SOFT X-RAY SPECTROSCOPY STUDY of WIDE BAND GAP SEMICONDUCTORS-M2PN3 (M=Mg, Zn) and BP3N6

The research on nitridophosphate materials has gained significant attention in recent years due to the abundance of elements like Mg, Zn, P, and N. The compound semiconductors comprising of these earth-abundant elements are important in terms of manufacturing cost of electronic devices and no detrimental impact on the environment. Here in this thesis, a detailed study of band gap and electronic structure of M2PN3 (M=Mg, Zn) and BP3N6 is presented using synchrotron-based soft X-ray spectroscopy measurements as well as Density Functional Theory (DFT) calculations. The experimental N K-edge X-ray emission spectroscopy (XES) and X-ray absorption spectroscopy (XAS) spectra are used to estimate the band gaps, which are compared with calculations along with the values available in literature. The band gap, which is essential for electronic device applications, is experimentally determined for the first time to be 5.3 ± 0.2 eV, 4.2 ± 0.2 eV, and 5.3 ± 0.2 for Mg2PN3, Zn2PN3, and BP3N6, respectively. The experimental band gaps agree well with the calculated band gaps of 5.4 eV for Mg2PN3, 3.9 eV for Zn2PN3, and 5.8 eV for BP3N6 using the modified Becke-Johnson (mBJ) exchange potential. The states that contribute to the band gap are investigated with the calculated density of states especially with respect to the non-equivalent N sites in the structure. The calculations and the measurements predict that all the three materials studied here in this thesis, have an indirect band gap. The wide band gap of M2PN3 (M=Mg, Zn) and BP3N6 could make it promising for the application in photovoltaic cells, high power RF applications, as well as power electronic devices

Action-oriented research for master’s level dissertations: an opportunity to improve practice

As academic staff become familiar with the qualifications framework of the UAE and work towards demonstrating alignment between learning outcomes, teaching/learning strategies and assessment, this is an opportune time to revisit traditional masters’ dissertation guidelines and methodologies to ensure students demonstrate mastery of a topic via achievement of a capstone project. Such a project is an opportunity to demonstrate both a contribution to knowledge and an improvement in practice. This discussion document takes the view that formal master’s level programs can miss this opportunity to use rich experiences of working professionals to produce knowledge and learning. An action-oriented approach to research is proposed as contributing to level 9 learning by its capability of generating practical learning and producing actionable knowledge. One example is outlined of a successful action-oriented project in the UAE, which formed part of a master’s program

Computerised prediction of the deterioration of concrete building facades caused by moisture and changes in temperature

Environmental conditions, mainly temperature and relative humidity, are the major factors in the physical, chemical, and biological deterioration of concrete building facades. The purpose of this thesis is to investigate the effect of the environmental conditions on the deterioration of concrete building facades, develop a system for monitoring these conditions, and predict the deterioration and remaining service life of these facades on the basis of the monitoring data. The thesis consists of a literature review, laboratory and field investigation, the development of a hygrothermal monitoring and deterioration predicting system, and its application to concrete building facades. The literature review covered the concrete deterioration caused by moisture and changes in temperature. Special attention was given to the corrosion of steel reinforcement as a result of carbonation, damage caused by repeated freezing and thawing, and mould growth. The laboratory investigation and field monitoring of relative humidity and temperature were carried out to assess the impact of environmental factors on the performance of the structure under concern. The laboratory and field measurement data were used to validate the concrete deterioration prediction models. The results confirm that the hygrothermal field monitoring is significant for: (i) collecting real relative humidity and temperature data within the concrete facades and the surrounding environmental conditions; (ii) integrating the effects of the measured data into different indicators for calculating the potential for deterioration, and (iii) working as an early warning alarm to ensure appropriate renovation and maintenance measures by comparing the predicted deterioration levels with different threshold values of deterioration indicators. Based on the monitoring data, the environmental factors that affect the deterioration of concrete building facades are characterized. The result of the research will improve the building construction industry by providing methodologies and systems for monitoring and predicting the performance of structures. A scientific relevance of this research will be the improved understanding between the results received in laboratory studies and observations of deterioration in practice. The implementation of the RHT-MAPS monitoring system developed can be carried out cost-effectively in comparison to the construction and maintenance expenses of concrete facades. The maintenance costs can also be optimized by the monitoring results

Towards Extended Bit Tracking for Scalable and Robust RFID Tag Identification Systems

The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradigm shift in the development of viable radio frequency identification technology (RFID)-based solutions for ubiquitous real-Time monitoring and tracking. Bit tracking-based anti-collision algorithms have attracted considerable attention, recently, due to its positive impact on decreasing the identification time. We aim to extend bit tracking to work effectively over erroneous channels and scalable multi RFID readers systems. Towards this objective, we extend the bit tracking technique along two dimensions. First, we introduce and evaluate a type of bit errors that appears only in bit tracking-based anti-collision algorithms called false collided bit error in single reader RFID systems. A false collided bit error occurs when a reader perceives a bit sent by tag as an erroneous bit due to channel imperfection and not because of a physical collision. This phenomenon results in a significant increase in the identification delay. We introduce a novel, zero overhead algorithm called false collided bit error selective recovery tackling the error. There is a repetition gain in bit tracking-based anti-collision algorithms due to their nature, which can be utilized to detect and correct false collided bit errors without adding extra coding bits. Second, we extend bit tracking to 'error-free' scalable mutli-reader systems, while leaving the study of multi-readers tag identification over imperfect channels for future work. We propose the multi-reader RFID tag identification using bit tracking (MRTI-BT) algorithm which allows concurrent tag identification, by neighboring RFID readers, as opposed to time-consuming scheduling. MRTI-BT identifies tags exclusive to different RFIDs, concurrently. The concept of bit tracking and the proposed parallel identification property are leveraged to reduce the identification time compared to the state-of-The-Art. 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 7-684-1-127. The work of A. Fahim and T. ElBatt was supported by the Vodafone Egypt Foundation.Scopu

Exploring the Relation between NPP-VIIRS Nighttime Lights and Carbon Footprint, Population Growth, and Energy Consumption in the UAE

Due to global warming and its detrimental effect, every country is responsible to join the global effort to reduce carbon emissions. In order to improve the mitigation plan of climate change, accurate es-timates of carbon emissions, population, and electricity consumption are critical. Carbon footprint is significantly linked to the socioeconomic development of the country which can be reflected in the city's infrastructure and urbanization. We may be able to estimate the carbon footprint, population growth, and electricity consumption of a city by observing the nighttime light reflecting its urbanization. This is more challenging in oil-producing countries where urbanization can be more complicated. In this study, we are therefore investigating the possibility of correlating the remotely sensed NPP-VIIRS Nighttime light (NTL) estimation with the aforementioned socioeconomic indicators. Daily NPP-VIIRS NTL were obtained for the period between 2012 to 2021 for the United Arab Emirates (UAE) which is one of the top oil producing countries. The socioeconomic indicators of the UAE, including the population, electricity consumption, and carbon dioxide emissions, have been obtained for the same period. The analysis of the correlation between the NTLs and the population indicates that there is a high correlation of more than 0.9. There is also a very good correlation of 0.7 between NTLs and carbon emissions and electricity consumption. However, these correlations differ from one city to another. For example, Dubai has shown the highest correlation between population and NTLs (R2 > 0.8). However, the correlation was the lowest in Al-Ain, a rural city (R2 < 0.4) with maximum electricity consumption of 1.1E04 GWh. These results demonstrate that NTLs can be considered as a promising proxy for carbon footprint and urbanization in oil-producing regions

The Social Network of High and Low Self- Monitors and it’s Impact on Organization’s Performance

Self-monitoring as a personality attribute is given particular attention by researchers in order to understand employee performance and productivity in the organizational context. Numerous studies focused on the impact of self-monitoring from individual employee perspective with limited focus on the impact of the organization’s performance. However, to what extent employees with high versus low self- monitors contribute to the organization’s performance needs to be theoretically augmented. Therefore, relying on exploratory method of research, the paper tries to address the impact of high and low self-monitors on organization’s performance. In this perspective, how self-monitoring helps employees to structure a social network within organizational setting and eventually contribute to it’s success is discussed in this paper. The paper addresses the literature review of self-monitoring principles with special interest in its contribution towards organizational performance through previous research compilation within the field of sociology and organization psychology

Generic code clone detection model for java applications

Code clone is a common term used for codes that are repeated multiple times in a program. There are Type 1, Type 2, Type 3 and Type 4 code clones. Various code clone detection approaches and models have been used to detect a code clone. However, a major challenge faced in detecting code clone using these models is the lack of generality in detecting all clone types. To address this problem, Generic Code Clone Detection (GCCD) model that consists of five processes which are Preprocessing, Transformation, Parameterization, Categorization and Match Detection process is proposed. Initially, a pre-processing process produces source units through the application of five combinatorial rules. This is followed by the transformation process to produce transformed source units based on the letter to number substitution concept. Next, a parameterization process produces parameters used in categorization and match detection process. Next, a categorization process groups the source units into pools. Finally, a match detection process uses a hybrid exact matching with Euclidean distance to detect the clones. Based on these processes, a prototype of the GCCD was developed using Netbeans 8.0. The model was compared with the Generic Pipeline Model (GPM). The comparisons showed that the GCCD was able to detect clone pairs of Type-1 until Type-4 while the GPM was able to detect clone pair for Type-1 only. Furthermore, the GCCD prototype was empirically tested with Bellons benchmark data and it was able to detect clones in Java applications with up to 203,000 line of codes. As a conclusion, the GCCD model is able to overcome the lack of generality in detecting all code clone types by detecting Type 1, Type 2, Type 3 and Type 4 clones

Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victims body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed models performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The systems performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease