Biotechnology harbors stunning potential to provide cutting-edge solutions that can mitigate arising environmental issues which impact developing countries more severely. Although considerable projects have been implemented in these developing nations to reduce the consequences of climate change, global warming, and food insecurity, most of the initiatives are deemed unsustainable or unfulfilling. Consequently, millions of individuals are still suffering from unhealthy environments and others have limited access to clean technologies. Accordingly, this manuscript is developed to act as a one-stop source for technical perspectives of microalgae cultivation and proposes the potential of scaling down of a photobioreactor (PBR) from industrial to household level to alleviate adverse environmental implications. The household PBR proposal is concerned with microalgae cultivation that contributes to mitigate CO2 from the surrounding environment and synthesize a product that could be of high nutritional value. Additionally, the business model of household PBR is developed in accordance to low and middle-income countries’ demands to facilitate future projects in this scope. The value proposition of this model relies on decreasing climate change impacts, enhancing wellbeing, and providing natural supplements. Scale of economy, appropriate technology, and socio-economic challenges for household PBR are highlighted.
In the present research, the impact of exposing Scenedesmus ecornis, S. communis, and Chlamydomonas sp. cultures to doses of gamma irradiation on their growth and productivity of lipids was investigated. Biomass concentration (g/L) of each microalga was periodically determined after exposure to a range of gamma irradiation doses of 0, 25, 50, 75, 100, 200, and 300 Gray (Gy) through 20 days of cultivation. Subsequently, the lipid content (%), and lipid productivity (mg/L day) of each species were evaluated. Results showed that the S. ecornis growth was positively affected by gamma irradiation, that the maximum concentration of biomass was obtained after 15 days at 1.3 g/L by the irradiated S. ecornis exposed to a dose of 300 Gy, while the non˗irradiated culture achieved up to 1.1 g/L. On the other hand, the growths of Chlamydomonas sp. and S. communis were reduced significantly by the radiation treatment. Significant variations have been also observed in the content of lipid and lipid productivity of each microalga. Irradiated S. ecornis at a dose of 300 Gy exhibited the highest content of lipid and lipid productivity to reach 28.4% and 24.9 mg/L day, respectively. Conversely, the best yields of lipid content and lipid productivity were achieved by the non˗irradiated culture of S. communis (24.4% and 16.6 mg/L day, respectively), compared to irradiated culture, regardless of the irradiation dose. The highest lipid content and lipid productivity gained by Chlamydomonas sp. were obtained by the cultures exposed to 25 Gy, being 27.3% and 21.3 mg/L day, respectively. In conclu-sion, results indicated that exposing cells of S. ecornis and Chlamydomonas sp. to specific doses of gamma˗rays has significantly stimulated lipid accumulation into cells, unlike S. communis which was negatively affected by gamma irradiation.
Training of multilayered feed-forward neural networks (MLFFNNs) is considered in this work. A procedure to derive high performance learning algorithm for updating the network weights is proposed. The proposed algorithm is based on heuristic technique that is developed from an analysis of the performance of the basicbackpropagation training algorithm. A unified formulation of the conventional learning algorithms including the basic-backpropagation algorithm, the momentum algorithm, and the exponential-smoothing algorithm alongside with the proposed learning algorithm is introduced. Recursive relations for updating the weights of the network are derived which greatly simplifies the application of these rules. Simulation results are presented and comparative studies are carried out to demonstrate the effectiveness of the new learning algorithm. The new algorithm can converge more than hundred times faster than the conventional algorithms.
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