Annotation. The article addresses the issues of identifying the impact of the independent indicators analyzed (i.e. insurance and financial services, gross capital formation, transport services, foreign direct investment, research and development expenditure, changes in inventories and inflation) at the level of the 22 European economies over a period of 23 years. The evolution of goods and merchandise exports, were considered dependent indicators. According to our findings within the econometric study, higher volume related to the exported goods and merchandise are associated with high levels of insurance, gross capital formation, transport services, FDI, R&D expenditure and inventories. Our analysis confirms that significant increase in prices due to cost-push inflation can negatively impact trade by making exports less competitive in international markets. Conversely, efforts to address trade imbalances through cost reduction measures may result in economic distress. Therefore, maintaining a balanced approach to inflation and trade policies is essential to ensuring overall economic stability. We have identified that clusters of EU countries having lower inflation, like North-Western European countries, tend to have higher trade activity. In the same time, the South Eastern-European countries show lower overall values, which might indicate developing or smaller economies. These results add value to the existing literature, concluding and formulating recommendations for policy support, to contribute and increase exports competitiveness both at the EU level and for South Eastern European.
This study explored an AI-enabled simultaneous producing hydrogen and bioremediation of municipal wastewater using Chlorella vulgaris. Algae from municipal wastewater sourced from Kuala Lumpur, Malaysia was immobilised in polyurethane foam and induced with light intensities of 100, 200 and 300 mu mol/m2/s at 4:20, 8:16, 12:12, 18:06, and 24:00 h photo periods to access their impact on hydrogen production. A model that accommodated both the exponential decay after maximum hydrogen production and algal behaviour to light was also introduced and optimised using AI for time series analysis. The result revealed that 12:12 photoperiod maximised hydrogen production (324.86mLH2/g microalgae). However, hydrogen production reduces with increasing experimental cycles. Also, 97.01 %, 88.13 %, 73.99 % and 78.54 % of BOD5, NH+4-N, NO-2-N, and NO-3-N respectively, were removed from the wastewater alongside hydrogen production. Thus, this study not only addressed the immediate necessity of implementing sustainable energy technologies but also contributed to wastewater management. The lifecycle assessment (LCA) of the process revealed a minimal impact on the environment and the AI model demonstrated an accuracy as low as 0.00192 for optimal photoperiods. Recommendations for larger-scale applications, microalgal research and energy integration were proposed.
This paper presents the design, modeling and experimental validation of an on-grid photovoltaic system with self-consumption, sized for the sustainable supply of a water pumping station. The system, composed of 68 photovoltaic panels, uses an architecture based on a Boost DC-DC converter controlled by the Perturb and Observe algorithm, raising the operating voltage to a high-voltage DC bus to maximize the conversion efficiency. The study integrates dynamic performance analysis through simulations in the Simulink environment, testing the stability of the DC bus under sudden irradiance shocks, with rigorous experimental validation based on field production data. The simulation results, which indicate a peak DC power of approximately 34 kW, are confirmed by real monitoring data that records a maximum of 35 kW, the error being justified by the high efficiency of the panels and system losses. Long-term validation, carried out over three years of operation (2023-2025), demonstrates the reliability of the technical solution, with the system generating a total of 124.68 MWh. The analysis of energy flows highlights a degree of self-consumption of 60.08%, while the absence of chemical storage is compensated for by injecting the surplus of 49.78 MWh into the national grid, which is used as an energy buffer. The paper demonstrates that using the grid to balance night-time or meteorological deficits, in combination with a stabilized DC bus, represents an optimal technical-economic solution for critical pumping infrastructures, eliminating the maintenance costs of the accumulators and ensuring continuous operation.
Abstract Japan’s energy transition represents a complex process balancing ambitious policy goals with structural and institutional constraints. Despite its long-standing reliance on fossil fuels, Japan aims to achieve a 36–38% share of renewables in the electricity mix by 2030 under the Strategic Energy Plan (2021), as part of its broader 2050 carbon neutrality target. Between 2020 and 2024, the renewable sector expanded significantly—solar capacity grew by 27.5%, wind by 41.5%, and bioenergy by 123%—while nuclear output nearly doubled. The shift from the Feed-in Tariff (FIT) to the Feed-in Premium (FIP) mechanism marks a key policy evolution, fostering competitive market participation and stimulating corporate PPAs. However, progress remains hindered by fragmented grid infrastructure, high curtailment levels (notably in Kyushu), elevated costs, and lengthy permitting procedures. The SWOT assessment underscores a strategic paradox: a well-developed institutional and legislative framework confronted by slow and uneven implementation. Overall, Japan holds substantial potential to emerge as a regional hub of the green transition, contingent on modernizing grid infrastructure, ensuring regulatory predictability, and strengthening participatory and multi-level governance mechanisms.
CSN stands for Catalin Silviu Nutu, and it is the acronym for the name of the author who has developed the theory related to the CSN Algorithm, such as presented in his previous work published so far. Current conventional and classical AI models today, mainly based on supervised and unsupervised learning, have almost reached their limits, and they are completely insufficient and totally inadequate to simulate and to model highly sophisticated and complicated brain processes involved in highly creative and extremely innovative brain processes. Based on the theory introduced, developed and presented by the author related to the CSN Algorithm and by introducing a new type of special matrix named CSN Matrix of Activations, this paper generates a quantum computing model named CSN Quantum Computing brain model. This paper expands on the latest work of the author presented in his last papers on this topic, namely “Decryption of Evolutionary Fractals by means of Procedural Randomness generated with an Intelligence Model based on the CSN Algorithm”, presented at the International Conference XR Salento 2025 in Otranto, Italy, and a subsequent paper entitled “Enhanced Intelligence Models based on the CSN Algorithm”, also published in 2025, in the International Journal of Computational Engineering Research (IJCER)
