An Analytical Study of the Reality of Ensuring the Quality of Education in Jordanian Universities

The purpose of this study was to find out to what extend application of the concepts of quality ensures of education in the formal Jordanian universities, and what are monitoring the strengths and weaknesses in the education system from the perspective of the entrance of ensure quality, and then sagest the factors that enhance the strengths and overcome the weaknesses. The study sample consisted of (63) Dean who work in official Jordanian universities. A questionnaire which developed by Abo varh (2004), was used ,which consisting of (94) items distributed on five areas each one covering a variable of the study. The study results showed that the quality assurance system in Jordanian universities is still below the expected level. Also the reality of ensuring quality of inputs is still not enough, and that there is decline significantly in the level of quality ensures universities education, as interest in universities to ensure quality outcomes and follow-up of its graduates in the labor market is still low interest. In the light of these results the study concluded a number of recommendations

Inference and optimal design for the k-level step-stress accelerated life test based on progressive Type-I interval censored power Rayleigh data

In this paper, a new generalization of the one parameter Rayleigh distribution called the Power Rayleigh (PRD) was employed to model the life of the tested units in the step-stress accelerated life test. Under progressive Type-I interval censored data, the cumulative exposure distribution was considered to formulate the life model, assuming the scale parameter of PRD has the inverse power function at each stress level. Point estimates of the model parameters were obtained via the maximum likelihood estimation method, while interval estimates were obtained using the asymptotic normality of the derived estimators and the bootstrap resampling method. An extensive simulation study of $k = 4$ levels of stress in different combinations of the life test under different progressive censoring schemes was conducted to investigate the performance of the obtained point and interval estimates. Simulation results indicated that point estimates of the model parameters are closest to their initial true values and have relatively small mean squared errors. Accordingly, the interval estimates have small lengths and their coverage probabilities are almost convergent to the 95% significance level. Based on the Fisher information matrix, the D-optimality and the A-optimality criteria are implemented to determine the optimal design of the life test by obtaining the optimum inspection times and optimum stress levels that improve the estimation procedures and give more efficient estimates of the model parameters. Finally, the developed inferential procedures were also applied to a real dataset

Ransomware anti-analysis and evasion techniques: a survey and research directions

Ransomware has been proven to constitute a severe threat to the world's digital assets. Resources or devices' recovery from a Crypto-Ransomware infection is practically infeasible unless an error in the malicious cryptographic implementation has been made, as robust encryption is irreversible. This paper attempts to justify as to why designing and deploying an effective and efficient detective solution against this particular malware category represents a formidable technical challenge. The paper starts with a recent presentation of the Ransomware's epidemic, as reported by the security industry. Subsequently, a taxonomy of Ransomware is presented. The anatomy of the malware's invariant intrusions and infection vectors are illustrated. In addition, the paper navigates and analyzes the various anti-analysis and evasive techniques that are deployable by Ransomware. In every context enumerated in the narrative, the technical difficulty being posed by this malware is illuminated. If a computer security researcher intends to devise a Crypto-Ransomware's preventive solution or a predictive or proactive one, then it is imperative to have a sound perception of the technical challenges that will manifest prior to launching the proposed research project - so as to be equipped to tackle the anticipated problems. This paper concludes with an advance notice underscoring the resilience of Ransomware intrusions and highlighting research open-problems

Statistical inference for the Power Rayleigh distribution based on adaptive progressive Type-II censored data

The Power Rayleigh distribution (PRD) is a new extension of the standard one-parameter Rayleigh distribution. To employ this distribution as a life model in the analysis of reliability and survival data, we focused on the statistical inference for the parameters of the PRD under the adaptive Type-II censored scheme. Point and interval estimates for the model parameters and the corresponding reliability function at a given time are obtained using likelihood, Bootstrap and Bayesian estimation methods. A simulation study is conducted in different settings of the life testing experiment to compare and evaluate the performance of the estimates obtained. In addition, the estimation procedure is also investigated in real lifetimes data. The results indicated that the obtained estimates gave an accurate and efficient estimation of the model parameters. The Bootstrap estimates are better than the estimates obtained by the likelihood estimation approach, and estimates obtained using the Markov Chain Monte Carlo method by the Bayesian approach under both the squared error and the general entropy loss functions have priority over other point and interval estimates. Under the adaptive Type-II censoring scheme, concluding results confirmed that the PRD can be effectively used to model the lifetimes in survival and reliability analysis

Assessment of Yeasts as Potential Probiotics: A Review of Gastrointestinal Tract Conditions and Investigation Methods

Probiotics are microorganisms (including bacteria, yeasts and moulds) that confer various health benefits to the host, when consumed in sufficient amounts. Food products containing probiotics, called functional foods, have several health-promoting and therapeutic benefits. The significant role of yeasts in producing functional foods with promoted health benefits is well documented. Hence, there is considerable interest in isolating new yeasts as potential probiotics. Survival in the gastrointestinal tract (GIT), salt tolerance and adherence to epithelial cells are preconditions to classify such microorganisms as probiotics. Clear understanding of how yeasts can overcome GIT and salt stresses and the conditions that support yeasts to grow under such conditions is paramount for identifying, characterising and selecting probiotic yeast strains. This study elaborated the adaptations and mechanisms underlying the survival of probiotic yeasts under GIT and salt stresses. This study also discussed the capability of yeasts to adhere to epithelial cells (hydrophobicity and autoaggregation) and shed light on in vitro methods used to assess the probiotic characteristics of newly isolated yeasts

Inactivation of <i>Cronobacter sakazakii</i> in reconstituted infant milk formula by plant essential oils

This study aimed to screen the in vitro antimicrobial activity of 10 plant essential oils (EOs) against 4 Cronobacter sakazakii strains, and use these oils or their combination to control C. sakazakii cocktail at low (3 log10 CFU/ml) and high (6 log10 CFU/ml) contamination levels in reconstituted infant milk formula (RIMF). Cinnamon and fir oils were the most inhibitory to C. sakazakii strains with inhibition zone of 32 to 40 mm at 20 µl/disc (the minimum inhibitory concentrations were 0.16 and 0.625 µl/ml, respectively). The addition of each of cinnamon or fir oil at 1% (v/v) reduced the C. sakazakii numbers in RIMF by 0.7-0.8 log10 CFU/ml when inoculated with high contamination level and by 2.5-3.1 log10 CFU/ml when inoculated with low contamination level. However, the combination of cinnamon and fir oils reduced C. sakazakii numbers at both inoculum levels to undetectable levels after 3 h of incubation at 37°C. The results of the current study indicated that a combination of cinnamon and fir oils has a potent antimicrobial activity which may potentially be used to control C. sakazakii in RIMF

The potential application of probiotics and prebiotics for the prevention and treatment of COVID-19

10.1038/s41538-020-00078-9npj Science of Food411

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from June 14 to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization

Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects

Background: Since the coronavirus disease 2019 (COVID-19) was declared a pandemic, there was no doubt that vaccination is the ideal protocol to tackle it. Within a year, a few COVID-19 vaccines have been developed and authorized. This unparalleled initiative in developing vaccines created many uncertainties looming around the efficacy and safety of these vaccines. This study aimed to assess the side effects and perceptions following COVID-19 vaccination in Jordan. Methods: A cross-sectional study was conducted by distributing an online survey targeted toward Jordan inhabitants who received any COVID-19 vaccines. Data were statistically analyzed and certain machine learning (ML) tools, including multilayer perceptron (MLP), eXtreme gradient boosting (XGBoost), random forest (RF), and K-star were used to predict the severity of side effects. Results: A total of 2213 participants were involved in the study after receiving Sinopharm, AstraZeneca, Pfizer-BioNTech, and other vaccines (38.2%, 31%, 27.3%, and 3.5%, respectively). Generally, most of the post-vaccination side effects were common and non-life-threatening (e.g., fatigue, chills, dizziness, fever, headache, joint pain, and myalgia). Only 10% of participants suffered from severe side effects; while 39% and 21% of participants had moderate and mild side effects, respectively. Despite the substantial variations between these vaccines in the presence and severity of side effects, the statistical analysis indicated that these vaccines might provide the same protection against COVID-19 infection. Finally, around 52.9% of participants suffered before vaccination from vaccine hesitancy and anxiety; while after vaccination, 95.5% of participants have advised others to get vaccinated, 80% felt more reassured, and 67% believed that COVID-19 vaccines are safe in the long term. Furthermore, based on the type of vaccine, demographic data, and side effects, the RF, XGBoost, and MLP gave both high accuracies (0.80, 0.79, and 0.70, respectively) and Cohen’s kappa values (0.71, 0.70, and 0.56, respectively). Conclusions: The present study confirmed that the authorized COVID-19 vaccines are safe and getting vaccinated makes people more reassured. Most of the post-vaccination side effects are mild to moderate, which are signs that body’s immune system is building protection. ML can also be used to predict the severity of side effects based on the input data; predicted severe cases may require more medical attention or even hospitalization