Sustainability assessment indicators and methodology for intelligent buildings

The sustainable intelligent building is a building that has the best combination of environmental, social, economic and technical values. And its sustainability assessment is related with system engineering methods and multi-criteria decision-making. Therefore firstly, the wireless monitoring system of sustainable parameters for intelligent buildings is achieved; secondly, the indicators and key issues based on the “whole life circle” for sustainability of intelligent buildings are researched; thirdly, the sustainable assessment model identified on the structure entropy and fuzzy analytic hierarchy process is proposed.

Investigating the impact of defects on key stakeholders in the UK new housing sector

The UK house building sector is facing dual pressures to expand supply, along with delivering against tougher Building Regulations’ requirements, predominantly in the areas of sustainability. A review of current literature has highlighted that the pressures the UK house building industry is currently under may be having a negative impact on build quality, causing an increase in defects. A review and synthesis of the current defect literature with respect to new-build housing and the wider construction sector has found that the prevailing emphasis is limited to the classification, causes, pathology and statistical analysis of defects. There is thus a need to better understand the overall impact of individual defects on key stakeholders within the new-build housing defect detection and remediation process. As part of ongoing research to develop and verify a defect impact assessment rating system, this paper seeks to contribute to our understanding of the impact of individual defects from a key stakeholder perspective by undertaking the literature review and synthesis phase. The literature review identifies the three distinct, but interrelated, dominant impact factors: cost, disruption, and health and safety. By pulling the strands of defect literature together the theoretical lens and key stakeholder sampling strategy is formed as the basis for the subsequent impact weighting development phase.

The representation of the West-African Monsoon vertical cloud structure in the Met Office Unified Model: an evaluation with CloudSat

Weather and climate model simulations of the West African Monsoon (WAM) have generally poor representation of the rainfall distribution and monsoon circulation because key processes, such as clouds and convection, are poorly characterized. The vertical distribution of cloud and precipitation during the WAM are evaluated in Met Office Unified Model simulations against CloudSat observations. Simulations were run at 40-km and 12-km horizontal grid length using a convection parameterization scheme and at 12-km, 4-km, and 1.5-km grid length with the convection scheme effectively switched off, to study the impact of model resolution and convection parameterization scheme on the organisation of tropical convection. Radar reflectivity is forward-modelled from the model cloud fields using the CloudSat simulator to present a like-with-like comparison with the CloudSat radar observations. The representation of cloud and precipitation at 12-km horizontal grid length improves dramatically when the convection parameterization is switched off, primarily because of a reduction in daytime (moist) convection. Further improvement is obtained when reducing model grid length to 4 km or 1.5 km, especially in the representation of thin anvil and mid-level cloud, but three issues remain in all model configurations. Firstly, all simulations underestimate the fraction of anvils with cloud top height above 12 km, which can be attributed to too low ice water contents in the model compared to satellite retrievals. Secondly, the model consistently detrains mid-level cloud too close to the freezing level, compared to higher altitudes in CloudSat observations. Finally, there is too much low-level cloud cover in all simulations and this bias was not improved when adjusting the rainfall parameters in the microphysics scheme. To improve model simulations of the WAM, more detailed and in-situ observations of the dynamics and microphysics targeting these non-precipitating cloud types are required.

Towards a meta-analysis and toolkit for port-related socio-economic impacts: a review of socio-economic impact studies conducted for seaports

Port authorities increasingly need to communicate with a variety of external stakeholders in order to maintain and strengthen the societal acceptance of seaport activities. The availability of socio-economic impact studies on port authority and regional development agency websites has often made this information accessible to the public at large. However, the differences in methodologies adopted, in terms of selecting, defining and measuring various types of socio-economic impacts, sometimes lead to misconceptions as well as misleading comparisons across ports within and between regions. In this paper, we suggest guidelines for the design and application of a potential best practice from an interregional perspective (UK, France and Belgium), based on research in the framework of a European Commission co-funded project, ‘IMPACTE’. The paper also aims to develop guidelines for comparing the socio-economic impacts of ports across regional and national borders and discusses the development of a European port economic impact measurement toolkit. We analyse a sample of 33 recent socio-economic impact assessment reports in terms of methodologies adopted and types of impacts measured. The review shows a great diversity among these studies, leading to important differences between the impacts of port activity communicated to stakeholders.

An Ensemble Smoother with Error Estimates

A smoother introduced earlier by van Leeuwen and Evensen is applied to a problem in which real obser vations are used in an area with strongly nonlinear dynamics. The derivation is new , but it resembles an earlier derivation by van Leeuwen and Evensen. Again a Bayesian view is taken in which the prior probability density of the model and the probability density of the obser vations are combined to for m a posterior density . The mean and the covariance of this density give the variance-minimizing model evolution and its errors. The assumption is made that the prior probability density is a Gaussian, leading to a linear update equation. Critical evaluation shows when the assumption is justified. This also sheds light on why Kalman filters, in which the same ap- proximation is made, work for nonlinear models. By reference to the derivation, the impact of model and obser vational biases on the equations is discussed, and it is shown that Bayes’ s for mulation can still be used. A practical advantage of the ensemble smoother is that no adjoint equations have to be integrated and that error estimates are easily obtained. The present application shows that for process studies a smoother will give superior results compared to a filter , not only owing to the smooth transitions at obser vation points, but also because the origin of features can be followed back in time. Also its preference over a strong-constraint method is highlighted. Further more, it is argued that the proposed smoother is more efficient than gradient descent methods or than the representer method when error estimates are taken into account

A review of climate risk information for adaptation and development planning

Although the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin-track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector-specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over-arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges. Copyright © 2009 Royal Meteorological Society

Optimal control of nonlinear systems represented by differential algebraic equations

An iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified simplified model based problem with parameter updating in such a manner that the correct solution of the original nonlinear problem is achieved.

The budget and the WTO: driving forces behind the ‘Health Check’?

The chapter examines the evidence for budget concerns or external (WTO) pressures being the drivers for the 'Health Check' reform of the European Union's common agricultural policy.

Estimating natural-ventilation potential considering both thermal comfort and IAQ issues

Natural-ventilation potential (NVP) value can provide the designers significant information to properly design and arrange natural ventilation strategy at the preliminary or conceptual stage of ventilation and building design. Based on the previous study by Yang et al. [Investigation potential of natural driving forces for ventilation in four major cities in China. Building and Environment 2005;40:739–46], we developed a revised model to estimate the potential for natural ventilation considering both thermal comfort and IAQ issues for buildings in China. It differs from the previous one by Yang et al. in two predominant aspects: (1) indoor air temperature varies synchronously with the outdoor air temperature rather than staying at a constant value as assumed by Yang et al. This would recover the real characteristic of natural ventilation, (2) thermal comfort evaluation index is integrated into the model and thus the NVP can be more reasonably predicted. By adopting the same input parameters, the NVP values are obtained and compared with the early work of Yang et al. for a single building in four representative cities which are located in different climates, i.e., Urumqi in severe cold regions, Beijing in cold regions, Shanghai in hot summer and cold winter regions and Guangzhou in hot summer and warm winter regions of China. Our outcome shows that Guangzhou has the highest and best yearly natural-ventilation potential, followed by Shanghai, Beijing and Urumqi, which is quite distinct from that of Yang et al. From the analysis, it is clear that our model evaluates the NVP values more consistently with the outdoor climate data and thus reveals the true value of NVP.

Association of low adiponectin levels with the metabolic syndrome--the Chennai Urban Rural Epidemiology Study (CURES-4)

The aim of the study was to assess the relation of adiponectin levels with the metabolic syndrome in Asian Indians, a high-risk group for diabetes and premature coronary artery disease. The study was conducted on 100 (50 men and 50 women) type 2 diabetic subjects and 100 age and sex matched subjects with normal glucose tolerance selected from the Chennai Urban Rural Epidemiology Study, an ongoing population study in Chennai in southern India. Metabolic syndrome was defined using modified Adult Treatment Panel III (ATPIII) guidelines. Adiponectin values were significantly lower in diabetic subjects (men: 5.2 vs 8.3 microg/mL, P=.00l; women: 7.6 vs 11.1 microg/mL, P<.00l) and those with the metabolic syndrome (men: 5.0 vs 6.8 microg/mL, P=.01; women: 6.5 vs 9.9 microg/mL, P=.001) compared with those without. Linear regression analysis revealed adiponectin to be associated with body mass index (P<.05), waist circumference (P<.01), fasting plasma glucose (P=.001), glycated hemoglobin (P<.001), triglycerides (P<.00l), high-density lipoprotein (HDL) cholesterol (P<.001), cholesterol/HDL ratio (P<.00l), and insulin resistance measured by homeostasis assessment model (P<.00l). Factor analysis identified 2 factors: factor 1, negatively loaded with adiponectin and HDL cholesterol and positively loaded with triglycerides, waist circumference, and insulin resistance measured by homeostasis assessment model; and factor 2, with a positive loading of waist circumference and systolic and diastolic blood pressure. Logistic regression analysis revealed adiponectin to be negatively associated with metabolic syndrome (odds ratio [OR], 0.365; P<.001) even after adjusting for age (OR, 0.344; P<.00l), sex (OR, 0.293; P<.001), and body mass index (OR, 0.292; P<.00l). Lower adiponectin levels are associated with the metabolic syndrome per se and several of its components, particularly, diabetes, insulin resistance, and dyslipidemia in this urban south Indian population.

Modelling methodology and forecast failure

We analyse by simulation the impact of model-selection strategies (sometimes called pre-testing) on forecast performance in both constant-and non-constant-parameter processes. Restricted, unrestricted and selected models are compared when either of the first two might generate the data. We find little evidence that strategies such as general-to-specific induce significant over-fitting, or thereby cause forecast-failure rejection rates to greatly exceed nominal sizes. Parameter non-constancies put a premium on correct specification, but in general, model-selection effects appear to be relatively small, and progressive research is able to detect the mis-specifications.

Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes

When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi-method global sensitivity analysis (MMGSA). We use an application of a finite element subsurface flow model (ESTEL-2D) on a flood inundation event on a floodplain of the River Severn to illustrate this new methodology. We demonstrate the utility of the method for model understanding and show how the prediction of state variables, such as Darcian velocity vectors, can be affected by such a MMGSA. This paper is a first attempt to use GSA with a numerically intensive hydrological model.