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Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Social stress is a psychological and biological pressure that stems from one's relationship with others in social environments, which has become the most serious humanitarian issue today. Learning environments are one of the most important environments for reducing or increasing social stress and concentration. This study aims to investigate the effect of classroom wall color on students' stress and concentration in four common types of classrooms.

This research is a survey of 275 university students with an age range of 20–24. The methodology is a combination of quantitative and qualitative research. Data analysis was performed by multiple variance analysis and the internal reliability of the questionnaire was calculated based on Cronbach's alpha.

Results show that classroom wall color has a significant effect on student stress and concentration. In class type one, wall color had an effect of 10.4% on stress and concentration; in the second type, this variable had an effect of 8.8%, also in the third type it had an effect of 7.3% and 8.8% in the fourth type.

It can be concluded that wall color has an effective role in understanding the level of stress and concentration of users in the classrooms, and considering this factor in designing classrooms improves students' behavior and the quality of education in learning environments.

The purpose of this paper is to explore the common learning challenges experienced by undergraduates in selected rural universities in Nigeria and South Africa. Rural universities are strategically established and expected to enhance sustainable development by meeting the needs of host communities. Hence, an attempt is made to trace factors hindering the attainment of the goals.

A quantitative research method was adopted for data collection. A self-designed questionnaire was administered to 2,335 randomly selected third-year students.

The outcome of the study shows that six common learning challenges: cognitive learning challenges, easy loss of concentration, previous learning experiences, distance, student–lecturer relationship as well as policy making and implementation are experienced by undergraduates in the two universities.

This research shows the common challenges experienced by undergraduates in rural universities. However, the study is limited to two selected universities in Nigeria and South Africa.

These results are useful in guiding education stakeholders in policy making and how quality education can be provided for rural-based undergraduates.

The research suggests various ways by which common learning challenges experienced by students in rural universities can be overcome. It will be of immense value to curriculum designers and implementers toward sustainable nation building.

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

The purpose of this paper is to delineate lessons for business schools seeking re-accreditation and that face previous peer-review improvement expectations, strategic and operational imperatives similar to those faced at College of Business Administration (CBA) in University of the State Capital, all pseudonyms to mask their true identity.

Based on qualitative case study method, CBA’s Assessment Director, Gabriel Mouton, again a pseudonym, serves as the central protagonist whose interactive dialogical and technology-enabled change processes provide instructive practical lessons around the management of assurance of learning (AoL) for re-accreditation.

This paper offers a tripartite change focus in AoL for re-accreditation: balancing program goal integration with discipline differentiation, adopting an interactive dialogical shared governance process over a top-down or bottom-up process and technology-enabled straddling program depth and breadth.

This paper is unique to CBA’s path-historical institutional change experiences in the USA with rich-shared faculty governance that may need to be first developed before emulation in institutions where such a tradition is absent.

The experiences narrated in this paper offer universal lessons for business schools aspiring to continuously improve their AoL and, in the process, uphold program meaning and quality standards for stakeholder relevance and re-accreditation.

The experiences narrated in this paper offers lessons for tying program quality to external stakeholders’ expectations in the community, including for international business schools.

This paper advances an original tripartite change focus specifically relevant for business schools seeking re-accreditation and that are concurrently grappling with multiple strategic and operational imperatives.

This paper aims to explore the relationship between music and learning in the mind/brain.

Taking a consilience approach, this paper briefly introduces how music affects the mind/brain, then moves through several historical highlights of the emergent understanding of the role of music in learning; for example, the much‐misunderstood Mozart effect. Then the role of music in learning is explored from a neuroscience perspective, with specific focus on its potential to achieve brain coherence. Finally, using a specific example of sound technology focused on achieving hemispheric synchronization, research findings, anecdotes and experiential interactions are integrated to touch on the potential offered by this new understanding.

Listening to music regularly (along with replaying tunes in one's brain) clearly helps keep the neurons active and alive and the synapses intact. Listening to the right music does appear to facilitate learning, and participating more fully in music making appears to provide additional cerebral advantages. Further, some music supports hemispheric synchronization, offering the opportunity to achieve brain coherence and significantly improve learning.

This paper brings together diverse research to demonstrate the potential of music to affect mind/brain learning. Further, it introduces and discusses a specific example of sound technology to achieve brain coherence.

This research examines the principal assumption underlying the learning organization literature that organizational learning leads to increased organizational performance and explores the role of organizational learning, culture and focused learning on organizational performance. The study is based on a stratified sample of 181 UK construction firms and adopts a structural equation methodology. As no scales exist from prior research, a new instrument is developed for a learning organization. The results suggest that double‐loop learning and cooperative cultures have a positive effect on organizational performance. The effect of competitive forces means that organizational learning focused on efficiency and proficiency leads to competitive advantage in the UK construction industry.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.