Jim Allen : radical drama beyond 'days of hope'

Due to a desire to establish television as a serious medium, television drama has often been seen as a forum for writers, with names such as David Mercer, Dennis Potter and Trevor Griffiths identified by critics as the driving force, or auteur, behind the works that bear their names rather than, as in much writing about film, the director. However, while this has been so, there are also many examples of writers whose contribution to television writing has been much less celebrated, often due to their close collaboration with a high-profile director who in many critics’ view remains the most influential contributor to the final piece of work. One practitioner who arguably has failed to get the critical credit he is due is Jim Allen, a writer still perhaps best known for his work with one such high-profile director, Ken Loach

Teacher Retention Policy Coherence: An Analysis of Policies and Practices Across Federal, State, and Division Levels

As part of a larger MERC study, this report provides an overview of federal, state, and regional policies and practices relevant to teacher retention. Using key informant interviews and document analysis, the report addresses the following research questions: What teacher retention policies exist at the federal, state, and local levels? How are these policies structured at the state level and local levels? How do teacher retention policies vary across MERC divisions? Following the findings, the report presents recommendations for policy and practice

Taking deliberative research online:Lessons from four case studies

Researchers using deliberative techniques tend to favour in-person processes. However, the covid-19 pandemic has added urgency to the question of whether meaningful deliberative research is possible in an online setting. This paper considers the reasons for taking deliberation online, including bringing people together more easily; convening international events; and reducing the environmental impact of research. It reports on four case studies: a set of stakeholder workshops considering greenhouse gas removal technologies, convened online in 2019, and online research workshops investigating local climate strategies; as well as two in-person processes which moved online due to covid-19: Climate Assembly UK, a Citizens’ Assembly on climate change, and the Lancaster Citizens’ Jury on Climate Change. It sets out learnings from these processes, concluding that deliberation online is substantively different from in-person meetings, but can meet the requirements of deliberative research, and can be a rewarding and useful process for participants and researchers alike

The Grizzly, February 14, 1986

Eckman Speaks on Corporate Takeovers • Students to Lose Booze? • Alcohol Restrictions Plague Neighboring Campuses • Campus Memo: Get and Stay Involved • Proposal Raises Serious Questions • Students Speak Out On Alcohol • Profile: Dr. Fago • Editorial: Drug Use Could Fill Vacuum • Letters: UC. Should Get Out of the Business; Alpha Chi Sigma Needs Support; Sauna Controversy Heats up; Fire Alarms are not Toys!; False Alarm Jeopardized Safety • Nursing Homes Part III: MCGRC\u27s Sordid Past • Bears Face Widener in the Big Game • Racich Praises Grapplers • Lady Swimmers Top Susquehanna • Women\u27s B-ball Finale • Confident \u27Mers\u27 Win Again • Track Records Set at Delaware • Heather Camp: Swimming\u27s Leading Lady • Forum: Human Rights in Latin America • Wenhold Awarded for Service • Ursinus in California • U.S. Trade Policy • A Peek at U.C.\u27s Favorite TVhttps://digitalcommons.ursinus.edu/grizzlynews/1157/thumbnail.jp

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping

Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture

In the smart agriculture system based on green-based technology of artificial intelligence (AI), flooding can be predicted early by forecasting the water surface and good agricultural irrigation. The process of rising and falling of the water surface in a water basin area can be explained theoretically, but since there are many related variables and the complexity of dependencies between variables, the mathematical model is difficult to construct. Forecasting water surface in the field of irrigation needs too many variable parameters, such as cross-sectional area, depth, volume of rivers and so on. Based on patterns in each period, forecasting can be done using a statistical method and AI. This study uses the support vector regression (SVR) method, regression, multiple linear regression, and algorithm backpropagation, all compared to one another. The results of tests carried out between SVR and multiple linear regression show that SVR is superior. This can be seen from the result of the mean square error (MSE) obtained for each method. SVR 0.03 and for multiple linear regression, 0.05. The result is also supported by the best MSE result in the regression method, which is 0.338, and the best MSE value in artificial neural network (ANN), which is 0.428. Keywords: forecasting; smart; agriculture; system; wate