Fire and Life Safety Report Orfalea College of Business

A comprehensive fire and life safety analysis was performed on the Orfalea College of Business. The report covers the prescriptive analysis of the building and the performance-based design analysis. The prescriptive analysis includes assessment of the relevant codes for egress system, water-based suppression system, alarm and detection system, and structural fire protection. The egress analysis under the Life Safety Code 2015 found the building meets the requirements of occupant load to exceed exit capacity, number of exits and its location, travel distances. Four rooms on the second floor that have not met the common path of travel distances limits were discussed in detail in this report. In addition, two locations where exits signs should have been located were investigated in this report. The water-based suppression system was analyzed, and an automatic fire sprinkler system was designed according to NFPA 13. The fire alarm and detection system throughout the building was reviewed. The notification device in Room 300 does not meet required candela rating. The smoke management system analysis indicates door magnetic holder for the exit enclosure on the third floor does not work properly. The analysis of the structural fire protection demonstrates that the building is in accordance to the requirements of the IBC 2015 for Type I-B construction. A performance based analysis of the Orfalea College of Business was performed to evaluate a design fire scenario. The fire scenario was modeled using Fire Dynamic Simulator (FDS), Pyrosim and Pathfinder. The tenability results of the simulation are compared to the established tenability criteria to determine the available safe egress time (ASET) for the scenario. Then, (ASET) is compared to the required safe egress time (RSET). The fire scenario evaluated an upholstered three seat sofa in a break room on the fourth floor. The required safe egress time (RSET) was calculated as 249 seconds and the available safe egress time (ASET) was determined to be 42 seconds when the visibility tenability criterion was exceeded. As part of the evaluation process, there are additional recommendations in the report including exit sings locations on the third floor, obstructions in the corridor on the fourth floor, smoke detection system and notification appliances coverage, and several other recommendations are discussed in more detail in the report

Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.</jats:p

Flow-induced order-order transitions in amyloid fibril liquid crystalline tactoids

Understanding and controlling the director field configuration, shape, and orientation in nematic and cholesteric liquid crystals is of fundamental importance in several branches of science. Liquid crystalline droplets, also known as tactoids, which spontaneously form by nucleation and growth within the biphasic region of the phase diagram where isotropic and nematic phases coexist, challenge our current understanding of liquid crystals under confinement, due to the influence of anisotropic surface boundaries at vanishingly small interfacial tension and are mostly studied under quiescent, quasi-equilibrium conditions. Here, we show that different classes of amyloid fibril nematic and cholesteric tactoids undergo out-of-equilibrium order-order transitions by flow-induced deformations of their shape. The tactoids align under extensional flow and undergo extreme deformation into highly elongated oblate shapes, allowing the cholesteric pitch to decrease as an inverse power law of the tactoids aspect ratio. Energy functional theory and experimental measurements are combined to rationalize the critical elongation ratio above which the director-field configuration of tactoids transforms from bipolar and uniaxial cholesteric to homogenous and to debate on the thermodynamic nature of these transitions. Our findings suggest new opportunities in designing self-assembled liquid crystalline materials where structural and dynamical properties may be tuned by non-equilibrium phase transitions

Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir

Recent developments in instrumentation, communication and software have enabled the integration of real-time data into the decision-making process of hydrocarbon production. Applications of real-time data integration in drilling operations and horizontal-well lateral placement are becoming industry common practice. In reservoir management, the use of real-time data has been shown to be advantageous in tasks such as improving smart-well performance and in pressure-maintenance programs. Such capabilities allow for a paradigm change in which reservoir management can be looked at as a strategy that enables a semi-continuous process of model updates and decision optimizations instead of being periodic or reactive. This is referred to as closed-loop reservoir management (CLRM). Due to the complexity of the dynamic physical processes, large sizes, and huge uncertainties associated with reservoir description, continuous model updating is a large-scale problem with a highly dimensional parameter space and high computational costs. The need for an algorithm that is both feasible for practical applications and capable of generating reliable estimates of reservoir uncertainty is a key element in CLRM. This thesis investigates the validity of Markov Chain Monte Carlo (MCMC) sampling used in a Bayesian framework as an uncertainty quantification and model-updating tool suitable for real-time applications. A 3-phase, dual-porosity, dual-permeability reservoir model is used in a synthetic experiment. Continuous probability density functions of cumulative oil production for two cases with different model updating frequencies and reservoir maturity levels are generated and compared to a case with a known geology, i.e., truth case. Results show continuously narrowing ranges for cumulative oil production, with mean values approaching the truth case as model updating advances and the reservoir becomes more mature. To deal with MCMC sampling sensitivity to increasing numbers of observed measurements, as in the case of real-time applications, a new formulation of the likelihood function is proposed. Changing the likelihood function significantly improved chain convergence, chain mixing and forecast uncertainty quantification. Further, methods to validate the sampling quality and to judge the prior model for the MCMC process in real applications are advised

Type-2 Fuzzy Logic based Systems for Adaptive Learning and Teaching within Intelligent E-Learning Environments

The recent years have witnessed an increased interest in e-learning platforms that incorporate adaptive learning and teaching systems that enable the creation of adaptive learning environments to suit individual student needs. The efficiency of these adaptive educational systems relies on the methodology used to accurately gather and examine information pertaining to the characteristics and needs of students and relies on the way that information is processed to form an adaptive learning context. The vast majority of existing adaptive educational systems do not learn from the users’ behaviours to create white-box models to handle the high level of uncertainty and that could be easily read and analysed by the lay user. The data generated from interactions, such as teacher–learner or learner–system interactions within asynchronous environments, provide great opportunities to realise more adaptive and intelligent e-learning platforms rather than propose prescribed pedagogy that depends on the idea of a few designers and experts. Another limitation of current adaptive educational systems is that most of the existing systems ignore gauging the students' engagements levels and mapping them to suitable delivery needs which match the students' knowledge and preferred learning styles. It is necessary to estimate the degree of students’ engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in small and large-scale online learning platforms. Furthermore, most of the current adaptive educational systems are used within asynchronous e-learning contexts as self-paced e-learning products in which learners can study in their own time and at their own speed, totally ignorant of synchronous e-learning settings of teacher-led delivery of the learning material over a communication tool in real time. This thesis presents novel theoretical and practical architectures based on computationally lightweight T2FLSs for lifelong learning and adaptation of learners’ and teachers’ behaviours in small- and large-scale asynchronous and synchronous e-learning platforms. In small-scale asynchronous and synchronous e-learning platforms, the presented architecture augments an engagement estimate system using a noncontact, low-cost, and multiuser support 3D sensor Kinect (v2). This is able to capture reliable features including head pose direction and hybrid features of facial expression to enable convenient and robust estimation of engagement in small-scale online and onsite learning in an unconstrained and natural environment in which users are allowed to act freely and move without restrictions. We will present unique real-world experiments in large and small-scale e-learning platforms carried out by 1,916 users from King Abdul-Aziz and Essex universities in Saudi Arabia and the UK over the course of teaching Excel and PowerPoint in which the type 2 system is learnt and adapted to student and teacher behaviour. The type-2 fuzzy system will be subjected to extended and varied knowledge, engagement, needs, and a high level of uncertainty variation in e-learning environments outperforming the type 1 fuzzy system and non-adaptive version of the system by producing better performance in terms of improved learning, completion rates, and better user engagements

The Impact of Board Composition on Corporate Dividends Pay-Out: "An Empirical Examination of Industrial Companies Listed in Amman Stock Exchange"

This study investigates the impact of board composition on corporate dividends pay-out of a sample of 30 Jordanian industrial companies listed on the Amman Stock Exchange (ASE) during the period (2007-2017). The study examine the  impact of a certain variables that represent board composition (Board size , Independent (non-executive ) director , duality of chief executive officer (CEO) and chairman position , Director nationality , Institutional investors ) .Panel-Data analysis was used to test the empirical model in the current study using a fixed affect model and Random effect model. Relevant data were collected from the (ASE) website and from the annual reports of the sampled companies.The result of the study revealed that there is a negative significant effect between Institutional investors, audit firm and dividend per share (DPS) at the 1% level. Moreover, there is a negative significant effect between Independent director and DPS at the 5% level.  In contrast, board of director size and firm profitability positively affect the DPS at the 5% level. Furthermore, Duality of CEO and chairman position, director nationality, firm size and financial leverage were found to have no effect on DPS at the 5% level. Keywords: Board Composition, Corporate Governance, Pay-out, ASE DOI: 10.7176/EJBM/11-27-16 Publication date:September 30th 201

Assessment of patients’ knowledge of tuberculosis and its impact on self-management ability

Purpose: To assess the knowledge of patients of tuberculosis (TB) and its relationship with patients’ self-management in Saudi Arabia. Methods: The study is based a prospective cross-sectional design. It included a sample of 176 cases with an active or latent diagnosis of TB. A survey was conducted in some hospitals, including King Abdul-Aziz University from November 2016 to January 2017. The collected data were statistically analyzed. Results: The survey showed that 70 % of the patients had inadequate information on TB and its treatment, while only 4 % showed awareness of the prevalence of TB. Moreover, a significant correlation was found between the educational level of patients and their knowledge of TB. Patients’ educational level substantially contributed to their understanding of health education. Conclusion: The findings suggest that active educational campaigns need to be initiated to enhance the patients’ awareness and knowledge of TB

ASSESSMENT OF MENSTRUAL HEALTH AND ANALGESICS USAGE IN YOUNG AGE WOMEN

Objective: The study focuses on assessing the incidence of menstrual irregularity among young women and the factors for a disturbance with the rationale to assess the use of analgesic drugs during Premenstrual Syndrome (PMS). Methods: A cross-sectional study was used. A total of 2500 randomly selected young female between the age of 11 and 30 y completed the study questionnaire to assess lifestyle pattern, variations in menstrual pattern, perceived stress, and to capture information about their menstrual cycle and related problems. In addition, the questionnaire assessed the use of analgesics for PMS. Results: 2481 participants completed the questionnaire. The mean age of participants’ menarche was 12.85±1.432 y. The prevalence of menstrual irregularities was 25.0 % (n=621) and about 8.5% (n=211) of respondents had severe pain that was not relieved by the use of analgesics. On the other hand, 50.9 % (n=1262) reported severe pain that was relieved by analgesics. A total of 1279 (51.6 %) of participants in this study used Over The Counter (OTC) analgesics to relieve PMS. Conclusion: Dysmenorrhea is the most common complaint among young females in Saudi Arabia. Low Body Mass Index (BMI), sedentary lifestyle, stress and early age of menarche are the most important factors associated with menstrual irregularities. Proper education programs and awareness among young girls about their menstrual health, and the provision of guidance in choosing effective analgesics and treatment options for dysmenorrhea are highly recommended

The optimal route of progesterone administration for luteal phase support in a frozen embryo transfer : a systematic review

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An interval type-2 fuzzy logic based system for improved instruction within intelligent e-learning platforms

E-learning is becoming increasingly more popular. However, for such platforms (where the students and tutors are geographically separated), it is necessary to estimate the degree of students' engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in large-scale online learning platforms. When the number of attendees is large, it is essential to obtain overall engagement feedback, but it is also challenging to do so because of the high levels of uncertainty associated with the environments and students. To handle such uncertainties, we present a type-2 fuzzy logic based system using visual RGB-D features including head pose direction and facial expressions captured from a low-cost but robust 3D camera (Kinect v2) to estimate the engagement degree of the students for both remote and on-site education. This system enriches another self- learning type-2 fuzzy logic system which provides the instructors with suggestions to vary their teaching means to suit the level of course students and improve the course instruction and delivery. This proposed dynamic e-learning environment involves on-site students, distance students, and a teacher who delivers the lecture to all attending onsite and remote students. The rules are learned from the students' behavior and the system is continuously updated to give the teacher the ability to adapt the lecture delivery instructional approach to varied learners' engagement levels. The efficiency of the proposed system has been evaluated through various real-world experiments in the University of Essex iClassroom on a sample of thirty students and six teachers. These experiments demonstrate the efficiency of the proposed interval type-2 fuzzy logic based system to handle the faced uncertainties and produce superior improved average learners' engagements when compared to type-1 fuzzy systems and nonadaptive systems