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Digital technologies have become central to social interaction and accessing goods and services. Development strategies and approaches to governance have increasingly deployed self-labelled ‘smart’ technologies and systems at various spatial scales, often promoted as rectifying social and geographic inequalities and increasing economic and environmental efficiencies. These have also been accompanied with similarly digitalized commercial and non-profit offers, particularly within the sharing economy. Concern has grown, however, over possible inequalities linked to their introduction. In this paper we critically analyse the role of sharing economies’ contribution to more inclusive, socially equitable
and spatially just transitions. Conceptually, this paper brings together literature on sharing economies, smart urbanism
and just transitions. Drawing on an explorative database of sharing initiatives within the cross-border region of Luxembourg and Germany, we discuss aspects of sustainability as they relate to distributive justice through spatial accessibility, intended benefits, and their operationalization. The regional analysis shows the diversity of sharing models, how they are appropriated in different ways and how intent and operationalization matter in terms of potential benefits.
Results emphasize the need for more fine-grained, qualitative research revealing who is, and is not, participating and
benefitting from sharing economies.
Background: The body-oriented therapeutic approach Somatic Experiencing® (SE) treats posttraumatic symptoms by changing the interoceptive and proprioceptive sensations associated with the traumatic experience. Filling a gap in the landscape of trauma treatments, SE has attracted growing interest in research and therapeutic practice, recently.
Objective: To date, there is no literature review of the effectiveness and key factors of SE. This review aims to summarize initial findings on the effectiveness of SE and to outline methodspecific key factors of SE.
Method: To gain a first overview of the literature, we conducted a scoping review including studies until 13 August 2020. We identified 83 articles of which 16 fit inclusion criteria and were systematically analysed.
Results: Findings provide preliminary evidence for positive effects of SE on PTSD-related symptoms. Moreover, initial evidence suggests that SE has a positive impact on affective and somatic symptoms and measures of well-being in both traumatized and non-traumatized
samples. Practitioners and clients identified resource-orientation and use of touch as methodspecific key factors of SE. Yet, an overall studies quality assessment as well as a Cochrane analysis of risk of bias indicate that the overall study quality is mixed.
Conclusions: The results concerning effectiveness and method-specific key factors of SE are promising; yet, require more support from unbiased RCT-research. Future research should focus on filling this gap.
The temporal stability of psychological test scores is one prerequisite for their practical usability. This is especially true for intelligence test scores. In educational contexts, high stakes decisions with long-term consequences, such as placement in special education programs, are often based on intelligence test results. There are four different types of temporal stability: mean-level change, individual-level change, differential continuity, and ipsative continuity. We present statistical methods for investigating each type of stability. Where necessary, the methods were adapted for the specific challenges posed by intelligence research (e.g., controlling for general intelligence in lower order test scores). We provide step-by-step guidance for the application of the statistical methods and apply them to a real data set of 114 gifted students tested twice with a test-retest interval of 6 months.
• Four different types of stability need to be investigated for a full picture of temporal stability in psychological research
• Selection and adaption of the methods for the use in intelligence research
• Complete protocol of the implementation
Background
Identifying pain-related response patterns and understanding functional mechanisms of symptom formation and recovery are important for improving treatment.
Objectives
We aimed to replicate pain-related avoidance-endurance response patterns associated with the Fear-Avoidance Model, and its extension, the Avoidance-Endurance Model, and examined their differences in secondary measures of stress, action control (i.e., dispositional action vs. state orientation), coping, and health.
Methods
Latent profile analysis (LPA) was conducted on self-report data from 536 patients with chronic non-specific low back pain at the beginning of an inpatient rehabilitation program. Measures of stress (i.e., pain, life stress) and action control were analyzed as covariates regarding their influence on the formation of different pain response profiles. Measures of coping and health were examined as dependent variables.
Results
Partially in line with our assumptions, we found three pain response profiles of distress-avoidance, eustress-endurance, and low-endurance responses that are depending on the level of perceived stress and action control. Distress-avoidance responders emerged as the most burdened, dysfunctional patient group concerning measures of stress, action control, maladaptive coping, and health. Eustress-endurance responders showed one of the highest levels of action versus state orientation, as well as the highest levels of adaptive coping and physical activity. Low-endurance responders reported lower levels of stress as well as equal levels of action versus state orientation, maladaptive coping, and health compared to eustress-endurance responders; however, equally low levels of adaptive coping and physical activity compared to distress-avoidance responders.
Conclusions
Apart from the partially supported assumptions of the Fear-Avoidance and Avoidance-Endurance Model, perceived stress and dispositional action versus state orientation may play a crucial role in the formation of pain-related avoidance-endurance response patterns that vary in degree of adaptiveness. Results suggest tailoring interventions based on behavioral and functional analysis of pain responses in order to more effectively improve patients quality of life.
Food waste is the origin of major social and environmental issues. In industrial societies, domestic households are the biggest contributors to this problem. But why do people waste food although they buy and value it? Answering this question is mandatory to design effective interventions against food waste. So far, however, many interventions have not been based on theoretical knowledge. Integrating food waste literature and ambivalence research, we propose that domestic food waste can be understood via the concept of ambivalence—the simultaneous presence of positive and negative associations towards the same attitude object. In support of this notion, we demonstrated in three pre-registered experiments that people experienced ambivalence towards non-perishable food products with expired best before dates. The experience of ambivalence was in turn associated with an increased willingness to waste food. However, two informational interventions aiming to prevent people from experiencing ambivalence did not work as intended (Experiment 3). We hope that the outlined conceptualization inspires theory-driven research on why and when people dispose of food and on how to design effective interventions.
Institutional and cultural determinants of speed of government responses during COVID-19 pandemic
(2021)
This article examines institutional and cultural determinants of the speed of government responses during the COVID-19 pandemic. We define the speed as the marginal rate of stringency index change. Based on cross-country data, we find that collectivism is associated with higher speed of government response. We also find a moderating role of trust in government, i.e., the association of individualism-collectivism on speed is stronger in countries with higher levels of trust in government. We do not find significant predictive power of democracy, media freedom and power distance on the speed of government responses.
Amphibian diversity in the Amazonian floating meadows: a Hanski core-satellite species system
(2021)
The Amazon catchment is the largest river basin on earth, and up to 30% of its waters flow across floodplains. In its open waters, floating plants known as floating meadows abound. They can act as vectors of dispersal for their associated fauna and, therefore, can be important for the spatial structure of communities. Here, we focus on amphibian diversity in the Amazonian floating meadows over large spatial scales. We recorded 50 amphibian species over 57 sites, covering around 7000 km along river courses. Using multi-site generalised dissimilarity modelling of zeta diversity, we tested Hanski's core-satellite hypothesis and identified the existence of two functional groups of species operating under different ecological processes in the floating meadows. ‘Core' species are associated with floating meadows, while ‘satellite' species are associated with adjacent environments, being only occasional or accidental occupants of the floating vegetation. At large scales, amphibian diversity in floating meadows is mostly determined by stochastic (i.e. random/neutral) processes, whereas at regional scales, climate and deterministic (i.e. niche-based) processes are central drivers. Compared with the turnover of ‘core' species, the turnover of ‘satellite' species increases much faster with distances and is also controlled by a wider range of climatic features. Distance is not a limiting factor for ‘core' species, suggesting that they have a stronger dispersal ability even over large distances. This is probably related to the existence of passive long-distance dispersal of individuals along rivers via vegetation rafts. In this sense, Amazonian rivers can facilitate dispersal, and this effect should be stronger for species associated with riverine habitats such as floating meadows.
We examined the long-term relationship of psychosocial risk and health behaviors on clinical events in patients awaiting heart transplantation (HTx). Psychosocial characteristics (e.g., depression), health behaviors (e.g., dietary habits, smoking), medical factors (e.g., creatinine), and demographics (e.g., age, sex) were collected at the time of listing in 318 patients (82% male, mean age = 53 years) enrolled in the Waiting for a New Heart Study. Clinical events were death/delisting due to deterioration, high-urgency status transplantation (HU-HTx), elective transplantation, and delisting due to clinical improvement. Within 7 years of follow-up, 92 patients died or were delisted due to deterioration, 121 received HU-HTx, 43 received elective transplantation, and 39 were delisted due to improvement. Adjusting for demographic and medical characteristics, the results indicated that frequent consumption of healthy foods (i.e., foods high in unsaturated fats) and being physically active increased the likelihood of delisting due improvement, while smoking and depressive symptoms were related to death/delisting due to clinical deterioration while awaiting HTx. In conclusion, psychosocial and behavioral characteristics are clearly associated with clinical outcomes in this population. Interventions that target psychosocial risk, smoking, dietary habits, and physical activity may be beneficial for patients with advanced heart failure waiting for a cardiac transplant.
Optimal mental workload plays a key role in driving performance. Thus, driver-assisting systems that automatically adapt to a drivers current mental workload via brain–computer interfacing might greatly contribute to traffic safety. To design economic brain computer interfaces that do not compromise driver comfort, it is necessary to identify brain areas that are most sensitive to mental workload changes. In this study, we used functional near-infrared spectroscopy and subjective ratings to measure mental workload in two virtual driving environments with distinct demands. We found that demanding city environments induced both higher subjective workload ratings as well as higher bilateral middle frontal gyrus activation than less demanding country environments. A further analysis with higher spatial resolution revealed a center of activation in the right anterior dorsolateral prefrontal cortex. The area is highly involved in spatial working memory processing. Thus, a main component of drivers’ mental workload in complex surroundings might stem from the fact that large amounts of spatial information about the course of the road as well as other road users has to constantly be upheld, processed and updated. We propose that the right middle frontal gyrus might be a suitable region for the application of powerful small-area brain computer interfaces.
With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. It shows that convolutional neural networks have potential to generalize better than pixel based models, since they do not rely on phenological development alone, but can also consider object geometry and texture. The UNET classifier was able to achieve the highest F1 scores, averaging 0.61 in temporal validation samples, and 0.77 in spatial validation samples. The theoretical potential for overfitting geometry and just memorizing the shape of fields that are maize has been shown to be insignificant in practical applications. In conclusion, kernel based convolutions can offer a large contribution in making agricultural classification models more transferable, both to other regions and to other years.