Infrastructure Asset Management Modeling through an Analysis of the Air Force Strategic Vision and Goals

Effective asset management requires an overarching model that establishes a framework for decision-makers. This research project develops a strategic level asset management model for varying types of infrastructure that provides guidance for effective asset management. The strategic model also incorporates Next Generation Information Technology initiatives into a single coherent system to streamline the top-down, bottom-up flow of information. The strategic model is applicable to agencies with a large, varying infrastructure inventory and limited resources. This research also develops an improved performance modeling tool, a critical component of the strategic model. This tool objectively prioritizes maintenance and repair projects according to measurable metrics as well as the strategic vision, established goals, and policies. Asset management of Air Force infrastructure provides an example of applicability for this strategic model and improved tool; thus, an asset management example of Air Force infrastructure is utilized throughout the research project to demonstrate the utility of the model and the tool. The strategic level model and improved tool enable policy-makers to make decisions that tie goals, infrastructure inventory, condition state, importance and criticality, and budget constraints to system performance. As a result, insight is gained on ways to maximize efficiency and optimize the performance of infrastructure

An Investigation of the Impact of Embedding Learning Strategies into Developmental Mathematics Curriculum

More than two-thirds of first-year community college students require remediation in reading and/or mathematics before they can take college-level courses. Among these underprepared students who take the recommended developmental mathematics courses to become college-ready, less than one-third successfully complete these courses, posing a barrier to their access to a college education. Developmental education has been criticized and has undergone various institutional reforms, but limited research has investigated the mathematical perspectives of community college students in developmental mathematics courses. The two research questions that I investigated were: what effects does taking a developmental mathematics course that incorporates learning and study strategies have on students’ strategic learning skills; and how do students in a developmental mathematics course incorporating learning and study strategies describe their learning experiences? The theoretical frameworks of social cognitive theory and the framework for student success (productive persistence) guided the design of my investigation. To conduct this investigation, I used a mixed methods design, gathering both quantitative and qualitative data from 65 participants enrolled in a developmental mathematics course using a reform-oriented curriculum at a community college. Participants took the 60-item Learning and Study Strategies Inventory (LASSI) early in the course and again near the end of the course, completing written reflections on a brief survey after each LASSI. I examined quantitative data addressing the first research question, followed by a qualitative analysis of survey data that address the second research question. To gain further insights, I conducted interviews with 10 participants after each LASSI. The quantitative analysis of the change in LASSI scores over the course of the semester revealed increases in some learning and study strategies. This was supported with comments from students in both written surveys and interviews. I present evidence of how taking a developmental mathematics course using reform-oriented curriculum both lowered Anxiety and improved Information Processing among the n=65, as well as improved Concentration and bolstered Using Academic Resources among the 10 interviewees. The qualitative analysis of the written surveys and interview data revealed more insights about the students’ learning experiences in the classroom both related to, and extending beyond, the 10 learning and study strategies assessed by the LASSI scales. Supporting evidence from written surveys and interview data led to five additional emergent themes impacting the student experience: Doing College, Barriers, Teacher Impact, Groupwork, and Growth Mindset. These factors were largely non-cognitive in nature, revealing psychological factors that influenced student persistence and student success. A discussion of limitations, suggestions, and recommendations for future research is also included

Email Thread Reassembly Using Similarity Matching

Email thread reassembly is the task of linking messages by parent-child relationships. In this paper, we present two approaches to address this problem. One exploits previously undocumented header information from the Microsoft Exchange Protocol. The other uses string similarity metrics and a heuristic algorithm to reassemble threads in the absence of header information. The pros and cons of both methods are discussed. The similarity matching method is evaluated using the Enron email corpus and found to perform well

Probability of Identification: A Statistical Model for the Validation of Qualitative Botanical Identification Methods

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given

One-class modeling for verification of botanical identity: a review

One-class modeling is a supervised multivariate botanical identification method based on principal component analysis (PCA) that constructs a model based only on the characteristics of the reference samples and uses the Q statistic as a combined metric. Test samples are judged to be similar (authentic) if their combined metric falls within the model limits or different (adulterated or contaminated) if the metric falls outside the model limits. This review initially considers three major factors affecting identification: the number of variables (univariate versus multivariate), the number of classes (one-class versus multi-class), and the type of analysis (quantitative versus qualitative). Multivariate analysis is commonly used for identification, providing a broader coverage of the identity specifications of the samples. With a combined metric, multivariate methods are analogous to univariate methods. One-class modeling and multi-class modeling employ different approaches for identification with one-class modeling being more flexible. While most methods to date have had a quantitative basis, qualitative methods are possible. This review focuses on multivariate, one-class modeling based on PCA. Examples are presented for the application of one-class modeling to identification of American ginseng (Panax quinquefolius), Echinacea purpurea, Black Cohosh (Actaea racemosa), and Maca (Lepidium meyenii). These examples demonstrate the utility and flexibility of one-class modeling

Dietary flavonoid intake and risk of incident depression in midlife and older women

Background: The impact of dietary flavonoid intakes on risk of depression is unclear. Objective: We prospectively examined associations between estimated habitual intakes of dietary flavonoids and depression risk. Design: We followed 82,643 women without a previous history of depression at baseline from the Nurses’ Health Study [(NHS) aged 53–80 y] and the Nurses’ Health Study II [(NHSII) aged 36–55 y]. Intakes of total flavonoids and subclasses (flavonols, flavones, flavanones, anthocyanins, flavan-3-ols, polymeric flavonoids, and proanthocyanidins) were calculated from validated food-frequency questionnaires collected every 2–4 y. Depression was defined as physician- or clinician-diagnosed depression or antidepressant use and was self-reported in response to periodic questionnaires. Cox proportional hazards models were performed to examine associations. Results: A total of 10,752 incident depression cases occurred during a 10-y follow-up. Inverse associations between flavonol, flavone, and flavanone intakes and depression risk were observed. Pooled multivariable-adjusted HRs (95% CIs) were 0.93 (0.88, 0.99), 0.92 (0.86, 0.98), and 0.90 (0.85, 0.96) when comparing the highest (quintile 5) with the lowest (quintile 1) quintiles, respectively, with evidence of linear trends across quintiles (P-trend = 0.0004–0.08). In flavonoid-rich food-based analyses, the HR was 0.82 (95% CI: 0.74, 0.91) among participants who consumed ≥2 servings citrus fruit or juices/d compared with <1 serving/wk. In the NHS only, total flavonoids, polymers, and proanthocyanidin intakes showed significant (9–12%) lower depression risks. In analyses among late-life NHS participants (aged ≥65 y at baseline or during follow-up), for whom we were able to incorporate depressive symptoms into the outcome definition, higher intakes of all flavonoid subclasses except for flavan-3-ols were associated with significantly lower depression risk; flavones and proanthocyanidins showed the strongest associations (HR for both: 0.83; 95% CI: 0.77, 0.90). Conclusions: Higher flavonoid intakes may be associated with lower depression risk, particularly among older women. Further studies are needed to confirm these associations

Multidimensional integration of absorbances: An approach to absolute analyte detection

The problem of absolute analyte detection is considered in this paper. It is shown that integration in absorbance, not in intensity, is a pre-requisite for absolute detection in atomic absorption spectrometry. A design for an atomic absorption spectrometer of the future is described which measures absorbance resolved in three key areas: wavelength, space and time. Intensity must be measured with sufficient temporal, spatial and spectral resolution to guarantee the accuracy of the computed absorbance. Technically, such measurements can be made simultaneously with a continuum source, a high resolution echelle spectrometer and a two dimensional solid-state array detector. All computed absorbances are fully background and stray light corrected. With such measurements, and a proper optical configuration, absolute analyte detection can become a reality and the possibility of absolute analysis becomes more obtainable