This overview study investigates integrating advanced manufacturing technologies, specifically metal additive manufacturing (AM) and laser powder bed fusion (LPBF) processes, within Industry 4.0 and Industry 5.0 frameworks, to enhance sustainability and efficiency in industrial production and prototyping. The manufacturing sector, a significant contributor to global greenhouse gas emissions and resource consumption, is increasingly adopting technologies that reduce environmental impact while maintaining economic growth. Selective laser melting (SLM), as the subsection LPBF technologies, is highlighted for its capability to produce high-performance, lightweight, and complex components with minimal material waste, thus aligning with circular economy goals for metal alloys. Life cycle assessment (LCA) and life cycle costing (LCC) analyses are essential methods for evaluating the sustainability of any new technology. Sustainable technologies could support the concepts of the factory of the future (FoF), fulfilling the requirements of digital transformation and digital twins. This overview study reveals that implementing AM—specifically SLM—has the potential to reduce the environmental impact of manufacturing. It underscores the ability of these technologies to promote sustainable and efficient manufacturing practices, thereby accelerating the shift from Industry 4.0 to Industry 5.0.

Gamma spectroscopy is an important analytical technique across various fields. Gamma spectroscopy uses the energy spectra of emitted gamma rays to examine the type and quantity of isotopes that exist in samples. Like any other analytical technique, traditional gamma spectroscopy encounters challenges that in some cases make the analysis uncertain. Machine Learning (ML) algorithms have been proposed as an approach for enhancing the precision and robustness of gamma spectroscopy. The current study introduces the basics behind ML and illustrates how they are applied in gamma spectroscopy using case studies. Major findings discussed herein demonstrate the developmental capability that ML has in improving gamma spectroscopy. Radioisotope identification, optimizing detector performance, and simplifying environmental monitoring processes have been the main areas in which ML algorithms have been deployed for improvement. These include the ability to predict and provide real-time spectrometry, among others, even though these opportunities come with their shortfalls such as the necessity for huge training datasets. This review explains that integrating ML into gamma spectroscopy marks a major change from current analytic techniques, with possible further developments in radiation detection and environmental science. It serves as very useful material for those studying or practicing AI and gamma spectroscopy and want to know more about it or need guidance on what is happening so far regarding AI integrated into gamma spectroscopy analysis.

As global fish consumption rises, a large amount of waste is generated that is generally neglected. Considering the value embedded in these resources, sustainable methods become more important in extracting valuable ingredients from fish processing residues. Enzymatic hydrolysis is a fast and easily reproducible method for recovering protein ingredients and obtaining valuable by-products. To confirm its advantages, an environmental and economic impact assessment is essential. This study overviewed the sustainability and economic viability of extracting protein compounds and oil from Atlantic mackerel processing residues using enzymatic hydrolysis. Life cycle assessment (LCA) and life cycle cost analysis (LCCA) methods were employed. It was found that the climate change impact of the whole process was 0.073 kg CO₂-eq per 1 g of fish protein hydrolysate (FPH). As the process produces FPH as the main product and fish oil as the by-product, economic allocation was used to distribute the impacts of FPH and fish oil. The findings of the LCCA showed that producing 1 g of FPH costs EUR 3.68. The contribution analysis indicated the crucial role of electricity and fish in environmental impacts. To ensure the accuracy of the calculation, the results of an LCA study published previously were recalculated. The sensitivity analysis showed that the results were susceptible to the region and source of electricity production. This research provides valuable insights into the sustainability and economic aspects of using enzymatic hydrolysis for extracting protein ingredients and oils from Atlantic mackerel. This can inform future investigations of environmentally friendly and economically viable solutions for extracting fish ingredients.

Chitosan is in high demand due to its wide range of applications, resulting in a reliable market. Conventional chemical extraction methods of chitosan are harsh, require strong acids and bases, and produce toxic waste products. High-pressure processing (HPP)-assisted chemical extraction of chitosan has the potential to result in a higher production yield. It is crucial to evaluate the environmental performance of this method. This paper presents a comprehensive comparative analysis of chitosan production methods from an environmental perspective, focusing on HPP-assisted and conventional techniques. Employing life cycle assessment (LCA) methodologies, the study evaluates the environmental footprints of conventional and HPP-assisted chitosan production processes. Results reveal that HPP-assisted production exhibits superior environmental performance, particularly in reducing climate change impact by 64% compared to conventional methods. Sensitivity and scenario analyses confirm the robustness of findings, considering changes in electricity production regions and alternative characterization methods. Uncertainty analysis indicates moderate uncertainty levels, affirming data reliability. The study concludes that HPP-assisted chitosan production offers a more sustainable approach with lower environmental footprints across various endpoints. These findings provide valuable guidance for stakeholders in the chitosan industry to enhance sustainability practices and minimize environmental impacts.