The world is evolving, and it has transformed from the industrial age to the era of connected and intelligent products in both organizations and competition. The advances in technology in the last decade have led to the introduction of a new term called Industry 4.0 or the fourth industrial revolution and that has led to the emergence of the term Quality 4.0. Quality 4.0 is the digitalization of traditional quality approaches and the focus on the use of digital tools to improve an organization’s ability to meet customers’ requirements with high quality. The purpose of this paper is to assess the environments of higher education institutions (HEIs) against the 11 axes of LNS Research Quality 4.0 framework and provide insights about their readiness for Quality 4.0 transformation. The framework helps the organizations digitalize their traditional quality practices and transform to Quality 4.0 through exploring the traditional quality—Quality 4.0 continuum of tools and/or concepts related to each axis so they can assess their transformation efforts accordingly. This paper uses these continuums to identify the quality implementation efforts conducted by HEIs through analyzing the continuums’ related practices adopted within their environments and find out what should be done to get to the full transformation to Quality 4.0 within the higher education field. The study shows the HEIs potential of adopting the Quality 4.0 tools and techniques of varies axes of the framework while revealing a limited adoption of most of them in the current times. This is due to several challenges the most impacting of which is having fragmented processes together with fragmented data systems and sources. The study is concluded with a proposed roadmap to assist HEIs to get the best out their efforts in the Quality 4.0 transformation process.

Amplified energy demand due to technologically advanced electrical and electronic appliances has accentuated the importance of energy efficiency to overcome energy shortage and environmental concerns. As adoption of energy efficient appliances depends on perception of the consumers, this study focuses on behavioral exploration of the consumers’ intentions towards the purchase of energy efficient appliances using an extended model of the theory of planned behavior (TPB). The study is based on a survey comprising 289 respondents. Partial least square (PLS) method is used to analyze the data. The results show that the attitude, perceived behavioral control, policy information campaigns, and past-purchase experiences significantly impact behavioral intentions of the consumers, whereas subjective and moral norms are insignificant in shaping behavioral intentions. Based on analyses, policy implications emphasizing (i) strong awareness campaigns, (ii) energy efficiency incentives, and (iii) replacement initiatives are proposed to help policy makers and administrators in achieving required goals of energy efficiency and conservation. The proposed research model and policy initiatives are a blueprint for synergies among policymakers, practitioners, and researchers in understanding and shaping consumers’ behaviors towards the purchase of energy efficient products, particularly, in developing countries.

In the digital multimedia era, digital forensics is becoming an emerging area of research thanks to the large amount of image and video files generated. Ensuring the integrity of such media is of great importance in many situations. This task has become more complex, especially with the progress of symmetrical and asymmetrical network structures which make their authenticity difficult. Consequently, it is absolutely imperative to discover all possible modes of manipulation through the development of new forensics detector tools. Although many solutions have been developed, tamper-detection performance is far from reliable and it leaves this problem widely open for further investigation. In particular, many types of multimedia fraud are difficult to detect because some evidences are not exploited. For example, the symmetry and asymmetry inconsistencies related to visual feature properties are potential when applied at multiple scales and locations. We explore here this topic and propose an understandable soft taxonomy and a deep overview of the latest research concerning multimedia forgery detection. Then, an in-depth discussion and future directions for further investigation are provided. This work offers an opportunity for researchers to understand the current active field and to help them develop and evaluate their own image/video forensics approaches.

Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision-making. Several important pollutants, such as SO₂, CO, PM₁₀, O₃, NOx, H₂S, location, and many others, have important effects on air quality. Air quality should be recorded and measured based on the total effect of pollutants that are collectively prescribed by a numerical value. In Canada, the Air Quality Health Index (AQHI) is used which is one numerical value based on the total effect of some concentrations. Therefore, evolution is required to consider the complex, ill-defined air pollutants, hence several naive and noble approaches are used to study AQHI. In this study, three approaches such as hybrid data-driven ANN, nonlinear autoregressive with external (exogenous) input (NARX) with a neural network, and adaptive neuro-fuzzy inference (ANFIS) approaches are used for estimating the air quality in an urban area (Jeddah city—industrial zone) for public health concerns. Over three years, 1771 data were collected for pollutants from 1 June 2016 until 30 September 2019. In this study, the Levenberg-Marquardt (LM) approach was employed as an optimization method for ANNs to solve the nonlinear least-squares problems. The NARX employed has a two-layer feed-forward ANN. On the other hand, the back-propagation multi-layer perceptron (BPMLP) algorithm was used with the steepest descent approach to reduce the root mean square error (RMSE). The RMSEs were 4.42, 0.0578, and 5.64 for ANN, NARX, and ANFIS, respectively. Essentially, all RMSEs are very small. The outcomes of approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of artificial intelligent techniques, the public’s early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed notable performance of NARX with a neural network, ANN, and ANFIS-based AQHI model for high dimensional data assessment.