Decarboxylating free fatty acids into fuels using subcritical water

Biofuel producers are looking into new avenues for utilization of their byproducts to improve their profitability in an environmentally friendly way. The biodiesel market has become saturated with very low margins and blenders cannot utilize this product in the winter. In order to address this problem, attention has now shifted towards a range of other possible products that can be made from non-food grade byproduct oils, including green diesel (a diesel having the same analytical signature as petroleum diesel), jet fuels, and other high value products. The major goal of this work was to investigate the hydrothermal conversion of bio-oil to fuel grade hydrocarbons as green diesel. Oleic acid was selected as a model compound of bio-oil to understand the reaction chemistry. Hydrothermal reactions were carried out in a CSTR (batch mode) from 300 to 450oC and reaction time was varied from 10 minutes to 6 hours. GC-FID and GC-TCD were used to analyze the liquid and gas samples, respectively. GC-TCD results showed that decarboxylation and decarbonylation of oleic acid was occurred whereas decarboxylation is the dominating chemical reaction. FTIR results also confirmed the decarboxylation of oleic acid and density measurement of the liquid proves that it falls between the diesel and kerosene. Please click Additional Files below to see the full abstract

PyDaddy: A Python package for discovering stochastic dynamical equations from timeseries data

Most real-world ecological dynamics, ranging from ecosystem dynamics to collective animal movement, are inherently stochastic in nature. Stochastic differential equations (SDEs) are a popular modelling framework to model dynamics with intrinsic randomness. Here, we focus on the inverse question: If one has empirically measured time-series data from some system of interest, is it possible to discover the SDE model that best describes the data. Here, we present PyDaddy (PYthon library for DAta Driven DYnamics), a toolbox to construct and analyze interpretable SDE models based on time-series data. We combine traditional approaches for data-driven SDE reconstruction with an equation learning approach, to derive symbolic equations governing the stochastic dynamics. The toolkit is presented as an open-source Python library, and consists of tools to construct and analyze SDEs. Functionality is included for visual examination of the stochastic structure of the data, guided extraction of the functional form of the SDE, and diagnosis and debugging of the underlying assumptions and the extracted model. Using simulated time-series datasets, exhibiting a wide range of dynamics, we show that PyDaddy is able to correctly identify underlying SDE models. We demonstrate the applicability of the toolkit to real-world data using a previously published movement data of a fish school. Starting from the time-series of the observed polarization of the school, pyDaddy readily discovers the SDE model governing the dynamics of group polarization. The model recovered by PyDaddy is consistent with the previous study. In summary, stochastic and noise-induced effects are central to the dynamics of many biological systems. In this context, we present an easy-to-use package to reconstruct SDEs from timeseries data

Noise-induced schooling of fish

We report on the dynamics of collective alignment in groups of the cichlid fish, Etroplus suratensis. Focusing on small-to-intermediate sized groups ($10<N<100$), we demonstrate that schooling (highly polarised and coherent motion) is noise-induced, arising from the intrinsic stochasticity associated with finite numbers of interacting fish. The fewer the fish, the greater the (multiplicative) noise and therefore the likelihood of alignment. Such empirical evidence is rare, and tightly constrains the possible underlying interactions between fish: computer simulations indicate that E. suratensis align with each other one at a time, which is at odds with the canonical mechanism of collective alignment, local direction-averaging. More broadly, our results confirm that, rather than simply obscuring otherwise deterministic dynamics, noise is fundamental to the characterisation of emergent collective behaviours, suggesting a need to re-appraise aspects of both collective motion and behavioural inference.Comment: Main manuscript: 8 pages (incl. refs), 4 figures. Supplementary: 11 pages, 5 figure

How honeybees respond to heat stress from the individual to colony level

A honey bee colony functions as an integrated collective, with individuals coordinating their behaviour to adapt and respond to unexpected disturbances. Nest homeostasis is critical for colony function; when ambient temperatures increase, individuals switch to thermoregulatory roles to cool the nest, such as fanning and water collection. While prior work has focused on bees engaged in specific behaviours, less is known about how responses are coordinated at the colony level, and how previous tasks predict behavioural changes during a heat stress. Using BeesBook automated tracking, we follow thousands of individuals during an experimentally induced heat stress, and analyse their behavioural changes from the individual to colony level. We show that heat stress causes an overall increase in activity levels and a spatial reorganization of bees away from the brood area. Using a generalized framework to analyse individual behaviour, we find that individuals differ in their response to heat stress, which depends on their prior behaviour and correlates with age. Examining the correlation of behavioural metrics over time suggests that heat stress perturbation does not have a long-lasting effect on an individual’s future behaviour. These results demonstrate how thousands of individuals within a colony change their behaviour to achieve a coordinated response to an environmental disturbance

Attack trees in Isabelle

In this paper, we present a proof theory for attack trees. Attack trees are a well established and useful model for the construction of attacks on systems since they allow a stepwise exploration of high level attacks in application scenarios. Using the expressiveness of Higher Order Logic in Isabelle, we succeed in developing a generic theory of attack trees with a state-based semantics based on Kripke structures and CTL. The resulting framework allows mechanically supported logic analysis of the meta-theory of the proof calculus of attack trees and at the same time the developed proof theory enables application to case studies. A central correctness and completeness result proved in Isabelle establishes a connection between the notion of attack tree validity and CTL. The application is illustrated on the example of a healthcare IoT system and GDPR compliance verification

Multi-band analyses of the bright GRB~230812B and the associated SN2023pel

GRB~230812B is a bright and relatively nearby ($z =0.36$) long gamma-ray burst that has generated significant interest in the community and therefore has been subsequently observed over the entire electromagnetic spectrum. We report over 80 observations in X-ray, ultraviolet, optical, infrared, and sub-millimeter bands from the GRANDMA (Global Rapid Advanced Network for Multi-messenger Addicts) network of observatories and from observational partners. Adding complementary data from the literature, we then derive essential physical parameters associated with the ejecta and external properties (i.e. the geometry and environment) and compare with other analyses of this event (e.g. Srinivasaragavan et al. 2023). We spectroscopically confirm the presence of an associated supernova, SN2023pel, and we derive a photospheric expansion velocity of v $\sim$ 17$\times10^3$ km $s^{-1}$. We analyze the photometric data first using empirical fits of the flux and then with full Bayesian Inference. We again strongly establish the presence of a supernova in the data, with an absolute peak r-band magnitude $M_r = - 19.41 \pm 0.10$. We find a flux-stretching factor or relative brightness $k_{\rm SN}=1.04 \pm 0.09$ and a time-stretching factor $s_{\rm SN}=0.68 \pm 0.05$, both compared to SN1998bw. Therefore, GRB 230812B appears to have a clear long GRB-supernova association, as expected in the standard collapsar model. However, as sometimes found in the afterglow modelling of such long GRBs, our best fit model favours a very low density environment ($\log_{10}({n_{\rm ISM}/{\rm cm}^{-3}}) = -2.16^{+1.21}_{-1.30}$). We also find small values for the jet's core angle $\theta_{\rm core}={1.70^{+1.00}_{-0.71}} \ \rm{deg}$ and viewing angle. GRB 230812B/SN2023pel is one of the best characterized afterglows with a distinctive supernova bump