University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Ontology learning: state of the art and open issues

Abstract: Ontology is one of the fundamental cornerstones of the semantic Web The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning We propose a new learning-oriented model for ontology development and a framework for ontology learning Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort The paper offers a road map and a variety of insights about this fast-growing field

A Pseudorigid-Body 3R Model for Determining Large Deflection of Cantilever Beams Subject to Tip Loads

Abstract: In this paper, a pseudo-rigid-body (PRB) 3R model which consists of four rigid links joined by three revolute joints and three torsion springs is proposed for approximating the de∞ection of a cantilever beam subject to a general tip load. The large de∞ection beam equations are solved through numerical integration. A comprehensive atlas of the tip de∞ection for various load modes is obtained. A three-dimensional search routine has been developed to flnd the optimal set of characteristic radius factors and spring stifiness of the PRB 3R model. Detailed error analysis has been done by comparing with the pre-computed tip de∞ection atlas. Our results show that the approximation error is much less than that of the conventional PBR 1R model. The beneflts of the PRB 3R model include (a) load independence which is critical for analysis/synthesis applications where loads vary signiflcantly, (b) high accuracy for large de∞ection beams, and (c) explicit kinematic and static constraint equations which are simpler to solve compared with the flnite element model. To demonstrate the use of the PRB 3R model, a compliant 4-bar linkage is studied and verifled by a numerical example. The result shows a maximum tip de∞ection error of 1:2% compared with the FEA model.

Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives

Abstract: Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. Indeed, aerosol particles constitute a highly variable atmospheric component characterized by a large number of parameters describing particle sizes, morphologies (including shape and internal structure), absorption and scattering properties, amounts, horizontal and vertical distribution, etc. Reliable monitoring of all these parameters is very challenging, and therefore the aerosol effects on climate and environment are considered to be among the most uncertain factors in climate and environmental research. In this regard, observations that provide both the angular distribution of the scattered atmospheric radiation as well as its polarization state at multiple wavelengths covering the UV–SWIR spectral range carry substantial implicit information on the atmospheric composition. Therefore, high expectations in improving aerosol characterization are associated with detailed passive photopolarimetric observations. The critical need to use space-borne polarimetry for global accurate monitoring of detailed aerosol properties was first articulated in the late 1980s and early 1990s. By now, several orbital instruments have already provided polarization observations from space, and a number of advanced missions are scheduled for launch in the coming years by international and national space agencies. The first and most extensive record of polarimetric imagery was provided by POLDER-I, POLDER-II, and POLDER/PARASOL multi-angle multi-spectral polarization sensors. Polarimetric observations with the POLDER-like design intended for collecting extensive multi-angular multi-spectral measurements will be provided by several instruments, such as the MAI/TG-2, CAPI/TanSat, and DPC/GF-5 sensors recently launched by the Chinese Space Agency. Instruments such as the 3MI/MetOp-SG, MAIA, SpexOne and HARP2 on PACE, POSP, SMAC, PCF, DPC–Lidar, ScanPol and MSIP/Aerosol-UA, MAP/Copernicus CO2 Monitoring, etc. are planned to be launched by different space agencies in the coming decade. The concepts of these future instruments, their technical designs, and the accompanying algorithm development have been tested intensively and analyzed using diverse airborne prototypes. Certain polarimetric capabilities have also been implemented in such satellite sensors as GOME-2/MetOp and SGLI/GCOM-C. A number of aerosol retrieval products have been developed based on the available measurements and successfully used for different scientific applications. However, the completeness and accuracy of aerosol data operationally derived from polarimetry do not yet appear to have reached the accuracy levels implied by theoretical sensitivity studies that analyzed the potential information content of satellite polarimetry. As a result, the dataset provided by MODIS is still most frequently used by the scientific community, yet this sensor has neither polarimetric nor multi-angular capabilities. Admittedly polarimetric multi-angular observations are highly complex and have extra sensitivities to aerosol particle morphology, vertical variability of aerosol properties, polarization of surface reflectance, etc. As such, they necessitate state-of-the-art forward modeling based on first-principles physics which remains rare, and conventional retrieval approaches based on look-up tables turn out to be unsuitable to fully exploit the information implicit in the measurements. Several new-generation retrieval approaches have recently been proposed to address these challenges. These methods use improved forward modeling of atmospheric (polarized) radiances and implement a search in the continuous space of solutions using rigorous statistically optimized inversions. Such techniques provide more accurate retrievals of the main aerosol parameters such as aerosol optical thickness and yield additional parameters such as aerosol absorption. However, the operational implementation of advanced retrieval approaches generally requires a significant extra effort, and the forward-modeling part of such retrievals still needs to be substantially improved. Ground-based passive polarimetric measurements have also been evolving over the past decade. Although polarimetry helps improve aerosol characterization, especially of the fine aerosol mode, the operators of major observational networks such as AERONET remain reluctant to include polarimetric measurements as part of routine retrievals owing to their high complexity and notable increase in effort required to acquire and interpret polarization data. In addition to remote-sensing observations, polarimetric characteristics of aerosol scattering have been measured in situ as well as in the laboratory using polar nephelometers. Such measurements constitute direct observations of single scattering with no contributions from multiple scattering effects and therefore provide unique data for the validation of aerosol optical models and retrieval concepts. This article overviews the above-mentioned polarimetric observations, their history and expected developments, and the state of resulting aerosol products. It also discusses the main achievements and challenges in the exploitation of polarimetry for the improved characterization of atmospheric aerosols.

The roles of religion and spirituality among African American survivors of domestic violence

Abstract: The investigation examined religious involvement, spirituality, religious coping, and social support as correlates of posttraumatic stress symptoms and depression symptoms in African American survivors of domestic violence. Sixty-five African American women who experienced domestic violence in the past year provided data on demographics, severity and frequency of physical and psychological abuse during the past year, aspects of current social support, types of current coping activities, religious involvement, spiritual experiences, and symptoms related to depression and posttraumatic stress disorder. Women who evinced higher levels of spirituality and greater religious involvement reported fewer depression symptoms. Religious involvement was also found to be negatively associated with posttraumatic stress symptoms. Women who reported higher levels of spirituality reported utilizing higher levels of religious coping strategies, and women who reported higher levels of religious involvement reported higher levels of social support. Results did not support hypotheses regarding social support and religious coping as mediators of the associations between mental health variables, religious involvement, and spirituality.

Imaging Time-Series to Improve Classification and Imputation

Abstract: Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time series classification and imputation. We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images. Our approaches achieve highly competitive results when compared to nine of the current best time series classification approaches. Inspired by the bijection property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on the GASF images of four standard and one synthesized compound dataset. The imputation MSE on test data is reduced by 12.18%-48.02% when compared to using the raw data. An analysis of the features and weights learned via tiled CNNs and DAs explains why the approaches work.