The objective of this article is to study the impact of environmental pollution on air, water, and soil quality with a focus on the role of environmental bacteria in bioremediation of pollutants. The research also addresses the ability of some strains of bacteria to remove heavy metals and petroleum hydrocarbons and degrade toxic substances, resulting in improved environmental quality. Outcomes: Empirical studies reveal that environmental pollution leads to significant health and environmental problems, such as a rise in respiratory disease as a result of air pollution, water pollution that affects aquatic life, and soil pollution that decreases crop output. Other bacterial strains such as Pseudomonas, Bacillus, and Streptomyces have also been shown to have an enormous capacity to biodegrade harmful organic toxicants and adsorb heavy metals such as lead, cadmium, and mercury. Bioremediation has also been proven useful in improving water and land contamination quality and thereby constituting a safe green technology option to regular chemical technology. Conclusions: The research indicates that environmental bacteria bioremediation is a sustainable and efficient means of reducing environmental pollution. It is possible to improve these practices through genetic engineering to make bacteria more efficient in removing pollutants. Research and technology support in this field is also required to make applications of bacteria in programs of environmental remediation, resulting in a cleaner and greene
This study investigated the bioethanol production from green algae Chlorella vulgaris depending on its carbohydrate-enriched biomass. Four different phosphorous concentrations were employed to stimulate bioethanol production from Chlorella vulgaris. The impact of various phosphorous values on Chlorella vulgaris growth rate as well as primary product (carbohydrate) were evaluated. High performance liquid chromatography was utilized in this work. The stationary phase was identified as day 14, 12, 10 and 6 in treatments 6, 4, 2 and g/L, respectively. The findings suggest that the treatment without phosphorous addition had the highest record of carbohydrate content (22.64% dry weight) as well as the highest bioethanol yield (20.66% dry weight).
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The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of
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The present research was conducted to reduce the sulfur content of Iraqi heavy naphtha by adsorption using different metals oxides over Y-Zeolite. The Y-Zeolite was synthesized by a sol-gel technique. The average size of zeolite was 92.39 nm, surface area 558 m2/g, and pore volume 0.231 cm3/g. The metals of nickel, zinc, and copper were dispersed by an impregnation method to prepare Ni/HY, Zn/HY, Cu/HY, and Ni + Zn /HY catalysts for desulfurization. The adsorptive desulfurization was carried out in a batch mode at different operating conditions such as mixing time (10,15,30,60, and 600 min) and catalyst dosage (0.2,0.4,0.6,0.8,1, and 1.2 g). The most of the sulfur compounds were removed at 10 min for all catalyst types. The maximum
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The temperature control process of electric heating furnace (EHF) systems is a quite difficult and changeable task owing to non-linearity, time delay, time-varying parameters, and the harsh environment of the furnace. In this paper, a robust temperature control scheme for an EHF system is developed using an adaptive active disturbance rejection control (AADRC) technique with a continuous sliding-mode based component. First, a comprehensive dynamic model is established by using convection laws, in which the EHF systems can be characterized as an uncertain second order system. Second, an adaptive extended state observer (AESO) is utilized to estimate the states of the EHF system and total disturbances, in which the observer gains are updated
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