43 research outputs found

    Determination of Natural Frequency of Aerofoil Section Blades Using Finite Element Approach, Study of Effect of Aspect Ratio and Thickness on Natural Frequency

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    In this paper an attempt has been made to calculate the natural frequencies of aerofoil section blades using the finite element method. The effect of varying the aspect ratio and thickness on the frequencies has also been studied. It is observed that by increasing the value of aspect ratio, the natural frequency in all the modes is increased. It is also concluded that, there is some optimum value of thickness at which the natural frequencies in different modes of vibration are minimum

    Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

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    Medical Image Segmentation is crucial in various clinical applications within the medical domain. While state-of-the-art segmentation models have proven effective, integrating textual guidance to enhance visual features for this task remains an area with limited progress. Existing segmentation models that utilize textual guidance are primarily trained on open-domain images, raising concerns about their direct applicability in the medical domain without manual intervention or fine-tuning. To address these challenges, we propose using multimodal vision-language models for capturing semantic information from image descriptions and images, enabling the segmentation of diverse medical images. This study comprehensively evaluates existing vision language models across multiple datasets to assess their transferability from the open domain to the medical field. Furthermore, we introduce variations of image descriptions for previously unseen images in the dataset, revealing notable variations in model performance based on the generated prompts. Our findings highlight the distribution shift between the open-domain images and the medical domain and show that the segmentation models trained on open-domain images are not directly transferrable to the medical field. But their performance can be increased by finetuning them in the medical datasets. We report the zero-shot and finetuned segmentation performance of 4 Vision Language Models (VLMs) on 11 medical datasets using 9 types of prompts derived from 14 attributes.Comment: 25 pages, 9 figure

    Epidemiological study of insular glioma – an institutional experience

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    Object: Insular gliomas are difficult and challenging tumors to operate and manage due to the inherent complexity and the adjacent vital structures around them. Our aim is to define the morbidity profile and the mortality associated with the maximum possible safe resection of these less common tumors.Methods: The study was conducted on patients who were diagnosed as insular gliomas on MRI. All grades of patients treated by microsurgical techniques at our center from 2010 to 2016 were included in the study.Results: Median age at diagnosis was 39.52 years, Mean KPS score was 92.Male patients constituted 23 of all patients.68% patients had right sided tumors.WHO grade 2 was most common grade and constituted 64% of all cases.One patient developed expressive aphasia and five patients developed hemiparesis .There was one mortality due to unrelated causes.Conclusion: With the use of meticulous microneurosurgical technique, safe yet aggressive resection of the insular gliomas can be accomplished with comparable morbidity and mortality rates even when the modern neurosurgical tool such as intraoperative neuromonitoring and neuronavigation systems are unavailable

    Genome-based trait prediction in multi- environment breeding trials in groundnut

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    Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

    Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

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    An amendment to this paper has been published and can be accessed via the original article

    Patient and stakeholder engagement learnings: PREP-IT as a case study

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    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat
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