Instructional Leadership, Teaching Quality, and Student Achievement: Suggestive Evidence from Three Urban School Districts

Does providing instruction-related professional development to school principals set in motion a chain of events that can improve teaching and learning in their schools? This report examines professional development efforts by the University of Pittsburgh's Institute for Learning in elementary schools in Austin, St. Paul, and New York City

Developing Methods to Support Collaborative Learning and Co-creation of Resilient Healthcare—Tips for Success and Lessons Learned From a Norwegian Hospital Cancer Care Study

Background There is a growing attention on the role of patients and stakeholders in resilience, but there is lack of knowledge and methods on how to support collaborative learning between stakeholders and co-creation of resilient healthcare. The aim of this article was to demonstrate how the methodological process of a consensus process for exploring aspects of next of kin involvement in hospital cancer care can be replicated as an effort to promote resilient healthcare through co-creation with multiple stakeholders in hospitals. Methods The study applied a modified nominal group technique process developed by synthesizing research findings across 4 phases of a research project with a mixed-methods approach. The process culminated in a 1-day meeting with 20 stakeholder participants (5 next of kin representatives, 10 oncology nurses, and 5 physicians) from 2 Norwegian university hospitals. Results The consensus method established reflexive spaces with collective sharing of experiences between the 2 hospitals and between the next of kin and healthcare professionals. The method promoted collaborative learning processes including identification and reflection upon new ideas for involvement, and reduction of the gap between healthcare professionals’ and next of kin experiences and expectations for involvement. Next of kin were considered as important resources for resilient performance, if involved with a proactive approach. The consensus process identified both successful and unsuccessful collaborative practices and resulted in a co-designed guide for healthcare professionals to support next of kin involvement in hospital cancer care. Conclusions This study expands the body of knowledge on methods development that is relevant for collaborative learning and co-creation of resilient healthcare. This study demonstrated that the consensus methods process can be used for creating reflexive spaces to support collaborative learning and co-creation of resilience in cancer care. Future research within the field of collaborative learning should explore interventions that include a larger number of stakeholders.publishedVersio

Una visión general sobre la implementación de metaheurísticas paralelas en la nube

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.Las metaheurísticas son uno de los métodos más populares en muchas áreas de la ciencia y la ingeniera para la resolución de problemas de optimización global difíciles. Su implementación paralela, aplicando técnicas de HPC, es una aproximación común a la hora de reducir el tiempo necesario para obtener una solución lo suficientemente buena con un uso eficiente de los recursos disponibles. Paradigmas como MPI u OMP son las opciones habituales cuando se ejecutan en clústeres o supercomputadores. Además, la utilización generalizada de la computación en la nube y la aparición de modelos de programación como MapReduce o Spark, han generado un interés creciente por portar aplicaciones HPC a la nube, como ocurre en el caso de las metaheursticas paralelas. En este trabajo recogemos una visión general de nuestra experiencia con diferentes opciones a la hora de portar metaheursticas paralelas a la nube, proporcionando información útil al lector interesado, que hemos ido adquiriendo a través de nuestra experiencia practica.Facultad de Informátic

Una visión general sobre la implementación de metaheurísticas paralelas en la nube

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.Las metaheurísticas son uno de los métodos más populares en muchas áreas de la ciencia y la ingeniera para la resolución de problemas de optimización global difíciles. Su implementación paralela, aplicando técnicas de HPC, es una aproximación común a la hora de reducir el tiempo necesario para obtener una solución lo suficientemente buena con un uso eficiente de los recursos disponibles. Paradigmas como MPI u OMP son las opciones habituales cuando se ejecutan en clústeres o supercomputadores. Además, la utilización generalizada de la computación en la nube y la aparición de modelos de programación como MapReduce o Spark, han generado un interés creciente por portar aplicaciones HPC a la nube, como ocurre en el caso de las metaheursticas paralelas. En este trabajo recogemos una visión general de nuestra experiencia con diferentes opciones a la hora de portar metaheursticas paralelas a la nube, proporcionando información útil al lector interesado, que hemos ido adquiriendo a través de nuestra experiencia practica.Facultad de Informátic

Una visión general sobre la implementación de metaheurísticas paralelas en la nube

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.Las metaheurísticas son uno de los métodos más populares en muchas áreas de la ciencia y la ingeniera para la resolución de problemas de optimización global difíciles. Su implementación paralela, aplicando técnicas de HPC, es una aproximación común a la hora de reducir el tiempo necesario para obtener una solución lo suficientemente buena con un uso eficiente de los recursos disponibles. Paradigmas como MPI u OMP son las opciones habituales cuando se ejecutan en clústeres o supercomputadores. Además, la utilización generalizada de la computación en la nube y la aparición de modelos de programación como MapReduce o Spark, han generado un interés creciente por portar aplicaciones HPC a la nube, como ocurre en el caso de las metaheursticas paralelas. En este trabajo recogemos una visión general de nuestra experiencia con diferentes opciones a la hora de portar metaheursticas paralelas a la nube, proporcionando información útil al lector interesado, que hemos ido adquiriendo a través de nuestra experiencia practica.Facultad de Informátic

Public Information Dissemination for Ramp Metering in the Pittsburgh, Pennsylvania Area

Although ramp metering systems have been in use throughout the U.S. for over 40 years,ramp meters are still a foreign concept in many places. Pittsburgh, Pennsylvania is one suchplace; therefore, successful implementation of ramp meters in the Pittsburgh area would require acomprehensive public education campaign. However, there are currently no standards inPennsylvania for such a campaign. This lack of information on public education raises thefollowing questions: How important is a public education campaign to the success of a rampmetering project in the Pittsburgh area? What have other states done in the past to educate thepublic on ramp metering? And what is the most effective way to inform the public about rampmeters?This study aims to answer these questions through the development, administration, andanalysis of a number of surveys. Eleven state departments of transportation were surveyed as apart of this study, and the trends of these states' experiences with ramp metering and publiceducation are discussed. A test group of thirty-one motorists representing the Pittsburgh area general public were also surveyed as part of the study. This survey was conducted in two parts(before and after reviewing informational material on ramp metering), and the results wereanalyzed both individually and as a comparison. The findings of all surveys are discussed, andrecommendations are made for a ramp metering public education campaign in the Pittsburgharea

Harnessing Markets for Water Quality

This issue of IMPACT is devoted to exploring and understanding the opportunities and challenges of harnessing markets to improve water quality. It looks at how markets could be implemented to address the growing concern of nonpoint source pollution as well as point sources. Recently, the EPA proposed a water quality trading proposal, which is summarized, reviewed, and critiqued

Lessons Learned From 10 Years of Preschool Intervention for Health Promotion: JACC State-of-the-Art Review.

Implementing a health promotion program for children is a complex endeavor. In this review, we outline the key lessons learned over 10 years of experience in implementing the SI! Program (Salud Integral-Comprehensive Health) for cardiovascular health promotion in preschool settings in 3 countries: Colombia (Bogotá), Spain (Madrid), and the United States (Harlem, New York). By matching rigorous efficacy studies with implementation science, we can help bridge the divide between science and educational practice. Achieving sustained lifestyle changes in preschool children through health promotion programs is likely to require the integration of several factors: 1) multidisciplinary teams; 2) multidimensional educational programs; 3) multilevel interventions; 4) local program coordination and community engagement; and 5) scientific evaluation through randomized controlled trials. Implementation of effective health promotion interventions early in life may induce long-lasting healthy behaviors that could help to curb the cardiovascular disease epidemic.This work is supported by the SHE Foundation and “la Caixa” Foundation (LCF/CE16/10700001). The project in Colombia was funded by Santo Domingo Foundation; the study in the United States (FAMILIA) was funded by the American Heart Association (grant no. 14SFRN20490315); and the study in Spain (SI! Program) was funded by the SHE Foundation, the research grant FIS-PI11/ 01885 (Fondo de Investigación Sanitaria del Instituto de Salud Carlos III), and Fundació la Marató de TV3 (369/C/2016). Dr SantosBeneit is the recipient of grant LCF/PR/MS19/12220001 funded by “la Caixa” Foundation (ID 100010434). Dr Fernández-Jiménez is the recipient of grant PI19/01704 funded by the Fondo de Investigación Sanitaria–Instituto de Salud Carlos III and co-funded by the European Regional Development Fund/European Social Fund “A way to make Europe”/“Investing in your future.” The Centro Nacional de Investigaciones Cardiovasculares is supported by the Instituto de Salud Carlos III, the Ministerio de Ciencia e Innovación, and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (CEX2020-001041-S). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.S