36 research outputs found

    Intrusion detection using decision tree classifier with feature reduction technique

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    The number of internet users and network services is increasing rapidly in the recent decade gradually. A Large volume of data is produced and transmitted over the network. Number of security threats to the network has also been increased. Although there are many machine learning approaches and methods are used in intrusion detection systems to detect the attacks, but generally they are not efficient for large datasets and real time detection. Machine learning classifiers using all features of datasets minimized the accuracy of detection for classifier. A reduced feature selection technique that selects the most relevant features to detect the attack with ML approach has been used to obtain higher accuracy. In this paper, we used recursive feature elimination technique and selected more relevant features with machine learning approaches for big data to meet the challenge of detecting the attack. We applied this technique and classifier to NSL KDD dataset. Results showed that selecting all features for detection can maximize the complexity in the context of large data and performance of classifier can be increased by feature selection best in terms of efficiency and accuracy

    Comparative analysis of N95 respirators fit testing with commercially available and in house reagent

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    Background: Due to COVID-19, thousands of healthcare workers have been affected and have lost their lives in the line of duty. For the protection of healthcare workers, WHO and CDC have made standard guidelines and requirements for PPE use. N95 masks are amongst the most readily used PPE by healthcare professionals and it is highly recommended by OSHA that every make and model of N95 should go through a fit test at least once in a year.Method: A total of 30 randomly selected healthcare professionals (who were a regular user of N95 respiratory masks) were subjected to assess in-house (saccharin sodium benzoate) reagent for use for standard qualitative fit testing in our hospital. Threshold testing with the in-house reagent at three different concentrations was performed prior to establish participants\u27 sensitivity to the reagent. After successful completion of threshold testing, fit test was performed on participants wearing an N95 mask.Results: All the participants included in the study passed the sensitivity testing with three concentrations of the reagents, while it was concluded that the concentration of the in-house reagent that was well suited for the sensitivity testing was a concentration of 1g/dl saccharin with 10g/dl sodium benzoate. For fit testing 12g/dl was found to be more appropriate.Discussion: Our study provided a low cost solution to ensure safety of healthcare workers who are regular users of N95 masks following guidelines implemented by OSHA and CDC.Conclusion: The in-house test solution prepared was found to be equally sensitive to its commercially available counterpart

    Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

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    Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

    Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

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    Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

    Surgical grand rounds at a university hospital. Applying publication presentation index to evaluate outcomes

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    Background and objective: Surgical grand rounds (SGR) are an important educational activity in all teaching hospitals however each institute has its own way of conducting them. At our institute, grand rounds in the Department of Surgery include an original research presentation by residents. The publication of the research work acts as a measure of its success. In this study we analyzed the outcome of this activity and review factors affecting their progression to publication.Methodology: We conducted a retrospective review of a prospectively maintained database of all presentations made at the Surgical Grand Round at a University Hospital from January 2001 to December 2010. Presentations with incomplete follow up records were excluded from analysis. A Publication-Presentation Index (PPI) was used to evaluate outcomes of SGRs and to study factors influencing outcomes. Differences in PPI in each category were calculated using the chi square test.Results: Total of 470 presentations were made. Majority presented retrospective studies (73%). Majority of the presentations were made by junior residents (year 1-3, 62%). Following presentation, 279 (59.4%) studies were presented at a national conference, 80 (17%) were presented at an international forum while only 99 (21.1%) studies were published. Mean presentation to publication time was 34.8 months. Study design, level of resident, section of surgery, sample size and national/international presentation were associated with conversion to a publication (all p \u3c 0.05). Overall PPI was 0.32. Randomized controlled trials had the highest PPI (0.67).Conclusion: The proportion of SGR presentations converted into national/international presentations and/or publications was found to be low. The PPI has a potential to be used as a tool to study the association of presentation to publication

    Aroma Component Analysis Using HS/SPME-FID Gas Chromatograph in Basmati Rice Varieties of Punjab, Pakistan

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    Aroma is a promising quality factor for rice grain that impacts consumer acceptability. The principal volatile compound that adds Basmati rice fragrance is 2-acetyl-1-pyrroline (2AP). Milled White rice of 04 promising varieties i.e., Super Basmati, Basmati-515, Basmati 2000and Basmati 370 were evaluated for volatile compounds by gas chromatography (GC) coupled with Solid Phase Micro Extraction unit (SPME) using Flame Ionizing Detector (FID). Six volatile compounds (nonanal, decanal, and alcohols such as benzyl alcohol, indole) were identified in the tested varieties, among them 2-AP is only present in aromatic rice varieties. This study confirmed the occurrence of 2-AP in all studied varieties with highest concentration in Super Basmati followed by Basmati-515, Basmati 2000 and Basmati 370
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