The Impact of Teacher Collaboration in a Professional Learning Community on Teacher Job Satisfaction

A dilemma in some local educational institutions is the lack of a supportive teacher community which could lead to decreased teacher job satisfaction. The purpose of this study was to describe to what extent teacher collaboration in a professional learning community (PLC) impacts teacher job satisfaction. Theoretical foundations by DuFour, Eaker and DuFour supported the relationships between PLC and teacher collaboration. Herzberg\u27s theory of motivation and Maslow\u27s hierarchy also served as frameworks in this study. An interpretive, qualitative research design was used to explore potential connections between collaboration and job satisfaction. Research questions addressed how scheduled school day collaborative time impacts teacher job satisfaction, job satisfaction changes due to a PLC environment, and the impact of teacher isolate on job satisfaction. Data included observations of collegial interactions, face-to-face interviews, recorded field notes and audio tapings captured during these data collecting events. Cross-referencing was applied between collection tools. Data were coded, categorized and analyzed following the process designed by Hatch. Ideal collaborative time and job satisfaction characteristics emerged as core themes. Specifically, scheduled collaboration provided a structure for developing strategies for meeting students\u27 needs, and principal support for collaboration was shown to enhance teachers\u27 perceptions of job satisfaction. Teachers also cited improved practices and enhanced collegial relationships as additional sources of increased job satisfaction. Implications for positive social change include improving teacher job satisfaction, which could assist in creating a positive, productive environment for teachers. This can result in more well planned learning environments and greater academic achievement for students

Artificial Intelligence and Image Processing

The evolution of artificial intelligence since the 1950s is discussed, especially as it is being applied in radiology to image processing. Developments in artificial intelligence are now being used to provide a new approach to image processing. Initially, the computer dealt with numeric representations using languages such as FORTRAN and BASIC. Now symbolic languages such as LISP and PROLOG have expanded the use of the computer into nonnumeric symbolic reasoning that is just being applied to image understanding. This paper explains the new languages and their application to image understanding

Comparison of two U.S. power-plant carbon dioxide emissions data sets

This paper is not subject to U.S. copyright. The definitive version was published in Environmental Science & Technology 42 (2008): 5688-5693, doi:10.1021/es800221q.Estimates of fossil-fuel CO2 emissions are needed to address a variety of climate-change mitigation concerns over a broad range of spatial and temporal scales. We compared two data sets that report power-plant CO2 emissions in the conterminous U.S. for 2004, the most recent year reported in both data sets. The data sets were obtained from the Department of Energy’s Energy Information Administration (EIA) and the Environmental Protection Agency’s eGRID database. Conterminous U.S. total emissions computed from the data sets differed by 3.5% for total plant emissions (electricity plus useful thermal output) and 2.3% for electricity generation only. These differences are well within previous estimates of uncertainty in annual U.S. fossil-fuel emissions. However, the corresponding average absolute differences between estimates of emissions from individual power plants were much larger, 16.9% and 25.3%, respectively. By statistical analysis, we identified several potential sources of differences between EIA and eGRID estimates for individual plants. Estimates that are based partly or entirely on monitoring of stack gases (reported by eGRID only) differed significantly from estimates based on fuel consumption (as reported by EIA). Differences in accounting methods appear to explain differences in estimates for emissions from electricity generation from combined heat and power plants, and for total and electricity generation emissions from plants that burn nonconventional fuels (e.g., biomass). Our analysis suggests the need for care in utilizing emissions data from individual power plants, and the need for transparency in documenting the accounting and monitoring methods used to estimate emissions.This study was supported by the National Research Program and the Earth System Dynamics Program of the U.S. Geological Survey

Development and application of landsat-based wetland vegetation cover and unvegetated-vegetated marsh ratio (UVVR) for the conterminous United States

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ganju, N. K., Couvillion, B. R., Defne, Z., & Ackerman, K. Development and application of landsat-based wetland vegetation cover and unvegetated-vegetated marsh ratio (UVVR) for the conterminous United States. Estuaries and Coasts, (2022), https://doi.org/10.1007/s12237-022-01081-x.Effective management and restoration of salt marshes and other vegetated intertidal habitats require objective and spatially integrated metrics of geomorphic status and vulnerability. The unvegetated-vegetated marsh ratio (UVVR), a recently developed metric, can be used to establish present-day vegetative cover, identify stability thresholds, and quantify vulnerability to open-water conversion over a range of spatial scales. We developed a Landsat-based approach to quantify the within-pixel vegetated fraction and UVVR for coastal wetlands of the conterminous United States, at 30-m resolution for 2014–2018. Here we present the methodology used to generate the UVVR from spectral indices, along with calibration, validation, and spatial autocorrelation assessments. We then demonstrate multiple applications of the data across varying spatial scales: first, we aggregate the UVVR across individual states and estuaries to quantify total vegetated wetland area for the nation. On the state level, Louisiana and Florida account for over 50% of the nation’s total, while on the estuarine level, the Chesapeake Bay Estuary and selected Louisiana coastal areas each account for over 6% of the nation’s total vegetated wetland area. Second, we present cases where this dataset can be used to track wetland change (e.g., expansion due to restoration and loss due to stressors). Lastly, we propose a classification methodology that delineates areas vulnerable to open-water expansion based on the 5-year mean and standard deviation of the UVVR. Calculating the UVVR for the period-of-record back to 1985, as well as regular updating, will fill a critical gap for tracking national status of salt marshes and other vegetated habitats through time and space.This work was supported by the U.S. Geological Survey’s Coastal and Marine Hazards/Resources Program

Beyond Outerplanarity

We study straight-line drawings of graphs where the vertices are placed in convex position in the plane, i.e., convex drawings. We consider two families of graph classes with nice convex drawings: outer $k$-planar graphs, where each edge is crossed by at most $k$ other edges; and, outer $k$-quasi-planar graphs where no $k$ edges can mutually cross. We show that the outer $k$-planar graphs are $(\lfloor\sqrt{4k+1}\rfloor+1)$-degenerate, and consequently that every outer $k$-planar graph can be $(\lfloor\sqrt{4k+1}\rfloor+2)$-colored, and this bound is tight. We further show that every outer $k$-planar graph has a balanced separator of size $O(k)$. This implies that every outer $k$-planar graph has treewidth $O(k)$. For fixed $k$, these small balanced separators allow us to obtain a simple quasi-polynomial time algorithm to test whether a given graph is outer $k$-planar, i.e., none of these recognition problems are NP-complete unless ETH fails. For the outer $k$-quasi-planar graphs we prove that, unlike other beyond-planar graph classes, every edge-maximal $n$-vertex outer $k$-quasi planar graph has the same number of edges, namely $2(k-1)n - \binom{2k-1}{2}$. We also construct planar 3-trees that are not outer $3$-quasi-planar. Finally, we restrict outer $k$-planar and outer $k$-quasi-planar drawings to \emph{closed} drawings, where the vertex sequence on the boundary is a cycle in the graph. For each $k$, we express closed outer $k$-planarity and \emph{closed outer $k$-quasi-planarity} in extended monadic second-order logic. Thus, closed outer $k$-planarity is linear-time testable by Courcelle's Theorem.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

A Model for Particle Microphysics, Turbulent Mixing, and Radiative Transfer in the Stratocumulus-Topped Marine Boundary Layer and Comparisons with Measurements

A detailed 1D model of the stratocumulus-topped marine boundary layer is described. The model has three coupled components: a microphysics module that resolves the size distributions of aerosols and cloud droplets, a turbulence module that treats vertical mixing between layers, and a multiple wavelength radiative transfer module that calculates radiative heating rates and cloud optical properties. The results of a 12-h model simulation reproduce reasonably well the bulk thermodynamics, microphysical properties, and radiative fluxes measured in an approx. 500-m thick, summertime marine stratocumulus cloud layer by Nicholls. However, in this case, the model predictions of turbulent fluxes between the cloud and subcloud layers exceed the measurements. Results of model simulations are also compared to measurements of a marine stratus layer made under gale conditions and with measurements of a high, thin marine stratocumulus layer. The variations in cloud properties are generally reproduced by the model, although it underpredicts the entrainment of overlying air at cloud top under gale conditions. Sensitivities of the model results are explored. The vertical profile of cloud droplet concentration is sensitive to the lower size cutoff of the droplet size distribution due to the presence of unactivated haze particles in the lower region of the modeled cloud. Increases in total droplet concentrations do not always produce less drizzle and more cloud water in the model. The radius of the mean droplet volume does not correlate consistently with drizzle, but the effective droplet radius does. The greatest impacts on cloud properties predicted by the model are produced by halving the width of the size distribution of input condensation nuclei and by omitting the effect of cloud-top radiative cooling on the condensational growth of cloud droplets. The omission of infrared scattering produces noticeable changes in cloud properties. The collection efficiencies for droplets less than 30-micrometers radius, and the value of the accommodation coefficient for condensational droplet growth, have noticeable effects on cloud properties. The divergence of the horizontal wind also has a significant effect on a 12-h model simulation of cloud structure. Conclusions drawn from the model are tentative because of the limitations of the 1D model framework. A principal simplification is that the model assumes horizontal homogeneity, and, therefore, does not resolve updrafts and downdrafts. Likely consequences of this simplification include overprediction of the growth of droplets by condensation in the upper region of the cloud, underprediction of droplet condensational growth in the lower region of the cloud, and underprediction of peak supersaturations

Historical influence of soil and water management on sediment and carbon budgets in the United States

This paper is not subject to U.S. copyright. The definitive version was published in Applied Geochemistry 26 (2011): S259, doi:10.1016/j.apgeochem.2011.03.118.The documented history of US soil and water management provides a unique opportunity to examine soil and sediment C storage under conditions of changing management practices. Historical acceleration of erosion due to cultivation has been moderated by improved soil management. Increased construction of dams and locks has expanded areas of aquatic sedimentation in reservoirs and ponds. Enhanced historical sediment deposition rates have been documented in lakes and estuaries. All of these changes have an impact on terrestrial C storage and turnover. The present-day C budget associated with erosion and burial cannot be determined without quantifying the time-dependent changes due to past and present soil and water management

SLT-VEGF Reduces Lung Metastases, Decreases Tumor Recurrence, and Improves Survival in an Orthotopic Melanoma Model

SLT-VEGF is a recombinant cytotoxin comprised of Shiga-like toxin (SLT) subunit A fused to human vascular endothelial growth factor (VEGF). It is highly cytotoxic to tumor endothelial cells overexpressing VEGF receptor-2 (VEGFR-2/KDR/Flk1) and inhibits the growth of primary tumors in subcutaneous models of breast and prostate cancer and inhibits metastatic dissemination in orthotopic models of pancreatic cancer. We examined the efficacy of SLT-VEGF in limiting tumor growth and metastasis in an orthotopic melanoma model, using NCR athymic nude mice inoculated with highly metastatic Line IV Cl 1 cultured human melanoma cells. Twice weekly injections of SLT-VEGF were started when tumors became palpable at one week after intradermal injection of 1 × 106 cells/mouse. Despite selective depletion of VEGFR-2 overexpressing endothelial cells from the tumor vasculature, SLT-VEGF treatment did not affect tumor growth. However, after primary tumors were removed, continued SLT-VEGF treatment led to fewer tumor recurrences (p = 0.007), reduced the incidence of lung metastasis (p = 0.038), and improved survival (p = 0.002). These results suggest that SLT-VEGF is effective at the very early stages of tumor development, when selective killing of VEGFR-2 overexpressing endothelial cells can still prevent further progression. We hypothesize that SLT-VEGF could be a promising adjuvant therapy to inhibit or prevent outgrowth of metastatic foci after excision of aggressive primary melanoma lesions

Making GDPR Usable: A Model to Support Usability Evaluations of Privacy

We introduce a new model for evaluating privacy that builds on the criteria proposed by the EuroPriSe certification scheme by adding usability criteria. Our model is visually represented through a cube, called Usable Privacy Cube (or UP Cube), where each of its three axes of variability captures, respectively: rights of the data subjects, privacy principles, and usable privacy criteria. We slightly reorganize the criteria of EuroPriSe to fit with the UP Cube model, i.e., we show how EuroPriSe can be viewed as a combination of only rights and principles, forming the two axes at the basis of our UP Cube. In this way we also want to bring out two perspectives on privacy: that of the data subjects and, respectively, that of the controllers/processors. We define usable privacy criteria based on usability goals that we have extracted from the whole text of the General Data Protection Regulation. The criteria are designed to produce measurements of the level of usability with which the goals are reached. Precisely, we measure effectiveness, efficiency, and satisfaction, considering both the objective and the perceived usability outcomes, producing measures of accuracy and completeness, of resource utilization (e.g., time, effort, financial), and measures resulting from satisfaction scales. In the long run, the UP Cube is meant to be the model behind a new certification methodology capable of evaluating the usability of privacy, to the benefit of common users. For industries, considering also the usability of privacy would allow for greater business differentiation, beyond GDPR compliance.Comment: 41 pages, 2 figures, 1 table, and appendixe