Search for: All records

Rainfall runoff and leaching are the main driving forces that nitrogen, an important non‐point source (NPS) pollutant, enters streams, lakes, and groundwater. Hydrological and transport processes thus play a pivotal role in NPS nitrogen pollution. Existing hydro‐environmental models for nitrogen pollution often over‐simplify the within‐watershed processes. It is unclear how such simplification affects the pollution assessment regarding the formation and distribution of denitrification hot spots—which is important for the design of land‐based countermeasures. To study this problem, we developed a model, DHSVM‐N, and its variant, DHSVM‐N_alt. DHSVM‐N is developed by integrating nitrogen‐related processes of SWAT into a comprehensive process‐based hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM). DHSVM‐N includes detailed representations of nitrate transport process at a fine spatial resolution with good landscape connectivity to accommodate interactions between hydrological and biogeochemical processes along the flow travel pathways. Because of the lack of spatially distributed observational data for validation, a model‐to‐model comparison study is conducted. Through comparison studies on a representative catchment using SWAT, DHSVM‐N and DHSVM‐N_alt, we quantify the critical roles of hydrological processes and nitrate transport processes in modeling the denitrification process. That is, the capabilities to give reasonable soil moisture estimates and to account for essential processes that take place along flow pathways are keys to simulate denitrification hot spots and their spatial variation. Furthermore, DHSVM‐N results show that terrestrial denitrification from hotspots alone can reach as high as 36% of the annual stream nitrate export of the watershed.

 

Datasets involving multivariate event streams are prevalent in numerous applications. We present a novel framework for modeling temporal point processes called clock logic neural networks (CLNN) which learn weighted clock logic (wCL) formulas as interpretable temporal rules by which some events promote or inhibit other events. Specifically, CLNN models temporal relations between events using conditional intensity rates informed by a set of wCL formulas, which are more expressive than related prior work. Unlike conventional approaches of searching for generative rules through expensive combinatorial optimization, we design smooth activation functions for components of wCL formulas that enable a continuous relaxation of the discrete search space and efficient learning of wCL formulas using gradient-based methods. Experiments on synthetic datasets manifest our model's ability to recover the ground-truth rules and improve computational efficiency. In addition, experiments on real-world datasets show that our models perform competitively when compared with state-of-the-art models. 

Efficient delivery of oxygen and nutrients to tissues requires an intricate balance of blood, lymphatic, and interstitial fluid pressures (IFPs), and gradients in fluid pressure drive the flow of blood, lymph, and interstitial fluid through tissues. While specific fluid mechanical stimuli, such as wall shear stress, have been shown to modulate cellular signaling pathways along with gene and protein expression patterns, an understanding of the key signals imparted by flowing fluid and how these signals are integrated across multiple cells and cell types in native tissues is incomplete due to limitations with current assays. Here, we introduce a multi-layer microfluidic platform (MμLTI-Flow) that enables the culture of engineered blood and lymphatic microvessels and independent control of blood, lymphatic, and IFPs. Using optical microscopy methods to measure fluid velocity for applied input pressures, we demonstrate varying rates of interstitial fluid flow as a function of blood, lymphatic, and interstitial pressure, consistent with computational fluid dynamics (CFD) models. The resulting microfluidic and computational platforms will provide for analysis of key fluid mechanical parameters and cellular mechanisms that contribute to diseases in which fluid imbalances play a role in progression, including lymphedema and solid cancer.

 

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.