On matrices, automata, and double counting in constraint programming

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables $\mathcal{M}$ , with the same constraint C defined by a finite-state automaton $\mathcal{A}$ on each row of $\mathcal{M}$ and a global cardinality constraint $\mathit{gcc}$ on each column of $\mathcal{M}$ . We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the $\mathit{gcc}$ constraints from the automaton $\mathcal{A}$ . The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We also provide a domain consistency filtering algorithm for the conjunction of lexicographic ordering constraints between adjacent rows of $\mathcal{M}$ and (possibly different) automaton constraints on the rows. We evaluate the impact of our methods in terms of runtime and search effort on a large set of nurse rostering problem instances.     

Evaporation from Lake Superior: 1. Physical controls and processes

The surface energy balance of Lake Superior was measured using the eddy covariance method at a remote, offshore site at 0.5-h intervals from June 2008 through November 2010. Pronounced seasonal patterns in the surface energy balance were observed, with a five-month delay between maximum summer net radiation and maximum winter latent and sensible heat fluxes. Late season (winter) evaporation and sensible heat losses from the lake typically occurred in two- to three-day-long events, and were associated with significant release of stored heat from the lake. The majority of the evaporative heat loss (70-88%) and sensible heat loss (97-99%) occurred between October and March, with 464 mm (2008-2009) and 645 mm (2009-2010) of evaporative water loss occurring over the water year starting October 1. Evaporation was proportional to the horizontal wind speed, inversely proportional to the ambient vapor pressure, and was well described by the ratio of wind speed to vapor pressure. This ratio remained relatively constant between the two water years, so the differences in evaporative water loss between years were largely associated with differences in lake surface conditions (e.g. water temperature, ice cover, and ice duration). Since late-season water temperature decline is driven by evaporative and sensible heat loss, the potential for a negative feedback mechanism between evaporation and ice cover is discussed.     

Relationships Between Satellite Observed Lit Area and Water Footprints

Global water resources are vulnerable to depletion due to the increasing demand of an ever-increasing human population. A country’s water footprint is a measure of the total volume of water needed to produce the goods and services consumed by the country, including water originating beyond its own borders. The water footprint can be a critical indicator of global water resource use, but its practical application is hindered by a lack of comparable data across national boundaries. The purpose of this article is to test the applicability of the nighttime imagery products produced by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) for the assessment of the global water footprint. To accomplish this purpose, the average areal extent of nighttime lighting (lit area) is calculated from 1997 to 2001. Next, lit area is regressed on the total water footprint for each country, as indicated by the Water Footprint Network (WFN), to estimate that country’s total water footprint using nighttime imagery. Model residuals are analyzed at the national scale to understand the appropriateness of nighttime imagery for assessing water consumption. Results indicate strong positive correlations between lit area and total water footprint (TWF), domestic water withdrawal (DWW), and industrial water consumption (IWC) at the national scale. Overall, the analyses reveal that the rate of agricultural water consumption to total water footprint (AWCR) and population density can affect the precision of estimates when lit area is selected as a proxy to estimate water footprints.     

Laminated paper-based analytical devices (LPAD): fabrication, characterization, and assays

Paper-based microfluidic devices have recently garnered an increasing interest in the literature. The majority of these devices were produced by patterning hydrophobic zones in hydrophilic paper via photoresist or wax. Others were created by cutting paper using a laser. Here, we present a fabrication method for producing devices by simple craft-cutting and lamination, in a way similar to making an identification (ID) card. The method employs a digital craft cutter and roll laminator to produce laminated paper-based analytical devices (LPAD). Lamination with a plastic backing provides the mechanical strength for a paper device. The approach of using a craft cutter and laminator makes it possible to rapid-prototype LPAD with no more difficulty than producing a typical ID card, at very low cost. Devices constructed using this method have been exploited for simultaneous detection of bovine serum albumin (BSA) and glucose in synthetic urine with colorimetric assays. Both BSA and glucose are detectable at clinically relevant concentrations, with the detection limit at 2.5 μM for BSA and 0.5 mM for glucose.     

An ontological engineering approach for automating inspection and quarantine at airports

Customs and quarantine departments are applying information systems to automate their inspection processes and improve their inspection efficiency and accuracy. The product codes from the Harmonized System (HS codes) are the essential elements of the system's integration, automation and intelligence. The identified HS codes are well-accepted and precise product references used by customs authorities, to match applicable policies to the products being inspected and taxed. Domain ontology for importing and exporting industry can be used to acquire HS codes for given products, and is a prerequisite for an integrated and intelligent automated inspection system. The authors have proposed and implemented an importing and exporting domain ontology. The ontology is composed of an integrated and comprehensive knowledge base derived from static dictionaries and the HS specification, and dynamic processing data. Based on this ontology, a reasoning engine is developed to generate HS codes intelligently for the given product names. Information systems can use the engine to get HS codes for submitted products and find applicable policies automatically. The ontology and the engine have been implemented in a Java-based platform and published as a HS Web service. In this paper, knowledge structure, reasoning mechanism and implementation details for the domain ontology and reasoning engine are presented. A test bed in the application environment has been conducted and experimental results have been obtained. The ontology and the service have the potential to be widely used by authorities and international traders of importing and exporting industry around the world.     

A superscalar simulation employing poisson distributed stalls

This paper presents a statistical approach to estimating the performance of a superscalar processor. Traditional trace-driven simulators can take a large amount time to conduct a performance evaluation of a machine, especially as the number of instructions increases. The result of this type of simulation is typically tied to the particular trace that was run. Elements such as dependencies, delays, and stalls are all a direct result of the particular trace being run, and can differ from trace to trace. This paper describes a model designed to separate simulation results from a specific trace. Rather than running a trace-driven simulation, a statistical model is employed, more specifically a Poisson distribution, to predict how these types of delay affects performance. Through the use of this statistical model, a performance evaluation can be conducted using a general code model, with specific stall rates, rather than a particular code trace. This model allows simulations to quickly run tens of millions of instructions and evaluate the performance of a particular micro-architecture while at the same time, allowing the flexibility to change the structure of the architecture.     

Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS

The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites image most of the Earth multiple times each day, providing useful data on fires that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures, which strongly influence how fires spread, the amount and chemistry of their gas and aerosol emissions, and their impacts on ecosystems. In this study, we use multiple endmember spectral mixture analysis (MESMA) to retrieve subpixel fire sizes and temperatures from MODIS, possibly overcoming some limitations of existing methods for characterizing fire intensities such as estimating the fire radiative power (FRP). MESMA is evaluated using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to assess the performance of FRP and MESMA retrievals of fire properties from a simultaneously acquired MODIS image, for a complex of fires in Ukraine from August 21, 2002. The MESMA retrievals of fire size described in this paper show a slightly stronger correlation than FRP does to fire pixel counts from the coincident ASTER image. Prior to this work, few studies, if any, had used MESMA for retrieving fire properties from a broad-band sensor like MODIS, or compared MESMA to higher-resolution fire data or other measures of fire properties like FRP. In the future, MESMA retrievals could be useful for fire spread modeling and forecasting, reducing hazards that fires pose to property and health, and enhancing scientific understanding of fires and their effects on ecosystems and atmospheric composition.     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.