Planets in the habitable zone of lower-mass stars are often assumed to be in a state of tidally synchronized rotation, which would considerably affect their putative habitability. Although thermal tides cause Venus to rotate retrogradely, simple scaling arguments tend to attribute this peculiarity to the massive Venusian atmosphere. Using a global climate model, we show that even a relatively thin atmosphere can drive terrestrial planets' rotation away from synchronicity. We derive a more realistic atmospheric tide model that predicts four asynchronous equilibrium spin states, two being stable, when the amplitude of the thermal tide exceeds a threshold that is met for habitable Earth-like planets with a 1 bar atmosphere around stars more massive than ∼ 0.5 − 0.7 M . Thus, many recently discovered terrestrial planets could exhibit asynchronous spinorbit rotation, even with a thin atmosphere.As we all experience in our everyday life, atmospheric temperatures oscillate following the diurnal insolation cycle. This, in turn, creates periodic large-scale mass redistribution inside the atmosphere, the so-called thermal atmospheric tides. But as we all have experienced too, the hottest moment of the day is actually not when the Sun is directly overhead, but a few hours later. This is due to the thermal inertia of the ground and atmosphere that creates a delay between the solar heating and thermal response (driving mass redistribution), causing the whole atmospheric response to lag behind the Sun (1).Because of this asymmetry in the atmospheric mass redistribution with respect to the sub-solar point, the gravitational pull exerted by the Sun on the atmosphere has a non-zero net torque that tends to accelerate or decelerate its rotation, depending on the direction of the solar motion (2, 3). Because the atmosphere and the surface are usually well coupled by friction in the atmospheric boundary layer, the angular momentum transferred from the orbit to the atmosphere is then transferred to the bulk of the planet, modifying its spin (4). On Earth, this effect is negligible because we are too far away from the Sun, but the atmospheric torque due to thermal tides can be very powerful, as seen on Venus. Indeed, although tidal friction inside the planet is continuously trying to spin it down to a state of synchronous rotation, thermal tides are strong enough to drive the planet out of synchronicity and to force the slow retrograde rotation that we see today (2-6). Very simple scaling arguments predict that the amplitude of the thermal tide is proportional to the ratio of the atmospheric mean surface pressure over its scale height (1). Everything else being equal, one would thus expect the thermal tide to be ∼ 50 times weaker if Venus had a less massive, cooler Earth-like atmosphere. Does this scaling really hold and how massive does an atmosphere need to be to affect the planetary rotation, this has not been completely worked out yet.These questions are of utmost importance as we now find many terrestrial planets in a situation ...

BackgroundFurther advancement in schistosomiasis prevention requires new tools to assess protective motivation, and promote innovative intervention program. This study aimed to develop and evaluate an instrument developed based on the Protection Motivation Theory (PMT) to predict protective behavior intention against schistosomiasis among adolescents in China.MethodsWe developed the Schistosomiasis PMT Scale based on two appraisal pathways of protective motivation- threat appraisal pathway and coping appraisal pathway. Data from a large sample of middle school students (n = 2238, 51 % male, mean age 13.13 ± 1.10) recruited in Hubei, China was used to evaluated the validity and reliability of the scale.ResultsThe final scale contains 18 items with seven sub-constructs. Cronbach’s Alpha coefficients for the entire instrument was 0.76, and for the seven sub-constructs of severity, vulnerability, intrinsic reward, extrinsic reward, response efficacy, self-efficacy and response cost was 0.56, 0.82, 0.75, 0.80, 0.90, 0.72 and 0.70, respectively. The construct validity analysis revealed that the one level 7 sub-constructs model fitted data well (GFI = 0.98, CFI = 0.98, RMSEA = 0.03, Chi-sq/df = 3.90, p < 0.001). Predictive validity showed that both the PMT instrument score and the 7 sub-construct scores were significantly correlated with the intention engaged in protective behavior against schistosomiasis (p < 0.05).ConclusionsThis study provides a reliable and valid tool to measure protective motivation in schistosomiasis prevention control. Further studies are needed to develop more effective intervention programs for schistosomiasis prevention.

Temporal information plays a significant role in video-based human action recognition. How to effectively extract the spatial-temporal characteristics of actions in videos has always been a challenging problem. Most existing methods acquire spatial and temporal cues in videos individually. In this article, we propose a new effective representation for depth video sequences, called hierarchical dynamic depth projected difference images that can aggregate the action spatial and temporal information simultaneously at different temporal scales. We firstly project depth video sequences onto three orthogonal Cartesian views to capture the 3D shape and motion information of human actions. Hierarchical dynamic depth projected difference images are constructed with the rank pooling in each projected view to hierarchically encode the spatial-temporal motion dynamics in depth videos. Convolutional neural networks can automatically learn discriminative features from images and have been extended to video classification because of their superior performance. To verify the effectiveness of hierarchical dynamic depth projected difference images representation, we construct a hierarchical dynamic depth projected difference images-based action recognition framework where hierarchical dynamic depth projected difference images in three views are fed into three identical pretrained convolutional neural networks independently for finely retuning. We design three classification schemes in the framework and different schemes utilize different convolutional neural network layers to compare their effects on action recognition. Three views are combined to describe the actions more comprehensively in each classification scheme. The proposed framework is evaluated on three challenging public human action data sets. Experiments indicate that our method has better performance and can provide discriminative spatial-temporal information for human action recognition in depth videos.

No abstract

No abstract

Automatic fall detection in videos could enable timely delivery of medical service to the injured elders who have fallen and live alone. Deep ConvNets have been used to detect fall actions. However, there still remain problems in deep video representations for fall detection. First, video frames are directly inputted to deep ConvNets. The visual features of human actions may be interfered with surrounding environments. Second, redundant frames increase the difficulty of time encoding for human actions. To address these problems, this paper presents trajectory-weighted deep-convolutional rank-pooling descriptor (TDRD) for fall detection, which is robust to surrounding environments and can describe the dynamics of human actions in long time videos effectively. First, CNN feature map of each frame is extracted through a deep ConvNet. Then, we present a new kind of trajectory attention map which is built with improved dense trajectories to optimally localize the subject area. Next, the CNN feature map of each frame is weighted with its corresponding trajectory attention map to get trajectory-weighted convolutional visual feature of human region. Further, we propose a cluster pooling method to reduce the redundancy of the trajectory-weighted convolutional features of a video in the time sequence. Finally, rank pooling method is used to encode the dynamic of the cluster-pooled sequence to get our TDRD. With TDRD, we get superior result on SDUFall dataset and get comparable performances on UR dataset and Multiple cameras dataset with SVM classifiers. INDEX TERMS Fall detection, descriptor, deep ConvNets, trajectory attention map, cluster pooling, rank pooling.

No abstract

The low-carbon supply chain is key to promoting sustainable development and solving environmental pollution. Government policies related to lowering carbon emissions deeply affect supply chains. This paper builds a supply chain decision-making model under three different regulatory policies: a pure carbon tax, a pure low-carbon subsidy, and a mixed policy with both a carbon tax and a low-carbon subsidy, then compares and analyzes the impacts of these three different regulatory policies on carbon emissions, manufacturer and retailer income, and marginal profit in order to determine the best course of action with respect to supply chain decision-making. Our results indicate that the supply chain decision-making model under the mixed carbon tax and low-carbon subsidy policy results in a unique Nash equilibrium solution between the retailer subsidy rate and the manufacturing carbon reduction rate in a non-cooperative game. Although a carbon tax is beneficial to the ecological environment, retailer income increases slightly as the carbon tax coefficient increases before declining rapidly. Manufacturer income has a negative linear relationship with carbon tax, and an excessive amount of carbon tax increases the burden on companies. Therefore, the government must establish reasonable standards for carbon tax collection while offering moderate low-carbon subsidies at the same time as a means of optimizing social welfare.