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ABSTRACTAims  To review the concept of binge drinking as a measure of risky single occasion drinking (RSOD), to illustrate its differential impact on selected health outcomes and to identify research gaps.Methods  Narrative literature review with focus on conceptual and methodological differences, trajectories of RSOD and effects of RSOD on fetal outcomes, coronary heart disease (CHD) and injuries.Results  Effects ascribed commonly to RSOD may often be the effects of an undifferentiated mixture of risky single occasions and regular heavy volume drinking, constituted by frequent, successive RSOD. This leads to the problem that additional risks due to RSOD are mis‐specified and remain unidentified or underestimated in some cases, such as for injuries or CHD, but are probably overstated for some chronic consequences or for effects of maternal drinking on newborns.Conclusion  A stronger focus should be placed upon methods that can differentiate the effects of RSOD from those due to frequent occasions of heavy drinking that result in heavy volume drinking.

Abstract The research community has worked on improving the design, performance, and operation of radiant system technologies; however, the interactions between architectural design decisions and radiant cooling systems design in the tropics – where radiant system design and operation represents a challenge due to high external heat gains and latent loads – have been left unexplored by existing work. This manuscript (1) addresses the holistic early-design of effective radiant cooling systems and the architectural environments that they serve in hot and humid tropical climates, (2) analyses how different design choices and their interaction affect buildings performance compared to conventional all-air systems, and (3) proposes preliminary early-design indications of the parameters values to adopt when designing radiant cooled office buildings in the tropics to achieve high multi-objective design goals. Morris sensitivity analysis is used to analyse the impact of 13 different building and system design parameters on comfort, system and energy performances for 3 radiant systems typologies applied to a parametric office building in the tropical climate of Singapore. The elementary effect (EE) of each parameter is analysed based on 8200 individual EnergyPlus simulation models being run, and the impact, interactions & (non) linear effects of different design factors are assessed. It is found that radiant cooling systems require different early-design phase considerations than conventional air-based cooling systems since different parameters prove to be dominant or significant. All three radiant system typologies, if coupled with efficient operational strategies and a complimentary set of design variables, ensure high multi-objective building performance, showing how the implementation of radiant cooling technologies could be beneficial for a tropical climate. The window solar heat gain coefficient and the facade window-to-wall ratio play a key role in this effort with respective values falling below or equal to 54% and 57%. The system set-point temperature has an increased importance for operational efficiency allowing high dry-bulb room temperatures ranging from 24.8 °C to 27 °C.

Abstract A multistage inexact-factorial fuzzy-probability programming (MIFP) method is developed for optimizing electric power systems with cost minimization and environmental-impact mitigation. MIFP is capable of addressing parameter uncertainties presented as intervals/fuzzy-probability distributions and their interactions in a systematic manner over a multistage context; it can also quantitatively evaluate the individual and interactive effects on system performance. The proposed MIFP method is then applied to planning electric power system for the City of Qingdao, where multiple scenarios that emission-reduction target is designed as random variable and electricity demand is specified as fuzzy-probability distribution over a long term are analyzed. Results reveal that various uncertainties in system components (e.g., fuel price, electricity-produce cost, emission-mitigation option, and electricity-demand level) have sound effects on the city’s future energy systems. High mitigation and high demand correspond to decisions with considerable efforts for developing more renewable energies to reduce pollutants and carbon dioxide emitted from fossil fuels. Results also disclose that the proportion of electricity generated by coal would shrink with time to reduce the environmental negative impacts. The imported electricity would eventually drop as the local renewable energy capacity becomes capable of meeting the city’s electricity demand. Through developing renewable energy, the city’s electric power system could finally be adjusted towards a cleaner and safer pattern. Results also show that factors of electricity demand and import-electricity expenditure have significant individual and/or joint effects on the system cost. The findings can not only optimize electricity-generation and -supply patterns with a cost-effective manner, but also help decision makers identify desired strategies for enhancing the mitigation of environmental impacts under uncertainty.

Abstract A number of conflicting issues such as growing economic demand, increasing energy supply, shrinking energy resources, changing climate condition, as well as tightening environmental requirement pose great challenges to decision makers for planning energy system towards cleaner production and sustainable development. In this study, an ensemble energy system model for China (named as CN-EES model) is developed through incorporating computable general equilibrium model and interval-parameter programming method within an energy system optimization framework. The CN-EES model is capable of predicting energy demands under different economic-development scenarios, reflecting uncertainties derived from long-term (2021–2050) planning period, and providing optimal solutions for China’s energy system transition and management. Results under multiple scenarios have been generated and are used for disclosing the impacts of economic development on the transition to cleaner production of China’s energy system. Our main findings are (a) fossil fuel (i.e., coal, oil and gas): would keep the leading position for China’s energy system, but its share in the total energy supply would decline to 61.09% by 2050; (b) China’s energy-supply security would be challenged because the fossil fuel import would be surpassed the international warning line in 2041–2045, even the speed of the economic development is slowed down; (c) renewable energy would replace fossil fuel to dominate the national electric power system after 2041; (d) China’s energy system would transfer towards a clean and low-carbon “modern energy system”; however, there is still enormous pressure on carbon dioxide and air pollutant emissions under high-speed economic development scenarios. The findings can help decision makers to well understand the effects of economic development on China’s energy system transition and effectively cope with issues of energy supply, environmental protection, and climate change.

Abstract A method of the scattering Phase Function Scaling (PFS) for determination of the direct normal irradiance (DNI) involving the circumsolar normal irradiance (CSNI) is developed. A fraction of the normalized phase function integrated over the field of view for a pyrheliometer is introduced in the radiative transfer equation so the scaled optical depth using this phase function is derived. The PFS method is tested in a radiative transfer scheme and a simple fast parametric scheme SUNFLUX using the observational datasets at 14 Baseline Surface Radiation Network (BSRN) stations. The evaluation results show that the modelled DNI in terms of the PFS method in the radiative transfer scheme is significantly improved compared with that using the original delta-Eddington scaling method and compared well with that determined using other two radiative transfer models of DISORT and SMARTS. The modelled DNI with the PFS method in the SUNFLUX scheme is also improved compared with that without using the PFS method and compared well with that from the SMARTS model. The CSNI is also evaluated against the results from the other three models. The results are generally poor in all cases. The poor results are mainly due to the small magnitude of CSNI and uncertainties in the input data. Uncertainty in the aerosol scattering phase function could be the main reason responsible for the poor comparisons.

This paper presents recent Canadian advances in nuclear-based production of hydrogen by electrolysis and the thermochemical copper-chlorine (Cu-Cl) cycle. This includes individual process and reactor developments within the Cu-Cl cycle, thermochemical properties, advanced materials, controls, safety, reliability, economic analysis of electrolysis at off-peak hours, and integrating hydrogen plants with Canada's nuclear power plants. These enabling technologies are being developed by a Canadian consortium, as part of the Generation IV International Forum (GIF) for hydrogen production from the next generation of nuclear reactors.

In this paper we present a one-dimensional transient model for the membrane electrode assembly of a polymer-electrolyte fuel cell. In earlier work we established a framework to describe the water balance in a steady-state, non-isothermal cathode model that explicitly included an agglomerate catalyst layer component. This paper extends that work in several directions, explicitly incorporating components of the anode, including a micro-porous layer, and accounting for electronic potential variations, gas convection and time dependance. The inclusion of temperature effects, which are vital to the correct description of condensation and evaporation, is new to transient modelling. Several examples of the modelling results are given in the form of potentiostatic sweeps and compared to experimental results. Excellent qualitative agreement is demonstrated, particularly in regard to the phenomenon of hysteresis, a manifestation of the sensitive response of the system to the presence of water. Results pertaining to pore size, contact angle and the presence of a micro-porous layer are presented and future work is discussed.

AbstractGroup living among ungulates has evolved mainly in species living in open habitats, such as grasslands and savannas, whereas in the forest, few ungulate species form groups and these tend to be small. Therefore, the white‐lipped peccary (Tayassu pecari), a Neotropical ungulate listed as Vulnerable by theIUCN, represents an almost unique social occurrence as it lives in large and cohesive groups, yet it inhabits dense tropical forests. Large variations in group sizes have been observed throughout the species range, with reports of herds with less than 10 to around 300 individuals. We examined factors that might cause variation in group size in white‐lipped peccary, including ecological and anthropogenic variables. We conducted an extensive literature review and used our original data to compile information on white‐lipped peccary's group size across its range. We built models to quantitate generalizations for group sizes distinguishing data from areas with high human influence, and areas that have not been significantly disturbed by humans for at least the last 20 years. We found that white‐lipped peccary's group size for all sites was most strongly predicted by a combination of the distances to the nearest human settlement and rainfall and its seasonality. Results from the undisturbed sites indicated that group size is positively influenced by rainfall. Our results contribute to understand why group size varies in different environments that are subjected to different ecological and human conditions. Information on these relationships is a key to advance our understanding of the socio‐ecological strategies of animal species living in groups.

The power grid has been evolving over the last 120 years, but it is seeing more changes in this decade and next than it has seen over the past century. In particular, the widespread deployment of intermittent renewable generation, smart loads and devices, hierarchical and distributed control technologies, phasor measurement units, energy storage, and widespread usage of electric vehicles will require fundamental changes in methods and tools for the operation and planning of the power grid. The resulting new dynamic and stochastic behaviors will demand the inclusion of more complexity in modeling the power grid. Solving such complex models in the traditional computing environment will be a major challenge. Along with the increasing complexity of power system models, the increasing complexity of smart grid data further adds to the prevailing challenges. In this environment, the myriad of smart sensors and meters in the power grid increase by multiple orders of magnitude, so do the volume and speed of the data. The information infrastructure will need to drastically change to support the exchange of enormous amounts of data as smart grid applications will need the capability to collect, assimilate, analyze and process the data, to meet real-time grid functions. High performance computing (HPC) holds the promise to enhance these functions, but it is a great resource that has not been fully explored and adopted for the power grid domain.