192 research outputs found
A collaborative perspective in green construction risk management
No full textMany risks existing in the supply chain of green construction projects are poorly managed by traditional non-collaborative approaches leading to problems such as higher prices, inappropriate indoor environment quality, technological failures and legal battles that in turn adversely affect all stakeholders. To reduce the cases of failure in the green construction industry, it is necessary for supply chain (SC) key players to collaboratively identify, analyse and treat risks, considering benefits and concerns of all stakeholders inside the network.
This paper presents a method for collaborative risk management to provide informed advice to supply chain stakeholders to manage risks in the green construction industry. Contribution of the proposed collaborative approach is illustrated in a case study carried out in a green construction development project in Melbourne, Australia. The case study introduced in this research is sufficiently robust to provide evidence that collaborative approaches can add value to traditional methods of risk management and presents a modelling and analysis framework for assessing supply chain risks in the green construction.
Authors: Mehrdad Arashpour and Mohammadreza Arashpour, School of Property, Construction and Project Management, RMIT University.
First published in Kamardeen, I, Newton, S, Lim, B and Loosemore, M (ed.) Proceedings of the 37th Annual Conference of the Australasian Universities Building Educators Association (AUBEA), Sydney, Australia, 4th - 6th July 2012, pp. 1-11
RabbitStamp Test Sequence
No full text# RabbitStamp sequence by LISA ULB
The test sequence "RabbitStamp" is provided by Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.
# License:
CC BY-NC-SA
ONLY Available for Academic Usage
# Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "RabbitStamp Test Sequence", 2021.
@misc{fachada_RabbitStamp_2021,
title = {{RabbitStamp} {Test} {Sequence}},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },
month = jul,
year = {2021},
doi = {10.5281/zenodo.5053771}
}
[2] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[DLF] Plenoptic 2.0 Multiview Lenslet Dataset and Preliminary Experiments [m56429]", 2021.
@article{fachada_RabbitStamp_2021,
title = {[DLF] {Plenoptic} 2.0 {Multiview} {Lenslet} {Dataset} and {Preliminary} {Experiments} [m56429]},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },
journal = {ISO/IEC JTC1/SC29/WG11},
month = apr,
year = {2021}
}
[3] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[LVC] Update for RabbitStamp: Plenoptic 2.0 Multiview Lenslet Dataset [m57100]", 2021.
@article{fachada_RabbitStamp_2021,
title = {[LVC] {Update} for {RabbitStamp}: {Plenoptic} 2.0 {Multiview} {Dataset} [m56429]},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },
journal = {ISO/IEC JTC1/SC29/WG11},
month = jul,
year = {2021}
}
[4] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[LVC] Exploration Experiments using RabbitStamp Multiview Lenslet Images [m57101]", 2021.
@article{fachada_RabbitStamp_2021,
title = {[LVC] {Exploration} {Experiments} {Using} {RabbitStamp} {Multiview} {Lenslet} {Images} [m56429]},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad},
journal = {ISO/IEC JTC1/SC29/WG11},
month = jul,
year = {2021}
}
# Production:
Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.
# Content:
This dataset contains a test scene acquired with a raytrix camera [1] array of 7x3 views. For details of the dataset, please refer to the references mentioned above.
The dataset contains:
- a `depth_7x3_center` depth maps computed with DERS reference software [2] in yuv40016ble format and json configuration files to do so,
- a `multiview_7x3_5x5_images` Calibrated subimages computed with RLC [3] in yuv42010ble format, the cameras.json with the camera parameters and view_synthesis.json with the view synthesis experiment.
- a `multiview_7x3_lenslets` folder containing the lenslet views in yuv42010ble format, the Raytrix xml calibration file and RLC cfg file for conversion to multiview.
# References and links:
[1] Raytrix, https://raytrix.de/
[2] S. Rogge and D. Bonatto and J. Sancho and R. Salvador and E. Juarez and A. Munteanu and G. Lafruit, "MPEG-I Depth Estimation Reference Software", in 2019 International Conference on 3D Immersion (IC3D), 2019.
[3] M. Teratani and T. Fujii, "[MPEG-I Visual] Conversion of Lenslet Data Capture by Single Focussed Plenoptic Camera to Multiview Video using RLC0.3 [N18567]", ISO/IEC JTC1/SC29/WG11, 201
Distribution of SCCmec Elements and Presence of Panton-Valentine Leukocidin in Methicillin-Resistant Staphylococcus epidermidis Isolated from Clinical Samples in a University Hospital of Isfahan City, Iran
No full textTransparent Magritte Test Sequence
No full textTransparent-Magritte sequence by LISA ULB
The test sequence "Transparent Magritte" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB, ULB.
License:
CC BY-NC-SA
Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Transparent Magritte Test Sequence", 2021.
@misc{fachada_transparent_2021,
title = {Transparent {Magritte} {Test} {Sequence}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.4488243}
}
[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.
@inproceedings{fachada_light_2021,
title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},
booktitle = {Electronic {Imaging}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
year = {2021}
}
Production
Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.
Content:
This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Bblender file and the rendered scene.
The scene contains a transparent refractive torus rendered in a regular camera array of 21x21 cameras.
In addition to the 3D model, two folders are available:
- `centered_cameras` resolution of 1000x1000, the cameras are centered on the refractive torus.
- `parallel_cameras` resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.
Each of these folders contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `parameters.cfg` generated with [2],
- a `texture` folder containing the rendered views in png format,
- a `depth` folder containing the associated depth maps in exr format.
References and links:
[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.
[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,
https://github.com/lightfield-analysis/blender-addon
https://github.com/dbonattoj/blender-addon
[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-formatAcknoledgments:
This work was supported by
Les Fonds de la Recherche Scientifique - FNRS, Belgium, under Grant n°3679514$, ColibriH
The European Commision project n°951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron.
Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgiu
Epidemiology of Listeria monocytogenes prevalence in foods, animals and human origin from Iran: a systematic review and meta-analysis
No full textAbstract Background Listeria monocytogenes as the main causative agent of human listeriosis is an intracellular bacterium that has the capability to infect a wide range of cell types. Human listeriosis is a sporadic foodborne disease, which is epidemiologically linked with consumption of contaminated food products. Listeriosis may range from mild and self-limiting diseases in healthy people to severe systemic infections in susceptible populations. This study aimed to investigate the prevalence of L. monocytogenes in food resources and human samples from Iran. Methods A systematic search was performed by using electronic databases from papers that were published by Iranian authors Since January of 2000 to the end of April 2017. Then, 47 publications which met our inclusion criteria were selected for data extraction and analysis by Comprehensive Meta-Analysis Software. Results The pooled prevalence of L. monocytogenes in human origin was 10% (95% CI: 7–12%) ranging from 0 to 28%. The prevalence of L. monocytogenes in animals was estimated at 7% (95% CI: 4–10%) ranging from 1 to 18%. Moreover, the pooled prevalence of L. monocytogenes in Iranian food samples was estimated at 4% (95% CI: 3–5%) ranging from 0 to 50%. From those 12 studies which reported the distribution of L. monocytogenes serotypes, it was concluded that 4b, 1/2a, and 1/2b were the most prevalent serotypes. Conclusions The prevalence of L. monocytogenes and prevalent serotypes in Iran are comparable with other parts of the world. Although the overall prevalence of human cross-contamination origin was low, awareness about the source of contamination is very important because of the higher incidence of infections in susceptible groups
Cinnamomum verum (Cinnamon): A promising natural alternative for urinary tract infection treatment
No full textNo abstract
Investigation of Class I, II, and III Integrons Among Acinetobacter Baumannii Isolates from Hospitalized Patients in Isfahan, Iran
No full textObjectives: This study aimed to determine the prevalence of class I, II, and III integrons among clinical Acinetobacter baumannii isolates collected from hospitalized patients. Methods: This cross-sectional study was conducted at two teaching hospitals in Isfahan, Iran, from October 2015 to October 2016. A total of 147 non-duplicate A. baumannii isolates were collected from clinical specimens and identified as A. baumannii using standard microbiological methods and confirmed by genotyping. Antimicrobial susceptibility was determined using disc diffusion method, and the presence of integron genes was performed using the polymerase chain reaction. Results: Out of 147 confirmed A. baumannii isolates, 97.3% of isolates were extensive drug-resistant (XDR) and 2.7% were multidrug-resistant (MDR). Class I and II integrons were detected in 63.9% and 78.2% of the A. baumannii, respectively. Class III integron was not detected in any of the isolates. Conclusion: Our results show a high prevalence of classes I and II integrons which may play a key role in the acquisition of MDR and XDR phenotype among A. baumannii isolates in our region. Therefore, use of appropriate infection control in clinical settings and implementation of treatment strategies is necessary for our hospitals
Magritte Sphere
No full text# Magritte-Sphere sequence by LISA ULB
The test sequence "Magritte Sphere" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.
# License:
CC BY-NC-SA
# Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Magritte Sphere Test Sequence", 2021.
@misc{fachada_magritte_2021,
title = {{Magritte} {Sphere} {Test} {Sequence}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.5048265}
}
[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.
@inproceedings{fachada_light_2021,
title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},
booktitle = {Electronic {Imaging}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
year = {2021}
}
# Production:
Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.
# Content:
This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Blender file and the rendered scene.
The scene contains a non-Lambertian (transparent-refractive (T) or mirror-specular (M)) sphere rendered in a regular camera array of 21x21 cameras.
In addition to the 3D model, we provide the rendered images : resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.
The dataset contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `parameters.cfg` generated with [2],
- a `texture_M` folder containing the rendered views in png format for the mirror object,
- a `texture_T` folder containing the rendered views in png format for the transparent object,
- a `mask` folder containing the mask indicating the sphere,
- a `depth` folder containing the associated depth maps in exr format.
# References and links:
[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.
[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,
https://github.com/lightfield-analysis/blender-addon
https://github.com/dbonattoj/blender-addon
[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-formatAcknowledgments:
This work was supported by:
Les Fonds de la Recherche Scientifique - FNRS, Belgium, under Grant n°3679514$, ColibriH,
The European Commision project n°951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron.
Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgiu
Investigation of antimicrobial susceptibility, class I and II integrons among Pseudomonas aeruginosa isolates from hospitalized patients in Isfahan, Iran
No full textAbstract Objectives The role of integrons in the transfer of antibiotic resistance is one of the important issues, therefore, this study is aimed to investigate antibiotic resistance pattern and prevalence of class 1 and 2 integrons in P. aeruginosa isolated. Results Out of 72 confirmed P. aeruginosa isolates, 50% were from ICU patients. Antibacterial susceptibility pattern showed that isolates were most resistant to ceftazidime (76.4%) and colistin was the most effective antibiotic (100%) and molecular analysis of class I and II integrons showed 55.5% and 29.1% of isolates were positive, respectively and the proportions of MDR isolates were significantly higher among integron-positive isolates with 73.6% compared to negative isolates with 22.9%. Our results showed that there was a correlation among class 1 and 2 integrons with MDR P. aeruginosa isolates. According to the importance of integrons in acquisition and dissemination of antibiotics resistance genes, the performance of antibiotic surveillance programs and investigating the role of integrons is recommended to control the spreading of antibiotics resistance genes
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