Coupling ground and airborne geophysical data with upscaling techniques for regional groundwater modeling of heterogeneous aquifers: Case study of a sedimentary aquifer intruded by volcanic dykes in Northern Ireland

In highly heterogeneous aquifer systems, conceptualization of regional groundwater flow models frequently results in the generalization or negligence of aquifer heterogeneities, both of which may result in erroneous model outputs. The calculation of equivalence related to hydrogeological parameters and applied to upscaling provides a means of accounting for measurement scale information but at regional scale. In this study, the Permo-Triassic Lagan Valley strategic aquifer in Northern Ireland is observed to be heterogeneous, if not discontinuous, due to subvertical trending low-permeability Tertiary dolerite dykes. Interpretation of ground and aerial magnetic surveys produces a deterministic solution to dyke locations. By measuring relative permeabilities of both the dykes and the sedimentary host rock, equivalent directional permeabilities, that determine anisotropy calculated as a function of dyke density, are obtained. This provides parameters for larger scale equivalent blocks, which can be directly imported to numerical groundwater flow models. Different conceptual models with different degrees of upscaling are numerically tested and results compared to regional flow observations. Simulation results show that the upscaled permeabilities from geophysical data allow one to properly account for the observed spatial variations of groundwater flow, without requiring artificial distribution of aquifer properties. It is also found that an intermediate degree of upscaling, between accounting for mapped field-scale dykes and accounting for one regional anisotropy value (maximum upscaling) provides results the closest to the observations at the regional scale.

Large panel-survey data demonstrated country-level and ethnic minority variation in consent for health record linkage

Objectives
To investigate individual, household and country variation in consent to health record linkage.

Study Design and Setting
Data from 50,994 individuals aged 16-74 years recruited to wave 1 of a large UK general purpose household survey (January 2009 – December 2010) were analysed using multi-level logistic regression models.

Results
Overall, 70.7% of respondents consented to record linkage. Younger age, marriage, tenure, car ownership and education were all significantly associated with consent, though there was little deviation from 70% in subgroups defined by these variables. There were small increases in consent rates in individuals with poor health when defined by self-reported long term limiting illness (adjusted OR 1.11; 95%CIs 1.06, 1.16), less so when defined by General Health Questionnaire score (adjusted OR=1.05; 95%CIs 1.00, 1.10), but the range in absolute consent rates between categories was generally less than 10%. Larger differences were observed for those of non-white ethnicity who were 38% less likely to consent (adjusted OR 0.62; 95%CIs 0.59, 0.66). Consent was higher in Scotland than England (adjusted OR 1.17; 95%CIs 1.06, 1.29) but lower in Northern Ireland (adjusted OR 0.56; 95%CIs 0.50, 0.63).

Conclusion
The modest overall level of systematic bias in consent to record linkage provides reassurance for record linkage potential in general purpose household surveys. However, the low consent rates amongst non-white ethnic minority survey respondents will further compound their low survey participation rates. The reason for the country-level variation requires further study.

The inclusion of open-ended questions on quantitative surveys of children: Dealing with unanticipated responses relating to child abuse and neglect

Web surveys have been shown to be a viable, and relatively inexpensive, method of data collection with children. For this reason, the Kids’ Life and Times (KLT) was developed as an annual online survey of 10 and 11 year old children. Each year, approximately 4,000 children participate in the survey. Throughout the six years that KLT has been running, a range of questions has been asked that are both policy-relevant and important to the lives of children. Given the method employed by the survey, no extremely sensitive questions that might cause the children distress are included. The majority of questions on KLT are closed yielding quantitative data that are analysed statistically; however, one regular open-ended question is included at the end of KLT each year so that the children can suggest questions that they think should be asked on the survey the following year. While most of the responses are innocuous, each year a small minority of children suggest questions on child abuse and neglect. This paper reports the responses to this question and reflects on how researchers can, and should, deal with this issue from both a methodological and an ethical perspective.

Extending Customer Relationship Management into a Social Context

Informed by the resource-based view, this study draws on customer relationship management (CRM) and value co-creation literature to develop a framework examining the impact of social networking sites on processes to manage customer relationships. Facilitating the depth and networked interactions necessary to truly engage customers, social networking sites act as a means of enhancing customer relationships through the co-creation of value, moving CRM into a social context. Tested and validated on a data set of hotels, the main contribution of the study to service research lies in the extension of CRM processes, termed relational information processes, to include value co-creation processes due to the social capabilities afforded by social networking sites. Information technology competency and social media orientation act as critical antecedents to these processes, which have a positive impact on both financial and non-financial aspects of firm performance. The theoretical and managerial implications of these findings are discussed accordingly.

PESSTO: Survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January - April and August - December inclusive, and typically has allocations of 10 nights per month. 

Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). 

Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5^m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345-9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935-2.53 μm and resolutions 23-33 Å) and imaging with broadband JHK<inf>s</inf> filters. 

Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012-2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ∼15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHK<inf>s</inf> imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. 

Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.

Vaccination and All Cause Child Mortality 1985-2011: Global Evidence from the Demographic and Health Surveys

Based on models with calibrated parameters for infection, case fatality rates, and vaccine efficacy, basic childhood vaccinations have been estimated to be highly cost effective. We estimate the association of vaccination with mortality directly from survey data. Using 149 cross-sectional Demographic and Health Surveys, we determine the relationship between vaccination coverage and under five mortality at the survey cluster level. Our data include approximately one million children in 68,490 clusters in 62 countries. We consider the childhood measles, Bacille Calmette-Guérin (BCG), Diphtheria-Pertussis-Tetanus (DPT), Polio, and maternal tetanus vaccinations. Using modified Poisson regression to estimate the relative risk of child mortality in each cluster, we also adjust for selection bias caused by the vaccination status of dead children not being reported. Childhood vaccination, and in particular measles and tetanus vaccination, is associated with substantial reductions in childhood mortality. We estimate that children in clusters with complete vaccination coverage have relative risk of mortality 0.73 (95% Confidence Interval: 0.68, 0.77) that of children in a cluster with no vaccination. While widely used, basic vaccines still have coverage rates well below 100% in many countries, and our results emphasize the effectiveness of increasing their coverage rates in order to reduce child mortality.

The use of patient experience survey data by out-of-hours primary care services: a qualitative interview study

Background English National Quality Requirements mandate out-of-hours primary care services to routinely audit patient experience, but do not state how it should be done.

Objectives We explored how providers collect patient feedback data and use it to inform service provision. We also explored staff views on the utility of out-of-hours questions from the English General Practice Patient Survey (GPPS).

Methods A qualitative study was conducted with 31 staff (comprising service managers, general practitioners and administrators) from 11 out-of-hours primary care providers in England, UK. Staff responsible for patient experience audits within their service were sampled and data collected via face-to-face semistructured interviews.

Results Although most providers regularly audited their patients’ experiences by using patient surveys, many participants expressed a strong preference for additional qualitative feedback. Staff provided examples of small changes to service delivery resulting from patient feedback, but service-wide changes were not instigated. Perceptions that patients lacked sufficient understanding of the urgent care system in which out-of-hours primary care services operate were common and a barrier to using feedback to enable change. Participants recognised the value of using patient experience feedback to benchmark services, but perceived weaknesses in the out-of-hours items from the GPPS led them to question the validity of using these data for benchmarking in its current form.

Conclusions The lack of clarity around how out-of-hours providers should audit patient experience hinders the utility of the National Quality Requirements. Although surveys were common, patient feedback data had only a limited role in service change. Data derived from the GPPS may be used to benchmark service providers, but refinement of the out-of-hours items is needed.

Studying Gender in Conference Talks -- data from the 223rd meeting of the American Astronomical Society

We present a study on the gender balance, in speakers and attendees, at the recent major astronomical conference, the American Astronomical Society meeting 223, in Washington, DC. We conducted an informal survey, yielding over 300 responses by volunteers at the meeting. Each response included gender data about a single talk given at the meeting, recording the gender of the speaker and all question-askers. In total, 225 individual AAS talks were sampled. We analyze basic statistical properties of this sample. We find that the gender ratio of the speakers closely matched the gender ratio of the conference attendees. The audience asked an average of 2.8 questions per talk. Talks given by women had a slightly higher number of questions asked (3.2$\pm$0.2) than talks given by men (2.6$\pm$0.1). The most significant result from this study is that while the gender ratio of speakers very closely mirrors that of conference attendees, women are under-represented in the question-asker category. We interpret this to be an age-effect, as senior scientists may be more likely to ask questions, and are more commonly men. A strong dependence on the gender of session chairs is found, whereby women ask disproportionately fewer questions in sessions chaired by men. While our results point to laudable progress in gender-balanced speaker selection, we believe future surveys of this kind would help ensure that collaboration at such meetings is as inclusive as possible.

Replicability of data collected for empirical estimation of relative pollen productivity

The effects of repeated survey and fieldwork timing on data derived from a recently proposed standard field methodology for empirical estimation of Relative Pollen Productivity have been tested. Seasonal variations in vegetation and associated pollen assemblages were studied in three contrasting cultural habitat types; semi-natural ancient woodlands, lowland heaths, and unimproved, traditionally managed hay meadows. Results show that in woodlands and heathlands the standard method generates vegetation data with a reasonable degree of similarity throughout the field season, though in some instances additional recording of woodland canopy cover should be undertaken, and differences were greater for woodland understorey taxa than for arboreal taxa. Large differences in vegetation cover were observed over the field season in the grassland community, and matching the phenological timing of surveys within and between studies is clearly important if RPP estimates from these sites are to be comparable. Pollen assemblages from closely co-located moss polsters collected on different visits are shown to be variable in all communities, to a greater degree than can be explained by the sampling error associated with pollen counting, and further study of moss polsters as pollen traps is recommended.

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management

Accuracy assessment of depth estimates from variation in pre- and post-processing of multi-beam SONAR surveys in a fjord environment

Senior thesis written for Oceanography 445

An exploratory study of how a Korean chaebol’s learning culture facilitates customer service

This paper explains how the organizational learning concept is used by managers in a global Korean company to promote group work, information sharing and an open communication style in order to produce a high level of customer service. Previously collected data from a set of in-depth personal interviews undertaken with three senior managers in a Korean electronics company were analyzed and interpreted using the grounded theory approach, and a number of propositions are put forward. The research findings show that managers in a chaebol deploy organizational learning to identify skilled and knowledgeable staff, and improve the organization’s capability by placing emphasis on developing harmonious, mutually oriented relationships that permeate throughout the organization. Top management demand that staff identify with government economic objectives and align the organization’s strategy accordingly so that the products produced are marketable. To achieve this, the organization fosters continual interaction among managers throughout the organization’s hierarchy. The chaebol’s organizational learning model encapsulates a “corollary” (continual communication) and “tools” (cultural influence and relationship management), and manifests in a unique strategy that allows management systems to evolve naturally.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.