@article{EmserJohnstonSteeleetal.2018,
  author    = {Emser, Theresa S. and Johnston, Blair A. and Steele, J. Douglas and Kooij, Sandra and Thorell, Lisa and Christiansen, Hanna},
  title     = {Assessing ADHD symptoms in children and adults: evaluating the role of objective measures},
  series = {Behavioral and Brain Functions},
  volume    = {14},
  journal   = {Behavioral and Brain Functions},
  number    = {11},
  doi       = {10.1186/s12993-018-0143-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-175717},
  year      = {2018},
  abstract  = {Background: Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Furthermore, it has been demonstrated that ratings and tests seem to assess somewhat different constructs. The use of objective measures might thus yield valuable information for diagnosing ADHD. This study aims at evaluating the role of objective measures when trying to distinguish between individuals with ADHD and controls. Our sample consisted of children (n = 60) and adults (n = 76) diagnosed with ADHD and matched controls who completed self- and observer ratings as well as objective tasks. Diagnosis was primarily based on clinical interviews. A popular pattern recognition approach, support vector machines, was used to predict the diagnosis. Results: We observed relatively high accuracy of 79\% (adults) and 78\% (children) applying solely objective measures. Predicting an ADHD diagnosis using both subjective and objective measures exceeded the accuracy of objective measures for both adults (89.5\%) and children (86.7\%), with the subjective variables proving to be the most relevant. Conclusions: We argue that objective measures are more robust against rater bias and errors inherent in subjective measures and may be more replicable. Considering the high accuracy of objective measures only, we found in our study, we think that they should be incorporated in diagnostic procedures for assessing ADHD.},
  language  = {en}
}
@article{MahmoudDukerConradetal.2016,
  author    = {Mahmoud, Mahmoud Ibrahim and Duker, Alfred and Conrad, Christopher and Thiel, Michael and Ahmad, Halilu Shaba},
  title     = {Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria},
  series = {Remote Sensing},
  volume    = {8},
  journal   = {Remote Sensing},
  number    = {3},
  doi       = {10.3390/rs8030220},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146644},
  pages     = {220},
  year      = {2016},
  abstract  = {This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa's fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82\%, 92\% and 92\%, while the AA derived from the error adjusted area (EAA) method stood at 69\%, 91\% and 91\% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11\% between 1986 and 2001. In contrast, this value rose to 17\% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.},
  language  = {en}
}