Protein-based nanomaterials and nanosystems for biomedical applications: A review

Advanced protein-based nanomaterials and nanosystems (PNNS) have attracted considerable scientific interest in recent decades due to their potential in bio-applications. Nowadays, the constructed PNNS exhibit different properties for various special applications based on the characteristics of different proteins. Herein, in this review article, a systematic summary and discussion focusing on designing multi-functional PNNS are presented. The latest developments in unique synthesis strategies and detailed classification of PNNS are reviewed. The functions of proteins in PNNS for biomedical applications, such as targeting proteins, carriers, enzymes, and fluorescent indicators, are summarized. Finally, the challenges and forward-looking perspectives of PNNS research are provided.     

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.     

Reducing numerical dissipation in smoke simulation

Numerical dissipation acts as artificial viscosity to make smoke viscous. Reducing numerical dissipation is able to recover visual details smeared out by the numerical dissipation. Great efforts have been devoted to suppress the numerical dissipation in smoke simulation in the past few years. In this paper we investigate methods of combating the numerical dissipation. We describe visual consequences of the numerical dissipation and explore sources that introduce the numerical dissipation into course of smoke simulation. Methods are investigated from various aspects including grid variation, high-order advection, sub-grid compensation, invariant conservation, and particle-based improvement, followed by discussion and comparison in terms of visual quality, computational overhead, ease of implementation, adaptivity, and scalability, which leads to their different applicability to various application scenarios.     

The working principle of hybrid perovskite gamma-ray photon counter

Gamma-ray spectroscopy that quantifies the gamma-ray energies is a critical technology widely needed in astrophysics, nuclear material detection and medical treatment. The key is to precisely count gamma-ray photons using sensitive detectors. In this paper, we investigate the operational principles of chlorine-doped methylammonium lead tribromide (MAPbBr_3−xCl_x) perovskite single crystal detectors that can efficiently count gamma-ray photon events with electrical pulses. Specifically, we find the main dark current originates from the thermally activated electron injection from the impurities, and using high work function contacts can block out the dark noise thus allows for efficient pulse collection at higher electrical fields ∼500 V/cm. As a result, we observe strong electrical pulses when exposing the detector under radioactive sources emitting gamma-ray photons at various energies. Our results also reveal the fundamental issues that prevent the reliable observation of photo-electric peak. This work suggest pathway towards energy resolved gamma-ray spectroscopy using perovskite crystal detectors.     

Residual learning rates in lead-acid batteries: Effects on emerging technologies

The low price of lead-acid, the most popular battery, is often used in setting cost targets for emerging energy storage technologies. Future cost reductions in lead acid batteries could increase investment and time scales needed for emerging storage technologies to reach cost-parity. In this paper the first documented model of cost reductions for lead-acid batteries is developed. Regression to a standard experience curve using 1989–2012 data yield a poor fit, with R² values of 0.17 for small batteries and 0.05 for larger systems. To address this problem, battery costs are separated into material and residual costs, and experience curves developed for residual costs. Depending on the year, residual costs account for 41–86% of total battery cost. Using running-time averages to address volatility in material costs, a 4-year time average experience curve for residual costs yield much higher R², 0.78 for small and 0.74 for large lead-acid batteries. The learning rate for residual costs in lead-acid batteries is 20%, a discovery with policy implications. Neglecting to consider cost reductions in lead-acid batteries could result in failure of energy storage start-ups and public policy programs. Generalizing this result, learning in incumbent technologies must be understood to assess the potential of emerging ones.     

The benefit of being physically present: A survey of experimental works comparing copresent robots,telepresent robots and virtual agents

The effects of physical embodiment and physical presence were explored through a survey of 33 experimental works comparing how people interacted with physical robots and virtual agents. A qualitative assessment of the direction of quantitative effects demonstrated that robots were more persuasive and perceived more positively when physically present in a user׳s environment than when digitally-displayed on a screen either as a video feed of the same robot or as a virtual character analog; robots also led to better user performance when they were collocated as opposed to shown via video on a screen. However, participants did not respond differently to physical robots and virtual agents when both were displayed digitally on a screen – suggesting that physical presence, rather than physical embodiment, characterizes people׳s responses to social robots. Implications for understanding psychological response to physical and virtual agents and for methodological design are discussed.     

Modelling consumer choice through the random regret minimization model: An application in the food domain

In line with findings on post-purchase food-choice regret, one can expect that pre-purchase anticipated regret with respect to forgone (non-chosen) alternatives has an impact on consumer food choices, especially when the choice is considered to be important. The traditional Random Utility Maximization (RUM) models for discrete choices may not fully capture this impact. This study investigates the usefulness and potential in the food domain of a discrete choice model that follows the regret minimization principle, the Random Regret Minimization (RRM) model, as an alternative and complement to existing RUM models. The two models are applied to consumer stated choices of cheese in a choice experiment. The study also investigates whether and to what extent a number of personality traits determine whether particular consumers rather choose according to utility-maximization, or regret-minimization principles. Results show that at the aggregate level the two models have a similar goodness of fit to the data and prediction ability. Still, each of them shows better fit for particular subgroups of consumers, based on personality traits. Hence, the present study reveals a potential for the RRM model applications in the food domain, and adds to the empirical literature supporting previous findings on the RRM model found in other contexts. Further research is needed to explore in which situations and for which consumer segments the RRM model is the most useful model.