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“I Don’t Think That’s Something I’ve Ever Thought About Really Before”: A Thematic Discursive Analysis of Lay People’s Talk about Legal Gender
This article examines three divergent constructions about the salience of legal gender in lay people’s everyday lives and readiness to decertify gender. In our interviews (and survey data), generally participants minimised the importance of legal gender. The central argument in this article is that feminist socio-legal scholars applying legal consciousness studies to legal reform topics should find scrutinizing the construction of interview talk useful. We illustrate this argument by adapting and applying Ewick and Silbey’s (1998) ‘The Common Place of Law: Stories from Everyday Life', ‘before’, ‘with’ and ‘against’ typology to interview talk about legal gender, and critique their cogn...
“I want it all” exploring the relationship between entrepreneurs’ satisfaction with work–life balance, well-being, flow and firm growth
Drawing from the conservation of resources theory, we explore how two personal resources (satisfaction with work–life balance and experience of flow at work) contribute to two important outcomes in entrepreneurship: entrepreneurs’ subjective well-being and firm growth. Although previous research has emphasized the importance of personal factors for firm growth and explored a variety of factors affecting entrepreneurs’ subjective well-being, little attention has been paid to the role of satisfaction with work–life balance as a critical personal resource for entrepreneurs. With this study, we find that entrepreneurs’ satisfaction with work–life balance is positively associated with subj...
ICE-GCN: An interactional channel excitation-enhanced graph convolutional network for skeleton-based action recognition
Thanks to the development of depth sensors and pose estimation algorithms, skeleton-based action recognition has become prevalent in the computer vision community. Most of the existing works are based on spatio-temporal graph convolutional network frameworks, which learn and treat all spatial or temporal features equally, ignoring the interaction with channel dimension to explore different contributions of different spatio-temporal patterns along the channel direction and thus losing the ability to distinguish confusing actions with subtle differences. In this paper, an interactional channel excitation (ICE) module is proposed to explore discriminative spatio-temporal features of acti...
ICU patients with infectious complications after abdominopelvic surgery Is thoracic CT in addition to abdominal CT helpful
The aim of this study was to assess the usefulness of adding thoracic CT to abdominal CT in intensive care unit (ICU) patients with signs of infection after abdominopelvic surgery.
Identification and Environmental Assessments for Different Scenarios of Repurposed Decommissioned Wind Turbine Blades
The rapidly growing wind industry poses a fundamental problem for wind turbine blade (WTB) disposal in many areas of the world. WTBs are primarily manufactured from composites consisting of a thermoset matrix and reinforcing fibers. Currently, there are no economically viable recycling technologies available for such large-scale composite products. Thus, other treatment strategies for disposed WTBs have to be considered. This study explores the repurpose of WTBs as a promising alternative approach from a processual and technological point of view. For this purpose, the study is guided by the categorization into four different types of repurposed applications: high-loaded complete stru...
This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A generalized method of moments estimation procedure is proposed, also employed by Peter Schmidt and his coauthors to address heterogeneity in time effects in panel data models. Using Monte Carlo simulations, we find that moments of the random coefficients can be estimated reasonably accurately, but large samples are required for the estimation of the parameters of the underlying categorical distribution.
Identification and validation of novel lung adenocarcinoma subtypes and construction of prognostic models: based on cuprotosis-related genes
Cuprotosis is a novel and unique form of cell death that is of great value in a variety of cancers. However, the prognostic role of cuprotosis-related genes (CRGs) in lung cancer remains undetermined. We compared the expression profile of CRGs in lung adenocarcinoma (LUAD) patients, revealing the genetic alterations and inter-gene correlations of CRGs. Based on 13 CRGs, LUAD patients could be well differentiated into two molecular subgroups, and the differentially expressed genes (DEGs) in these molecular subtypes were identified. Furthermore, 10 cuprotosis pattern-related DEGs with a significant prognostic value were obtained for constructing a prognostic model.
Identification of biomarkers related to copper metabolism in patients with pulmonary arterial hypertension
The pathogenesis of pulmonary arterial hypertension (PAH) and associated biomarkers remain to be studied. Copper metabolism is an emerging metabolic research direction in many diseases, but its role in PAH is still unclear.
Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the i...
Convolutional neural networks (CNNs), a representative type of deep neural networks, are used in various fields. There are problems that should be solved to operate CNN in the real-world. In real-world operating environments, the CNN’s performance may be degraded due to data of untrained types, which limits its operability. In this study, we propose a method for identifying data of a type that the model has not trained on based on the neuron cluster, a set of neurons activated based on the type of input data. In experiments performed on the ResNet model with the MNIST, CIFAR-10, and STL-10 datasets, the proposed method identifies data of untrained and trained types with an accuracy of...
Identifying essential skills and competencies towards building a training framework for future operators of autonomous ships: a qualitative study
Past and ongoing research in the design, development, and implementation of fully autonomous and unmanned ships has revealed operational, environmental, and financial benefits for the maritime industry. However, with the benefits of being highly intuitive and intelligent systems, there are risks of mistakes and failures caused by their operators i.e. the unavoidable human element. With predictions of both seafarers and non-seafarers to be involved in the critical operations of autonomous vessels, it was imperative to identify key maritime stakeholders and conduct research which would investigate their beliefs and perceptions on the training requirements of the future shore-based opera...
Autonomous vehicle advancements and communication technologies such as V2V, V2I, and V2X have enabled the development of connected and autonomous vehicles. Because CAVs are directly effective in traffic, their application in traffic management and incident management appears promising. They can immediately begin regulating traffic and acting as sensors due to their connectivity to the infrastructure. This research proposes Incident Detection Included Linear Management (IDILIM), a CAV-based incident management algorithm that regulates CAV and traffic speeds based on dynamic and predicted shockwave speeds. The SUMO simulations are carried out on a 10.4-km-long, three-lane facility with ...
Provides a comprehensive study of illicit markets in the contexts of international security and economic development
Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation
Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time and expertise. The aim of this study was to propose an externally validated deep learning model with uncertainty quantification and image segmentation for the automated assessment of ETT placement on ICU chest radiographs.
In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. T...
Image processing based horizon sensor for estimating the orientation of sounding rockets, launch vehicles and spacecraft
The paper describes how the attitude of a sounding rocket, launch vehicle or satellite with respect to the Earth can be estimated from camera images of the Earth horizon. Details about detecting the horizon in the camera image, fitting hyperbolae or ellipses to the detected horizon curve and deriving the Earth nadir vector and the corresponding error covariance from the fitted conic section are given. The presented method works at low heights, where the projected horizon mostly appears to be hyperbolic, as well as at large heights, where the projected horizon mostly appears to be elliptic and it is irrelevant if the Earth is fully or only partially in the field of view of the camera.<...
This article seeks to investigate the role that a symbol—connected to a legal event and a collective trauma—has in the construction of a past imaginary. It begins with a theoretical reflection on the role of the symbol as proposed by Juri Lotman and the function of repetition in the consolidation of collective memory. It subsequently focuses on the semiotic resonance of one specific object: the bulletproof cabin of the Nazi criminal Adolf Eichmann, used during his trial in Jerusalem, in 1961. I consider the ‘afterlives’ of this object, examining the different ‘remakes’ of Eichmann’s cabin in several mediatic and artistic contexts, focusing on the modalities that have anchored it to th...
We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We evaluated the extension using four classifiers and four regressors, two discretizers, and 119 responses from a wide variety of datasets in different domains.
Immigrant and Refugee Families: Global Perspectives on Displacement and Resettlement Experiences offers an interdisciplinary perspective on immigrant and refugee families' challenges and resilience across multiple domains, including economic, political, health, and human rights. This new edition has been revised and updated from the original 2016 edition.