Effect of temperature on raw whole milk density and its potential impact on milk payment in the dairy industry

The objective of this study was to determine the effect of temperature on whole milk density measured at four different temperatures: 5, 10, 15, and 20 °C. A total of ninety-three individual milk samples were collected from morning milking of thirty-two Holstein Friesian dairy cows, of national average genetic merit, once every two weeks over a period of 4 weeks and were assessed by Fourier transform infrared spectroscopy for milk composition analysis. Density of the milk was evaluated using two different analytical methods: a portable density meter DMA35 and a standard desktop model DMA4500M (Anton Paar GmbH, UK). Milk density was analysed with a linear mixed model with the fixed effects of sampling period, temperature and analysis method; triple interaction of sampling period x analysis method x temperature; and the random effect of cow to account for repeated measures. The effect of temperature on milk density (ρ) was also evaluated including temperature (t) as covariate with linear and quadratic effects within each analytic method. The regression equation describing the curvature and density–temperature relationship for the DMA35 instrument was ρ = 1.0338−0.00017T−0.0000122T² (R² = 0.64), while it was ρ = 1.0334 + 0.000057T−0.00001T² (R² = 0.61) for DMA4500 instrument. The mean density determined with DMA4500 at 5 °C was 1.0334 g cm⁻³, with corresponding figures of 1.0330, 1.0320 and 1.0305 g cm⁻³ at 10, 15 and 20 °C, respectively. The milk density values obtained in this study at specific temperatures will help to address any bias in weight–volume calculations and thus may also improve the financial and operational control for the dairy processors in Ireland and internationally.     

A red code triggers an unintended approach motivation toward sweet ultra-processed foods: Possible implications for front-of-pack labels

Front-of-package labels (FOPL) are recommended to reduce consumer intake of ultra-processed food products (UPP). The multiple traffic-light label is one example of FOPL that indicates the content of target nutrients in products by displaying red (high), amber (medium), and/or green (low) color-coding. The red code may implicitly enhance sweetness perception and approach dispositions toward sweet UPP via cross-modal visual-taste interactions. We conducted two experiments to examine the possibility of contradictory influence of explicitly learned and implicit cross-modal associations on the emotional responses evoked by UPP pictures. In both experiments, we first explicitly associated the color codes with health-related meanings. In Experiment I (n = 78), a psychometric tool estimated the emotional responses (pleasantness and arousal ratings) evoked by UPP pictures when preceded by red, amber, or green color-codes. In Experiment II (n = 24), we recorded participants’ electrocortical brain activity to assess the early posterior negativity (EPN) component as an index of the emotional responses to UPP. The reported pleasantness (Experiment I) and the EPN amplitude (Experiment II) were greater for sweet UPP relative to salty UPP when primed with red codes but not when primed with green or amber. A red code increased positive emotions toward sweet UPP despite its explicit association with increased health-risks. Thus, the use of multiple traffic-lights might lead to an unintended implicit approach behavior toward sweet UPP. Designers, researchers, and policy makers may consider color-taste cross-modal associations when designing, testing, and applying FOPL.     

Piezoresistive in-situ strain sensing of composite laminate structures

Various methods have been developed to monitor the health and strain state of carbon fiber reinforced polymers, each with a unique set of pros and cons. This research assesses the use of piezoresistive sensors for in situ strain measurement of carbon fiber and other composite structures in multidirectional laminates. The piezoresistive sensor material and the embedded circuitry are both evaluated. For the piezoresistive sensor, a conductive nickel nanocomposite sensor is compared with the piezoresistivity of the carbon fiber itself. For the circuit, the use of carbon fibers already present in the structure is compared with the use of nickel coated carbon fiber. Successful localized strain sensing is demonstrated for several sensor and circuitry configurations. Numerous engineering applications are possible in the ever-growing field of carbon-composites.     

Physical characteristics of AFEX-pretreated and densified switchgrass,prairie cord grass,and corn stover

The goal of the study was to evaluate and compare the physical properties of control, pretreated and densified corn stover, switchgrass, and prairie cord grass samples. Ammonia Fiber Expansion (AFEX) pretreated switchgrass, corn stover, and prairie cord grass samples were densified by using the comPAKco device developed by Federal Machine Company of Fargo, ND. The densified biomass were referred as “PAKs” in this study. All feedstocks were ground into three different grind size of 2, 4 and 8 mm prior to AFEX pretreatment and the impact of grinding on pellet properties was studied. The results showed that the physical properties of AFEX-PAKed material were not influenced by the initial grind size of the feedstocks. The bulk density of the AFEX-PAKed biomass increased by 1.2–6 fold as compared to untreated and AFEX-pretreated materials. The durability of the AFEX-PAKed materials were between 78.25 and 95.2%, indicating that the AFEX-PAKed biomass can be transported easily. To understand the effect of storage on the physical properties of these materials, samples were stored in the ambient condition (20 ± 2 °C and 70 ± 5% relative humidity) for six months. After storage, thermal properties of the biomass did not change but glass transition temperature decreased. The water absorption index and water solubility index of AFEX-treated and AFEX-PAKed biomass showed mixed trends after storage. Moisture content decreased and durability increased upon storage.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

多先验融合的图像显著性目标检测算法

为了更加准确地检测出图像中的显著性目标，提出了多先验融合的显著性目标检测算法。针对传统中心先验对偏离图像中心的显著性目标会出现检测失效的情况，提出在多颜色空间下求显著性目标的最小凸包交集来确定目标的大致位置，以凸包区域中心计算中心先验。同时通过融合策略将凸包区域中心先验、颜色对比先验和背景先验融合并集成到特征矩阵中。最后通过低秩矩阵恢复模型生成结果显著图。在公开数据集MSRA1000和ESSCD上的仿真实验结果表明，MPLRR能够得到清晰高亮的显著性目标视觉效果图，同时F，AUC，MAE等评价指标也比现有的许多方法有明显提升。