Can managed grasslands enhance pollinators in intensively farmed areas?

Wild flower strips is a common agri-environmental scheme used by farmers and land managers in order to improve biodiversity of pollinators. However, managed grasslands may also provide flower resources for flower visiting insects in agricultural landscapes. Botanically diverse grasslands on arable farms may support a range of wild pollinators, enhancing pollination services of crops. Intensively managed leys, on the other hand, typically contain only a few high-yielding, competitively strong species. One of the aims of the Multiplant project (2014-2018) was to test perennial seed mixtures targeted for bio-energy, feed protein and biodiversity, in order to develop multi-functional seed mixtures for grasslands. In the current study, we specifically investigated if yield (biomass production) and floral resources for pollinators could be simultaneously optimized by varying botanical composition of mixtures and cutting frequency. We tested four different perennial seed mixtures (3-, 5-, 11- and 13-species mixtures) at three sites varying in surrounding environment using three cutting strategies (no cutting, two cuts per year, four cuts per year). We measured flower production during the season, composition of flower-visitors (in functional groups), and biomass production of all plant species in the seed mixtures. The 11- and 13-species mixtures, which were designed to enhance pollinators, produced similar or higher yield than the 3- and 5- species mixtures under certain cutting regimes. The 3- and 5- species mixtures had a high accumulated flower abundance due to excessive flowering of lucerne under the two-cut strategy and white clover under the four-cut strategy. However, the 11- and 13 species mixtures presented a higher diversity of flowers during the flowering season. Interestingly, accumulated flower abundance was not significantly reduced under the two-cut strategy compared to no cut. Pollinator profiles (visits by different functional groups of insects) were plant-species specific, i.e. at all sites, plant species attracted similar types of insects. Legume species mainly attracted large bees (honey bees and bumblebees), while herbs attracted other insect groups, in particular syrphids and other flies. Our results suggest that multi-species grassland mixtures can be designed to support a higher diversity of pollinators without compromising herbage yield. In particular, adding forbs to the grass-legume mixtures and using a two-cut strategy rather than four cuts per year, may increase flower resources available for a larger range of wild pollinators

Kinetics of in situ epoxidation of hemp oil under heterogeneous reaction conditions: an overview with preliminary results

Epoxidised hemp oil (EHO) was synthesised in the laboratory by reacting hemp oil (HO) with peroxyacetic acid (PA) in a batch reactor. The peroxyacetic acid was formed in situ from acetic acid (AA) and hydrogen peroxide (H2O2) in the presence on an acidic ion exchange resin (Amberlite IR-120) as catalyst. The overall reaction can be thought of as having two components. The first being epoxidation, a homogenous reaction which occurs at the interface of the aqueous phase and the HO phase while the second is the formation of PA, a heterogeneous reaction at the interface of the aqueous phase and the solid catalyst phase. The overall reaction kinetics were modelled by applying the Langmuir-Hinshelwood-Hougen-Watson (LHHW) model to heterogeneous reactions. Of the steps in the reaction it is postulated that the formation of PA is rate limiting, while the epoxidation occurs comparatively fast negating the requirement for an additional homogenous model. The diffusion steps in the reaction are also ignored in the kinetic model as it is believed that their effects are negligible due to intensive mixing in the batch reactor. Experiments were used to determine the optimal molar ratios of reactants and it was found that at these conditions 88% conversion of double bonds to epoxy groups occurred. The kinetic model was found to be in good agreement with the experimental results

Impact of a smoking ban in hospitality venues on second hand smoke exposure : a comparison of exposure assessment methods

In May 2010, Switzerland introduced a heterogeneous smoking ban in the hospitality sector. While the law leaves room for exceptions in some cantons, it is comprehensive in others. This longitudinal study uses different measurement methods to examine airborne nicotine levels in hospitality venues and the level of personal exposure of non-smoking hospitality workers before and after implementation of the law.; Personal exposure to second hand smoke (SHS) was measured by three different methods. We compared a passive sampler called MoNIC (Monitor of NICotine) badge, to salivary cotinine and nicotine concentration as well as questionnaire data. Badges allowed the number of passively smoked cigarettes to be estimated. They were placed at the venues as well as distributed to the participants for personal measurements. To assess personal exposure at work, a time-weighted average of the workplace badge measurements was calculated.; Prior to the ban, smoke-exposed hospitality venues yielded a mean badge value of 4.48 (95%-CI: 3.7 to 5.25; n = 214) cigarette equivalents/day. At follow-up, measurements in venues that had implemented a smoking ban significantly declined to an average of 0.31 (0.17 to 0.45; n = 37) (p = 0.001). Personal badge measurements also significantly decreased from an average of 2.18 (1.31-3.05 n = 53) to 0.25 (0.13-0.36; n = 41) (p = 0.001). Spearman rank correlations between badge exposure measures and salivary measures were small to moderate (0.3 at maximum).; Nicotine levels significantly decreased in all types of hospitality venues after implementation of the smoking ban. In-depth analyses demonstrated that a time-weighted average of the workplace badge measurements represented typical personal SHS exposure at work more reliably than personal exposure measures such as salivary cotinine and nicotine

Differential Phase-contrast Interior Tomography

Differential phase contrast interior tomography allows for reconstruction of a refractive index distribution over a region of interest (ROI) for visualization and analysis of internal structures inside a large biological specimen. In this imaging mode, x-ray beams target the ROI with a narrow beam aperture, offering more imaging flexibility at less ionizing radiation. Inspired by recently developed compressive sensing theory, in numerical analysis framework, we prove that exact interior reconstruction can be achieved on an ROI via the total variation minimization from truncated differential projection data through the ROI, assuming a piecewise constant distribution of the refractive index in the ROI. Then, we develop an iterative algorithm for the interior reconstruction and perform numerical simulation experiments to demonstrate the feasibility of our proposed approach

Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem

This paper enhances a well-known dynamic portfolio management algorithm, the BGSS algorithm, proposed by Brandt et al. (Review of Financial Studies, 18(3):831–873, 2005). We equip this algorithm with the components from a recently developed method, the Stochastic Grid Bundling Method (SGBM), for calculating conditional expectations. When solving the first-order conditions for a portfolio optimum, we implement a Taylor series expansion based on a nonlinear decomposition to approximate the utility functions. In the numerical tests, we show that our algorithm is accurate and robust in approximating the optimal investment strategies,which are generated b

On pre-commitment aspects of a time-consistent strategy for a mean-variance investor

In this paper, a link between a time-consistent and a pre-commitment investment strategy is established. We define an implied investment target, which is implicitly con- tained in a time-consistent strategy at a given time step and wealth level. By imposing the implied investment target at the initial time step on a time-consistent strategy, we form a hybrid strategy which may generate better mean-variance efficient frontiers than the time-consistent strategy. We extend the numerical algorithm proposed in Cong and Oosterlee (2016b) to solve constrained time-consistent mean-variance optimization pro- blems. Since the time-consistent and the pre-commitment strategies generate different terminal wealth distributions, time-consistency is not always inferior to pre-commitment

Pricing Bermudan options under Merton jump-diffusion asset dynamics

In this paper, a recently developed regression-based option pricing method, the Stochastic Grid Bundling Method (SGBM), is considered for pricing multidimensional Bermudan options.We compare SGBM with a traditional regression-based pricing approach and present detailed insight in the application of SGBM, including how to configure it and how to reduce the uncertainty of its estimates by control variates. We consider the Merton jump-diffusion model, which performs better than the geometric Brownian motion in modelling the heavy-tailed features of asset price distributions. Our numerical tests show that SGBM with appropriate set-up works highly satisfactorily for pricing multidimensional options under jump-diffusion asset dynamics

Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation

We propose a simulation-based approach for solving the constrained dynamic mean– variance portfolio managemen tproblem. For this dynamic optimization problem, we first consider a sub-optimal strategy, called the multi-stage strategy, which can be utilized in a forward fashion. Then, based on this fast yet sub-optimal strategy, we propose a backward recursive programming approach to improve it. We design the backward recursion algorithm such that the result is guaranteed to converge to a solution, which is at leas tas good as the one generated by the multi-stage strategy. In our numerical tests, highly satisfactory asset allocations are obtained for dynamic portfolio management problems with realistic constraints on the control variable

On robust multi-period pre-commitment and time-consistent mean-variance portfolio optimization

We consider robust pre-commitment and time-consistent mean-variance optimal asset allocation strategies, that are required to perform well also in a worst-case scenario regarding the development of the asset price. We show that worst-case scenarios for both strategies can be found by solving a specific equation each time step. In the unconstrained asset allocation case, the robust pre-commitment as well as the time-consistent strategy are identical to the corresponding robust myopic strategies, by which investors perform robust portfolio control only for one time step and conduct a risk-free strategy afterwards. In the experiments, the robustness of pre-commitment and time-consi