Effects of red-wine grape pomace on the quality and sensory attributes of beef hamburger patty

This study was conducted to evaluate the effect of red-wine grape pomaces on the quality and sensory attributes of beef hamburger patties. Both phenolic content and antioxidant activity were assessed using Syrah, Merlot and Cabernet Sauvignon pomaces. Following the assessment, hamburger patties were prepared with Merlot pomace at 0%, 2% and 4% for the patty quality and sensory attributes. Grape seeds possessed significantly higher phenolics and antioxidant activities over the seedless pomace (P < 0.05), whereas no significant difference was found for phenolics and antioxidant activities within the seeds and seedless pomaces. The patty pH decreased as the pomace was added for 2% and 4%. Colour values (L*, a* and b*) of patties lowered as the pomace was added. Allo-Kramer shear force and hardness values increased while cooking yield decreased (P < 0.05) with the addition of pomace. No significant difference between control and Merlot patties was found for flavour, juiciness and colour, whereas lower sensory attributes were observed for texture, taste and overall acceptability. It is observed that the addition of fermented red-wine grape pomace provides hamburger patties with health promoting factors such as antioxidant and other functional components, but it also provided darker, sourer and lower cooking yield.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

High-Throughput Screening for Phase-Change Memory Materials

Phase change memory (PCM) is an emerging non-volatile data storage technology concerned by the semiconductor industry. To improve the performances, previous efforts have mainly focused on partially replacing or doping elements in the flagship Ge-Sb-Te (GST) alloy based on experimental “trial-and-error” methods. Here, the current largest scale PCM materials searching is reported, starting with 124 515 candidate materials, using a rational high-throughput screening strategy consisting of criteria related to PCM characteristics. In the results, there are 158 candidates screened for PCM materials, of which ≈68% are not employed. By further analyses, including cohesive energy, bond angle analyses, and Born effective charge, there are 52 materials with properties similar to the GST system, including Ge₂Bi₂Te₅, GeAs₄Te₇, GeAs₂Te₄, so on and other candidates that have not been reported, such as TlBiTe₂, TlSbTe₂, CdPb₃Se₄, etc. Compared with GST, materials with close cohesive energy include AgBiTe₂, TlSbTe₂, As₂Te₃, TlBiTe₂, etc., indicating possible low power consumption. Through further melt-quenching molecular dynamic calculation and structural/electronic analyses, Ge₂Bi₂Te₅, CdPb₃Se₄, MnBi₂Te₄, and TlBiTe₂ are found suitable for optical/electrical PCM applications, which further verifies the effectiveness of this strategy. The present study will accelerate the exploration and development of advanced PCM materials for current and future big-data applications.     

From enfant terrible to problem-solver? Tracing the competing discourse to explain blockchain-related technological diffusion

Since the inception of blockchain-related technologies over a decade ago, investors’ uptake of the technologies has grown rapidly. But even with the advancement in standing from conceptual beginnings to real-world experimentation, mainstream adoption of the technologies in either organizational or individual contexts has yet to eventuate. To resolve this paradox, we examine progression of the technologies’ diffusion instead, and in the process, seek to uncover the underlying dynamics of sentiments at play. Diffusion involves multiple actions by different parties, with social discourses via diverse media playing an essential role. In demonstrating the interplay of support to resistance via media framing and the diffusion of innovation theory, findings from our paper contribute to the technology adoption literature. Understanding resistant behavior toward a new technology area could help resolve potential issues that may arise. This would hopefully lead to better technological implementation outcomes in future.     

Overland Flow along Stone Covered Slope Land Simulated with Semi-Analytical and Numerical Models

Overland flow is an important hydrological response of catchments to rainstorms and contributes to soil erosion and nutrient loss. The kinematic wave model is known to describe the transformation of rainfall to overland flow. Through this, field studies were conducted on a hillslope to simulate water scouring from upstream with a complex surface condition, which was covered with different sizes and percentages of stones. Existing semi-analytical and numerical models were adopted to describe the overland flow in the field. Results indicate that both semi-analytical and numerical models could be applied to describe the process of overland flow. Furthermore, predicted outflow rates by the semi-analytical and the numerical model showed strong correlation with the field measured outflow rates, respectively (NS?=?(0.926, 0.942, 0.992), RE?=?(5.5%, 4.7%, 1.7%) for the semi-analytical model, and NS?=?(0.817, 0.952, 0.992), RE?=?(5.5%, 5.5%, 2.1%) for the numerical model). Besides, hydraulic parameters (Reynolds number-Re, Froude numbers-Fr, Darcy-Weisbach-f, hydraulic shear stress-τ, stream power-ω, water wave celerity-v_w) at any time and distance could be described by the semi-analytical method, and the parameter n/h (an important factor indicating the energy of water and wave flow celerity) could successfully characterize the average hydraulic parameters, and all of the hydraulic parameters are fitted to the expression of y?=?a(n/h)^b.

     

Energy price shocks,household location patterns and housing crises: Theory and implications

The U.S. housing collapse in 2007 is widely blamed for inducing a financial crisis that spread to the real economy and caused a severe and prolonged downturn. This paper develops a model to investigate the role of gasoline price shocks in triggering the housing market collapse and identifies a new channel through which energy price shocks affect the financial market and the macro economy. Results suggest that unanticipated gasoline price shocks increase the cost of work commutes, lower the value of homes away from the city center, and increase foreclosure rates as homeowners either cannot afford mortgage payments amid elevated gas expenditures or seek to abandon underwater homes. The model predicts that gasoline price shocks disproportionately affect suburban households that face greater exposure due to longer commutes and lower incomes. Empirical evidence from the 2007–08 housing collapse is presented to corroborate this theory.     

Novel nanorod Ti0·7Ir0·3O2 prepared by facile hydrothermal process: A promising non-carbon support for Pt in PEMFCs

Late transition metal doped TiO₂ has been exploited for generating efficient catalyst support by enhancing electrical conductivity and modifying properties of TiO₂. The Ti_0·7Ir_0·3O₂ nanorod (NRs), a novel catalyst support for Pt nanoparticles, was prepared for the first time via single-step hydrothermal process at low temperature using IrCl₃·3H₂O and TiCl₄ as starting materials. We found that the Ti_0·7Ir_0·3O₂ NRs with 70–80 nm in length and 25–30 nm in width is successful prepared at 210 °C for 12 h without utilizing surfactants or stabilizers. In addition, the Ti_0·7Ir_0·3O₂ NRs was presented principally as a single-phase solid with the TiO₂ is in the rutile form with high crystallinity without using further treatment after synthesis. More importantly, we found that the Ti_0·7Ir_0·3O₂ NRs possesses high electrical conductivity (0.028 S cm⁻¹) dealing the intrinsically non-conducted drawback of TiO₂. The Pt nanoparticles were then deposited on the support of Ti_0·7Ir_0·3O₂ NRs via chemical reduction method. The properties of 20 wt % Pt/Ti_0·7Ir_0·3O₂ NRs electrocatalyst were characterized by X-ray diffraction (XRD), Transmission electron microscopy (TEM), the cyclic voltammetry (CV). The uniformly distributed small Pt nanoparticles (3–4 nm diameter) were well adhered to the Ti_0·7Ir_0·3O₂ NRs. The electrochemically active surface area (ECSA) of 20 wt % Pt/Ti_0·7Ir_0·3O₂ NRs was higher than that of the commercial 20 wt % Pt/C (E-TEK) due to the small size and good dispersion of Pt nanoparticles on the surface of Ti_0·7Ir_0·3O₂ NRs. Moreover, the ECSA value of the Pt/Ti_0·7Ir_0·3O₂ NRs retained up to 88% after 2000 cycles of cyclic voltammetry, suggesting the high stability of catalyst resulted from strong metal support interaction (SMSI) of Titania-based materials with the noble metals. More importantly, the onset potential of Pt/Ti_0·7Ir_0·3O₂ NRs catalyst towards oxygen reduction reaction is more positive (∼80 mV) compared to commercial Pt/C, indicating the high catalytic activity of the Pt/Ti_0·7Ir_0·3O₂ NRs catalyst. The results of this research suggested that novel Ti_0·7Ir_0·3O₂ NRs could be applied as promising robust non-carbon support for Pt. This research also creates a preliminary step for investigating systematically promising Iridium doped Titania materials.