Pediatric Posterior Fossa Epidural Hematomas

Aim: Posterior fossa epidural hematoma (PFEDH) in the pediatric age group is a very rare condition and the treatment method is still a matter of debate. The aim of this study is to evaluate the observation results in the management of pediatric patients with PFEDH in our tertiary care hospital and to investigate the importance of the relationship of the hematoma with the cerebral venous sinuses, which has not been investigated before in the literature, in the treatment decision. Materials and Methods: This is a retrospective study conducted at Selcuk University, Faculty of Medicine. All patients (≤ 17 years) diagnosed with PFEDH between January 2010 and May 2022 were included in this study. Demographic data, clinical signs, trauma type and symptoms at presentation, CT findings, type of treatment, and outcomes were collected. CT findings including hematoma thickness, hydrocephalus, presence of fourth ventricular compression, relation with cerebral venous sinuses and other associated brain injuries were evaluated. Results: The patient group consists of two girl and six boy. The most common cause of PFEDH was a fall from a height resulting in a blow to the back of the head in four patients. Vomiting was the most frequent presenting symptom. Four patients had a relation between cerebral venous sinuses and hematoma, and two of these patients underwent surgical treatment Conclusion: In addition to criteria such as hematoma thickness, GCS, hydrocephalus, and compression of the fourth ventricle, we determined that the relationship of hematoma with venous sinuses is a criterion to be evaluated

Co-pyrolysis of waste polyolefins with waste motor oil

The co-pyrolysis of waste polyolefins [waste polyethylene (PE) and waste polypropylene (PP)] with waste motor oil (WMO) was performed at different ratios under a nitrogen atmosphere at 500 degrees C. The effects of WMO on the pyrolysis of waste polyolefins and their blends were investigated under identical conditions. The addition of WMO into waste polyolefins not only increased the liquid yields but also improved the properties of liquid products. In the most cases, the co-pyrolysis process had a positive synergistic effect on the liquid yields when compared with the calculated co-pyrolysis yields. The naphtha and paraffinic contents of the liquid products obtained from the co-pyrolysis of PE/WMO, PE/PP/WMO blends were higher than liquid products obtained from the pyrolysis of the individual waste polyolefins. The trace elements as well as heavy metals in the liquid products from the pyrolysis of WMO alone or the co-pyrolysis of waste polyolefins with WMO were observed to be lower than the WMO feed. The prominent gas products obtained from the pyrolysis of individual waste polyolefins and WMO or the co-pyrolysis of waste polyolefins/WMO blends were hydrocarbons and hydrogen. The heating values of the pyrolysis and co-pyrolysis gases were found to be in the range of 27.6-32.4 MJ Nm(-3). (C) 2016 Elsevier B.V. All rights reserved

Lewis acid catalyzed diesel-like fuel production from raw corn oil

The pyrolysis of raw corn oil in he absence and presence of Lewis acids was carried out at 500 degrees C. The catalytic effect of AlCl3 was better than that of FeCl3. The physico-chemical properties of diesel-like fuels produced by Lewis acid catalyzed are close to that of commercial diesel fuel. The diesel-like fuels obtained from catalytic runs can be evaluated as diesel fuels. Copyright (C) 2008 John Wiley & Sons, Ltd

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data

There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated features are extracted and validated by multi-label classification techniques, Binary Relevance (BR), Classifier Chains (CC), Pruned Sets (PS), and Random k-Labelset (RAkEL). Base classifiers such as Support Vector Machines (SVM), J48-Decision Tree (J48), and Logistic Regression (LR) are performed in the classification phase of the algorithms. Comprehensive experiments are carried out with local feature descriptors extracted from two multi-label data sets, the well-known MIR-Flickr dataset and a Wireless Multimedia Sensor (WMS) dataset that we have generated from our video recordings. The prediction accuracy levels are improved by 6.36% and 25.7% for the MIR-Flickr and WMS datasets respectively while the number of features is significantly reduced. The results verify that the algorithms presented in this new framework outperform the state-of-the-art algorithms

The influence of the waste ethylene vinyl acetate copolymer on the thermal degradation of the waste polypropylene

WOS: 000261281600027The Pyrolysis of the waste polypropylene (PP), the waste ethylene vinyl acetate copolymer (EVA) and their blends has been carried out in a fixed bed reactor at 500 degrees C. The effect of different ratios of the waste EVA in the waste PP/EVA blends on the thermal degradation of the waste PP was investigated in terms of both product distributions and liquid fuel properties. The compositions of pyrolysis products were characterized in detail. The liquid products from the pyrolysis of the waste PP, the waste EVA and their blends were analyzed using different analytical techniques and fuel properties of pyrolytic liquids were investigated in comparison with commercial diesel. There were no synergistic effects between products from the waste PP and products from the waste EVA. While the ratio of the waste EVA increased in the waste PP/EVA blends, aromatic content of the pyrolytic liquids increased and subsequently paraffinic content of the pyrolytic liquids decreased. In addition, the boiling point distributions of pyrolytic liquids derived from the waste PP/EVA blends were found to be similar for all tested ratios of the waste PP/EVA blends. (C) 2008 Elsevier B.V. All rights reserved.Dokuz Eylul UniversityDokuz Eylul UniversityThis work was supported by Dokuz Eylul University

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets. In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder. We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used. More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection. Some of these algorithms use feature selection techniques to improve the accuracy of the classification. Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings. Significant improvements in the accuracy performance of the algorithms are observed while the number of features is being reduced.Publisher's Versio

Machine Learning-Enabled Prediction of 3D-Printed Microneedle Features

Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have emerged in the field of MNs for their time- and cost-efficient manufacturing. Tuning 3D printing parameters with artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is an emerging multidisciplinary field for optimization of manufacturing biomedical devices. Herein, we presented an AI framework to assess and predict 3D-printed MN features. Biodegradable MNs were fabricated using fused deposition modeling (FDM) 3D printing technology followed by chemical etching to enhance their geometrical precision. DL was used for quality control and anomaly detection in the fabricated MNAs. Ten different MN designs and various etching exposure doses were used create a data library to train ML models for extraction of similarity metrics in order to predict new fabrication outcomes when the mentioned parameters were adjusted. The integration of AI-enabled prediction with 3D printed MNs will facilitate the development of new healthcare systems and advancement of MNs’ biomedical applications

Coronary Disease Risk Curve of Serum Creatinine Is Linear in Turkish Men, U-Shaped in Women

Objectives: The highest levels of glomerular filtration rate are associated with increased coronary heart disease (CHD) risk, an issue we investigated in separate sexes in a population prone to metabolic syndrome

Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study

Abstract Objective To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning. Methods We used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning. We used the area under the receiver operating characteristic (AUROC) curve to evaluate patient-level performance in detecting csPCa. Results The PI-CAI training data had 425 scans with csPCa, while the in-house testing and fine-tuning data had 288 and 50 scans with csPCa, respectively. The nnU-Net model achieved an AUROC of 0.888 and 0.889 on the hidden validation and testing data. The model performed with an AUROC of 0.886 on the in-house testing data, with a slight decrease in performance to 0.870 using transfer learning. Conclusions The state-of-the-art deep learning method using prostate masks trained on large-scale bi-parametric MRI data provides high performance in detecting csPCa in internal and external testing data with different characteristics, demonstrating the robustness and generalizability of deep learning within and across datasets. Clinical relevance statement A self-adapting deep network, utilizing prostate masks and trained on large-scale bi-parametric MRI data, is effective in accurately detecting clinically significant prostate cancer across diverse datasets, highlighting the potential of deep learning methods for improving prostate cancer detection in clinical practice. Graphical Abstrac