This document analyzes extreme precipitation events in the Gulf region from 1949-2017, with a focus on Hurricane Harvey. It examines rainfall totals from Harvey and other events using grid boxes of different sizes. Harvey had rainfall exceed 50 inches in some locations over multiple days. The analysis ranks the top 100 rainfall events and finds that 24 of these were caused by tropical cyclones, while most others were associated with fronts. It discusses the challenges of analyzing extreme precipitation at different spatial and temporal scales, from individual thunderstorms to hemispheric weather patterns.
A study on comparision of runoff estimated by Empirical formulae with Measure...Ahmed Ali S D
MAIN PUPOSE OF THIS PPT PRESENTATION IS TO SELECT SIUTABLE DISCHARGE FORMULA FOR A RIVER BASIN TO ESTIMATE RUNOFF ONLY BY USING PRECIPITATION DATA ONLY. IF WE KNOW RAINFALL DATA WE EASILY ESTIMATE FUTURE RUNOFF ALSO.
A study on comparision of runoff estimated by Empirical formulae with Measure...Ahmed Ali S D
MAIN PUPOSE OF THIS PPT PRESENTATION IS TO SELECT SIUTABLE DISCHARGE FORMULA FOR A RIVER BASIN TO ESTIMATE RUNOFF ONLY BY USING PRECIPITATION DATA ONLY. IF WE KNOW RAINFALL DATA WE EASILY ESTIMATE FUTURE RUNOFF ALSO.
Flood frequency analysis of river kosi, uttarakhand, india using statistical ...eSAT Journals
Abstract In the present study, flood frequency analysis has been applied for river Kosi in Uttarakhand. The river Kosi is an important tributary of Ganga river system, which arising from Koshimool near Kausani, Almora district flows on the western side of the study area and to meet at Ramganga River. The annual flood series analysis has been carried out to estimate the flood quantiles at different return period at Kosi barrage site of river Kosi. The statistical approach provided a significant advantage of estimation of flood at any sites in the homogenous region with very less or no data. In the at –site analysis of annual flood series the Normal, Log normal, Pearson type III, Log Pearson type III, Gumbel and Log Gumbel distribution were applied using method of moments . From the analysis of different goodness of fit tests, it has been found that the Log Gumbel distribution with method of moment as parameters estimation found to be the best-fit distribution for Kosi River and other sites in the region. It is recommended that the regional parameters for Kosi Basin may be used only for primary estimation of flood and should be reviewed when more regional data available. Keywords: Flood Frequency Analysis, River Kosi, Annual Peak Flood discharge, Return Period, Goodness of fit Test.
this is my presentation of hydraulic and water resources engineering. I have discussed in this ppt about network density for given rain gauge and calculations and index of witness.
Summary of my most recent results, with a note on potential applications. Was presented at the the 2010 CG/AR (Center for Geosciences and Atmospheric Research) Annual Program Review.
Presentation given by Peter Gibbs, Met Office and BBC broadcast meteorologist, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
Record Setting: The Origins of Extreme Hail on 19 March 2018 during VORTEX-SEDeanMeyer14
Authors Dean Meyer and Ryan Wade. UAH Department of Atmospheric Science. Completed as Dean Meyer's student research as part of the UAH RCEU program at SWIRLL.
It is based on Journal Paper named
"Mukherjee, M.K.2013, ’Flood Frequency Analysis of River Subernarekha, India, Using Gumbel’s extreme Value Distribution’, IJCER,Vol-3,Issue-7,pp-12-18."
I have studied the journal and make a PPT in the following.
I
Hurricanes and Global Warming- Dr. Kerry EmanuelJohn Atkeison
Dr. Kerry Emanuel explains how Global Warming increased the power of hurricanes. Hurricane Katrina is discussed, with the conclusion that Katrina probably would not have had the power to break the New Orleans levees in a pre-Global Warming world. April 2009 webinar presented by the Southern Allicance for Clean Energy (http://www.cleanenergy.org/) and the Gulf Restoration Network (http://healthygulf.org/) SlideCast by John Atkeison of the Alliance for Affordable Energy. There is a very small amount of phone noise.
Flood frequency analysis of river kosi, uttarakhand, india using statistical ...eSAT Journals
Abstract In the present study, flood frequency analysis has been applied for river Kosi in Uttarakhand. The river Kosi is an important tributary of Ganga river system, which arising from Koshimool near Kausani, Almora district flows on the western side of the study area and to meet at Ramganga River. The annual flood series analysis has been carried out to estimate the flood quantiles at different return period at Kosi barrage site of river Kosi. The statistical approach provided a significant advantage of estimation of flood at any sites in the homogenous region with very less or no data. In the at –site analysis of annual flood series the Normal, Log normal, Pearson type III, Log Pearson type III, Gumbel and Log Gumbel distribution were applied using method of moments . From the analysis of different goodness of fit tests, it has been found that the Log Gumbel distribution with method of moment as parameters estimation found to be the best-fit distribution for Kosi River and other sites in the region. It is recommended that the regional parameters for Kosi Basin may be used only for primary estimation of flood and should be reviewed when more regional data available. Keywords: Flood Frequency Analysis, River Kosi, Annual Peak Flood discharge, Return Period, Goodness of fit Test.
this is my presentation of hydraulic and water resources engineering. I have discussed in this ppt about network density for given rain gauge and calculations and index of witness.
Summary of my most recent results, with a note on potential applications. Was presented at the the 2010 CG/AR (Center for Geosciences and Atmospheric Research) Annual Program Review.
Presentation given by Peter Gibbs, Met Office and BBC broadcast meteorologist, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
Record Setting: The Origins of Extreme Hail on 19 March 2018 during VORTEX-SEDeanMeyer14
Authors Dean Meyer and Ryan Wade. UAH Department of Atmospheric Science. Completed as Dean Meyer's student research as part of the UAH RCEU program at SWIRLL.
It is based on Journal Paper named
"Mukherjee, M.K.2013, ’Flood Frequency Analysis of River Subernarekha, India, Using Gumbel’s extreme Value Distribution’, IJCER,Vol-3,Issue-7,pp-12-18."
I have studied the journal and make a PPT in the following.
I
Hurricanes and Global Warming- Dr. Kerry EmanuelJohn Atkeison
Dr. Kerry Emanuel explains how Global Warming increased the power of hurricanes. Hurricane Katrina is discussed, with the conclusion that Katrina probably would not have had the power to break the New Orleans levees in a pre-Global Warming world. April 2009 webinar presented by the Southern Allicance for Clean Energy (http://www.cleanenergy.org/) and the Gulf Restoration Network (http://healthygulf.org/) SlideCast by John Atkeison of the Alliance for Affordable Energy. There is a very small amount of phone noise.
We present a survey of computational and applied mathematical techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties.
Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.
Numerous studies have found an average increase in extreme precipitation for both the U.S. and Northern Hemisphere mid-latitude land areas, consistent with the expectations arising from the observed increase in greenhouse gas concentrations (now more than 40% above pre-industrial levels). However, there are important regional variations in these trends that are not fully explained. These trend studies are typically based on direct analyses of observational station data. Such analyses confront multiple challenges, such as incomplete data and uneven spatial coverage of stations. Central scientific questions related to this general finding are: Are there changes in weather system phenomenology that are contributing to this observed increase? What is the contribution of increases in atmospheric water vapor? There are also questions related to application of potential future changes in planning. Because of the rarity (by definition) of extreme events, trends are mostly found only when aggregating over space. When would we expect to see a signal at the local level? What are the uncertainties surrounding future changes and their potential incorporation into future design? Further development of statistical/mathematical methods, or innovative application of existing methods, is desirable to aid scientists in exploring these central scientific questions. This talk will describe characteristics of the observation record and the issues surrounding the above questions.
Hurricane Threat and Risk Analysis in Rhode Islandriseagrant
Hurricane Threat and Risk Analysis in Rhode Island presented at the July 24, 2014 Beach Special Area Management Plan Stakeholder meeting.
Dr. Isaac Ginis, URI Graduate School of Oceanography
View the video here: http://new.livestream.com/universityofrhodeisland/StormRecoveryRI
WATER PRESENT AT OUR EARTH IN THE FORM OF CONDENSATION ,LIQUID AND PROVIDE RAINFALL INTENSITIES AFTER VARIABLE PERIODS OF TIME CAN BE ANALYSED BY VARIOUS METHODS ,GIVEN AND SHOWN IN THIS PPT
Dr. Cynthia Rosenzweig, Senior Research Scientist, NASA Goddard Institute for Space Studies Senior Research Scientist, Earth Institute at Columbia University Co-Chair Mayor Bloomberg’s Climate Change Commission Co-Director Urban Climate Change Research Network (UCCRN); National Institute for Coastal & Harbor Infrastructure, John F. Kennedy Center, Boston, Nov. 12, 2013: "The Triple Threat of Rising Sea Levels, Extreme Storms and Aging Infrastructure: Coastal Community Responses and The Federal Role" See http://www.nichiusa.org or http://www.nichi.us
ICLR Forecast Webinar: 2014 Canadian hurricane season (June 20, 2014) glennmcgillivray
On June 20, 2014, the Institute for Catastrophic Loss Reduction (ICLR) conducted a Webinar with Bob Robichaud, Environment Canada's Warning Preparedness Meteorologist for Eastern Canada.
The interactive webinar included a review of the 2013 North-Atlantic hurricane season and concluded with a seasonal outlook for the 2014 North-Atlantic hurricane season.
Robichaud received his B.Sc. in meteorology from Lyndon State College, Vermont in 1995. After a few years as a weather forecaster in the private sector, he joined Environment Canada in 1998 as an aviation forecaster in Gander NL where he eventually became aviation weather program manager for Atlantic Canada. In 2003, Robichaud managed the National Aviation Weather Services contract with NAV CANADA and he has
also written a book on aviation weather for eastern Canada.
Robichaud moved to Halifax in 2004 to fill the new warning preparedness meteorologist role in Atlantic Canada where his primary focus is working closely with emergency management officials on a variety of different weather related issues including training,
exercising and support during actual weather events.
Recently, the machine learning community has expressed strong interest in applying latent variable modeling strategies to causal inference problems with unobserved confounding. Here, I discuss one of the big debates that occurred over the past year, and how we can move forward. I will focus specifically on the failure of point identification in this setting, and discuss how this can be used to design flexible sensitivity analyses that cleanly separate identified and unidentified components of the causal model.
I will discuss paradigmatic statistical models of inference and learning from high dimensional data, such as sparse PCA and the perceptron neural network, in the sub-linear sparsity regime. In this limit the underlying hidden signal, i.e., the low-rank matrix in PCA or the neural network weights, has a number of non-zero components that scales sub-linearly with the total dimension of the vector. I will provide explicit low-dimensional variational formulas for the asymptotic mutual information between the signal and the data in suitable sparse limits. In the setting of support recovery these formulas imply sharp 0-1 phase transitions for the asymptotic minimum mean-square-error (or generalization error in the neural network setting). A similar phase transition was analyzed recently in the context of sparse high-dimensional linear regression by Reeves et al.
Many different measurement techniques are used to record neural activity in the brains of different organisms, including fMRI, EEG, MEG, lightsheet microscopy and direct recordings with electrodes. Each of these measurement modes have their advantages and disadvantages concerning the resolution of the data in space and time, the directness of measurement of the neural activity and which organisms they can be applied to. For some of these modes and for some organisms, significant amounts of data are now available in large standardized open-source datasets. I will report on our efforts to apply causal discovery algorithms to, among others, fMRI data from the Human Connectome Project, and to lightsheet microscopy data from zebrafish larvae. In particular, I will focus on the challenges we have faced both in terms of the nature of the data and the computational features of the discovery algorithms, as well as the modeling of experimental interventions.
Bayesian Additive Regression Trees (BART) has been shown to be an effective framework for modeling nonlinear regression functions, with strong predictive performance in a variety of contexts. The BART prior over a regression function is defined by independent prior distributions on tree structure and leaf or end-node parameters. In observational data settings, Bayesian Causal Forests (BCF) has successfully adapted BART for estimating heterogeneous treatment effects, particularly in cases where standard methods yield biased estimates due to strong confounding.
We introduce BART with Targeted Smoothing, an extension which induces smoothness over a single covariate by replacing independent Gaussian leaf priors with smooth functions. We then introduce a new version of the Bayesian Causal Forest prior, which incorporates targeted smoothing for modeling heterogeneous treatment effects which vary smoothly over a target covariate. We demonstrate the utility of this approach by applying our model to a timely women's health and policy problem: comparing two dosing regimens for an early medical abortion protocol, where the outcome of interest is the probability of a successful early medical abortion procedure at varying gestational ages, conditional on patient covariates. We discuss the benefits of this approach in other women’s health and obstetrics modeling problems where gestational age is a typical covariate.
Difference-in-differences is a widely used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale-dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, Angrist and Pischke (2009) show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting.
We develop sensitivity analyses for weak nulls in matched observational studies while allowing unit-level treatment effects to vary. In contrast to randomized experiments and paired observational studies, we show for general matched designs that over a large class of test statistics, any valid sensitivity analysis for the weak null must be unnecessarily conservative if Fisher's sharp null of no treatment effect for any individual also holds. We present a sensitivity analysis valid for the weak null, and illustrate why it is conservative if the sharp null holds through connections to inverse probability weighted estimators. An alternative procedure is presented that is asymptotically sharp if treatment effects are constant, and is valid for the weak null under additional assumptions which may be deemed reasonable by practitioners. The methods may be applied to matched observational studies constructed using any optimal without-replacement matching algorithm, allowing practitioners to assess robustness to hidden bias while allowing for treatment effect heterogeneity.
The world of health care is full of policy interventions: a state expands eligibility rules for its Medicaid program, a medical society changes its recommendations for screening frequency, a hospital implements a new care coordination program. After a policy change, we often want to know, “Did it work?” This is a causal question; we want to know whether the policy CAUSED outcomes to change. One popular way of estimating causal effects of policy interventions is a difference-in-differences study. In this controlled pre-post design, we measure the change in outcomes of people who are exposed to the new policy, comparing average outcomes before and after the policy is implemented. We contrast that change to the change over the same time period in people who were not exposed to the new policy. The differential change in the treated group’s outcomes, compared to the change in the comparison group’s outcomes, may be interpreted as the causal effect of the policy. To do so, we must assume that the comparison group’s outcome change is a good proxy for the treated group’s (counterfactual) outcome change in the absence of the policy. This conceptual simplicity and wide applicability in policy settings makes difference-in-differences an appealing study design. However, the apparent simplicity belies a thicket of conceptual, causal, and statistical complexity. In this talk, I will introduce the fundamentals of difference-in-differences studies and discuss recent innovations including key assumptions and ways to assess their plausibility, estimation, inference, and robustness checks.
We present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. Specific topics covered in this talk include several recent projects with robust and flexible methods developed for the above research area. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (T-RL) method, which builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through a purity measure constructed with augmented inverse probability weighted estimators. T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. At the end of this talk, we will also present a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs.
A fundamental feature of evaluating causal health effects of air quality regulations is that air pollution moves through space, rendering health outcomes at a particular population location dependent upon regulatory actions taken at multiple, possibly distant, pollution sources. Motivated by studies of the public-health impacts of power plant regulations in the U.S., this talk introduces the novel setting of bipartite causal inference with interference, which arises when 1) treatments are defined on observational units that are distinct from those at which outcomes are measured and 2) there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. Interference in this setting arises due to complex exposure patterns dictated by physical-chemical atmospheric processes of pollution transport, with intervention effects framed as propagating across a bipartite network of power plants and residential zip codes. New causal estimands are introduced for the bipartite setting, along with an estimation approach based on generalized propensity scores for treatments on a network. The new methods are deployed to estimate how emission-reduction technologies implemented at coal-fired power plants causally affect health outcomes among Medicare beneficiaries in the U.S.
Laine Thomas presented information about how causal inference is being used to determine the cost/benefit of the two most common surgical surgical treatments for women - hysterectomy and myomectomy.
We provide an overview of some recent developments in machine learning tools for dynamic treatment regime discovery in precision medicine. The first development is a new off-policy reinforcement learning tool for continual learning in mobile health to enable patients with type 1 diabetes to exercise safely. The second development is a new inverse reinforcement learning tools which enables use of observational data to learn how clinicians balance competing priorities for treating depression and mania in patients with bipolar disorder. Both practical and technical challenges are discussed.
The method of differences-in-differences (DID) is widely used to estimate causal effects. The primary advantage of DID is that it can account for time-invariant bias from unobserved confounders. However, the standard DID estimator will be biased if there is an interaction between history in the after period and the groups. That is, bias will be present if an event besides the treatment occurs at the same time and affects the treated group in a differential fashion. We present a method of bounds based on DID that accounts for an unmeasured confounder that has a differential effect in the post-treatment time period. These DID bracketing bounds are simple to implement and only require partitioning the controls into two separate groups. We also develop two key extensions for DID bracketing bounds. First, we develop a new falsification test to probe the key assumption that is necessary for the bounds estimator to provide consistent estimates of the treatment effect. Next, we develop a method of sensitivity analysis that adjusts the bounds for possible bias based on differences between the treated and control units from the pretreatment period. We apply these DID bracketing bounds and the new methods we develop to an application on the effect of voter identification laws on turnout. Specifically, we focus estimating whether the enactment of voter identification laws in Georgia and Indiana had an effect on voter turnout.
We study experimental design in large-scale stochastic systems with substantial uncertainty and structured cross-unit interference. We consider the problem of a platform that seeks to optimize supply-side payments p in a centralized marketplace where different suppliers interact via their effects on the overall supply-demand equilibrium, and propose a class of local experimentation schemes that can be used to optimize these payments without perturbing the overall market equilibrium. We show that, as the system size grows, our scheme can estimate the gradient of the platform’s utility with respect to p while perturbing the overall market equilibrium by only a vanishingly small amount. We can then use these gradient estimates to optimize p via any stochastic first-order optimization method. These results stem from the insight that, while the system involves a large number of interacting units, any interference can only be channeled through a small number of key statistics, and this structure allows us to accurately predict feedback effects that arise from global system changes using only information collected while remaining in equilibrium.
We discuss a general roadmap for generating causal inference based on observational studies used to general real world evidence. We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for realistic (i.e, infinite dimensional) statistical models. TMLE is a two stage procedure that first involves using ensemble machine learning termed super-learning to estimate the relevant stochastic relations between the treatment, censoring, covariates and outcome of interest. The super-learner allows one to fully utilize all the advances in machine learning (in addition to more conventional parametric model based estimators) to build a single most powerful ensemble machine learning algorithm. We present Highly Adaptive Lasso as an important machine learning algorithm to include.
In the second step, the TMLE involves maximizing a parametric likelihood along a so-called least favorable parametric model through the super-learner fit of the relevant stochastic relations in the observed data. This second step bridges the state of the art in machine learning to estimators of target estimands for which statistical inference is available (i.e, confidence intervals, p-values etc). We also review recent advances in collaborative TMLE in which the fit of the treatment and censoring mechanism is tailored w.r.t. performance of TMLE. We also discuss asymptotically valid bootstrap based inference. Simulations and data analyses are provided as demonstrations.
We describe different approaches for specifying models and prior distributions for estimating heterogeneous treatment effects using Bayesian nonparametric models. We make an affirmative case for direct, informative (or partially informative) prior distributions on heterogeneous treatment effects, especially when treatment effect size and treatment effect variation is small relative to other sources of variability. We also consider how to provide scientifically meaningful summaries of complicated, high-dimensional posterior distributions over heterogeneous treatment effects with appropriate measures of uncertainty.
Climate change mitigation has traditionally been analyzed as some version of a public goods game (PGG) in which a group is most successful if everybody contributes, but players are best off individually by not contributing anything (i.e., “free-riding”)—thereby creating a social dilemma. Analysis of climate change using the PGG and its variants has helped explain why global cooperation on GHG reductions is so difficult, as nations have an incentive to free-ride on the reductions of others. Rather than inspire collective action, it seems that the lack of progress in addressing the climate crisis is driving the search for a “quick fix” technological solution that circumvents the need for cooperation.
This seminar discussed ways in which to produce professional academic writing, from academic papers to research proposals or technical writing in general.
Machine learning (including deep and reinforcement learning) and blockchain are two of the most noticeable technologies in recent years. The first one is the foundation of artificial intelligence and big data, and the second one has significantly disrupted the financial industry. Both technologies are data-driven, and thus there are rapidly growing interests in integrating them for more secure and efficient data sharing and analysis. In this paper, we review the research on combining blockchain and machine learning technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more researches on deeper integration of the two promising technologies.
In this talk, we discuss QuTrack, a Blockchain-based approach to track experiment and model changes primarily for AI and ML models. In addition, we discuss how change analytics can be used for process improvement and to enhance the model development and deployment processes.
More from The Statistical and Applied Mathematical Sciences Institute (20)
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Chapter 3 - Islamic Banking Products and Services.pptx
Climate Extremes Workshop - Historical Perspective on Hurricane Harvey Rainfall - Ken Kunkel, May 16, 2018
1. 1
Kenneth E. Kunkel
NOAA Cooperative Institute for Climate and Satellites, North
Carolina State University
Historical Perspective on
Hurricane Harvey Rainfall
2. 2
Hurricane Harvey
• One focus of working group
o Late August 2017
o Multi-day rainfall exceeded 50 inches in some
locations
o This reached Probable Maximum Precipitation
levels
o Massive flooding
6. 6
Extreme Precipitation – climate analysis
• Define an overlapping grid of cells separated by 1/10 degree in latitude and 1/10
degree in longitude covering longitudes 80–100°W and latitudes 25–35 °N (large
blue box on following figure).
• Within the grid, consider all possible 2-degree by 2-degree boxes (all boxes like the
red box in the figure). This represents an approximate area of 40,000 km2.
• Compute daily precipitation for 1949-present as a simple average of all stations in
each box. All boxes that are wholly or partly over water are not included in this
analysis.
• For each grid box, identify top 5-day precipitation totals.
• Pool everything together and identify the top 100 events for 1949–2017 across the
entire region, ignoring those that overlap in time or space with larger event.
• Rank and plot these.
• Also did same analysis on other grid sizes from 1° to 3°
17. 17
• Complex temporal and spatial coherence and
variability of extreme precipitation events –
– Individual thunderstorm cells – hour, a few km
– Thunderstorm complexes – a few hours, tens-100+ km
– Spiral rain bands in hurricanes – a few hours, tens-
100+ km
– Low pressure wave – day, 100s of km
– Hurricanes – day, 100s of km
– Synoptic low pressure system – days, 1000+ km
– Hemispheric jet stream wave patterns – weeks, 1000s
of km
The Challenge
17
18. 18
• Weather system based inquiry of historical
trends and future changes
– Statistical trend significance tests
– Automated tools
– How do we incorporate this approach into the
standard statistical approaches for estimating
design values
Research
18