Improving the nutritional performance of gluten-free pasta with potato peel autohydrolysis extract

The potato processing industry produces peels, a good source of fibres, minerals and antioxidants, which could be recovered and used in the production of added-value products, such as gluten-free (GF) foods especially designed for the celiac population.This work is focused on the application of the bioactive fraction extracted from potato peels into GF pasta. Subcritical water extraction (autohydrolysis, AH) was performed on potato peel, and the obtained AH liquid extract was characterized in terms of total phenolic content and antioxidant activity. The selected AH temperature (220 °C at 2.2 MPa) was applied to peels from Kennebec, Neiker and Agria potato varieties, and the Agria extract was selected for application in GF pasta, as this was the one with higher antioxidant activity.The impact of Agria potato peel autohydrolysis extract on the nutritional composition and cooking quality of pasta was assessed. Results confirmed that the GF pasta enriched with potato peel extract presented suitable technological properties, coupled with attractive colour and with increased total phenolic content and antioxidant activity, which can contribute to improve the offer of GF products in the market.     

Graph-based video fingerprinting using double optimal projection

A double optimal projection method that involves projections for intra-cluster and inter-cluster dimensionality reduction are proposed for video fingerprinting. The video is initially set as a graph with frames as its vertices in a high-dimensional space. A similarity measure that can compute the weights of the edges is then proposed. Subsequently, the video frames are partitioned into different clusters based on the graph model. Double optimal projection is used to explore the optimal mapping points in a low-dimensional space to reduce the video dimensions. The statistics and geometrical fingerprints are generated to determine whether a query video is copied from one of the videos in the database. During matching, the video can be roughly matched by utilizing the statistics fingerprint. Further matching is thereafter performed in the corresponding group using geometrical fingerprints. Experimental results show the good performance of the proposed video fingerprinting method in robustness and discrimination.     

Assessing the cost-effectiveness of electric vehicles in European countries using integrated modeling

Electric vehicles (EVs) are considered alternatives to internal combustion engines due to their energy efficiency and contribution to CO₂ mitigation. The adoption of EVs depends on consumer preferences, including cost, social status and driving habits, although it is agreed that current and expected costs play a major role. We use a partial equilibrium model that minimizes total energy system costs to assess whether EVs can be a cost-effective option for the consumers of each EU27 member state up to 2050, focusing on the impact of different vehicle investment costs and CO₂ mitigation targets. We found that for an EU-wide greenhouse gas emission reduction cap of 40% and 70% by 2050 vis-à-vis 1990 emissions, battery electric vehicles (BEVs) are cost-effective in the EU only by 2030 and only if their costs are 30% lower than currently expected. At the EU level, vehicle costs and the capability to deliver both short- and long-distance mobility are the main drivers of BEV deployment. Other drivers include each state’s national mobility patterns and the cost-effectiveness of alternative mitigation options, both in the transport sector, such as plug-in hybrid electric vehicles (PHEVs) or biofuels, and in other sectors, such as renewable electricity.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

Approximate computational approaches for Bayesian sensor placement in high dimensions

Since the cost of installing and maintaining sensors is usually high, sensor locations should always be strategically selected to extract most of the information. For inferring certain quantities of interest (QoIs) using sensor data, it is desirable to explore the dependency between observables and QoIs to identify optimal placement of sensors. Mutual information is a popular dependency measure, however, its estimation in high dimensions is challenging as it requires a large number of samples. This also comes at a significant computational cost when samples are obtained by simulating complex physics-based models. Similarly, identifying the optimal design/location requires a large number of mutual information evaluations to explore a continuous design space. To address these challenges, two novel approaches are proposed. First, instead of estimating mutual information in high-dimensions, we map the limited number of samples onto a lower dimensional space while capturing dependencies between the QoIs and observables. We then estimate a lower bound of the original mutual information in this low dimensional space, which becomes our new dependence measure between QoIs and observables. Second, we use Bayesian optimization to search for optimal sensor locations in a continuous design space while reducing the number of lower bound evaluations. Numerical results on both synthetic and real data are provided to compare the performance of the lower bound with the estimate of mutual information in high dimensions, and a puff-based dispersion model is used to evaluate the sensor placement of the Bayesian optimization for a chemical release problem. The results show that the proposed approaches are both effective and efficient in capturing dependencies and inferring the QoIs.     

Measuring changing strain fields in composites with Distributed Fiber-Optic Sensing using the optical backscatter reflectometer

Background/purposeMeasurements of strains in critical components are often required in addition to finite element calculations when evaluating a structure.MethodsThis paper describes how standard optical fibers, bonded to the surface or embedded in a laminate, can measure strain fields along the entire length of the fiber, using the optical backscatter reflectometer.ResultsA strain field measurement can be much better compared to simulations than the more common single point measurements with strain gauges or Bragg Gratings. Changes of the strain field can be related to damage development and can be used for structural health monitoring. Practical aspects of using the fibers are also discussed.ConclusionDistributed Fiber-Optic Sensing was successfully embedded and bonded to a composite joint. Adhesive damage was identified and the strain field agreed well with FE-Analysis.     

Employing cost sharing to motivate the efficient implementation of distributed energy resources

We consider the design of cost-sharing policies to motivate electricity distribution utilities to manage the costs of distributed energy resource (DER) projects. The optimal share of realized cost savings (s) that is awarded to the utility takes a relatively simple form in certain settings. More generally, s can vary with the prevailing environment in subtle and sometimes counterintuitive ways. For instance, s may increase as cost savings become less onerous for the utility to secure. Gains from affording the utility a choice among cost-sharing policies typically are minimal.     

Enabling supercapacitors to compete for ancillary services: An important step towards 100 % renewable energy

While wind and solar generation has increased dramatically over the past decade, there has been a much larger increase in gas generation (eia, 2019). This is driven in part by low gas prices but also in part by how electricity markets are organized. The intermittent nature of wind and solar generation increases the need for more flexible and reliable generation; a role gas plants fill well. However, current market structures and rules unfairly tip the balance in their favor compared to energy storage systems. They have created market barriers restrictive enough to prevent market participation of a technology key to a 100 % renewable grid: supercapacitors. Adjusting markets to remove the market barriers to supercapacitor and other energy storage systems will allow for increased renewable penetration while simultaneously improving grid performance and reducing costs.     

p-Median and p-dispersion problems: A bi-criteria analysis

Given a collection of n locations and a symmetric measure of distance (difference) between each pair of locations, we seek to identify (select) a subset of p locations so as to achieve two distinct objectives. The first objective is to use the selected locations as centers (medians) of p groups that would partition the entire collection and minimize the total distance between the locations and their respective group medians. The second objective is to maximize the minimum distance (diversity) among the selected locations themselves. We study this problem as a multi-objective optimization problem and propose an iterative algorithm to obtain its non-dominated frontier. At each iteration we construct and solve a 0–1 integer programming problem. Through a computational experiment we show that this algorithm is computationally effective for small to medium size instances of the problem. We also propose a Lagrangian heuristic algorithm for solving larger instances of this problem.     

Optimal sales-mix and generation plan in a two-stage electricity market

A bi-level stochastic programming problem is used to model the optimal decision of a risk averse electricity producer, interacting in a two-stage market with cost minimizer competitors. His decision variables include the distribution of production (which plant of different technologies and variable costs to operate) and the sales-mix (how much generation to commit to bilateral contracts and spot market). To enhance computation times, the bi-level problem is transformed into a Mixed-Integer Linear Problem (MILP) by applying sophisticated linearization techniques. Electricity demand, Renewable Energy Sources (RES) generation and production costs are different sources of uncertainty. A copula method is used to generate scenarios under different correlations values (between RES generation and demand), to analyze the impact of correlation on the optimal solution. The model is tested through extensive numerical simulations based on data from the Spanish electricity market. The results show that correlation and risk aversion have a relevant impact on how sales-mix and generation plan decisions should combine optimally.