Farmers' preferences for adopting on-farm concentration of raw milk: Results from a discrete choice experiment in Germany

Milk concentrates are used in the manufacturing of dairy products such as yogurt and cheese or are processed into milk powder. Processes for the nonthermal separation of water and valuable milk ingredients are becoming increasingly widespread at farm level. The technical barriers to using farm-manufactured milk concentrate in dairies are minimal, hence the suspicion that the practice of on-farm raw milk concentration is still fairly uncommon for economic reasons. This study, therefore, set out to investigate farmers' potential willingness to adopt a raw milk concentration plant. The empirical analysis was based on discrete choice experiments with 75 German dairy farmers to identify preferences and the possible adoption of on-farm raw milk concentration. The results showed that, in particular, farmers who deemed the current milk price to be insufficient viewed on-farm concentration using membrane technology as an option for diversifying their milk sales. We found no indication that adoption would be impeded by a lack of trustworthy information on milk processing technologies or capital.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

非常规气藏储层改造体积评价方法研究

为精确描述非常规气藏压裂后的复杂流动特征及定量评价储层改造体积（SRV），利用自主研发的缝网重构算法“破裂树生长法”建立压后缝网模型，并以此为基础提出了使用拟稳态流动时特定的压力等值线来确定SRV范围的定量评价方法，最后以长宁201井区为例进行了矿场实例分析。该方法根据微地震监测点的位置，重构出微裂缝网的连通关系。在复杂微裂缝网的基础上建立离散裂缝地质模型并进行生产数值模拟，根据数值模拟结果的压力分布精确划定SRV的范围。利用该方法计算出长宁201井区的SRV体积为0.052 1 km3，以该缝网模型的数值模拟产量预测结果符合实际生产规律，方法实用性较好。     

Predictive environmental hydrogen embrittlement on fracture toughness of commercial ferritic steels with hydrogen-modified fracture strain model

In this work, a practical numerical model with few parameters was proposed for the prediction of environmental hydrogen embrittlement. The proposed method adopts hydrogen enhanced plasticity-based mechanism in a fracture strain model to describe hydrogen embrittlement. Fracture toughness degradation of three commercial steels SA372J70, AISI4130 and X80 in high pressure hydrogen environment were investigated. Firstly, governing equations for hydrogen distribution and material damage evolution was established. Hydrogen enhanced localized flow softening effect was coupled within fracture strain dependency on stress triaxiality. Then, the numerical implementation and identification process of model parameters was described. Model parameters of the investigated steels were determined based on experiment results from literatures. Finally, with the calibrated model, fracture toughness reduction of the steels was predicted in a wide range of hydrogen pressure. The prediction results were compared with experimental results. Reasonable accuracy was reached. The proposed method is an attempt to reach balance between physical accurate prediction and engineering practicality. It is promising to provide a simplified numerical tool for the design and fit for service evaluation of hydrogen storage vessels.     

Experimental study and model predictions on helium release in an enclosure with single or multiple vents

This paper presents experiments performed at Canadian Nuclear Laboratories (CNL) to examine the dispersion behaviour of helium in a polycarbonate enclosure that was representative of a residential parking garage. The purpose was to gain a better understanding of the effect of buoyancy- or wind-driven natural ventilation on hydrogen dispersion behaviour. Although hydrogen dispersion studies have been reported extensively in the literature, gaps still exist in predictive methods for hazard analysis. Helium, a simulant for hydrogen, was injected near the centre of the floor with a flow rate ranging from 5 to 75 standard litres per minute through an upward-facing nozzle, resulting in an injection Richardson number ranging between 10^?1 and 10². The location of the nozzle varied from the bottom of the enclosure to near the ceiling to examine the impact of the nozzle elevation on the development of a stratified layer in the upper region of the enclosure. When the injection nozzle was placed at a sufficiently low elevation, the vertical helium profile always consisted of a homogenous layer at the top overlaying a stratified layer at the bottom. To simulate outdoor environmental conditions, a fan was placed in front of each vent to examine the effect of opposing or assisting wind on the dispersion. The helium transients in the uniform layer predicted with analytical models were in good agreement with the measured transients for most tests. Model improvements are required for adequately predicting transients with primarily stratified profiles or strong opposing wind.     

Flow distribution and mass removal in floating treatment wetlands arranged in series and spanning the channel width

Floating treatment wetlands (FTWs) use plants’ roots for water quality improvement. The plants are supported by a buoyant structure deployed at the water surface. The roots form a porous zone beneath the structure and remove pollutants carried in suspension through filtering, absorption and uptake. This paper used CFD simulation to model FTWs arranged in series and spanning the channel width and to study the effects of root length and spacing between FTWs on flow distribution and mass removal. The root zone was modelled as a porous media, and removal was computed using first-order decay, for which a range of removal constants was tested. Longer roots increased the reactive volume of the root zone, which increased the fraction of pollutant inflow entering the FTWs. Increasing the distance between FTWs allowed greater mixing between water that went through and beneath the upstream FTW. This increased the concentration entering each FTW, which enhanced mass removal per FTW. However, a larger distance between FTWs reduced the number of FTWs in the channel, reducing the reactive volume. In the tradeoff between mixing and reactive volume, the reactive volume was more important, such that total removal in the channel increased with longer roots and more units of FTW (shorter gap distance). However, removing the gap entirely was detrimental, as FTWs in series removed more mass than a continuous FTW of same volume. This study points to two design recommendations for FTWs in series. First, if resources for building FTWs are not limiting, but the channel length is, it is preferable to prioritize higher reactive volume (shorter gap distance) to achieve maximum removal per channel length. Second, if resources for FTWs are limiting, but channel length is not, it is better to place the FTWs with a longer gap distance, preferably along enough to allow mixing over the full depth between FTWs, as this will achieve maximum removal per FTW.     

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

Liquefaction behavior of fiber-reinforced calcareous sands in unidirectional and multidirectional simple shear tests

A study on the liquefaction resistance of calcareous sands reinforced with polypropylene fibers was reported. Stress-controlled cyclic simple shear tests were conducted on specimens prepared at a relative density of 50%, with and without fiber reinforcements. The liquefaction behavior was investigated by considering the effects of fiber contents ranging from 0% to 1%, fiber lengths varying from 3 mm to 12 mm and loading patterns. The results indicated that increasing fiber content and fiber length resulted in a decrease in the deformation, a reduction in pore pressure accumulation rate, and improved the liquefaction resistance of calcareous sands. Additionally, the risk of soil liquefaction could be significantly reduced when the fiber content was greater than 0.8%. The multidirectional loading had a considerable effect in reducing the liquefaction resistance compared to unidirectional loading. Further, the stiffness degradation of calcareous sands decreased with increasing fiber content and fiber length. The pore pressure generated in the cyclic tests was analyzed and was found to be affected by fiber content. A pore pressure prediction model was proposed to obtain the pore pressure characteristics of fiber-reinforced calcareous sands under various fiber content conditions.     

Concentration dependence of physical properties of low temperature processed ZnO quantum dots thin films on polyethylene terephthalate as potential electron transport material for perovskite solar cell

Colloidal Zinc oxide quantum dots (ZnO QDs) prepared with varying concentrations through precipitation method were deposited on flexible ITO/PET substrates using spin-coating technique. Various characterization tools were utilized to investigate the morphological, structural, electrical and optical properties of the films. The crystallinity of the films was found to improve with increasing ZnO QD concentration (ZQ_C) as evident from the X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) studies. Crystallographic and optical parameters were evaluated and explained in depth. The average nanograin size and bandgap were increased and decreased respectively, from ～5 nm to ～8 nm and 3.29 eV–3.24 eV with an increase in ZQ_C from 10 mg/mL to 70 mg/mL. Columnar structure growth of the films is revealed by AFM results. The films showed decent optical transparency up to 81%. All the ZnO films exhibited n-type semiconducting property as indicated by the electrical measurements with carrier mobility and low resistivity of 12.21–26.63 cm²/Vs and 11.84 × 10^?3 to 13.16 × 10^?3 Ω cm respectively. Based on the experimental findings, ZnO QD nanostructure film grown at 50 mg/mL is envisaged to be a potential candidate for flexible perovskite photovoltaic application.