ABSTRACT
Noxious chemicals, coupled with morphine treatment, are often used in studies on pain in vertebrates. Here we show that injection of morphine caused several behavioural changes in the crab, Carcinus maenas, including reduced pressing against the sides of the enclosure and more rubbing and picking at the mouth parts and, at least for a short time, more defensive displays. Subsequent injection of acetic acid into one rear leg caused rubbing of the injected leg and the injected leg was held vertically off the ground. These activities directed at or involving the specific leg are consistent with previous observations of directed behaviour following noxious stimuli and are consistent with the idea that decapods experience pain. Further, acetic acid but not injection of water induced autotomy of the injected leg in these animals. Because autotomy is temporally associated with directed behaviour, it is possible that the autotomy is a pain-related response. Acetic acid is clearly a noxious substance when applied to decapods. However, morphine had no effect on the activities associated with acetic acid injection and thus there is no evidence for an analgesic effect. Further, the injection of acetic acid did not interfere with behavioural effects of morphine. The activities directed towards the site of injection are like those observed with injection, or with external application, of various noxious substances and the present study adds to a growing body of knowledge about possible pain in decapods.
ABSTRACT
The suggestion that decapod crustaceans might experience pain has been dismissed by some authors who claim decapods only respond to noxious stimuli by nociceptive reflexes. Because reflexes do not require complex neuronal processing, but pain does, demonstrating reflex responses to noxious stimuli would not support the case for pain. Here, we report an experiment in which shore crabs are repeatedly placed in a light area (20 trials), but the animals can avoid the light by moving to a dark shelter. However, some crabs received an electric shock of 6 or 12 volts each time they entered the shelter. Those receiving either level of shock swiftly reduced their use of shelters and remained in the light. However, the magnitude of shelter avoidance was influenced by the brightness of the arena and the intensity of the shock. Shelter use was subsequently reduced to a greater extent if the shock level was high and the light intensity low. That is, crabs traded their avoidance of shock for their avoidance of bright light. Further, these animals showed avoidance learning and demonstrated activities suggesting anxiety, such as contact with the tank wall in the light area and increased latency to enter shelters when making the decision to enter the shelter if they had received shock in earlier trials. These results fulfil three key behavioural criteria for pain and, thus, are consistent with the idea that decapods can experience pain.
ABSTRACT
Acceptance of the possibility of pain in animals usually requires that various criteria are fulfilled. One such criterion is that a noxious stimulus or wound would elicit directed rubbing or grooming at the site of the stimulus. There is also an expectation that local anaesthetics would reduce these responses to damage. These expectations have been fulfilled in decapod crustaceans but there has been criticism of a lack of replication. Here, we report an experiment on the effects of a noxious chemical, sodium hydroxide, applied to one eyestalk of the glass prawn. This caused an immediate escape tail-flick response. It then caused nipping and picking with the chelipeds at the treated eyestalk but much less so at the alternative eyestalk. Prior treatment with benzocaine also caused an immediate tail-flick and directed behaviour, suggesting that this agent is aversive. Subsequently, however, it reduced the directed behaviour caused by caustic soda. We thus demonstrated responses that are consistent with the idea of pain in decapod crustaceans.
ABSTRACT
Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.
ABSTRACT
Failure of critical national infrastructures can result in major disruptions to society and the economy. Understanding the criticality of individual assets and the geographic areas in which they are located is essential for targeting investments to reduce risks and enhance system resilience. Within this study we provide new insights into the criticality of real-life critical infrastructure networks by integrating high-resolution data on infrastructure location, connectivity, interdependence, and usage. We propose a metric of infrastructure criticality in terms of the number of users who may be directly or indirectly disrupted by the failure of physically interdependent infrastructures. Kernel density estimation is used to integrate spatially discrete criticality values associated with individual infrastructure assets, producing a continuous surface from which statistically significant infrastructure criticality hotspots are identified. We develop a comprehensive and unique national-scale demonstration for England and Wales that utilizes previously unavailable data from the energy, transport, water, waste, and digital communications sectors. The testing of 200,000 failure scenarios identifies that hotspots are typically located around the periphery of urban areas where there are large facilities upon which many users depend or where several critical infrastructures are concentrated in one location.
ABSTRACT
Future development in cities needs to manage increasing populations, climate-related risks, and sustainable development objectives such as reducing greenhouse gas emissions. Planners therefore face a challenge of multidimensional, spatial optimization in order to balance potential tradeoffs and maximize synergies between risks and other objectives. To address this, a spatial optimization framework has been developed. This uses a spatially implemented genetic algorithm to generate a set of Pareto-optimal results that provide planners with the best set of trade-off spatial plans for six risk and sustainability objectives: (i) minimize heat risks, (ii) minimize flooding risks, (iii) minimize transport travel costs to minimize associated emissions, (iv) maximize brownfield development, (v) minimize urban sprawl, and (vi) prevent development of greenspace. The framework is applied to Greater London (U.K.) and shown to generate spatial development strategies that are optimal for specific objectives and differ significantly from the existing development strategies. In addition, the analysis reveals tradeoffs between different risks as well as between risk and sustainability objectives. While increases in heat or flood risk can be avoided, there are no strategies that do not increase at least one of these. Tradeoffs between risk and other sustainability objectives can be more severe, for example, minimizing heat risk is only possible if future development is allowed to sprawl significantly. The results highlight the importance of spatial structure in modulating risks and other sustainability objectives. However, not all planning objectives are suited to quantified optimization and so the results should form part of an evidence base to improve the delivery of risk and sustainability management in future urban development.
ABSTRACT
Critical infrastructure networks, including transport, are crucial to the social and economic function of urban areas but are at increasing risk from natural hazards. Minimizing disruption to these networks should form part of a strategy to increase urban resilience. A framework for assessing the disruption from flood events to transport systems is presented that couples a high-resolution urban flood model with transport modelling and network analytics to assess the impacts of extreme rainfall events, and to quantify the resilience value of different adaptation options. A case study in Newcastle upon Tyne in the UK shows that both green roof infrastructure and traditional engineering interventions such as culverts or flood walls can reduce transport disruption from flooding. The magnitude of these benefits depends on the flood event and adaptation strategy, but for the scenarios considered here 3-22% improvements in city-wide travel times are achieved. The network metric of betweenness centrality, weighted by travel time, is shown to provide a rapid approach to identify and prioritize the most critical locations for flood risk management intervention. Protecting just the top ranked critical location from flooding provides an 11% reduction in person delays. A city-wide deployment of green roofs achieves a 26% reduction, and although key routes still flood, the benefits of this strategy are more evenly distributed across the transport network as flood depths are reduced across the model domain. Both options should form part of an urban flood risk management strategy, but this method can be used to optimize investment and target limited resources at critical locations, enabling green infrastructure strategies to be gradually implemented over the longer term to provide city-wide benefits. This framework provides a means of prioritizing limited financial resources to improve resilience. This is particularly important as flood management investments must typically exceed a far higher benefit-cost threshold than transport infrastructure investments. By capturing the value to the transport network from flood management interventions, it is possible to create new business models that provide benefits to, and enhance the resilience of, both transport and flood risk management infrastructures. Further work will develop the framework to consider other hazards and infrastructure networks.
ABSTRACT
A number of criteria have been suggested for testing if pain occurs in animals, and these include an analgesic effect of opiates (Bateson, 1991). Morphine reduces responses to noxious stimuli in crustaceans but also reduces responsiveness in a non-pain context. Here we use a paradigm in which shore crabs receive a shock in a preferred dark shelter but not if they remain in an unpreferred light area. Analgesia should thus enhance movement to the preferred dark area because they should not experience 'pain'. However, morphine inhibits rather than enhances this movement even when no shock is given. Morphine produces a general effect of non-responsiveness rather than a specific analgesic effect and this could also explain previous studies claiming analgesia. However, we question the utility of this criterion of pain and suggest instead that behavioural criteria be employed.