204 research outputs found
Awada (Hassan) Le Liban et le flux islamiste
No full textAwada (Hassan) Le Liban et le flux islamiste. In: Archives de sciences sociales des religions, n°67/2, 1989. p. 212
Awada (Hassan) Le Liban et le flux islamiste
No full textAwada (Hassan) Le Liban et le flux islamiste. In: Archives de sciences sociales des religions, n°67/2, 1989. p. 212
Project planning and scheduling : an application of project scheduling on AUB’s registration process - by Ghinwa Hassan Awada.
No full textProject (M.B.A.)--American University of Beirut, Suliman S. Olayan School of Business, 2011.;"First Reader : Dr. Tony Feghali, Assistant Professor, Suliman S. Olayan School of Business Second Reader : Dr. Ibrahim Osman, Professor, Suliman S. Olayan SchooIncludes bibliographical references (leaves 75-77)The importance of administrative services in universities has become an important aspect for researchers and administration in terms of increasing enrollment, retaining students, and beating the competition. The registration process is one of the major p
Forested Infiltration Areas as a Natural Solution for Nitrate Mitigation in Contaminated Water
No full textA Managed Aquifer Recharge (MAR) approach is being utilized through a Forested Infiltration Area (FIA) to mitigate nitrate contamination in an aquifer situated within a Nitrate Vulnerable Zone. This innovative technique is deployed at a pilot site where the groundwater nitrate contents is higher than the threshold of regulatory limits defined by the EU Directive 91/676/EEC. Drainage water, sourced from a nearby dewatering pumping station and containing an average nitrate concentration of 70 mg L−1, is channeled into the area. The infiltration process occurs through six recharge trenches, totaling 300 m in length and one meter in depth, filled with a mixture of eucalyptus wood chips (50% by volume), inert material, clay and iron oxides. This blend termed the “Passive Treatment System” (PTS), enhances the activity of denitrifying bacteria that transform nitrate (NO3−) into nitrogen gas (N2). The objective is to lower nitrate levels in the drainage water entering the trenches and subsequently use this treated water to recharge and dilute the polluted underlying aquifer. Hydrogeochemical monitoring results indicate a notable decrease in NO3− in the infiltrated water, demonstrating that the FIA method can be an effective Nature-Based Solution for the remediation of nitrate-contaminated water
Rainfall variability and drought in West Africa: challenges and implications for rainfed agriculture
No full textThis research investigates rainfall variability and drought patterns in West Africa and their consequential impacts on rainfed agriculture, with a particular focus on vulnerability linked to weather extremes Utilizing NASA POWER/Agro-climatology data, cross-validated against observed meteorological records in the targeted countries, this study spans the years 1981 to 2021, with a particular focus on Ghana and Burkina Faso. The Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and different statistical methods were employed to evaluate the variations in rainfall, including intensity and frequency, as well as analyze drought patterns in the study areas. Despite increased rainfall in the last decade, seasonal and decadal shifts have been noticed, and drought and irregular patterns still threaten the study areas. Temporal analysis reveals fluctuations in temperature and rainfall. SPI and SPEI results indicated a decline in drought frequency, aligned with global trends, though the monthly scale showed no evident decline. The spatial analysis highlights regional variations in rainfall and drought dynamics. The study emphasizes the importance of region-specific mitigation and adaptation strategies, providing valuable insights for informed decision-making in West Africa's agriculture and water resource management under climate change. The findings underscore the continued threat of irregular rainfall patterns and drought, emphasizing the need for tailored approaches to address these challenge
Modelling soil moisture and daily actual evapotranspiration: Integrating remote sensing surface energy balance and 1D Richards equation
No full textEvapotranspiration (ET) is a crucial component of the soil - plant-atmosphere system. In semi -arid Mediterranean regions, most land water loss occurs through ET, encompassing both evaporation from the earth ' s surface and plant transpiration. A comprehensive understanding of the actual ET spatiotemporal dynamics is critically important for hydrological modelling and effective water resource management. This significance is further pronounced considering the growing stress on water resources and the potential influence of climate change on water fluxes. Remote sensing (RS) provides long-term, high-resolution data that can contribute to the monitoring and management of natural ecosystems. Surface energy balance (SEB) methods relying on satellite remote sensing have proven effective in measuring actual evapotranspiration (ET a eb ) across different scales. However, their applicability may be constrained by interruptions in image acquisition caused by cloud cover and/or the spatio-temporal resolution limitations of satellites. In this research, a model-based methodology is suggested for simulating the dynamics of the soil - plant-atmosphere system and for estimating the daily actual evapotranspiration (ET p act ) of a Mediterranean Maquis ecosystem in northwest Sardinia. The model integrates ET a eb estimates obtained from the SEBAL model utilizing Landsat-8 data, satellite-derived vegetation indices, on-site measurements of potential evapotranspiration, and the mono-dimensional transient flow Richards equation for simulating soil moisture within the root zone. By combining these elements, the proposed model provides a more comprehensive and accurate estimate of the ET p act between Landsat acquisitions. The SEBAL model showed satisfactory performance in estimating actual evapotranspiration (ET a eb ) on satellite acquisition days, with an average error of 17 % compared to Eddy Covariance measurements. In addition, the integrated modelling approach yielded an ET p act average estimation error of +/- 37 % in the whole studied period. The soil moisture simulation by the model had a notable accuracy with an average error of 7.1 %. Temporal analysis showed that the model effectively simulated ET p act and soil moisture under both dry and wet conditions, exhibiting similar monthly and daily variations as observed data. Furthermore, a sensitivity analysis revealed that the stress index significantly improved the model ' s accuracy, while vegetation dynamics had a lower impact. Overall, the proposed model is a valuable tool for estimating ET p act in semi -arid Mediterranean regions, providing important information for water resource management and conservation efforts. Further application and validation of the model are recommended as new data becomes accessible, especially in areas characterized by cropped and irrigated agriculture. In future work, we aim to spatialize the Richards equation and integrate a multidimensional water balance hydrologic model
Assessing the performance of a large-scale irrigation system by estimations of actual evapotranspiration obtained by Landsat satellite images resampled with cubic convolution
No full textRemote sensing techniques allow monitoring the Earth surface and acquiring worthwhile information that can be used efficiently in agro-hydrological systems. Satellite images associated to computational models represent reliable resources to estimate actual evapotranspiration fluxes, ET a , based on surface energy balance. The knowledge of ET a and its spatial distribution is crucial for a broad range of applications at different scales, from fields to large irrigation districts. In single plots and/or in irrigation districts, linking water volumes delivered to the plots with the estimations of remote sensed ET a can have a great potential to develop new cost-effective indicators of irrigation performance, as well as to increase water use efficiency. With the aim to assess the irrigation system performance and the opportunities to save irrigation water resources at the “SAT Llano Verde” district in Albacete, Castilla-La Mancha (Spain), the Surface Energy Balance Algorithm for Land (SEBAL) was applied on cloud-free Landsat 5 Thematic Mapper (TM) images, processed by cubic convolution resampling method, for three irrigation seasons (May to September 2006, 2007 and 2008). The model allowed quantifying instantaneous, daily, monthly and seasonal ET a over the irrigation district. The comparison between monthly irrigation volumes distributed by each hydrant and the corresponding spatially averaged ET a , obtained by assuming an overall efficiency of irrigation network equal to 85%, allowed the assessment of the irrigation system performance for the area served by each hydrant, as well as for the whole irrigation district. It was observed that in all the investigated years, irrigation volumes applied monthly by farmers resulted generally higher than the corresponding evapotranspiration fluxes retrieved by SEBAL, with the exception of May, in which abundant rainfall occurred. When considering the entire irrigation seasons, it was demonstrated that a considerable amount of water could have been saved in the district, respectively equal to 26.2, 28.0 and 16.4% of the total water consumption evaluated in the three years
Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes
No full textMyelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients’ molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research
Satellite-Based Machine Learning for Soil Moisture Prediction and Land Conservation Practice Assessment in West African Drylands
No full textEssential Thrombocythemia and Acquired von Willebrand Syndrome: The Shadowlands between Thrombosis and Bleeding
No full textOver the past decade, new insights have emerged on the pathophysiology of essential thrombocythemia (ET), its clinical management, and associated thrombohemostatic disturbances. Here, we review the latest diagnostic and risk stratification modalities of ET and its therapeutics. Moreover, we discuss the clinical evidence-based benefits, deriving from major clinical trials, of using cytoreductive therapy and antiplatelet agents to lower the risk of fatal vascular events. Also, we focus on the condition of extreme thrombocytosis (>1000 × 109/L) and bleeding risk, the development and pathogenesis of acquired von Willebrand syndrome, and the clinical approach to this paradoxical scenario in ET
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