Promoting employee safety performance in the Chinese construction industry

In the construction industry, safety leadership has been widely recognised as an indispensable factor that affects organisational safety performance. However, in China specifically, research on safety leadership in the construction domain is not adequately developed. This paper examines the role of organisational leadership in promoting safety performance, as moderated by safety climate. The study adopts quantitative research method through questionnaire survey with 106 construction professionals leading or participating in safety management work in the Chinese construction sectors. The results show that exerting certain leadership strategies that encourage construction stakeholders to comply with safety practices will improve safety performance. At a moment when the whole industry is suffering from momentous safety challenges, transformation is required; these findings are intended to guide construction managers in their commitment to programme safety management. The study reinforces the interaction between upper layer and lower layer employees thereby improving the safety performance via improvements in the safety climate. In addition to being rooted in the full-range leadership model, this paper considered the impo rtant (and often ignored) characteristics of Chinese culture. The study recommends the early involvement of contractors in the design process and considers site hazards when making design decisions

Improved two-stream model for human action recognition

This paper addresses the recognitions of human actions in videos. Human action recognition can be seen as the automatic labeling of a video according to the actions occurring in it. It has become one of the most challenging and attractive problems in the pattern recognition and video classification fields. The problem itself is difficult to solve by traditional video processing methods because of several challenges such as the background noise, sizes of subjects in different videos, and the speed of actions. Derived from the progress of deep learning methods, several directions are developed to recognize a human action from a video, such as the long-short-term memory (LSTM)-based model, two-stream convolutional neural network (CNN) model, and the convolutional 3D model.In this paper, we focus on the two-stream structure. The traditional two-stream CNN network solves the problem that CNNs do not have satisfactory performance on temporal features. By training a temporal stream, which uses the optical flow as the input, a CNN can have the ability to extract temporal features. However, the optical flow only contains limited temporal information because it only records the movements of pixels on the x-axis and the y-axis. Therefore, we attempt to design and implement a new two-stream model by using an LSTM-based model in its spatial stream to extract both spatial and temporal features in RGB frames. In addition, we implement a DenseNet in the temporal stream to improve the recognition accuracy. This is in-contrast to traditional approaches which typically utilize the spatial stream for extracting only spatial features. The quantitative evaluation and experiments are conducted on the UCF-101 dataset, which is a well-developed public video dataset. For the temporal stream, we choose the optical flow of UCF-101. Images in the optical flow are provided by the Graz University of Technology. The experimental result shows that the proposed method outperforms the traditional two-stream CNN method with an accuracy of at least 3%. For both spatial and temporal streams, the proposed model also achieves higher recognition accuracies. In addition, compared with the state of the art methods, the new model can still have the best recognition performance

Rpgrip1 is required for rod outer segment development and ciliary protein trafficking in zebrafish

The authors would like to thank the Royal Society of London, the National Eye Research Centre, the Visual Research Trust, Fight for Sight, the W.H. Ross Foundation, the Rosetrees Trust, and the Glasgow Children’s Hospital Charity for supporting this work. This work was also supported by the Deanship of Scientific Research at King Saud University for funding this research (Research Project) grant number ‘RGP – VPP – 219’.Mutations in the RPGR-interacting protein 1 (RPGRIP1) gene cause recessive Leber congenital amaurosis (LCA), juvenile retinitis pigmentosa (RP) and cone-rod dystrophy. RPGRIP1 interacts with other retinal disease-causing proteins and has been proposed to have a role in ciliary protein transport; however, its function remains elusive. Here, we describe a new zebrafish model carrying a nonsense mutation in the rpgrip1 gene. Rpgrip1homozygous mutants do not form rod outer segments and display mislocalization of rhodopsin, suggesting a role for RPGRIP1 in rhodopsin-bearing vesicle trafficking. Furthermore, Rab8, the key regulator of rhodopsin ciliary trafficking, was mislocalized in photoreceptor cells of rpgrip1 mutants. The degeneration of rod cells is early onset, followed by the death of cone cells. These phenotypes are similar to that observed in LCA and juvenile RP patients. Our data indicate RPGRIP1 is necessary for rod outer segment development through regulating ciliary protein trafficking. The rpgrip1 mutant zebrafish may provide a platform for developing therapeutic treatments for RP patients.Publisher PDFPeer reviewe

Investigation on learning approaches of the whole-time college nursing students

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Creating, moving and merging Dirac points with a Fermi gas in a tunable honeycomb lattice

Dirac points lie at the heart of many fascinating phenomena in condensed matter physics, from massless electrons in graphene to the emergence of conducting edge states in topological insulators [1, 2]. At a Dirac point, two energy bands intersect linearly and the particles behave as relativistic Dirac fermions. In solids, the rigid structure of the material sets the mass and velocity of the particles, as well as their interactions. A different, highly flexible approach is to create model systems using fermionic atoms trapped in the periodic potential of interfering laser beams, a method which so far has only been applied to explore simple lattice structures [3, 4]. Here we report on the creation of Dirac points with adjustable properties in a tunable honeycomb optical lattice. Using momentum-resolved interband transitions, we observe a minimum band gap inside the Brillouin zone at the position of the Dirac points. We exploit the unique tunability of our lattice potential to adjust the effective mass of the Dirac fermions by breaking inversion symmetry. Moreover, changing the lattice anisotropy allows us to move the position of the Dirac points inside the Brillouin zone. When increasing the anisotropy beyond a critical limit, the two Dirac points merge and annihilate each other - a situation which has recently attracted considerable theoretical interest [5-9], but seems extremely challenging to observe in solids [10]. We map out this topological transition in lattice parameter space and find excellent agreement with ab initio calculations. Our results not only pave the way to model materials where the topology of the band structure plays a crucial role, but also provide an avenue to explore many-body phases resulting from the interplay of complex lattice geometries with interactions [11, 12]

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

Peer reviewedPublisher PD

NuRV: A nuXmv Extension for Runtime Verification

We present NuRV, an extension of the nuXmv model checker for assumption-based LTL runtime verification with partial observability and resets. The tool provides some new commands for online/offline monitoring and code generations into standalone monitor code. Using the online/offline monitor, LTL properties can be verified incrementally on finite traces from the system under scrutiny. The code generation currently supports C, C++, Common Lisp and Java, and is extensible. Furthermore, from the same internal monitor automaton, the monitor can be generated into SMV modules, whose characteristics can be verified by Model Checking using nuXmv. We show the architecture, functionalities and some use scenarios of NuRV, and we compare the performance of generated monitor code (in Java) with those generated by a similar tool, RV-Monitor. We show that, using a benchmark from Dwyer's LTL patterns, besides the capacity of generating monitors for long LTL formulae, our Java-based monitors are about 200x faster than RV-Monitor at generation-time and 2–5x faster at runtime

Aging and pattern complexity effects on the visual span: evidence from Chinese character recognition

Research suggests that pattern complexity (number of strokes) limits the visual span for Chinese characters, and that this may have important consequences for reading. With the present research, we investigated age differences in the visual span for Chinese characters by presenting trigrams of low, medium or high complexity at various locations relative to a central point to young (18-30 years) and older (60+ years) adults. A sentence reading task was used to assess their reading speed. The results showed that span size was smaller for high complexity stimuli compared to low and medium complexity stimuli for both age groups, replicating previous findings with young adult participants. Our results additionally showed that this influence of pattern complexity was greater for the older than younger adults, such that while there was little age difference in span size for low and medium complexity stimuli, span size for high complexity stimuli was almost halved in size for the older compared to the young adults. Finally, our results showed that span size correlated with sentence reading speed, confirming previous findings taken as evidence that the visual span imposes perceptual limits on reading speed. We discuss these findings in relation to age-related difficulty reading Chinese

Applications of Direct Injection Soft Chemical Ionisation-Mass Spectrometry for the Detection of Pre-blast Smokeless Powder Organic Additives

Analysis of smokeless powders is of interest from forensics and security perspectives. This article reports the detection of smokeless powder organic additives (in their pre-detonation condition), namely the stabiliser diphenylamine and its derivatives 2-nitrodiphenylamine and 4-nitrodiphenylamine, and the additives (used both as stabilisers and plasticisers) methyl centralite and ethyl centralite, by means of swab sampling followed by thermal desorption and direct injection soft chemical ionisation-mass spectrometry. Investigations on the product ions resulting from the reactions of the reagent ions H3O+ and O2+ with additives as a function of reduced electric field are reported. The method was comprehensively evaluated in terms of linearity, sensitivity and precision. For H3O+, the limits of detection (LoD) are in the range of 41-88 pg of additive, for which the accuracy varied between 1.5 and 3.2%, precision varied between 3.7 and 7.3% and linearity showed R20.9991. For O2+, LoD are in the range of 72 to 1.4 ng, with an accuracy of between 2.8 and 4.9% and a precision between 4.5 and 8.6% and R20.9914. The validated methodology was applied to the analysis of commercial pre-blast gun powders from different manufacturers.(VLID)4826148Accepted versio