A qualitative study exploring transgender youths’ experiences of using social media

Background The rise of the Internet in recent decades, along with social media and communication platforms, has created an opportunity for transgender individuals to seek out a common alternative identity that may reduce the societal pressure of fitting into a particular gender role dictated by biological sex. The developmental period that adolescents go through is accompanied by an array of challenges, more so for a young person whose biological sex is incongruent to their felt gendered sense. Research in social media use within the trans population is still developing, given the growing interest in how social media impacts on our sense of identity. Given the importance of identity development in adolescence, this highlights the need for research into this specific population. This study thus aims to contribute to the existing literature by exploring the experiences of transgender adolescents in using social media. Method A qualitative research methodology was employed, using a thematic analysis approach. A total of 11 participants between the ages of 15 to 18 were interviewed. Recruitment took place at the Tavistock and Portman NHS Foundation trust as well as using snowballing sampling. Results Participants described using a varied range of social media platforms. A total of 3 main themes were developed from the data, with participants describing how social media played an initial role in helping them explore their trans identity, how they find themselves aligning with particular trans narratives on social media and lastly how participants make use of social media to present an image of themselves to others. Discussion The participants’ experiences on social media mirror and intersect with the transitional journey many of them take in changing their gender and this has implications for how clinicians can take into account social media influences when working with young trans individuals

Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints

Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the training process in order to yield better predictions in terms of the quality of the predicted solution under the true parameters. Almost all prior works have focused on the special case where the unknowns appear only in the optimization objective and not the constraints. Hu et al.~proposed the first adaptation of Predict+Optimize to handle unknowns appearing in constraints, but the framework has somewhat ad-hoc elements, and they provided a training algorithm only for covering and packing linear programs. In this work, we give a new \emph{simpler} and \emph{more powerful} framework called \emph{Two-Stage Predict+Optimize}, which we believe should be the canonical framework for the Predict+Optimize setting. We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework. Experimental results demonstrate the superior prediction performance of our training framework over all classical and state-of-the-art methods

Non-parametric Probabilistic Time Series Forecasting via Innovations Representation

Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical in risk-based decision-making and planning under uncertainties. Existing approaches are primarily based on parametric or semi-parametric time-series models that are restrictive, difficult to validate, and challenging to adapt to varying conditions. This paper proposes a nonparametric method based on the classic notion of {\em innovations} pioneered by Norbert Wiener and Gopinath Kallianpur that causally transforms a nonparametric random process to an independent and identical uniformly distributed {\em innovations process}. We present a machine-learning architecture and a learning algorithm that circumvent two limitations of the original Wiener-Kallianpur innovations representation: (i) the need for known probability distributions of the time series and (ii) the existence of a causal decoder that reproduces the original time series from the innovations representation. We develop a deep-learning approach and a Monte Carlo sampling technique to obtain a generative model for the predicted conditional probability distribution of the time series based on a weak notion of Wiener-Kallianpur innovations representation. The efficacy of the proposed probabilistic forecasting technique is demonstrated on a variety of electricity price datasets, showing marked improvement over leading benchmarks of probabilistic forecasting techniques

A Test of the Mobile Phone Appropriation Model: A Comparison between Chinese and US Samples

The published version is being made available with the permission of the publisher.The mobile phone appropriation (MPA; Wirth et al., 2007, 2008) model is an integrative model that seeks to explain attitudes and behaviors related to mobile phone usage from a communication perspective, proposing a dynamic loop of metacommunication, evaluations, and usage patterns. Following a previous study (Lee & Cioena, 2023), the current research tests the MPA model with a Chinese sample collected through an online survey (N = 510) and compares it with the U.S. sample (N = 501) collected by Lee and Cionea (2023) using multigroup confirmatory factor analysis and multigroup structural equation modeling. Although the core structure of MPA model was shown to be tenable cross-culturally, the results of comparative analysis reveal some noticeable cultural differences in mobile phone appropriation and call for further model revisions. Noticeably, relational and social implications of mobile communication penetrate more aspects of mobile phone appropriation with greater strength in the Chinese sample, potentially due to the collectivistic Chinese culture, and the results demonstrate a paradox between perceived affordability and usage. The more Chinese participants evaluated the cost of mobile phone usage as a restrictive factor of MPA, corroborate the more they used it for relationship maintenance and daily schedule management. In addition, the results indicate some tensions between instrumental purposes and entertainment and symbolic usage unique to the Chinese context

Understanding Singleness: A Phenomenological Study of Single Women in Beijing and Singapore

The aim of this phenomenological study was to gain a better understanding in the lives of single women by exploring their thoughts and experiences of being single. Data were collected from semi-structured interviews of a group of six well-educated, ethnic Chinese single women aged between 30 and 45 living in Beijing and Singapore. Transcribed interviews were analysed through reading and rereading and culling for like phrases and themes that are then grouped to form clusters of meaning. Through this process, we found four salient themes: (a) the women had equivocal feelings over the reasons they were single; (b) they recognized the advantages, disadvantages, and ambivalence of singlehood; (c) they took a pragmatic approach towards their singleness; and (d) they coped singleness with various practical strategies. Implications related to clinical practice and areas of further research are discussed

Ultra‐high elastic strain energy storage in hybrid metal‐oxide infiltrated polymer nanocomposites

An understanding of the mechanical properties of materials at nanometer length scales, including a material’s ability to store and release elastic strain energy, is of great significance in the effective miniaturization of actuators, sensors and resonators for use in micro-/nano-electromechanical systems (MEMS/NEMS) as well as advanced development of artificial muscles for locomotion in soft robots. The measure of a material’s ability to store and release elastic strain energy, the modulus of resilience (R), is a crucial parameter in realizing such advanced mechanical actuation technologies. Typically, engineering a material system with a large R requires large increases in the material’s yield strength yet conservative increase in Young’s modulus, an engineering challenge as the two mechanical properties are strongly coupled; generally, strengthening methods results in considerable stiffening or increase in the Young’s modulus. Here, we present hybrid composite polymer nanopillars which achieve the highest specific R ever reported, by utilizing vapor-phase aluminum oxide infiltrations into lithographically patterned polymer resist SU-8. In-situ nanomechanical measurements reveal high, metallic-like yield strengths (~500 MPa) combined with a compliant, polymeric-like Young’s modulus (~7 GPa), a unique pairing never observed in known engineering materials. It is these exceptional elastic properties of our hybrid composite which allows for realization of R per density (Rs) values ~ 11200 J/kg, orders of magnitude greater than those in most engineering material systems. The high elastic energy storage/release capability of this material, as well as its compatibility with lithographic techniques, makes it an attractive candidate in the design of MEMS devices, which require an ultra-high elastic component for advanced actuation and sensor technologies. Furthermore, an opportunity for tunability of the elastic properties of the SU-8 polymeric material exists with this fabrication technique by varying the number of infiltration cycles or the organometallic precursor Please click Additional Files below to see the full abstract

Test for rare variants by environment interactions in sequencing association studies

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142484/1/biom12368_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142484/2/biom12368.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142484/3/biom12368-sup-0001-SuppData.pd

Carbon Dioxide-Catalyzed Stereoselective Cyanation Reaction

© 2019 American Chemical Society.We report a Michael-type cyanation reaction of coumarins by using CO2 as a catalyst. The delivery of the nucleophilic cyanide was realized by catalytic amounts of CO2, which forms cyanoformate and bicarbonate in the presence of water. Under ambient conditions, CO2-catalyzed reactions afforded high chemo- A nd diastereoselectivity of β-nitrile carbonyls, whereas only low reactivities were observed under argon or N2. Computational and experimental data suggest the catalytic role of CO2, which functions as a Lewis acid, and a protecting group to mask the reactivity of the product, suppressing byproducts and polymerization. The utility of this convenient method was demonstrated by preparing biologically relevant heterocyclic compounds with ease11sciescopu

MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms

This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages. We train a multilingual transformer model (XLM-RoBERTa) to disambiguate potentially euphemistic terms (PETs) in multilingual and cross-lingual settings. In line with current trends, we demonstrate that zero-shot learning across languages takes place. We also show cases where multilingual models perform better on the task compared to monolingual models by a statistically significant margin, indicating that multilingual data presents additional opportunities for models to learn about cross-lingual, computational properties of euphemisms. In a follow-up analysis, we focus on universal euphemistic "categories" such as death and bodily functions among others. We test to see whether cross-lingual data of the same domain is more important than within-language data of other domains to further understand the nature of the cross-lingual transfer