Preliminary evidence that daily light exposure enhances the antibody response to influenza vaccination in patients with dementia.

Enhancing lighting conditions in institutions for individuals with dementia improves their sleep, circadian rhythms and well-being. Here, we report first findings that exposure to brighter light during daytime may support the immune response to the annual influenza vaccination. Eighty older institutionalised patients suffering from dementia (54 women and 26 men) continuously wore an activity tracker for 8 weeks to assess individual light exposure and rest-activity cycles. We analysed the patients' immune response from two blood samples taken before and 4 weeks after the annual influenza vaccination. Individual antibody concentrations to three influenza virus strains (H3N2, H1N1, IB) were quantified via hemagglutination inhibition assays. By quantifying individual light exposure profiles (including daylight), we classified the patients into a low and a high light exposure group based on a median illuminance of 392.6 lux. The two light exposure groups did not differ in cognitive impairment severity, age or gender distribution. However, patients in the high light exposure group showed a significantly greater circadian rest-activity amplitude (i.e., more daytime activity and less nighttime activity) along with a significantly greater antibody titer increase to the H3N2 vaccine than patients in the low light exposure group, despite similar pre-vaccination concentrations. Sufficient seroprotective responses to all three influenza virus strains were attained for ≥75% of participants. These data provide preliminary evidence for a potentially enhanced immune response in patients with dementia when they received more daily light. Future studies are needed to determine whether regular daily light exposure may have beneficial effects on the human immune system, either directly or via a stabilising circadian sleep-wake rhythms.

Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential.

With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of 250 × 250  m 2 for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km 2 of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02219-w.

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior.

Several contrasting effects are reported in the existing literature concerning the impact assessment of the COVID-19 outbreak on the use of energy in buildings. Following an in-depth literature review, we here propose a GIS-based approach, based on pre-pandemic, partial, and full lockdown scenarios, using a bottom-up engineering model to quantify these impacts. The model has been verified against measured energy data from a total number of 451 buildings in three urban neighborhoods in the Canton of Geneva, Switzerland. The accuracy of the engineering model in predicting the energy demand has been improved by 10%, in terms of the mean absolute percentage error, as a result of adopting a data-driven correction with a random forest algorithm. The obtained results show that the energy demand for space heating and cooling tended to increase by 8% and 17%, respectively, during the partial lockdown, while these numbers rose to 13% and 28% in the case of the full lockdown. The study also reveals that the introduced detailed occupancy scenarios are the key to improving the accuracy of urban building energy models (UBEMs). Finally, it is shown that the proposed GIS-based approach can be used to mitigate the expected impacts of any possible future pandemic in urban neighborhoods.

Optimized office lighting advances melatonin phase and peripheral heat loss prior bedtime.

Improving indoor lighting conditions at the workplace has the potential to support proper circadian entrainment of hormonal rhythms, sleep, and well-being. We tested the effects of optimized dynamic daylight and electric lighting on circadian phase of melatonin, cortisol and skin temperatures in office workers. We equipped one office room with an automated controller for blinds and electric lighting, optimized for dynamic lighting (= Test room), and a second room without any automated control (= Reference room). Young healthy participants (n = 34) spent five consecutive workdays in each room, where individual light exposure data, skin temperatures and saliva samples for melatonin and cortisol assessments were collected. Vertical illuminance in the Test room was 1177 ± 562 photopic lux (mean ± SD) , which was 320 lux higher than in the Reference room (p < 0.01). Melanopic equivalent daylight (D65) illuminance was 931 ± 484 melanopic lux in the Test room and 730 ± 390 melanopic lux in the Reference room (p < 0.01). Individual light exposures resulted in a 50 min earlier time of half-maximum accumulated illuminance in the Test than the Reference room (p < 0.05). The melatonin secretion onset and peripheral heat loss in the evening occurred significantly earlier with respect to habitual sleeptime in the Test compared to the Reference room (p < 0.05). Our findings suggest that optimized dynamic workplace lighting has the potential to promote earlier melatonin onset and peripheral heat loss prior bedtime, which may be beneficial for persons with a delayed circadian timing system.

Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology.

The urban form and extreme microclimate events can have an important impact on the energy performance of buildings, urban comfort and human health. State-of-the-art building energy simulations require information on the urban microclimate, but typically rely on ad-hoc numerical simulations, expensive in-situ measurements, or data from nearby weather stations. As such, they do not account for the full range of possible urban microclimate variability and findings cannot be generalized across urban morphologies. To bridge this knowledge gap, this study proposes two data-driven models to downscale climate variables from the meso to the micro scale in arbitrary urban morphologies, with a focus on extreme climate conditions. The models are based on a feedforward and a deep neural network (NN) architecture, and are trained using results from computational fluid dynamics (CFD) simulations of flow over a series of idealized but representative urban environments, spanning a realistic range of urban morphologies. Both models feature a relatively good agreement with corresponding CFD training data, with a coefficient of determination R2 = 0.91 (R2 = 0.89) and R2 = 0.94 (R2 = 0.92) for spatially-distributed wind magnitude and air temperature for the deep NN (feedforward NN). The models generalize well for unseen urban morphologies and mesoscale input data that are within the training bounds in the parameter space, with a R2 = 0.74 (R2 = 0.69) and R2 = 0.81 (R2 = 0.74) for wind magnitude and air temperature for the deep NN (feedforward NN). The accuracy and efficiency of the proposed CFD-NN models makes them well suited for the design of climate-resilient buildings at the early design stage.

The influence of urban form on the grid integration of renewable energy technologies and distributed energy systems.

Standard and newly designed building blocks for complex urban sites- also designated by urban archetypes - are used in this study to quantify the influence of urban forms on their energy demand and energy systems design. An energy hub, which consists on a multi-carrier energy system involving multiple energy conversion, storage and/or network technologies, is employed to quantify the impact of the urban morphology on the energy system requirements. This study reveals that urban archetypes have a notable influence on the heating and cooling energy demands of city districts that can be characterized using form factors and floor area ratio. However, the influence on demand profiles cannot be assessed based on the aforementioned indicators. The cost of energy systems can increase up to 50% due to the impact of urban forms that are well beyond the increase of peak and/or annual energy demands. In addition, renewable energy integration to the grid as well its utilization in districts is influenced by urban forms. This makes it essential to consider energy system design as a part of the urban planning process moving even beyond building simulation.

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment.

Bright Light Delights: Effects of Daily Light Exposure on Emotions, Restactivity Cycles, Sleep and Melatonin Secretion in Severely Demented Patients.

OBJECTIVE: We tested whether the effects of a dynamic lighting system are superior to conventional lighting on emotions, agitation behaviour, quality of life, melatonin secretion and circadian restactivity cycles in severely demented patients. As a comparison, an age matched control patient group was exposed to conventional lighting. For none of the output measures were significant differences between the two lighting conditions found during the 8 study weeks in fall/winter. METHODS: Thus, we divided the patient cohort (n = 89) into two groups, solely based on the median of their daily individual light exposure. Patients with higher average daily light exposure (>417 lx) showed significantly longer emotional expressions of pleasure and alertness per daily observations than patients with lower daily light exposure. Moreover, they had a higher quality of life, spent less time in bed, went to bed later and initiated their sleep episodes later, even though the two groups did not differ with respect to age, severity of cognitive impairment and mobility. In general, men were more agitated, had shorter sleep with more wake episodes, had a lower circadian amplitude of relative rest-wake activity and interdaily circadian stability than women. In particular, lower daily light exposures significantly predicted lower circadian amplitudes of rest-activity cycles in men but not in women. This may indicate sex specific susceptibility to daily light exposures for rest-activity regulation in older demented patients. RESULTS: Our results provide evidence that a higher daily light exposure has beneficial effects on emotions and thus improved quality of life in a severely demented patient group.

CFSpro: ray tracing for design and optimization of complex fenestration systems using mixed dimensionality approach.

Advanced optical ray tracing software, CFSpro, was developed for the study and optimization of complex fenestration systems (CFSs). Using an algorithm mixing 2D and 3D approaches, accurate computation of large numbers of rays in extruded geometries can be performed and visualized in real time. A thin film model was included to assess the spectral control provided by coatings. In this paper, the ray tracing model is described and validated. A novel glazing, engineered with this simulation tool, is presented. It combines the functions of daylight provision, glare protection, and seasonal thermal control while conserving a view to the outside at near normal incidence.

Effects of prior light exposure on early evening performance, subjective sleepiness, and hormonal secretion.

In sighted humans, light intensity, timing, exposure duration, and spectral composition of light are important to entrain the endogenous circadian pacemaker to the 24-h day-night cycle. We tested the impact of two realistic office lighting conditions during the afternoon on subjective sleepiness, hormonal secretion, and cognitive performance in the early evening hours. Twenty-nine young subjects came twice and spent 8 h (12:00-20:00) in our laboratory, where they were exposed for 6 h to either artificial light (AL) or to mainly daylight (DL). In the early evening, we assessed their salivary cortisol and melatonin secretion, subjective sleepiness, and cognitive performance (n-back test) under dim light conditions. Subjects felt significantly more alert at the beginning of the evening after the DL condition, and they became sleepier at the end of the evening after the AL condition. For cognitive performance we found a significant interaction between light conditions, mental load (2- or 3-back task) and the order of light administration. On their first evening, subjects performed with similar accuracy after both light conditions, but on their second evening, subjects performed significantly more accurately after the DL in both n-back versions and committed fewer false alarms in the 2-back task compared to the AL group. Lower sleepiness in the evening was significantly correlated with better cognitive performance (p < .05). In summary, even short-term lighting conditions during the afternoon had an impact on cognitive task performance in the evening. This rapid effect was only distinguishable on the second day of training, when a difficult task had been sufficiently practiced.