Stimulating Critical Thinking in U.S Business Students through the Inclusion of International Students

Students enroll in college with different expectations, aptitudes, and cultural backgrounds. Research supports the need for faculty to create an atmosphere where students feel comfortable discussing cultural sensitive issues. As the business world is becoming more global, critical thinking and the ability to form and articulate thoughts proficiently have become essential competencies for effective communication in all avenues of life. Discussion and Multicultural Education as methods of teaching have the potential in aiding the development of critical thinking skills of students. Moreover, students’ intellectual progress by means of the discussions enhances from critically evaluating views from global perspectives. This paper provides an argument as to the importance of faculty being knowledgeable of the cultural and sociopolitical history of international students to better foster a classroom climate of respect to ensure that the discussion perspectives from all students are inclusive and value

Pearl Lunt Robinson Correspondence

Entries include brief biographical information, a handwritten biography and reply to Fuller\u27s request to list Robinson in a Who\u27s Who of Maine Poets in the Maine Library Bulletin, and the typed transcript of correspondence with a note concerning Robinson\u27s visit to the Maine State Library to present her book

How to model mutually exclusive events based on independent causal pathways in Bayesian network models

This is supported by ERC project ERC-2013-AdG339182-BAYES_KNOWLEDGE

The National Childrens Study: Recruitment Outcomes Using the Provider-Based Recruitment Approach

In 2009, the National Children’s Study (NCS) Vanguard Study tested the feasibility of household-based recruitment and participant enrollment using a birth-rate probability sample. In 2010, the NCS Program Office launched 3 additional recruitment approaches. We tested whether provider-based recruitment could improve recruitment outcomes compared with household-based recruitment

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support

OBJECTIVES: 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way

When and where to transfer for Bayesian network parameter learning

This work is supported by the European Research Council (ERC-2013-AdG339182-BAYES-KNOWLEDGE) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640891. YZ is supported by China Scholarship Council (CSC)/Queen Mary Joint PhD scholarships and National Natural Science Foundation of China (61273322, 71471174)

Assessment of the response of pollinator abundance to environmental pressures using structured expert elicitation

Policy-makers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those dealing with ecosystem services. For policy-makers wishing to make evidence-based decisions which will best support pollinator abundance and pollination services, one of the problems faced is how to access the information and evidence they need, and how to combine it to formulate and evaluate candidate policies. This is even more complex when multiple factors provide influence in combination. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees, and other pollinators are well documented, but incomplete. In order to estimate the potential effectiveness of various candidate policy choices, there is an urgent need to quantify the effect of various combinations of factors on the pollination ecosystem service. Using high-quality experimental evidence is the most robust approach, but key aspects of the system may not be amenable to experimentation or may be prohibitive based on cost, time and effort. In such cases, it is possible to obtain the required evidence by using structured expert elicitation, a method for quantitatively characterizing the state of knowledge about an uncertain quantity. Here we report and discuss the outputs of the novel use of a structured expert elicitation, designed to quantify the probability of good pollinator abundance given a variety of weather, disease, and habitat scenarios

Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved

When developing a causal probabilistic model, i.e. a Bayesian network (BN), it is common to incorporate expert knowledge of factors that are important for decision analysis but where historical data are unavailable or difficult to obtain. This paper focuses on the problem whereby the distribution of some continuous variable in a BN is known from data, but where we wish to explicitly model the impact of some additional expert variable (for which there is expert judgment but no data). Because the statistical outcomes are already influenced by the causes an expert might identify as variables missing from the dataset, the incentive here is to add the expert factor to the model in such a way that the distribution of the data variable is preserved when the expert factor remains unobserved. We provide a method for eliciting expert judgment that ensures the expected values of a data variable are preserved under all the known conditions. We show that it is generally neither possible, nor realistic, to preserve the variance of the data variable, but we provide a method towards determining the accuracy of expertise in terms of the extent to which the variability of the revised empirical distribution is minimised. We also describe how to incorporate the assessment of extremely rare or previously unobserved events

A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study

Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project

Genomic loss of heterozygosity and survival in the REAL3 trial

Background: Homologous recombination deficiency (HRD) measured using a genomic signature for loss of heterozygosity (LOH) predicts benefit from rucaparib in ovarian cancer. We hypothesized that some oesophagogastric cancers will have high-LOH which would be prognostic in patients treated with platinum chemotherapy. Methods: Diagnostic biopsy DNA from patients treated in the REAL3 trial was sequenced using the Foundation Medicine T5 next-generation sequencing (NGS) assay. An algorithm quantified the percentage of interrogable genome with LOH. Multidimensional optimization was performed to identify a cut-off dichotomizing the population into LOH-high and low groups associated with differential survival outcomes. Results: Of 158 available samples, 117 were successfully sequenced; LOH was derived for 74 of these. A cut-off of 21% genomic LOH defined an LOH-high subgroup (n=10, 14% of population) who had median overall survival (OS) of 18.3 months (m) versus 11m for the LOH-low group (HR 0.55 95% CI 0.19-0.97, p= 0.10). Progression free survival (PFS) for LOH-high and LOH-low groups was 10.7m and 7.3m (HR 0.61 (95% CI 0.21 – 1.09, p=0.09). Sensitivity analysis censoring operated patients (n=4), demonstrated OS of 18.3m vs. 10.2m (HR 0.43, 95% CI (0.20-0.92), p=0.02; PFS was 10.5m vs. 7.2m (HR 0.55, (95% CI 0.26-1.17), p=0.09 for LOH-high and LOH-low. Conclusion: HRD assessment using an algorithmically derived LOH signature on a standard NGS panel identifies oesophagogastric cancer patients with high LOH who have prolonged survival when treated with platinum chemotherapy. Validation work will determine the signature’s predictive value in patients treated with a PARP inhibitor and with platinum chemotherapy