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

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

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

Non prescribed sale of antibiotics in Riyadh, Saudi Arabia: A Cross Sectional Study

Background Antibiotics sales without medical prescriptions are increasingly recognized as sources of antimicrobial misuse that can exacerbate the global burden of antibiotic resistance. We aimed to determine the percentage of pharmacies who sell antibiotics without medical prescriptions, examining the potential associated risks of such practice in Riyadh, Saudi Arabia, by simulation of different clinical scenarios. Methods A cross sectional study of a quasi-random sample of pharmacies stratified by the five regions of Riyadh. Each pharmacy was visited once by two investigators who simulated having a relative with a specific clinical illness (sore throat, acute bronchitis, otitis media, acute sinusitis, diarrhea, and urinary tract infection (UTI) in childbearing aged women). Results A total of 327 pharmacies were visited. Antibiotics were dispensed without a medical prescription in 244 (77.6%) of 327, of which 231 (95%) were dispensed without a patient request. Simulated cases of sore throat and diarrhea resulted in an antibiotic being dispensed in (90%) of encounters, followed by UTI (75%), acute bronchitis (73%), otitis media (51%) and acute sinusitis (40%). Metronidazole (89%) and ciprofloxacin (86%) were commonly given for diarrhea and UTI, respectively, whereas amoxicillin/clavulanate was dispensed (51%) for the other simulated cases. None of the pharmacists asked about antibiotic allergy history or provided information about drug interactions. Only 23% asked about pregnancy status when dispensing antibiotics for UTI-simulated cases. Conclusions We observed that an antibiotic could be obtained in Riyadh without a medical prescription or an evidence-based indication with associated potential clinical risks. Strict enforcement and adherence to existing regulations are warranted

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

The state of HRM in the Middle East:Challenges and future research agenda

Based on a robust structured literature analysis, this paper highlights the key developments in the field of human resource management (HRM) in the Middle East. Utilizing the institutional perspective, the analysis contributes to the literature on HRM in the Middle East by focusing on four key themes. First, it highlights the topical need to analyze the context-specific nature of HRM in the region. Second, via the adoption of a systematic review, it highlights state of development in HRM in the research analysis set-up. Third, the analysis also helps to reveal the challenges facing the HRM function in the Middle East. Fourth, it presents an agenda for future research in the form of research directions. While doing the above, it revisits the notions of “universalistic” and “best practice” HRM (convergence) versus “best-fit” or context distinctive (divergence) and also alternate models/diffusion of HRM (crossvergence) in the Middle Eastern context. The analysis, based on the framework of cross-national HRM comparisons, helps to make both theoretical and practical implications

TRUST IN CROSS-CULTURAL B2B FINANCIAL SERVICE RELATIONSHIPS: THE ROLE OF SHARED VALUES

This is the accepted version of the following article: Houjeir, R. & Brennan, R. J, 'Trust in cross-cultural b2b financial service relationships: The role of shared values', Journal of Financial Services Marketing, June 2016, Vol 21(2): 90-102 The final publication is available at Springer via http://dx.doi.org/10.1057/fsm.2016.4Trust in business-to-business supplier–customer relationships in financial services is an area of considerable research interest. The bulk of prior empirical research in this field has concentrated on trust in business relationships within a Western cultural context. However, shared values are acknowledged to be an important antecedent to trust. The premise of this study is that in circumstances where there are substantial cultural differences between parties to a supplier–customer relationship, these differences will be reflected in shared values, which will in turn be reflected in differences in the nature of trust. A qualitative study was conducted among business bankers and their corporate clients in the context of the United Arab Emirates. In all 170 respondents were interviewed; of these, 160 were paired respondents, that is, where a client and banker from the same business relationship were interviewed (yielding 80 interview dyads). Substantial differences with respect to trust were found between relationships that involved only Emiratis, those that involved Emiratis and non-Emiratis, and those that involved only non-Emiratis. For Emiratis mutual trust is substantially based on family and clan ties and exhibits strongly affective characteristics. For non-Emiratis trust is largely based on business considerations, and exhibits strongly cognitive characteristics.Peer reviewedFinal Accepted Versio

Decompressive cervical laminectomy and lateral mass screw-rod arthrodesis. Surgical analysis and outcome

<p>Abstract</p> <p>Background</p> <p>This study evaluates the outcome and complications of decompressive cervical Laminectomy and lateral mass screw fixation in 110 cases treated for variable cervical spine pathologies that included; degenerative disease, trauma, neoplasms, metabolic-inflammatory disorders and congenital anomalies.</p> <p>Methods</p> <p>A retrospective review of total 785 lateral mass screws were placed in patients ages 16-68 years (40 females and 70 males). All cases were performed with a polyaxial screw-rod construct and screws were placed by using Anderson-Sekhon trajectory. Most patients had 12-14-mm length and 3.5 mm diameter screws placed for subaxial and 28-30 for C1 lateral mass. Screw location was assessed by post operative plain x-ray and computed tomography can (CT), besides that; the facet joint, nerve root foramen and foramen transversarium violation were also appraised.</p> <p>Results</p> <p>No patients experienced neural or vascular injury as a result of screw position. Only one patient needed screw repositioning. Six patients experienced superficial wound infection. Fifteen patients had pain around the shoulder of C5 distribution that subsided over the time. No patients developed screw pullouts or symptomatic adjacent segment disease within the period of follow up.</p> <p>Conclusion</p> <p>decompressive cervical spine laminectomy and Lateral mass screw stabilization is a technique that can be used for a variety of cervical spine pathologies with safety and efficiency.</p