A new splitting-based displacement prediction approach for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage

Capital Budgeting Practices: The Case of Qatar

The purpose of this project was to investigate the capital budgeting practice in the largest firms in Qatar. A survey was conducted of the 170 largest firms and corporations. A total of 55 completed surveys were received, for a response rate of 34 percent. The results showed that Qatari companies in general tend to adopt the discounted cash flow methods, with Net Present Value (NPV), Profitability Index (PI) and the Internal Rate of Return (IRR) being the most widely used methods. Capital asset Pricing Model (CAPM) including some extra “risk factors” was used to estimate the cost of capital in more than half of the companies. Companies tend to use the cost of debt plus some premium as the discount rate, and they frequently reviewed and adjusted that rate, mainly as to the expected changes in the project’s risk. The terminal value was commonly estimated using the present value of future cash flow in perpetuity and multiples of terminal earnings

In Vitro Effect of Sorghum (Sorghum bicolor) Seed Extracts as a Biological Acaricidal Against Some Hard Tick (Ixodidae) in Sulaimani Governorate - Kurdistan Region/Iraq

This study was conducted in Sulaimani governorate in order to identify the biological control of some Ixodidae genera among different flocks of cattle, sheep and goats. Four genera of Ixodidae; Boophilus spp., Hyalomma spp., Rhipicephalus spp. and Haemaphysalis spp., were identified in these infested animals. According to chi–square test, the highest distribution of Boophulis spp., was recorded in cattle (56.51%), and the highest distribution of Hyalomma spp., (49.82%) and Rhipicephalus spp., (28.16%) which were in sheep. The highest number of Haemophasylas spp., was obtained from goats (6.67%), whereas the lowest number of this genus (2.88% and 2.89%) was collected from cattle and sheep respectively. The toxicity of Sorghum bicolor seed extract was tested against the more distributed Ixodidae genera (Boophilus spp. and Hyalomma spp.) by immersion method on mature ticks, four concentrations (23.2, 17.4, 11.6 and 5.8 mg/dl), in addition to the control treatment (0 mg/dl) of the seed were used to evaluate the engorged females in vitro. The results showed that 100% of absolute cumulative mortality of Boophilus spp., was gain after 72 hr by 23.2 mg/dl extract concentration, followed by 17.4 mg/dl which gave 90% mortality, whereas 100% absolute cumulative mortality for Hyalomma spp., was obtained by 23.2 mg/dl extract concentration after 48 hr, followed by 17.4, 11.6 and 5.8 mg/dl concentration that gave 90%, 80% and 40% mortality after 72 hr

A battery-less power supply using supercapacitor as energy storage powered by solar

This paper presents a battery-less power supply using supercapacitor as energy storage powered by solar. In this study the supercapacitor as energy storage, as opposed to batteries, has widely researched in recent years. Supercapacitors act like other capacitors, but their advantage is having enormous power storage capabilities. Maximum charging voltage and capacitance are two variables of storage in the supercapacitor. The supercapacitor is used as energy storage to charge a low power device wirelessly and act as a power supply. The solar energy is used as a backup power supply if there is no electricity in the remote or isolated area to charge the supercapacitor. The time taken to charge the supercapacitor depend on the amount of current rating of the solar panel. The higher the current, the shorter the time taken to charges the supercapacitor. Power supply using supercapacitor can store up to 30 Vdc using a DC-DC boost converter

An Ensemble Self-Structuring Neural Network Approach to Solving Classification Problems with Virtual Concept Drift and its Application to Phishing Websites

Classification in data mining is one of the well-known tasks that aim to construct a classification model from a labelled input data set. Most classification models are devoted to a static environment where the complete training data set is presented to the classification algorithm. This data set is assumed to cover all information needed to learn the pertinent concepts (rules and patterns) related to how to classify unseen examples to predefined classes. However, in dynamic (non-stationary) domains, the set of features (input data attributes) may change over time. For instance, some features that are considered significant at time Ti might become useless or irrelevant at time Ti+j. This situation results in a phenomena called Virtual Concept Drift. Yet, the set of features that are dropped at time Ti+j might return to become significant again in the future. Such a situation results in the so-called Cyclical Concept Drift, which is a direct result of the frequently called catastrophic forgetting dilemma. Catastrophic forgetting happens when the learning of new knowledge completely removes the previously learned knowledge. Phishing is a dynamic classification problem where a virtual concept drift might occur. Yet, the virtual concept drift that occurs in phishing might be guided by some malevolent intelligent agent rather than occurring naturally. One reason why phishers keep changing the features combination when creating phishing websites might be that they have the ability to interpret the anti-phishing tool and thus they pick a new set of features that can circumvent it. However, besides the generalisation capability, fault tolerance, and strong ability to learn, a Neural Network (NN) classification model is considered as a black box. Hence, if someone has the skills to hack into the NN based classification model, he might face difficulties to interpret and understand how the NN processes the input data in order to produce the final decision (assign class value). In this thesis, we investigate the problem of virtual concept drift by proposing a framework that can keep pace with the continuous changes in the input features. The proposed framework has been applied to phishing websites classification problem and it shows competitive results with respect to various evaluation measures (Harmonic Mean (F1-score), precision, accuracy, etc.) when compared to several other data mining techniques. The framework creates an ensemble of classifiers (group of classifiers) and it offers a balance between stability (maintaining previously learned knowledge) and plasticity (learning knowledge from the newly offered training data set). Hence, the framework can also handle the cyclical concept drift. The classifiers that constitute the ensemble are created using an improved Self-Structuring Neural Networks algorithm (SSNN). Traditionally, NN modelling techniques rely on trial and error, which is a tedious and time-consuming process. The SSNN simplifies structuring NN classifiers with minimum intervention from the user. The framework evaluates the ensemble whenever a new data set chunk is collected. If the overall accuracy of the combined results from the ensemble drops significantly, a new classifier is created using the SSNN and added to the ensemble. Overall, the experimental results show that the proposed framework affords a balance between stability and plasticity and can effectively handle the virtual concept drift when applied to phishing websites classification problem. Most of the chapters of this thesis have been subject to publicatio

Implementation of Human Rights Principles in School Administration: Perceptions of Principals and Teachers of Arab Schools at Jerusalem Governorate

This study was undertaken during the 2009 /2010 academic year to explore the Arab schools principals' and teachers' perceptions of the degree to which human rights principles were implemented in school administration in Jerusalem Governorate. A stratified random sample of (36) principals, and (475) teachers was chosen; and a 54-item questionnaire covering five fields of human right was developed to solicit data. Both the validity and reliability of the questionnaire were examined. Results showed that the application of human rights principles in school administration -as perceived by principals and teachers- was “moderate”. Results also showed that there were no statistical significant differences (α ≤ 0.05) between the means due to gender, educational qualification, years of experience, and supervising body; while significant differences were found between means due to job title and school stage

Prevalence and Risk Factors of Gestational Diabetes in Twin Pregnancies: Population Based Study

Objective: To assess the prevalence and risk factors of gestational diabetes (GDM) in twin compared with singleton pregnancies. Methods: Population-based study using CDC birth data from 2016-2020. Higher order pregnancies and pre-pregnancy diabetes were excluded. A Chi-square test of independence was performed to identify significant factors associated with GDM in twin versus singleton pregnancies and within each group independently. Multivariable regression analyses were performed first to assess risk factors that are significantly associated with GDM in twins and second to assess the risk of GDM in twin compared with singletons, adjusted for the significant risk factors. P value<0.01 was considered statistically significant Results: Total of 18,173,365 singleton and 611,043 twin pregnancies were included during the study period. Following the regression model, maternal age≥30 years, nulliparous, IVF, chronic hypertension, Hispanic and Non-Hispanic (NH) Asian, foreign-born, overweight and obesity class I/II/II remained significantly associated with GDM in twins. However, maternal age<25 years, NH Black, and W.I.C program reduced that risk. Factors that more than doubled the risk in twins were maternal age≥40 years (OR 2.06 (1.97 – 2.14), P<0.001), NH Asian (OR 2.12 (2.04 – 2.20), P<0.001), and obesity class I, II, and III (OR: 2.22 (2.16 – 2.29), P<0.001, OR:3.01 (2.92 – 3.11), P<0.001, OR: 3.80 (3.67 – 3.93), p<0.001, respectively). Following adjustment for all the significant risk factors, twin pregnancy remained significantly associated with increasing the risk of GDM in twin compared to singleton pregnancies (OR 1.22 (1.21 – 1.23), P<0.001). Conclusion: Of the significant risk factors, maternal age≥40 years, NH Asian, and obesity class I, II, and III more than doubled the risk of GDM in twins. Regardless of maternal demographics, obstetric history, and endocrine factors, twin pregnancy remained significantly associated with GDM compared to singleton pregnancies. These factors can be used in risk prediction models to better counsel and manage twin pregnancies