Abstract
This research provides an overview of the online consumer experience of luxury brands in Portugal. The purpose of this study was to identify the significant factors that represent customers’ perceptions of the online shopping experience for luxury products. Using a quantitative approach, the authors conducted an online survey. 327 usable responses were obtained. Descriptive and factorial statistical analyzes were used to provide the empirical findings. This study proposes and empirically tests a model of the factorial structure of the online shopping experience for luxury goods. We found an eight-factor dimension structure that proposes the main contributors to understand the factors that represent consumer perceptions about buying luxury products online. The findings suggest that the eight ranked significant factors that represent the customer’s perception of the online luxury shopping experience are in this order: e-buying experience, e-loyalty, e-risk, e-satisfaction, luxury value, luxury useless, luxury future buy, and e-buying influence. The work provides empirical evidence that the eight significant factors represent the customer’s perception of the luxury shopping experience online, that help to understand how luxury brands should be managed online in order to enhance customer e-buying experience, e-satisfaction, e-loyalty, and luxury value proposition. This study provides several contributions for online luxury brand managers and some directions for further research. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
We study last-mile delivery with the option of crowd shipping, where a company makes use of occasional drivers to complement its vehicle's fleet in the activity of delivering products to its customers. We model it as a data-driven distributionally robust optimization approach to the capacitated vehicle routing problem. We assume the marginals of the defined uncertainty vector are known, but the joint distribution is difficult to estimate. The presence of customers and available occasional drivers can be random. We adopt a strategic planning perspective, where an optimal a priori solution is calculated before the uncertainty is revealed. Therefore, without the need for online resolution performance, we can experiment with exact solutions. Solving the problem defined above is challenging: not only the first-stage problem is already NP-Hard, but also the uncertainty and potentially the second-stage decisions are binary of high dimension, leading to non-convex optimization formulations that are complex to solve. We propose a branch-price-and-cut algorithm taking into consideration measures that exploit the intrinsic characteristics of our problem and reduce the complexity to solve it.

Abstract
There are significant changes occurring both in the electricity system and the natural gas system. These two energy carries can be combined to form what is known as an energy hub. These energy hubs can play a significant role in the energy system and thus understanding of their optimization, especially their costs, is important. This paper proposes a risk management framework for an energy-hub through the utilization of the information-gap decision theory (IGDT). The uncertainties introduced from the various load profiles, such as the electric and heating loads, are considered in this risk management framework. The modeled energy-hub consists of several distributed generation systems such as a microcombined heat and power (mu CHP), electric heat pump (EHP), electric heater (EH), absorption chiller (AC) and an energy storage system (ESS). A demand response (DR) program is also considered to shift a percentage of electric load away from the peak period to minimize the operational cost of the hub. A feasible test system is also applied to demonstrate the proposed model's effectiveness.

Abstract
Foraminifers are widespread, highly abundant protists and active participants in marine carbon cycling. Their biomass might represent almost half of the total meiobenthic biomass in the deep sea. Foraminiferal biomass is frequently assessed through geometric models and biovolume estimates due to its non-destructive nature, which allows estimates of individuals from palaeoecological, museum, and living samples. To increase the accuracy of foraminiferal biovolume and biomass assessment we evaluate and propose geometric models for 207 foraminiferal taxa and the species’ average cell occupancy of the test. Individual test dimensions were measured to calculate volume (µm3), and the percent of cell occupancy (PCO) of the test was measured to assess the biovolume (µm3). These data were converted into individual biomass measurements (µg Corg ind-1). Our high intra- and interspecific PCO variance suggest that a mean PCO for each species represents the natural variability of occupancy more accurately than a predetermined fixed percentage for the whole assemblage, as previously asserted in the literature. Regression equations based on the relationship between test dimensions and volumes are presented. The geometric models, the PCO adjustment, and the equations will reduce time, effort, and discrepancies in foraminiferal biovolume and biomass assessments. Therefore, these results can improve the use and reliability of foraminiferal biomass in the future, facilitating its use in (1) distinct approaches including carbon flux estimations, (2) determining the effects of climate change on the marine trophic webs, and (3) environmental monitoring programs.

Abstract
Surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) are among the most common and powerful label-free refractive index-based biosensing techniques available nowadays. Focusing on LSPR sensors, their performance is highly dependent on the size, shape, and nature of the nanomaterial employed. Indeed, the tailoring of those parameters allows the development of LSPR sensors with a tunable wavelength range between the ultra-violet (UV) and near infra-red (NIR). Furthermore, dealing with LSPR along optical fiber technology, with their low attenuation coefficients at NIR, allow for the possibility to create ultra-sensitive and long-range sensing networks to be deployed in a variety of both biological and chemical sensors. This work provides a detailed review of the key science underpinning such systems as well as recent progress in the development of several LSPR-based biosensors in the NIR wavelengths, including an overview of the LSPR phenomena along recent developments in the field of nanomaterials and nanostructure development towards NIR sensing. The review ends with a consideration of key advances in terms of nanostructure characteristics for LSPR sensing and prospects for future research and advances in this field.

Abstract
Mobile handheld devices, such as smartphones and tablets, have become some of the most prominent ubiquitous terminals within the information and communication technology landscape. Their transformative power within the digital music domain changed the music ecosystem from production to distribution and consumption. Of interest here is the ever-expanding number of mobile music applications. Despite their growing popularity, their design in terms of interaction perception and control is highly arbitrary. It remains poorly addressed in related literature and lacks a clear, systematized approach. In this context, our paper aims to provide the first steps towards defining guidelines for optimal sonic interaction design practices in mobile music applications. Our design approach is informed by user data in appropriating mobile handheld devices. We conducted an experiment to learn links between control gestures and musical parameters, such as pitch, duration, and amplitude. A twofold action-reflection protocol and tool-set for evaluating the aforementioned links-are also proposed. The results collected from the experiment show statistically significant trends in pitch and duration control gesture mappings. On the other hand, amplitude appears to elicit a more diverse mapping approach, showing no definitive trend in this experiment.