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Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases are commonly considered some of the most obvious effects of climate change. However, current climate models lack a representation of vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modeling approach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data for analyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of several meteorological variables were used as predictors and phenological anomalies of the flowering date of the test plant Forsythia suspensa as predictand. Several cross-validated prediction models using various numbers and differently constructed predictors were developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observations per phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust and successful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its average coefficient of determination and root mean square error (RMSE) per station are 60% and ± 8.6 days, respectively. However, the prediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparable level of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants and regions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century compared to the phenology of the reference period (1961–1990).

The Asian longhorned beetle Anoplophora glabripennis (ALB) is a dreaded quarantine pest that attacks a wide range of hardwood tree species. However, some of the specified host plants which have to be eradicated in infestation zones such as lime and beech trees almost never get infested or may even be resistant to the ALB. As a result, the specified host plant list needs to be revised based on the data from the infestation areas in the EU, in order to avoid further felling of whole lime avenues and major clear-cutting in parks with old growth. As large differences may exist between species of the same genus, exact species not merely genera should be specified on the host list.

BackgroundAlthough psychoeducation is generally recommended for the treatment of adult attention-deficit hyperactivity disorder (ADHD), participation in clinical psychoeducation groups is impeded by waiting times and the constrained number of patients who can simultaneously attend a group. Digital psychoeducation attempts are promising, but the rapidly expanding number of apps lack evidence and are mostly limited to only a few implemented interactive elements.AimsTo determine the potential of digital, self-guided psychoeducation for adult ADHD, a newly developed interactive chatbot was compared with a previously validated, conventional psychoeducation app.MethodForty adults with ADHD were randomised, of whom 17 participants in each group completed self-guided psychoeducation based on either a chatbot or conventional psychoeducation app between October 2020 and July 2021. ADHD core symptoms were assessed before and after the 3-week interventions, using both the blinded observer-rated Integrated Diagnosis of ADHD in Adulthood interview and the self-rated ADHD Self-Assessment Scale (ADHS-SB).ResultsObserver- and patient-rated ADHD symptoms were significantly reduced from pre- to post-intervention (observer-rated: mean difference −6.18, 95% CI −8.06 to −4.29; patient-rated: mean difference −2.82, 95% CI −4.98 to −0.67). However, there were no group × intervention interaction effects that would indicate a stronger therapeutic benefit of one of the interventions. Likewise, administered psychoeducational knowledge quizzes did not show differences between the groups. No adverse events were reported.ConclusionsSelf-guided psychoeducation based on a chatbot or a conventional app appears similarly effective and safe for improving ADHD core symptoms. Future research should compare additional control interventions and examine patient-related outcomes and usability preferences in detail.

The assessment of the perceived exertion during physical activity can be a valuable addition to a healthy training. Modern smartwatches provide a good opportunity to asses the perceived exertion, but entering information is quite challenging due to the very small displays. In this paper, we present an evaluation of five different input methods for entering the perceived exertion on the Borg-RPE scale on smartwatches. Our evaluation shows that speech input is inappropriate for entering the rating while using ranges is promising.

In cardiac rehabilitation, physical activity is essential, but the intensity of exertion as defined by the heart rate should be kept within specific limits. A heart rate monitor helps the user maintain the target range of physical exertion for maximum benefit. According to experts, visual heart rate display are often distracting and uncomfortable. In this paper, we present the results of a less intrusive approach where heart rate zones are encoded with continuous vibro-tactile feedback. In a participatory design study, we obtained designs for tactile heart rate displays, and in a user study with 16 participants we evaluated one design in comparison to a common design. In a second study with 20 participants, we showed that users are able to assess and maintain their optimal heart rate significantly better using our design.

Increasing volumes of spatial data about urban areas are captured and made available via volunteered geographic information (VGI) sources, such as OpenStreetMap (OSM). Hence, new opportunities arise for regional exploration that can lead to improvements in the lives of citizens through spatial decision support. We believe that the VGI data of the urban environment could be used to present a constructive overview of the regional infrastructure with the advent of web technologies. Current location-based services provide general map-based information for the end users with conventional local search functionality, and hence, the presentation of the rich urban information is limited. In this work, we analyze the OSM data to classify the geo entities into consequential categories with facilities, landscape and land use distribution. We employ a visual overlay of heat map and interactive visualizations to present the regional characterization on OSM data classification. In the proposed interface, users are allowed to express a variety of spatial queries to exemplify their geographic interests. They can compare the characterization of urban areas with respect to multiple spatial dimensions of interest and can search for the most suitable region. The search experience is further enhanced via efficient optimization and interaction methods to support the decision making of end users. We report the end user acceptability and efficiency of the proposed system via usability studies and performance analysis comparison.

Based on a previously reported method for monitoring eggs of the grape leafhopper (Böll and Herrmann 2001) stated as Leda (Leafhopper Egg Detection by Autofluorescence), a simplified but equally reliable version of this method, Sleda (Simplified Leafhopper Egg Detection by Autofluorescence), is introduced. Both methods are based on the fact that the leafhopper eggs show an intense green autofluorescence under blue light excitation. However, in contrast to Leda, for Sleda a fluorescent light microscope is no longer prerequisite, only an optic light source equipped with a blue exciter and a commercial blue cutter are required.

Currently, lane change decision aid systems primarily address foveal vision and thus compete for drivers’ attention with interfaces of other assistant systems. Also, alternative modalities such as acoustic perception (Mahapatra et al., in: 2008 International conference on advanced computer theory and engineering, pp 992–995. https://doi.org/10.1109/ICACTE.2008.165 , 2008 ), tactile perception (Löcken et al., in: Adjunct proceedings of the 7th international conference on automotive user interfaces and interactive vehicular applications, AutomotiveUI ’15, pp 32–37. ACM, New York, NY, USA. https://doi.org/10.1145/2809730.2809758 , 2015 ), or peripheral vision (Löcken et al., in: Proceedings of the 7th international conference on automotive user interfaces and interactive vehicular applications, AutomotiveUI ’15, pp 204–211. ACM, New York, NY, USA. https://doi.org/10.1145/2799250.2799259 , 2015 ), have been introduced for lane change support. We are especially interested in ambient light displays (ALD) addressing peripheral vision since they can adapt to the driver’s attention using changing saliency levels (Matthews et al., in: Proceedings of the 17th Annual ACM symposium on user interface software and technology, UIST ’04, pp 247–256, ACM. https://doi.org/10.1145/1029632.1029676 , 2004 ). The primary objective of this research is to compare the effect of ambient light and focal icons on driving performance and gaze behavior. We conducted two driving simulator experiments. The first experiment evaluated an ambient light cue in a free driving scenario. The second one focused on the difference in gaze behavior between ALD and focal icons, called “abstract faces with emotional expressions” (FEE). The results show that drivers decide more often for safe gaps in rightward maneuvers with ambient light cues. Similarly, drivers decide to overtake more often when the gaps are big enough with both displays in the second experiment. Regarding gaze behavior, drivers looked longer towards the forward area, and less often and shorter into the side mirrors when using ALD. This effect supports the assumption that drivers perceive the ALD with peripheral vision. In contrast, FEE did not significantly affect the gaze behavior when compared to driving without assistance. These results help us to understand the effect of different modalities on performance and gaze behavior, and to explore appropriate modalities for lane change support.

Semantic understanding of images has been an important topic in the research community for a long time as it is an important prerequisite to build meaningful retrieval systems which are accessible by both users and automatic reasoning algorithms. Recently, especially with the growing trend to share photos online, the social aspect of image retrieval becomes more and more prevalent and image retrieval more and more focusses specifically on photos and their special characteristics, especially on information outside the image itself. Researchers are starting to explore how and why photos are shot, shared and used and try to incorporate this additional knowledge to aid image analysis and retrieval. Several survey papers have been written in the past reviewing works in the general field of image analysis and retrieval. However, the social aspect of image retrieval and the focus on digital photos has not sufficiently been addressed in these works. In this article we give an overview over the current research field of semantic photo understanding, annotation and retrieval. We review over 160 contributions in the field and identify trending topics and implications for future directions of research.

The paper introduces the theoretical framework and methods/instruments used by the Physical Activity and Health Equity: Primary Prevention for Healthy Ageing (AEQUIPA) prevention research network as an interdisciplinary approach to tackle key challenges in the promotion of physical activity among older people (65+). Drawing on the social-ecological model, the AEQUIPA network developed an interdisciplinary methodological design including quantitative/qualitative studies and systematic reviews, while combining expertise from diverse fields: public health, psychology, urban planning, sports sciences, health technology and geriatrics. AEQUIPA tackles key challenges when promoting physical activity (PA) in older adults: tailoring of interventions, fostering community readiness and participation, strengthening intersectoral collaboration, using new technological devices and evaluating intervention generated inequalities. AEQUIPA aims to strengthen the evidence base for age-specific preventive PA interventions and to yield new insights into the explanatory power of individual and contextual factors. Currently, the empirical work is still underway. First experiences indicate that thenetwork has achieved a strong regional linkage with communities, local stakeholders and individuals. However, involving inactive persons and individuals from minority groups remained challenging. A review of existing PA intervention studies among the elderly revealed the potential to assess equity effects. The results will add to the theoretical and methodological discussion on evidence-based age-specific PA interventions and will contribute to the discussion about European and national health targets.