Understanding Controlled Evaporation Of Microdroplets Towards Scalable Micro/Nano Manufacturing

This research investigates the controlled evaporation of microdroplets to the nano scale regime for scalable micro/nano manufacturing. A customized direct write inkjet printing system was utilized to generate monodisperse microdroplets of different fluid types. Two novel approaches were employed to achieve the research objective. The first approach incorporated a convective heat source (i.e. resistive heated ring) to induce controlled heat flux for microdroplet evaporation after ejection from the inkjet system

Alif: Building a Bridge: Arabic Calligraphy and Arabic Typography in Design

The project is centered around the exploration of Arabic typography and its multi-faceted uses. Arabic calligraphy is an old practice and tradition that has been studied and refined since it was founded. Comparatively, Arabic typography is a much more recent creative study that is still developing, thus there has not been much literature that covers it. Therefore, the main goal of the project is to highlight and integrate Arabic typography with Arabic calligraphy. By doing so, the project builds a bridge within Arabic graphic design, connecting the rich Arabic calligraphy to its newer counterpart, Arabic typography. In our day and age of inclusivity and fast communication, it is proving to be crucial to create a bridge between languages. Design using Roman and Arabic letterforms is challenging, but worthwhile when it all comes together into one harmonious design. The contrast between the Arabic letterforms and Roman ones is striking when the designer uses the appropriate fonts and calligraphic styles. By bridging that linguistic gap in a functional and aesthetic way, the second goal for the project is to build a bridge between the Arabic design world and the English-speaking design world. Finally, English-speakers who want to learn Arabic and love art will find this as a great starting point due to the educational nature of the content

Debating medicalization of Female Genital Mutilation/Cutting (FGM/C) : learning from (policy) experiences across countries

Background: Although Female Genital Mutilation/Cutting (FGM/C) is internationally considered a harmful practice, it is increasingly being medicalized allegedly to reduce its negative health effects, and is thus suggested as a harm reduction strategy in response to these perceived health risks. In many countries where FGM/C is traditionally practiced, the prevalence rates of medicalization are increasing, and in countries of migration, such as the United Kingdom, the United States of America or Sweden, court cases or the repeated issuing of statements in favor of presumed minimal forms of FGM/C to replace more invasive forms, has raised the debate between the medical harm reduction arguments and the human rights approach. Main body: The purpose of this paper is to discuss the arguments associated with the medicalization of FGM/C, a trend that could undermine the achievement of Sustainable Development Goal 5.3. The paper uses four country case studies, Egypt, Indonesia, Kenya and UK, to discuss the reasons for engaging in medicalized forms of FGM/C, or not, and explores the ongoing public discourse in those countries concerning harm reduction versus human rights, and the contradiction between medical ethics, national criminal justice systems and international conventions. The discussion is structured around four key hotly contested ethical dilemmas. Firstly, that the WHO definition of medicalized FGM/C is too narrow allowing medicalized FGM to be justified by many healthcare professionals as a form of harm reduction which contradicts the medical oath of do no harm. Secondly, that medicalized FGM/C is a human rights abuse with lifelong consequences, no matter who performs it. Thirdly, that health care professionals who perform medicalized FGM/C are sustaining cultural norms that they themselves support and are also gaining financially. Fourthly, the contradiction between protecting traditional cultural rights in legal constitutions versus human rights legislation, which criminalizes FGM/C. Conclusion: More research needs to be done in order to understand the complexities that are facilitating the medicalization of FGM/C as well as how policy strategies can be strengthened to have a greater de-medicalization impact. Tackling medicalization of FGM/C will accelerate the achievement of the Sustainable Development Goal of ending FGM by 2030

Law and the Demand for Property-Casualty Insurance Consumption

This paper examines the importance of legal rights and enforcement in influencing property-casualty insurance consumption. We extend the existing literature by examining the role of legal factors in determining insurance density across countries. Also, measures of risk aversion, loss probability and price, which overcome limitations of proxies used in the existing literature on insurance demand are analysed. Using a panel data set we apply a Generalized Methods of Moments dynamic system estimator, which relaxes the assumption of strict exogeneity of the regressors and produces unbiased and efficient estimates. The results show a strong positive relationship between the protection of property rights and insurance consumption, which is robust to various model specifications and estimation techniques. Moreover, the results show the purchase of property-casualty insurance is significantly and positively related to loss probability and income, as well as providing weaker evidence of a negative relationship with price.

Learning Learning Algorithms

Machine learning models rely on data to learn any given task and depending on the universal diversity of each of the elements of the task and the design objectives, multiple data may be required for better performance, which in turn could exponentially increase learning time and computational cost. Although most of the training of machine learning models today are done using GPUs (Graphics Processing Unit) to speed up the training process, most however, depending on the dataset, still require a huge amount of training time to attain good performance. This study aims to look into learning learning algorithms or popularly known as metalearning which is a method that not only tries to improve the learning speed but also the model performance and in addition it requires fewer data and entails multiple tasks. The concept involves training a model that constantly learns to learn novel tasks at a fast rate from previously learned tasks. For the review of the related work, attention will be given to optimization-based methods and most precisely MAML (Model Agnostic MetaLearning), because first of all, it is one of the most popular state-of-the-art metalearning method, and second of all, this thesis focuses on creating a MAML based method called MAML-DBL that uses an adaptive learning rate technique with dynamic bounds that enables it to attain quick convergence at the beginning of the training process and good generalization towards the end. The proposed MAML variant aims to try to prevent vanishing learning rates during training and slowing down at the end where dense features are prevalent, although further hyperparameter tunning might be necessary for some models or where sparse features may be prevalent, for improved performance. MAML-DBL and MAML, were tested on the most commonly used datasets for metalearning models, and based on the results of the experiments, the proposed method showed a rather competitive performance on some of the models and even outperformed the baseline in some of the carried out tests. The results obtained with both MAML-DBL (in one of the dataset) and MAML, show that metalearning methods are highly recommendable solutions whenever good performance, less data and a multi-task or versatile model are required or desired.Os modelos de aprendizagem automática dependem dos dados para aprender qualquer tarefa e, dependendo da diversidade de cada um dos elementos da tarefa e dos objetivos do projeto, a quantidade de dados pode ser elevada, o que, por sua vez, pode aumentar exponencialmente o tempo de aprendizagem e o custo computacional. Embora a maioria do treino dos modelos de aprendizagem automática hoje seja feito usando GPUs (unidade de processamento gráfico), ainda é necessária uma quantidade enorme de tempo de treino para obter o desempenho desejado. Este trabalho tem como objetivo analisar os algoritmos de aprendizagem de aprendizagem ou popularmente conhecidos como metalearning, que são métodos que não apenas tentam melhorar a velocidade de aprendizagem, mas também o desempenho do modelo e, além disso, requerem menos dados e envolvem várias tarefas. O conceito envolve o treino de um modelo que aprende constantemente a aprender tarefas novas em ritmo acelerado, a partir de tarefas aprendidas anteriormente. Para a revisão do trabalho relacionado, será dada atenção aos métodos baseados em otimização e, mais precisamente, ao MAML (Model Agnostic MetaLearning), porque em primeiro lugar é um dos métodos de metalearning mais populares e em segundo lugar, esta tese foca a criação de um método baseado em MAML, chamado MAML-DBL, que usa uma técnica de taxa de aprendizagem adaptável com limites dinâmicos que permite obter convergência rápida no início do processo de treino e boa generalização no fim. A proposta variante de MAML tem como objetivo tentar evitar o desaparecimento das taxas de aprendizagem durante o treino e a desaceleração no fim onde entradas densas são predominantes, embora possa ser necessário um ajuste adicional dos hiperparâmetros para alguns modelos ou onde entradas esparsas podem ser predominantes, para melhorar o desempenho. O MAML-DBL e o MAML foram testados nos conjuntos de dados mais comumente usados para modelos de metalearning, e com base nos resultados das experiências, o método proposto mostrou um desempenho bastante competitivo em alguns dos modelos e até superou o baseline em alguns dos testes realizados. Os resultados obtidos com o MAML e MAML-DBL (num dos conjuntos de dados) mostram que os métodos de metalearning são soluções altamente recomendáveis sempre que um bom desempenho, menos dados e um modelo versátil ou com várias tarefas são necessários ou desejados

Indoor Navigation for Visually Impaired / Blind People Using Smart Cane and Mobile Phone: Experimental Work

This paper suggests a navigation structure for visually impaired/blind people. The proposed structure is consisted of builder simulator, smart phone with built-in compass and cane. The aim is to guide visually impaired/blind people from a required location to a desired location. Multi tests are run where modeled in C sharp and java languages and MATLAB program using A* algorithm. Experimental results show good performance for the proposed scheme that the visually impaired/blind people reach the desired location successfully without error. In conclusion, A* path finding algorithm using C sharp language for builder simulator, and java language for smart phone, were valid and reliable methods to guide the visually impaired / blind people from a required location to a desired location in indoor environment with or without obstacles. Keywords: Smart phone, built in compass, cane, planning the path, avoidance obstacles, visually impaired/blind people, A* algorithm, path finding, and indoor navigatio

Rapid evidence assessment: Quality of studies assessing interventions to support FGM/C abandonment

The last decade has seen increased focus and investment in interventions to eliminate female genital mutilation/cutting (FGM/C), along with the need to accelerate its abandonment. The UK Department for International Development (DFID) commissioned the Evidence to End FGM/C: Research to Help Girls and Women Thrive project to: 1) assess the quality of studies that have evaluated different interventions for the prevention of FGM/C, and 2) describe the FGM/C interventions that were evaluated by high-quality studies. The quality of evidence on the effectiveness and impact of FGM/C interventions is generally moderate to low. In addition, few baseline surveys are conducted prior to implementing interventions, making assessment of effect and generalizability difficult. Despite a high concentration of studies evaluating anti-FGM/C interventions from sub-Saharan Africa, few emphasize adequate reporting on cultural sensitivity and contexts during the design stage, or interpretation of findings for local policy. This rapid evidence assessment provides valuable methodological lessons for the design of future high-quality assessments or evaluations of FGM/C interventions

Sexuality experiences of secondary school students in Nakuru, Kenya: a cross-sectional study

Background: Adolescence is a phase where one is inquisitive about sex and sexuality. It is but natural to exchange the half-baked feelings and experiences with peers. These days the environment that includes public media has sexually suggestive flavors.Objective: This study was conducted to find out the sexual experiences of a selected, few school going adolescents.Methods: A cross-sectional study utilizing stratified sampling to enroll 200 students from secondary schools in Nakuru County, Kenya. A self-administered questionnaire was used to collect data. The data was entered and analyzed in SPSS® version 22. Chi-square test of independence and Fisher’s Exact Test were performed to test for associations.Results: The study found that a large number have had sexual experiences of varying type. It was interesting to learn from the survey that a large number of older adolescents wish that their parents should have talked to them about sexual matters. There is a general conservative outlook that the students had. Although a small percentage had sexual experiences at a very young age, many of them valued ethics and morality.Conclusion: There is urgency for intervention by the parents and the church in filing the gap in sexual knowledge.Keywords: Sexuality experiences, secondary school students, Nakuru, Kenya