The glass transition temperature (Tg) of alkali borate glasses is strongly composition-dependent and difficult to predict from first principles due to the structural complexity of the boron network. Here, we apply physics-informed symbolic regression (combining evolutionary search with physically meaningful descriptors) to derive an interpretable closed-form expression for Tg for the xM2O⋅(100−x)B2O3 glass family, with M = Li, Na and K, and x expressed in mol%, followed by extrapolation to M = Rb and Cs. The resulting model achieves a root mean square error of 14–16 K while maintaining clear physical interpretability, explicitly capturing the interplay among Tg, structural dissociation energy, and network packing. Critically, models built on the Rigid Unit Packing Fraction (RUPF) yield substantially more realistic Tg predictions than those using the conventional Atomic Packing Fraction (APF), as APF overestimates structural rigidity at intermediate compositions. The fitted dissociation energies are further validated against the revised Makishima–Mackenzie model, confirming that the inferred parameters are physically consistent, not merely statistically effective, within the alkali borate family. Finally, Monte Carlo uncertainty quantification reveals that prediction uncertainty is highest in the compositional regions associated with the boron anomaly, directly linking model limitations to a known structural transition in these glasses. This result highlights the potential of physics-informed symbolic regression as a transparent and interpretable alternative to black-box models for property prediction in glass systems.
Eucalyptus species are widely cultivated globally, with over 22.5 million hafv planted, owing to their rapid growth, adaptability, versatility for cellulose, wood, and energy production, and their role in carbon fixation. Other species, including pine, acacia, teak, poplar, rubber tree, and birch, are also widely cultivated for various uses. Measuring tree height in plantations is fundamental for estimating volume and biomass and for assessing the plantation condition, a key variable in forest inventories. Although challenging, various techniques, including trigonometric methods and technologies such as Light Detection and Ranging (LiDAR) sensors, photogrammetry, and Mobile Laser Scanner (MLS), have been employed to obtain accurate height measurements. This study presents a new algorithm for estimating the total height (H) of trees in forest plantations using MLS data. It incorporates characteristics of organized plantations to improve trunk search and uses the theory of natural tree taper to obtain more accurate height estimates. Validated in two study areas, it demonstrated reliability, especially for short trees, and high accuracy, achieving Root Mean Squared Error (RMSE) (%) values of 3.32% for area 1 and 3.26% for area 2. In contrast, traditional algorithms such as, TreeLS yielded RMSE (%) values of 14.70% for area 1 and 14.40% for area 2, whereas the 3DFin algorithm obtained RMSE (%) values of 12.49% for area 1 and 21.43% for area 2. Although it requires more processing time than the 3DFin algorithm, the proposed method provides more accurate results for forest inventories.
This study investigates the relationship between infrared (IR), ultraviolet (UV), leakage current, and meteorological parameters for assessing saline contamination in high-voltage insulation systems operating in a coastal environment. A multispectral monitoring methodology was applied to seven 500 kV disconnect switches installed at a coastal substation located approximately 1.5 km from the sea. Infrared thermal attributes, ultraviolet partial discharge indicators, RMS leakage current measurements, and a precipitation-based drought index (derived from rainfall data) were collected and analyzed over several months under real operating conditions. Thermal monitoring was performed using region-based maximum threshold temperature variation (Delta Tthreshold ), while ultraviolet analysis employed maximum persistence (Pmax) and corona discharge area (aF) as condition indicators. The monitored parameters exhibited similar temporal behavior, with reductions following significant rainfall events and progressive increases during extended dry periods, indicating the cumulative influence of saline contamination. Correlation analyses indicated strong and statistically significant associations between the monitored attributes and the number of days elapsed since precipitation events >= 5 mm. Pearson correlation coefficients ranged from 0.890 to 0.970, while Spearman coefficients reached 0.976 (p <= 0.01). Strong associations were also observed among thermal, ultraviolet, and leakage current indicators, suggesting consistency within the proposed multispectral framework. The results suggest that the integrated analysis of IR, UV, leakage current, and precipitation history may provide useful condition indicators for tracking contamination severity and supporting condition-based maintenance of high-voltage assets operating in coastal environments.
Sorghum is a resilient crop of great agricultural importance, notable for its high efficiency in water and energy use. However, factors such as low soil nutrient availability and abiotic stresses can limit its productive potential. In this context, plant growth-promoting bacteria (PGPB) have proven to be sustainable alternatives as they can stimulate crop growth through biological nitrogen fixation (BNF) and tolerance to adverse stresses. This study aimed to conduct a bibliometric analysis with a narrative synthesis of scientific production on the use of PGPB in sorghum cultivation between 2014 and 2024. To this end, a search for scientific articles was conducted in the Scopus and Web of Science databases. The bibliographic data were refined and analyzed in the R software using the Bibliometrix package. It was possible to identify the main authors, institutions, journals, keywords, and scientific collaboration networks. An increase was observed in global scientific production, with Brazil and the United States of America standing out as leaders in publications. The research mainly addressed the effects of PGPB on sorghum growth, BNF, and mitigation of abiotic stresses, such as drought and heavy metal contamination. The use of PGPB was also related to phytoremediation, standing out as a strategy for environmental recovery and increased agricultural productivity. These results contribute to understanding trends and advances in the application of PGPB in sorghum cultivation, as well as providing subsidies for future research.
Fruit processing generates large volumes of residues that are often underutilized, despite their functional and nutritional value being comparable to or even higher than that of fresh fruits. Genipap (Genipa americana L.) stands out due to its richness in bioactive compounds, including iridoids, phenolics, and flavonoids, as well as the natural blue pigment derived from the fruit. In juice production, approximately 40
