Texture classification using transform analysis

The work presented in this thesis deals with the application of spectral methods for texture classification. The aim of the present work is to introduce a hybrid methodology for texture classification based on a spatial domain global pre-classifier together with a spectral classifier that utilizes multiresolution transform analysis. The reason for developing a spatial pre-classifier is that many discriminating features of textures are present in the spatial domain of the texture. Of these, global features such as intensity histograms and entropies can still add significant information to the texture classification process. The pre-classifier uses texture intensity histograms to derive histogram moments that serve as global features. A spectral classifier that uses Hartley transform follows the pre-classifier. The choice of such transform was due to the fact that the Fast Hartley Transform has many advantages over the other transforms since it results in real valued arrays and requires less memory space and computational complexity. To test the performance of the whole classifier, 900 texture images were generated using mathematical texture generating functions. The images generated were of three different classes and each class is sub-classified into three sub-classes. Half of the generated samples was used to build the classifier, while the other half was used to test it. The pre-classifier was designed to identify texture classes using an Euclidean distance matching for 4 statistical moments of the intensity histograms. The pre-classifier matching accuracy is found to be 99.89%. The spectral classifier is designed on the basis of the Hartley transform to determine the image sub-class. Initially, a full resolution Hartley transform was used to obtain two orthogonal power spectral vectors. Peaks in these two vectors were detected after applying a 10% threshold and the highest 4 peaks for each image are selected and saved in position lookup tables. The matching accuracy obtained using the two classification phases (pre-classifier and spectral classifier) is 99.56%. The accuracy achieved for the single resolution classifier is high but that was achieved on the expense of space for the lookup tables. In order to investigate the effect of lowering the resolution on the size of the information needed for matching the textures, we have applied a multiresolution technique to the Hartley Transform in a restricted way by computing the Hartley spectra in decreasing resolution. In particular, a one-step resolution decrease achieves 99% matching efficiency while saving memory space by 40%. This is a minor sacrifice of less than 1% in the matching efficiency with a considerable decrease in the complexity of the present methodology

Television human-puppet talk show: sensationalism, conflict and emotional concerns case study of Abla Fahita Live

This study examines the effect of sensationalism in human-puppet talk shows and the rate of adoption or rejection of viewers to the new innovation for Egyptian Television “Abla Fahita.†Two theories are used as a framework: diffusion of innovation theory (DOI) and cultivation theory. The study’s main hypotheses were H1: Youngesters adopt innovativeness earlier than others in their social system. H2: Audience who watch more show episode’s segments, the more they tend to adopt sensational contents spontaneously. H3: Abla Fahita human-puppet talk show’s heavy viewers tend to watch the episodes on YouTube channel than television. The primary research linked sensationalism to television talk shows’ aspects to examine whether the rate of adoption or rejection to human-puppet talk show (as new innovation to Egyptian television) is due to the sensational contents or the time spent watching human-puppet shows. This study processed with conducting quantitative survey for sample of three generations; teeangers (university students), parents (second generation), and grandparents (first generation) to measure the relative speed of adoption or rejection rate to human-puppet shows across generations. The findings support the assumption that the rate of adoption to Abla Fahita human-puppet talk show as new innovation to Egyptian television increases by the decrease of age; i.e. young third generation adopt innovation earlier than others in social system. The more sensational contents presented in the episode’s segments, the more viewers tend to adopt the innovation spontaneously. The third hypothesis was rejected as the data collected showed that heavy viewers change their viewership medium from television to YouTube depending on preference and comfort, and not for show contents nor television censorship

Using Direct PCR for Disaster Victim Identification Reference Samples

Forensic genetic testing is an important tool for the identification of victims after a mass fatality event but degradation of remains, presence of PCR inhibitors, and limited amounts of sample can make testing difficult. Standard protocols typically include extraction of genetic material from recovered post-mortem samples, and from ante-mortem reference samples or families of a missing person. This is a time-consuming and laborious process and may result in the loss of trace amounts of DNA available for amplification. Incorporating workflows that bypass the extraction step and directly amplify recovered DNA for short tandem repeat (STR) profile generation has the potential to help expedite the process of victim identification and improve the success rates for small samples. To evaluate the effectiveness of a direct PCR workflow in disaster victim identification (DVI) settings, bone and muscle tissue were subjected to direct amplification for STR profile generation. Furthermore, two possible reference sample types, formalin fixed tissue and slides, and personal belongings such as toothbrushes, hair from hairbrushes, glasses and razors were evaluated with different direct PCR methods. Bone, muscle, hair and toothbrushes were all consistently successful with direct PCR workflows, while razors and glasses were less consistent. formalin fixed samples were found to be inappropriate for use with direct PCR, and should be avoided if possible when constructing reference STR profiles

Diving in: Using a “Shark Tank” approach to teach business skills to future DNP leaders

Doctor of Nursing Practice (DNP) education prepares graduates to lead clinical improvement and innovation across practice settings. Advanced clinical knowledge, leadership skills, and the development of quality/safety competencies uniquely prepare the DNP program graduates to drive organizational change. Adding business and financial competencies to the skill set of DNP graduates strengthens the impact and value of their role on financial, quality, and operational outcomes. The Organizational Systems and Healthcare Financing course in a DNP program was redesigned to engage learners using an innovative approach to teach business and financial principles. This paper aims to (a) describe a novel “Shark Tank” approach whereby students develop and “pitch” their business proposals to a panel of healthcare executives; (b) share examples of impactful change projects by student teams; (c) report DNP course and program evaluations including students’ satisfaction and perceptions of value and knowledge gained in business principles; and (d) report opportunities for bidirectional mentorship, faculty recruitment, and succession planning. The success of this innovative team-based approach for teaching business/financial skills better prepares future DNP leaders and has implications for other DNP programs. Using this teaching strategy created opportunities for faculty recruitment, succession planning, and bidirectional mentorship of DNP-prepared nurse leaders

Supervised Learning Method for the Prediction of Subcellular Localization of Proteins Using Amino Acid and Amino Acid Pair Composition

Background Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein\u27s subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy. Results We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to predict the subcellular location based on amino acid composition and amino acid pair composition. Results were calculated after 10-fold cross validation. Radial Basis Function (RBF) outperformed polynomial and linear kernel functions. Total prediction accuracy reached to 71.8% for amino acid composition and 77.0% for amino acid pair composition. In order to observe the impact of number of subcellular locations we constructed two more datasets of nine and five subcellular locations. Total accuracy was further improved to 79.9% and 85.66%. Conclusions A new SVM based approach is presented based on amino acid and amino acid pair composition. Result shows that data simulation and taking more protein features into consideration improves the accuracy to a great extent. It was also noticed that the data set needs to be crafted to take account of the distribution of data in all the classes

Busting contraception myths and misconceptions among youth in Kwale County, Kenya: results of a digital health randomised control trial

Objectives: The objective of this randomised controlled trial in Kenya was to assess the effect of delivering sexual and reproductive health (SRH) information via text message to young people on their ability to reject contraception-related myths and misconceptions. Design and setting: A three-arm, unblinded randomised controlled trial with a ratio of 1:1:1 in Kwale County, Kenya. Participants and interventions: A total of 740 youth aged 18–24 years were randomised. Intervention arm participants could access informational SRH text messages on-demand. Contact arm participants received once weekly texts instructing them to study on an SRH topic on their own. Control arm participants received standard care. The intervention period was 7 weeks. Primary outcome: We assessed change myths believed at baseline and endline using an index of 10 contraception- related myths. We assessed change across arms using difference of difference analysis. Results: Across arms, \u3c5% of participants did not have any formal education, \u3c10% were living alone, about 50% were single and \u3e80% had never given birth. Between baseline and endline, there was a statistically significant drop in the average absolute number of myths and misconceptions believed by intervention arm (11.1%, 95% CI 17.1% to 5.2%), contact arm (14.4%, 95% CI 20.5% to 8.4%) and control arm (11.3%, 95% CI 17.4% to 5.2%) participants. However, we observed no statistically significant difference in the magnitude of change across arms. Conclusions: We are unable to conclusively state that the text message intervention was better than text message ‘contact’ or no intervention at all. Digital health likely has potential for improving SRH-related outcomes when used as part of multifaceted interventions. Additional studies with physical and geographical separation of different arms is warranted

Life cycle assessment of plastic waste into furniture using open LCA software

Plastic waste management is one of the most severe environmental issues confronting municipalities worldwide, and it is the most serious environmental issue in Malaysia. Furniture gains attention in the life cycle assessment (LCA) of a net-zero energy building. It was responsible for 10% of the building's impact on global warming and nonrenewable energy demand. Therefore, it shall be considered in the building's design. This study evaluates the environmental effects of recycled high-density polyethylene (HDPE) eco-furniture using the Open LCA software. The scope of the study considered the cradle-to-gate boundary of recycling and manufacturing 1 kg of the eco-furniture functional unit. This paper assesses the LCA through Open LCA in obtaining the environmental impact of waste-to-wealth product generation. Primary data (amount of plastic waste, electricity, emission, and water) were gathered in a local recycling centre, EZ plast Plastic, data from the European Life Cycle Data database and data from a previous study for the electricity. In addition, the CML Baseline impact method, readily available in the Eco Invent LCIA database, is employed to determine plastic waste performance in their impact categories. Nine environmental impact categories were considered. The result shows that the consumption of electricity and HDPE during the manufacture of eco-furniture resulted in the most significant amount of environmental loading, up to 78% to 90% on all the impact categories

The Effect of Switching to Second-Line Antiretroviral Therapy on the Risk of Opportunistic Infections Among Patients Infected With Human Immunodeficiency Virus in Northern Tanzania

Background. Due to the unintended potential misclassifications of the World Health Organization (WHO) immunological failure criteria in predicting virological failure, limited availability of treatment options, poor laboratory infrastructure, and healthcare providers’ confidence in making switches, physicians delay switching patients to second-line antiretroviral therapy (ART). Evaluating whether timely switching and delayed switching are associated with the risk of opportunistic infections (OI) among patients with unrecognized treatment failure is critical to improve patient outcomes

Hybrid 2D Nanomaterials as Dual-mode Contrast Agents in Cellular Imaging

The design of multifunctional nanofluids is highly desirable for biomedical therapy/cellular imaging applications.[1–4] The emergence of hybrid nanomaterials with specific properties, such as magnetism and fluorescence, can lead to an understanding of biological processes at the biomolecular level.[1] Various hybrid systems have been analyzed in the recent past for several possible biomedical applications.[5–9] Carbon-based hybrid systems such as carbon nanotubes with various nanoparticles are being widely tested for their biological applications because of their ability to cross cell membranes and their interesting thermal and electrical properties.[10,11] Graphene oxide (GO) is a fairly new graphene-based system with a 2D carbon honeycomb lattice decorated with numerous functional groups attached to the backbone: these functional groups make it an excellent platform for further attachment of nanoparticles and synthesis of hybrid materials. Cell viability studies on GO have been recently attempted, showing biocompatibility. [12,13] Moreover, the intrinsic photoluminescence (PL) properties of GO can be utilized for cellular imaging.[13] The large surface area and non-covalent interactions with aromatic molecules make GO an excellent system for biomolecular applications and drug attachment