The dielectric relaxation and optical properties of the chlorobenzene-iodobenzene-n-hexane ternary system were investigated in the microwave region (lambda = 0.75-6.32 cm) over the temperature range from -40 to +30 degrees C using dielectric spectroscopy combined with Density Functional Theory (DFT) calculations. The influence of temperature and composition on dielectric relaxation behavior was analyzed. It was established that increasing iodobenzene content enhances relaxation time and dielectric strength due to stronger dipole-dipole interactions and higher molecular polarizability, whereas the addition of n-hexane weakens intermolecular interactions and reduces relaxation times. The relaxation processes exhibit thermally activated non-Debye behavior. DFT calculations provided microscopic insight into the dielectric and optical response through analysis of the dielectric function, polarizability, refractive index, optical conductivity, and electric susceptibility. The results indicate that iodine-induced polarizability and n-hexane-mediated dielectric screening govern the relaxation behavior and optical response of the ternary system. These findings contribute to the understanding of dielectric relaxation in multicomponent aromatic liquid systems and may be relevant for the design of dielectric and microwave materials.
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.
Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable/smart devices or multimodal biosignals (e.g., EEG/MEG, ECG/HRV, EMG, EDA/GSR, and sleep/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress/anxiety management (18%), post-traumatic stress disorder (PTSD)/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time/frequency/nonlinear indices such as entropy and complexity), and machine learning/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice.
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.
