A comparison of academic achievement of Montessori and non -Montessori students in a public school setting

Relationships between academic achievement and type of curriculum delivery system, Montessori or traditional, in a diverse group of learners from a public school district were examined in this study. In a repeated measures, within subjects design, students from an elementary Montessori program were paired with agemates from a traditional group on the basis of similar Stanford Achievement Test Scores in reading or math during the baseline year. Two subsequent administrations of the Stanford were observed for each subject to elucidate possible differences which might emerge based on program affiliation over the three year duration of the study. ^ Mathematics scores for both groups were not observed to be significantly different, although following the initial observation, the Montessori group continued to produce higher mean scores than did the traditional students. Marginal significance between the groups suggests that the data analysis should continue in an effort to elucidate a possible trend toward significance at the .05 level. ^ Reading scores for the groups demonstrated marginally significant differences by one analytical method, and significant differences when analyzed with a second method. In the second and third years of the study, Montessori students produced means which consistently outperformed the traditional group. ^ Recommendations included tracking subsequent administrations of the Stanford Achievement Test for all pairs of subjects in order to evaluate emerging trends in both subject areas. ^

The Next Generation of Scientists: Examining the Experiences of Graduate Students in Network-Level Social-Ecological Science

By integrating the research and resources of hundreds of scientists from dozens of institutions, network-level science is fast becoming one scientific model of choice to address complex problems. In the pursuit to confront pressing environmental issues such as climate change, many scientists, practitioners, policy makers, and institutions are promoting network-level research that integrates the social and ecological sciences. To understand how this scientific trend is unfolding among rising scientists, we examined how graduate students experienced one such emergent social-ecological research initiative, Integrated Science for Society and Environment, within the large-scale, geographically distributed Long Term Ecological Research (LTER) Network. Through workshops, surveys, and interviews, we found that graduate students faced challenges in how they conceptualized and practiced social-ecological research within the LTER Network. We have presented these conceptual challenges at three scales: the individual/project, the LTER site, and the LTER Network. The level of student engagement with and knowledge of the LTER Network was varied, and students faced different institutional, cultural, and logistic barriers to practicing social-ecological research. These types of challenges are unlikely to be unique to LTER graduate students; thus, our findings are relevant to other scientific networks implementing new social-ecological research initiatives.

Relational restorative justice pedagogy in educator professional development

What would a professional development experience rooted in the philosophy, principles, and practices of restorative justice look and feel like? This article describes how such a professional development project was designed to implement restorative justice principles and practices into schools in a proactive, relational and sustainable manner by using a comprehensive dialogic, democratic peacebuilding pedagogy. The initiative embodied a broad, transformative approach to restorative justice, grounded in participating educators’ identifying, articulating and applying personal core values. This professional development focused on diverse educators, their relationships, and conceptual understandings, rather than on narrow techniques for enhancing student understanding or changing student behaviour. Its core practice involved facilitated critical reflexive dialogue in a circle, organized around recognizing the impact of participants’ interactions on others, using three central, recurring questions: Am I honouring? Am I measuring? What message am I sending? Situated in the context of relational theory (Llewellyn, 2012), this restorative professional development approach addresses some of the challenges in implementing and sustaining transformative citizenship and peacebuilding pedagogies in schools. A pedagogical portrait of the rationale, design, and facilitation experience illustrates the theories, practices, and insights of the initiative, called Relationships First: Implementing Restorative Justice From the Ground Up.

Comparative study of CXC chemokines modulation in brown trout (Salmo trutta) following infection with a bacterial or viral pathogen

We would like to acknowledge Richard Paley, Tom Hill and Georgina Rimmer for their collaboration during brown trout infection challenges in CEFAS-Weymouth biosecurity facilities. Bartolomeo Gorgoglione, Stephen W. Feist and Nick G. H. Taylor were supported by a DEFRA grant (F1198).

Colorado's Piceance Basin: Variation in the local public finance effects of oil and gas development

A large increase in natural gas production occurred in western Colorado’s Piceance basin in the mid- to late-2000s, generating a surge in population, economic activity, and heavy truck traffic in this rural region. We describe the fiscal effects related to this development for two county governments: Garfield and Rio Blanco, and two city governments: Grand Junction and Rifle. Counties maintain rural road networks in Colorado, and Garfield County’s ability to fashion agreements with operators to repair roads damaged during operations helped prevent the types of large new costs seen in Rio Blanco County, a neighboring county with less government capacity and where such agreements were not made. Rifle and Grand Junction experienced substantial oil- and gas-driven population growth, with greater challenges in the smaller, more isolated, and less economically diverse city of Rifle. Lessons from this case study include the value of crafting road maintenance agreements, fiscal risks for small and geographically isolated communities experiencing rapid population growth, challenges associated with limited infrastructure, and the desirability of flexibility in the allocation of oil- and gas-related revenue.

Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Conflicting expectations in transforming government service processes: the story of e-payment for social welfare in Ireland

Despite its clear potential and attractiveness as a solution to a broad range of societal problems, E-Government has not been adopted to levels predicted in early 2000 literature. Whilst case studies of punctual development of E-Government initiatives abound, few countries have progressed to high levels of maturity in the systematic use of ICT in the relationship between government and citizens. At the same time, the current period brings challenges in terms of access to public services and costs of delivering these services which make the large scale use of ICT by governments more attractive than ever, if not even a necessity. This paper presents a detailed case study of a specific E-Government initiative in Ireland in the area of E-payments for G2C, in the social welfare area. Locating the current initiative in its historical context, it analyses the varied motivations and conflicting requirements of the numerous stakeholders and discusses the constraints that bear on the potential scenarios that could be followed at this point in time.

The Influence of Household Economic Status and Food Consumption Patterns on Obesity Rates in Peru – Evidence from a Field Study in 11 Communities of Madre de Dios

The globalization contributes to rapid economic developments and great changes of lifestyle in Madre de Dios of Peru, both of which have influenced the health status of local people in direct and indirect ways. The high overweight and obesity rate has become one of the biggest health challenges in this region. This study quantitatively analyzed the impact of household economic status and food consumption patterns on overweight and obesity, and tried to establish their relationship with local economic activities. People living in mining communities are more likely to be overweight or obese. Increased family incomes and lacks of health knowledge are two important reasons. The large consumption of soda and alcohol are positively associated with overweight and obesity. In addition, lack of physical activities is also one of the risk factors of overweight and obesity.

Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine?

Precision medicine is an emerging approach to disease treatment and prevention that considers variability in patient genes, environment, and lifestyle. However, little has been written about how such research impacts emergency care. Recent advances in analytical techniques have made it possible to characterize patients in a more comprehensive and sophisticated fashion at the molecular level, promising highly individualized diagnosis and treatment. Among these techniques are various systematic molecular phenotyping analyses (e.g., genomics, transcriptomics, proteomics, and metabolomics). Although a number of emergency physicians use such techniques in their research, widespread discussion of these approaches has been lacking in the emergency care literature and many emergency physicians may be unfamiliar with them. In this article, we briefly review the underpinnings of such studies, note how they already impact acute care, discuss areas in which they might soon be applied, and identify challenges in translation to the emergency department (ED). While such techniques hold much promise, it is unclear whether the obstacles to translating their findings to the ED will be overcome in the near future. Such obstacles include validation, cost, turnaround time, user interface, decision support, standardization, and adoption by end-users.

Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.

The quality of financial information in the extractive industries: a value relevance analysis

The extractive industry is characterized by high levels of risk and uncertainty. These attributes create challenges when applying traditional accounting concepts (such as the revenue recognition and matching concepts) to the preparation of financial statements in the industry. The International Accounting Standards Board (2010) states that the objective of general purpose financial statements is to provide useful financial information to assist the capital allocation decisions of existing and potential providers of capital. The usefulness of information is defined as being relevant and faithfully represented so as to best aid in the investment decisions of capital providers. Value relevance research utilizes adaptations of the Ohlson (1995) to assess the attribute of value relevance which is one part of the attributes resulting in useful information. This study firstly examines the value relevance of the financial information disclosed in the financial reports of extractive firms. The findings reveal that the value relevance of information disclosed in the financial reports depends on the circumstances of the firm including sector, size and profitability. Traditional accounting concepts such as the matching concept can be ineffective when applied to small firms who are primarily engaged in nonproduction activities that involve significant levels of uncertainty such as exploration activities or the development of sites. Standard setting bodies such as the International Accounting Standards Board and the Financial Accounting Standards Board have addressed the financial reporting challenges in the extractive industry by allowing a significant amount of accounting flexibility in industryspecific accounting standards, particularly in relation to the accounting treatment of exploration and evaluation expenditure. Therefore, secondly this study examines whether the choice of exploration accounting policy has an effect on the value relevance of information disclosed in the financial reports. The findings show that, in general, the Successful Efforts method produces value relevant information in the financial reports of profitable extractive firms. However, specifically in the oil & gas sector, the Full Cost method produces value relevant asset disclosures if the firm is lossmaking. This indicates that investors in production and non-production orientated firms have different information needs and these needs cannot be simultaneously fulfilled by a single accounting policy. In the mining sector, a preference by large profitable mining companies towards a more conservative policy than either the Full Cost or Successful Efforts methods does not result in more value relevant information being disclosed in the financial reports. This finding supports the fact that the qualitative characteristic of prudence is a form of bias which has a downward effect on asset values. The third aspect of this study is an examination of the effect of corporate governance on the value relevance of disclosures made in the financial reports of extractive firms. The findings show that the key factor influencing the value relevance of financial information is the ability of the directors to select accounting policies which reflect the economic substance of the particular circumstances facing the firms in an effective way. Corporate governance is found to have an effect on value relevance, particularly in the oil & gas sector. However, there is no significant difference between the exploration accounting policy choices made by directors of firms with good systems of corporate governance and those with weak systems of corporate governance.

Comparative Administrative Reform: The Rhetoric and Reality of the Civil Service Reform Programs in Uganda and Tanzania

Administrative reform is a challenging endeavor for both developed and developing countries alike. For developing countries, the challenge is greater because numerous reforms are implemented concurrently sometimes under conditions of resource scarcity and political instability. So far there is no consensus as to what makes some reforms succeed and others fail. The current study seeks to fill that gap by offering an empirical comparative analysis of the administrative reforms initiated in Uganda and Tanzania since the early 1990s. The purpose of the study is to explain the similarities and differences, and give reasons for the successes and failures of the reform programs in the two countries. It focuses on four major areas; the size of the civil service, pay reform, capacity building, and ethics and accountability. Data were collected via in-depth face to face interviews with 35 key government officials and the content analysis of various documents. The results indicate that the reforms generated initial substantial reduction in the size of the public services in both countries. In Uganda, the traditional civil service was reduced from 140,500 in 1990 to 41,730 in 2004; while in Tanzania Ministries, Departments, and Agencies were reduced by 25%. Pay reform has generated substantial increases in civil servants’ salaries in both countries but in Uganda, the government has not been able to abide by the pay strategy while in Tanzania the strategy guides the increments. Civil Service capacity building efforts have focused on enhancing the skills of the personnel. Training needs assessments were undertaken in all ministries in Uganda and a training policy was formulated. In Tanzania, the training needs assessments are still under way and a training policy has not yet been developed. Ethics and accountability are great challenges in both countries, but in Tanzania, there is more political will and commitment to improve the integrity of the civil service. The findings reveal that although Uganda started the reform with much more rigor and initial success, Tanzania has surpassed it and has a more stable, consistent, and promising reform record. This is because Uganda’s leadership lacks political legitimacy. The country has since the late 1990s experienced a civil war in the northern and western parts of the country while Tanzania has benefitted from relative peace and high level political legitimacy.

New Frontiers in Energy-Efficient Computing

Energy-efficient computing remains a critical challenge across the wide range of future data-processing engines — from ultra-low-power embedded systems to servers, mainframes, and supercomputers. In addition, the advent of cloud and mobile computing as well as the explosion of IoT technologies have created new research challenges in the already complex, multidimensional space of modern and future computer systems. These new research challenges led to the establishment of the IEEE Rebooting Computing Initiative, which specifically addresses novel low-power solutions and technologies as one of the main areas of concern.With this in mind, we thought it timely to survey the state of the art of energy-efficient computing.