Fast Bayesian identification of a class of elastic weakly nonlinear systems using backbone curves

This paper introduces a method for the identification of the parameters of nonlinear structures using a probabilistic Bayesian framework, employing a Markov chain Monte Carlo algorithm. This approach uses analytical models to describe the unforced, undamped dynamic responses of structures in the frequency–amplitude domain, known as the backbone curves. The analytical models describing these backbone curves are then fitted to measured responses, found using the resonant-decay method. To investigate the proposed identification method, a nonlinear two-degree-of-freedom example structure is simulated numerically and analytical expressions describing the backbone curves are found. These expressions are then used, in conjunction with the backbone curve data found through simulated experiment, to estimate the system parameters. It is shown that the use of these computationally-cheap analytical expressions allows for an extremely efficient method for modelling the dynamic behaviour, providing an identification procedure that is both fast and accurate. Furthermore, for the example structure, it is shown that the estimated parameters may be used to accurately predict the existence of dynamic behaviours that are well-away from the backbone curve data provided; specifically the existence of an isola is predicted

Progressive Transient Photon Beams

In this work we introduce a novel algorithm for transient rendering in participating media. Our method is consistent, robust, and is able to generate animations of time-resolved light transport featuring complex caustic light paths in media. We base our method on the observation that the spatial continuity provides an increased coverage of the temporal domain, and generalize photon beams to transient-state. We extend the beam steady-state radiance estimates to include the temporal domain. Then, we develop a progressive version of spatio-temporal density estimations, that converges to the correct solution with finite memory requirements by iteratively averaging several realizations of independent renders with a progressively reduced kernel bandwidth. We derive the optimal convergence rates accounting for space and time kernels, and demonstrate our method against previous consistent transient rendering methods for participating media

Psychobiotics Regulate the Anxiety Symptoms in Carriers of Allele A of IL-1β Gene: A Randomized, Placebo-Controlled Clinical Trial

Background. Probiotic oral intake, via modulation of the microbiota-gut-brain axis, can impact brain activity, mood, and behavior; therefore, it may be beneficial against psychological distress and anxiety disorders. Inflammatory cytokines can influence the onset and progression of several neurodegenerative mood disorders, and the IL-1β rs16944 SNP is related to high cytokine levels and potentially affects mood disorders. The aim of this study was to examine the combined effect of IL-1β polymorphism and probiotic administration in mood disorder phenotypes in the Italian population. Methods. 150 subjects were randomized into two different groups, probiotic oral suspension group (POSG) and placebo control group (PCG), and received the relative treatment for 12 weeks. Psychological profile assessment by Hamilton Anxiety Rating Scale (HAM-A), Body Uneasiness Test (BUT), and Symptom Checklist 90-Revised (SCL90R) was administered to all volunteers. Genotyping was performed on DNA extracted from salivary samples. Results. After 12 weeks of intervention, a significant reduction of HAM-A total score was detected in the POSG (p < 0.01), compared to the PCG. Furthermore, IL-1β carriers have moderate risk to develop anxiety (OR = 5.90), and in POSG IL-1β carriers, we observed a reduction of HAM-A score (p = 0.02). Conclusions. Consumption of probiotics mitigates anxiety symptoms, especially in healthy adults with the minor A allele of rs16944 as a risk factor. Our results encourage the use of probiotics in anxiety disorders and suggest genetic association studies for psychobiotic-personalized therapy

General model with experimental validation of electrical resonant frequency tuning of electromagnetic vibration energy harvesters

This paper presents a general model and its experimental validation for electrically tunable electromagnetic energy harvesters. Electrical tuning relies on the adjustment of the electrical load so that the maximum output power of the energy harvester occurs at a frequency which is different from the mechanical resonant frequency of the energy harvester. Theoretical analysis shows that for this approach to be feasible the electromagnetic vibration energy harvester’s coupling factor must be maximized so that its resonant frequency can be tuned with the minimum decrease of output power. Two different-sized electromagnetic energy harvesters were built and tested to validate the model. Experimentally, the micro-scale energy harvester has a coupling factor of 0.0035 and an untuned resonant frequency of 70.05 Hz. When excited at 30 mg, it was tuned by 0.23 Hz by changing its capacitive load from 0 to 4000 nF; its effective tuning range is 0.15 Hz for a capacitive load variation from 0 to 1500 nF. The macro-scale energy harvester has a coupling factor of 552.25 and an untuned resonant frequency of 95.1 Hz and 95.5 Hz when excited at 10 mg and 25 mg, respectively. When excited at 10 mg, it was tuned by 3.8 Hz by changing its capacitive load from 0 to 1400 nF; it has an effective tuning range of 3.5 Hz for a capacitive load variation from 0 to 1200 nF. When excited at 25 mg, its resonant frequency was tuned by 4.2 Hz by changing its capacitive load from 0 to 1400 nF; it has an effective tuning range of about 5 Hz. Experimental results were found to agree with the theoretical analysis to within 10%

Uncertainty in Simulating Wheat Yields Under Climate Change

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking

Mycotoxin mixtures in food and feed: holistic, innovative, flexible risk assessment modelling approach: MYCHIF

Mycotoxins are toxic compounds mainly produced by fungi of the genera Aspergillus, Penicillium and Fusarium. They are present, often as mixtures, in many feed and food commodities including cereals, fruits and vegetables. Their ubiquitous presence represents a major challenge to the health and well being of humans and animals. Hundreds of compounds are listed as possible mycotoxins occurring in raw and processed materials destined for human food and animal feed. In this study, mycotoxins of major toxicological relevance to humans and target animal species were investigated in a range of crops of interest (and their derived products). Extensive Literature Searches (ELSs) were undertaken for data collection on: (i) ecology and interaction with host plants of mycotoxin producing fungi, mycotoxin production, recent developments in mitigation actions of mycotoxins in crop chains (maize, small grains, rice, sorghum, grapes, spices and nuts), (ii) analytical methods for native, modified and co-occurring mycotoxins (iii) toxicity, toxicokinetics, toxicodynamics and biomarkers relevant to humans and animals (poultry, suidae (pig, wild boar), bovidae (sheep, goat, cow, buffalo), rodents (rats, mice) and others (horses, dogs), (iv) modelling approaches and key reference values for exposure, hazard and risk modelling. Comprehensive databases were created using EFSA templates and were stored in the MYCHIF platform. A range of approaches were implemented to explore the modelling of external and internal exposure as well as dose-response of mycotoxins in chicken and pigs. In vitro toxicokinetic and in vivo toxicity databases were exploited, both for single compounds and mixtures. However, large data gaps were identified particularly with regards to absence of common statistical and study designs within the literature and constitute an obstacle for the harmonisation of internal exposure and dose-response modelling. Finally, risk characterisation was also performed for humans as well as for two animal species (i.e. pigs and chicken) using available tools for the modelling of internal dose and a component-based approach for selected mycotoxins mixtures