The growing demand for efficient and sustainable energy solutions underscores the importance of advancing solar energy technologies, particularly Concentrated Solar Power (CSP) systems. This review presents a structured evaluation of two key innovation domains in CSP: the application of nanofluids and the adoption of Industry 4.0 technologies. The first part analyzes experimental and simulation-based studies on nanofluid-enhanced CSP systems, covering four major collector types—parabolic trough, solar power tower, solar dish, and Fresnel reflectors. Nanofluids have been shown to significantly enhance thermal efficiency, with hybrid formulations offering the greatest improvements. The second part examines the role of Industry 4.0 technologies—including artificial intelligence (AI), machine learning (ML), and digital twins (DT)—in improving CSP system monitoring, performance prediction, and operational reliability. Although a few recent studies explore the combined use of nanofluids and Industry 4.0 tools in CSP systems, most research addresses these areas independently. This review identifies this lack of integration as a gap in the current literature. By presenting separate yet complementary analyses, the study offers a comprehensive overview of emerging pathways for CSP optimization. Key research challenges and future directions are highlighted, particularly in nanofluid stability, system cost-efficiency, and digital implementation at scale.

Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations.

The bakery industry is undergoing a profound digital transformation driven by the increasing need for enhanced energy efficiency, operational resilience, and a commitment to environmental sustainability. Digital Twin (DT) technology, recognized as a fundamental component of Industry 4.0, provides advanced capabilities for intelligent energy management across bakery operations. This paper utilizes a narrative and integrative review approach, conceptually integrating emerging developments in using DT with respect toenergy management in the baking industry, including real-time energy monitoring, predictive maintenance, dynamic optimization of production processes, and the seamless integration of renewable energy sources. The study underscores the transformative benefits of adopting DT technologies, such as improvements in energy utilization, greater equipment reliability, increased operational transparency, and stronger alignment with global sustainability objectives. It also critically examines the technical, organizational, and financial barriers limiting broader adoption, particularly among small and medium-sized enterprises (SMEs). Future research directions are identified, emphasizing the potential of artificial intelligence-driven DTs, the adoption of edge computing, the development of scalable and modular platforms, and the necessity of supportive policy frameworks. By integrating DT technologies, bakeries can shift from traditional reactive energy practices to proactive, data-driven strategies, paving the way for greater competitiveness, operational excellence, and a sustainable future.