Thermal injury in tonsils and its relation to postoperative pain—a histopathological and clinical study

Objectives: The aim of this study was to compare thermal injury and depth of necrosis of using different monopolar power settings in partial tonsillectomy and correlate the results with the postoperative pain score. Results: The study included a total of 15 patients with mean of age of 5.7 ± 2.57 years. The mean depth of injury was significantly higher for the 25 W side (0.973 ± 0.613) versus the 15 W side (0.553 ± 0.218) (p = 0.023). The postoperative pain score showed no significant differences between both sides. Conclusion: The histopathologic depth of thermal injury is significantly higher with the 25 W monopolar microdissection in comparison to the 15 W; however, it does not seem to correlate with the postoperative pain level. Apparently, power settings of 25 W can be safely used for pediatric intracapsular tonsillectomies, without added postoperative morbidity despite the deeper tissue injury observed in the tonsil.The authors are grateful to the Histology and Electron Microscopy Service (HEMS) team at the i3S (Institute for Research and Innovation in Health, University of Porto) for providing the necessary equipment and the technical support for the electron microscopic analysis

The Safe Learning Environment in the United Arab Emirates Schools and Its Relationship to the Development of Creative Thinking Among Students

The study aimed to assess the relationship between a safe learning environment in Emirati schools and the development of student's creative thinking. Using a descriptive method with stratified random sampling, the researchers selected a sample of 500 male and female teachers. Two questionnaires were employed: one assessing the safe learning environment (20 items) and another measuring creative thinking (20 items). Results indicated a high teacher perception of a safe learning environment, with statistically significant chi-square values for all items. Similarly, teachers perceived a high level of creative thinking development, with significant chi-square values for all items. Gender and experience did not show statistically significant differences in the perception of a safe learning environment. However, teachers with over 10 years of experience demonstrated higher levels of creative thinking development. Notably, a significant correlation was found between a safe learning environment and the development of students' creative thinking in Emirati schools. This study aligns with the UAE Ministry of Education's mission to create a safe and creative educational system that meets the needs of a globally competitive knowledge society. Doi: 10.28991/ESJ-2023-SIED2-014 Full Text: PD

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Entropically damped artificial compressibility for the discretization corrected particle strength exchange method in incompressible fluid mechanics

peer reviewedWe present a consistent mesh-free numerical scheme for solving the incompressible Navier–Stokes equations. Our method is based on entropically damped artificial compressibility for imposing the incompressibility con- straint explicitly, and the Discretization-Corrected Particle Strength Exchange (DC-PSE) method to consistently discretize the differential operators on mesh-free particles. We further couple our scheme with Brinkman penalization to solve the Navier–Stokes equations in complex geometries. The method is validated using the 3D Taylor–Green vortex flow and the lid-driven cavity flow problem in 2D and 3D, where we also compare our method with hr-SPH and report better accuracy for DC-PSE. In order to validate DC-PSE Brinkman penalization, we study flow past obstacles, such as a cylinder, and report excellent agreement with previous studies.R-AGR-3952 - C20/MS/14610324-PorSol (01/02/2021 - 31/01/2024) - OBEIDAT Ana

An improved dandelion optimizer algorithm for spam detection next-generation email filtering system

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), Chimp Optimization Algorithm (ChOA), Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO al-gorithm, which represents a promising approach for solving high-dimensional optimization prob-lems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning

Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials

Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

Abstract Objectives: To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. Methods: This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. Results: A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Conclusions: Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention

The Chemical Speciation of Trace-Metals in Street Dusts of Irbid, Jordan

Abstract Street dust samples were collected from different locations in Irbid city, Jordan. The concentrations of Pb, Cu, Zn, Cd, Ni, Mn, Cr and Al in these samples were determined usin

The Pharmacogenomics of Inhaled Corticosteroids and Lung Function Decline in COPD

Inhaled corticosteroids (ICS) are widely prescribed for patients with chronic obstructive pulmonary disease (COPD), yet with variable outcomes and adverse reactions which may be genetically determined. The primary aim of the study was to identify the genetic determinants for FEV1 changes related to ICS therapy. In the Lung Health Study 2 (LHS-2), 1116 COPD patients were randomised to the ICS, triamcinolone acetonide (n=559), or placebo (n=557) with spirometry performed every 6 months for 3 years. We performed a pharmacogenomic genome-wide association study (GWAS) for the genotype-by-ICS treatment effect on 3 years of forced expiratory volume in 1 s (FEV1) changes (estimated as slope) in 802 genotyped LHS-2 participants. Replication was performed in 199 COPD patients randomised to the ICS, fluticasone or placebo. A total of five loci showed genotype-by-ICS interaction at p&lt;5×10-6; of these, SNP rs111720447 on chromosome 7 was replicated (discovery p=4.8×10-6, replication p=5.9×10-5) with the same direction of interaction effect. ENCODE data revealed that in glucocorticoid treated (dexamethasone) A549 alveolar cell line, glucocorticoid receptor binding sites were located near SNP rs111720447. In stratified analyses of LHS-2, genotype at SNP rs111720447 was significantly associated with rate of FEV1 decline in patients taking ICS (C allele beta=56.35 mL·year-1, 95% confidence interval (CI)=29.96, 82.76 mL·yr-1) and also in patients who were assigned to placebo, though the relationship was weaker and in the opposite direction than that in the ICS group (C allele beta=-27.57 mL·year-1, 95% CI=-53.27, -1.87 mL·yr-1). The study uncovered genetic factors associated with FEV1 changes related to ICS in COPD patients, which may provide new insight on the potential biology of steroid responsiveness in COPD.</p