An Assessment of Statistical Process Control-Based Approaches for Charting Student Evaluation Scores

We compare three control charts for monitoring data from student evaluations of teaching (SET) with the goal of improving student satisfaction with teaching performance. The two charts that we propose are a modified p chart and a z-score chart. We show that these charts overcome some of the shortcomings of the more traditional charts for analyzing SET data. A comparison of three charts (an individual’s chart, the modified p chart, and the z-score chart) reveals that the modified p chart is the best approach for analyzing SET data because it utilizes distributions that are appropriate for categorical data, and its interpretation is more straightforward. We conclude that administrators and faculty alike can benefit by using the modified p chart to monitor and improve teaching performance as measured by student evaluations

Constructing synthetics from deep earth tomographic models

Recent studies of deep mantle structure indicate strong heterogeneity. To conduct high-resolution waveform modelling of these structures, we have developed a new method to construct 2-D synthetics directly from block-style tomographic models. Unlike the WKBJ approximation, which utilizes rays overshooting and undershooting receivers, our method (WKM approximation) uses rays that arrive at the receiver. First, the ray paths from the 1-D layered reference model are used to localize each ray segment, where the anomalous velocities are applied by overlay, as in tomography. Next, new p_i (t_i) (p_i ray parameter, ti traveltime) are computed to satisfy Snell’s law along with their numerical derivative (δp/δt), which is used to construct a synthetic seismogram similar to the WKBJ method. As a demonstration of the usefulness of this method, we generated WKM synthetics for the D″ region of high velocities beneath Central America based on Grand’s tomography model. Reasonable fits to broad-band data are obtained by condensing his distributed anomalies into his lowermost mantle layer; such a 2-D model predicts synthetics containing a laterally varying S_cd triplication similar to observations

The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services

Prior research examines customer satisfaction in retailing and e-commerce settings, yet online financial services have received little research attention. To understand customer satisfaction with this fast-growing service, this study investigates the role of flow experience, a sensation that occurs as a result of significant cognitive involvement. The study examines how service system characteristics affect the cognitive states of the flow experience, which determines customer satisfaction. The flow construct and total experience design suggest a structural model that is empirically tested using responses from a large sample of online investors. In support of the model and most of the hypotheses it suggests, the empirical results clarify the important antecedents and consequence of flow experience in online financial services and suggest the viability of using a dual-layer experience construct to investigate customer satisfaction. These findings can help researchers and service providers understand when, where, and how flow experience is formulated in online financial services

Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment

Constructing a robust protein-protein interaction network by integrating multiple public databases

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.</p> <p>Methods</p> <p>In this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called <it>k</it>-votes to create seven different integrated networks by using values of <it>k</it> ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.</p> <p>Results</p> <p>Each integrated human PPI network was constructed based on the number of votes (<it>k</it>) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for <it>k</it> was determined by the functional module analysis. Our results demonstrate that the <it>k</it>-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at <it>k</it>=2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.</p> <p>Conclusions</p> <p>We determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.</p

USE OF A HYBRID MCDM METHOD TO EVALUATE KEY SOLUTIONS INFLUENCING SERVICE QUALITY AT A PORT LOGISTICS CENTER IN TAIWAN

The main purpose of this article is to develop a hybrid multiple criteria decision-making (MCDM) model to evaluate key solutions influencing service quality at a port logistics center in Taiwan. At first, this article proposes the use of a hybrid MCDM model incorporating the Analytic Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytic Network Process (ANP) techniques to assess the causal relationships between criterion variables, including dependent relationships and feedback mechanisms. Then, using a hypothetical port logistics center case as an example, this study focuses port logistics center service quality solutions, and applies the hybrid MCDM model constructed in this article in order to explain its functioning and assessment procedures. Finally, via the various operating steps of the hybrid MCDM model, the key logistics center service quality solutions are ranked. Furthermore, some discussions and conclusion are provided in the last of this article

Velocity variations in the uppermost mantle beneath the southern Sierra Nevada and Walker Lane

We model Pn waveforms from two earthquakes in the southwestern United States (Mammoth Lakes, California, and western Nevada) to determine a velocity model of the crustal and mantle structure beneath the southern Sierra Nevada and Walker Lane. We derive a one-dimensional velocity model that includes a smooth crust-mantle transition east of Death Valley and extending south into the eastern Mojave desert. West of Death Valley and toward the Sierra Nevada a low-velocity mantle (V_p = 7.6 km/s) directly below the crust indicates the lithosphere is absent. At the base of this low-velocity structure (at 75–100 km depth) the P wave velocity jumps discontinuously to V_p 8.0 km/s. The area of low velocity is bounded by the Garlock Fault to the south and the Sierra Nevada to the west, but we cannot resolve its northern extent. However, on the basis of teleseismic travel times we postulate that the anomaly terminates at about 38°N. The presence of a low-velocity, upper mantle anomaly in this area agrees with geochemical research on xenoliths from the southern Sierras and recent studies of receiver functions, refraction profiles, tomography, and gravity. However, the velocity discontinuity at 75–100 km is a new discovery and may represent the top of the once present, now unaccounted for and possibly sunken Sierra Nevada lithosphere

Aryl hydrocarbon receptor deficiency causes the development of chronic obstructive pulmonary disease through the integration of multiple pathogenic mechanisms

Emphysema, a component of chronic obstructive pulmonary disease (COPD), is characterized by irreversible alveolar destruction that results in a progressive decline in lung function. This alveolar destruction is caused by cigarette smoke, the most important risk factor for COPD. Only 15%-20% of smokers develop COPD, suggesting that unknown factors contribute to disease pathogenesis. We postulate that the aryl hydrocarbon receptor (AHR), a receptor/transcription factor highly expressed in the lungs, may be a new susceptibility factor whose expression protects against COPD. Here, we report that Ahr-deficient mice chronically exposed to cigarette smoke develop airspace enlargement concomitant with a decline in lung function. Chronic cigarette smoke exposure also increased cleaved caspase-3, lowered SOD2 expression, and altered MMP9 and TIMP-1 levels in Ahr-deficient mice. We also show that people with COPD have reduced expression of pulmonary and systemic AHR, with systemic AHR mRNA levels positively correlating with lung function. Systemic AHR was also lower in never-smokers with COPD. Thus, AHR expression protects against the development of COPD by controlling interrelated mechanisms involved in the pathogenesis of this disease. This study identifies the AHR as a new, central player in the homeostatic maintenance of lung health, providing a foundation for the AHR as a novel therapeutic target and/or predictive biomarker in chronic lung disease

Superconductivity in SmFe1-xMxAsO (M = Co, Rh, Ir)

In this paper we report the comparative study of superconductivity by 3d (Co), 4d (Rh), 5d (Ir) element doping in SmFeAsO. X-ray diffraction patterns indicate that the material has formed the ZrCuSiAs-type structure with a space group P4/nmm. It is found that the antiferromagnetic spin-density-wave (SDW) order in the parent compounds is rapidly suppressed by Co, Rh, and Ir doping, and superconductivity emerges. Both electrical resistance and magnetization measurements show superconductivity up to around 10 K in SmFe1-xMxAsO (M = Co, Rh, Ir). Co, Rh and Ir locate in the same column in the periodic table of elements but have different electronic band structure, so comparative study would add more ingredients to the underlying physics of the iron-based superconductors.Comment: 16 pages, 4 figures, 1 tabl