What Makes Hotel Expatriates Remain in Their Overseas Assignments: A Grounded Theory Study

In this study the researcher uses a qualitative research design to discover what makes hotel expatriates remain in their overseas assignments. In-depth interviews, participant observations, and personal documents are used as data collection methods. Four hotel expatriates are recruited as participants of the study. The collected interview transcripts and fieldnotes are further analyzed through the use of grounded theory. Five selective codes found as the dominant themes in this study are hotel expatriates’ : (a) personality characteristics, (b) motivations to work overseas, (c) challenges derived from overseas assignments, (d) competencies, and (e) roles/identities in overseas assignments. These five main themes are further analyzed and concluded with a coherent theory that explains why hotel expatriates remain in their assignments

AI-based thermal bridge detection of building rooftops on district scale using aerial images

Thermal bridges are weak areas of building envelopes that conduct more heat to the outside than surrounding envelope areas. They lead to increased energy consumption and the formation of mold. With a neural network approach, we demonstrate a method of automatically detecting thermal bridges on building rooftops from panorama drone images of whole city districts. To train the neural network, we created a dataset including 917 images and 6895 annotations. The images in the dataset contain thermal information for detecting thermal bridges and a height map for rooftop recognition in addition to regular RGB information. Due to the small dataset, our approach currently only has an average recall of 9.4% @IoU:0.5-0.95 (14.4% for large objects). Nevertheless, our approach reliably detects structures only on rooftops and not on other parts of buildings, without any additional segmentation effort of building parts

Contour Integration over Time: Psychophysical and fMRI Evidence.

The brain integrates discrete but collinear stimuli to perceive global contours. Previous contour integration (CI) studies mainly focus on integration over space, and CI is attributed to either V1 long-range connections or contour processing in high-visual areas that top-down modulate V1 responses. Here, we show that CI also occurs over time in a design that minimizes the roles of V1 long-range interactions. We use tilted contours embedded in random orientation noise and moving horizontally behind a fixed vertical slit. Individual contour elements traveling up/down within the slit would be encoded over time by parallel, rather than aligned, V1 neurons. However, we find robust contour detection even when the slit permits only one viewable contour element. Similar to CI over space, CI over time also obeys the rule of collinearity. fMRI evidence shows that while CI over space engages visual areas as early as V1, CI over time mainly engages higher dorsal and ventral visual areas involved in shape processing, as well as posterior parietal regions involved in visual memory that can represent the orientation of temporally integrated contours. These results suggest at least partially dissociable mechanisms for implementing the Gestalt rule of continuity in CI over space and time

Deep learning approaches to building rooftop thermal bridge detection from aerial images

Thermal bridges are weak points of building envelopes that can lead to energy losses, collection of moisture, and formation of mould in the building fabric. To detect thermal bridges of large building stocks, drones with thermographic cameras can be used. As the manual analysis of comprehensive image datasets is very time-consuming, we investigate deep learning approaches for its automation. For this, we focus on thermal bridges on building rooftops recorded in panorama drone images from our updated dataset of Thermal Bridges on Building Rooftops (TBBRv2), containing 926 images with 6,927 annotations. The images include RGB, thermal, and height information. We compare state-of-the-art models with and without pretraining from five different neural network architectures: MaskRCNN R50, Swin-T transformer, TridentNet, FSAF, and a MaskRCNN R18 baseline. We find promising results, especially for pretrained models, scoring an Average Recall above 50% for detecting large thermal bridges with a pretrained Swin-T Transformer model

MiR-21 Is Induced by Hypoxia and Down-Regulates RHOB in Prostate Cancer

Tumour hypoxia is a well-established contributor to prostate cancer progression and is also known to alter the expression of several microRNAs. The over-expression of microRNA-21 (miR-21) has been consistently linked with many cancers, but its role in the hypoxic prostate tumour environment has not been well studied. In this paper, the link between hypoxia and miR-21 in prostate cancer is investigated. A bioinformatic analysis of The Cancer Genome Atlas (TCGA) prostate biopsy datasets shows the up-regulation of miR-21 is significantly associated with prostate cancer and clinical markers of disease progression. This up-regulation of miR-21 expression was shown to be caused by hypoxia in the LNCaP prostate cancer cell line in vitro and in an in vivo prostate tumour xenograft model. A functional enrichment analysis also revealed a significant association of miR-21 and its target genes with processes related to cellular hypoxia. The over-expression of miR-21 increased the migration and colony-forming ability of RWPE-1 normal prostate cells. In vitro and in silico analyses demonstrated that miR-21 down-regulates the tumour suppressor gene Ras Homolog Family Member B (RHOB) in prostate cancer. Further a TCGA analysis illustrated that miR-21 can distinguish between different patient outcomes following therapy. This study presents evidence that hypoxia is a key contributor to the over-expression of miR-21 in prostate tumours, which can subsequently promote prostate cancer progression by suppressing RHOB expression. We propose that miR-21 has good potential as a clinically useful diagnostic and prognostic biomarker of hypoxia and prostate cancer

The Effect of Prenatal Exposure to Radiation on Birth Outcomes: Exploiting a Natural Experiment in Taiwan

We estimate the effect of prenatal exposure to radiation on infant health. By exploiting the 1983 Taiwanese radiation-contaminated buildings (RCBs) accident as a natural experiment, we compare birth outcomes between siblings and cousins exposed to different radiation levels. Given the 1983 accident was unanticipated and exposed cohorts were unaware of the risk until 1992, our design isolates the effect of radiation exposure during pregnancy from other effects. We provide the first evidence that prenatal exposure to a continuous low-level dose of radiation significantly reduces gestational length and increases the probabilities of prematurity and low birth weight

Phonological abilities of Cantonese-speaking hearing- impaired children with cochlear implants or hearing aids

Thesis (B.Sc.)--University of Hong Kong, 2003."A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, April 30, 2003."Also available in print.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science

Thermal Bridges on Building Rooftops

Thermal Bridges on Building Rooftops (TBBR) is a multi-channel remote sensing dataset. It was recorded during six separate UAV fly-overs of the city center of Karlsruhe, Germany, and comprises a total of 926 high-resolution images with 6927 manually-provided thermal bridge annotations. Each image provides five channels: three color, one thermographic, and one computationally derived height map channel. The data is pre-split into training and test data subsets suitable for object detection and instance segmentation tasks. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository. This work provides a comprehensive data descriptor for the TBBR dataset to facilitate broad community uptake

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world

Practical Delegatable Attribute-Based Anonymous Credentials with Chainable Revocation

Delegatable Anonymous Credentials (DAC) are an enhanced Anonymous Credentials (AC) system that allows credential owners to use credentials anonymously, as well as anonymously delegate them to other users. In this work, we introduce a new concept called Delegatable Attribute-based Anonymous Credentials with Chainable Revocation (DAAC-CR), which extends the functionality of DAC by allowing 1) fine-grained attribute delegation, 2) issuers to restrict the delegation capabilities of the delegated users at a fine-grained level, including the depth of delegation and the sets of delegable attributes, and 3) chainable revocation, meaning if a credential within the delegation chain is revoked, all subsequent credentials derived from it are also invalid. We provide a practical DAAC-CR instance based on a novel primitive that we identify as structure-preserving signatures on equivalence classes on vector commitments (SPSEQ-VC). This primitive may be of independent interest, and we detail an efficient construction. Compared to traditional DAC systems that rely on non-interactive zero-knowledge (NIZK) proofs, the credential size in our DAAC-CR instance is constant, independent of the length of delegation chain and the number of attributes. We formally prove the security of our scheme in the generic group model and demonstrate its practicality through performance benchmarks