This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO₂ emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.

This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds’ mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R² scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics.

This study investigates integrating circular economy principles—such as closed-loop systems and economic decoupling—into industrial sectors, including refining, clean energy, and electric vehicles. The primary objective is to quantify the impact of circular practices on resource efficiency and environmental sustainability. A mixed-methods approach combines qualitative case studies with quantitative modelling using the Brazilian Land-Use Model for Energy Scenarios (BLUES) and Autoregressive Integrated Moving Average (ARIMA). These models project long-term trends in emissions reduction and resource optimization. Significant findings include a 20–25% reduction in waste production and an improvement in recycling efficiency from 50% to 83% over a decade. Predictive models demonstrated high accuracy, with less than a 5% deviation from actual performance metrics, supported by error metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Statistical validations confirm the reliability of these forecasts. The study highlights the potential for circular economy practices to reduce reliance on virgin materials and lower carbon emissions while emphasizing the critical role of policy support and technological innovation. This integrated approach offers actionable insights for industries seeking sustainable growth, providing a robust framework for future resource efficiency and environmental management applications.

This review research investigates the potential of Polylactic Acid (PLA)/Hydroxyapatite (HA) composites in bone regeneration, focusing on the composites’ synthesis methods, mechanical properties, and biocompatibility. Through an extensive examination of various preparation techniques, such as solvent evaporation, phase separation, electrospinning, and lyophilisation, the study assesses how these methods influence the physical and biological properties of PLA/HA composites. Significant findings from the review highlight that PLA/HA composites enhance osteoblast activity and proliferation, demonstrating an increase in cell adhesion by up to 25% compared to PLA alone. These composites substantially improve mechanical properties, increasing compressive strength and fracture toughness by approximately 30% and 50%, respectively. These enhancements are pivotal for applications requiring robust, load-bearing materials supporting bone tissue integration and regeneration. In conclusion, due to their optimised mechanical strength, biodegradability, and bioactivity, PLA/HA composites are promising biomaterials for orthopaedic and dental applications. The review suggests future research directions focused on long-term clinical outcomes and further material refinement to maximise clinical efficacy and patient compatibility.

This research paper explores the development of AI-optimized lattice structures for biomechanics scaffold design, aiming to enhance bone implant functionality by utilizing advanced human–AI systems. The primary objective is to create scaffold structures that mimic the mechanical properties of natural bone and improve bioactivity and biocompatibility, adapting to patient-specific needs. We employed polylactic acid (PLA), calcium hydroxyapatite (cHAP), and reduced graphene oxide (rGO) as base materials, leveraging their synergistic properties. The scaffolds were intricately designed using nTopology software (nTop 5.12) and fabricated via 3D printing techniques, optimizing for biomechanical load-bearing and cellular integration. The study’s findings highlight a notable enhancement in the mechanical properties of the scaffolds, with the Gyroid lattice design demonstrating a 20% higher energy-absorption capacity than traditional designs. Thermal and chemical analysis revealed a 15% increase in the thermal stability of the composites, enhancing their resilience under physiological conditions. However, the research identified minor inconsistencies in filament diameter during 3D printing, which could affect scaffold uniformity. These findings underscore the potential of integrating AI-driven design with advanced material composites in revolutionizing orthopedic implant technologies.

This research explores the enhancement of electrochemical performance in magnesium batteries by optimising magnesium alloy anodes, explicitly focusing on Mg-Al and Mg-Ag alloys. The study’s objective was to determine the impact of alloy composition on anode voltage stability and overall battery efficiency, particularly under extended cycling conditions. The research assessed the anodes’ voltage behaviour and internal resistance across magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI)₂) electrolyte formulations using a systematic setup involving cyclic voltammetry on the anode and electrochemical impedance spectroscopy. The Mg-Al alloy demonstrated superior performance, with minimal voltage drop and lower resistance increase than the Mg-Ag alloy. The results showed that the Mg-Al alloy maintained over 85% energy efficiency after 100 cycles, significantly outperforming the Mg-Ag alloy, which exhibited increased degradation and efficiency reduction to approximately 80%. These findings confirm that incorporating aluminium into magnesium anodes stabilises the anode voltage and enhances the overall battery efficiency by mitigating degradation mechanisms. Consequently, the Mg-Al alloy is identified as an up-and-coming candidate for use in advanced battery technologies, offering energy density and cycle life improvements. This study lays the groundwork for future research to refine magnesium alloy compositions further to boost battery performance.