Purpose: Quality 4.0 is an emerging research topic concerned with rethinking how quality management needs to be adopted in the digital era. The purpose of this research is to conduct a systematic review on the state of the research in the field of Industry 4.0 impact on improving quality management aspects and how technology can be leveraged to enhance its practices.Design/methodology/approach: A systematic review of the literature published in the last 5 years is conducted. 52 papers were selected, mapped based on the technology they focused on and categorized based on the addressed quality aspects.Findings: The review revealed various areas where quality management can benefit from Industry 4.0 technologies, identified several research gaps and suggested new directions for future research. Firstly, the literature provided some insights about industry 4.0 potential contributions but lacks further detail on the exact applications and solutions through use cases and case studies. Secondly, there has been a focus on the potential benefits provided for quality control while there is a clear scarcity in terms of the other quality management tools and methodologies. Thirdly, there is a lack of studies on economic analysis or detailed impacts on quality costs that justifies the substantial investments needed. Finally, there is a need for including more studies about the mapping and integration of ISO 9001 requirements and Industry 4.0 features.Originality/value: This is the first attempt to conduct a comprehensive review on the ways industry 4.0 technologies can be leveraged for the field of quality management. Based on this review, several directions for further research in this area are identified.

Pyrolysis offers a sustainable and efficient approach to resource utilization and waste management, transforming organic materials into valuable products. The quality and distribution of the pyrolysis products highly depend on the constituents' properties and set process parameters. This research aims to investigate and model this dependency, offering decision-makers a tool to guide them when designing the process for a particular application. Experimental data on the pyrolysis of various types of feedstocks processed at a wide range of pyrolysis temperatures (350-650 °C) are utilized to develop the prediction models. Four variables are modeled: the yield and energy content for both the biochar and bio-oil as a function of the pyrolysis temperature and feedstock characteristics. The models developed had very good prediction power with the coefficient of determination above 90%. The results highlight the advantages of food waste (leftover) as a suitable feedstock to produce biochar at the pyrolysis temperature within the range of 450-550 °C. Furthermore, the biofuels produced from food waste are found to be of good quality, with the bio-oil exceptionally high in energy content (HHV = 34.6 MJ/kg), which is almost 80% of that of diesel. The developed models provide a tool for predicting the biofuel yield and quality based on the feedstock selection and process temperature.

Purpose: Cold supply chain (CSC) distribution systems are vital in preserving the integrity and freshness of transported temperature sensitive products. CSC is also known to be energy intensive with a significant emission footprint. As a result, CSC requires strict monitoring and control management system during storage and transportation to improve safety and reduce profit losses. In this research, a systematic review of recent literature related to the distribution of food CSC products is presented and possible areas to extend research in modeling and decision-making are identified.Design/methodology/approach: The paper analyzes the content of 65 recent articles related to CSC and perishable foods. Several relevant keywords were used in the initial search, which generated a list of 214 articles. The articles were screened based on content relevance in terms of food vehicle routing modeling and quality. Selected articles were categorized and analyzed based on cost elements, modeling framework and solution approach. Finally, recommendations for future research are suggested.Findings: The review identified several research gaps in CSC logistics literature, where more focused research is warranted. First, the review suggests that dynamic vehicle modeling and routing while considering products quality and environmental impacts is still an open area for research. Second, there is no consensus among researchers in terms of quality degradation models used to assess the freshness of transported cold food. As a result, an investigation of critical parameters and quality modeling is warranted. Third, and due to the problem complexity, there is a need for developing heuristics and metaheuristics to solve such models. Finally, there is a need for extending the single product single compartment CSC to multi-compartment multi-temperature routing modeling.Originality/value: The article identified possible areas to extend research in CSC distribution modeling and decision-making. Modified models that reflect real applications will help practitioners, food authorities and researchers make timely and more accurate decisions that will reduce food waste and improve the freshness of transported food.

Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells’ performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels’ performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors.

Background: According to the World Health Organization (WHO), pneumonia is the leading infectious cause of death in children below 5 years old. Hence, the early detection of pediatric pneumonia is crucial to reduce its morbidity and mortality rates. Even though chest radiography is the most commonly employed modality for pneumonia detection, recent studies highlight the existence of poor interobserver agreement in the chest X-ray interpretation of healthcare practitioners when it comes to diagnosing pediatric pneumonia. Thus, there is a significant need for automating the detection process to minimize the potential human error. Since Artificial Intelligence tools such as Deep Learning (DL) and Machine Learning (ML) have the potential to automate disease detection, many researchers explored how such tools can be implemented to detect pneumonia in chest X-rays. Notably, the majority of efforts tackled this problem from a DL point of view. However, ML has shown a higher potential for medical interpretability while being less computationally demanding than DL. Objective: The aim of this paper is to automate the early detection process of pediatric pneumonia using ML as it is less computationally demanding than DL. Methods: The proposed approach entails performing data augmentation to balance the classes of the utilized dataset, optimizing the feature extraction scheme, and evaluating the performance of several ML models. Moreover, the performance of this approach is compared to a TL benchmark to evaluate its candidacy. Results: Using the proposed approach, the Quadratic SVM model yielded an accuracy of 97.58%, surpassing the accuracies reported in the current ML literature. In addition, this model classification time was significantly smaller than that of the TL benchmark. Conclusion: The results strongly support the candidacy of the proposed approach in reliably detecting pediatric pneumonia.