Prediction Of Future Activity Research Articles

Radon signature of CO2 flux constrains the depth of degassing: Furnas volcano (Azores, Portugal) versus Syabru-Bensi (Nepal Himalayas)

Substantial terrestrial gas emissions, such as carbon dioxide (CO2), are associated with active volcanoes and hydrothermal systems. However, while fundamental for the prediction of future activity, it remains difficult so far to determine the depth of the gas sources. Here we show how the combined measurement of CO2 and radon-222 fluxes at the surface constrains the depth of degassing at two hydrothermal systems in geodynamically active contexts: Furnas Lake Fumarolic Field (FLFF, Azores, Portugal) with mantellic and volcano-magmatic CO2, and Syabru-Bensi Hydrothermal System (SBHS, Central Nepal) with metamorphic CO2. At both sites, radon fluxes reach exceptionally high values (> 10 Bq m−2 s−1) systematically associated with large CO2 fluxes (> 10 kg m−2 day−1). The significant radon‒CO2 fluxes correlation is well reproduced by an advective–diffusive model of radon transport, constrained by a thorough characterisation of radon sources. Estimates of degassing depth, 2580 ± 180 m at FLFF and 380 ± 20 m at SBHS, are compatible with known structures of both systems. Our approach demonstrates that radon‒CO2 coupling is a powerful tool to ascertain gas sources and monitor active sites. The exceptionally high radon discharge from FLFF during quiescence (≈ 9 GBq day−1) suggests significant radon output from volcanoes worldwide, potentially affecting atmosphere ionisation and climate.

국내 비의도적 주요 배출원의 지역별 수은 대기 배출량 산정 및 미래 활동도 변화와 최적가용기술 적용 시 배출량 추이

Abstract This study discusses the present status of mercury emission and distribution from major anthropogenic sources in Korea and the future trend of mercury emission by activity changes and application of BATs. Atmospheric mercury emission from major anthropogenic sources based on Annex D of Minamata convention was estimated to around 4.89 tonne in 2012. Emission ratios of cement clinker production, coal-fired power plant, waste incineration and non-ferrous metal smelting were 68.68%, 24.75%, 6.29% and 0.28%, respectively. High mercury emission regions were characterized by the presence of cement clinker production facilities and coal-fired power plants. Prediction of future activities was carried out by linear regression of the previous year data. The (total) mercury emission was estimated to decrease up to 48% Under the scenario of BATs to be applied and the change of future activities. Emissions from coal-fired powerplants and cement clinkers were expected to decrease significantly.

Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach

Society is rapidly accepting the use of video cameras in many new and varied locations, but effective methods to utilize and manage the massive resulting amounts of visual data are only slowly developing. This paper presents a framework for live video analysis in which the behaviors of surveillance subjects are described using a vocabulary learned from recurrent motion patterns, for real-time characterization and prediction of future activities, as well as the detection of abnormalities. The repetitive nature of object trajectories is utilized to automatically build activity models in a 3-stage hierarchical learning process. Interesting nodes are learned through Gaussian mixture modeling, connecting routes formed through trajectory clustering, and spatio-temporal dynamics of activities probabilistically encoded using hidden Markov models. Activity models are adapted to small temporal variations in an online fashion using maximum likelihood regression and new behaviors are discovered from a periodic retraining for long-term monitoring. Extensive evaluation on various data sets, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based activity analysis.

A spatio-temporal modelling framework for assessing the fluctuations of avalanche occurrence resulting from climate change: application to 60 years of data in the northern French Alps

Based on a previous township-scale model, a spatio-temporal framework is proposed to study the fluctuations of avalanche occurrence possibly resulting from climate change. The regional annual component is isolated from the total variability using a two-factor nonlinear analysis of variance. Moreover, relying on a Conditional AutoRegressive sub-model for the spatial effects, the structured time trend is distinguished from the random noise with different time series sub-models including autocorrelative, periodic and change-point models. The hierarchical structure obtained takes into account the uncertainty related to the estimation of the annual component for the quantification of the time trend. Bayesian inference is performed using Monte Carlo simulations. This allows a comparison of the different time series models and the prediction of future activity in an explicit unsteady context. Application to the northern French Alps illustrates the information provided by the model’s different components, mainly the spatial and temporal terms as well as the spatio-temporal fluctuation of the relative risk. For instance, it shows no strong modifications in mean avalanche activity or in the number of winters of low or high activity over the last 60 years. This suggests that climate change has recently had little impact on the avalanching rhythm in this region. However, significant temporal patterns are highlighted: a complex combination of abrupt changes and pseudo-periodic cycles of approximately 15 years. For anticipating the future response of snow avalanches to climate change, correlating them with fluctuations of the constraining climatic factors is now necessary.

Automatic mapping and modeling of human networks

Abstract Mobile telephones, company ID badges, and similar common devices form a sensor network which can be used to map human activity, and especially human interactions. The most informative sensor data seem to be measurements of person-to-person proximity, and statistics of vocalization and body movement measurements. Using this data to model individual behavior as a stochastic process allows prediction of future activity, with the greatest predictive power obtained by modeling the interactions between individual processes. Experiments show that between 40% and 95% of the variance in human behavior may be explained by such models.