| 1. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
- 2024
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Ingår i: Potato Research. - : Springer Nature. - 0014-3065 .- 1871-4528.
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Tidskriftsartikel (refereegranskat)abstract
- Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
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| 2. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Winter Potato Water Footprint Response to Climate Change in Egypt
- 2022
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Ingår i: Atmosphere. - : MDPI. - 2073-4433 .- 2073-4433. ; 13:7
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Tidskriftsartikel (refereegranskat)abstract
- The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity.
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| 3. |
- Ebeed, Mohamed, et al.
(författare)
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Solving stochastic optimal reactive power dispatch using an Adaptive Beluga Whale optimization considering uncertainties of renewable energy resources and the load growth
- 2024
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Ingår i: Ain Shams Engineering Journal. - : ELSEVIER. - 2090-4479 .- 2090-4495. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- The electrical system performance can be improved considerably by controlling the reactive power flow in the system. The reactive power control can be achieved by optimal reactive power dispatch (ORPD) problem solution and optimal integration of the FACTS devices. With high penetration of renewable energy sources (RESs) and the load growth, the ORPD solution became a challenging and a complex task due to the stochastic nature of the RERs and the load growth. In this regard, the aim of this paper is to solve the stochastic optimal reactive power dispatch (SORPD) with optimal inclusion of PV units, wind turbines and the unified power flow controller (UPFC) under uncertainties of the load growth and the generated powers. An Adaptive Beluga Whale Optimization (ABWO) is proposed for solving the SORPD which is based on the Fitness-Distance Balance Selection (FDBS) strategy and the territorial solitary males' strategy of the Mountain Gazelle Optimizer. The proposed ABWO is tested on IEEE 30-bus system and a comparison with other optimization techniques for solving the ordinary ORPD is presented for validating the proposed ABWO. The obtained results reveal that the TEPL is reduced from 5.3168 MW to 3.97985 MW with optimal integration of the RERs and UPFC. Likewise, the TEVD is reduced from 0.1794p.u. to 0.10689p.u. and the TVSI is decreased from 0.1289p.u. to 0.0476p.u.
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| 4. |
- Henning, Petra, 1974, et al.
(författare)
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Toll-like receptor-2 induced inflammation causes local bone formation and activates canonical Wnt signaling.
- 2024
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Ingår i: Frontiers in immunology. - : Frontiers Media S.A.. - 1664-3224. ; 15:5
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Tidskriftsartikel (refereegranskat)abstract
- It is well established that inflammatory processes in the vicinity of bone often induce osteoclast formation and bone resorption. Effects of inflammatory processes on bone formation are less studied. Therefore, we investigated the effect of locally induced inflammation on bone formation. Toll-like receptor (TLR) 2 agonists LPS from Porphyromonas gingivalis and PAM2 were injected once subcutaneously above mouse calvarial bones. After five days, both agonists induced bone formation mainly at endocranial surfaces. The injection resulted in progressively increased calvarial thickness during 21 days. Excessive new bone formation was mainly observed separated from bone resorption cavities. Anti-RANKL did not affect the increase of bone formation. Inflammation caused increased bone formation rate due to increased mineralizing surfaces as assessed by dynamic histomorphometry. In areas close to new bone formation, an abundance of proliferating cells was observed as well as cells robustly stained for Runx2 and alkaline phosphatase. PAM2 increased the mRNA expression of Lrp5, Lrp6 and Wnt7b, and decreased the expression of Sost and Dkk1. In situ hybridization demonstrated decreased Sost mRNA expression in osteocytes present in old bone. An abundance of cells expressed Wnt7b in Runx2-positive osteoblasts and ß-catenin in areas with new bone formation. These data demonstrate that inflammation, not only induces osteoclastogenesis, but also locally activates canonical WNT signaling and stimulates new bone formation independent on bone resorption.
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