This chapter discusses air quality in Lebanon. It identifies the key drivers affecting ambient and indoor air quality, including transportation, energy production, industry, agriculture, and other human activities. It also discusses natural phenomena that can influence air quality such as dust storms and wildfires. The chapter then examines Lebanon's current air quality situation based on preliminary monitoring programs. It outlines the country's key actors and laws related to air quality. Finally, it explores some responses to air quality issues and emerging issues going forward, such as plans to improve monitoring programs and public transportation systems.
O documento apresenta um cronograma de tópicos sobre AngularJS II, incluindo filters, validação de formulários, injeção de dependências, services, scopes e testes unitários. É fornecido detalhes sobre como usar diferentes filters embutidos como currency, date e json para formatar dados, e como validar formulários com requisitos, tamanhos mínimos e máximos, e padrões.
This document discusses trend analysis of time series data. It defines time series as measurements of a variable taken at regular intervals over time. Time series can show trends, seasonal variations, cyclical variations, and irregular variations. Trend analysis determines if there is a significant increasing or decreasing trend in the data over time. Linear regression and non-parametric Mann-Kendall tests are common methods used to test for trends and estimate their magnitude. The selection of an appropriate trend analysis method depends on characteristics of the water resources data such as distributions, outliers, and missing values.
Hero's Tookit: Start Your Rugged DevOps Journey with Nexus, Jenkins and DockerSeniorStoryteller
This document discusses starting a Rugged DevOps journey using Nexus, Jenkins, and Docker. It presents a Rugged Software Factory model with continuous integration/continuous delivery (CI/CD) pipelines for building software from source code in public repositories, running builds and tests, and deploying to development, QA, user acceptance testing, and production environments. The model emphasizes early feedback, policy enforcement through trusted containers, and monitoring for new issues.
This document discusses Teaching Kids Programming (TKP), a global non-profit that provides free, open-source coding curriculum to teach kids ages 10-17 Java and other programming languages. TKP has taught over 4,000 students through 70 trained teachers using a 40-hour Java curriculum that allows students to start coding within 90 seconds. They partner with schools, non-profits, and organizations around the world and provide teaching resources, curriculum, and support to address the lack of computer science education for kids. The goal is to help more young people, especially girls, gain skills in programming and consider careers in technology.
This chapter discusses air quality in Lebanon. It identifies the key drivers affecting ambient and indoor air quality, including transportation, energy production, industry, agriculture, and other human activities. It also discusses natural phenomena that can influence air quality such as dust storms and wildfires. The chapter then examines Lebanon's current air quality situation based on preliminary monitoring programs. It outlines the country's key actors and laws related to air quality. Finally, it explores some responses to air quality issues and emerging issues going forward, such as plans to improve monitoring programs and public transportation systems.
O documento apresenta um cronograma de tópicos sobre AngularJS II, incluindo filters, validação de formulários, injeção de dependências, services, scopes e testes unitários. É fornecido detalhes sobre como usar diferentes filters embutidos como currency, date e json para formatar dados, e como validar formulários com requisitos, tamanhos mínimos e máximos, e padrões.
This document discusses trend analysis of time series data. It defines time series as measurements of a variable taken at regular intervals over time. Time series can show trends, seasonal variations, cyclical variations, and irregular variations. Trend analysis determines if there is a significant increasing or decreasing trend in the data over time. Linear regression and non-parametric Mann-Kendall tests are common methods used to test for trends and estimate their magnitude. The selection of an appropriate trend analysis method depends on characteristics of the water resources data such as distributions, outliers, and missing values.
Hero's Tookit: Start Your Rugged DevOps Journey with Nexus, Jenkins and DockerSeniorStoryteller
This document discusses starting a Rugged DevOps journey using Nexus, Jenkins, and Docker. It presents a Rugged Software Factory model with continuous integration/continuous delivery (CI/CD) pipelines for building software from source code in public repositories, running builds and tests, and deploying to development, QA, user acceptance testing, and production environments. The model emphasizes early feedback, policy enforcement through trusted containers, and monitoring for new issues.
This document discusses Teaching Kids Programming (TKP), a global non-profit that provides free, open-source coding curriculum to teach kids ages 10-17 Java and other programming languages. TKP has taught over 4,000 students through 70 trained teachers using a 40-hour Java curriculum that allows students to start coding within 90 seconds. They partner with schools, non-profits, and organizations around the world and provide teaching resources, curriculum, and support to address the lack of computer science education for kids. The goal is to help more young people, especially girls, gain skills in programming and consider careers in technology.
ASEE 2017 WORKSHOP SPECIFIC AND GENERIC PERFORMANCE INDICATORS FOR THE COMPRE...WAJID HUSSAIN
This workshop aims to teach participants how to develop specific and generic performance indicators and rubrics to comprehensively assess student learning as defined by ABET outcomes. The workshop will cover mapping ABET outcomes to Bloom's three learning domains, analyzing outcomes with a model, and correlating outcomes, course objectives, and performance indicators for alignment of assessments and teaching strategies. Participants will learn through case studies with video examples and have hands-on practice developing indicators and rubrics to measure student performance related to outcomes. The goal is to present an effective approach for assessment that supports authentic outcomes-based education and continuous improvement.
This document describes a digitally automated outcomes assessment system developed by the Faculty of Engineering at the Islamic University in Madinah. The key aspects of the system include:
1. Measuring outcomes at the introductory, reinforced, and mastery levels of courses to better track student performance throughout their education.
2. Using evaluation software and a standardized course assessment report format to automate the collection of outcomes data from existing student assessments and evaluations.
3. Classifying specific performance indicators according to Bloom's three domains of learning and three levels of skills to facilitate measuring outcomes across different learning levels.
4. Designing assessments to directly measure individual performance indicators to obtain precise outcomes data for continuous improvement purposes
This document presents a novel methodology for collecting robust assessment data on ABET student learning outcomes and course learning outcomes in shorter time frames. The methodology utilizes EvalTools® software to design unique assessments with high relative coverage (70% or more) of specific performance indicators related to outcomes. Existing assessments are split into questions or sections to obtain high coverage of single indicators. Weighting factors can be applied to assessments. Student performance data is aggregated and categorized as Excellent, Adequate, Minimal or Unsatisfactory to calculate weighted averages for indicators, outcomes, and programs. Continuous improvement is enabled through comprehensive evaluation and closing of action item loops at the course and program level.
ASEE 2017 WORKSHOP SPECIFIC AND GENERIC PERFORMANCE INDICATORS FOR THE COMPRE...WAJID HUSSAIN
This workshop aims to teach participants how to develop specific and generic performance indicators and rubrics to comprehensively assess student learning as defined by ABET outcomes. The workshop will cover mapping ABET outcomes to Bloom's three learning domains, analyzing outcomes with a model, and correlating outcomes, course objectives, and performance indicators for alignment of assessments and teaching strategies. Participants will learn through case studies with video examples and have hands-on practice developing indicators and rubrics to measure student performance related to outcomes. The goal is to present an effective approach for assessment that supports authentic outcomes-based education and continuous improvement.
This document describes a digitally automated outcomes assessment system developed by the Faculty of Engineering at the Islamic University in Madinah. The key aspects of the system include:
1. Measuring outcomes at the introductory, reinforced, and mastery levels of courses to better track student performance throughout their education.
2. Using evaluation software and a standardized course assessment report format to automate the collection of outcomes data from existing student assessments and evaluations.
3. Classifying specific performance indicators according to Bloom's three domains of learning and three levels of skills to facilitate measuring outcomes across different learning levels.
4. Designing assessments to directly measure individual performance indicators to obtain precise outcomes data for continuous improvement purposes
This document presents a novel methodology for collecting robust assessment data on ABET student learning outcomes and course learning outcomes in shorter time frames. The methodology utilizes EvalTools® software to design unique assessments with high relative coverage (70% or more) of specific performance indicators related to outcomes. Existing assessments are split into questions or sections to obtain high coverage of single indicators. Weighting factors can be applied to assessments. Student performance data is aggregated and categorized as Excellent, Adequate, Minimal or Unsatisfactory to calculate weighted averages for indicators, outcomes, and programs. Continuous improvement is enabled through comprehensive evaluation and closing of action item loops at the course and program level.