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"Khan, Noreen"
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The veil in Kuwait : gender, fashion, identity
\"The Veil in Kuwait explores the complex and compelling reasons behind why young women in Kuwait wear the hijab, abaya, and/or niqab, analyzing--along the way--the ways in which these women are perceived by those who do not veil. In April 2013, Thorsten Botz-Bornstein and Noreen Abdullah-Khan conducted a survey of Islamic veiling at the Gulf University for Science and Technology in Kuwait. The purpose of the survey was to examine the veil through the prism of recent international developments that have transformed veiling, at least partially, into a fashion phenomenon. The first of its kind, their study considers the embracing of the veil in a fashion context within a unique Muslim society and asks pertinent questions about the intentions and motivations behind its use. In The Veil in Kuwait, the authors examine the findings of this singular study. Among other questions and discussions, they investigate whether or not the present re-veiling wave in Kuwait is an expression of frustration and resentment in the face of broken promises of modernity and whether there is a real desire among young Kuwaitis to return to the values of the past. The important influence of religion, culture, family, and fashion are all explored through the eyes of Kuwaitis themselves; and the study is incredibly unique in its inclusion of veiled and non-veiled participants, as well as males and their perceptions of women who veil. Attitudes towards women, religion, culture, and fashion are carefully examined to provide insight into Kuwaiti society\"-- Provided by publisher.
Book
Important Flavonoids and Their Role as a Therapeutic Agent
Flavonoids are phytochemical compounds present in many plants, fruits, vegetables, and leaves, with potential applications in medicinal chemistry. Flavonoids possess a number of medicinal benefits, including anticancer, antioxidant, anti-inflammatory, and antiviral properties. They also have neuroprotective and cardio-protective effects. These biological activities depend upon the type of flavonoid, its (possible) mode of action, and its bioavailability. These cost-effective medicinal components have significant biological activities, and their effectiveness has been proved for a variety of diseases. The most recent work is focused on their isolation, synthesis of their analogs, and their effects on human health using a variety of techniques and animal models. Thousands of flavonoids have been successfully isolated, and this number increases steadily. We have therefore made an effort to summarize the isolated flavonoids with useful activities in order to gain a better understanding of their effects on human health.
Journal Article
College preparation for a medical career in the United States
A college degree is required to enter medical school in the United States. A remarkably high percentage of students entering college have pre-medical aspirations but relatively few end up as medical students. As an \"applied science\", education about medicine is usually thought to be beyond the purview of a liberal arts curriculum. Students therefore receive little education about a medical career, or information about the many alternative careers in health science. Instead, they take courses for Medical College Admission Test (MCAT) preparation and medical school application prerequisites in biology, chemistry, physics, and math. These classes give them little insight into a real medical career. The current report considers this mismatch between student needs in health science and available resources in colleges across the United States.
A Collective Case Series framework was used to obtain qualitative data. Key informant interviews were requested from a convenience sample of representatives from 20 colleges, with six colleges providing extensive data. Three institutions collected data specifically on students who matriculated college interested in a career as a physician.
At these schools, one-half to one-quarter of students who said they were interested in medicine at the beginning of college ended up not applying to medical school. At each of the six schools, we saw a wide range of generally sparse academic and professional advising involvement and a very limited number of classes that discussed concepts directly related to careers in health science.
Looking at this data, we provide a novel conceptual model as a potential testable solution to the problem of an underexposed and unprepared student population interested in medicine. This includes a brief series of courses intended to inform students about what a career in medicine would fully entail to help foster core competencies of empathy, compassion and resilience.
Journal Article
An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network
Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.
Journal Article
MACRS: An Enhanced Directory-Based Resource Sharing Framework for Mobile Ad Hoc Networks
Recent technological developments have caused a rapid increase in the use of portable devices around the globe. However, these devices comprise limited processing resources that restrict their performance. To overcome this issue, the existing literature provides several frameworks that enable resource sharing through ad hoc clouds. However, these frameworks lack the ability to cater to the omni-directional movements of devices, which adversely affects the cloud stability, thereby, restricting the resource sharing process. To this end, this paper proposes a novel framework, namely Mobility-aware Ad hoc Cloud-based Resource Sharing (MACRS), which aims to enhance resource sharing among devices. To achieve this aim, MACRS proposes a new mobility-aware clustering algorithm that improves cloud stability. Moreover, the proposed framework prevents unfair resource exploitation and introduces an enhanced technique to handle emergency tasks. Furthermore, we employed event-triggered energy valuations’ synchronization, instead of periodic updates, which minimizes network congestion, hence staving off bandwidth wastage. Additionally, MACRS proposes to maintain the local directory at each node, instead of the cellular service provider, to reduce end-to-end delay during energy valuations’ verification and to minimize the overall execution time of tasks. The simulation results demonstrated that MACRS provides considerably improved cloud stability and resource sharing in comparison with eminent frameworks.
Journal Article
A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization
Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees.
Journal Article
Patient and Therapist Perceptions of a Publicly Funded Internet-Based Cognitive Behavioral Therapy (iCBT) Program for Ontario Adults During the COVID-19 Pandemic: Qualitative Study
To address the anticipated rise in mental health symptoms experienced at the population level during the COVID-19 pandemic, the Ontario government provided 2 therapist-assisted internet-delivered cognitive behavioral therapy (iCBT) programs to adults free of charge at the point of service.
The study aims to explore the facilitators of and barriers to implementing iCBT at the population level in Ontario, Canada, from the perspective of patients and therapists to better understand how therapist-assisted iCBT programs can be effectively implemented at the population level and inform strategies for enhancing service delivery and integration into the health care system.
Using a convenience sampling methodology, semistructured interviews were conducted with 10 therapists who delivered iCBT and 20 patients who received iCBT through either of the publicly funded programs to explore their perspectives of the program. Interview data were analyzed using inductive thematic analysis to generate themes.
Six salient themes were identified. Facilitators included the therapist-assisted nature of the program; the ease of registration and the lack of cost; and the feasibility of completing the psychoeducational modules given the online and self-paced nature of the program. Barriers included challenges with the online remote modality for developing the therapeutic alliance; the program's generalized nature, which limited customization to individual needs; and a lack of formal integration between the iCBT program and the health care system.
Although the program was generally well-received by patients and therapists due to its accessibility and feasibility, the digital format of the program presented both benefits and unique challenges. Strategies for improving the quality of service delivery include opportunities for synchronous communication between therapists and patients, options for increased customization, and the formal integration of iCBT into a broader stepped-care model that centralizes patient referrals between care providers and promotes continuity of care.
Journal Article
Phytochemical screening and antibacterial assay of the crude extract and fractions of Ferula oopoda
The principal objective of the current study was to analyse phytochemical constituents and to determine the antimicrobial activity of the crude methanol extract and fractions of chloroform, ethyl acetate and hexane from the whole plant of Ferula oopoda against three bacterial strains Escherichia coli, Salmonella typhi and Staphylococcus aureus. Phytochemical assay confirmed the presence of terpenoid, flavonoids, saponins, tannins, phenolic compounds, carbohydrates, steroids and glycosides. Agar disc diffusion method was used to determine the zone of inhibition of the tested sample for antimicrobial activity. The crude methanolic extract showed activity against E. coli ZOI, 30.00±1.060 mm, for ethyl acetate fractions 50.00±4.18 mm, for chloroform fraction 27.00±0.060 mm and for n-hexane fraction 24.00±0.353 mm. This observation shows that ethyl acetate fraction possesses great potential against E. coli. Inhibition zone for Salmonella typhi was 23.25±1.050 mm for ethyl acetate, 14.00±0.353 mm for crude methanol extract, 22.00±1.753 mm for chloroform fraction and 08.00±0.352 mm for n-hexane fraction. This observation shows that n-hexane fraction possesses low potential against Salmonella typhi. Anti-bacterial potential against Staphylococcus aureus strain was maximum in ethyl acetate fraction and showed ZOI, 34.00±1.767 mm, for chloroform fraction 21.24±2.636 mm, for crude methanol extract 19.00±1.060 mm and for n-hexane fraction 16.00±1.412 mm respectively.
Journal Article
