Hydrodynamics study of the bubble columns with intense vertical heat-exchanging tubes using gamma-ray computed tomography and radioactive particle tracking techniques

Understanding the hydrodynamics of bubble columns with and without vertical heat-exchanging tubes is a necessity for the proper design, scale-up, and operation of these reactors. To achieve this goal, systematic experiments were performed to visualize and quantify the influence of the presence of vertical internal tubes on the gas holdup distributions and their profiles, axial liquid velocity, and turbulent parameters (i.e., normal and shear stresses; turbulent kinetic energy) by using advanced gamma-ray computed tomography (CT) and radioactive particle tracking (RPT). In this study, the experiments were conducted in 6- and 18-inch bubble columns with an air-water system as the working fluid, under a wide range of superficial gas velocities (5-45 cm/s). Three configurations of vertical internals (i.e., hexagonal, circular without a central tube, and circular with a central tube plus vertical internals), as well as the vertical internals sizes, were examined in this study. These three configurations were designed to cover 25% of the column\u27s cross-sectional area (CSA) to represent the percentage of the covered area utilized in the Fischer-Tropsch process. Reconstructed CT images reveal that the configurations of the vertical internal tubes significantly impacted the gas holdup distribution over the CSA of the column. Additionally, the bubble column equipped with 1-inch vertical internals exhibited a more uniform gas holdup distribution than the column with 0.5-inch internals. Moreover, a remarkable increase in the gas holdup values at the wall region was achieved in the churn turbulent flow regime due to the insertion of vertical internals inside the column. Furthermore, pronounced peaks of the gas holdup and axial liquid velocity were observed in the inner gaps between the vertical internals --Abstract, page iv

Endovascular Management of Traumatic Iliac Vessel Disruption—Report of Two Cases

AbstractVascular injuries in a multi-trauma patient are associated with significant cardiovascular instability and organ injury. Injuries with active bleeding are best treated with a quick, safe and the least less invasive procedure available to the trauma surgeon. We report two cases of blunt trauma induced common and external iliac vessel injury, managed by endovascular treatment. In the second case, endovascular treatment prevented histological examination of the artery, which would have revealed an alternative diagnosis

Prediction of Heat Transfer Coefficient of Pool Boiling Using Back propagation Neural Network

Artificial neural network (ANN), in comparison with empirical correlations, has recently received more attention. The present paper includes predictive modeling of heat transfer coefficient for binary mixtures in pool boiling for hydrocarbon compounds, using Back propagation techniques through Multilayer Perceptron, one of the types of the artificial neural networks. To train and learn the system, predictive neural network was found, which is capable of understanding and predicting the preset output which is heat transfer coefficient. The principle operation of such neural networks is based on the experimental data collected from some researchers [1-4]. A new ANN model is proposed using five inputs (mole fraction, temperature difference, heat flux, density and viscosity) to predict the heat transfer coefficient. The prediction using ANN shows 0.0026 AARE (Absolute Average Relative Error) with most widely known correlations namely those of Calus, Fujita and Thome which have given 0.086, 0.066 and 0.038 respectively

Automatic neonatal sleep stage classification:A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

Novel Conserved-region T-cell Mosaic Vaccine With High Global HIV-1 Coverage Is Recognized by Protective Responses in Untreated Infection

An effective human immunodeficiency virus type 1 (HIV-1) vaccine is the best solution for halting the acquired immune deficiency syndrome epidemic. Here, we describe the design and preclinical immunogenicity of T-cell vaccine expressing novel immunogens tHIVconsvX, vectored by DNA, simian (chimpanzee) adenovirus, and poxvirus modified vaccinia virus Ankara (MVA), a combination highly immunogenic in humans. The tHIVconsvX immunogens combine the three leading strategies for elicitation of effective CD8+ T cells: use of regions of HIV-1 proteins functionally conserved across all M group viruses (to make HIV-1 escape costly on viral fitness), inclusion of bivalent complementary mosaic immunogens (to maximize global epitope matching and breadth of responses, and block common escape paths), and inclusion of epitopes known to be associated with low viral load in infected untreated people (to induce field-proven protective responses). tHIVconsvX was highly immunogenic in two strains of mice. Furthermore, the magnitude and breadth of CD8+ T-cell responses to tHIVconsvX-derived peptides in treatment-naive HIV-1+ patients significantly correlated with high CD4+ T-cell count and low viral load. Overall, the tHIVconsvX design, combining the mosaic and conserved-region approaches, provides an indisputably better coverage of global HIV-1 variants than previous T-cell vaccines. These immunogens delivered in a highly immunogenic framework of adenovirus prime and MVA boost are ready for clinical development

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes’ response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error

SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweight, and fast (having less inference time), designed particularly to detect botnet attacks in resource constrained IoT devices. This paper proposes SkipGateNet, a novel deep learning model designed for detecting Mirai and Bashlite botnet attacks in resource constrained IoT and fog computing environments. SkipGateNet is a lightweight, fast model combining 1D-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers. The novelty of this model lies in the integration of ‘Learnable Skip Connections’. These connections feature gating mechanisms that enhance detection by focusing on relevant features and ignoring irrelevant ones. They add adaptability to the architecture, performing feature selection and propagating only essential features to deeper layers. Tested on the N-BaIoT dataset, SkipGateNet efficiently detects ten types of botnet attacks, with a remarkable test accuracy of 99.91%. It is also compact (2596.87 KB) and demonstrates a quick inference time of 8.0 milliseconds, suitable for real-time implementation in resource-limited settings. While evaluating its performance, parameters like precision, recall, accuracy, and F1 score were considered, along with statistical reliability measures like Cohen’s Kappa Coefficient and Matthews Correlation Coefficient. These highlight its reliability and effectiveness in IoT security challenges. The paper also compares SkipGateNet to existing models and four other deep learning architectures, including two sequential CNN architectures, a simple CNN+LSTM architecture, and a CNN+LSTM with standard skip connections. SkipGateNet surpasses all in accuracy and inference time, demonstrating its superiority in addressing IoT security issues

Automatic neonatal sleep stage classification: A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

A new approach to prevention of knee osteoarthritis: reducing medial load in the contralateral knee

Background. Few if any prevention strategies are available for knee osteoarthritis (OA). In those with symptomatic medial OA, the contralateral knee may be at high risk of disease and a reduction in medial loading in that knee might prevent disease or its progression there. Lateral wedge insoles reduce loading across an affected medial knee but their effect on the contralateral knee is unknown. Methods: To determine the proportion of persons with medial knee OA who had concurrent medial contralateral OA or developed contralateral medial OA later, we examined knee radiographs from the longitudinal Framingham Osteoarthritis Study. Then, to examine an approach to reducing medial load in the contralateral knee, 51 people from a separate study with painful medial tibiofemoral OA underwent gait analysis wearing bilateral controlled shoes with i) no insoles ii) two types of lateral wedge insoles laterally posted by 5 degrees. Primary outcome was the external knee adduction moment (EKAM) in the contralateral knee. Non-parametric confidence intervals were constructed around the median differences in percentage change in the affected and contralateral sides. Results: Of Framingham subjects with medial radiograph knee OA, 137/152 (90%) either had concurrent contralateral medial OA or developed it within 10 years. 43/67 (64%) of those with medial symptomatic knee OA had or developed the same disease state in the contralateral knee. Compared to a control shoe, medial loading was reduced substantially on both the affected (median percentage EKAM change =-4.84%; 95% CI -11.33% to -0.65%) and contralateral sides (median EKAM percentage change -9.34% (95% CI -10.57% to -6.45%). Conclusions: In persons with medial OA, the contralateral knee is also at high risk of medial OA. Bilateral reduction in medial loading in knees by use of strategies such as lateral wedge insoles might not only reduce medial load in affected knees but prevent knee OA or its progression on the contralateral side

HIV-1 remission following CCR5Δ32/Δ32 haematopoietic stem cell transplantation

HIV-1 cure remains elusive with only one reported case a decade ago. Termed the “Berlin Patient”, the individual underwent 2 allogeneic Hematopoietic Stem Cell Transplantation (allo-HSCT) procedures using a donor with a homozygous mutation in the HIV coreceptor CCR5 (CCR5Δ32/Δ32) to treat his acute myeloid leukemia. Total body irradiation was given with each HSCT. Critically, it is unclear which treatment or patient parameters contributed to this only documented case of long term HIV remission. Here we show that HIV-1 remission may be possible with a less aggressive and toxic approach. An HIV-1-infected adult underwent allo-HSCT for Hodgkin’s Lymphoma using cells from a CCR5Δ32/Δ32 donor. He experienced mild gut graft versus host disease. Antiretroviral therapy was interrupted 16 months after transplantation. HIV-1 remission has been maintained through a further 18 months. Plasma HIV-1 RNA has been undetectable at <1 copy/ml along with undetectable HIV-1 DNA in peripheral CD4 T lymphocytes. Quantitative viral outgrowth assay from peripheral CD4 T lymphocytes shows no reactivatable virus using a total of 24 million resting CD4 T cells. CCR5-tropic, but not CXCR4-tropic viruses were identified in HIV-1 DNA from CD4 T cells of the patient prior to transplant. CD4 T cells isolated from peripheral blood post-transplant did not express CCR5 and were only susceptible to CXCR4-tropic virus ex vivo. HIV-1 Gag-specific CD4 and CD8 T cell responses were lost after transplantation whilst Cytomegalovirus (CMV)-specific responses were detectable. Likewise, HIV-1-specific antibodies and avidities fell to levels comparable to those in the Berlin patient following transplantation. Although at 18 months post-treatment interruption it is premature to conclude that this patient has been cured, these data suggest that single allo-HSCT with homozygous CCR5Δ32 donor cells may be sufficient to achieve HIV-1 remission with reduced intensity conditioning and no irradiation, and the findings further support the development of HIV remission strategies based on preventing CCR5 expression.NIHR AmFA