Spin dynamics in semiconductors

This article reviews the current status of spin dynamics in semiconductors which has achieved a lot of progress in the past years due to the fast growing field of semiconductor spintronics. The primary focus is the theoretical and experimental developments of spin relaxation and dephasing in both spin precession in time domain and spin diffusion and transport in spacial domain. A fully microscopic many-body investigation on spin dynamics based on the kinetic spin Bloch equation approach is reviewed comprehensively.Comment: a review article with 193 pages and 1103 references. To be published in Physics Reports

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)

Who’s the Real Victim? Marriott’s Victimization and Customers’ Perception of It - Marriott Data Breach Crisis in 2018

This study examines the Marriott data breach crisis of 2018, analyzing Marriott\u27s crisis communication through the lens of Situational Crisis Communication Theory (SCCT). It explores how Marriott framed and responded to the crisis and assesses public perception of these actions. Marriott\u27s official statements were analyzed to determine if there was a match between the company\u27s crisis response strategies and public comments from online news articles were analyzed to explore how the public actually perceived the crisis. The findings indicate that while Marriott employed rebuilding and diminishing strategies to position the crisis in a victim cluster, the public largely saw the crisis fit in a preventable cluster. This discrepancy highlights the critical role of aligning crisis response strategies with public expectations to manage reputation and mitigate negative impacts. The findings contribute to the literature on data breach crises applying SCCT and provide practical insights for organizations and stakeholders facing data breaches about what the public demands

Servicescape Effects on Hotel Guests’ Willingness to Pay Premiums at Different Stages of Pandemic: A Multi-Phase Study

Drawing on servicescape theory, this research investigates guests’ perceptions of and responses to the protection and prevention practices launched by hotels at different stages of the pandemic. The research finds that hotel guests’ general response-efficacy beliefs positively influence their perception of the effectiveness of the protection and prevention practices adopted in hotels’ physical and social servicescapes, and such positive relationships also show a significant increase from 2020 to 2021. The servicescape effects’ downstream results show that hotel guests are willing to pay premium prices for safety servicescapes manifested as protection and prevention practices implemented at the private space or related to employees. This research sheds light on servicescape theory by deconstructing the overall hotel servicescape concept into multiple dimensions, particularly in a health threat situation such as the pandemic, and empirically examining each dimension’s effects on guests’ monetary response at different timepoints. From a practical perspective, this study provides managerial insights into which servicescape dimensions warrant operational investments by hotels

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