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Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. [ABSTRACT FROM AUTHOR]

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model.

In this paper, we describe a model designed to simulate seasonal dynamics of warm and cool season grasses and forbs, as well as the dynamics of woody plant succession through five seral stages, in each of nine different plant communities on the Rob and Bessie Welder Wildlife Refuge. The Welder Wildlife Refuge (WWR) is located in the Gulf Coastal Prairies and Marshes ecoregion of Texas. The model utilizes and integrates data from a wide array of research projects that have occurred in south Texas and WWR. It is designed to investigate the effects of alternative livestock grazing programs and brush control practices, with particular emphasis on prescribed burning, the preferred treatment for brush on the WWR. We evaluated the model by simulating changes in the plant communities under historical (1974-2000) temperature, rainfall, livestock grazing rotation, and brush control regimes, and comparing simulation results to field data on herbaceous biomass and brush canopy cover collected on the WWR over the same period. We then used the model to simulate the effects of 13 alternative management schemes, under each of four weather regimes, over the next 25 years. We found that over the simulation period, years 1974-2000, the model does well in simulating the magnitude and seasonality of herbaceous biomass production and changes in percent brush canopy cover on the WWR. It also does well in simulating the effects of variations in cattle stocking rates, grazing rotation programs, and brush control regimes on plant communities, thus providing insight into the combined effects of temperature, precipitation, cattle stocking rates, grazing rotation programs, and brush control on the overall productivity and state of woody plant succession on the WWR. Simulation of alternative management schemes suggests that brush canopy removal differs little between summer and winter prescribed burn treatments when precipitation remains near the long-term average, but during periods of low precipitation canopy removal is greater under winter prescribed burning. The model provides a useful tool to assist refuge personnel with developing long-term brush management and livestock grazing strategies.

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity. [ABSTRACT FROM AUTHOR]

The Texas Medication Algorithm Project is a program designed to improve the quality of care of persons with serious mental disorders across sites in the Texas public mental health system and to create a uniform clinical environment from which cost estimates can be made. This paper describes the process of developing a pharmacological treatment algorithm for schizophrenia that addresses use of antipsychotics as well as other medications for side effects and co-existing symptoms. Input from clinicians, consultants, and consumers informed development of the algorithm, which was based on existing expert consensus guidelines. Information about the project can be found on the Internet at www.mhmr.state.tx.us/meds/tmap.htm. The authors present and describe the original and current versions of the algorithm, outline the feedback process by which it will be refined, and discuss how new medications will be incorporated as they enter the market.

To fully automate building code compliance checking, regulatory requirements need to be automatically extracted and transformed from building codes. Existing regulatory requirement processing efforts mainly focus on building code requirements in text. A more efficient approach for processing regulatory requirements in other parts of building codes, such as tables or charts, remains to be addressed. The ability to process building code requirements in all parts and formats is necessary for an automated code compliance checking system to achieve full coverage of checkable building code requirements. To address this gap, the authors propose a semiautomated information extraction and transformation method. The proposed method can extract building code requirements in tables and convert extracted information to logic rules. Automated code compliance checking systems can utilize the logic rules. The proposed method includes two main steps: (1) tabular information extraction, and (2) rule generation. The tabular information extraction semiautomatically detects the layout of tables, extracts building code requirements from tables, and transforms extracted information to databases. The rule generation step automatically generates logic rules that can be directly executed by logic reasoners. The rule generation step also provides options for users to further refine the generated rules. The development of the tabular information extraction algorithm takes an iterative approach. An experiment was conducted to develop a tabular information extraction algorithm from a section of existing code. The primary version of the algorithm correctly processed 91.67% of tables in a second sample section. After iterative refinements, the updated tabular information extraction algorithm correctly processed all tables in this section. The rule generation algorithm correctly generated logic rules that successfully represented the applicable building code requirements for a testing building, a convenience store in Texas based on the tabular information. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes and demonstrates a new interactive framework for sensitivity-informed de Novo planning to confront the deep uncertainty within water management problems. The framework couples global sensitivity analysis using Sobol’ variance decomposition with multiobjective evolutionary algorithms (MOEAs) to generate planning alternatives and test their robustness to new modeling assumptions and scenarios. We explore these issues within the context of a risk-based water supply management problem, where a city seeks the most efficient use of a water market. The case study examines a single city’s water supply in the Lower Rio Grande Valley (LRGV) in Texas, using a suite of 6-objective problem formulations that have increasing decision complexity for both a 10-year planning horizon and an extreme single-year drought scenario. The de Novo planning framework demonstrated illustrates how to adaptively improve the value and robustness of our problem formulations by evolving our definition of optimality while discovering key tradeoffs. [Copyright &y& Elsevier]

Abstract: In this paper, I analyze immigration''s effect on the SAT-scores and college application patterns of high school students in California and Texas. The student-level dataset used is longitudinal in nature and is matched via a unique algorithm to the Census 2000 summary tabulation files to determine immigration at the local census-place level. The Census measure of immigration developed here is extremely specific and improves upon existing immigration measures. Using empirical strategies to account for issues of selection, this essay''s main finding is that the 1990s immigration did not harm, and possibly benefited the student outcomes of U.S. citizens. [Copyright &y& Elsevier]

Abstract: The Center for Space Research (CSR) continues to focus on developing methods to improve correlations between satellite-based aerosol optical thickness (AOT) values and ground-based, air pollution observations made at continuous ambient monitoring sites (CAMS) operated by the Texas commission on environmental quality (TCEQ). Strong correlations and improved understanding of the relationships between satellite and ground observations are needed to formulate reliable real-time predictions of air quality using data accessed from the moderate resolution imaging spectroradiometer (MODIS) at the CSR direct-broadcast ground station. In this paper, improvements in these correlations are demonstrated first as a result of the evolution in the MODIS retrieval algorithms. Further improvement is then shown using procedures that compensate for differences in horizontal spatial scales between the nominal 10-km MODIS AOT products and CAMS point measurements. Finally, airborne light detection and ranging (lidar) observations, collected during the Texas Air Quality Study of 2000, are used to examine aerosol profile concentrations, which may vary greatly between aerosol classes as a result of the sources, chemical composition, and meteorological conditions that govern transport processes. Further improvement in correlations is demonstrated with this limited dataset using insights into aerosol profile information inferred from the vertical motion vectors in a trajectory-based forecast model. Analyses are ongoing to verify these procedures on a variety of aerosol classes using data collected by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (Calipso) lidar. [Copyright &y& Elsevier]

Important decisions regarding governmental expenditures for the public welfare require accurate assessment of preferences of the citizenry as one input. Often, strong disagreement exists regarding relative merit of various projects competing for limited resources. This paper proposes a procedure for establishing a complete social preference ordering on the set of alternative projects when the group preference is fuzzy because of individual differences. An extension of current algorithms is developed and applied to a problem concerning public preferences for expenditures on civic projects in Houston, Texas. One thousand male and female heads-of-household were interviewed in depth to determine their preferences for investment among forty-one projects in the areas of social services, business and job development, public transportation, arts and leisure, and education. A complete preference order for group decision was obtained for a situation in which significant disagreement exists among group members and where funding is limited. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Mesoscale-to-microscale coupling (MMC) aims to address the limited scope of traditional large-eddy simulations by driving the microscale flow with information concerning large-scale weather patterns provided by mesoscale models. We present a new offline MMC technique for horizontally homogeneous microscale flow conditions, in which internal forcing terms are computed based on mesoscale time–height profiles of mean-flow quantities. The advantage of such an approach is that it can be used to drive a microscale simulation with either mesoscale or observational data, and that it does not rely on specific terms in the mesoscale budget equations, which are typically not part of the default output of a mesoscale solver. The performance of the proposed profile assimilation technique is assessed based on the simulation of a typical diurnal cycle over the Scaled Wind Farm Technology site in west Texas. Results indicate that simple data assimilation techniques lead to unphysically high levels of shear and turbulence caused by the algorithm's inability to cope with inaccuracies in the mesoscale time–height profiles. Modifying the algorithm to account for vertical coherence in the mesoscale source terms gives the microscale solver a greater ability to correct the provided mesoscale time–height profiles, leading to improved predictions of shear and turbulence statistics. The resulting turbulence statistics are in good agreement with meteorological tower observations and simulation results obtained with state-of-the-art coupling techniques using mesoscale budget components. [ABSTRACT FROM AUTHOR]

Background: Kulldorff's spatial scan statistic has been one of the most widely used statistical methods for automatic detection of clusters in spatial data. One limitation of this method lies in the fact that it has to rely on scan windows with predefined shapes in the search process, and therefore it cannot detect cluster with arbitrary shapes. We employ a new neighbor-expanding approach and introduce two new algorithms to detect cluster with arbitrary shapes in spatial data. These two algorithms are called the maximum-likelihood-first (MLF) algorithm and non-greedy growth (NGG) algorithm. We then compare the performance of these two new algorithms with the spatial scan statistic (SaTScan), Tango's flexibly shaped spatial scan statistic (FlexScan), and Duczmal's simulated annealing (SA) method using two datasets. Furthermore, we utilize the methods to examine clusters of murine typhus cases in South Texas from 1996 to 2006. Result: When compared with the SaTScan and FlexScan method, the two new algorithms were more flexible and sensitive in detecting the clusters with arbitrary shapes in the test datasets. Clusters detected by the MLF algorithm are statistically more significant than those detected by the NGG algorithm. However, the NGG algorithm appears to be more stable when there are no extreme cluster patterns in the data. For the murine typhus data in South Texas, a large portion of the detected clusters were located in coastal counties where environmental conditions and socioeconomic status of some population groups were at a disadvantage when compared with those in other counties with no clusters of murine typhus cases. Conclusion: The two new algorithms are effective in detecting the location and boundary of spatial clusters with arbitrary shapes. Additional research is needed to better understand the etiology of the concentration of murine typhus cases in some counties in south Texas. [ABSTRACT FROM AUTHOR]

A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabilities are known to occur at "sharp" transitions in discrete geometry, but the identification of problem locations has been a matter of modeler's art and a roadblock to implementing large-scale SVE simulations. The new method implements techniques from graph theory applied to a steady-state 1D shallow-water equation solver to recursively examine the numerical stability of each flowpath through the channel network. The SSA is validated with a short river reach and tested by the simulation of ten complete river systems of the Texas–Gulf Coast region by using the extreme hydrological conditions recorded during hurricane Harvey. The SSA successfully identified the problematic channel sections in all tested river systems. Subsequent modification of the problem sections allowed stable solution by an unsteady SVE numerical solver. The new SSA approach permits automated and consistent identification of problem channel geometry in large open-channel network data sets, which is necessary to effectively apply the fully dynamic Saint-Venant equations to large-scale river networks or for city-wide stormwater networks. [ABSTRACT FROM AUTHOR]

We developed a three-dimensional unstructured grid coastal and estuarine circulation model, named the General Ocean Model (GOM). Combining the finite volume and finite difference methods, GOM achieved both the exact conservation and computational efficiency. The propagation term was implemented by a semi-implicit numerical scheme, the so-called θ scheme, and the time-explicit Eulerian–Lagrangian method was used to discretize the nonlinear advection term to remove the major limitation of the time step, which appears when solving shallow water equations, by the Courant–Friedrichs–Lewy stability condition. Because the GOM uses orthogonal unstructured computational grids, allowing both triangular and quadrilateral grids, considerable flexibility to resolve complex coastal boundaries is allowed without any transformation of governing equations. The GOM was successfully verified with five analytical solutions, and it was also validated when applied to the Texas coast, showing an overall skill value of 0.951. The verification results showed that the algorithm used in GOM was correctly coded, and it is efficient and robust. [ABSTRACT FROM AUTHOR]