Publications by authors named "Juergen Pilz"

3 Publications

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A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006-2017).

BMC Public Health 2020 Jun 25;20(1):995. Epub 2020 Jun 25.

Quaid-i-Azam University, Islamabad, 45000, Pakistan.

Background: In Pakistan, dengue fever has become a major concerning factor, given that it is a relatively new disease compared to malaria. The number of people affected by dengue fever has increased at least 10-fold in the last 15 years in specific areas of Pakistan. Therefore, it is necessary to analyse this disease to reduce or prevent the effects of dengue fever in the region.

Methods: Geographical information system (GIS) maps are used to identify the intensity of the spread according to the count of affected people in our study area. Generalised linear modelling (GLM) is used to study the significance of factors associated with dengue fever.

Results: The dengue virus is present throughout the year in specific areas of Pakistan. Karachi and Lahore are most significantly affected with cases in these two most populous cities of Pakistan reported every year. In the study period (2006-2017), 2011 was the most devastating year for Pakistan. Lahore recorded more than 17,000 confirmed cases with 290 deaths in a single year. The GLM analysis shows rainfall, the average maximum temperature, and hospitals to be significant factors in the prevalence of dengue fever.

Conclusion: This study finds that Sindh and Khyber Pakhtunkhwa are two of the primarily vulnerable provinces for the spread of dengue fever. Punjab has observed sporadic increases in dengue fever cases. In Pakistan, dengue cases increase in the rainfall season, especially during monsoon season. Lack of proper hospitals and clinics are another major factor, and mobile hospitals are needed in remote hotspot regions often affected by dengue fever. Finally, improved sanitation systems in metropoles would facilitate reducing breeding grounds for Aedes Aegypti larvae.
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http://dx.doi.org/10.1186/s12889-020-08846-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318413PMC
June 2020

Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks.

IEEE Trans Neural Netw Learn Syst 2021 Mar 1;32(3):1037-1051. Epub 2021 Mar 1.

In this article, a novel approach for training deep neural networks using Bayesian techniques is presented. The Bayesian methodology allows for an easy evaluation of model uncertainty and, additionally, is robust to overfitting. These are commonly the two main problems classical, i.e., non-Bayesian architectures have to struggle with. The proposed approach applies variational inference in order to approximate the intractable posterior distribution. In particular, the variational distribution is defined as the product of multiple multivariate normal distributions with tridiagonal covariance matrices. Every single normal distribution belongs either to the weights or to the biases corresponding to one network layer. The layerwise a posteriori variances are defined based on the corresponding expectation values, and furthermore, the correlations are assumed to be identical. Therefore, only a few additional parameters need to be optimized compared with non-Bayesian settings. The performance of the new approach is evaluated and compared with other recently developed Bayesian methods. Basis of the performance evaluations are the popular benchmark data sets MNIST and CIFAR-10. Among the considered approaches, the proposed one shows the best predictive accuracy. Moreover, extensive evaluations of the provided prediction uncertainty information indicate that the new approach often yields more useful uncertainty estimates than the comparison methods.
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http://dx.doi.org/10.1109/TNNLS.2020.2980004DOI Listing
March 2021

Portable chamber system for measuring chloroform fluxes from terrestrial environments--methodological challenges.

Environ Sci Technol 2013 Dec 21;47(24):14298-305. Epub 2013 Nov 21.

University of British Columbia , Vancouver, Canada.

This study describes a system designed to measure chloroform flux from terrestrial systems, providing a reliable first assessment of the spatial variability of flux over an area. The study takes into account that the variability of ambient air concentrations is unknown. It includes quality assurance procedures, sensitivity assessments, and testing of materials used to ensure that the flux equation used to extrapolate from concentrations to fluxes is sound and that the system does not act as a sink or a source of chloroform. The results show that many materials and components commonly used in sampling systems designed for CO2, CH4, and N2O emit chloroform and other volatile chlorinated compounds (VOCls) and are thus unsuitable in systems designed for studies of such compounds. To handle the above-mentioned challenges, we designed a system with a non-steady-state chamber and a closed-loop air-circulation unit returning scrubbed air to the chamber. Based on empirical observations, the concentration increase during a deployment was assumed to be linear. Four samples were collected consecutively and a line was fitted to the measured concentrations. The slope of the fitted line and the y-axis intercept were input variables in the equation used to transform concentration change data to flux estimates. The soundness of the flux equation and the underlying assumptions were tested and found to be reliable by comparing modeled and measured concentrations. Fluxes of chloroform in a forest clear-cut on the east coast of Vancouver Island, BC, during the year were found to vary from -130 to 620 ng m(-2) h(-1). The study shows that the method can reliably detect differences of approximately 50 ng m(-2) h(-1) in chloroform fluxes. The statistical power of the method is still comparatively strong down to differences of 35 ng m(-2) h(-1), but for smaller differences, the results should be interpreted with caution.
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http://dx.doi.org/10.1021/es403062cDOI Listing
December 2013