, biofilm thickness was measured using confocal laser scanning microscopy, and biofilm morphology was observed by scanning electron microscopy.ResultsThe biofilm forming capacity of E. coli under anaerobic environment was similar to that in a 5% CO2 environment. The biofilm forming capacity of G. vaginalis and E. coli was stronger at 106:105 CFU/mL than at other ratios (P<0.05). Their thicknesses were greater at 106:105 CFU/mL than at the other ratios, with the exception of 106:102 CFU/mL (P<0.05), under laser scanning microscopy. Scanning electron microscopy revealed increased biofilm formation at 106:105 CFU/mL and 106:102 CFU/mL, but no discernible E. coli was observed at 106:102 CFU/mL.ConclusionG. vaginalis and E. coli showed the greatest biofilm forming capacity at a concentration of 106:105 CFU/mL at 48 hours and could be used to simulate a mixed infection of bacterial vaginosis and aerobic vaginitis in vitro

Local dominance unveils clusters in networks

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data

Local dominance unveils clusters in networks

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data

Local dominance unveils clusters in networks

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data

Empirical analysis of web-based user-object bipartite networks

Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This Letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative clustering coefficient, to quantify the clustering behavior based on the collaborative selection. Accordingly, the clustering properties and clustering-degree correlations are investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.Comment: 6 pages, 7 figures and 1 tabl

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe