Background: Chronic low back pain (CLBP) is a leading cause of disability, impacting quality of life (QoL), function, and work productivity. Traditional rehabilitation faces challenges in accessibility and adherence. Remote rehabilitation and virtual reality (VR) interventions using motion sensors offer real-time movement tracking, biofeedback, and personalized exercises. This systematic review evaluates their effectiveness in pain reduction, functional improvement, adherence, and QoL. Methods: A systematic search was performed across PubMed, Scopus, Web of Science, and PEDro (2015–2025), including randomized controlled trials, observational, and feasibility studies on adults with CLBP undergoing sensor-based digital rehabilitation. The primary outcomes included pain, functional mobility, and movement biomechanics; secondary outcomes included adherence, QoL, and cost-effectiveness. Eight studies involving 7166 participants were included. Overall, sensor-based remote rehabilitation and VR interventions demonstrated positive effects on pain, function, and adherence. Pain reductions ranged from modest short-term decreases to over 60% in long-term programs (e.g., −68.5% in VAS). Functional improvements included lumbar ROM gains up to +9.9° and better movement control. Adherence was consistently high, with some programs reporting completion rates between 73% and 90%, particularly those incorporating gamification or real-time feedback. Selected studies also showed QoL improvements (e.g., +9.10 points on SF-36) and reductions in work impairment by over 60%. A few trials reported significant decreases in inflammatory markers (e.g., CRP −1.16 mg/L, TNF-α −8.9 pg/mL). Conclusions: Motion sensor-based remote rehabilitation and VR interventions show promising results in pain management, mobility, and adherence for individuals with CLBP. Gamification and biofeedback features enhance engagement, addressing a key challenge of conventional rehabilitation. However, more long-term RCTs and economic evaluations are needed to confirm their effectiveness and cost-efficiency.

Virtual reality (VR) technology improves engineering activities, enabling a better understanding of products and processes. A complete conversion from the existing CAx software to a VR environment could improve product and process design activities, leading to new practices. This article aims to provide key insights, technical evidences, and related impacts on the engineering activities of industrial companies while integrating the existing systems with VR as a supporting tool for product and process design. The results indicate improved design and limited negative impacts related to errors during the manufacturing phase. An action research study involving Oil&Gas manufacturing companies was conducted to explore the following four CAx-VR scenarios: CAD–VR for ergonomics/reachability, CAE(CFD)–VR and CAE(FEM)–VR for abstract data visualization, and CAM–VR for packaging. For each scenario, applicative cases focusing on Oil&Gas products, such as multistage centrifugal pumps and ball valves, have been investigated. Furthermore, a detailed discussion of the involved chain of software applications and impacts on the engineering activities for each scenario establishes the reliability of the integrated environment for CAD, CAE, and CAM data immersive visualization.

Building Information Modeling (BIM) and Product Lifecycle Management (PLM) have been associated many times in recent literature and the possibilities for their integration or to be mutually used as a source of lesson learned has been envisaged. The paper proposes to analyze, through a systematic literature review approach, the existing state of art of previous studies that has already examined relations between BIM and PLM. The main objective of the paper is to understand the real nature of BIM-PLM association for better directing future research developments.

Background: Non-communicable diseases (NCDs) represent a leading cause of global mortality, demanding innovative approaches to management. Voice assistants (VAs) have emerged as promising tools in healthcare, offering support for self-management, behavioral engagement, and patient care. This systematic review evaluates the role of VAs in NCD management, analyzing their impact on clinical and behavioral outcomes, quality of life, usability, and user experiences while identifying barriers to their adoption. Methods: A systematic search was conducted in PubMed, Scopus, and Web of Science from January 2014 to October 2024. Studies were selected based on predefined inclusion and exclusion criteria using the PRISMA guidelines. Data extraction focused on outcomes such as usability, acceptability, adherence, clinical metrics, and quality of life. The risk of bias was assessed using the Cochrane Risk of Bias (RoB) 2 and ROBINS-I tools. Results: Eight studies involving 541 participants were included, examining VAs across various NCD contexts such as diabetes, cardiovascular diseases, and mental health. While VAs demonstrated good usability and moderate adherence, their clinical and quality-of-life outcomes were modest. Behavioral improvements, such as increased physical activity and problem-solving skills, were noted in some interventions. Key challenges included privacy concerns, speech recognition errors, and accessibility issues. Conclusions: VAs show potential as supportive tools in NCD management, especially for enhancing patient engagement and self-management, and their impact on clinical outcomes and long-term usability requires further investigation. Future research should focus on diverse populations, standardized metrics, and comparative studies with alternative technologies.

The transition to sustainable mobility is one of the most pressing and complex challenges for the automotive industry, with impacts that extend beyond the mere reduction of emissions. Electric vehicles, while at the center of this evolution, raise questions about the consumption of natural resources, such as lithium, copper, and cobalt, and their long-term sustainability. In addition, the introduction of advanced technologies, including artificial intelligence (AI) and autonomous systems, brings new challenges related to the management of components and materials needed for their production, creating a significant impact on supply chains. The growing demand for electric and autonomous vehicles is pushing the industry to rethink production models, favoring the adoption of circular economy principles to minimize waste and optimize the use of resources. To better understand the implications of this transition, this study adopts a multiple case study methodology, which allows in-depth exploration of different contexts and scenarios, and analysis of real cases of dismantling and recycling of internal combustion engines (ICEs) and electric vehicles (EVs). The research includes a financial simulation and a comparison of revenues from the dismantling of ICE and EV vehicles, highlighting differences in the value of recycled materials and the effectiveness of circular economy practices applied to the two types of vehicles. This approach provides a detailed overview of the economic benefits and challenges related to the management of the end of life of vehicles, helping to outline optimal strategies for a sustainable and cost-effective future in the automotive sector.

The full development of the energy services company (ESCO) industry is still inhibited by the difficulties during the negotiation between customers and ESCOs. Contractual arrangements are traditionally based on energy performance contracting (EPC). Improvements on EPC schemes are required in order to achieve the success of the negotiation. This work focuses on a particular type of EPC, named guaranteed savings (GS) contract, where a minimum energy saving is guaranteed to the customer by the ESCO. A model based on real options theory to share risks among contractual parties is proposed in order to estimate the fair value of main contractual parameters. A Monte Carlo simulation is adopted for evaluating the most critical factors influencing the overall risk sharing. A numerical example concerning a cogeneration plant of a paper mill is presented. A two level full factorial design of experiments (DOE) analysis is carried out in order to estimate single and compound effects of model parameters.