Characterization of biosorption potential of <i>Brevibacillus</i> biomass isolated from contaminated water resources for removal of Pb (II) ions

Abstract Various activities of different industries are found to be the main reason for water pollution with heavy metals. Use of microorganisms that are tolerant even of a high concentration of metal ions could be a valuable tool for remediation of contaminated water resources. In the present study, microorganisms that showed high resistance to lead ions were isolated and evaluated for biosorption efficiency for removal of lead ions from waste water. Biochemical identification and 16S rRNA gene sequence analysis indicated that the isolated strain was Brevibacillus. The conditions of pH, biomass concentration, temperature, time, agitation and Initial concentration of metal for biosorption of Pb (II) were optimized. Based on induction coupled plasma optical emission spectroscopy (ICP-OES) analysis, the biosorption efficiency of Brevibacillus at optimized conditions of initial metal concentration of 150 μg/mL, 1 g/L of biomass dose, pH 6.0, 40 °C, for 12 h at 80 rpm was 78.58% and the biosorption capacity (qe) is 128.58 mg/g of the biosorbent. Of the three isotherm models investigated, the Freundlich isotherm model was identified as a good fit with high correlation coefficient, while kinetic data followed the pseudo first order model as best fit. Surface characterization by scanning electron microscopy (SEM) analysis revealed morphological changes with a bulged rod-shape cell having metal depositions and rough texture. The presence of lead within the cell was detected by transmission emission microscopy (TEM). The key functional groups that participate in biosorption were analyzed by Fourier transform infrared (FTIR) spectroscopy and were found to be carboxyl, hydroxyl, amino and phosphate groups. From the real-time study, it proves that the biomass of Brevibacillus can be used as a promising biosorbent for removal of metals including lead from waste water.</jats:p

Multi-Objective Ant Colony Optimization (MOACO) Approach for Multi-Document Text Summarization

The demand for creating automatic text summarization methods has significantly emerged as a result of the web&rsquo;s explosive growth in textual data and the challenge of finding re-quired information within this massive volume of data. Multi-document text summarizing (MDTS) is an effective method for creating summaries by grouping texts that are relevant to a similar subject. With the aid of optimization methods, this strategy can be optimized. The majority of optimization algorithms used in the scientific literature are single-objective ones, but more recently, multi-objective optimization (MOO) techniques have been created, and their findings have outperformed those of single-objective methods. Metaheuristics-based techniques are also increasingly being used effectively in the study of MOO. The MDTS issue is therefore solved by the Multi-Objective Ant Colony Optimization (MOACO) method. This multi-objective metaheuristic algorithm is based on the Pareto optimization. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics have been used to assess the outcomes of experiments using Document Understanding Conferences (DUC) datasets. Additionally, they have consistently outperformed other referenced summarizer systems

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models

There have been several initiatives taken to guarantee higher yields and higher-quality crops as the agriculture sector grows. The agriculture industry is severely impacted by plant and agricultural illnesses and deficits. Several techniques and technologies have been developed to aid in the diagnosis, management, and eventual eradication of plant diseases. The efficient and accurate identification of plant diseases could be aided by the development of a quick and accurate model. The use of deep convolutional neural networks for image categorization has greatly improved accuracy. In this paper, we present a framework for automating disease detection by the use of a tailored DL architecture. Both the Plant Village dataset and the real-time field dataset are utilized in the testing process. Our model&rsquo;s results are compared to those of other spatial exploitation models. The results show that the proposed method is superior to the standard deep-learning classifier. This proves the network&rsquo;s potential for usage in real-time applications by extracting high-level features that boost the efficiency and accuracy while reducing the risk introduced by a manual procedure. In order to enable a prompt reaction, and perhaps a targeted pesticide application, the suggested method has the ability to provide the early diagnoses of plant vital health