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In large polymer electrolyte membrane (PEM) fuel cell stacks, monitoring and control of the local changes in membrane humidity inside the cathode channel is critical. In this study, a control-oriented dynamic model capable of describing the spatial distribution of voltage and relative humidity (RH) in a large fuel cell stack is developed and experimentally validated. The model tracks energy and mass flow inside the cathode, anode, and coolant channels, as well as the fuel cell stack body. Validation tests show that the model agrees well with the experimental data. The new modeling framework developed in this study can be used to predict the localized effects of humidity on the performance of a fuel cell stack. Also, given its accurate prediction of RH in the stack, this model can be used as an observer to predict local humidity variations that are, otherwise, not available. This capability would allow PEM fuel cells to avoid membrane damage due to low operating humidities as well as efficiency losses due to catalyst layer flooding.

C57BL/6J mice showed dose dependent devreases in locomotor activity with increasing IP doses of ethanol (0.0, 0.75, 1.50 and 2.25 g/kg), while BALB/cJ mice showed dose dependent increases in activity; both strains were equally active with saline. Whether this finding represents decreased CNS responsivity in C57BL mice to ethanol's excitatory effect or increased response to its depressant action at sub-hypnotic doses is unclear, since anesthetic doses produce anesthesia of far shorter duration in the C57BL strain than in the BALB strain. It is possible that the biphasic action of alcohol is under the control of separate and distinct mechanisms, rather than a common one, and that these two mechanisms are differentially affected by alcohol. Endogenous as well as ethanol-induced neurochemical differences in biogenic amines may also be correlated with the gentic variation in CNS responsivity towards alcohol.

Abstract This paper describes a computer simulation model constructed in the summer of 2000. It was used to simulate the general patterns of power plant construction that might appear in an electric system with approximately the same loads, resources and markets as those in California. The paper begins with background on restructuring in California and a review of previous research on construction. The paper then describes the computer model by presenting several simulations with qualitatively different patterns of construction. The simulations reveal that construction could appear in a steady, even fashion, causing power plants to come on line exactly in time to meet the profitability goals of investors. But this is not the dominant pattern. The more likely pattern shows construction lagging behind the growth in demand, allowing prices to climb to surprisingly high values during peak periods in the summer. When new power plants are completed, they come on line in great numbers causing a bust in wholesale prices. The boom/bust pattern of construction is common in industries such as commodities and real estate, and there are good reasons to believe that a boom could appear in the electric industry. Electricity consumers would certainly benefit from a boom in construction. Unfortunately, waiting for the boom is a difficult challenge with the current mix of state and federal rules in California. The paper concludes with a summary of recent events that have led to the demise of the California approach to deregulation and to the state's entry into the power business.

Serum lipoproteins have received considerable notoriety as risk factors for atherosclerotic disease, yet the kinetic factors that determine serum concentrations are often unappreciated. Simple compartmental models for lipoprotein kinetics are herein presented which integrate key features of lipoprotein metabolism and allow prediction of very low-density lipoprotein and low-density lipoprotein levels in a wide variety of clinical circumstances. Possible changes in kinetic parameters responsible for hyperlipidemia in obesity, insulin resistance, diabetes, carbohydrate (sugar)-induced hypertriglyceridemia, alcoholic type V hyperlipemia, polyunsaturated fat diets, and several pharmacological interventions are discussed. Key features of lipoprotein metabolism are briefly reviewed.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

The Nigerian government is committed to sustaining rice production to meet national demand. Nevertheless, political tension and climate-induced stressors remain crucial constraints in achieving policy targets. This study examines whether climate change and political instability significantly threaten rice production in Nigeria. First, we employed nonparametric methods to estimate the country's rainfall and temperature trends between 1980Q1 and 2015Q4. Second, we employed the autoregressive distributed lag (ARDL) technique to examine the effects of climate change and political instability on rice production. The results show that while temperature has an increasing pattern, rainfall exhibits no significant trend. The findings from the ARDL estimate reveal that rice production responds negatively to temperature changes but is less sensitive to changes in rainfall. In addition, political instability adversely affects rice production in Nigeria. We argue that Nigeria's slow growth in rice production can be traced back to the impact of climate change and political tension in rice farming areas. As a result, reducing the overall degree of conflict to ensure political stability is critical to boosting the country's self-sufficiency in rice production. We also recommend that farmers be supported and trained to adopt improved rice varieties less prone to extreme climate events while supporting them with irrigation facilities to facilitate rice production.

• Premise of the study: Climate change is associated with phenological shifts in an increasing number of organisms worldwide. However, accurate estimates of these shifts are dependent on long‐term data sets that include phenological observations from before annual average temperatures began to rise.• Methods: We compared the first flowering times of native prairie plants between 2007 and 2010 with historical data recorded by O. A. Stevens from 1910 to 1961. By merging climate variable data from the same time period, it also was possible to correlate first flowering dates with associated climate variables.• Key results: Over the past 100 years, spring temperatures in the Red River Valley near Fargo, North Dakota, USA, have increased, and growing seasons have lengthened significantly. Seventy‐five percent of the 178 species observed by Stevens had flowering times that were sensitive to at least one variable related to temperature or precipitation. Over the past 4 yr, 5% to 17% of the species observed have significantly shifted their first flowering time either earlier or later relative to the previous century.• Conclusions: The results of this study indicate that as spring temperatures in the northern Great Plains have increased and the growing season has lengthened, some spring flowering species have advanced their first flowering time, some fall species have delayed their first flowering, and some species have not changed. Given the importance of flowering timing for reproductive success, the changing climate in the Great Plains is expected to have long‐term ecological and evolutionary consequences for native plant species.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.