Cardiogenic Shock Challenges and Priorities: A Clinician Survey.

BACKGROUND: Cardiogenic shock (CS) is common and survival outcomes have not substantially improved. Australia's geography presents unique challenges in the management of CS. The challenges and research priorities for clinicians pertaining to CS identification and management have yet to be described. METHOD: We used an exploratory sequential mixed methods design. Semi-structured interviews were conducted with 10 clinicians (medical and nursing) to identify themes for quantitative evaluation. A total of 143 clinicians undertook quantitative evaluation through online survey. The interviews and surveys addressed current understanding of CS, status of cardiogenic systems and future research priorities. RESULTS: There were 143 respondents: 16 (11%) emergency, cardiology 22 (16%), 37 (26%) intensive care, 54 (38%) nursing. In total, 107 (75%) believe CS is under-recognised. Thirteen (13; 9%) of respondents indicated their hospital had existing CS teams, all from metropolitan hospitals, and 40% thought additional access to mechanical circulatory support devices was required. Five (5; 11%) non-tertiary hospital respondents had not experienced a delay in transfer of a patient in CS. All respondents felt additional research, particularly into the management of CS, was required. CONCLUSIONS: Clinicians report that CS is under-recognised and further research into CS management is required. Access to specialised CS services is still an issue and CS protocolised pathways may be of value

Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation

To have a sustainable economy and environment, several countries have widely inclined to the utilization of non-fossil fuels like biomass fuels to produce heat and electricity. The advantage of employing biomass for combustion is emerging as a potential renewable energy, which is regarded as a cheap fuel. Chemical constituents or elements are essential properties in biomass applications, which would be costly and labor-intensive to experimentally estimate them. One of the criteria to evaluate the energy of biomass from an economic perspective is the higher heating value (HHV). In the present work, we have applied multilayer perceptron artificial neural network (MLP-ANN), least-squares support vector machine (LSSVM), ant colony-adaptive neuro-fuzzy inference system (ACO-ANFIS), particle swarm optimization- ANFIS (PSO-ANFIS), genetic algorithm-radial basis function (GA-RBF) and new multivariate nonlinear regression (MNR) as accurate correlation methods to estimate HHVs of biomass fuels based on the ultimate analysis. 535 experimental data were gathered from literature and categorized into eight classes of by-products of fruits, agri-wastes, wood chips/tree species, grasses/leaves/fibrous materials, other waste materials, briquettes/charcoals/pellets, cereal and Industrial wastes. In the term of statistical analysis, average absolute relative deviation (AARD) authenticates that MNR and GA-RBF algorithm with %AARD of 3.5 and 3.4 could be used to estimate HHV. In addition, developed models results were compared to the results of 69 recently previously published empirical correlations and it confirms the reliability of our results. Relevency factor shows the impact of biomass elements on HHV and outlier analysis indicates the unreliable experimental data. The results of this study can be used by researchers to design and optimize biomass combustion systems

Polymers in contemporary art objects at the Art Gallery of New South Wales: a study using infrared spectroscopy

The common occurrence of polymer-based objects in museum and gallery collections means that conservators and curators require a knowledge of the polymer composition used in order to best address the care of an object. Polymer-based artworks can be examined and characterised using infrared spectroscopy. In this study, a variety of polymers from contemporary artworks in the collection of the Art Gallery of New South Wales were examined using this technique. In addition to providing guidance on polymer components, the findings of this study have demonstrated the importance of the identification of additives in the formulation of the polymer systems employed by artists. Additives including fillers, plasticisers and processing agents present in appreciable concentrations in commercial polymers, complicate the interpretation of the infrared spectra of these materials. The findings of this collaborative study contribute to a growing resource of information on polymers in heritage collections

Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods

Capture of air pollutant gases using novel and green solvents is obtaining widespread attention. Accurate estimation of this process is complex. We have estimated the absorption of CO2, CH4, H2S, N2O, SO2 and CO gases in ionic liquids (ILs). We have applied Multilayer Perceptron-Artificial Neural Networks (MLP-ANN), Hybrid-Adaptive Neuro Fuzzy Inference System (Hybrid-ANFIS), Particle Swarm Optimization-Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Coupled Simulated Annealing-Least Squares Support Vector Machine (CSA-LSSVM). We have gathered 3060 data of 72 IL-Gas mixtures for 40 types of ILs. The inputs of these models are: Temperature (T), pressure (P), IL molecular weight (MwIL), IL critical temperature (Tc, IL), IL critical pressure (Pc, IL), IL acentric factor (ωIL), gas molecular weight (Mwgas), gas critical temperature (Tc, gas), gas critical pressure (Pc, gas), gas kinetic diameter (d) and the acentric factor (ωgas). The CSA-LSSVM model produces best estimation with an Average Absolute Relative Deviation (AARD) of 8.7%. The results suggest the solubilities of the gases in ILs are correlated with structural factors of ILs. Estimation of the equilibrium behaviors in ionic liquids is of importance in simulation and design of solvent-based pollutant gas capture processes

International Diversification Ambidexterity of Emerging Economy Contractors: A Matching Perspective With Industrial Features

This article explores the mechanisms through which international diversification strategies and the global construction industrial context shape the competitiveness of emerging economic contractors. Drawing on an internal-external matching perspective, this study analyzes the matching between a contractor's international diversification ambidexterity (depth or breadth strategy) and industrial features (munificence or dynamism). Using panel data on Chinese contractors listed in Engineering News Record TOP 225/250 between 2012 and 2020, the empirical findings indicate that positive relationships exist between a contractor's diversification ambidexterity and competitiveness. Furthermore, we find two positive matching effects on contractor competitiveness: one is between depth strategy and industrial munificence, and the other is between breadth strategy and industrial dynamism. This study makes valuable contributions to both the theory and practice of international businesses for emerging economy contractors and the global construction market

Colorectal cancer survivors' experiences and views of shared and telehealth models of survivorship care: A qualitative study.

OBJECTIVES: The number of colorectal cancer (CRC) survivors is increasing and current models of survivorship care are unsustainable. There is a drive to implement alternative models of care including shared care between general practitioners (GPs) and hospital-based providers. The primary objective of this study was to explore perspectives on facilitators and barriers to shared care. The secondary objective was to explore experiences of telehealth-delivered care. METHOD: Qualitative data were collected via semi-structured interviews with participants in the Shared Care for Colorectal Cancer Survivors (SCORE) randomised controlled trial. Interviews explored patient experiences of usual and shared survivorship care during the SCORE trial. In response to the COVID pandemic, participant experiences of telehealth appointments were also explored. Interviews were recorded and transcribed for thematic analysis. RESULTS: Twenty survivors of CRC were interviewed with an even number in the shared and usual care arms; 14 (70%) were male. Facilitators to shared care included: good relationships with GPs; convenience of GPs; good communication between providers; desire to reduce public health system pressures. Barriers included: poor communication between clinicians; inaccessibility of GPs; beliefs about GP capacity; and a preference for follow-up care with the hospital after positive treatment experiences. Participants also commonly expressed a preference for telehealth-based follow-up when there was no need for a clinical examination. CONCLUSIONS: This is one of few studies that have explored patient experiences with shared and telehealth-based survivorship care. Findings can guide the implementation of these models, particularly around care coordination, communication, preparation, and personalised pathways of care

Deep Learning-Based Hybrid Algorithm for Detecting Cyber-Attacks

The Cybersecurity of the Industrial Internet of Services IIoS requires substantial consideration due to its extensive nature of interconnected devices and data communication which increasingly relies on wireless networks The increased dependence of wireless networks and interconnected devices on the Internet increases the value of exploiting the IloS The traditional methods used to detect cyber threats can no longer rely on a single aspect of information instead they must be able to adapt to new threats by learning from various sources Deep learning has gained popularity in recent years for its ability to learn complex inferences from significant data sources This research aims to propose a deep learning based hybrid algorithm that combines Multi Layer Perceptron MLP and Bidirectional Long Short Term Memory BiLSTM networks MLP and BiLSTM networks enhance the model s capability to detect complex cyber attack patterns Experimental results reveal better adequacy in terms of accuracy and recall than traditional methods The findings suggested that the proposed hybrid model can be a valuable approach for detecting sophisticated attack vector

Few-shot class incremental learning via robust transformer approach

Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built upon the Convolutional Neural Networks (CNNs)performing sub-optimally compared to the transformer approaches. Our paper presents Robust Transformer Approach (ROBUSTA)built upon the Compact Convolutional Transformer (CCT). The issue of overfitting due to few samples is overcome with the notion of the stochastic classifier, where the classifier's weights are sampled from a distribution with mean and variance vectors, thus increasing the likelihood of correct classifications, and the batch-norm layer to stabilize the training process. The issue of CFis dealt with the idea of delta parameters, small task-specific trainable parameters while keeping the backbone networks frozen. A non-parametric approach is developed to infer the delta parameters for the model's predictions. The prototype rectification approach is applied to avoid biased prototype calculations due to the issue of data scarcity. The advantage of ROBUSTAis demonstrated through a series of experiments in the benchmark problems where it is capable of outperforming prior arts with big margins without any data augmentation protocols