• Energy Research
• Closed Access close
• US close
• GB close
• IT close
• ES close

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.

• 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.

Abstract With the aging of current isotope-producing reactors and the rising demand for medical isotopes, future shortages are predicted to greatly increase the cost of Mo-99. Molten salt reactors have been proposed for the production of Mo-99, and have the potential for safe, efficient operation at a variety of scales. However, while Mo-99 is a major fission product, it is unclear how accessible it will be for direct extraction from a fuel salt given its relatively short half-life. In this study, a computationally efficient mathematical model was used to analyze fission product accumulation in an MSR, with the specific goal of assessing Mo-99 production by continuous or near-continuous extraction from the reactor. The model includes generation and removal of isotopes in an MSR from fission, decay, activation and extraction. The extraction process was assumed to be mass-transfer limited, owing to the noble behavior and relatively low concentration of molybdenum in the reactor. It was found that the concentration at which Mo was present in the reactor had a profound effect on the specific activity of the Mo-99 that could be successfully extracted. If molybdenum is allowed to buildup in the reactor, the majority of Mo-99 is lost by decay or activation, leaving only a small fraction of Mo-99 in the extracted molybdenum. Therefore, separation processes that require a high relative molybdenum concentration for extraction are not desirable. On the other hand, separation processes become more difficult at low concentrations. Thus, a trade-off exists between the difficulty of the separation and effectiveness of the extraction as measured by the recovery fraction and product quality. It was found that increasing the power density enabled improved Mo-99 recovery for separation processes at increased concentrations. These results demonstrate the use of a model to explore the viability of Mo-99 extraction from an MSR, the influence of operating parameters on that extraction, and the conditions under which Mo-99 extraction can be optimized.

The third edition of this text on electric machinery has reference-frame theory at its core, the reviewer states, and is recommended for "those with a need for electric machinery modeling and simulation." Analysis of Electric Machinery and Drive Systems, Third Edition by Paul Krause, Oleg Wasynczuk, Scott Sudhoff, and Steven Pekarek. Analysis is a word that means a scrutiny, enquiry, explanation, or dissection. Analysis is the perfect word to describe this book. This work gets to the details of modeling electric machines, motors and generators, and drives systems. As stated in the preface, "...reference-frame theory is at the core of this book."

Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

The paper proposes a critical assessment of municipal solid waste gasification today, starting from basic aspects of the process (process types and steps, operating and performance parameters) and arriving to a comparative analysis of the reactors (fixed bed, fluidized bed, entrained bed, vertical shaft, moving grate furnace, rotary kiln, plasma reactor) as well as of the possible plant configurations (heat gasifier and power gasifier) and the environmental performances of the main commercially available gasifiers for municipal solid wastes. The analysis indicates that gasification is a technically viable option for the solid waste conversion, including residual waste from separate collection of municipal solid waste. It is able to meet existing emission limits and can have a remarkable effect on reduction of landfill disposal option.