Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Phosphorus and Potassium Rates and Placement Methods for Corn and Soybean Managed with No-till or Tillage

No-till management for corn and soybean results in little or no incorporation of crop residues and fertilizer with soil. Subsurface banding phosphorus (P) and potassium (K) fertilizers with planter attachments could be more effective than broadcast fertilization, because in no-till with broadcast fertilizer, both nutrients accumulate at or near the soil surface. A long-term study was initiated in 1994 at the ISU Northwest Research Farm to evaluate P and K fertilizer placement for corn and soybean managed with no-till and chiselplow tillage.

Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research

The microfoundations research agenda presents an expanded theoretical perspective because it considers individuals, their characteristics, and their interactions as relevant variables to help us understand firm-level strategic issues. However, microfoundations empirical research faces unique challenges because processes take place at different levels of analysis and these multilevel processes must be considered simultaneously. We describe multilevel modeling and mixed methods as methodological approaches whose use will allow for theoretical advancements. We describe key issues regarding the use of these two types of methods and, more importantly, discuss pressing substantive questions and topics that can be addressed with each of these methodological approaches with the goal of making theoretical advancements regarding the microfoundations research agenda and strategic management studies in general.

The panorama of science and art; embracing the principal sciences and arts; the methods of working in wood and metal; and a miscellaneous selection of useful and interesting processes and experiments.

Mode of access: Internet.

Profitable lumber retailing; a presentation of profit-creating management practices and sales promotion methods as applied to the retailing of all building materials,

Mode of access: Internet.

The impact of leadership and change management strategy on organizational culture and individual acceptance of change during a merger

This article reports a longitudinal study that examined mergers between three large multi-site public-sector organizations. Both qualitative and quantitative methods of analysis are used to examine the effect of leadership and change management strategies on acceptance of cultural change by individuals. Findings indicate that in many cases the change that occurs as a result of a merger is imposed on the leaders themselves, and it is often the pace of change that inhibits the successful re-engineering of the culture. In this respect, the success or otherwise of any merger hinges on individual perceptions about the manner in which the process is handled and the direction in which the culture is moved. Communication and a transparent change process are important, as this will often determine not only how a leader will be regarded, but who will be regarded as a leader. Leaders need to be competent and trained in the process of transforming organizations to ensure that individuals within the organization accept the changes prompted by a merger.

Perceptions and experiences of the implementation, management, use and optimisation of electronic prescribing systems in hospital settings:protocol for a systematic review of qualitative studies

Introduction: There is increasing evidence that electronic prescribing (ePrescribing) or computerised provider/physician order entry (CPOE) systems can improve the quality and safety of healthcare services. However, it has also become clear that their implementation is not straightforward and may create unintended or undesired consequences once in use. In this context, qualitative approaches have been particularly useful and their interpretative synthesis could make an important and timely contribution to the field. This review will aim to identify, appraise and synthesise qualitative studies on ePrescribing/CPOE in hospital settings, with or without clinical decision support. Methods and analysis: Data sources will include the following bibliographic databases: MEDLINE, MEDLINE In Process, EMBASE, PsycINFO, Social Policy and Practice via Ovid, CINAHL via EBSCO, The Cochrane Library (CDSR, DARE and CENTRAL databases), Nursing and Allied Health Sources, Applied Social Sciences Index and Abstracts via ProQuest and SCOPUS. In addition, other sources will be searched for ongoing studies (ClinicalTrials.gov) and grey literature: Healthcare Management Information Consortium, Conference Proceedings Citation Index (Web of Science) and Sociological abstracts. Studies will be independently screened for eligibility by 2 reviewers. Qualitative studies, either standalone or in the context of mixed-methods designs, reporting the perspectives of any actors involved in the implementation, management and use of ePrescribing/CPOE systems in hospital-based care settings will be included. Data extraction will be conducted by 2 reviewers using a piloted form. Quality appraisal will be based on criteria from the Critical Appraisal Skills Programme checklist and Standards for Reporting Qualitative Research. Studies will not be excluded based on quality assessment. A postsynthesis sensitivity analysis will be undertaken. Data analysis will follow the thematic synthesis method. Ethics and dissemination: The study does not require ethical approval as primary data will not be collected. The results of the study will be published in a peer-reviewed journal and presented at relevant conferences.

A study on uncertain dynamic disaster management tasks, knowledge sharing, and task performance

Each disaster presents itself with a unique set of characteristics that are hard to determine a priori. Thus disaster management tasks are inherently uncertain, requiring knowledge sharing and quick decision making that involves coordination across different levels and collaborators. While there has been an increasing interest among both researchers and practitioners in utilizing knowledge management to improve disaster management, little research has been reported about how to assess the dynamic nature of disaster management tasks, and what kinds of knowledge sharing are appropriate for different dimensions of task uncertainty characteristics. ^ Using combinations of qualitative and quantitative methods, this research study developed the dimensions and their corresponding measures of the uncertain dynamic characteristics of disaster management tasks and tested the relationships between the various dimensions of uncertain dynamic disaster management tasks and task performance through the moderating and mediating effects of knowledge sharing. ^ Furthermore, this research work conceptualized and assessed task uncertainty along three dimensions: novelty, unanalyzability, and significance; knowledge sharing along two dimensions: knowledge sharing purposes and knowledge sharing mechanisms; and task performance along two dimensions: task effectiveness and task efficiency. Analysis results of survey data collected from Miami-Dade County emergency managers suggested that knowledge sharing purposes and knowledge sharing mechanisms moderate and mediate uncertain dynamic disaster management task and task performance. Implications for research and practice as well directions for future research are discussed.^

Barriers and facilitators to asthma self-management in adolescents:a systematic review of qualitative and quantitative studies

BACKGROUND: Many adolescents have poor asthma control and impaired quality of life despite the availability of modern pharmacotherapy. Research suggests that poor adherence to treatment and limited engagement in self-management could be contributing factors. OBJECTIVE: To conduct a systematic review of the barriers and facilitators to self-management of asthma reported by adolescents using a narrative synthesis approach to integrate the findings. DESIGN: MEDLINE, EMBASE, CINAHL, and PsycINFO were searched for all types of study design. Full papers were retrieved for study abstracts that included data from participants aged 12-18 years referring to barriers or facilitators of asthma self-management behaviors. RESULTS: Sixteen studies (5 quantitative and 11 qualitative) underwent data extraction, quality appraisal, and thematic analysis. Six key themes were generated that encompassed barriers and/or facilitators to self-management of asthma in adolescents: Knowledge, Lifestyle, Beliefs and Attitudes, Relationships, Intrapersonal Characteristics, and Communication. CONCLUSIONS: There is a pressing need to prepare adolescents for self-management, using age-appropriate strategies that draw on the evidence we have synthesized. Current clinical practice should focus on ensuring adolescents have the correct knowledge, beliefs, and positive attitude to self-manage their illness. This needs to be delivered in a supportive environment that facilitates two-way communication, fosters adolescents' self-efficacy to manage their disease, and considers the wider social influences that impinge on self-management. Pediatr Pulmonol. 2016; 9999:XX-XX. © 2016 Wiley Periodicals, Inc.

COSMOS - Congestion management strategies and methods in urban sites.

The paper describes the strategies for Congestion and Incident Management (CIM) on the basis of Automatic Congestion and Incident Detection (ACID) that COSMOS will develop, implement in SCOOT, UTOPIA and MOTION, and validate and demonstrate in London, Piraeus and Torino. Four levels of operation were defined for CIM: strategies, tactics, tools and realisation. The strategies for CIM form the top level of this hierarchy. They have to reflect the strategic requirements of the system operators. The tactics are the means that can be employed by the strategies to achieve particular goals in particular situations. The tools that are used by the tactics relate to the elements of the signal plan and the ways in which they can be modified. Strategies, tactics and tools are generally common to all three systems, while the realisation of individual strategies and tactical decisions, through the use of particular common sets of tools, will generally be system specific. For the covering abstract, see IRRD 490001.

Using Quantitative Methods to Explore Political Leverage, Trade Policy, and Food Security: A Case Study of the Middle East

This dissertation explores why some states consistently secure food imports at prices higher than the world market price, thereby exacerbating food insecurity domestically. I challenge the idea that free market economics alone can explain these trade behaviors, and instead argue that states take into account political considerations when engaging in food trade that results in inefficient trade. In particular, states that are dependent on imports of staple food products, like cereals, are wary of the potential strategic value of these goods to exporters. I argue that this consideration, combined with the importing state’s ability to mitigate that risk through its own forms of political or economic leverage, will shape the behavior of the importing state and contribute to its potential for food security. In addition to cross-national analyses, I use case studies of the Gulf Cooperation Council states and Jordan to demonstrate how the political tools available to these importers affect their food security. The results of my analyses suggest that when import dependent states have access to forms of political leverage, they are more likely to trade efficiently, thereby increasing their potential for food security.

The Effect of using online homework exercises on student achievement in a Quantitative methods course

Abstract: Quantitative Methods (QM) is a compulsory course in the Social Science program in CEGEP. Many QM instructors assign a number of homework exercises to give students the opportunity to practice the statistical methods, which enhances their learning. However, traditional written exercises have two significant disadvantages. The first is that the feedback process is often very slow. The second disadvantage is that written exercises can generate a large amount of correcting for the instructor. WeBWorK is an open-source system that allows instructors to write exercises which students answer online. Although originally designed to write exercises for math and science students, WeBWorK programming allows for the creation of a variety of questions which can be used in the Quantitative Methods course. Because many statistical exercises generate objective and quantitative answers, the system is able to instantly assess students’ responses and tell them whether they are right or wrong. This immediate feedback has been shown to be theoretically conducive to positive learning outcomes. In addition, the system can be set up to allow students to re-try the problem if they got it wrong. This has benefits both in terms of student motivation and reinforcing learning. Through the use of a quasi-experiment, this research project measured and analysed the effects of using WeBWorK exercises in the Quantitative Methods course at Vanier College. Three specific research questions were addressed. First, we looked at whether students who did the WeBWorK exercises got better grades than students who did written exercises. Second, we looked at whether students who completed more of the WeBWorK exercises got better grades than students who completed fewer of the WeBWorK exercises. Finally, we used a self-report survey to find out what students’ perceptions and opinions were of the WeBWorK and the written exercises. For the first research question, a crossover design was used in order to compare whether the group that did WeBWorK problems during one unit would score significantly higher on that unit test than the other group that did the written problems. We found no significant difference in grades between students who did the WeBWorK exercises and students who did the written exercises. The second research question looked at whether students who completed more of the WeBWorK exercises would get significantly higher grades than students who completed fewer of the WeBWorK exercises. The straight-line relationship between number of WeBWorK exercises completed and grades was positive in both groups. However, the correlation coefficients for these two variables showed no real pattern. Our third research question was investigated by using a survey to elicit students’ perceptions and opinions regarding the WeBWorK and written exercises. Students reported no difference in the amount of effort put into completing each type of exercise. Students were also asked to rate each type of exercise along six dimensions and a composite score was calculated. Overall, students gave a significantly higher score to the written exercises, and reported that they found the written exercises were better for understanding the basic statistical concepts and for learning the basic statistical methods. However, when presented with the choice of having only written or only WeBWorK exercises, slightly more students preferred or strongly preferred having only WeBWorK exercises. The results of this research suggest that the advantages of using WeBWorK to teach Quantitative Methods are variable. The WeBWorK system offers immediate feedback, which often seems to motivate students to try again if they do not have the correct answer. However, this does not necessarily translate into better performance on the written tests and on the final exam. What has been learned is that the WeBWorK system can be used by interested instructors to enhance student learning in the Quantitative Methods course. Further research may examine more specifically how this system can be used more effectively.

Geometrical Methods in Multivariate Risk Management: Algorithms and Applications

This thesis builds a framework for evaluating downside risk from multivariate data via a special class of risk measures (RM). The peculiarity of the analysis lies in getting rid of strong data distributional assumptions and in orientation towards the most critical data in risk management: those with asymmetries and heavy tails. At the same time, under typical assumptions, such as the ellipticity of the data probability distribution, the conformity with classical methods is shown. The constructed class of RM is a multivariate generalization of the coherent distortion RM, which possess valuable properties for a risk manager. The design of the framework is twofold. The first part contains new computational geometry methods for the high-dimensional data. The developed algorithms demonstrate computability of geometrical concepts used for constructing the RM. These concepts bring visuality and simplify interpretation of the RM. The second part develops models for applying the framework to actual problems. The spectrum of applications varies from robust portfolio selection up to broader spheres, such as stochastic conic optimization with risk constraints or supervised machine learning.