Patient and Doctor Perceptions of Hypertension and its Treatment: a Qualitative Study in Urban Hospitals of Pakistan

Hypertension (HTN) is a chronic disease that has become a growing public health problem in countries around the world, including Pakistan. Successful HTN control is an essential cornerstone in the prevention of morbidity and mortality associated with uncontrolled HTN. However, patients’ beliefs about their disease, treatment and control are related to the outcome of successful HTN control and management. Likewise, doctors’ understanding of HTN and its treatment is equally important and can affect their practice and HTN management. There is little qualitative research considering patients’ and doctors’ understanding of HTN, its treatment and how it influences HTN management in Pakistan. Therefore, the current study aimed to elicit patients’ and doctors’ perceptions, attitudes and beliefs about HTN and its treatment in urban areas of Pakistan. A qualitative study that drew on grounded theory principles was undertaken in two public hospitals of Pakistan. Thirty in-depth semi-structured interviews with hypertensive patients and thirty interviews with doctors were conducted in two hospitals. Interviews were translated and transcribed from Urdu into English and NVivo was used to organise the data in a systematic way. Data were analysed using a constant comparative approach based on the principles of grounded theory. The study revealed that patients’ (n=30) beliefs were complex, deep-rooted and influenced their attitude towards HTN treatment. Patients’ beliefs were informed by understanding gleaned from the socio-cultural environment (local norms, social relations, religion), individual factors (e.g. income, co-morbidities) and interactions with doctors. In contrast, doctors’ (n=30) own understandings on what constitutes successful HTN management often contradicted patients’ beliefs. Doctors’ reported that time restraints and work burden affected their approach to treatment and the provision of information to patients. Findings also revealed an overlap between patients’ and doctors’ beliefs, however, in relation to adopting lifestyle changes for management of HTN. In general, though doctors paid less consideration to patients’ beliefs in routine clinical practice and evaluated patients through the filter of their own beliefs. The findings suggest that doctors could provide a better service care by aligning with their patients on a common understanding about HTN management and providing culturally appropriate information. Doctors should be aware of the understanding hypertensive patients attach to HTN and avoid providing treatment based on their own beliefs. Doctors must engage with patients’ beliefs and identify their particular healthcare needs in order to achieve control of HTN in Pakistan

Numerical Optical Centroid Measurements

Optical imaging methods are typically restricted to a resolution of order of the probing light wavelength $\lambda_p$ by the Rayleigh diffraction limit. This limit can be circumvented by making use of multiphoton detection of correlated $N$-photon states, having an effective wavelength $\lambda_p/N$. But the required $N$-photon detection usually renders these schemes impractical. To overcome this limitation, recently, so-called optical centroid measurements (OCM) have been proposed which replace the multi-photon detectors by an array of single-photon detectors. Complementary to the existing approximate analytical results, we explore the approach using numerical experiments by sampling and analyzing detection events from the initial state wave function. This allows us to quantitatively study the approach also beyond the constraints set by the approximate analytical treatment, to compare different detection strategies, and to analyze other classes of input states.Comment: 15 pages, 18 figure

Genetic Programming based Feature Manipulation for Skin Cancer Image Classification

Skin image classification involves the development of computational methods for solving problems such as cancer detection in lesion images, and their use for biomedical research and clinical care. Such methods aim at extracting relevant information or knowledge from skin images that can significantly assist in the early detection of disease. Skin images are enormous, and come with various artifacts that hinder effective feature extraction leading to inaccurate classification. Feature selection and feature construction can significantly reduce the amount of data while improving classification performance by selecting prominent features and constructing high-level features. Existing approaches mostly rely on expert intervention and follow multiple stages for pre-processing, feature extraction, and classification, which decreases the reliability, and increases the computational complexity. Since good generalization accuracy is not always the primary objective, clinicians are also interested in analyzing specific features such as pigment network, streaks, and blobs responsible for developing the disease; interpretable methods are favored. In Evolutionary Computation, Genetic Programming (GP) can automatically evolve an interpretable model and address the curse of dimensionality (through feature selection and construction). GP has been successfully applied to many areas, but its potential for feature selection, feature construction, and classification in skin images has not been thoroughly investigated. The overall goal of this thesis is to develop a new GP approach to skin image classification by utilizing GP to evolve programs that are capable of automatically selecting prominent image features, constructing new high level features, interpreting useful image features which can help dermatologist to diagnose a type of cancer, and are robust to processing skin images captured from specialized instruments and standard cameras. This thesis focuses on utilizing a wide range of texture, color, frequency-based, local, and global image properties at the terminal nodes of GP to classify skin cancer images from multiple modalities effectively. This thesis develops new two-stage GP methods using embedded and wrapper feature selection and construction approaches to automatically generating a feature vector of selected and constructed features for classification. The results show that wrapper approach outperforms the embedded approach, the existing baseline GP and other machine learning methods, but the embedded approach is faster than the wrapper approach. This thesis develops a multi-tree GP based embedded feature selection approach for melanoma detection using domain specific and domain independent features. It explores suitable crossover and mutation operators to evolve GP classifiers effectively and further extends this approach using a weighted fitness function. The results show that these multi-tree approaches outperformed single tree GP and other classification methods. They identify that a specific feature extraction method extracts most suitable features for particular images taken from a specific optical instrument. This thesis develops the first GP method utilizing frequency-based wavelet features, where the wrapper based feature selection and construction methods automatically evolve useful constructed features to improve the classification performance. The results show the evidence of successful feature construction by significantly outperforming existing GP approaches, state-of-the-art CNN, and other classification methods. This thesis develops a GP approach to multiple feature construction for ensemble learning in classification. The results show that the ensemble method outperformed existing GP approaches, state-of-the-art skin image classification, and commonly used ensemble methods. Further analysis of the evolved constructed features identified important image features that can potentially help the dermatologist identify further medical procedures in real-world situations

Macroeconomic and idiosyncratic factors of non-performing loans: Evidence from Pakistan’s banking sector

Using panel data approach in the Pakistan banking sector over the period 2010 to 2016, we examine the bank-specific and macroeconomic determinants of non-performing loans.  We use quantitative research design with OLS random effect model. Furthermore, we use various regression and correlation analysis in this study. We find that rise in capital adequacy ratio, bank size, GDP growth rate, and inflation, reduces the non-performing loans (NPL) ratio. Our results also show that a rise in loan loss provisions enhances the NPL ratio. Our results suggest that banks with poor asset-quality can sabotage the growth of fiscal as well as the economic sector. Outcomes of the study emphasis on the need to clear-out the NPLs to keep financial sector sound. NPLs can cause high loan loss provisions which affect the capitalization of banks that ultimately impacts fiscal and economic growth. Bank supervisory agencies should therefore pay attention to monitory and macroeconomic policies of the banks. This study examines the impact of idiosyncratic and macroeconomic determinants of non-performing loans on banks’ asset quality using recent data from 2010 to 2016, the time period when major banking sector reforms were launched

Health Risks Caused by Wireless Technologies

There are many health issues related to the use of cellular phones, wireless local area networks, and other devices that emit electromagnetic radiation (EMR). Some of these systems have become a part of our daily lives and many of us are in direct or indirect contact for extended period of times with these devices. However, the general public is unaware of the health risks associated with the use of these devices. Our research covers studies done by individuals as well as organizations on the harmful effects on the health of people from these devices and their claims. We also present information about research studies that refute some of these claims. Research under way to find health effects of wireless devices is also covered

Novel methods of object recognition and fault detection applied to non-destructive testing of rail’s surface during production

A series of rail image inspection algorithms have been developed for Tata Steels Scunthorpe rail production line. The following thesis describes the contributions made by the author in the design and application of these algorithms. A fully automated rail inspection system that has never been implemented before in any such company or setup has been developed. An industrial computer vision system (JLI) already exists for the image acquisition of rails during production at a rail manufacturing plant in Scunthorpe. An automated inspection system using the same JLI vision system has been developed for the detection of rail‟s surface defects during manufacturing process. This is to complement the human factor by developing a fully automated image processing based system to recognize the faults with an improved efficiency and to allow an exhaustive detection on the entire rail in production. A set of bespoke algorithms has been developed from a plethora of available image processing techniques to extract and identify components in an image of rail in order to detect abnormalities. This has been achieved through offline processing of the rail images using the blended use of different object recognition and image processing techniques, in particular, variation of standard image processing techniques. Several edge detection methods as well as adapted well known Artificial Neural Network and Principal Component Analysis techniques for fault detection on rail have been developed. A combination of customised existing image algorithms and newly developed algorithms have been put together to perform the efficient defect detection. The developed system is fast, reliable and efficient for detection of unique artefacts occurring on the rail surface during production followed by fault classification on the rail imaging system. Extensive testing shows that the defect detection techniques developed for automated rail inspection is capable of detecting more than 90% of the defects present in the available data set of rail images, which has more than 100,000 images under investigation. This demonstrates the efficiency and accuracy of the algorithms developed in this work

Creation, applications and detection of entanglement in quantum optical systems

In this work, three applications aimed at studying or exploiting various aspects of entanglement are considered. In the first project, the entanglement between two atoms inside a multimode resonator is investigated in the presence of retardation. Retardation is associated with the finite time required by a photon to propagate between atoms and cavity boundaries. It is found that retardation affects the atomic populations as well as the entanglement dynamics to a large degree. The second project is a study of entangled states of light to obtain an enhanced resolution. We have simulated optical centroid measurements for spatial resolution enhancement with various types of non-classical input states. By numerically simulating the measurement scheme, we optimize the detection parameters for an experimental implementation and also study the multiphoton absorption required for quantum lithography. The third project uses the scattered light from a resonantly driven correlated system to obtain information about the system. Techniques have been proposed using which in certain detection directions, n-atom correlations can be directly accessed in an experiment via light scattering with a significant count rate. Moreover, such detection of correlations is not limited to a particular spatial geometry but can be utilized for generalized geometries, too