Development of a spatially distributed model of Arctic thermal and hydrologic processes (MATH)

Thesis (Ph.D.) University of Alaska Fairbanks, 1998A process based, spatially distributed hydrologic model with the acronym MATH (Model of Arctic Thermal and Hydrologic Processes) is constructed to quantitatively simulate the energy and mass transfer processes and their interactions within arctic regions. The impetus for development of this model was the need to have spatially distributed soil moisture data for use in models of trace gas fluxes (carbon dioxide and methane) generated from the carbon-rich soils of this region. The model is applied against the data from the Imnavait watershed (2.2 \rm km\sp2) and the Upper Kuparuk River basin (146 \rm km\sp2) located on the North Slope of Alaska. Both point and spatially distributed data such as precipitation, radiation, air temperature, and other meteorological data have been used as model inputs. Based on the digital elevation data, one component of the model determines drainage area, channel networks, and the flow directions in a watershed that is divided into many triangular elements. Simulated physical processes include hydraulic routing of subsurface flow, overland flow and channel flow, evapotranspiration (ET), snow ablation, and active layer thawing and freezing. This hydrologic model simulates the dynamic interactions of each of these processes and can predict spatially distributed snowmelt, soil moisture, and ET over a watershed at each time step as well as discharge at any point(s) of interest along a channel. Modeled results of spatially distributed soil moisture content, discharge at gauging stations and other results yield very good agreement, both spatially and temporally, with independently derived data sets, such as Synthetic Aperture Radar (SAR) generated soil moisture data, field measurements of snow ablation, measured discharge data and water balance computations. The timing of simulated discharge results do not compare well to the measured data during snowmelt periods because the effect of snow damming on runoff generation is not considered in the model. It is concluded that this model can be used to simulate spatially distributed hydrologic processes within the arctic regions provided that suitable data sets for input are available. This physically based model also has the potential to be coupled with atmospheric and biochemical models

CCOM-HuQin: an Annotated Multimodal Chinese Fiddle Performance Dataset

HuQin is a family of traditional Chinese bowed string instruments. Playing techniques(PTs) embodied in various playing styles add abundant emotional coloring and aesthetic feelings to HuQin performance. The complex applied techniques make HuQin music a challenging source for fundamental MIR tasks such as pitch analysis, transcription and score-audio alignment. In this paper, we present a multimodal performance dataset of HuQin music that contains audio-visual recordings of 11,992 single PT clips and 57 annotated musical pieces of classical excerpts. We systematically describe the HuQin PT taxonomy based on musicological theory and practical use cases. Then we introduce the dataset creation methodology and highlight the annotation principles featuring PTs. We analyze the statistics in different aspects to demonstrate the variety of PTs played in HuQin subcategories and perform preliminary experiments to show the potential applications of the dataset in various MIR tasks and cross-cultural music studies. Finally, we propose future work to be extended on the dataset.Comment: 15 pages, 11 figure

Dynamical alterations of brain function and gut microbiome in weight loss

ObjectiveIntermittent energy restriction (IER) is an effective weight loss strategy. However, little is known about the dynamic effects of IER on the brain-gut-microbiome axis.MethodsIn this study, a total of 25 obese individuals successfully lost weight after a 2-month IER intervention. FMRI was used to determine the activity of brain regions. Metagenomic sequencing was performed to identify differentially abundant gut microbes and pathways in from fecal samples.ResultsOur results showed that IER longitudinally reduced the activity of obese-related brain regions at different timepoints, including the inferior frontal orbital gyrus in the cognitive control circuit, the putamen in the emotion and learning circuit, and the anterior cingulate cortex in the sensory circuit. IER longitudinally reduced E. coli abundance across multiple timepoints while elevating the abundance of obesity-related Faecalibacterium prausnitzii, Parabacteroides distasonis, and Bacterokles uniformis. Correlation analysis revealed longitudinally correlations between gut bacteria abundance alterations and brain activity changes.ConclusionsThere was dynamical alteration of BGM axis (the communication of E. coli with specific brain regions) during the weight loss under the IER

Collaborative replenishment in the presence of intermediaries

In complex supply chains, downstream buyers would often replenish individually from intermediaries instead of directly dealing with original manufacturers. Although collaborative replenishment from intermediaries might generate benefits, significant cost reductions could be achieved when direct replenishments from manufacturers are considered. This paper constructs a general model to study collaborative replenishment in multi-product chains with alternative sources of supply—i.e., manufacturers and intermediaries. A collaborative organization determines the optimal choices of replenishment sources on behalf of its members to minimize collective costs. We introduce a class of cooperative games associated with these situations and give sufficient conditions for their concavity. We investigate the choice of allocation rule and its effect on supply chain efficiency when buyers strategically participate in the collaborative organization. We prove that the Shapley value coordinates the supply chain, i.e., it makes complete participation the best strategy for buyers even under asymmetric information. This setting is compared with an alternative structure where buyers can only collaborate in source-specific replenishment organizations that purchase all requested products either from intermediaries or manufacturers. Although there are always participation strategies that result in minimum collective cost, it is impossible to find allocation rules for source-specific replenishment organizations that always motivate the buyers to choose such strategies

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations

Currently, the river forecasting system deployed in each of 13 River Forecast Centers of the National Weather Service primarily uses lumped parameter models to generate hydrologic simulations. With the deployment of the weather surveillance radar 1988 Doppler radars, more and more precipitation data with high spatial and temporal resolution have become available for hydrologic modeling. Hydrologists inside and outside the National Weather Service are now investigating how to effectively use these data to enhance river-forecasting capabilities. In this paper, six years of continuously simulated hydrographs from an eight-subbasin model are compared to those from a single-basin (or lumped) model, both applied to the Blue River basin (1,232 km2) in Oklahoma. The Sacramento soil moisture accounting model is used to generate runoff in all cases. Synthetic unit hydrographs for each subbasin convey the water to the outlet of the basin without explicit flow routing. Subdividing the basin into eight subbasins captures spatially variable rainfall reflected in the next generation weather radar products and produces improved results without greatly increasing the computational and data requirements. Strategies for calibrating the hydrologic model parameters for multiple subbasins are explored

Lumped and Semi-distributed Modeling using NEXRAD Stage-III Data: Results from Continuous Multi-year Simulations

Proceedings of the 1999 Georgia Water Resources Conference, March 30 and 31, Athens, Georgia.Nexrad precipitation products appear to be a viable data source for river forecasting model in the National Weather Service (NWS). Good simulation results at an hourly lumped scale were achieved for 3 basins near Tulsa, Oklahoma. In some cases, semidistributed representation of these basins led to slightly better results. However, problems with parameterizing and calibrating a semi-distributed model with limited information arose. Possible explanations for the similarity between the lumped and semi-d istributed simulations are discussed.Sponsored and Organized by: U.S. Geological Survey, Georgia Department of Natural Resources, The University of Georgia, Georgia State University, Georgia Institute of TechnologyThis book was published by the Institute of Ecology, The University of Georgia, Athens, Georgia 30602-2202 with partial funding provided by the U.S. Department of Interior, geological Survey, through the Georgia Water Research Insttitute as authorized by the Water Research Institutes Authorization Act of 1990 (P.L. 101-397). The views and statements advanced in this publication are solely those of the authors and do not represent official views or policies of the University of Georgia or the U.S. Geological Survey or the conference sponsors

The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments

The Office of Hydrologic Development (OHD) of the National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) conducted the second phase of the Distributed Model Intercomparison Project (DMIP 2). After DMIP 1, the NWS recognized the need for additional science experiments to guide its research-to-operations path towards advanced hydrologic models for river and water resources forecasting. This was accentuated by the need to develop a broader spectrum of water resources forecasting products (such as soil moisture) in addition to the more traditional river, flash flood, and water supply forecasts. As it did for DMIP 1, the NWS sought the input and contributions from the hydrologic research community. DMIP 1 showed that using operational precipitation data, some distributed models could indeed perform as well as lumped models in several basins and better than lumped models for one basin. However, in general, the improvements were more limited than anticipated by the scientific community. Models combining so-called conceptual rainfall-runoff mechanisms with physically-based routing schemes achieved the best overall performance. Clear gains were achieved through calibration of model parameters, with the average performance of calibrated models being better than uncalibrated models. DMIP 1 experiments were hampered by temporally-inconsistent precipitation data and few runoff events in the verification period for some basins. Greater uncertainty in modeling small basins was noted, pointing to the need for additional tests of nested basins of various sizes. DMIP 2 experiments in the Oklahoma (OK) region were more comprehensive than in DMIP 1, and were designed to improve our understanding beyond what was learned in DMIP 1. Many more stream gauges were located, allowing for more rigorous testing of simulations at interior points. These included two new gauged interior basins that had drainage areas smaller than the smallest in DMIP 1. Soil moisture and routing experiments were added to further assess if distributed models could accurately model basin-interior processes. A longer period of higher quality precipitation data was available, and facilitated a test to note the impacts of data quality on model calibration. Moreover, the DMIP 2 calibration and verification periods contained more runoff events for analysis. Two lumped models were used to define a robust benchmark for evaluating the improvement of distributed models compared to lumped models. Fourteen groups participated in DMIP 2 using a total of sixteen models. Ten of these models were not in DMIP 1. This paper presents the motivation for DMIP 2 Oklahoma experiments, discusses the major project elements, and describes the data and models used. In addition, the paper introduces the findings, which are covered in a companion results paper (Smith et al., this issue). Lastly, the paper summarizes the DMIP 1 and 2 experiments with commentary from the NWS perspective. Future papers will cover the DMIP 2 experiments in the western USA mountainous basins