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  1. Three Cd2+ resistant bacterium's minimal inhibition concentrations were assessed and their percentages of Cd2+ accumulation were determined by measurements using an atomic absorption spectrophotometer (AAS). The results revealed that two isolates Bacillus paramycoides (PM51) and Bacillus tequilensis (PM52), identified by 16S rDNA gene sequencing, showed a higher percentage of Cd2+ accumulation i.e., 83.78% and 81.79%, respectively. Moreover, both novel strains can tolerate Cd2+ levels up to 2000 mg/L isolated from district Chakwal. Amplification of the czcD, nifH, and acdS genes was also performed. Batch bio-sorption studies revealed that at pH 7.0, 1 g/L of biomass, and an initial 150 mg/L Cd2+ concentration were the ideal bio-sorption conditions for Bacillus paramycoides (PM51) and Bacillus tequilensis (PM52). The experimental data were fit to Langmuir isotherm measurements and Freundlich isotherm model R2 values of 0.999 for each of these strains. Bio sorption processes showed pseudo-second-order kinetics. The intra-diffusion model showed Xi values for Bacillus paramycoides (PM51) and Bacillus tequilensis (PM52) of 2.26 and 2.23, respectively. Different surface ligands, was investigated through Fourier-transformation infrared spectroscopy (FTIR). The scanning electron microscope SEM images revealed that after Cd2+ adsorption, the cells of both strains became thick, adherent, and deformed. Additionally, both enhanced Linum usitatissimum plant seed germination under varied concentrations of Cd2+ (0 mg/L, 250 mg/L,350 mg/L, and 500 mg/L). Current findings suggest that the selected strains can be used as a sustainable part of bioremediation techniques. 
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    Free, publicly-accessible full text available June 1, 2025
  2. null (Ed.)
  3. Huge amounts of data generated on social media during emergency situations is regarded as a trove of critical information. The use of supervised machine learning techniques in the early stages of a crisis is challenged by the lack of labeled data for that event. Furthermore, supervised models trained on labeled data from a prior crisis may not produce accurate results, due to inherent crisis variations. To address these challenges, the authors propose a hybrid feature-instance-parameter adaptation approach based on matrix factorization, k-nearest neighbors, and self-training. The proposed feature-instance adaptation selects a subset of the source crisis data that is representative for the target crisis data. The selected labeled source data, together with unlabeled target data, are used to learn self-training domain adaptation classifiers for the target crisis. Experimental results have shown that overall the hybrid domain adaptation classifiers perform better than the supervised classifiers learned from the original source data. 
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  4. Franco, Z. ; González, J.J. ; Canós, J.H. (Ed.)