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F. Fraser, M.B. B.A.O., M.B.B.Ch., Ph.D.

Assistant Professor, University of Nebraska College of Medicine

Expanding the functionality of demand response and inherent storage provides opportunities for stakeholder action, including: 9. Flexibility of resources, which can be either generation or flexible demand or storage, is generally defined as the ability to change states quickly. Thus fast ramping and short start-up, shut-down, and up-/down-times are measures of flexibility. It is also necessary to inform power system operators which practices work and which do not by disseminating findings via publications, workshops, and conferences. This activity provides the scientific background necessary to help promulgate operating best practices, such as sub-hourly energy scheduling and balancing over larger areas, which have the potential to significantly reduce wind integration costs. This activity also illustrates the need for more flexible resources such as fast-starting conventional generation and increased demand response, which can also substantially reduce wind integration costs. Increased wind integration levels, appropriate amounts of operating reserves, reduced curtailment, lower integration costs. Note: Increased balancing area size and faster scheduling reduce regulation requirements, and therefore reduce power system operating costs. Figure 4-1 demonstrates that requirements for regulation-a relatively expensive balancing service-are reduced substantially as balancing area size is increased and the dispatch interval is decreased. Analysis may be required to quantify benefits in regions that are not already implementing sub-hourly energy scheduling or that operate with small balancing areas. Such techniques and studies should seek to accurately encompass multiple balancing areas and regions as well as help promulgate best practices, such as optimization of flexibility reserve. These advanced methods can be used to address technology neutrality concerns, assuring that all technologies are treated equally in reliability rules and market structures. Allows power system operator access to additional flexibility from wind plants, when it is economical or necessary for reliability. Key Themes: Reduce Wind Costs; Expand Developable Areas Markets Addressed: Land; Offshore Action 5. The Wind Vision Study Scenario includes the construction and integration of multiple offshore wind plants. Each project is individually responsible for the interconnection that brings power to shore. These power delivery systems will be built on public waterways and connected to the on-shore grid infrastructure. Under this activity, aggregating the power export systems for multiple offshore facilities is expected to lower the cost of offshore transmission and minimize impacts to coastal ecosystems where cables are routed. Several strategies are under consideration in the United States to develop optimized architectures for the orderly construction of an offshore grid. As part of this effort, close coordination between state and federal agencies is needed to streamline the offshore permitting process and reduce regulatory uncertainty. Wind turbines are being developed that can help with voltage control, regulation (automatic generation control), synthetic inertial response, and frequency regulation. Some of these features are untested, and, in many parts of the United States, wind turbine owners and operators have no incentive to provide these services because no market mechanism exists to pay the owners for providing these added capabilities. There is also a need to provide controls at the wind plant level, which would allow wind plants to behave more like conventional generation. The wind stakeholder community can collaborate with others to develop needed control strategies at the wind plant level, building upon newly emerging turbine capabilities. The grid effects of distributed wind generation, alone and integrated with other forms of distributed generation, need to be better understood in order to facilitate mitigation and removal of integration barriers and to accelerate deployment. Better distribution system modeling tools, informed utilities, and standards development can reduce costs and increase confidence in distributed wind integration. This revision will establish a framework for distributed generation that supports the grid and allows high levels of penetration. Increased utility confidence in offshore wind and reduced cost of offshore wind due to aggregation of power, lower environmental footprint, reduced transmission congestion, and possible higher capacity value. Key Themes: Reduce Wind Costs; Expand Developable Areas Markets Addressed: Offshore Offshore wind electricity will typically be injected into heavily congested urban centers. As such, the integration of offshore wind in certain markets will have global utility effects that reduce the market price of electricity, at least for the near term. The capacity value of offshore wind differs from that of land-based wind and, in some regions, provides stronger matching with load during peak summer months. Both of these effects significantly influence the economics of offshore wind technology for the Wind Vision Study Scenario.

Wind technology improvements are characterized through a combination of capital cost reductions, operations expenditure cost reductions, and capacity factor improvements. Figure 3-11 also shows the multiple variable or combined sensitivities analyzed including the Favorable (Low Wind Costs coupled with High Fuel Cost) and Unfavorable (High Wind Costs coupled with Low Fuel Cost) conditions, respectively. When considered together, these multivariable sensitivities are referred to as the Combined sensitivities. The seven Study Scenario sensitivities are compared with three Baseline Scenario sensitivities. The Central Baseline Scenario provides a reference for the three Study Scenario sensitivities that rely on the central fossil fuel cost case, and the Baseline Scenario sensitivities under High and Low Fuel Cost assumptions provide references for the Study Scenario sensitivities with the corresponding fuel cost assumptions. Baseline Scenario sensitivities with different wind technology improvement trajectories are not needed because no new wind capacity is installed. Many of the results presented in this chapter focus on the full range of analysis sensitivities. Reported impacts including wind capacity additions, economic impacts, electric system impacts, and transmission and grid integration impacts rely on data from the full set of scenario sensitivities modeled. Even in those instances in which impacts are calculated based on the Central Study Scenario, a range of results is presented to reflect the uncertainties associated with these impacts. Impacts calculated from the full set of scenarios are clearly distinguished from those calculated from the Central Study Scenario alone. This distinction is important, but does introduce challenges for direct comparisons across the reported impact metrics. These scenarios and their respective sensitivities provide a means to quantify the impacts of higher wind deployment. In particular, the scenario framework is designed to provide general bounding assessments specific to wind technology and fossil fuel market variables. Ultimately, however, this framework primarily demonstrates the changes in the results as a function of those variables alone. Other market factors, including electricity demand growth and non-wind power costs, can also impact results and introduce uncertainty; however, modeling the sensitivity of results to these factors is outside the scope of this particular scenario analysis. In addition, other than the prescribed wind penetration levels in the Study Scenario, the modeling analysis only considers existing policies as enacted as of January 1, 2014. Proposed or new legislation or regulations that would impact future wind deployment are excluded from the results and analysis reported here. The assumption of no new policies, beyond the prescribed wind penetration levels, does not represent policy forecasts or recommendations. It is important to note that-while the Wind Vision analysis is policy-agnostic and focused entirely on the electric sector-the impacts, costs, and benefits of the Study Scenario and respective sensitivities will be dependent on the policy and market factors used to yield wind deployment levels consistent with the Wind Vision, and on larger economy interactions. Alternative approaches to reaching the same deployment levels, through policy drivers and/or market dynamics, would be expected to yield different results. Research has generally found that energy policies that are specifically intended to internalize so-called "external" costs. Although the Central Study Scenario reflects a central estimate, it has not been assigned a higher probability (in fact, no probabilities are explicitly assigned to any single scenario) and should not be construed as a most likely outcome. It is simply the central estimate given the range of potential input variables that exist as of 2013. Inputs of particular importance for the Wind Vision analysis include generation capacity cost and performance from 2014 to 2050 for wind technologies, other renewable technologies, and non-renewable technologies. Key market variables that also serve as important modeling inputs through 2050 include anticipated generation plant retirements, future load growth, and fossil fuel prices. This section summarizes the values applied for the inputs and, where applicable, describes the methods by which these inputs were developed. Data reflect costs to build and operate new plants only and apply to the Study Scenario and the Baseline Scenario. For supplemental detail on these inputs, as well as operating costs associated with the existing plants, transmission costs, and storage costs, see Appendices G and H.

Research would include the appropriate use of validated screening instruments, testing to rule out alternative diagnoses, and laboratory tests for alcohol and other drug use. Initial Assessment: Areas for further research in alcohol withdrawal assessment include the development and testing of scales to predict the risk of alcohol withdrawal (and the risk of severe withdrawal). Further research on assessing the risk of severe alcohol withdrawal would include the relative importance of predictors, as well as additional research on individual risk factors for complicated withdrawal/complications of withdrawal. Furthermore, for clinicians in ambulatory settings, further research on triaging patients based on risk would help guide clinical practice. In particular, evidence-based improvements in the assessment of the recovery environment and available social support networks would be helpful to determine appropriateness for ambulatory management. Ambulatory Management: Further research on optimal monitoring intervals at various levels of care would be useful in guiding clinical practice. The literature revealed a wide variety of recommendations for monitoring frequency and intensity. While the importance of supportive care is widely recognized, it is not well-researched. Additional research on individualizing nutritional supplementation and alternative interventions for symptom management. Comparative effectiveness studies of various models and strategies for linkages to care would be particularly helpful, as would investigation into the moderating or mediating influence of patient and setting factors. Inpatient Management: Several promising medications have not yet been well-researched. Hence, large, well-controlled studies of specific medications would be helpful in expanding the options for individualization of alcohol withdrawal management. Although the Guideline Committee agreed with the one study conducted by Sellers in 1983, there is insufficient evidence to support the use of other medications to control for alcohol-induced psychosis during withdrawal. Further research on differentiating between alcohol-induced intoxication and alcohol-induced withdrawal as well as the management for both is warranted. Specific Settings and Populations: the literature and Guideline Committee agreed that clinically significant alcohol withdrawal is rare among adolescents, and this special population was beyond the scope of the current guideline. However, further research on potential modifications to alcohol withdrawal management protocols for adolescents would be useful. Other special populations in need of further research include the elderly and criminal justice populations. Recurrent Detoxification May Elevate Alcohol Craving as Measured by the Obsessive Compulsive Drinking Scale. Multiple Previous Detoxifications are Associated with Less Responsive Treatment and Heavier Drinking During an Index Outpatient Detoxification. Clinical Guidelines for Withdrawal Management and Treatment of Drug Dependence in Closed Setting. Practice Guideline for the Pharmacological Treatment of Patients with Alcohol Use Disorder. Alcohol-Use Disorders: Diagnosis, Assessment and Management of Harmful Drinking and Alcohol Dependence. A Strategy of Escalating Doses of Benzodiazepines and Phenobarbital Administration Reduces the Need for Mechanical Ventilation in Delirium Tremens. Resistant alcohol withdrawal: does an unexpectedly large sedative requirement identify these patients early? Improved Outcomes in Patients With Head and Neck Cancer Using a Standardized Care Protocol for Postoperative Alcohol Withdrawal. Admission Criteria and Patient Placement Guidelines for Ambulatory Alcohol Medical Detoxification. Guidelines for Biological Treatment of Substance Use and Related Disorders, Part 1: Alcoholism. Predictors of Severe Alcohol Withdrawal Syndrome: A Systematic Review and Meta-Analysis.

For many instructors, quantitative skills were not part of their own background in biology. For this reason, Bio Intro Lab01 01 a few specific examples of how quantitative skills inform various areas of biology are included to provide background context. The hope is that these examples will serve to stimulate your own further investigations into quantitative biology with your students. Chapter 5 T25 Counting, Measuring, and Calculating Counting is one of the most common activities of biological observation, yet it is often undervalued. For example, each of the following is a routine operation: counting colonies on a petri plate, fruit flies with a particular set of traits, the number of rings in a tree slice, the trichome hairs on a petiole of a Wisconsin Fast Plant, or the number of stomata on a leaf. However, more difficult counts involve the number of individuals in a population, such as ants in an anthill, the number of red blood cells in a milliliter of blood on a hemocytometer slide, or the number of leaves on a tree. These counts often involve sampling a subpopulation, using a technique such as capture-mark and-recapture, or employing a quadrat or transect in field work followed by simple calculations, such as the Lincoln-Peterson estimator3 of population size. Explore the strengths and shortcomings of various sampling strategies and techniques in order to instill in your students an understanding of the reasoning that goes into such decisions. One of the fundamental quantitative expectations of biology teachers is to help their students gain an appreciation for dimensions and the appropriate units of measurement. Too often, students have learned mathematical skills without the context of units. It is doubtful that you can overemphasize the importance of keeping track of units of measurement. It is only through extensive practice with, and attention to , units in calculations that a student begins to develop an intuitive understanding of scale between various levels of biology. For example, the calculations in Investigation 10: Energy Dynamics are relatively simple, but making sure that the units of energy and mass are accounted for is often a challenge for students. Students (and the general public) typically lack an intuitive understanding of exponential processes, such as compounded interest, population growth, or radioactive decay. Without an explicit connection to the logarithmic scale of pH measurement, the student often develops an understanding of pH scale that is deficient and overly simplistic - a mental model that will not hold up if the student is asked to compare the acidity of various soils or rain water, for example. Serial dilutions are another example of a lack of student understanding; somehow it seems counterintuitive to most students to add one mL of media to 9 mL of water to get a 1-to-10 dilution. Or, if they compound two successive serial dilutions by adding 10 mL to 1 mL, mix, remove a 1 mL aliquot, and then dilute with 10 mL of water, students will get a 1-to-121 dilution instead of the 1-to-100 dilution they were anticipating. The point is to emphasize that mathematics underlies almost every biological experiment performed, even before any analysis of results. Too often, laboratory investigations are designed and implemented in a manner where most of these preliminary calculations are done for the student. By having the students work through these skills, you are laying the foundation for further work. T26 Chapter 5 Graphing, Mapping, and Ordering Students need to graph different data sets, and graphing by scatter plots is an ordinary scientific activity (see Figure 2). Often we would like a straight-line relationship between our predicted value (temperature) based on actual measurements of another variable (number of chirps) and our observed value (temperature). The more data students graph, the sooner students begin to understand that certain plot shapes or forms are easily associated with models that make it easier to infer causal mechanisms. For example, a bell-shaped curve (see Figure 3) is associated with random samples and normal distributions; a concave upward curve is associated with exponentially increasing functions, such as occur in the early stages of bacterial growth; an S-shaped curve is associated with a carrying capacity of the environment (a logistic curve); and a sine-wave-like curve (see Figure 4) is associated with a biological rhythm. Such shapes are quite familiar to a biologist, and with more frequent exposures, your students will become familiar with these shapes and their biological implications. A graph of circadian rhythms in Arabidopsis (Note the sinusoidal behavior over time. Another important skill is ordering, such as understanding the sequence of a protein or a nucleic acid; the sequence of genes along a chromosome; the pecking order of a hierarchically organized social group within a chicken coop; the trophic levels from producers to primary, secondary, tertiary consumers in a food chain; or the substrateproduct series of metabolic pathways or signal cascades. Problem Solving Students learn to become good problem solvers by doing problem solving, rather than just hearing or reading about how scientists solve problems. Even socalled "plug-and-chug" problem solving, where students follow an algorithm or apply a formula, helps students to develop an intuition about how sensitive or robust a model is. Interactive software, which lets students vary parameters over wide ranges with multiple variables and visualize results, provides a particularly effective tool to help students better draw inferences about the fit of a particular model to a biological problem.