GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management.

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.

Parametric Study of Gold Nanoparticles Synthesis under Micro-Continuous Flow Conditions.

The synthesis of gold nanoparticles (GNPs) using chemical reduction in batch and microreactor methods has been reported. A parametric study of the effect of several parameters on the size of gold nanoparticles was performed in batch synthesis mode using the modified Martin method. The best-obtained conditions were used to synthesize gold nanoparticles using a glass chip microreactor, and the size of the resulting GNPs from both methods was compared. The presence of polyvinyl alcohol (SC) was used as a capping agent, and sodium borohydride (SB) was used as a reducing agent. Several parameters were studied, including HAuCl4, SC, SB concentrations, the volumetric ratio of SB to gold precursor, pH, temperature, and mixing speed. Various techniques were used to characterize the resulting nanoparticles, including Atomic Absorbance spectroscopy (AAS), Ultraviolet-visible spectroscopy (UV-Vis), and dynamic light scratching (DLS). Optimum conditions were obtained for the synthesis of gold nanoparticles. Under similar reaction conditions, the microreactor consistently produced smaller nanoparticles in the range of 10.42-11.31 nm with a reaction time of less than 1 min.

Biochemical Characterization of Halotolerant Bacillus safensis PM22 and Its Potential to Enhance Growth of Maize under Salinity Stress.

Salinity stress is one of the primary abiotic stresses limiting crop growth and yield. Plants respond to salinity stress with several morphophysiological, molecular, and biochemical mechanisms, however, these mechanisms need to be improved further to cope with salt stress effectively. In this regard, the use of plant growth-promoting (PGP) and halotolerant bacteria is thought to be very efficient for enhancing growth and salinity tolerance in plants. The current study aims to assess Bacillus safensis PM22 for its ability to promote plant growth and resistance to salt. The PM22 produced substantial amounts of exopolysaccharides, indole-3-acetic acid, siderophore, and 1-aminocyclopropane-1-carboxylic acid deaminase (ACC-deaminase) under saline conditions. Additionally, inoculation of the halotolerant bacteria PM22 reduced the severity of salinity stress in plants and increased root and shoot length at various salt concentrations (0, 180, 240, and 300 mM). Furthermore, PM22-inoculated plants showed markedly enhanced photosynthetic pigment, carotenoid, leaf relative water content, 2,2-diphenyl-1-picrylhydrazyl (DPPH) activity, salt tolerance index, total soluble sugar, total protein, and ascorbic acid contents compared to non-inoculated control maize plants. PM22 substantially increased antioxidant (enzymatic and non-enzymatic) activities in maize plants, including ascorbate peroxidase, peroxidase, superoxide dismutase, catalase, total flavonoid, and phenol levels. Maize plants inoculated with PM22 also exhibited a significant reduction in electrolyte leakage, hydrogen peroxide, malondialdehyde, glycine betaine, and proline contents compared to non-inoculated control plants. These physiological appearances were further validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), which revealed the upregulation of expression in genes responsible for stress tolerance. In the current investigation, Bacillus safensis PM22 showed plant growth-promoting and salt tolerance attributes and can be utilized as a bio-inoculant to improve yield in salt stress affected areas.