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The esteemed Alphonso mango, cherished in India for its taste, saffron color, texture, and extended shelf life, holds global commercial appeal. Unfortunately, the prevalent spongy tissue disorder in Alphonso mangoes results in a soft and corky texture, with up to 30% of mangoes within a single batch affected. This issue leads to outright rejection during export due to delayed disorder identification. The current assessment method involves destructive sampling, causing substantial fruit loss, and lacks assurance for overall batch quality. In light of the mentioned challenges, this current study focuses on utilizing visible-near infrared (Vis-NIR) spectroscopy as a non-invasive method to assess the internal quality of mangoes. It also enables innovative classification models for automated binary categorization (healthy vs spongy tissue-affected). Through preprocessing and principal component analysis of spectral reflectance data, wavelength ranges of 670–750 nm, 900–970 nm, and 1100–1170 nm were identified for distinguishing healthy and damaged mangoes. Soft independent modelling of class analogy (SIMCA) modelling is a novel approach that can be used to classify mango into healthy and spongy tissue-affected categories for better postharvest management. The accuracy of SIMCA models for classifying mangoes into healthy and spongy tissue-affected classes was highest in the wavelength regions of 670–750 nm and 900–970 nm, being 94.4% and 96.7%, respectively. The spectral reflectance between wavelength region 650–970 nm gave significant and visible differentiation between all stages of spongy tissue, that is, mild, medium, and severe. Furthermore, the application of Vis-NIR spectroscopy alongside SIMCA modelling offers a viable avenue for examining internal abnormalities resulting from diseases or injuries in fruits, broadening its utility for diverse inspection needs. [ABSTRACT FROM AUTHOR]

The article investigates the mitigation of copper (Cu) toxicity with copper tolerant plant growth promoting rhizobacteria (PGPR) strains in marigold plant growth. The study discusses the in vitro testing of Cu-tolerant Providentia, Paenibacillus and Lysinibacillus PGPR strains and their effect on Cu accumulation by marigold. It measures plant growth promoting activities including production of siderophores, indole acetic acid, hydrogen cyanide and phosphate solubilization.

Totally 300 demonstrations were initiated in four districts (Dharwad, Belagavi, Bagalkot and Vijayapur) of northern Karnataka, but 50 farmers terminated the demonstrations half way due to use of the produce for consumption or marketing of produce. Hence, the inferences were drawn from 250 successful demonstrations. Immediately after the harvest, the seeds should be dried properly and stored. For cereals, as a preventive measure, to reduce the pest infestation, mix sweet flag rhizome powder @ 1 per cent (100 g/q) or neem seed powder @ 2 per cent (200 g/q) in case of cereals such as sorghum, maize, wheat and paddy. For pulses, as a preventive measure, to reduce the pest infestation, mix sweet flag rhizome powder @ 1 per cent (100 g/q) or smear groundnut oil @ 5 per cent (500 ml/q) in case of pulses such as bengal gram, red gram, cowpea and green gram. For oilseeds, as a preventive measure, to reduce the pest infestation, mix sweet flag rhizome powder @ 1 per cent (100 g/q) in oilseeds such as groundnut, sunflower and soybean. These give protection against storage pests for a period of six months. As curative measure, in the infested seeds/grains of cereals or pulses, place Aluminium Phosphide tablets (@ 3 tablets per tonne tied in the muslin cloth) in the middle of the stack in the storage structures under airtight conditions for seven days. After this, open the gunny bags and air dry for 48 hours and sieve the produce to remove dead insects and store/use further. [ABSTRACT FROM AUTHOR]

This research investigates the influence of temperature on the performance of Cadmium Selenium (CdSe) semiconductor-sensitized solar cells (SSSCs) with tin oxide (SnO2) deposition. CdSe thin films were synthesized at different temperatures (room temperature, 55 and 70 °C) and characterized for their optical and structural properties. The results reveal temperature-dependent variations in the bandgap energy and crystal structure of CdSe, with higher temperatures leading to a red shift in absorption spectra and increased crystallinity. The CdSe-coated SnO2 films showed enhanced nanoparticle density at higher bath temperatures, indicating improved particle binding and aggregation. Moreover, the elemental analysis confirmed the successful loading of CdSe onto SnO2 substrates without impurities. Solar cells constructed with these materials exhibited temperature-dependent efficiency, with maximum efficiency achieved at room temperature due to optimal bandgap characteristics and reduced recombination rates. The solar cell with the optimal SnO2:(FTO)/SnO2/CdSe/CuS nanostructure array electrode produced a short-circuit current density of 4.155 mA/cm2 and a power conversion efficiency of 0.26% when exposed to one sun's rays. These findings suggest that temperature control during CdSe synthesis plays a crucial role in optimizing the performance of SSSCs, highlighting the importance of understanding temperature effects in semiconductor-based solar cell technologies. [ABSTRACT FROM AUTHOR]

This study presents a novel hybrid deep neural network (DNN) with the White Shark optimization (WSO) algorithm to predict and optimize the gas sensing performance of Ni0.6−xMgxCo0.4Fe2O4 nanoparticles at different sizes (x = 0.1, 0.2, 0.3, 0.4, and 0.5). In order to create Ni0.6−xMgxCo0.4Fe2O4, the sol–gel process was employed, with x varying from 0.1 to 0.5 ppm. This study utilized precursors such as Ni(NO3)2·6H2O, Mg(NO3)2·6H2O, Co(NO3)2·6H2O, and Fe(NO3)3·9H2O. Response–recovery curves were analyzed, and a hybrid DNN-WSO model was created to predict gas sensing properties for Ni0.6−xMgxCo0.4Fe2O4 ferrite materials with different Mg levels. Fit statistics like the sum of squares (SSE), R-squared (R2), degrees of freedom (DFE), adjusted R2, and RMSE were calculated. In XRD analysis, the (311) peak shifted from 35.6561° to 35.5614° for Ni0.6−xMgxCo0.4Fe2O4. At 200 °C, the sensor is more responsive to NO2 gas than NH3 gas, with Ni0.6−xMgxCo0.4Fe2O4(x = 0.5) showing the highest response due to increased oxygen adsorption sites for gas oxidation. Smaller particle sizes (x = 0.1) in SEM analysis exhibited well-defined nanocrystalline structures with high crystallinity and smooth surfaces, enhancing sensitivity. The TGA results confirm the stability and functionality of the proposed materials up to the operating temperature of 250 °C. The Ni0.6−xMgxCo0.4Fe2O4(x=0.2) concentration exhibited the best predictive accuracy with high R2, high adjusted R2, enhanced DFE, and low RMSE. The hybrid DNN-WSO algorithm effectively predicts and optimizes gas sensing performance, which is significant for advancing efficient gas sensor development. [ABSTRACT FROM AUTHOR]