Factors associated with pesticide use behaviors among farmworkers based on health belief model

[No abstract available

Genetic Toolkit for Assessment and Prediction of Population-Level Impacts of Bridge Construction on Birds

Recent studies have highlighted alarming rates of declines in bird populations across the country. The State of California is home to over 650 resident and migrant avian species. Legislation for protecting these species has existed for over a century now, yet tools for identifying populations and understanding seasonal movement remain limited. Recently, genetic and genomic tools have provided a method for understanding population structure, allowing for more informed delineation of management units. The goal of this project was to create a genetic toolkit for identifying breeding populations and assigning individuals to those populations. Ultimately, such tools could be used to assess population-level impacts when there are conflicts with birds at infrastructure construction sites. As a test case, we sequenced entire genomes for 40 individual Anna’s hummingbirds (Calypte anna) from across the state. Based on this initial data, we found low levels of differentiation between sampled locations, suggesting that C. anna in California are not subdivided into different population units. However, there was a weak signal of geography suggesting there may be localized genetic differences in a small proportion of the genome. Follow-up work will focus on a broader sampling across the state of California to clarify any possible population subdivision or geographical patterns of differentiation.View the NCST Project Webpag

Moving Toward a Holistic Menstrual Hygiene Management: An Anthropological Analysis of Menstruation and Practices in Western and Non-Western Societies

Educating girls is not only their human right, but also proposed as one of the best investments for improving quality of life in developing countries (Montgomery et al. 2016, 2). Although menstruation is a universal, biological process, it is fraught with cultural stigmas and taboos throughout Western and non-Western societies. Menstrual-related absenteeism is believed to be a primary cause of missed attendance and early dropout rates, so the developing field of menstrual hygiene management (MHM) is seeking to understand and evaluate what factors are contributing to these findings. After the analyzation of the current literature, a more holistic, nine-pronged approach to menstrual hygiene management is proposed for interventions in the Global South through girls’ access to sanitary products, clean toilets, hand washing facilities, puberty education, pain mitigation, a head woman teacher, inclusion of boys and men, improving support at home, and destigmatization. Because of the country’s rich recent history of international attention and aid, Uganda will be utilized as a backdrop to understand what menstrual hygiene management research is currently being conducted, positive and negative conclusions from the studies, and to uncover gaps for future research

Women’s Health Seeking Behavior in Rural Uganda

My project analyzes the usage of traditional healers and biomedical healthcare by pregnant women in the rural region of the Nakaseke District, Uganda. This is of concern to medical anthropologists because childbirth decision-making is a result of social change, a topic that is rapidly becoming more important in international development research. With data that Kimber Haddix McKay and Catherine Sanders have collected in 2011 and 2013, I utilized inferential and descriptive statistical analyses to determine my predicted hypotheses that age and education level are the most influential factors during prenatal care and delivery, that women who have a more traditional view of gender roles will be more likely to give birth close to home, and that traditional healers are more accessible than biomedical facilities. With the same data sets, I then determined the impact of socioeconomic status on these decision-making processes using multiple regressions. This research is important to the understanding of how social change is affecting decisions about childbirth is rural Uganda

Angular variation of the magnetoresistance of the superconducting ferromagnet UCoGe

We report a magnetoresistance study of the superconducting ferromagnet UCoGe. The data, taken on single-crystalline samples, show a pronounced structure at $B^* = 8.5$~T for a field applied along the ordered moment $m_0$. Angle dependent measurements reveal this field-induced phenomenon has an uniaxial anisotropy. Magnetoresistance measurements under pressure show a rapid increase of $B^*$ to 12.8~T at 1.0~GPa. We discuss $B^*$ in terms of a field induced polarization change. Upper critical field measurements corroborate the unusual S-shaped $B_{c2}(T)$-curve for a field along the $b$-axis of the orthorhombic unit cell.Comment: 6 pages, 5 figures; accepted for publication in Phys. Rev.

Analysis of multi-sensor data, 12 September - 11 December 1968

Analysis of multi-sensor data obtained by Earth Resources Aircraft Progra

Superconductivity and magnetic order in the non-centrosymmetric Half Heusler compound ErPdBi

We report superconductivity at $T_c = 1.22$ K and magnetic order at $T_N = 1.06$ K in the semi-metallic noncentrosymmetric Half Heusler compound ErPdBi. The upper critical field, $B_{c2}$, has an unusual quasi-linear temperature variation and reaches a value of 1.6 T for $T \rightarrow 0$. Magnetic order is found below $T_c$ and is suppressed at $B{_M} \sim 2.5$ T for $T \rightarrow 0$. Since $T_c \simeq T_N$, the interaction of superconductivity and magnetism is expected to give rise to a complex ground state. Moreover, electronic structure calculations show ErPdBi has a topologically nontrivial band inversion and thus may serve as a new platform to study the interplay of topological states, superconductivity and magnetic order.Comment: 6 pages, 5 figures; accepted for publication in Europhysics Letter

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015