Self-Services - Do Not Leave Your Customers Alone with the Technology

New arising technologies change the modes of interaction between companies and their customers. So-called self-service technologies (SSTs) allow integrating customers as active participants into companies\u27 business processes and thereby are expected to generate not only more efficient processes but also positive effects on customer satisfaction. As some customers do not consider their integration as an improvement and others are not able to use the SSTs, companies have to pro-vide personal support offering direct response, assurance and social interaction. As for many companies the corresponding economic effects remain unclear, the aim of this paper is to develop a quantitative decision model that allows to decide on the integration of customers in business processes while considering of the necessary customer support on an economically well-grounded basis. To demon-strate the applicability of the model and its practical utility, we conduct a case study

Estimating the global burden of endemic canine rabies

Background: Rabies is a notoriously underreported and neglected disease of lowincome countries. This study aims to estimate the public health and economic burden of rabies circulating in domestic dog populations, globally and on a country-by-country basis, allowing an objective assessment of how much this preventable disease costs endemic countries.<p></p> Methodology/Principal Findings: We established relationships between rabies mortality and rabies prevention and control measures, which we incorporated into a model framework. We used data derived from extensive literature searches and questionnaires on disease incidence, control interventions and preventative measures within this framework to estimate the disease burden. The burden of rabies impacts on public health sector budgets, local communities and livestock economies, with the highest risk of rabies in the poorest regions of the world. This study estimates that globally canine rabies causes approximately 59,000 (95% Confidence Intervals: 25- 159,000) human deaths, over 3.7 million (95% CIs: 1.6-10.4 million) disability-adjusted life years (DALYs) and 8.6 billion USD (95% CIs: 2.9-21.5 billion) economic losses annually. The largest component of the economic burden is due to premature death (55%), followed by direct costs of post-exposure prophylaxis (PEP, 20%) and lost income whilst seeking PEP (15.5%), with only limited costs to the veterinary sector due to dog vaccination (1.5%), and additional costs to communities from livestock losses (6%).<p></p> Conclusions/Significance: This study demonstrates that investment in dog vaccination, the single most effective way of reducing the disease burden, has been inadequate and that the availability and affordability of PEP needs improving. Collaborative investments by medical and veterinary sectors could dramatically reduce the current large, and unnecessary, burden of rabies on affected communities. Improved surveillance is needed to reduce uncertainty in burden estimates and to monitor the impacts of control efforts.<p></p&gt

Estimating the global burden of endemic canine rabies

Rabies is a fatal viral disease largely transmitted to humans from bites by infected animals —predominantly from domestic dogs. The disease is entirely preventable through prompt administration of post-exposure prophylaxis (PEP) to bite victims and can be controlled through mass vaccination of domestic dogs. Yet, rabies is still very prevalent in developing countries, affecting populations with limited access to health care. The disease is also grossly underreported in these areas because most victims die at home. This leads to insufficient prioritization of rabies prevention in public health agendas. To address this lack of information on the impacts of rabies, in this study, we compiled available data to provide a robust estimate of the health and economic implications of dog rabies globally. The most important impacts included: loss of human lives (approximately 59,000 annually) and productivity due to premature death from rabies, and costs of obtaining PEP once an exposure has occurred. The greatest risk of developing rabies fell upon the poorest regions of the world, where domestic dog vaccination is not widely implemented and access to PEP is most limited. A greater focus on mass dog vaccination could eliminate the disease at source, reducing the need for costly PEP and preventing the large and unnecessary burden of mortality on at-risk communities.S1 Text. Supporting bibliography.S1 Table. Estimates by country of rabies deaths, exposures, PEP use, prevented deaths, dog vaccination coverage, probability that a dog is rabid (RP), of bite victims receiving PEP (PP), DALYs, costs and 95% confidence intervals of estimates. Clusters to which countries are assigned are shown and inputs used for estimating parameters including the human development index and whetehr a country s rabies-free or endemic (RISK). Estimates of years of life lost (YLL) and DALYs (due to rabies and to adverse events from the use of nerve tissue vaccines) are shown under different assumptions (estimates under the assumption of no time discounting or age-weighting should be directly comparable to the 2010 Global Burden of Disease study).S1 Fig. Division of costs associated with rabies, prevention and control across sectors by cluster. Inset shows proportional expenditure in different clusters. Full details of countries by cluster are given in S1 Table. Asia 4 comprises: Philippines, Sri Lanka, Thailand (High PEP use); Asia 3 comprises Bhutan, Nepal, Bangladesh, Pakistan (Himalayan region); Asia 2 comprises Cambodia, Myanmar, Laos, Vietnam and Democratic People’s Republic of Korea; SADC comprises countries in the Southern African Development Community, Eurasia comprises Afghanistan, Kazakhstan, Kyrgyzstan, Mongolia, the Russian Federation, Turkmenistan, Tajikistan, and Uzbekistan.S1 Dataset. Model code and input data files including references, rationale and detail of Delphi process. The code folder contains seven R scripts: burden_model.R runs the model using data compiled in burden_1.R, after estimating parameters using: FitCovInc.R, FitPP.R, and creating Fig 2 (RabiesBurdenFig2.R). The script burden_results.R summarizes findings using the output of burden_model.R and burden_sensitivity.R runs the sensitivity analyses. The data folder contains 12 csv files called by the R code for the analyses, and one excel file (Vet. xlsx) with additional details about the data sources in vcountry2.csv and vcluster2.csv and with Delphi process estimates for dog vaccination coverage. Data sources are detailed in the relevant data sources and the details of the sources of data used in the analysis are in the supporting bibliography, S1 text.This study was funded by the UBS Optimus Foundation (http://www.ubs.com/optimusfoundation) and the Wellcome Trust (095787/Z/11/Z).http://www.plosntds.orgam201

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

A QUALITY ASSURANCE/QUALITY CONTROL ANALYSIS OF RAINFALL DATA COLLECTED BY VOLUNTEERS IN TUCSON, ARIZONA FOR THE RAINLOG.ORG PROGRAM

Scientists now recognize how quickly environmental conditions are changing, yet to monitor and understand these spatially distributed changes more dispersed quantitative and qualitative data are needed than ever before. The need for more comprehensive and robust data has created the burgeoning field of citizen science, which engages volunteers to monitor environmental changes and report this information to scientists. Precipitation monitoring networks like RainLog.org are considered one of the oldest types of citizen science with many networks in existence for over 100 years. RainLog.org is a more modern version of these original networks and was developed in response to a need to better characterize precipitation events and provide stakeholders with more robust precipitation totals and distributions throughout Arizona.RainLog.org is a statewide precipitation monitoring network that relies on volunteers across Arizona to report daily precipitation into an online reporting system. To ensure that these data are reliable, a quality assurance and quality control analysis (QA/QC) was completed on a subset of gauges in the Tucson area. Results indicate that although there are many errors inherent with any precipitation network, whether volunteer or scientist driven, these errors are for the most part identified using basic interpolation methods. This paper analyzes a range of user reporting and gauge type errors, discusses the significance of each error type and provides recommendations for mitigating reporting errors in any citizen science network