Lorestan University

This study aimed to analyze changes in the production capacity of mountain forests that have faced decreased rainfall and drought occurrence in recent decades, with field sampling, a long-term time series analysis of satellite imagery and climate data. To achieve this goal, first during field sampling and when measuring the crown diameter of all the trees in the sample plots, the allometric equations developed for Quercus brantii Lindle in mountain forest habitats were used, and the aboveground biomass (AGB) value of forests was calculated for 2020. To investigate changes in the AGB amount, a regression model was established between the Ratio Vegetation Index (RVI; extracted from the 2020 Landsat satellite images) and the AGB amount in 2020. By running the developed regression model on the 35-year time series (1986-2020) of RVI maps, the 35-year time series of AGB was prepared. To prepare the 35-year time series (1986-2020) of the Standardized Precipitation Index (SPI), the 35-year time series (1986-2020) of monthly rainfall data was applied. The analysis of changes in drought occurrence revealed that 2007 was the most important change point in the studied time series, with a significant difference between the mean rainfall values before and after 2007. Hence, rainfall increased somewhat after 2007. An examination of the relations between AGB changes and drought occurrence variations during the study period demonstrated that there was a suitable correlation (R2 = 0.64) between these two variables, and the amounts of both biomass and rainfall displayed similar increasing trends during the study period.

Plasmonic nanostructures with photothermal effect have emerged as a powerful tool for optofluidic systems. Many studies have explored the photothermal application of Ti3C2Tx MXene in micro- or nanoscale devices, while its plasmonic potential in photothermal-induced fluid convection remains unexplored. Here, we numerically investigate a Ti3C2Tx MXene-based plasmonic metasurface capable of photoinduced heat generation and driving nanoscale fluid convection. By combining optics, thermodynamics, and hydrodynamics simulations, we demonstrate that the arrays of Ti3C2Tx MXene nanodisks exhibit localized surface plasmonic resonances in an aqueous medium in the near-infrared frequencies. The wavelength-dependent photoinduced temperature profile of the structure closely follows the absorption spectra in a wide wavelength region of visible and near-infrared incident illumination, indicating efficient photothermal conversion. The photothermal-induced fluid convection across the incident wavelength also shows strong qualitative agreement with the spectral-dependent photoinduced heat profile and absorption spectra, which points to its significant practical application in nanophotonics and optofluidics.

Background: The integration of Deep Learning (DL) in neuro-oncology has significantly improved diagnostic accuracy; however, the "black-box" nature of these models remains a fundamental barrier to clinical trust and regulatory approval. This study addresses the critical need for interpretability in medical AI by developing a transparent diagnostic framework. Methods: Building upon our previous architectural innovations in two-stage transfer learning and asymmetric class weighting, we propose a fine-tuned MobileNetV2-based system enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM). The model incorporates decision threshold optimization (0.3 instead of 0.5) to eliminate False Negatives (FN), a strategy validated in our earlier research. We evaluate the model's spatial reasoning on a diverse test set of 10 unseen MRI scans, comprising both pathological and healthy samples. Results: The system achieved a remarkable training accuracy of 100% and a validation accuracy of 99.51% (𝐿𝑜𝑠𝑠 = 0.0185). By implementing the optimized threshold, we secured a 100% recall for the tumor class. Grad-CAM visualizations demonstrated a high degree of spatial fidelity, where the model consistently localized hyper-intense necrotic and enhancing regions of tumors with high precision. Conclusion: The proposed framework offers a dual-validation mechanism: superior statistical performance and visual accountability. This transparency allows clinicians to verify the biological relevance of the AI's focus, bridging the gap between computational power and clinical diagnostic safety.