Investigating the value dairy farmers place on a reduction of lameness in their herds using a willingness to pay approach

A survey was conducted to elicit dairy farmers’ willingness to pay (WTP) to reduce the prevalence of lameness in their herds. A choice experiment questionnaire was administered using face-to-face interviews of 163 farmers in England and Wales. Whole herd lameness assessments by trained researchers recorded a mean lameness prevalence of nearly 24% which was substantially higher than that estimated by farmers. Farmers’ responses to a series of attitudinal questions showed that they strongly agreed that cows can suffer a lot of pain from lameness and believed that they could reduce lameness in their herds. Farmers’ mean WTP to avoid lameness amounted to UK£411 per lame cow but with considerable variation across the sample. Median WTP of UK£249 per lame cow was considered a better measure of central tendency for the sample. In addition, the survey found that farmers had a substantial WTP to avoid the inconvenience associated with lameness control (a median value of UK£97 per lame cow) but that they were generally prepared to incur greater inconvenience if it reduced lameness. The study findings suggest that farmers need a better understanding of the scale and costs of lameness in their herds and the benefits of control. To encourage action, farmers need to be convinced that lameness control measures perceived as inconvenient will be cost effective.

Solvent-Dependent Formation of Os(0) Complexes by Electrochemical Reduction of [Os(CO)(2,2′-bipyridine)(L)Cl2]; L = Cl−,PrCN

Cyclic voltammetry and ultraviolet−visible/infrared (UV−vis/IR) spectroelectrochemistry were used to study the cathodic electrochemical behavior of the osmium complexes mer-[OsIII(CO) (bpy)Cl3] (bpy = 2,2′-bipyridine) and trans(Cl)-[OsII(CO) (PrCN)(bpy)Cl2] at variable temperature in different solvents (tetrahydrofuran (THF), butyronitrile (PrCN), acetonitrile (MeCN)) and electrolytes (Bu4NPF6, Bu4NCl). The precursors can be reduced to mer-[OsII(CO) (bpy•−)Cl3]2− and trans(Cl)-[OsII(CO)(PrCN) (bpy•−)Cl2]−, respectively, which react rapidly at room temperature, losing the chloride ligands and forming Os(0) species. mer-[OsIII(CO) (bpy)Cl3] is reduced in THF to give ultimately an Os−Os-bonded polymer, probably [Os0(CO) (THF)-(bpy)]n, whereas in PrCN the well-soluble, probably mononuclear [Os0(CO) (PrCN)(bpy)], species is formed. The same products were observed for the 2 electron reduction of trans(Cl)-[OsII(CO)(PrCN) (bpy)Cl2] in both solvents. In MeCN, similar to THF, the[Os0(CO) (MeCN)(bpy)]n polymer is produced. It is noteworthy that the bpy ligand in mononuclear [Os0(CO) (PrCN)(bpy)] is reduced to the corresponding radical anion at a significantly less negative potential than it is in polymeric [Os0(CO) (THF)(bpy)]n: ΔE1/2 = 0.67 V. Major differences also exist in the IR spectra of the Os(0) species: the polymer shows a broad ν(CO) band at much smaller wavenumbers compared to the soluble Os(0) monomer that exhibits a characteristic ν(Pr-CN) band below 2200 cm−1 in addition to the intense and narrow ν(CO) absorption band. For the first time, in this work the M0-bpy(M = Ru, Os) mono- and dicarbonyl species soluble in PrCN have been formulated as a mononuclear complex. Density functional theory (DFT) and time-dependent-DFT calculations confirm the Os(0) oxidation state and suggest that [Os0(CO)(PrCN)(bpy)] is a square planar moiety. The reversible bpy-based reduction of [Os0(CO) (PrCN)(bpy)] triggers catalytic reduction of CO2 to CO and HCOO−.

An analysis of UK policies for domestic energy reduction using an agent based tool

This paper introduces a new agent-based model, which incorporates the actions of individual homeowners in a long-term domestic stock model, and details how it was applied in energy policy analysis. The results indicate that current policies are likely to fall significantly short of the 80% target and suggest that current subsidy levels need re-examining. In the model, current subsidy levels appear to offer too much support to some technologies, which in turn leads to the suppression of other technologies that have a greater energy saving potential. The model can be used by policy makers to develop further scenarios to find alternative, more effective, sets of policy measures. The model is currently limited to the owner-occupied stock in England, although it can be expanded, subject to the availability of data.

The real-time optimisation of DNO owned storage devices on the LV network for peak reduction

Energy storage is a potential alternative to conventional network reinforcementof the low voltage (LV) distribution network to ensure the grid’s infrastructure remainswithin its operating constraints. This paper presents a study on the control of such storagedevices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, wherethe objective is to achieve the greatest peak reduction in demand, for a given storagedevice specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-pointcontroller and bench marked against a control algorithm with a perfect forecast. A specificcase study, using storage on the LV network, is presented, and the results of each algorithmare compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

A peak reduction scheduling algorithm for storage devices on the low voltage network

Reinforcing the Low Voltage (LV) distribution network will become essential to ensure it remains within its operating constraints as demand on the network increases. The deployment of energy storage in the distribution network provides an alternative to conventional reinforcement. This paper presents a control methodology for energy storage to reduce peak demand in a distribution network based on day-ahead demand forecasts and historical demand data. The control methodology pre-processes the forecast data prior to a planning phase to build in resilience to the inevitable errors between the forecasted and actual demand. The algorithm uses no real time adjustment so has an economical advantage over traditional storage control algorithms. Results show that peak demand on a single phase of a feeder can be reduced even when there are differences between the forecasted and the actual demand. In particular, results are presented that demonstrate when the algorithm is applied to a large number of single phase demand aggregations that it is possible to identify which of these aggregations are the most suitable candidates for the control methodology.

A phosphoramidite-based [FeFe]H2ase functional mimic displaying fast electrocatalytic proton reduction

A phosphoramidite modified [FeFe]H2ase mimic is studied as a model for photodriven production of H2. On cathodic activation, the pyridyl–phosphoramidite complex exhibits a strongly enhanced rate of proton reduction over the previously reported pyridylphosphine model at the same overpotential. Analysis of the cyclic voltammograms shows an apparent H2 evolution rate strongly influenced by the presence of both side-bound pyridyl and phosphorous-bound dimethylamino moieties at the phosphoramidite ligands. This difference is ascribed to the basic amines acting as proton relays.

Ring-opening polymerization in molten PEEK: transient reduction of melt-viscosity by macrocyclic aromatic thioetherketones

Blends of PEEK with macrocyclic thioether-ketones show initial melt-viscosities reduced by more than an order of magnitude relative to the polymer itself, enabling more facile processing and fabrication. On raising the temperature of the melt, however, the macrocycle undergoes spontaneous, entropically-driven ring-opening polymerization (ED-ROP), so that the properties of the final polymer should not, in principle, be compromised by the presence of low-MW macrocyclic material.

Electrocatalytic reduction of carbon dioxide with a manganese(I)tricarbonyl complex containing a nonaromatic α‑diimine ligand

The 2e reduced anion [Mn(CO)3(iPr-DAB)]− (DAB = 1,4- diazabuta-1,3-diene, iPr = isopropyl) was shown to convert in the presence of CO2 and a small amount of water to the unstable complex [Mn(CO)3(iPr-DAB)(η1-OCO2H)] (OCO2H− = unidentate bicarbonate) that was further reductively transformed to give a stable catalytic intermediate denoted as X2, showing νs(OCO) 1672 and 1646 (sh) cm−1. The subsequent cathodic shift by ca. 650 mV in comparison to the single 2e cathodic wave of the parent [Mn(CO)3(iPr-DAB)Br] triggers the reduction of intermediate X2 and catalytic activity converting CO2 to CO. Infrared spectroelectrochemistry has revealed that the high excess of CO generated at the cathode leads to the conversion of [Mn(CO)3(iPr-DAB)]− to inactive [Mn(CO)5]−. In contrast, the five-coordinate anion [Mn(CO)3(pTol-DAB)]−(pTol = 4-tolyl) is completely inert toward both CO2 and H2O (solvolysis). This detailed spectroelectrochemical study is a further contribution to the development of sustainable electro- and photoelectrocatalysts of CO2 reduction based on abundant first-row transition metals, in particular manganese.

Effects of high hydrostatic pressure and chemical reduction on the emulsification properties of gum arabic

Gum arabic is widely used in the food industry as an additive, both as a thickener and an emulsifier. This study has compared the emulsification properties of two types of gums, KLTA (Acacia senegal) and GCA (Acacia seyal), both in their native/untreated forms and after exposure to high pressure (800 MPa). Further studies were undertaken to chemically modify the disulphide linkages present and to investigate the effects of their reduction on the diffusion of the carbohydrate materials. The emulsification properties of the gum samples were examined by determining the droplet size distribution in a ‘‘model’’ oil-in-water system. Results showed that high pressure treatment and chemical reduction of gums changed the emulsification properties of both gums. The high molecular weight component in arabinogalactanproteins (AGP/GP), and more ‘‘branched’’ carbohydrates present in gum arabic, may be responsible for the emulsification properties of GCA gum, indicating that the emulsification mechanisms for KLTA and GCA were different.

[M(CO)4(2,2′-bipyridine)] (M = Cr, Mo, W) as efficient catalysts for electrochemical reduction of CO2 to CO at a gold electrode

Group 6 complexes of the type [M(CO)4(bpy)] (M=Cr, Mo, W) are capable of behaving as electrochemical catalysts for the reduction of CO2 at potentials less negative than those for the reduction of the radical anions [M(CO)4(bpy)].−. Cyclic voltammetric, chronoamperometric and UV/Vis/IR spectro-electrochemical data reveal that five-coordinate [M(CO)3(bpy)]2− are the active catalysts. The catalytic conversion is significantly more efficient in N-methyl-2-pyrrolidone (NMP) compared to tetrahydrofuran, which may reflect easier CO dissociation from 1e−-reduced [M(CO)4(bpy)].− in the former solvent, followed by second electron transfer. The catalytic cycle may also involve [M(CO)4(H-bpy)]− formed by protonation of [M(CO)3(bpy)]2−, especially in NMP. The strongly enhanced catalysis using an Au working electrode is remarkable, suggesting that surface interactions may play an important role, too.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

Gaussian processes autoencoder for dimensionality reduction

Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing. The autoencoder is a powerful dimensionality reduction technique based on minimizing reconstruction error, and it has regained popularity because it has been efficiently used for greedy pretraining of deep neural networks. Compared to Neural Network (NN), the superiority of Gaussian Process (GP) has been shown in model inference, optimization and performance. GP has been successfully applied in nonlinear Dimensionality Reduction (DR) algorithms, such as Gaussian Process Latent Variable Model (GPLVM). In this paper we propose the Gaussian Processes Autoencoder Model (GPAM) for dimensionality reduction by extending the classic NN based autoencoder to GP based autoencoder. More interestingly, the novel model can also be viewed as back constrained GPLVM (BC-GPLVM) where the back constraint smooth function is represented by a GP. Experiments verify the performance of the newly proposed model.