Floristic analysis and biogeography of Tubiflorae in Egypt

The species distribution and biogeography of the Egyptian Tubiflorae were exam-ined in detail. We found 284 species of vascular plants belonging to 96 genera and 12 families, making the Egyptian Tubiflorae richer in species than that of other arid region floras: Libya and Saudi Arabia. The most species rich families were Scrophulariaceae, Boraginaceae, Labiatae, Convolvulaceae and Solanaceae, constituting more than 85% of the totál species in the order. The generic spectrum dominated by a suite of species-rich genera (Convolvulus, Heliotropium, Veronica, Solanum, Salvia, Cuscuta, Echium, Ipomoea and Orobanche). Therophytes were the most dominant life forms among the families, followed by chamaephytes and hemicryptophytes. Boraginaceae and Scrophulariaceae had the highest share of annuals. Remarkable distribution patterns of the life forms in the seven studied biogeographic zones were noticed. Trees were dominant in the Mediterranean zone, while shrubs, perennial herbs and therophytes were dominant in the Sinai. Altogether 8 endemic species and 14 near-endemics were included in the Tubiflorae of Egypt; mostly from Southern Sinai. We found that Labiatae and Scrophulariaceae were the families with higher concentration of endemics. Notably, Teucrium was among the genera of the Mediterranean Africa with highest endemism. Gamma diversity varied from 171 in the Sinai Peninsula to 43 and 39 in the Oases of the western Desert and along the Red Sea, respectively. Interestingly, highest significant values of similarity and species turnover (béta diversity) were observed between the Oases and the Nile lands. It is worthy noting the com-bined effect of both temperature and precipitation on gamma diversity of Tubiflorae in the 7 biogeographic zones. Our results indicated that almost one-half of the species showed a certain degree of consistency, i.e., with narrow geographic expansion. On the basis of UPGMA clustering and PCoA analysis, 4 floristic groups were recognized, each include one or more biogeographic zone. The occurrence of the species of Tubiflorae in the adjacent régiónál arid floras and their phytochorological afflnities, were discussed

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods