Supervisor conflict management, justice and strain : multi-level relationships

Purpose The purpose of this paper is to test a multilevel model of the main and mediating effects of supervisor conflict management style (SCMS) climate and procedural justice (PJ) climate on employee strain. It is hypothesized that workgroup-level climate induced by SCMS can fall into four types: collaborative climate, yielding climate, forcing climate, or avoiding climate; that these group-level perceptions will have differential effects on employee strain, and will be mediated by PJ climate. Design/methodology/approach Multilevel SEM was used to analyze data from 420 employees nested in 61 workgroups. Findings Workgroups that perceived high supervisor collaborating climate reported lower sleep disturbance, job dissatisfaction, and action-taking cognitions. Workgroups that perceived high supervisor yielding climate and high supervisor forcing climate reported higher anxiety/depression, sleep disturbance, job dissatisfaction, and action-taking cognitions. Results supported a PJ climate mediation model when supervisors’ behavior was reported to be collaborative and yielding. Research limitations/implications The cross-sectional research design places limitations on conclusions about causality; thus, longitudinal studies are recommended. Practical implications Supervisor behavior in response to conflict may have far-reaching effects beyond those who are a party to the conflict. The more visible use of supervisor collaborative CMS may be beneficial. Social implications The economic costs associated with workplace conflict may be reduced through the application of these findings. Originality/value By applying multilevel theory and analysis, we extend workplace conflict theory.

The MyD88+ phenotype is an adverse prognostic factor in epithelial ovarian cancer

The prognosis of epithelial ovarian cancer is poor in part due to the high frequency of chemoresistance. Recent evidence points to the Toll-like receptor-4 (TLR4), and particularly its adaptor protein MyD88, as one potential mediator of this resistance. This study aims to provide further evidence that MyD88 positive cancer cells are clinically significant, stem-like and reproducibly detectable for the purposes of prognostic stratification. Expression of TLR4 and MyD88 was assessed immunohistochemically in 198 paraffin-embedded ovarian tissues and in an embryonal carcinoma model of cancer stemness. In parallel, expression of TLR4 and MyD88 mRNA and regulatory microRNAs (miR-21 and miR-146a) was assessed, as well as in a series of chemosensitive and resistant cancer cells lines. Functional analysis of the pathway was assessed in chemoresistant SKOV-3 ovarian cancer cells. TLR4 and MyD88 expression can be reproducibly assessed via immunohistochemistry using a semi-quantitative scoring system. TLR4 expression was present in all ovarian epithelium (normal and neoplastic), whereas MyD88 was restricted to neoplastic cells, independent of tumour grade and associated with reduced progression-free and overall survival, in an immunohistological specific subset of serous carcinomas, p<0.05. MiR-21 and miR-146a expression was significantly increased in MyD88 negative cancers (p<0.05), indicating their participation in regulation. Significant alterations in MyD88 mRNA expression were observed between chemosensitive and chemoresistant cells and tissue. Knockdown of TLR4 in SKOV-3 ovarian cells recovered chemosensitivity. Knockdown of MyD88 alone did not. MyD88 expression was down-regulated in differentiated embryonal carcinoma (NTera2) cells, supporting the MyD88+ cancer stem cell hypothesis. Our findings demonstrate that expression of MyD88 is associated with significantly reduced patient survival and altered microRNA levels and suggest an intact/functioning TLR4/MyD88 pathway is required for acquisition of the chemoresistant phenotype. Ex vivo manipulation of ovarian cancer stem cell (CSC) differentiation can decrease MyD88 expression, providing a potentially valuable CSC model for ovarian cancer.

Audio signalling as a backup communication channel for multi-robot systems

This paper presents a low-bandwidth multi-robot communication system designed to serve as a backup communication channel in the event a robot suffers a network device fault. While much research has been performed in the area of distributing network communication across multiple robots within a system, individual robots are still susceptible to hardware failure. In the past, such robots would simply be removed from service, and their tasks re-allocated to other members. However, there are times when a faulty robot might be crucial to a mission, or be able to contribute in a less communication intensive area. By allowing robots to encode and decode messages into unique sequences of DTMF symbols, called words, our system is able to facilitate continued low-bandwidth communication between robots without access to network communication. Our results have shown that the system is capable of permitting robots to negotiate task initiation and termination, and is flexible enough to permit a pair of robots to perform a simple turn taking task.

Electrochemical pathway for the quantification of SERS enhancement factor

This communication presents a new pathway for the more precise quantification of surface-enhanced Raman scattering (SERS) enhancement factor via deducing resonance Raman scattering (RRS) effect from surface-enhanced resonance Raman scattering (SERRS). To achieve this, a self-assembled monolayer of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) is formed on plasmon inactive glassy carbon (GC) and plasmon active GC/AuNPs surface. The surfaces are subsequently used as common probes for electrochemical and Raman (RRS and SERRS) studies. The most crucial parameters required for the quantification of SERS substrate enhancement factor (SSEF) such as real surface area of GC/AuNPs substarte and the number of 4α-CoIITAPc molecules contributing to RRS (on GC) and SERRS (on GC/AuNPs) are precisely estimated by cyclic voltammetry experiments. The present approach of SSEF quantification can be applied to varieties of surfaces by choosing an appropriate laser line and probe molecule for each surface.

The effects of metabolic work rate and ambient environment on physiological tolerance times while wearing explosive and chemical personal protective equipment

This study evaluated the physiological tolerance times when wearing explosive and chemical (>35kg) personal protective equipment (PPE) in simulated environmental extremes across a range of differing work intensities. Twelve healthy males undertook nine trials which involved walking on a treadmill at 2.5, 4 and 5.5 km.h-1 in the following environmental conditions, 21, 30 and 37 °C wet bulb globe temperature (WBGT). Participants exercised for 60 min or until volitional fatigue, core temperature reached 39 °C, or heart rate exceeded 90% of maximum. Tolerance time, core temperature, skin temperature, mean body temperature, heart rate and body mass loss were measured. Exercise time was reduced in the higher WBGT environments (WBGT37

Multi-ciphersuite security of the Secure Shell (SSH) protocol

The Secure Shell (SSH) protocol is widely used to provide secure remote access to servers, making it among the most important security protocols on the Internet. We show that the signed-Diffie--Hellman SSH ciphersuites of the SSH protocol are secure: each is a secure authenticated and confidential channel establishment (ACCE) protocol, the same security definition now used to describe the security of Transport Layer Security (TLS) ciphersuites. While the ACCE definition suffices to describe the security of individual ciphersuites, it does not cover the case where parties use the same long-term key with many different ciphersuites: it is common in practice for the server to use the same signing key with both finite field and elliptic curve Diffie--Hellman, for example. While TLS is vulnerable to attack in this case, we show that SSH is secure even when the same signing key is used across multiple ciphersuites. We introduce a new generic multi-ciphersuite composition framework to achieve this result in a black-box way.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30 : a confirmatory versus exploratory approach

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

It wasn't my fault : does crash culpability improve predictive outcomes in a self-report study?

Introduction: Research that has focused on the ability of self-report assessment tools to predict crash outcomes has proven to be mixed. As a result, researchers are now beginning to explore whether examining culpability of crash involvement can subsequently improve this predictive efficacy. This study reports on the application of the Manchester Driver Behaviour Questionnaire (DBQ) to predict crash involvement among a sample of general Queensland motorists, and in particular, whether including a crash culpability variable improves predictive outcomes. Surveys were completed by 249 general motorists on-line or via a pen-and-paper format. Results: Consistent with previous research, a factor analysis revealed a three factor solution for the DBQ accounting for 40.5% of the overall variance. However, multivariate analysis using the DBQ revealed little predictive ability of the tool to predict crash involvement. Rather, exposure to the road was found to be predictive of crashes. An analysis into culpability revealed 88 participants reported being “at fault” for their most recent crash. Corresponding between and multi-variate analyses that included the culpability variable did not result in an improvement in identifying those involved in crashes. Conclusions: While preliminary, the results suggest that including crash culpability may not necessarily improve predictive outcomes in self-report methodologies, although it is noted the current small sample size may also have had a deleterious effect on this endeavour. This paper also outlines the need for future research (which also includes official crash and offence outcomes) to better understand the actual contribution of self-report assessment tools, and culpability variables, to understanding and improving road safety.

Moderate physical activity level as a protective factor against metabolic syndrome in middle-aged and older women

Aims and objectives To investigate whether physical activity is a protective factor against metabolic syndrome in middle-aged and older women. Background Socio-demographic and lifestyle behaviour factors contribute to metabolic syndrome. To minimise the risk of metabolic syndrome, several global guidelines recommend increasing physical activity level. However, only limited research has investigated the relationship between physical activity levels and metabolic syndrome in middle-aged and older women after adjusting for socio-demographic and lifestyle behaviour factors. Design Cross-sectional design. Methods A convenience sample of 326 middle-aged and older women was recruited. Metabolic syndrome was confirmed according to the National Cholesterol Education Program, Adult Treatment Panel III guidelines, and physical activity levels were measured by the International Physical Activity Questionnaire. Results The sample had a mean age of 60•9 years, and the prevalence of metabolic syndrome was 43•3%. Postmenopausal women and women with low socioeconomic status (low-education background, without personal income and currently unemployed) had a significantly higher risk of developing metabolic syndrome. After adjusting for significant socio-demographic and lifestyle behaviour factors, the women with moderate or high physical activity levels had a significantly lower (OR = 0•10; OR = 0•11, p < 0•001) risk of metabolic syndrome and a lower risk for each specific component of metabolic syndrome, including elevated fasting plasma glucose (OR = 0•29; OR = 0•26, p = 0•009), elevated blood pressure (OR = 0•18; OR = 0•32, p = 0•029), elevated triglycerides (OR = 0•41; OR = 0•15, p = 0•001), reduced high-density lipoprotein (OR = 0•28; OR = 0•27, p = 0•004) and central obesity (OR = 0•31; OR = 0•22, p = 0•027). Conclusions After adjusting for socio-demographic and lifestyle behaviour factors, physical activity level was a significant protective factor against metabolic syndrome in middle-aged and older women. Higher physical activity levels (moderate or high physical activity level) reduced the risk of metabolic syndrome in middle-aged and older women. Relevance to clinical practice Appropriate strategies should be developed to encourage middle-aged and older women across different socio-demographic backgrounds to engage in moderate or high levels of physical activity to reduce the risk of metabolic syndrome.

Multi-agent systems for modeling high penetration photovoltaic system impacts in distribution networks

This paper reviews the use of multi-agent systems to model the impacts of high levels of photovoltaic (PV) system penetration in distribution networks and presents some preliminary data obtained from the Perth Solar City high penetration PV trial. The Perth Solar City trial consists of a low voltage distribution feeder supplying 75 customers where 29 consumers have roof top photovoltaic systems. Data is collected from smart meters at each consumer premises, from data loggers at the transformer low voltage (LV) side and from a nearby distribution network SCADA measurement point on the high voltage side (HV) side of the transformer. The data will be used to progressively develop MAS models.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

Prevalence of active foot disease and foot disease risk factors in a subacute inpatient rehabilitation facility: a cross-sectional prevalence study

Background Australian subacute inpatient rehabilitation facilities face significant challenges from the ageing population and the increasing burden of chronic disease. Foot disease complications are a negative consequence of many chronic diseases. With the rapid expansion of subacute rehabilitation inpatient services, it seems imperative to investigate the prevalence of foot disease and foot disease risk factors in this population. The primary aim of this cross-sectional study was to determine the prevalence of active foot disease and foot disease risk factors in a subacute inpatient rehabilitation facility. Methods Eligible participants were all adults admitted at least overnight into a large Australian subacute inpatient rehabilitation facility over two different four week periods. Consenting participants underwent a short non-invasive foot examination by a podiatrist utilising the validated Queensland Health High Risk Foot Form to collect data on age, sex, medical co-morbidity history, foot disease risk factor history and clinically diagnosed foot disease complications and foot disease risk factors. Descriptive statistics were used to determine the prevalence of clinically diagnosed foot disease complications, foot disease risk factors and groups of foot disease risk factors. Logistic regression analyses were used to investigate any associations between defined explanatory variables and appropriate foot disease outcome variables. Results Overall, 85 (88%) of 97 people admitted to the facility during the study periods consented; mean age 80 (±9) years and 71% were female. The prevalence (95% confidence interval) of participants with active foot disease was 11.8% (6.3 – 20.5), 32.9% (23.9 – 43.5) had multiple foot disease risk factors, and overall, 56.5% (45.9 – 66.5) had at least one foot disease risk factor. A self-reported history of peripheral neuropathy diagnosis was independently associated with having multiple foot disease risk factors (OR 13.504, p = 0.001). Conclusion This study highlights the potential significance of the burden of foot disease in subacute inpatient rehabilitation facilities. One in eight subacute inpatients were admitted with active foot disease and one in two with at least one foot disease risk factor in this study. It is recommended that further multi-site studies and management guidelines are required to address the foot disease burden in subacute inpatient rehabilitation facilities. Keywords: Subacute; Inpatient; Foot; Complication; Prevalence

Non-monetised multi-objective decision making system for road management

This paper presents a performance-based optimisation approach for conducting trade-off analysis between safety (roads) and condition (bridges and roads). Safety was based on potential for improvement (PFI). Road condition was based on surface distresses and bridge condition was based on apparent age per subcomponent. The analysis uses a non-monetised optimisation that expanded upon classical Pareto optimality by observing performance across time. It was found that achievement of good results was conditioned by the availability of early age treatments and impacted by a frontier effect preventing the optimisation algorithm from realising of the long-term benefits of deploying actions when approaching the end of the analysis period. A disaggregated bridge condition index proved capable of improving levels of service in bridge subcomponents.

Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: A multivariate statistical approach

The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na–Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na–HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous–Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls.

Multi-modal deformable medical image registration

Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between images acquired from different individuals or atlases. Furthermore, certain applications require this type of registration to operate across images acquired from different imaging modalities. One popular local approach for estimating this registration is a block matching procedure utilising the mutual information criterion. However, previous block matching procedures generate a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. This paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo statistical procedure to optimally select grid points of interest. Three different methods are then compared to propagate the estimated sparse deformation field to the entire image including a thin-plate spline warp, Gaussian convolution, and a hybrid fluid technique. Results show that non-rigid registration can be improved by using the proposed algorithm to optimally select grid points of interest.