An Examination Of The Feasibility Of Converting Atmospheric Or Solar Energy To Stored Energy In The Form Of Electrolytically Generated Hydrogen Using Low Or Intermediate Technology.

The object of this work is to establish if hydrogen can satisfy the fuel requirements of isolated, often third world, communities; also to consider how hydrogen can be produced electrolytically, using mainly low and intermediate technology. A study of the literature has shown that hydrogen could be used a) to provide heat using a conventional gas burner or catalytic heater and b), to do mechanical work via an internal combustion engine or fuel cell. Electricity required for hydrogen production on a small scale can be generated by wind turbines and water wheels, which themselves involve only low technology fabrication. Larger scale operations necessitate use of more sophisticated equipment, needing to be manufactured elsewhere. Solar cells, although requiring high technology manufacture, are robust, durable and readily installed. Production of hydrogen can be achieved using tank, or filter press, electrolytic cells; the latter can be made with solid polymer electrolytes, in place of alkaline solutions. Performance and operational safety of electrolytic cells have been assessed. The hydrogen produced can be stored either in low pressure gas holders, high pressure gas cylinders or chemically combined, as metal hydrides. The amount of power that could be produced from stored hydrogen by linking existing technological processes has been calculated for selected conditions. Results show that about 25% of the original electrical energy could be converted to useful work using the internal combustion engine, compared with 38% using a fuel cell. Other means of energy storage have been considered, either as an alternative to, or in association with hydrogen production. Lastly, energy requirements of two contrasting social groups have been assessed. It is concluded that use of hydrogen to store energy is technically feasible for isolated communities with no ready access to fossil fuels

Science in the New Zealand Curriculum e-in-science

This milestone report explores some innovative possibilities for e-in-science practice to enhance teacher capability and increase student engagement and achievement. In particular, this report gives insights into how e-learning might be harnessed to help create a future-oriented science education programme. “Innovative” practices are considered to be those that integrate (or could integrate) digital technologies in science education in ways that are not yet commonplace. “Future-oriented education” refers to the type of education that students in the “knowledge age” are going to need. While it is not yet clear exactly what this type of education might look like, it is clear that it will be different from the current system. One framework used to differentiate between these kinds of education is the evolution of education from Education 1.0 to Education 2.0 and 3.0 (Keats & Schmidt, 2007). Education 1.0, like Web 1.0, is considered to be largely a one-way process. Students “get” knowledge from their teachers or other information sources. Education 2.0, as defined by Keats and Schmidt, happens when Web 2.0 technologies are used to enhance traditional approaches to education. New interactive media, such as blogs, social bookmarking, etc. are used, but the process of education itself does not differ significantly from Education 1.0. Education 3.0, by contrast, is characterised by rich, cross-institutional, cross-cultural educational opportunities. The learners themselves play a key role as creators of knowledge artefacts, and distinctions between artefacts, people and processes become blurred, as do distinctions of space and time. Across these three “generations”, the teacher’s role changes from one of knowledge source (Education 1.0) to guide and knowledge source (Education 2.0) to orchestrator of collaborative knowledge creation (Education 3.0). The nature of the learner’s participation in the learning also changes from being largely passive to becoming increasingly active: the learner co-creates resources and opportunities and has a strong sense of ownership of his or her own education. In addition, the participation by communities outside the traditional education system increases. Building from this framework, we offer our own “framework for future-oriented science education” (see Figure 1). In this framework, we present two continua: one reflects the nature of student participation (from minimal to transformative) and the other reflects the nature of community participation (also from minimal to transformative). Both continua stretch from minimal to transformative participation. Minimal participation reflects little or no input by the student/community into the direction of the learning—what is learned, how it is learned and how what is learned will be assessed. Transformative participation, in contrast, represents education where the student or community drives the direction of the learning, including making decisions about content, learning approaches and assessment

Missional readiness amoung Christian men : how a study on John 13-17 can impact missional readiness

https://place.asburyseminary.edu/ecommonsatsdissertations/2459/thumbnail.jp

Semi-nonparametric Estimation of Operational Risk Capital with Extreme Loss Events

Bank operational risk capital modeling using the Basel II advanced measurement approach (AMA) often lead to a counter-intuitive capital estimate of value at risk at 99.9% due to extreme loss events. To address this issue, a flexible semi-nonparametric (SNP) model is introduced using the change of variables technique to enrich the family of distributions to handle extreme loss events. The SNP models are proved to have the same maximum domain of attraction (MDA) as the parametric kernels, and it follows that the SNP models are consistent with the extreme value theory peaks over threshold method but with different shape and scale parameters from the kernels. By using the simulation dataset generated from a mixture of distributions with both light and heavy tails, the SNP models in the Frechet and Gumbel MDAs are shown to fit the tail dataset satisfactorily through increasing the number of model parameters. The SNP model quantile estimates at 99.9 percent are not overly sensitive towards the body-tail threshold change, which is in sharp contrast to the parametric models. When applied to a bank operational risk dataset with three Basel event types, the SNP model provides a significant improvement in the goodness of fit to the two event types with heavy tails, yielding an intuitive capital estimate that is in the same magnitude as the event type total loss. Since the third event type does not have a heavy tail, the parametric model yields an intuitive capital estimate, and the SNP model cannot provide additional improvement. This research suggests that the SNP model may enable banks to continue with the AMA or its partial use to obtain an intuitive operational risk capital estimate when the simple non-model based Basic Indicator Approach or Standardized Approach are not suitable per Basel Committee Banking Supervision OPE10 (2019).Comment: There are 32 pages, including tables, figures, appendix and reference. The research was presented at the MATLAB Annual Computational Finance Conference, September 27-30, 202