Purpose: The correct interaction between the workforce and augmented, virtual, and mixed reality technologies represents a crucial aspect of the success of the smart factory. This interaction is, indeed, affected by the variability of human behavior and its reliability, which can strongly influence the quality, safety, and productivity standards. For this reason, this paper aims to provide a clear and complete analysis of the impacts of these technologies on the performance of operators.Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify peer-reviewed papers that focused on the implementation of augmented and virtual technologies in manufacturing systems and their effects on human performance.Findings: In total, 61 papers were selected and thoroughly analyzed. The findings of this study reveal that Augmented, Virtual and Mixed Reality can be applied for several applications in manufacturing systems with different types of devices, that involve various advantages and disadvantages. The worker’s performance that are influencing by the use of these technologies are above all time to complete a task, error rate and mental and physical workload. Originality/value: Over the years Augmented, Virtual and Mixed Reality technologies in manufacturing systems have been investigated by researchers. Several studies mostly focused on technological issues, have been conducted. The role of the operator, whose tasks may be influenced positively or negatively by the use of new devices, has been hardly ever analyzed and a deep analysis of human performance affected by these technologies is missing. This study represents a preliminary analysis to fill this gap. The results obtained from the SLR allowed us to develop a conceptual framework that investigates the current state-of-the-art knowledge about the topic and highlights gaps in the current research.

Production planning and control (PPC), responsible for all the activities that keep production running regularly, plays an essential role in the transition to more sustainable manufacturing systems. PPC decision-making processes need to be driven by sustainable principles even if this makes them more effortful and complex from the strategic to operative level. This study aims to review the scientific literature relating to sustainable PPC. A bibliometric analysis of 437 papers published on the Scopus database was performed to identify the most relevant articles, authors, and journals and to provide the current topic trends and future research themes and gaps. The findings revealed the increasing interest in this topic mainly since 2018. China and the USA are the most productive countries, whereas the Journal of Cleaner Production and Sustainability are the most productive journals. The analysis has also highlighted the ways to address sustainability issues in PPC, e.g., by integrating in scheduling models objectives related to sustainability or by removing barriers to reverse logistics and circular economy at the PPC level. The following topics, instead, deserve further research: attention to the social issues in PPC and the development of decision support systems that will improve companies’ PPC decision-making capabilities in sustainable optics.

Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs.

Human–robot interaction (HRI), in all its different variants, has gained significant attention in industry. The combination of human skills and robotics significantly enhances manufacturing, improving operations quality and efficiency. This is particularly relevant in highly variable contexts like remanufacturing, which aims to preserve the value of products and components over time and involves complex, non-standardized tasks such as inspection, disassembly, reprocessing, and reassembly. Uncertainties related to the conditions of products to remanufacture cannot be managed by pure automation making HRI highly beneficial. Although researchers have focused on the use of HRI in several fields of application (from construction to logistics), no extensive mapping of the research trends has specifically addressed the intersection of HRI and remanufacturing contexts. To fill this gap, this paper presents a comprehensive study based on a bibliometric analysis to explore the current state of research on this topic. Based on 120 articles from the Scopus database, this study points out the main countries, authors, and affiliations involved in the field. In addition, the key topics were analyzed. A predominant focus, above all in the last three years, on Human-Robot Collaboration (HRC) among the different levels of HRI, and disassembly processes was highlighted, while other related activities, such as reassembly, have received comparatively less attention. An unexplored frontier is the integration of Artificial Intelligence and Machine Learning with HRC tasks in remanufacturing. These findings offer valuable insights for researchers, scholars, and industrial professionals aiming to advance HRI in remanufacturing.

Background/Objectives: Bladder cancer is a multifactorial disease, ranking as the 10th most common cancer globally and the fourth most common cancer in men and the ninth in women in the Western world. This bibliometric analysis aims to identify and evaluate scientific literature addressing new biomarkers for bladder cancer diagnosis, as well as to identify the most prolific organizations, authors, journals, countries, and keywords within this research domain.Methods: An electronic search was conducted using Elsevier's Scopus database. From a total of 940 retrieved papers (published between 2019 and 2024), 493 were selected. For data analysis and visualization, the titles of articles, year of publication, countries, authors, journals, articles, and keywords were analyzed using Microsoft Excel, VOSviewer, and Biblioshiny. Results: China published the most papers (200 articles) and received the highest number of citations, followed by the USA. While some countries, such as Egypt and India, published exclusively Single Country Publications (SCPs), others demonstrated a higher level of international collaboration, with at least half of their publications being Multi-Country Publications (MCPs). Countries with higher rates of MCPs were Greece (66.6%), Italy (53.8%), Korea, and France (50%). The journals that produced the most publications and received the highest number of citations were Cancers, International Journal of Molecular Sciences, and Frontiers in Oncology, confirming their role in producing high-impact research. Conclusions: The consistent distribution of publications over the years considered indicates a sustained interest in this field.