Sustainable development goals (SDG) involve not only environmental issues but also economic, social, and cultural concerns. Higher education plays a key role in promoting sustainable development initiatives and in empowering people to change their thinking and to strive for a sustainable future. However, the main issue that needs to be presently resolved is how leaders, teachers, and students in higher education can achieve sustainable development in their system vision, mission and values, strategic plans, and organizational culture. Morocco is a country with a long history of higher education and has continuous reforms for sustainable development. In the process of responding to the wave of globalization, the Moroccan government has begun to formulate a higher education reform plan to maintain its competitiveness and achieve the SDG standards. Therefore, this study is focused on the quality of the higher education system through which the sustainability of higher education reform can be implemented. With this in mind, an organized approach that involved a questionnaire using the SWOT (strengths, weaknesses, opportunities, and threats) decision-making model with integration of analytic hierarchy process (AHP) and Entropy method was developed. The questionnaires were filled out by the experts, staff, and students of the higher education system (universities) to obtain the important key factors for the SWOT analysis. The AHP was used for the qualitative analysis of the weights of the SWOT factors, while the Entropy method was applied for the objective analysis of the number of different weight attributes. After integration of AHP with Entropy, the finalized variables were ranked; these results are more reliable and realistic to decision-makers. Finally, the SWOT matrix was established based on the questionnaire assessment and the AHP with Entropy weights to help implement the higher education reform policy and to monitor the quality of the current education system. The results also indicate that higher education reform must incorporate many changes, including effective budget planning, skilled experts, internationalization, improved and expanded infrastructure, reformed study curriculum, and latest training.
Introduction The primary aim was to develop convolutional neural network (CNN)‐based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X‐ray (CXR) triaging. A secondary aim was to perform interpretability analysis on the best‐performing candidate model to determine whether the model's predictions were susceptible to bias or confounding. Method A CANDID‐PTX dataset, that included 19,237 anonymized and manually labelled CXRs, was used for training and testing candidate models for pneumothorax classification and segmentation. Evaluation metrics for classification performance included Area under the receiver operating characteristic curve (AUC‐ROC), sensitivity and specificity, whilst segmentation performance was measured using mean Dice and true‐positive (TP)‐Dice coefficients. Interpretability analysis was performed using Grad‐CAM heatmaps. Finally, the best‐performing model was implemented for a triage simulation. Results The best‐performing model demonstrated a sensitivity of 0.93, specificity of 0.95 and AUC‐ROC of 0.94 in identifying the presence of pneumothorax. A TP‐Dice coefficient of 0.69 is given for segmentation performance. In triage simulation, mean reporting delay for pneumothorax‐containing CXRs is reduced from 9.8 ± 2 days to 1.0 ± 0.5 days (P‐value < 0.001 at 5% significance level), with sensitivity 0.95 and specificity of 0.95 given for the classification performance. Finally, interpretability analysis demonstrated models employed logic understandable to radiologists, with negligible bias or confounding in predictions. Conclusion AI models can automate pneumothorax detection with clinically acceptable accuracy, and potentially reduce reporting delays for urgent findings when implemented as triaging tools.
Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model’s inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.
During the epidemic period, primary emissions across the world were significantly reduced, while the response to secondary pollution such as ozone differed from region to region. To study the impact of the strict control measures of the new COVID-19 epidemic on the air quality of Anhui in early 2020, the air quality monitoring data of Anhui, from 2019 to 2021, specifically 1 January to 30 August, was examined to analyze the characteristics of the temporal and spatial distribution. Regression and path analysis were used to extract the relationship between the variable. PM 10 and O 3 , on average, increased by 6%, and 2%, while PM 2.5 , SO 2 decreased by 15% and 10% in the post-COVID-19 period. All air quality pollutants decreased during the active-COVID-19 period, with a maximum decrease of 21% observed in PM 10 , followed by 19% of PM 2.5 , and a minimum decrease of 2% observed in O 3 . Changes in air pollutants from 2017 to 2021 were also compared, and a decrease in all pollutants through 2020 was found. The air quality index (AQI) recorded a low decrease of 3% post-COVID-19, which shows that air quality will worsen in the future, but it decreased by 16% during the active-COVID-19 period. A path analysis model was developed to further understand the relationship between the AQI and air quality patterns. This path analysis shows a strong correlation between the AQI and PM 10 and PM 2.5 , however, its correlation with other air pollutants is weak. Regression analysis shows a similar pattern
Recent studies in data anonymization techniques have primarily focused on MapReduce. However, these existing MapReduce based approaches often suffer from many performance overheads due to their inappropriate use of data allocation, expensive disk I/O access and network transfer, and no support for iterative tasks. We propose “SparkDA” which is a new novel anonymization technique that is designed to take the full advantage of Spark platform to generate privacy-preserving anonymized dataset in the most efficient way possible. Our proposal offers a better partition control, in-memory operation and cache management for iterative operations that are heavily utilised for data anonymization processing. Our proposal is based on Spark’s Resilient Distributed Dataset (RDD) with two critical operations of RDD, such as FlatMapRDD and ReduceByKeyRDD, respectively. The experimental results demonstrate that our proposal outperforms the existing approaches in terms of performance and scalability while maintaining high data privacy and utility levels. This illustrates that our proposal is capable to be used in a wider big data applications that demands privacy.
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