E-commerce adoption by SMEs in developing countries: evidence from Indonesia

This study aims to provide an overview of e-commerce adoption by SMEs in developing countries and, in particular, the extent of the adoption of e-commerce by Indonesian SMEs. It identifies the e-commerce benefits realized by these SMEs and investigates the relationship between the levels of e-commerce adoption and the benefits thus realized. The study was motivated by the limited studies related to e-commerce adoption by SMEs, especially in developing countries. In addition, it seems that most e-commerce studies are focused more on upstream issues: to see the factors that facilitate, or barriers faced regarding e-commerce adoption, rather than downstream issues: to see post-adoption benefits. This certainly limits our understanding about e-commerce adoption by SMEs in developing countries, as well as the post-adoption benefits of e-commerce. Indonesia was chosen as the place in which to conduct the study. A survey of 292 SMEs shows that the majority of them are still at an early stage in their adoption of e-commerce. Their use of e-commerce is dominated by marketing and purchasing and procurement activities. “Extending market reach”, “increased sales”, “improved external communication”, “improved company image”, “improved speed of processing”, and “increased employee productivity” are reported as the top six e-commerce benefits perceived by these SMEs. This study also shows that SMEs at the higher level of e-commerce adoption experience greater e-commerce benefits than those at other levels of adoption

Value of syndromic surveillance within the Armed Forces for early warning during a dengue fever outbreak in French Guiana in 2006

<p>Abstract</p> <p>Background</p> <p>A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.</p> <p>Methods</p> <p>Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.</p> <p>Results</p> <p>It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.</p> <p>Conclusion</p> <p>Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.</p

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Protection of Visual Functions by Human Neural Progenitors in a Rat Model of Retinal Disease

BACKGROUND: A promising clinical application for stem and progenitor cell transplantation is in rescue therapy for degenerative diseases. This strategy seeks to preserve rather than restore host tissue function by taking advantage of unique properties often displayed by these versatile cells. In studies using different neurodegenerative disease models, transplanted human neural progenitor cells (hNPC) protected dying host neurons within both the brain and spinal cord. Based on these reports, we explored the potential of hNPC transplantation to rescue visual function in an animal model of retinal degeneration, the Royal College of Surgeons rat. METHODOLOGY/PRINCIPAL FINDINGS: Animals received unilateral subretinal injections of hNPC or medium alone at an age preceding major photoreceptor loss. Principal outcomes were quantified using electroretinography, visual acuity measurements and luminance threshold recordings from the superior colliculus. At 90–100 days postnatal, a time point when untreated rats exhibit little or no retinal or visual function, hNPC-treated eyes retained substantial retinal electrical activity and visual field with near-normal visual acuity. Functional efficacy was further enhanced when hNPC were genetically engineered to secrete glial cell line-derived neurotrophic factor. Histological examination at 150 days postnatal showed hNPC had formed a nearly continuous pigmented layer between the neural retina and retinal pigment epithelium, as well as distributed within the inner retina. A concomitant preservation of host cone photoreceptors was also observed. CONCLUSIONS/SIGNIFICANCE: Wild type and genetically modified human neural progenitor cells survive for prolonged periods, migrate extensively, secrete growth factors and rescue visual functions following subretinal transplantation in the Royal College of Surgeons rat. These results underscore the potential therapeutic utility of hNPC in the treatment of retinal degenerative diseases and suggest potential mechanisms underlying their effect in vivo

Exploring Web-Based University Policy Statements on Plagiarism by Research-Intensive Higher Education Institutions

Plagiarism may distress universities in the US, but there is little agreement as to exactly what constitutes plagiarism. While there is ample research on plagiarism, there is scant literature on the content of university policies regarding it. Using a systematic sample, we qualitatively analyzed 20 Carnegie-classified universities that are “Very High in Research.” This included 15 public state universities and five high-profile private universities. We uncovered highly varied and even contradictory policies at these institutions. Notable policy variations existed for verbatim plagiarism, intentional plagiarism and unauthorized student collaboration at the studied institutions. We conclude by advising that the American Association of University Professors (AAUP), the American Association of Colleges and Universities (AACU) and others confer and come to accord on the disposition of these issues

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time