Purpose: We investigated the effects of threat and trait anxiety on state anxiety and how that affects police officers’ actions during an arrest. Most experiments on police performance under anxiety test the performance of one particular skill. Yet, police work often involves concerted use of a combination of skills. Methods: We created situations – with two different levels of threat – in which officers had to choose and initiate their actions to control and arrest a non-cooperative suspect. We examined whether threat, trait anxiety and state anxiety influenced decision-making (e.g., choosing the appropriate actions) and performance (e.g., quality of communication and the execution of skills). Results: Trait anxiety affected the level of state anxiety, but not any of the decision-making and performance variables. As for decision-making, results showed that only threat determined which action officers took to gain control over the suspect. As for performance, higher levels of state anxiety were accompanied by lower scores of overall performance, communication, proportionality of applied force and quality of skill execution. Conclusion: It is concluded that state anxiety not only impairs performance of single perceptual-motor tasks, but also relevant accompanying skills such as communicating and applying appropriate force. We argue that police training should focus on an integrated set of decision-making and perceptual-motor skills and not just on the performance of isolated motor skills.
Introduction Negative pain-related cognitions are associated with persistence of low-back pain (LBP), but the mechanism underlying this association is not well understood. We propose that negative pain-related cognitions determine how threatening a motor task will be perceived, which in turn will affect how lumbar movements are performed, possibly with negative long-term effects on pain. Objective To assess the effect of postural threat on lumbar movement patterns in people with and without LBP, and to investigate whether this effect is associated with task-specific pain-related cognitions. Methods 30 back-healthy participants and 30 participants with LBP performed consecutive two trials of a seated repetitive reaching movement (45 times). During the first trial participants were threatened with mechanical perturbations, during the second trial participants were informed that the trial would be unperturbed. Movement patterns were characterized by temporal variability (CyclSD), local dynamic stability (LDE) and spatial variability (meanSD) of the relative lumbar Euler angles. Pain-related cognition was assessed with the task-specific ‘Expected Back Strain’-scale (EBS). A three-way mixed Manova was used to assess the effect of Threat, Group (LBP vs control) and EBS (above vs below median) on lumbar movement patterns. Results We found a main effect of threat on lumbar movement patterns. In the threat-condition, participants showed increased variability (MeanSDflexion-extension, p<0.000, η2 = 0.26; CyclSD, p = 0.003, η2 = 0.14) and decreased stability (LDE, p = 0.004, η2 = 0.14), indicating large effects of postural threat. Conclusion Postural threat increased variability and decreased stability of lumbar movements, regardless of group or EBS. These results suggest that perceived postural threat may underlie changes in motor behavior in patients with LBP. Since LBP is likely to impose such a threat, this could be a driver of changes in motor behavior in patients with LBP, as also supported by the higher spatial variability in the group with LBP and higher EBS in the reference condition.
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Instagram has become an unsuspecting pulpit – seemingly caught off guard – for those determined to spread a militant message of Islamic State terror. Graphic, fanatical and oftentimes heavily photoshopped images weave through Instagram’s labyrinth of sunset snaps and gym selfies to advance a curious manifestation of cause-related self-promotion.
Chemical preservation is an important process that prevents foods, personal care products, woods and household products, such as paints and coatings, from undesirable change or decomposition by microbial growth. To date, many different chemical preservatives are commercially available, but they are also associated with health threats and severe negative environmental impact. The demand for novel, safe, and green chemical preservatives is growing, and this process is further accelerated by the European Green Deal. It is expected that by the year of 2050 (or even as soon as 2035), all preservatives that do not meet the ‘safe-by-design’ and ‘biodegradability’ criteria are banned from production and use. To meet these European goals, there is a large need for the development of green, circular, and bio-degradable antimicrobial compounds that can serve as alternatives for the currently available biocidals/ preservatives. Anthocyanins, derived from fruits and flowers, meet these sustainability goals. Furthermore, preliminary research at the Hanze University of Applied Science has confirmed the antimicrobial efficacy of rose and tulip anthocyanin extracts against an array of microbial species. Therefore, these molecules have the potential to serve as novel, sustainable chemical preservatives. In the current project we develop a strategy consisting of fractionation and state-of-the-art characterization methods of individual anthocyanins and subsequent in vitro screening to identify anthocyanin-molecules with potent antimicrobial efficacy for application in paints, coatings and other products. To our knowledge this is the first attempt that combines in-depth chemical characterization of individual anthocyanins in relation to their antimicrobial efficacy. Once developed, this strategy will allow us to single out anthocyanin molecules with antimicrobial properties and give us insight in structure-activity relations of individual anthocyanins. Our approach is the first step towards the development of anthocyanin molecules as novel, circular and biodegradable non-toxic plant-based preservatives.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
