Open Access

El-Salam Canal Project aims at increasing the Egyptian agricultural productivity through agricultural and stock development by irrigating about 263,500 ha gross of new lands. In order to stretch the limited water supply to cover these reclaimed areas, fresh River Nile water is augmented with agriculture drainage water from Hadus and Lower Serw drains to meet crop requirements, especially during summer months (peak demand). With a growing population and intensified industrial and agricultural activities, water pollution is spreading in Egypt, especially in main drains, which receive almost all kinds of wastes (municipal, rural, domestic and industrial wastes). The medical records indicate that significant numbers of waterborne-disease cases (bilharzias, typhoid, paratyphoid, diarrhoea, hepatitis A, B and C) have been reported in many areas in Egypt (MOHP, 2000). The National Water Quality Monitoring Program (NWQMP) in Egypt covers the Nile River, irrigation canals, drains and groundwater aquifers to assess the status of water quality for different water uses and users. The overall objective of this research is to introduce a rationalization technique for the drainage water quality-monitoring network for Hadus drain as a main feeder of El-Salam Canal Project. Later on, this technique can be applied for other parts in the NWQMP. The rationalization process started firstly with assessing and reformulating the current objectives of the network. Then, the monitoring locations were identified using integrated logical and statistical approaches. Finally, a sampling frequency regime was recommended to facilitate proper and integrated information management. The monitoring objectives were classified into three classes: design oriented, short-term and long-term deductible objectives. Mainly, the objectives “assess compliance with standards”, “define water quality problems”, “determine fate and transport of pollutants”, “make waste-load allocations” and “detect possible trends” were considered in the redesign process of the network. A combination of uni-, bi-, and multi-variate statistical techniques supported by spatial and temporal analysis for the important tributaries (key players) in Hadus drain system, were used for locating the monitoring sites. The key players analysis was carried out in the light of monitoring objectives. As a result, the monitoring network was divided into three priority levels (Layers I, II and III) as following: Layer I: It has the highest priority level and includes eight monitoring locations Layer II: It has the second priority level and includes three monitoring locations Layer III: It has the lowest priority level and includes five monitoring locations Using the method proposed by Lettenmaier (1976), the sampling frequencies were initially estimated and then evaluated for 36 water quality parameters, which were collected on monthly basis during the period from August 1997 to January 2005. The evaluation process was carried out by generating new data sets (subsets) from the original data. Then, the common required statistics from the monitoring network were extracted. The information obtained from different data sets was assessed using visual and statistical comparisons. Three integrated validation methods were employed to ensure that any decisions concerning the proposed program would not affect its ability to accomplish the monitoring objectives. These validation methods employed: descriptive statistics, regression analysis and linear multiple regression in an integrated approach. The validation results ensured that excluding the monitoring locations in layer III did not significantly affect the information produced by the monitoring network. Therefore, a monitoring network including only 11 sites (out of 16) representing the layers I and II was recommended. Based on the evaluation of sampling frequencies, it is recommended to have 6 (instead of 12) samples per year for 18 water quality parameters (COD, TSS, TVS, N-NO3, Pb, Ca, Na, Cl, Visib, BOD, Cu, Fe, Mn, pH, TDS, K, SO4_m and DO). The measured parameter SO4m will automatically replace the SO4 (calculated). SAR and Adj. SAR also can be calculated from the other parameters. For the other fifteen parameters (Mg, EC, Br, Ni, Sal, Cd, TN, TP, Temp, Fecal, Coli and N-NH4, Zn, P and Turb), it is recommended to continue with twelve samples per year. These recommendations may ensure significant reduction in the total cost of the monitoring network. This facilitates a fiscal resource, which is a key prerequisite in developing a successful program. The rescued budget can be redirected to achieve better performance in terms of improving the current resources. In addition, a frame of stakeholders-participation mechanism was proposed to not only facilitate a better coordination among the Egyptian Ministries involved in the water sector but also guarantee effective landowners/farmers involvement. However, applying such a mechanism requires more detailed studies of all the previous experiences gained by many projects trying to achieve better integration between objectives, plans and activities for the different environmental institutions in Egypt.

Personally meaningful tourist experiences foster subjective mental wellbeing. Modern, human-centred technologies such as gamified technology have been recognised as a promising means to support tourists in their co-creation of meaningful tourist experiences. However, a deeper understanding and conceptualisation of tourists’ engagement with gamified technologies in the tourist experience has remained absent so far. This study draws on positive psychology as the guiding theoretical lens to conceptualise and explore tourists’ underlying motives for engaging with gamified technology, as well as the gratifications thereof for the tourist experience. In doing so, this thesis identifies how tourists generate meaning through interacting with gamified technology in the tourist experience, thereby fostering the co-creation of meaningful tourist experiences and contributing to subjective mental wellbeing. Being among the first studies to link the concepts of positive psychology, gamified technology, and tourist experiences, the results of this thesis provide rich findings on the underlying motives for tourists to engage with gamified technology during vacation, as well as the gratifications of gamified technology for the creation of meaning in the tourist experience. Using the theoretical lens of positive psychology and achievement motivation theory as the main theoretical underpinning, this study is positioned at the intersection of social psychology, human-computer interaction, and tourism as the field of application. Conceptually, this thesis provides an in-depth understanding of tourists’ engagement with gamified technology, including the socio-psychological motivators for engagement and the outcomes thereof for the tourist experience. This thesis contributes to the theoretical advancement of two principal streams: a) tourists’ motives for engaging with gamified technologies and the gratifications thereof in the vacation context and b) the understanding of motivational affordances of gamified technology in general. The substantial theoretical contribution of this study is the advancement of knowledge as it demonstrates the value of gamified technologies in the tourist experience. The findings eventually provide tourism destinations with nuanced insights into the feature-specific values of gamified technology to contribute to the co-creation of meaningful tourist experiences.

The Authority, through its planners, implements its housing and urbanizing policy at the macro level by preparing the master plan and zoning processes. How far can the Authority intervene and limit the bundle of rights of the private ownership tenure? And how can externalities be minimized in the process of transformation from the traditional quarter to the new settlement? Political values, whether tacit or explicit, are manifested in the resultant designs. A theoretical base for new urban planning guidelines involving a comprehensive study of housing in Damascus with emphasis on social and cultural factors is the ultimate aim of this research. The research starts with a historical review of the Muslim City in general and distinguishes between cities that existed before Islam and then were conquered and modified by Muslims and cities established by Muslim Authority. The focus is only on the residential quarters in the city and the local market, mosque and etc (outside the old walled city of Damascus). Other Muslim city´ urban elements such as Grand mosque, caliph´s residency, Citadel and etc. are not in the scope of this study. A brief historical review of Damascus before and during Islam and the development of residential quarters are illustrated. Later, the study analyzes the traditional residential quarter and explores the building guidelines that governed the evolution of the built form of the quarter. (Literature reviews based mainly on researches conducted by others). Then, the study explores the multi-faceted changes (economic, social and political) that the Middle Eastern region went through, in the last century, in general and the effect of those changes on the city form, case of Damascus. The effect will be traced through examining the decrees that the Authority issued in order to govern land reform and manage public and private domains. Then, the study looks at the ramifications of those decrees on the urban form of Damascus. It also investigates the decrees that were the guide for new planning and organizing developments. The study will inspect the end products of the planning and organizing process by studying several cases of building permits. Then, provides morphology of the new residential sub-quarter and its urban form. Based on lessons learned from the previous decades of housing policy, the study will recommend foundation for governmental norms to produce responsive physical and social urban forms.

The emission of anthropogenic trace substances into the aquatic environment continuously poses challenges to water suppliers. The contamination of raw waters with organic trace substances requires complex water treatment processes to secure drinking water quality. The routine monitoring of these raw waters as well as the behavior and fate of organic trace substances during different treatment processes is of great interest to recognize and counter potential dangers at an early stage. Chromatographic separation techniques coupled to triple quadrupole mass spectrometers are conventionally used for the reliable monitoring of traces of known polar substances. However, such analytical techniques usually fail to recognize unknown compounds. This weakness presents a serious restriction with regard to the monitoring of treatment processes, since transformation products are often not - or not sufficiently - characterized and are thus only detected sporadically. Non-target screening using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allows the detection of thousands of compounds within a single run and covers known as well as unknown substances. Compared to the established analytical techniques, this is a decisive advantage for the monitoring of raw and process waters during water treatment. While the analytical technique LC-HRMS has undergone significant developments in recent years, the algorithms for data processing reveal clear weaknesses. This dissertation therefore deals with reliable processing strategies for LC-HRMS data. The first part of this work seeks to highlight the problematics of false positive and false negative findings. Based on repeated measurements, various strategies of data processing were assessed with regard to the repeatability of the results. To ensure that real peaks were barely or not removed by the filtering procedure, samples were spiked with isotope-labeled standards. The results emphasize that the processing of sample triplicates results in sufficient repeatability and that the signal fluctuation across the triplicates emerged as a powerful filtering criteria. The number of false positives and false negatives could be significantly reduced by the developed strategies which consequently improve the validity of the data. The second part of this thesis addresses the development of processing strategies particularly aimed at assessing water treatment processes. The detected signals were tracked across the treatment process and classified based on their fold changes. A more reliable signal classification was achieved by implementing a recursive integration approach. Special integration algorithms allow a reliable signal classification even though the signal to be compared was below the intensity threshold. Different combinations of replicates of process influents and effluents were processed for evaluating the repeatability. The good repeatability was indicated by the results of both the plausibility checks and the ozonation process (ozonation of pretreated river water) and thus points to high reliability. The developed procedure enables the assessment of water treatment processes based on the changes in the pattern of all detected signals and offers a more comprehensive picture of the treatment efficiency. Particularly with regard to transformation products, existing knowledge gaps can be reduced by this approach, albeit the entire variety of chemicals cannot be covered completely. The applicability of the developed strategies to real world applications is demonstrated in the last part of this work. Besides the prioritization of the generated results, the main focus was the identification of recognized compounds. The developed strategies clearly improve the validity of the underlying data. The combination of LC-HRMS analysis with reliable processing strategies opens up multiple possibilities for a more comprehensive monitoring of water resources and for the assessment of water treatment processes. The processing strategies and validation concepts may be easily transferred to other research fields.

The wide accessibility of the Internet and web-based programs enable an increased volume of online interventions for mental health treatment. In contrast to traditional face-to-face therapy, online treatment has the potential to overcome some of the barriers such as improved geographical accessibility, individual time planning, and reduced costs. The availability of clients’ treatment data fuels research to analyze the collected data to obtain a better understanding of the relationship among symptoms in mental disorders and derive outcome and symptom predictions. This research leads to predictive models that can be integrated into the online treatment process to assist clinicians and clients. This dissertation discusses different aspects of the development of predictive modeling in online treatment: Categorization of predictive models, data analyses for predictive purposes, and model evaluation. Specifically, the categorization of predictive models and barriers against the uptake of mental health treatment are discussed in the first part of this dissertation. Data analysis and predictive modeling are emphasized in the second part by presenting methods for inference and prediction of mood as well as the prediction of treatment outcome and costs. Prediction of future and current mood can be beneficial in many aspects. Inference of users’ mood levels based on unobtrusive measures or diary data can provide crucial information for intervention scheduling. Prediction of future mood can be used to assess clients’ response to the treatment and expected treatment outcome. Prediction of the expected treatment costs and outcomes for different treatment types allows simultaneous optimization of these objectives and to increase the cost-effectiveness of the treatment. In the third part, a systematic predictive model evaluation incorporating simulation analyses is demonstrated and a method for model parameter estimation for computationally limited devices is presented. This dissertation aims to overcome the current challenges of predictive model development and its use in online treatment. The development of predictive models for varies data collected in online treatment is demonstrated and how these models can be applied in practice. The derived results contribute to computer science and mental health research with client individual data analysis, the development ofpredictive models, and their statistical evaluation.