Search

Water scarcity and increasing salinity stress are significant challenges in the farming sector as they often exacerbate each other, as limited water availability can concentrate salts in the soil, further hindering plant growth. Lettuce, a crucial leafy vegetable with high nutritional value, is susceptible to water availability and quality. This study investigates the growth and development of lettuce plants under water scarcity and varying levels of salinity stress to identify effective strategies for reducing water consumption while maintaining or improving plant productivity. Field experiments were designed to simulate three drought levels (50, 75, and 100% of class A pan evaporation) and three salinity stress levels (control, 1500, and 3000 ppm NaCl), assessing their impact on lettuce's morphological and biochemical parameters. The combination of reduced water supply and high salinity significantly hindered growth, underscoring the detrimental effects of simultaneous water deficit and salinity stress on plant development. Non-stressed treatment enhanced nitrogen, phosphorus, and potassium contents and progressively decreased with the reduction in water supply from 100% to 50%. Interestingly, higher salinity levels increased total phenolic, flavonoid, and antioxidant activity, suggesting an adaptive stress response. Moreover, antioxidant activity, evaluated through DPPH and ABTS assays, peaked in plants irrigated with 75% ETo, whether under control or 1500 ppm salinity conditions. The Yield Stability Index was highest at 75% ETo (0.95), indicating robust stability under stress. The results indicated that lettuce could be cultivated with up to 75% of the water requirement without significantly impacting plant development or quality. Furthermore, the investigation demonstrated that lettuce could thrive when irrigated with water of moderate salinity (1500 ppm). These findings highlight the potential for reducing water quantities and saline water in lettuce production, offering practical solutions for sustainable farming in water-scarce regions. [ABSTRACT FROM AUTHOR]

Leaching of calcium phosphate with sulfuric acid was carried out to study phosphogypsum (PG) crystal growth and size distributions in presence and absence of phosphogypsum seed crystals (PGSC) and sorbitol surfactant (SS) additives. PG crystallization simulates industrial Dihydrate process for phosphoric acid production. PG crystals grow in a medium containing 26±1% P2O5 phosphoric acid and 2±0.5 % free sulfuric acid. Better filterability of phosphoric acid can be achieved with large crystals. The formed PG crystals are larger with addition of either 100 ppm Sorbitol surfactant, 5%, 10% or 15% PGSC compared to the baseline (without additives). With addition of surfactant or increasing the % of PGSC from 5% to 15%, both the mean diameter and d90 of crystalized PG are increased due to enhancing the crystallization growth rates. [ABSTRACT FROM AUTHOR]

The slow evaporation technique was used to produce nonlinear optical ammonium pentaborate dihydrate (APBDH) single crystals. The crystal lattice parameters and structure of APBDH crystal was determined by single crystal X-ray diffraction (S-XRD) technique with the triclinic space group. The optical band gap of the grown crystal was determined using UV–Vis spectrophotometer. There is no absorption peak was observed in the entire visible region of the spectrum. The functional groups of the APBDH crystal were identified through Fourier transforms infrared (FTIR) and Fourier transform Raman (FR-Raman) analyses. The thermal decomposition and thermal stability of the grown crystal was measured using TG-DTA analyses. The laser damage threshold (LDT) analysis was used to know the ability to withstand the influence of laser on the grown crystal. Photoconductivity of the grown crystal was studied and its photocurrent and dark current behavior was examined. The surface morphology of the APBDH crystal was evaluated using HR-SEM analysis. The APBDH crystal was exhibited second harmonic generation behavior and its efficiency was calculated with different particle sizes. [ABSTRACT FROM AUTHOR]

Viral and bacterial infection diseases are the most common things caused by microbes. Infection diseases are serious issues because of the growth of COVID‐19. Because of the current living situation, clinical pathogens are difficult to identify. Therefore, biosensors have been widely utilized to sense the biomolecules relevant to viruses and bacteria. The biosensors observe the nanoparticles from the pathogens and help improve the infection analysis. The sensor information is processed using machine learning techniques because it consists of several learning patterns. However, the existing methods have multi‐objective optimization problems while analysing the changes in the nanoparticles. This work utilizes a mayfly optimized convoluted neural network (MOCNN) to overcome this research issue. The grid uses the fully convolution layer that processes the extracted biosensor features to determine the infections. The network performance is optimized by applying the exploitation and exploration properties of nuptial dance that help to escape from the local optima solutions. The effective utilization of the optimized training patterns improves the convergence speed and convergence rate compared to traditional methods. From the results, MOCNN ensures 98.97% accuracy, 0.388 error rate, and 0.322833 convergence rate on various iterations with different learning rates. [ABSTRACT FROM AUTHOR]