The Impact of Linguistic Styles on Message Delivery in Encouraging the Use of Leftover Bags for Food Waste Reduction

Plate leftovers are a major cause of food waste in restaurants. To reduce food waste, many restaurants encourage customers to use “doggy bags” to take away their plate leftovers. However, the efficiency of adopting such leftover bags is still questionable as some customers may feel embarrassed to use leftover bags. Hence, the current research aims to explore how to leverage different linguistic styles (figurative vs. literal language) to encourage the usage of leftover bags for food waste reduction purposes. Furthermore, this research will investigate the linguistic style efficiency in various restaurant dining contexts across two empirical studies. Study 1 will examine customers’ responses to different linguistic style messages when they make orders either with or without the restaurant server around (server taking the order vs. customer ordering by using the table tablet). Study 2 will examine whether there are any differences in terms of linguistic impacts on food waste reduction when customers are with or without other customers around (solo diners vs. group diners)

Service differentiation in OFDM-Based IEEE 802.16 networks

IEEE 802.16 network is widely viewed as a strong candidate solution for broadband wireless access systems. Various flexible mechanisms related to QoS provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the standards. Among the mechanisms, bandwidth request scheme can be used to indicate and request bandwidth demands to the base station for different services. Due to the diverse QoS requirements of the applications, service differentiation (SD) is desirable for the bandwidth request scheme. In this paper, we propose several SD approaches. The approaches are based on the contention-based bandwidth request scheme and achieved by the means of assigning different channel access parameters and/or bandwidth allocation priorities to different services. Additionally, we propose effective analytical model to study the impacts of the SD approaches, which can be used for the configuration and optimization of the SD services. It is observed from simulations that the analytical model has high accuracy. Service can be efficiently differentiated with initial backoff window in terms of throughput and channel access delay. Moreover, the service differentiation can be improved if combined with the bandwidth allocation priority approach without adverse impacts on the overall system throughput

Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks

Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure-temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps $P$ to $T$. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.Comment: Accepted for publication in Astronomy & Astrophysic

Atmospheric retrievals for LIFE and other future space missions: the importance of mitigating systematic effects

Atmospheric retrieval studies are essential to determine the science requirements for future generation missions, such as the Large Interferometer for Exoplanets (LIFE). The use of heterogeneous absorption cross-sections might be the cause of systematic effects in retrievals, which could bias a correct characterization of the atmosphere. In this contribution we quantified the impact of differences in line list provenance, broadening coefficients, and line wing cut-offs in the retrieval of an Earth twin exoplanet orbiting a Sun-like star at 10 pc from the observer, as it would be observed with LIFE. We ran four different retrievals on the same input spectrum, by varying the opacity tables that the Bayesian retrieval framework was allowed to use. We found that the systematics introduced by the opacity tables could bias the correct estimation of the atmospheric pressure at the surface level, as well as an accurate retrieval of the abundance of some species in the atmosphere (such as CO$_2$ and N$_2$O). We argue that differences in the line wing cut-off might be the major source of errors. We highlight the need for more laboratory and modeling efforts, as well as inter-model comparisons of the main radiative transfer models and Bayesian retrieval frameworks. This is especially relevant in the context of LIFE and future generation missions, to identify issues and critical points for the community to jointly work together to prepare for the analysis of the upcoming observations.Comment: 24 pages, 12 figures. Proceedings SPIE Volume 12180, Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave; 121803L (2022

A systematic study of \ce{CO2} planetary atmospheres and their link to the stellar environment

The Milky Way Galaxy is literally teeming with exoplanets; thousands of planets have been discovered, with thousands more planet candidates identified. Terrestrial-like planets are quite common around other stars, and are expected to be detected in large numbers in the future. Such planets are the primary targets in the search for potentially habitable conditions outside the solar system. Determining the atmospheric composition of exoplanets is mandatory to understand their origin and evolution, as atmospheric processes play crucial roles in many aspects of planetary architecture. In this work we construct and exploit a 1D radiative transfer model based on the discrete-ordinates method in plane-parallel geometry. Radiative results are linked to a convective flux that redistributes energy at any altitude producing atmospheric profiles in radiative-convective equilibrium. The model has been applied to a large number (6250) of closely dry synthetic \ce{CO2} atmospheres, and the resulting pressure and thermal profiles have been interpreted in terms of parameter variability. Although less accurate than 3D general circulation models, not properly accounting for e.g., clouds and atmospheric and ocean dynamics, 1D descriptions are computationally inexpensive and retain significant value by allowing multidimensional parameter sweeps with relative ease.Comment: 12 pages, 9 figures, accepted for publication in MNRA

Direct in situ synthesis of a 3D interlinked amorphous carbon nanotube/graphene/BaFe12O19 composite and its electromagnetic wave absorbing properties

2016-2017 > Academic research: refereed > Publication in refereed journalbcrcVersion of RecordPublishe

In situ synthesis of interlinked three-dimensional graphene foam/polyaniline nanorod supercapacitor

Three-dimensional (3-D) graphene foam/PANI nanorods were fabricated by hydrothermal treatment of graphene oxide (GO) solution and sequentially in-situ synthesis of PANI nanorods on the surface of graphene hydrogel. 3-D graphene foam was used as substrate for the growth of PANI nanorods and it increases the specific surface area as well as the double layer capacitance performance of the graphene foam/PANI nanorod composite. The length of the PANI nanorod is about 340 nm. PANI nanorods exhibited a short stick shape. These PANI nanorods agglomerate together and the growth orientation is anisotropic. The highest specific capacitance of 3-D graphene/PANI nanorod composite electrodes is 352 F g−1 at the scan rate of 10 mV s−1.Institute of Textiles and Clothin