School of Engineering and Architecture of Fribourg

Abstract Current models of snow cover distribution, soil moisture, surface runoff and river discharge typically have very simple parameterizations of surface processes, such as degree‐day factors or single‐layer snow cover representation. For the purpose of reproducing catchment runoff, simple snowmelt routines have proven to be accurate, provided that they are carefully calibrated specifically for the catchment they are applied to. The use of more detailed models is, however, useful to understand and quantify the role of individual surface processes for catchment hydrology, snow cover status and soil moisture distribution. We introduce ALPINE3D, a model for the high‐resolution simulation of alpine surface processes, in particular snow processes. The model can be driven by measurements from automatic weather stations or by meteorological model outputs. As a preprocessing alternative, specific high‐resolution meteorological fields can be created by running a meteorological model. The core three‐dimensional ALPINE3D modules consist of a radiation balance model (which uses a view‐factor approach and includes shortwave scattering and longwave emission from terrain and tall vegetation) and a drifting snow model solving a diffusion equation for suspended snow and a saltation transport equation. The processes in the atmosphere are thus treated in three dimensions and are coupled to a distributed (in the hydrological sense of having a spatial representation of the catchment properties) one‐dimensional model of vegetation, snow and soil (SNOWPACK) using the assumption that lateral exchange is small in these media. The model is completed by a conceptual runoff module. The model can be run with a choice of modules, thus generating more or less detailed surface forcing data as input for runoff generation simulations. The model modules can be run in a parallel (distributed) mode using a GRID infrastructure to allow computationally demanding tasks. In a case study from the Dischma Valley in eastern Switzerland, we demonstrate that the model is able to simulate snow distribution as seen from a NOAA advanced very high‐resolution radiometer image. We then analyse the sensitivity of simulated snow cover distribution and catchment runoff to the use of different surface process descriptions. We compare model runoff simulations with runoff data from 10 consecutive years. The quantitative analysis shows that terrain influence on the radiation processes has a significant influence on catchment hydrology dynamics. Neglecting the role of vegetation and the spatial variability of the soil, on the other hand, had a much smaller influence on the runoff generation dynamics. We conclude that ALPINE3D is a valuable tool to investigate surface dynamics in mountains. It is currently used to investigate snow cover dynamics for avalanche warning and permafrost development and vegetation changes under climate change scenarios. It could also serve to test the output of simpler soil–vegetation–atmosphere transfer schemes used in larger scale climate or meteorological models and to create accurate soil moisture assessments for meteorological and flood forecasting. Copyright © 2006 John Wiley & Sons, Ltd.

BACKGROUND: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. RESULTS: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. CONCLUSION: ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.

Approximate Bayesian computation (ABC) techniques permit inferences in complex demographic models, but are computationally inefficient. A Markov chain Monte Carlo (MCMC) approach has been proposed (Marjoram et al. 2003), but it suffers from computational problems and poor mixing. We propose several methodological developments to overcome the shortcomings of this MCMC approach and hence realize substantial computational advances over standard ABC. The principal idea is to relax the tolerance within MCMC to permit good mixing, but retain a good approximation to the posterior by a combination of subsampling the output and regression adjustment. We also propose to use a partial least-squares (PLS) transformation to choose informative statistics. The accuracy of our approach is examined in the case of the divergence of two populations with and without migration. In that case, our ABC-MCMC approach needs considerably lower computation time to reach the same accuracy than conventional ABC. We then apply our method to a more complex case with the estimation of divergence times and migration rates between three African populations.

Buildings consume a significant amount of energy, estimated at about one-third of total primary energy resources. Building energy efficiency has turned out to be a major issue in limiting the increasing energy demands of the sector. Literature shows that building user behavior can increase the efficiency of the energy used in the building and different strategies have been tested to address and support this issue. These strategies often combine the quantification of energy savings and qualitative interpretation of occupant behavior in order to foster energy efficiency. Strategies that influence building occupant behaviors include eco-feedback, social interaction, and gamification. This review paper presents a study conducted on the state of the art related to the impact of building user behavior on energy efficiency, in order to provide the research community with a better understanding and up-to-date knowledge of energy, comfort-related practices, and potential research opportunities. Achieving and maintaining energy-efficient behavior without decreasing the comfort of building occupants still represents a challenge, despite emerging technologies and strategies as well as general research progress made over the last decade. Conclusions highlight eco-feedback as an effective way to influence behavior, and gamification as a new opportunity to trigger behavioral change. The impact of user behavior is difficult to quantify for methodological reasons. Factors influencing human behavior are numerous and varied. Multi-disciplinary approaches are needed to provide new insights into the inner dynamic nature of occupant’s energy behavior.

Morphogenesis occurs in 3D space over time and is guided by coordinated gene expression programs. Here we use postembryonic development in Arabidopsis plants to investigate the genetic control of growth. We demonstrate that gene expression driving the production of the growth-stimulating hormone gibberellic acid and downstream growth factors is first induced within the radicle tip of the embryo. The center of cell expansion is, however, spatially displaced from the center of gene expression. Because the rapidly growing cells have very different geometry from that of those at the tip, we hypothesized that mechanical factors may contribute to this growth displacement. To this end we developed 3D finite-element method models of growing custom-designed digital embryos at cellular resolution. We used this framework to conceptualize how cell size, shape, and topology influence tissue growth and to explore the interplay of geometrical and genetic inputs into growth distribution. Our simulations showed that mechanical constraints are sufficient to explain the disconnect between the experimentally observed spatiotemporal patterns of gene expression and early postembryonic growth. The center of cell expansion is the position where genetic and mechanical facilitators of growth converge. We have thus uncovered a mechanism whereby 3D cellular geometry helps direct where genetically specified growth takes place.

Food webs depict who eats whom in communities. Ecologists have examined statistical metrics and other properties of food webs, but mainly due to the uneven quality of the data, the results have proved controversial. The qualitative data on which those efforts rested treat trophic interactions as present or absent and disregard potentially huge variation in their magnitude, an approach similar to analyzing traffic without differentiating between highways and side roads. More appropriate data are now available and were used here to analyze the relationship between trophic complexity and diversity in 59 quantitative food webs from seven studies (14-202 species) based on recently developed quantitative descriptors. Our results shed new light on food-web structure. First, webs are much simpler when considered quantitatively, and link density exhibits scale invariance or weak dependence on food-web size. Second, the "constant connectance" hypothesis is not supported: connectance decreases with web size in both qualitative and quantitative data. Complexity has occupied a central role in the discussion of food-web stability, and we explore the implications for this debate. Our findings indicate that larger webs are more richly endowed with the weak trophic interactions that recent theories show to be responsible for food-web stability.

When modelling outdoor microclimates, researchers and designers need to be aware of the modelling capabilities and limitations of tools. This comparative study attempts to understand how tools such as CitySim Pro, ENVI-met, RayMan, Grasshopper plug-ins Honeybee / Ladybug and Autodesk CFD, evaluate the Mean Radiant Temperature (MRT), one of the main parameters governing human energy balance. To this purpose, the space underneath and surrounding the Rolex Learning Center, located on the EPFL campus in Lausanne, were modelled. Significant variations of MRT predictions were recorded. This led to the review of the physical modelling assumptions that each of the calculation engines operates. Based on the tools’ available documentation, answers to forums, interviews with the developers, and tool codes, the paper lists how all the variables that affect MRT are considered. Although not exhaustively, the paper lists the main differences among tools, leading to the understanding of the types of physical context that they could simulate.

solely from plastic production, with 99% of all plastics being produced from fossil fuel sources, while those that are produced from renewable sources use food products as feedstocks. In 2019, 29 Mt of plastic waste was collected in Europe. It is estimated that 32% was recycled, 43% was incinerated and 25% was sent to landfill. It has been estimated that life-sciences (biology, medicine, etc.) alone create plastic waste of approximately 5.5 Mt/yr, the majority being disposed of by incineration. The vast majority of this plastic waste is made from fossil fuel sources, though there is a growing interest in the possible use of bioplastics as a viable alternative for single-use lab consumables, such as petri dishes, pipette tips, etc. However, to-date only limited bioplastic replacement examples exist. In this review, common polymers used for labware are discussed, along with examining the possibility of replacing these materials with bioplastics, specifically polylactic acid (PLA). The material properties of PLA are described, along with possible functional improvements dure to additives. Finally, the standards and benchmarks needed for assessing bioplastics produced for labware components are reviewed.

Tsunamis, landslide-generated waves, and dam failures are rare, but highly destructive phenomena, associated with extreme loading on infrastructure. Recent events showed that specific measures must be taken to guarantee safety of both people and the built environment. This experimental study investigates the forces and moments exerted on free-standing buildings that are induced by both surges and bores. The hydrodynamic impact was characterized by high splash, subsequently followed by a quasi-steady flow around the structure. For dry bed surges, the time history of the horizontal force was proportional to the momentum flux per unit width. For wet bed bores, an attenuation of the peak force due to the presence of an aerated front was observed and the introduction of a reduction coefficient was necessary to achieve a realistic force estimation. Additional force analysis in terms of peak time, wave height at maximum force and impulse also pointed out some key differences between forces exerted by dry bed surges and wet bed bores. The occurrence of the maximum tilting moment on the building coincided with the maximum horizontal force and an evaluation of the cantilever arm was possible. These findings provide engineers with practical information for the design of safer coastal structures.

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.

Living organisms have evolved complex signaling networks to drive appropriate physiological processes in response to changing environmental conditions. Amongst them, electric signals are a universal method to rapidly transmit information. In animals, bioelectrical activity measurements in the heart or the brain provide information about health status. In plants, practical measurements of bioelectrical activity are in their infancy and transposition of technology used in human medicine could therefore, by analogy provide insight about the physiological status of plants. This paper reports on the development and testing of an innovative electrophysiological sensor that can be used in greenhouse production conditions, without a Faraday cage, enabling real-time electric signal measurements. The bioelectrical activity is modified in response to water stress conditions or to nycthemeral rhythm. Furthermore, the automatic classification of plant status using supervised machine learning allows detection of these physiological modifications. This sensor represents an efficient alternative agronomic tool at the service of producers for decision support or for taking preventive measures before initial visual symptoms of plant stress appear.

The co-simulation of both urban and building-level models leverages the advantages of both platforms. It better accounts for the localized effects of surrounding buildings, geography and climate conditions while maintaining high-fidelity building systems representation. This paper describes the co-simulation process of the building and urban-scale models of two university campuses in Switzerland using EnergyPlus and CitySim. In the first case study, on-site measured performance data is compared to the co-simulation results. The second case study examines the results of the two engines. The results show that coupling of EnergyPlus with CitySim resulted in a −15.5% and −7.5% impact on cooling consumption and a +6.5% and +4.8% impact on heating use as compared to solo simulations.The co-simulation process was able to better model realistic conditions for heating, but not cooling in one case study. It was able to substantially reduce the discrepancies in prediction between the engines in the other study.

We performed the first large-scale investigation of indoor air quality (IAQ), energy and occupant behavior and satisfaction, in 650 energy-efficient dwellings in western Switzerland. The investigation included comparative assessment of 217 green-certified Minergie (M) and 433 energy-renovated (R) dwellings. Data were collected through a combination of questionnaire survey of building characteristics and occupancy symptoms/satisfaction, as well as field measurements of radon, total volatile organic compounds (TVOC), formaldehyde and fungi. The results showed that 90% of M dwellings relied on renewable and low-carbon energy sources for space and water heating, as compared to only 40% of R dwellings. The annual electricity consumptions of M and R dwellings were similar (~33 kWh/m2), however, R dwellings consumed more gas and heating oil, thus contributing more to greenhouse gas emissions. Concentration of sampled air pollutants in the two dwelling types was generally below the maximum guideline values. Interestingly, concentration of all air pollutants was significantly lower in M relative to R dwellings: Radon (48 vs. 91 Bq/m3), TVOC (167 vs. 259 μg/m3), formaldehyde (12 vs. 15 μg/m3) and fungal colony forming units (33 vs. 48 CFUs). Statistical comparisons revealed that residents of naturally ventilated R dwellings tended to open window more frequently, while occupants of M dwellings relied on mechanical ventilation. We found no differences in occupant satisfaction and self-reported symptoms between the two dwelling types. The findings of this study are of potential utility for interpreting impacts of growing building energy renovation initiatives on indoor air quality, ventilation design and occupant satisfaction.

Wine lees are one of the main residues formed in vast quantities during the fermentation of wine. While toxic when applied to plants and wetlands, it is a biodegradable material, and several alternatives have been proposed for its valorization as: dietary supplement in animal feed, source for various yeast extracts and bioconversion feedstock. The implementation of stricter environment protection regulations resulted in increasing costs for wineries as their treatment process constitutes an unavoidable and expensive step in wine production. We propose here an alternative method to reduce waste and add value to wine production by exploiting this rich carbon source and use it as a raw material for producing carbon quantum dots (CQDs). A complete synthetic pathway is discussed, comprising the carbonization of the starting material, the screening of the most suitable solvent for the extraction of CQDs from the carbonized mass and their hydrophobic or hydrophilic functionalization. CQDs synthesized with the reported procedure show a bright blue emission ( λ max = 433 ± 13 nm) when irradiated at 366 nm, which is strongly shifted when the wavelength is increased (e.g. emission at around 515 nm when excited at 460 nm). Yields and luminescent properties of CQDs, obtained with two different methods, namely microwave and ultrasound-based extraction, are discussed and compared. This study shows how easy a residue can be converted into an added-value material, thus not only reducing waste and saving costs for the wine-manufacturing industry but also providing a reliable, affordable and sustainable source for valuable materials.

We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is challenging both from a detection point of view, as there are no markers on the targets available, and from a tracking perspective, due to misdetection and false positives. Furthermore, the methods need to be computationally light, despite the complexity of computer vision algorithms, to be used on UAVs with limited payload. To address these issues we propose a multi-staged framework that incorporates fast object detection using an AdaBoost-based approach, coupled with an on-line visual-based tracking algorithm and a recent sensor fusion and state estimation method. Our framework allows for achieving real-time performance with accurate object detection and tracking without any need of markers and customized, high-performing hardware resources.

Future buildings will more and more rely on advanced Building Management Systems (BMS) connected to a variety of sensors, actuators and dedicated networks. Their objectives are to observe the state of rooms and apply automated rules to preserve or increase comfort while economizing energy. In this work, we advocate for the inclusion of a dedicated system for sensors data storage and processing, based on Big Data technologies. This choice enables new potentials in terms of data analytics and applications development, the most obvious one being the ability to scale up seamlessly from one smart building to several, in the direction of smart areas and smart cities. We report in this paper on our system architecture and on several challenges we met in its elaboration, attempting to meet requirements of scalability, data processing, flexibility, interoperability and privacy. We also describe current and future end-user services that our platform will support, including historical data retrieval, visualisation, processing and alarms. The platform, called BBData - Big Building Data, is currently in production at the Smart Living Lab of Fribourg and is offered to several research teams to ease their work, to foster the sharing of historical data and to avoid that each project develops its own data gathering and processing pipeline.

Abstract Sustainably producing plastics with performance properties across a variety of materials chemistries is a major challenge—especially considering that most performance materials use aromatic precursors that are still difficult to source sustainably. Here we demonstrate catalyst-free, melt polymerization of dimethyl glyoxylate xylose, a stabilized carbohydrate that can be synthesized from agricultural waste with 97% atom efficiency, into amorphous polyamides with performances comparable to fossil-based semi-aromatic alternatives. Despite the presence of a carbohydrate core, these materials retain their thermomechanical properties through multiple rounds of high-shear mechanical recycling and could be chemically recycled. Techno-economic and life-cycle analyses suggest selling prices close to those of nylon 66 with a reduction of global warming potential of up to 75%. This work illustrates the versatility of a carbohydrate moiety to impart performance that can compete with that of semi-aromatic polymers across two important materials chemistries.

We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided into 10 categories: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave oven, printers, and televisions (LCD or LED). We measured their consumption in terms of real power (W), reactive power (var), RMS current (A) and phase of voltage relative to current (φ). We now give free access to this ACS-Fl database. The proposed test protocols will help the scientific community to objectively compare new algorithms.

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

Over the last couple of decades, many peer-reviewed publications focused on the bioeconomy, which it is frequently argued to be a key part of the solution to global challenges (climate change, ecosystem degradation). This study investigates the scientific literature on forest bioeconomy by applying a social network analysis to the bibliometric science. The bibliometric network analysis was performed over the time-frame of 2003-2020 to provide an overview on the main aspects characterising the forest bioeconomy issue. The results show that 225 documents on forest bioeconomy were published by 567 organisations from 44 countries. Finland and Canada are the two most productive countries with 32.8% and 12.7% of forest bioeconomy documents respectively. The co-occurrence network map of the keywords shows that the forest bioeconomy is related to three main concepts: sustainable development, bioenergy production, climate change mitigation.