1 Decembrie 1918 University, Alba Iulia

The transition to clean and renewable energy sources is of prime importance in addressing environmental challenges related to the consumption of fossil fuels. Hydrogen, being a clean fuel with a high energy density, has huge potential, especially when it can be obtained through solar-driven PEC water splitting. Herein, MoS2 photocatalysts were synthesized by sulfurizing MoO3 thin films, using a CVD technique. The deposited MoO3 films by thermal evaporation at 450 degrees C were further sulfurized at 500 degrees C, 550 degrees C, and 600 degrees C. XRD results confirmed the successful conversion of MoO3 into MoS2. The optical properties showed a bandgap reduction from 2.50 eV to 1.30 eV, which leads to better absorption of light in the visible region.The photoelectrochemical experiment shows that the S-MoO3-600 degrees C thin film has the best performance, and the solar-to-hydrogen conversion efficiency reaches 0.11385% at an applied bias of 1.0 V versus Ag/AgCl, which is about 189 times higher than that of the pristine MoO3 thin film.

This paper introduces a new extended rectangular fuzzy b-metric-like space that generalizes the existing frameworks of the extended rectangular and fuzzy b-metric spaces. Within this setting, we establish several fixed-point theorems for Ciric-type and Banach-type contractions, accompanied by a series of corollaries, propositions, and conditions that further illustrate the proposed concept. These results unify and extend many known theorems in fuzzy metric theory. Moreover, we provided several non-trivial examples to validate of the main results. A flow diagram was provided to demonstrate the generalized structure. Additionally, we applied the fuzzy integral equation to establish the uniqueness and existence of our main result.

Linguistic interval-valued q-rung orthopair fuzzy (LIVq-ROF) sets offer a powerful framework for modeling uncertainty and vagueness in complex decision-making environments. This study leverages the expressive strength of LIVq-ROF sets to develop novel aggregation operators-specifically, the LIVq-ROF Choquet integral averaging and geometric operators-which are designed to capture interdependencies among attributes in multiple attribute group decision-making (MAGDM) scenarios. The theoretical properties of these operators are rigorously established. Building on this foundation, we propose a Choquet integral-based grey relational analysis (GRA) method tailored for MAGDM under uncertainty. The proposed model is applied to a real-world case study involving the selection of the optimal neural network model for predicting crop yields. Results demonstrate the model's effectiveness in identifying the best-performing alternative. A thorough sensitivity analysis and comparison with existing approaches confirm the robustness and superior performance of the proposed method.

In recent years, industry development has become closely connected with Information and Communication Technology (ICT) and trade openness. This research explores how industry, ICT, economic activity, and trade openness affect the environment, highlighting the importance of investing in low-carbon technologies and energy-efficient machinery. The goal of this research is to investigate the short- and long-run impacts of industrialization, ICT, economic activity, and trade openness on per capita carbon emissions in Lithuania from 2000 to 2024. This study employs the ARDL econometric model along with several diagnostic tests. The Breusch-Godfrey Serial Correlation test indicated no serial correlation, while the Breusch-Pagan-Godfrey test indicated no heteroscedasticity. The Ramsey RESET test confirmed that the model specification is appropriate and significant for the research. Additionally, the VIF test for multicollinearity indicates that no multicollinearity exists among the research variables. The research results show that industrialization and economic activity are positively associated with per capita carbon emissions and environmental harm. In contrast, trade openness and ICT are negatively associated with per capita carbon emissions in Lithuania, thereby contributing to environmental sustainability. The novelty of this research: a specific combination of variables combining key structural (industrialization), integration (trade openness), and digital diffusion (ICT penetration) determinants of CO2 emissions within a specific single-country context, applying the ARDL framework for the Baltic EU member state, Lithuania. While prior studies primarily relied on multi-country panels and often treat ICT through heterogeneous proxies, this study operationalizes ICT as internet-user penetration to capture digital integration effects-an important distinction for small open economies where energy-intensive digital infrastructure may be located abroad. By separating short-run from long-run dynamics, the analysis offers evidence on how the environmental effects of openness, growth, and digitalization unfold over time, using recent data up to 2024 and providing policy recommendations encouraging decarbonization strategies.

This paper introduces a novel cubic Fermatean fuzzy (CFF) Sugeno-Weber (SuW) aggregation-based method for multi-criteria group decision-making (MCGDM) under uncertainty. First, we define new SuW operational laws for CFF sets and investigate their key properties. Based on these, we develop several power aggregation operators (AOs) that effectively combine evaluation information in fuzzy environments. These AOs are then integrated into a novel MCGDM framework employing two types of maximizing deviation models to determine unknown criteria weights. The proposed method is validated through a real-world case study assessing project team member performance across multiple evaluation criteria. Furthermore, sensitivity analysis and comparative evaluation demonstrate the robustness and superiority of the proposed method over existing aggregation techniques, confirming its practical significance.