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Probabilistic Risk Framework for Nuclear- and Fossil-Powered Vessels: Analyzing Casualty Event Severity and Sub-Causes
No full textMaritime activities pose significant safety risks, particularly with the growing presence of nuclear-powered vessels (NPVs) alongside traditional fossil-powered vessels (FPVs). This study employs a probabilistic risk assessment (PRA) approach to evaluate and compare accident hazards involving NPVs and FPVs. By analyzing historical data from 1960 to 2024, this study identifies risk patterns, accident frequency (probability), and severity levels. The methodology focuses on incidents such as marine incidents, marine casualties, and very serious cases with sub-causes. Key findings reveal that Russia exhibits the highest risk for very serious incidents involving both NPVs and FPVs, with a significant 100% risk for NPVs. China has the highest FPV risk, while France and the USA show above-average risks, particularly for marine casualties and very serious incidents. Moreover, collision is the most significant global risk, with a 26% risk for NPVs and 34% for FPVs, followed by fire hazards, which also pose a major concern, with a 17% risk for NPVs and 16% for FPVs, highlighting the need for enhanced safety and fire-prevention measures. In conclusion, comparative analysis highlights the need for enhanced stability improvements, fire prevention, and maintenance practices, particularly in the UK, France, Russia, and China. This study underscores the importance of targeted safety measures to mitigate risks, improve ship design, and promote safer maritime operations for both nuclear- and fossil-fueled vessels
Clinical and microbiological evaluation of the efficacy of herbal mouthwashes in gingivitis
Full text linkOBJECTIVE: This randomize controlled clinical trial (RCT) aims to clinically and
microbiologically evaluate the effectiveness of different herbal mouthwashes as an adjunct
treatment to non-surgical periodontal therapy (NSPT) in patients with gingivitis.
MATERIALS AND METHODS: A total of 52 patients diagnosed with gingivitis were
incorporated into the RCT and categorized into four groups as follows: Tea tree oil
(Tebodont®), thyme hydrosol (Arifoglu®), essential oil (Listerine®), and placebo. The
patients were advised to wash with15 ml of prescribed mouthwash twice a day for a
period of 3
rd months after NSPT. Clinical periodontal measurements; including plaque
index (PI), gingival index (GI) and probing pocket depth (PPD) and microbiological
sampling, were performed before enrollment and in 3
rd months. Plaque samples were
examined by quantitative polymerase chain reaction (qPCR).
RESULTS: All four groups had significant reductions in PI, GI, and PPD levels from
beginning to the third month (p<0.05). Intergroup comparisons demonstrated a
significantly higher reduction in PPD in the essential oil and thyme hydrosol groups
compared to the control group (p<0.05). The PI reduction was significantly higher in the
tea tree oil group when comparing with the essential oil group at the third month (p<0.05). The essential oil group exhibited the least drop in GI scores. The levels of P.
gingivalis significantly decreased in all groups (p<0.05).
CONCLUSION: It has been demonstrated that, with regular use, herbal mouth rinses are
effective and may serve as an however, studies with a positive control group, larger study
population and longer period are needed to further evaluate the efficacy of herbal
mouthwashes. Also, in-vitro studies to clarify the mechanism of anti-bacterial action of
herbal mouthwashes are needed
A machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materials
No full textIn the realm of polymeric materials, the delicate balance between long-range order and disorder dictates crystal properties, influencing their performance in various applications. To unravel this enigma, we embarked on a machine learning (ML) and data-driven quest, compiling 2500 data points from literature. By harnessing the power of Support Vector Machines (SVM) and Radial Basis Functions (RBF), we trained our model to decipher the intricate relationships between molecular descriptors and crystal properties. Introducing a novel pass/fail system, we screened polymers based on their calculated descriptors, revealing that combining multiple descriptors significantly enhances model performance. Identifying 1200 polymers that failed to meet crystallization requirements provides valuable insights for designing materials with tailored structural features. This groundbreaking study pioneers a data-oriented approach to understanding polymeric materials, paving the way for the creation of novel crystals with optimized properties. By uncovering the hidden patterns of order and disorder, we unlock the secrets of polymeric materials, revolutionizing their applications in various fields.Taif University, Saudi Arabia [TU-DSPP-2024-93]The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-93)
Investigation of Heat Transfer and Swirl Flow Between Concentric Cylinders
No full textSwirl flows, especially in decaying forms, have shown promise in enhancing thermal performance with minimal mechanical complexity, providing a practical alternative to traditional forced convection methods in annular geometries. The swirling flows are created by introducing tangential air inflow, which promotes mixing and disrupts thermal boundary layers, thereby increasing heat transfer. This approach is valuable in applications where efficient thermal management is critical, such as in heat exchangers, reactors, and rotating machinery. This study highlights the potential effectiveness of swirling decaying flows in enhancing heat transfer, as initially proposed by Talbot. Local Nusselt numbers were experimentally measured in an annular space between concentric cylinders under a constant heat flux boundary condition. Swirling air motion was generated by tangential inlet slots, allowing air to enter with varying slot numbers and tangency angles. The resulting flow field within the annulus maintained a Reynolds number below 2000, aligning with the study's focus on low-Reynolds, laminar flow conditions for examining the impact of swirl flow on heat transfer between concentric cylinders. Careful consideration was given to keeping track of errors from their collection of sources and their propagation in the results. In heat transfer enhancement, swirling flow showed real promise; augmentation of up to 24 % could be achieved, while in power consumption, swirling flow was considerably more demanding than the fully developed non-working laminar flow. Even with the acceptable accuracy of the experimental measurements, the data reduction yielded substantial errors - up to 300 % of the calculated value - thereby undermining the results and the assumptions
An advanced mixed finite element formulation for flexural analysis of laminated composite plates incorporating HSDT and transverse stretching effect
No full textThe modeling and analysis of laminated composite plates are performed using a unified Higher Order Shear Deformation Theory (HSDT) that accounts for transverse stretching effect. The adopted unified HSDT formulation allows the implementation of various shear functions. To derive a weak form from the generalized displacement fields of HSDTs, a variational principle is applied within a two-field mixed approach. The stationarity of the functional for laminated plate structures is obtained through the application of the Hellinger-Reissner variational principle. Hence, displacements and stress resultants, namely two independent fields, are included in finite element equations. Four-noded, quadrilateral elements are employed for the discretization of the plate's domain. While the generated functional initially had C1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document} continuity, benefiting from the two-fields property of the mixed finite element formulation, integration by parts is performed that results with a functional requiring only C0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document} continuity. To effectively capture the nonlinear and parabolic variation of transverse shear stress, it is determined that even with varying functions, the results are theoretically consistent with the elasticity method and the employed HSDT model. Also, when compared to the theories that are already accessible in the literature, for the bending behavior of composite plates, incorporating the stretching effect converges the exact results for laminated composite plates more than the studies where that effect is neglected
3D Face Anti-Spoofing With Dense Squeeze and Excitation Network and Neighborhood-Aware Kernel Adaptation Scheme
Full text linkFace anti-spoofing is a critical challenge in biometric security systems, where sophisticated spoofing techniques pose significant threats. To enhance the effectiveness and efficiency of the methods employed for face anti-spoofing, this paper presents a new lightweight 3D face anti-spoofing framework characterized by several advanced mechanisms. To this purpose, the proposed architecture introduces DenseNet, a Squeeze and Excitation mechanism, and a new computational component called Neighborhood-Aware Kernel Adaptation (NAKA) that adaptively modifies 3D convolution kernels according to spatial proximity. Initially, an adaptive thresholding-based wavelet decomposition is employed for image denoising, followed by cross-channel attention to improve feature learning. Finally, Multiple Instance Learning (MIL) is used to address face anti-spoofing for the first time by modeling the spatial and temporal variations across facial areas. We validate our framework on two publicly available datasets: CelebA-Spoof and CASIA-SURF. We compared the performance of our proposed framework with several state-of-the-art methods using Classification accuracy, Precision, Recall, F1-score, ACER, APCER, and BPCER. Our model realizes 99.62% on the CelebA-Spoof and 99.86% on CASIA-SURF datasets. The proposed approach realized superior results in terms of high classification accuracy (99.62% and 99.86%), precision (99.85% and 99.83%), recall (99.39% and 99.84%), F-score (99.62% and 99.84%), ACER (0.0038/ 0.0014), FPR (0.0015/ 0.0013), APCER (0.0015/0.0016), and BPCER (0.0061/0.0013). These results are compared with 10 state-of-the-art methods to show the effectiveness of our approach in outperforming existing methods. The global comparative results reveal that the proposed approach is relatively effective in determining masked and true face scans
Medication adherence in the curricula of future European physicians, pharmacists and nurses - a cross-sectional survey
No full textAim: Many patients are not taking their medicines. It has substantial negative medical and economic consequences for patients and healthcare systems but there is limited knowledge on how medication adherence is integrated in medical education. This study seeks to investigate to what extent students in medicine, pharmacy and nursing in Europe are taught about medication adherence. Methods: A cross-sectional online survey was distributed to 731 persons teaching relevant courses across 142 European universities between February and June 2024. The survey addressed definitions of adherence and The ABC Taxonomy; methods to support adherence, methods to identify and monitor non-adherence; consequences and outcomes of non-adherence, and methods applied in teaching. They were also asked to provide links to their curricula. Responses from quantitative questions were analyzed descriptively. Word frequency and qualitative thematic analysis was used for the curricula inventory and analysis of free-text answers, respectively. Results: In total, 212 participants from 114 universities in 34 countries completed the survey. Respondents agreed to similar level on the need to enhance medication adherence teaching, with 72% in pharmacy, 71% medical, and 59% agreement in nursing education. The most taught topic across educations was the clinical impact of non-adherence, according to 89% in pharmacy, 84% medical, and 76% in nursing education. The ABC Taxonomy was taught in more than half of all pharmacy (73%), nursing (60%) and medical education (52%). In the qualitative analysis of free text-answers respondents emphasized the value of early, mixed method teaching. They reported a lack of guidance in teaching medication adherence, causing inconsistency in the educational quality and depth. Time constraints were highlighted as a significant challenge, while interprofessional collaboration and use of medication adherence technologies were seen as opportunities, though not widely implemented in teaching. The curricula inventory showed a substantial variance in how medication adherence content was described. Conclusion: There is a lack of consistent teaching on medication adherence in Europe, underlining the necessity to establish a unified curriculum incorporating the ABC taxonomy, and to include a more patient-centred approach to support medication adherence
Identification of common genes associated with development of resistance against tamoxifen and doxorubicin in MCF7 cells
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Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design
Full text linkThis study presents an automated circuit design approach using neural networks to optimize the dynamic range (DR) of active filters, illustrated through the design of a 7th-order Chebyshev low-pass filter. Traditional design methods rely heavily on designer expertise, often resulting in time-intensive and energy-consuming processes. Two techniques are proposed: inverse modeling and forward modeling. In inverse modeling, artificial neural networks (ANNs) predict circuit parameters to meet specific performance goals. A randomly selected subset, comprising 0.05% of the 1,953,125 possible circuit configurations, was used to train and validate the model, providing an accurate representation of the entire dataset without requiring full-scale data analysis. In forward modeling, the same subset was used to train the network, which was then used to predict DR values for the remaining dataset. This approach enabled the identification of circuit parameters that resulted in optimal DR values. The results confirm the effectiveness of these techniques, with both inverse modeling and forward modeling outperforming the standard circuit design. At 160 kHz, a critical frequency for the operation of the designed filter, inverse modeling achieved a DR of 140.267 dB and forward modeling reached 136.965 dB, compared to 132.748 dB for the standard circuit designed using the traditional approach. These findings demonstrate that ANN-based methods can significantly enhance design accuracy, reduce time requirements, and improve energy efficiency in analog circuit optimization
A Comprehensive Evaluation of Augmentation Techniques in Medical Data
No full textPredicting medical conditions from tabular data is often challenged by class imbalance and a limited sample size, which can diminish the model's performance and affect its generalization. Data augmentation has emerged as a crucial strategy to address these issues, evolving from traditional oversampling techniques like Synthetic Minority Oversampling Technique (SMOTE) to advanced generative adversarial networks (GANs) that synthesize realistic tabular healthcare data. These methods are particularly valuable in healthcare, where data scarcity, imbalance, and privacy concerns are prevalent. In this study, we investigate the effectiveness of multiple data augmentation approaches on one publicly available imbalanced medical datasets. Specifically, we generate synthetic samples using random oversampling; the SMOTE and its variants; the Conditional Tabular GAN (CTGAN); and the Wasserstein GAN with gradient penalty (WGAN-GP). Our objective is to transform imbalanced datasets into balanced, trainable forms that enable robust predictive modeling rather than defaulting to the majority class. By training commonly used classification algorithms on the augmented data, we systematically evaluate the relative performance gains attributable to each augmentation method. Our analysis reveals which augmentation strategies most effectively boost the minority-class F1 score and improve macro-F1 relative to a non-augmented baseline. Although CTGAN achieved the highest macro-F1 in one model, its advantage did not generalize across all classifiers; WGAN-GP delivered the strongest overall performance, while standard SMOTE and its variants remained both competitive and computationally efficient. This work provides practical insights into selecting appropriate data augmentation strategies tailored to medical tabular data, ultimately facilitating more accurate and generalizable medical condition prediction models