Irrigated agriculture, water pricing and water savings in the Lower Jordan River Basin (in Jordan)

Farming systems / Irrigated farming / Water conservation / Groundwater / Water policy / Water rates / Water costs / Pricing / Cost recovery / Economic impact / Jordan / Lower Jordan River Basin / Jordan Valley / Amman-Zarqa Basin / Yarmouk Basin

Design and analysis of a hybrid timber-steel floating substructure for a 15 MW semisubmersible-type FWT

Wind energy has developed to be among the most promising sources of renewable energy. Furthermore, floating offshore wind turbines have presented the opportunity for higher power production in intermediate (45-150 m) and deep water (> 150 m). However, the manufacturing, installation, and operation of wind turbines in general, and floating wind turbines in particular, can result in significant amounts of greenhouse gas emissions (GHG). This thesis proposes a novel design of a hybrid timber-steel floating substructure for the IEA 15 MW floating wind turbine. The new design presents a modified version of the UMaine VolturnUS-S semisubmersible platform that was initially developed for the same turbine. The main objective of the new design is to reduce the turbine’s overall CO2 footprint. This objective is achieved by replacing structural steel with glued laminated timber, a more sustainable material known for its environmental benefits. Firstly, a robust design methodology is introduced. Secondly, Ansys workbench 2020 R1 is utilized to compare and then select between three preliminary hybrid timber-steel models based on a set of criteria that are extracted from relevant standards for both timber and steel. Compared to the UMaine VolturnUS-S semisubmersible platform, the selected hybrid configuration provides a considerable reduction in the steel mass (around 590 t). Subsequently, fully coupled aero-hydro-servo-elastic dynamic analysis is carried out using OpenFAST to validate the selected model. Only the ultimate limit state design (ULS) for the turbine under extreme and normal operating conditions is considered. The results from the numerical analysis show that the selected model fulfills all design criteria with a utilization factor that varies between 74- 94% for the different design load cases. In the end, the work concludes that the glulam-based supporting structure offers an effective load-bearing solution for the IEA 15 MW turbine, contributing to the development of floating wind energy with minimal cost and CO2 footprint. However, a series of tasks and suggestions are proposed to enhance the process of developing an optimal timber-steel design

Atomistic Thermo-mechanical Description of the Deformation Behavior, Scaling Laws, and Constitutive Modeling of Nanoporous Gold

Metallic foams, or nanoporous (NP) metals as it is widely referred to in literature, with ligament sizes up to a few tens of nm show exceptional mechanical properties such as high strength and stiffness per weight ratio under different loading scenarios due to their high surface area to solid volume ratio. Therefore, they can be utilized in a wide range of applications making them of great interest to researchers. While their elasticity and yield strength have been the subject of several studies, very limited attention was given to the effect of size, strain rate, and temperature on the material plastic response. Moreover, despite the significant attention in the literature that is directed towards the development of scaling laws that relate the properties of nanoporous metals to bulk materials, the literature still lacks a specific model that predicts the material mechanical properties based on a combination of parameters capturing the effect of surface area, ligament size, relative density, strain rate, and temperature. Therefore, the effect of ligament size, strain rate, and temperature are investigated using large-scale atomistic simulations to probe the elastic response, plastic response, and deformation mechanisms of nanoporous gold under uniaxial compression and tension and up to strains in excess of 60 percent for strain rates in the range of 106 ��−1 and 109 ��−1 at temperatures between 300K and 700K. This work explores the full range of the material response, focusing on the modifications to strain hardening and densification under compression and on the ductility and failing mechanisms under tension. Additionally, by utilizing the literature reported results, scaling laws that account for the effect of surface area to solid volume ratio, ligament size, relative density, strain rate, and temperature to predict the elastic modulus, yield stress, and ultimate stress are proposed. Finally, a size, relative density, strain rate, and temperature dependent dislocation based constitutive model that describes the plastic flow in NP-Au is proposed. The results reported in this work will eventually help enhancing the design of novel metallic foams with tailored mechanical response

Neural Networks for Flow Bottom Hole Pressure Prediction

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps.  However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells.  The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy

Bronchial Asthma and Salivary Surfactant Protein D: Review Article

Background: Chronic bronchial inflammation underlies asthma, which is a complex disease with varied and largely reversible blockage of the respiratory route. Asthma is a major public health issue that affects people of all ages around the world. Many countries are seeing an increase in the prevalence of this disease, particularly among children. Among children, asthma is the most frequent long-term condition, accounting for more than half of all missed school days, emergency room consultations, and hospitalizations. Surfactant Protein D, a pattern-recognition molecule, dampens elevated levels of particular antibodies, alveolar macrophage accumulation, eosinophilia, and subepithelial fibrosis and mucous metaplasia, as well as airway hyper-reactivity in allergic asthma in vivo. Objective: In order to discover the connection between children's bronchial asthma and surfactant protein D. Conclusion: Salivary SP-D is a simple, low-cost, quick, and noninvasive way to collect saliva from children. Salivary SP-D levels may be linked to asthma exacerbation severity and peripheral airway resistance

Determinants Of The Voluntary Formation Of A Company Audit Committee: Evidence From Palestine

Drawing on agency theory, this paper investigates the determinants of voluntary audit committee (AC) formation among non-bank firms listed on the Palestine Stock Exchange (PSE). We used the annual reports of nearly all non-bank companies listed on the PSE as well as the company guides issued by the PSE for the period between 2010 and 2012. Logistic regression analysis was performed to identify the influence of a set of corporate governance mechanisms, ownership structures and company characteristics on the voluntary formation of ACs among non-bank Palestinian firms. The result of the analysis demonstrated that the AC is influenced by foreign ownership, institutional ownership, board diligence and external auditor type. This paper adds to the limited AC literature in Middle Eastern countries in general and in the Arab World in particular. This paper not only examines the determinants of the voluntary formation of ACs but also attempts to theorise about this formation

Multifunctional Nanoparticles for Imaging Guided Interventions

We describe multifunctional magnetic nanoparticles (MNPs) encapsulated in thermosensitive, drug-bearing shells and delivered to the tumor site by genetically modified and non-pathogenic strains of bacteria with known affinity to tumors for an effective and minimally invasive protocol for tumor management. The magnetic nanoparticles also serve as a non-invasive imaging contrast agent, heating agent as well as thermometry monitoring agents. We have shown an efficient tumor management on a mouse model utilizing the MNPs. Our studies showed that these novel MNPs significantly reduce the progress of tumor and prolong the animal life and function as an imaging contrast to visually monitor the tumor treatment and evolution

Does Corporate Governance Constrain Earnings Management in an Unstable Economic and Political Environment?

The purpose of this research is to examine the effects of corporate governance structures on earnings management behavior in a weakly governed and politically unstable environment. A panel of data from 35 non-bank companies listed on the Palestine Exchange between 2012 and 2019 was employed. A fixed effects regression model was used to examine the impact of certain board characteristics (board size, board meetings, and audit committee formation) and ownership structures (institutional ownership, foreign ownership, and ownership concentration) on earnings management in the volatile and risky political and economic environment of Palestine. The findings indicate that corporate governance and ownership systems in Palestine appear to be ineffective in constraining earnings management practices. None of the board attributes appear to constrain earnings management practices. However, there is weak evidence to show that ownership concentration has some effect in curbing earnings manipulation. The findings of this study are expected to increase awareness among Palestinian regulators, investors, and other policymakers regarding the role of boards of directors and institutional and foreign shareholders in monitoring Palestinian listed companies to enhance corporate governance and the quality of financial reporting