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Validated ensemble variable selection of laser-induced breakdown spectroscopy data for coal property analysis
No full textLaser-induced breakdown spectroscopy (LIBS), an emerging elemental analysis technique, provides a fast and low-cost solution for coal characterization without complex sample preparation. However, LIBS spectra contain a large number of uninformative variables, resulting in reduction in the predictive ability and learning speed of a multivariate model. Variable selection based on a single criterion usually leads to a lack of diversity in the selected variables. Coupled with spectral uncertainty in LIBS measurements, this can degrade the reliability and robustness of the multivariate model when analysing spectra obtained at different times and conditions. This work proposes a validated ensemble method for variable selection which uses six base algorithms and combines the returned variable subsets based on the cross-validation results. The proposed method is tested on two sets of LIBS spectra obtained within one month under variable experimental conditions to quantify the properties of coal, including fixed carbon, volatile matter, ash, calorific value and sulphur. The results show that the multivariate model based on the proposed method outperforms those using benchmark variable selection algorithms in six out of the seven tasks by 0.3%–2% in the coefficient of determination for prediction. This study suggests that variable selection based on ensemble learning improves the predictive ability and computational efficiency of the multivariate model in coal property analysis. Moreover, it can be used as a reliable method when the user is not sure which variables to choose in LIBS application
Cyber threat predictive analytics for improving cyber supply chain security
Get PDFCyber Supply Chain(CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, Procedure (TTP), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement
Epidemiological characteristics of sporadic nosocomial COVID-19 infections from June 2020 to June 2021 in China: an overview of vaccine breakthrough infection events
Get PDFThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has wreaked havoc to human beings around the world. Although China quickly brought the Coronavirus disease (COVID-19) pandemic under control, there have been several sporadic outbreaks in different regions of China since then. This article describes the chronological nosocomial COVID-19 infection events that related to several sporadic outbreaks of SARS-CoV-2 in different regions of China. We reported epidemiological characteristics and management measures of sporadic nosocomial COVID-19 infections from June 2020 to June 2021 in China, specially focused on domestic COVID-19 breakthrough infection in China — a vaccinated healthcare professional working in the isolation ward of a designated COVID-19 hospital
An exploration and analysis of the relative importance of employability skills for undergraduate nursing students as perceived by stakeholders - a mixed methods approach
No full textBackground: Current challenges in healthcare environment require nurses to develop and be equipped
with transferable employability skills in addition to discipline specific skills. Recently, Higher Education
Institutions and healthcare providers have focused on more collaboration and working in partnerships to
develop the requisite employability skills in undergraduates. Objective: To identify and develop an
employability skills questionnaire as informed by the stakeholders including service users. Methods: A
mixed method study using the Delphi technique to develop items of the Employability Skills
Questionnaire, and statistics to examine quantitative results. Setting: A modern university in London.
Undergraduate nurses, academics, managers in healthcare and service users who regularly use National
Health Service participated in the study. Focus groups and Delphi technique was used to obtain the general
consensus in the Questionnaire, and surveys to obtain numerical data to establish the reliability of the
questionnaire and the differences in the stakeholders‟ responses applying ANOVA. Results: The Delphi
technique yielded a consensus of 75% to the Questionnaire. Quantitative findings showed good internal
reliability of the Questionnaire and all stakeholders attributed high value to all the items therein. Data
indicated that service users play a significant role in identifying undergraduates‟ employability skills as
consumers of the healthcare service and gave more weight to the importance of evidence-based
information demonstrating their awareness of the importance of evidence-based practice in healthcare.
Conclusion: It is crucial that nurses keep abreast of current research to translate research findings into
practice and to adapt to different ways of working to improve patient care
The ‘pop-up’ recovery arts café: growing resilience through the staging of recovery community
Get PDFReflecting on a prototype event, A Recovery Arts Café, this article examines how recovery communities can be staged through collaborative performance events that directly engage with what it means to be in recovery from addiction. I theorise recovery and performance practice as particular forms of affective ecology, or processes of relation between the human and nonhuman, and challenge neoliberal ideas of self-care and ‘good’ citizenship. Drawing on posthumanist concepts of ‘life-living’ (Manning 2016) and ‘making kin’ (Haraway 2016), I identify how recovery-engaged performance events can operate as dynamic modes of growing resilience amidst societal contexts that impede capacity for recovery
Lignin modified PVDF membrane with antifouling properties for oil filtration
Get PDFLignin is a sustainable chemical that can be extracted from a wide range of lignocellulosic biowaste. It was blended into polymeric membranes to improve membrane morphology for filtration. Lignin dissolved in NaOH solution can be coated on different substrates to improve the surface hydrophilicity. In this work, the polyvinylidene fluoride (PVDF) membrane was coated with lignin to improve the filtration of oily water. Lignin was dissolved in NaOH solution with varied alkaline concentration (0.25–1.50 wt%) and lignin concentration (0.25–1.00 wt%). The PVDF membrane degraded in the highly alkaline solution, but the increasing lignin content reduced the membrane pore size for the effective rejection of oil emulsion. The PVDF membrane modified with 0.75 wt% of lignin in 0.5 wt% of NaOH solution attained a permeate flux about 70 L·m−2·h−1, but a slightly lower permeate flux of 55 L·m−2·h−1was recorded after immersed in alkaline solution 12 h. The lignin modified membrane rejected up to 99.30% of oil, while the neat PVDF membrane only rejected 83.30% of oil. The lignin modified membrane showed slightly lower but stable flux than the neat PVDF membrane due to the reduction of membrane fouling
Investigation of metabolic kinetics in different brain regions of awake rats using the [1H-13C]-NMR technique
Get PDFEnergy metabolism and neurotransmission are necessary for sustaining normal life activities. Hence, neurological or psychiatric disorders are always associated with changes in neurotransmitters and energy metabolic states in the brain. Most studies have only focused on the most important neurotransmitters, particularly GABA and Glu, however, other metabolites such as NAA and aspartate which are also very important for cerebral function are rarely investigated. In this study, most of the metabolic kinetics information of different brain regions was investigated in awake rats using the [1H-13C]-NMR technique. Briefly, rats (n = 8) were infused [1-13C] glucose through the tail vein for two minutes. After 20 min of glucose metabolism, the animals were sacrificed and the brain tissue was extracted and treated. Utilizing the 1H observed/13C-edited nuclear magnetic resonance (POCE-NMR), the enrichment of neurochemicals was detected which reflected the metabolic changes in different brain regions and the metabolic connections between neurons and glial cells in the brain. The results suggest that the distribution of every metabolite differed from every brain region and the metabolic rate of NAA was relatively low at 8.64 ± 2.37 μmol/g/h. In addition, there were some correlations between several 13C enriched metabolites, such as Glu4-Gln4 (p = 0.062), Glu4-GABA2 (p < 0.01), Glx2-Glx3 (p < 0.001), Asp3-NAA3 (p < 0.001). This correlativity reflects the signal transmission between astrocytes and neurons, as well as the potential interaction between energy metabolism and neurotransmission. In conclusion, the current study systematically demonstrated the metabolic kinetics in the brain which shed light on brain functions and the mechanisms of various pathophysiological states
Network representation learning enhanced by partial community information that is found using game theory
Get PDFPresently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving network topology properties in learned low-dimension representations. Community structure is a prominent characteristic of complex networks and thus should be well maintained. However, the difficulty lies in the fact that the properties of community structure are multivariate and complicated; therefore, it is insufficient to model community structure using a predefined model, the way that is popular in most state-of-the-art network embedding algorithms explicitly considering community structure preservation. In this paper, we introduce a multi-process parallel framework for network embedding that is enhanced by found partial community information and can preserve community properties well. We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. A series of experiments are conducted to evaluate the performance of our methods and six state-of-the-art algorithms. The results demonstrate that our methods can effectively preserve community properties of networks in their low-dimension representations. Specifically, compared to the involved baselines, our algorithms behave the best and are the runners-up on networks with high overlapping diversity and density
Challenges in preventive practices and risk communication towards COVID-19: a cross-sectional study in Bangladesh
Get PDFBangladesh recently experienced a COVID-19 second wave, resulting in the highest number of new cases and deaths in a single day. This study aims to identify the challenges for COVID-19 preventive practices and risk communications and associated factors among Bangladeshi adults. A cross-sectional survey was conducted between December 2020 and January 2021 involving 1,382 Bangladeshi adults (aged ≥18-years) in randomly selected urban and rural areas from all eight divisions in Bangladesh. Descriptive data analysis was conducted to highlight the challenges for preventive practices and risk communications for COVID-19. Multiple logistic regression analysis was used to determine the sociodemographic groups vulnerable to these challenges. Lack of availability of protective equipment (44.4%), crowded living situations/workspaces (36.8%), inadequate information on the proper use of protective measures (21.9%), inadequate handwashing and sanitation facilities (17.6%), and negative influences on family/friends (17.4%) were identified as barriers to COVID-19 preventive practices. It was also found that males (OR=1.3, 95% CI=1.01,1.7), rural residents (OR=1.5, 95% CI=1.2,2), respondents with a low level of education: No schooling vs. ≥ higher secondary (OR=3.5, 95% CI=2.3,5.2), Primary vs. ≥ higher secondary (OR=2.5, 95%CI=1.7,3.8), respondents engaged in agricultural (OR=1.7, 95%CI=1.2,2.4), laboring (OR=3.2, 95% CI=2,5), and domestic (OR=1.6, 95% CI=1.07,2.5) works, and people with disabilities (OR=1.7, 95% CI=1.1,2.6) were all likely to have difficulty in practicing effective COVID-19 protective behaviors. Respondents’ education and occupation were significant predictors of inadequate understanding of COVID-19 risk communications and was identified as a problem among 17.4% of the respondents. A substantial percentage of Bangladeshi adults have difficulty practising COVID-19 protective behaviours and have poor comprehension of risk communications, particularly in rural areas and among those with low education. This research can aid policymakers in developing tailored COVID-19 risk communications and mitigation strategies to help prevent future waves of the pandemic
The influence of anaemia on pressure ulcer healing in elderly patients
Get PDFAnaemia is a common and multifactorial blood disorder in elderly individuals. This condition may be a significant barrier to pressure ulcers healing as it is associated with a decreased level of oxygen being supplied to body tissues. Some nutritional deficiencies such as iron, vitamin B12 and folate may also cause anaemia and have a negative impact on pressure ulcer healing. An increased iron demand in hard-to-heal pressure ulcers is a significant factor associated with the risk of anaemia of chronic disease in elderly patients. Anaemia screening and correction may need to be considered as well as iron supplementation if required in pressure ulcer prevention and management