Cross-sectional study assessing HIV related knowledge, attitudes and behavior in Namibian public sector employees in capital and regional settings

The study objective was to assess the current status of HIV knowledge, attitudes and behavior (KAB) among employees of Namibian ministries. As most HIV campaigning takes place in the capital of Windhoek, an additional aim was to compare Windhoek to four regions (Hardap, Erongo, Oshana, and Caprivi). Between January and March 2011 a cross-sectional survey was conducted in two Namibian ministries, with participants selected randomly from the workforce. Data collection was based on questionnaires. 832 participants were included in the study (51.6% male). Nearly 90% of participants reported to have been tested for HIV before. Knowledge about HIV transmission ranged from 67% to 95% of correct answers, with few differences between the capital and regions. However, a knowledge gap regarding HIV transmission and prevention was seen. In particular, we found significantly lower knowledge regarding transmission from mother-to-child during pregnancy and higher rate of belief in a supernatural role in HIV transmission. In addition, despite many years of HIV prevention activities, a substantial proportion of employees had well-known HIV risk factors including multiple concurrent partnership rates (21%), intergenerational sex (19%), and lower testing rates for men (82% compared to women with 91%)

Effects of Ginkgo biloba in dementia: systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>The benefit of Ginkgo biloba has been discussed controversially. The aim of this review was to assess the effects of Ginkgo biloba in Alzheimer's disease as well as vascular and mixed dementia covering a variety of outcome domains.</p> <p>Methods</p> <p>We searched MEDLINE, EMBASE, the Cochrane databases, CINAHL and PsycINFO for controlled trials of ginkgo for Alzheimer's, vascular or mixed dementia. Studies had to be of a minimum of 12 weeks duration with at least ten participants per group. Clinical characteristics and outcomes were extracted. Meta-analysis results were expressed as risk ratios or standardized mean differences (SMD) in scores.</p> <p>Results</p> <p>Nine trials using the standardized extract EGb761^®met our inclusion criteria. Trials were of 12 to 52 weeks duration and included 2372 patients in total. In the meta-analysis, the SMDs in change scores for cognition were in favor of ginkgo compared to placebo (-0.58, 95% confidence interval [CI] -1.14; -0.01, p = 0.04), but did not show a statistically significant difference from placebo for activities in daily living (ADLs) (SMD = -0.32, 95% CI -0.66; 0.03, p = 0.08). Heterogeneity among studies was high. For the Alzheimer subgroup, the SMDs for ADLs and cognition outcomes were larger than for the whole group of dementias with statistical superiority for ginkgo also for ADL outcomes (SMD = -0.44, 95% CI -0.77; -0.12, p = 0.008). Drop-out rates and side effects did not differ between ginkgo and placebo. No consistent results were available for quality of life and neuropsychiatric symptoms, possibly due to the heterogeneity of the study populations.</p> <p>Conclusions</p> <p>Ginkgo biloba appears more effective than placebo. Effect sizes were moderate, while clinical relevance is, similar to other dementia drugs, difficult to determine.</p

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

In this paper, we present a novel pose normalization method for indoor mapping point clouds and triangle meshes that is robust against large fractions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries is determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces is conducted. Subsequently, a rotation around the resulting vertical axis is determined that aligns the dataset horizontally with the coordinate axes. The proposed method is evaluated quantitatively against several publicly available indoor mapping datasets. Our implementation of the proposed procedure along with code for reproducing the evaluation will be made available to the public upon acceptance for publication

Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy δ1:25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions

Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy {\delta}1.25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions

Automatic Feature-Based Point Cloud Registration for a Moving Sensor Platform

The automatic and accurate alignment of multiple point clouds is a basic requirement for an adequate digitization, reconstruction and interpretation of large 3D environments. Due to the recent technological advancements, modern devices are available which allow for simultaneously capturing intensity and range images with high update rates. Hence, such devices can even be used for dynamic scene analysis and for rapid mapping which is particularly required for environmental applications and disaster management, but unfortu-nately, they also reveal severe restrictions. Facing challenges with respect to noisy range measurements, a limited non-ambiguous range, a limited field of view and the occurrence of scene dynamics, the adequate alignment of captured point clouds has to satisfy additional constraints compared to the classical registration of terrestrial laser scanning (TLS) point clouds for describing static scenes. In this paper, we propose a new methodology for point cloud registration which considers such constraints while maintaining the fundamental properties of high accuracy and low computational effort without relying on a good initial alignment or human interaction. Exploiting 2D image features and 2D/2D correspondences, sparse point clouds of physically almost identical 3D points are derived. Subsequently, these point clouds are aligned with a fast procedure directly taking into account the reliability of the detected correspondences with respect to geometric and radiometric information. The proposed methodology is evaluated and its performance is demonstrated for data captured with a moving sensor platform which has been designed for monitoring from low altitudes. Due to the provided reliability and a fast processing scheme, the proposed methodology offers a high potential for dynamic scene capture and analysis.

PREFACE: TECHNICAL COMMISSION I

For ISPRS Technical Commission I (TC I), 76 submissions for the 2021 Congress edition of ISPRS Annals and ISPRS Archives were received. This included both full paper and abstract submissions from all over the world. Continuing the success of double blind paper reviewing in preparation of the 2016 Prague congress, the 2018 Karlsruhe symposium as well as the 2020 edition of the ISPRS congress, also this time, TC I organized a strict peer-reviewing process. This included double-blind reviewing for full papers as well as a two-stage evaluation of abstract submissions – first stage based on the submitted abstracts (“conditional acceptance”), and second stage after submission of the respective final papers.For the 2021 edition of the ISPRS congress, we received 32 full paper submissions and 46 abstract submissions, which underlines the trend towards full paper submissions compared to previous TC I events.17 full papers passed the double-blind peer-review process and were accepted for publication in the ISPRS Annals (acceptance rate 53%); none of the papers was reviewed by less than two reviewers; for the majority of full papers, three or four reviews were obtained. Abstract submissions and their respective final papers were reviewed by a team of professionals. In total, 28 papers were accepted to the ISPRS Archives.Strict peer-reviewing relies on many voluntary reviewers spending their time for reading and evaluating the submissions. We would like to express our thanks to our reviewing team – especially since they were again willing to contribute to the review process just after completion of the 2020 edition of the ISPRS congress.The manuscripts in both the ISPRS Annals and Archives cover a broad range of topics related to remote sensing platforms, technologies, systems and related methods and reflect the current trends in algorithmic research and developments in sensing and data acquisition methods. Noteworthy is that numerous contributions were again submitted to Intercommission WGs of TC I with TC II and TC IV, which underlines the continuing trend towards an integral approach to sensors, systems and methods in photogrammetry, remote sensing and mobile mapping.</p