Development of a Fuzzy Fire Risk Evaluation Model for Building Construction Sites in Hong Kong

Earlier research works on fire risk evaluation indicated that an objective,reliable, comprehensive, and practical fire risk evaluation model is essentialfor mitigating fire occurrence in building construction sites. Nevertheless,real empirical studies in this research area are quite limited. This journalpaper gives an account of the second stage of a research study aiming atdeveloping a fuzzy fire risk evaluation model for building construction sitesin Hong Kong. The empirical research findings showed that the overall firerisk level of building construction sites is 3.6427, which can be interpretedas “moderate risk”. Also, the survey respondents perceived that “Restrictionsfor On-Site Personnel” is the most vital fire risk factor; with “Storage ofFlammable Liquids or Dangerous Goods” being the second; and “Attitudeof Main Contractor” the third. The proposed fuzzy fire risk evaluationmodel for building construction sites can be used to assess the overall firerisk level for a building construction site, and to identify improvementareas needed. Although the fuzzy fire risk evaluation model was developeddomestically in Hong Kong, the research could be reproduced in othernations to develop similar models for international comparisons. Suchan extension would provide a deeper understanding of the fire riskmanagement on building construction sites

Computational narrative mapping for the acquisition and representation of lessons learned knowledge

Lessons learned knowledge is traditionally gained from trial and error or narratives describing past experiences. Learning from narratives is the preferred option to transfer lessons learned knowledge. However, learners with insufficient prior knowledge often experience difficulties in grasping the right information from narratives. This paper introduces an approach that uses narrative maps to represent lessons learned knowledge to help learners understand narratives. Since narrative mapping is a time-consuming, labor-intensive and knowledge-intensive process, the proposed approach is supported by a computational narrative mapping (CNM) method to automate the process. CNM incorporates advanced technologies, such as computational linguistics and artificial intelligence (AI), to identify and extract critical narrative elements from an unstructured, text-based narrative and organize them into a structured narrative map representation. This research uses a case study conducted in the construction industry to evaluate CNM performance in comparison with existing paragraph and concept mapping approaches. Among the results, over 90% of respondents asserted that CNM enhanced their understanding of the lessons learned. CNM’s performance in identifying and extracting narrative elements was evaluated through an experiment using real-life narratives from a reminiscence study. The experiment recorded a precision and recall rate of over 75%

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

Open data from the third observing run of LIGO, Virgo, KAGRA, and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages

Effect of fuels on cooking fume emissions

Emissions of cooking fumes across a wide range of settings were investigated for two commonly used fuels in Hong Kong-town gas and electricity. The effect of cooking process on the emissions of fumes was controlled by repeating cooking processes using these two fuels. The measurement results showed that the cooking process and energy source could have an effect on the concentrations of PM10 and total volatile organic compound (TVOC) in kitchens and the concentrations of extractable organic material in kitchen exhausts. Gas cooking produced higher concentrations of PM10, TVOC and extractable organic material than electric cooking for stir frying, pan frying and deep frying in the domestic kitchens but inclusive results were obtained for deep frying, griddle frying and char-broiling in the commercial kitchens. Paired-sample t-tests revealed that the concentrations of TVOC generated from gas cooking were significantly higher than those generated from electric cooking at the 5% level, but not for PM10 when measurements in the domestic and commercial environments were combined. Analyses of variance indicated that the emissions of extractable organic material depended significantly on the cooking fuel and the cooking process in domestic kitchens and commercial kitchens. © The Author(s) 2011

A systematic review of axillary web syndrome (AWS)

Introduction Axillary web syndrome (AWS) can result in early post-operative and long-term difficulties following lymphadenectomy for cancer and should be recognised by clinicians. This systematic review was conducted to synthesise information on AWS clinical presentation and diagnosis, frequency, natural progression, grading, pathoaetiology, risk factors, symptoms, interventions and outcomes. Methods Electronic searches were conducted using Cochrane, Pubmed, MEDLINE, CINAHL, EMBASE, AMED, PEDro and Google Scholar until June 2013. The methodological quality of included studies was determined using the Downs and Black checklist. Narrative synthesis of results was undertaken. Results Thirty-seven studies with methodological quality scores ranging from 11 to 26 on a 28-point scale were included. AWS diagnosis relies on inspection and palpation; grading has not been validated. AWS frequency was reported in up to 85.4 % of patients. Biopsies identified venous and lymphatic pathoaetiology with five studies suggesting lymphatic involvement. Twenty-one studies reported AWS occurrence within eight post-operative weeks, but late occurrence of greater than 3 months is possible. Pain was commonly reported with shoulder abduction more restricted than flexion. AWS symptoms usually resolve within 3 months but may persist. Risk factors may include extensiveness of surgery, younger age, lower body mass index, ethnicity and healing complications. Low-quality studies suggest that conservative approaches including analgesics, non-steroidal anti-inflammatory drugs and/or physiotherapy may be safe and effective for early symptom reduction. Conclusions AWS appears common. Current evidence for the treatment of AWS is insufficient to provide clear guidance for clinical practice. Implications for Cancer Survivors Cancer survivors should be informed about AWS. Further investigation is needed into pathoaetiology, long-term outcomes and to determine effective treatment using standardised outcomes