An unexpectedly large count of trees in the West African Sahara and Sahel.

A large proportion of dryland trees and shrubs (hereafter referred to collectively as trees) grow in isolation, without canopy closure. These non-forest trees have a crucial role in biodiversity, and provide ecosystem services such as carbon storage, food resources and shelter for humans and animals1,2. However, most public interest relating to trees is devoted to forests, and trees outside of forests are not well-documented3. Here we map the crown size of each tree more than 3 m2 in size over a land area that spans 1.3 million km2 in the West African Sahara, Sahel and sub-humid zone, using submetre-resolution satellite imagery and deep learning4. We detected over 1.8 billion individual trees (13.4 trees per hectare), with a median crown size of 12 m2, along a rainfall gradient from 0 to 1,000 mm per year. The canopy cover increases from 0.1% (0.7 trees per hectare) in hyper-arid areas, through 1.6% (9.9 trees per hectare) in arid and 5.6% (30.1 trees per hectare) in semi-arid zones, to 13.3% (47 trees per hectare) in sub-humid areas. Although the overall canopy cover is low, the relatively high density of isolated trees challenges prevailing narratives about dryland desertification5-7, and even the desert shows a surprisingly high tree density. Our assessment suggests a way to monitor trees outside of forests globally, and to explore their role in mitigating degradation, climate change and poverty.

["Dark patterns": manipulative design strategies in digital health applications]. / Dark Patterns: manipulative Designstrategien in digitalen Gesundheitsanwendungen.

Among other things, digital health applications offer users support in better understanding their physical and mental health through digital data, thereby promoting positive health behavior. In addition to state-approved digital health applications (DiGA) and digital care applications (DiPA), there is a wide array of other commercial health applications available to users. Particularly in non-approved applications, developers often deploy manipulative design strategies (dark patterns), intentionally or unintentionally, to deceive users into making specific decisions. This article provides an overview of current and widespread dark patterns and assesses the risks posed by them in digital health applications.In the future, "light" should be shed on dark patterns by creating more transparency for users, providing regulators with a more accurate understanding of dark patterns, and paying more attention to the implementation of guidelines. Thus, users may gain autonomy using healthcare applications and their data can be better protected.

Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies.

BACKGROUND: Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms. OBJECTIVE: This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19-related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality. METHODS: A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants' feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users. RESULTS: A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3). CONCLUSIONS: Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19-related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19-related misinformation on social media.

Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies.

In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people's mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

Multisensory Proximity and Transition Cues for Improving Target Awareness in Narrow Field of View Augmented Reality Displays.

Augmented reality applications allow users to enrich their real surroundings with additional digital content. However, due to the limited field of view of augmented reality devices, it can sometimes be difficult to become aware of newly emerging information inside or outside the field of view. Typical visual conflicts like clutter and occlusion of augmentations occur and can be further aggravated especially in the context of dense information spaces. In this article, we evaluate how multisensory cue combinations can improve the awareness for moving out-of-view objects in narrow field of view augmented reality displays. We distinguish between proximity and transition cues in either visual, auditory or tactile manner. Proximity cues are intended to enhance spatial awareness of approaching out-of-view objects while transition cues inform the user that the object just entered the field of view. In study 1, user preference was determined for 6 different cue combinations via forced-choice decisions. In study 2, the 3 most preferred modes were then evaluated with respect to performance and awareness measures in a divided attention reaction task. Both studies were conducted under varying noise levels. We show that on average the Visual-Tactile combination leads to 63% and Audio-Tactile to 65% faster reactions to incoming out-of-view augmentations than their Visual-Audio counterpart, indicating a high usefulness of tactile transition cues. We further show a detrimental effect of visual and audio noise on performance when feedback included visual proximity cues. Based on these results, we make recommendations to determine which cue combination is appropriate for which application.

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.

Crew time and workload in the EDEN ISS greenhouse in Antarctica.

The goal of the EDEN ISS project is to research technologies for future greenhouses as a substantial part of planetary surface habitats. In this paper, we investigate crew time and workload needed to operate the space analogue EDEN ISS greenhouse on-site and remotely from the Mission Control Center. Within the almost three years of operation in Antarctica, different vegetable crops were cultivated, which yielded an edible biomass of 646 kg during the experiment phase 2018 and 2019. Operating in such a remote environment, analogue to future planetary missions, both greenhouse systems and remote support capabilities must be carefully developed and assessed to guarantee a reliable and efficient workflow. The investigation of crew time and workload is crucial to optimize processes within the operation of the greenhouse. For the Antarctic winter seasons, 2019 and 2020, as well as the summer season 2019/2020, the workload of the EDEN ISS greenhouse operators was assessed using the NASA Task Load Index. In addition, crew time was measured for the winter season 2019. The participants consisted of on-site operators, who worked inside the EDEN ISS greenhouse in Antarctica and the DLR remote support team, who worked in the Mission Control Center at the DLR Institute of Space Systems in Bremen (Germany). The crew time results show that crew time for the whole experiment phase 2019 required by the on-site operator team 2019 is approximately four times higher than the crew time of the corresponding remote support team without considering planning activities for the next mission. The total crew time for the experiment phase 2019 amounts to 694.5 CM-h or 6.31 CM-h/kg. With the measurements of the experiment phase 2019 it was possible to develop a methodology for crew time categorization for the remote support activities, which facilitates the analysis and increases the comparability of crew time values. In addition, the development of weekly and monthly crew time demand over the experiment phase is presented. The workload investigations indicate that the highest workload is perceived by the remote support team 2019 + 2020, followed by the summer maintenance team 2019/2020. The on-site operator team 2019 and on-site operator team 2020 showed the lowest values. The values presented in this paper indicate the need to minimize crew time as well as workload demands of the operators involved in the operation of future planetary surface greenhouses.

Comparing Non-Visual and Visual Guidance Methods for Narrow Field of View Augmented Reality Displays.

Current augmented reality displays still have a very limited field of view compared to the human vision. In order to localize out-of-view objects, researchers have predominantly explored visual guidance approaches to visualize information in the limited (in-view) screen space. Unfortunately, visual conflicts like cluttering or occlusion of information often arise, which can lead to search performance issues and a decreased awareness about the physical environment. In this paper, we compare an innovative non-visual guidance approach based on audio-tactile cues with the state-of-the-art visual guidance technique EyeSee360 for localizing out-of-view objects in augmented reality displays with limited field of view. In our user study, we evaluate both guidance methods in terms of search performance and situation awareness. We show that although audio-tactile guidance is generally slower than the well-performing EyeSee360 in terms of search times, it is on a par regarding the hit rate. Even more so, the audio-tactile method provides a significant improvement in situation awareness compared to the visual approach.

Large scale detailed mapping of dengue vector breeding sites using street view images.

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale.