1,721,038 research outputs found
A novel approach to isolation and screening of calcifying bacteria for biotechnological applications
Full text linkBacterial calcium-carbonate precipitation (BCP) has been studied for multiple applications such as remediation, consolidation, and cementation. Isolation and screening of strong calcifying bacteria is the main task of BCP-technique. In this paper, we studied CaCO3 precipitation by different bacteria isolated from a rhizospheric soil in both solid and liquid media. It has been found, through culture-depending studies, that bacteria belonging to Actinobacteria, Gammaproteobacteria, and Alphaproteobacteria are the dominant bacteria involved in CaCO3 precipitation in this environment. Pure and mixed cultures of selected strains were applied for sand biocementation experiments. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) analyses of the biotreated samples revealed the biological nature of the cementation and the effectiveness of the biodeposition treatment by mixed cultures. X-ray diffraction (XRD) analysis confirmed that all the calcifying strains selected for sand biocementation precipitated CaCO3, mostly in the form of calcite. In this study, Biolog® EcoPlate is evaluated as a useful method for a more targeted choice of the sampling site with the purpose of obtaining interesting candidates for BCP applications. Furthermore, ImageJ software was investigated, for the first time to our knowledge, as a potential method to screen high CaCO3 producer strains
Localization of Azospirillum brasilense Cd in inoculated tomato (Lycopersicon esculentum Mill.) roots
No full textTomato (Lycopersicon esculentum Mill.) seeds inoculated with Azospirillum brasilense Cd were cultured in vitro on solid medium with and without combined nitrogen. Inoculated and control roots from 30-day-old plants were examined by light microscopy (LM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM), to monitor the adhesion, penetration, and localization of bacteria on and within the plant. We also studied the first colonisation steps of three-day-old plants under the microscope and bacteria in culture. Azospirillum brasilense Cd was identified by immunogold or immunofluorescence labelling. In culture, this bacterium has a thick polar flagellum and many thin peritrichous flagella. In aged dark cultures, small cells appeared within cyst-like forms. The bacteria used the polar flagellum to adhere to the epidermis, root hair and root cap areas of tomato roots. They also penetrated the epidermis, root hairs and outer cortex cells. Within the root, A. brasilense formed cyst-like cells that did not divide inside and were rich in poly-β-hydroxybutyrate granules and glycogen. Bacteria within the root had a thick capsule, many granules of different types, and high levels of SOD activity, suggesting that they can fix nitrogen in the intercellular spaces of tomato roots
Immunocytochemical characterisation of endophytic bacteria Azospirillum brasilense, Herbaspirillum seropedicae, Burkholderia ambifaria and Gluconacetobacter diazotrophicus
No full textIn the present work an immunocytochemical characterisation of four endophytic bacterial species has been made by using polyclonal antiserum produced against each of the four bacterial strains previously heated at 60 degrees C. The aim of this research is to identify common elements among bacteria associated with their endophytic behaviour. Analysis of extracts of each strain by immunoblotting and ELISA confirmed the presence of proteins from different bacterial strains made up of common epitopes. However, antisera produced against Herbaspirillum seropedicae and Burkholderia ambifaria show a high number of bands recognised on each extracts, while antisera against Azospirillum brasilense and Gluconacetobacter diazotrophicus show a low number of bands recognised on each extract. Immunogold labelling showed that epitopes are located both on the cell wall and in the cytoplasm; most likely they could be precursor cell wall proteins synthesized inside the cytoplasm and subsequently transported onto cell wall. Finally, the common bands among bacterial strains revealed by immunoblotting could play a role as active hydrolases involved in host tissue penetration
Production of polysaccharides by Arthrobacter globiformis associated with Anabaena azollae in Azolla leaf cavity.
No full textSystematic Literature Review of Artificial Intelligence in production scheduling problems in real cases
No full textIndustry 4.0 has revolutionized the scheduling problem by providing real-time data availability, predictive maintenance capabilities, optimization algorithms, flexibility, integration of supply chain data, and collaborative platforms. These advancements empower schedulers to make data-driven decisions, adapt to changes more efficiently, optimize resource allocation, and improve overall scheduling effectiveness. As a result, companies can enhance their operational efficiency, reduce downtime, and gain a competitive edge in the market. Consequently, scheduling AI algorithms can play a significant role in enhancing a company competitiveness in today fast-paced business environment with their related benefits. In this context and for this purpose, the European project AIDEAS ‘Fabrication Optimiser’ tool wants to realise an AI-based scheduling tool. Therefore, the first step to its realisation was to define the state of the art. In literature, there are several contributions on using artificial intelligence to solve production scheduling problems. The first search was conducted on Scopus database selecting the years 2011 to 2022 and considering only publications in the field of engineering. Since Neural Networks and Particle Swarm Optimization are the most widely used strategy for solving such problems, this study aims at analysing how authors solve real production scheduling problems, in what context and what benefits they have obtained. The results of this study show how neural network and particle swarm optimization allow for solving different types of multi-objective and single-objective programming problems in dynamic production environments while generating benefits for the company according to their needs. © 2023, AIDI - Italian Association of Industrial Operations Professors. All rights reserved
A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment
Full text linkProduction problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a self-learning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied
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