Impact of adjustment strategies on building design process in different climates oriented by multiple performance

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China's typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios

Quantum cryptography with squeezed states

A quantum key distribution scheme based on the use of displaced squeezed vacuum states is presented. The states are squeezed in one of two field quadrature components, and the value of the squeezed component is used to encode a character from an alphabet. The uncertainty relation between quadrature components prevents an eavesdropper from determining both with enough precision to determine the character being sent. Losses degrade the performance of this scheme, but it is possible to use phase-sensitive amplifiers to boost the signal and partially compensate for their effect.Comment: 15 pages, no figure

Adapting and Mitigating to Climate Change: Balancing the Choice under Uncertainty

Nowadays, as stressed by important strategic documents like for instance the 2009 EU White Paper on Adaptation or the recent 2009 “Copenhagen Accord”, it is amply recognized that both mitigation and adaptation strategies are necessary to combat climate change. This paper enriches the rapidly expanding literature trying to devise normative indications on the optimal combination of the two introducing the role of catastrophic and spatial uncertainty related to climate change damages. Applying a modified version of the Nordhaus’ Regional Dynamic Integrated Model of Climate and the Economy it is shown that in both cases uncertainty works in the direction to make mitigation a more attractive strategy than adaptation. When catastrophic uncertainty is concerned mitigation becomes relatively more important as, by curbing emissions, it helps to reduce temperature increase and hence the probability of the occurrence of the event. Adaptation on the contrary has no impact on this. It is also shown that optimal mitigation responses are much less sensitive than adaptation responses to spatial uncertainty. Mitigation responds to global damages, while adaptation to local damages. The first, being aggregated, change less than the second in the presence of spatial uncertainty as higher expected losses in some regions are compensated by lower expected losses in other. Accordingly, mitigation changes less than adaptation. Thus if it cannot be really claimed that spatial uncertainty increases the weight of mitigation respect to that of adaptation, however its presence makes mitigation a “safer” or more robust strategy to a policy decision maker than adaptation.Climate Change, Mitigation, Adaptation, Uncertainty, Integrated Assessment Model

New resonance approach to competitiveness interventions in lagging regions: the case of Ukraine before the armed conflict

Regional competitiveness is considered to be an alternative basis for the determination of regional interventions. However, the composite competitiveness indicator is quite sensitive to the weights of sub-indicators, no matter what methodology is being used. To avoid this uncertainty in the determination of regional interventions, we proposed a new non-compensatory resonance approach that is focused on the hierarchical coincidence between weaknesses of NUTS 1 and NUTS 2 regions measuring the extensive and intensive components of competitiveness. Such a coincidence, being perceived as a resonance effect, is supposed to increase the effectiveness of interventions triggering synergetic effects and stirring up local regional potentials. The components of competitiveness are obtained through synthesising DEA methodology and Hellwig's index, correspondingly focusing on the measurement of technical efficiency and resource level. In analysing Ukrainian regions, no correlation between resonance interventions and the composite competitiveness indicator or GDP per capita was found, pointing toward a completely different direction in resonance approach. In western Ukraine, the congestion of six NUTS 2 regions was defined as a homogeneous area of analogous resonance interventions focused on improving business efficiency.Web of Science171562

Monte Carlo analysis of uncertainty propagation in a stratospheric model. 2: Uncertainties due to reaction rates

A concise stratospheric model was used in a Monte-Carlo analysis of the propagation of reaction rate uncertainties through the calculation of an ozone perturbation due to the addition of chlorine. Two thousand Monte-Carlo cases were run with 55 reaction rates being varied. Excellent convergence was obtained in the output distributions because the model is sensitive to the uncertainties in only about 10 reactions. For a 1 ppby chlorine perturbation added to a 1.5 ppby chlorine background, the resultant 1 sigma uncertainty on the ozone perturbation is a factor of 1.69 on the high side and 1.80 on the low side. The corresponding 2 sigma factors are 2.86 and 3.23. Results are also given for the uncertainties, due to reaction rates, in the ambient concentrations of stratospheric species

Reliable estimation of prediction uncertainty for physico-chemical property models

The predictions of parameteric property models and their uncertainties are sensitive to systematic errors such as inconsistent reference data, parametric model assumptions, or inadequate computational methods. Here, we discuss the calibration of property models in the light of bootstrapping, a sampling method akin to Bayesian inference that can be employed for identifying systematic errors and for reliable estimation of the prediction uncertainty. We apply bootstrapping to assess a linear property model linking the 57Fe Moessbauer isomer shift to the contact electron density at the iron nucleus for a diverse set of 44 molecular iron compounds. The contact electron density is calculated with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91, PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error diagnostics and reliable, locally resolved uncertainties for isomer-shift predictions. Pure and hybrid density functionals yield average prediction uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter being close to the average experimental uncertainty of 0.02 mm/s. Furthermore, we show that both model parameters and prediction uncertainty depend significantly on the composition and number of reference data points. Accordingly, we suggest that rankings of density functionals based on performance measures (e.g., the coefficient of correlation, r2, or the root-mean-square error, RMSE) should not be inferred from a single data set. This study presents the first statistically rigorous calibration analysis for theoretical Moessbauer spectroscopy, which is of general applicability for physico-chemical property models and not restricted to isomer-shift predictions. We provide the statistically meaningful reference data set MIS39 and a new calibration of the isomer shift based on the PBE0 functional.Comment: 49 pages, 9 figures, 7 table