A stochastic model of the influence of buffer gas collisions on Mollow spectra

In this paper we consider the influence of collisional fluctuations on the Mollow spectra of resonance fluorescence (RF). The fluctuations are taken into account by a simple shift of the constant detuning, involved in a set of optical Bloch equations by collision frequency noise which is modelled by a two-step random telegraph signal (RTS). We consider in detail the Mollow spectra for RF in the case of an arbitrary detuning of the laser frequency, where the emitter is a member of a statistical ensemble in thermodynamic equilibrium with the buffer gas at temperature $T$ which is treated as a colored environment, and velocity $v$ is distributed with the Maxwell-Boltzmann density

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model\u27s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.<br /

Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors

Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sensors for improving recognition performance. The proposed FS-AHMM is an extension of the Abstract Hidden Markov Model which applies the concept of factored state representations to compactly represent the state transitions. The parameters of the FS-AHMM are estimated using the EM algorithm from the data acquired through multiple multi-modal sensors and cameras. The model is evaluated and compared with other existing models on real-world data. The results show that the proposed model outperforms other models and that the integrated sensor information helps in recognizing activity more accurately

A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.<br /

Magneto-transport properties of monolayer borophene in perpendicular magnetic field: influence of electron-phonon interaction

The magneto-transport properties of a borophene monolayer in a perpendicular magnetic field B are studied via calculating the conductivity tensor and resistance under electron-optical phonon interaction by using the linear response theory. Numerical results are obtained and discussed for some specific parameters. The magnetic field-dependent longitudinal conductivity shows the magneto-phonon resonance effect that describes the transition of electrons between Landau levels by absorbing/emitting an optical phonon. The Hall conductivity increases first and then decreases with the magnetic field strength. Also, the longitudinal resistance increases significantly with increasing temperature, which shows the metal behaviour of the material. Practically, the observed magneto-phonon resonance can be applied to experimentally determine some material parameters, such as the distance between Landau levels and the optical phonon energy

Utilization of services provided by village based ethnic minority midwives in mountainous villages of Vietnam

Introduction: Since 2011, the Vietnam’s Ministry of Health implemented the ethnic minority midwives (EMMs) scheme in order to increase the utilization of maternal health services by women from ethnic minorities and those living in hard-to-reach mountainous areas. This paper analyzes the utilization of antenatal, delivery, and postpartum care provided by EMMs and reports the key determinants of utilization of EMM services as perceived by service users. Methods: A structured questionnaire was administered in 2015 to all mothers (n=320) who gave birth to a live-born during a 1-year period in 31 villages which had EMM in two provinces, Dien Bien and Kon Tum. A multivariate logistic regression model was used to examine the association between all potential factors and the use of services provided by EMMs. Results: We found that EMMs provided more antenatal care and postnatal care as compared with delivery services, which corresponded to their job descriptions. The results also showed that utilization of antenatal care provided by EMMs was lower than that of postnatal care. The proportion of those who never heard about EMM was high (24%). Among the mothers who knew about EMM services, 33.4% had antenatal checkups, 20.1% were attended during home deliveries, and 57.3% had postnatal visits by an EMM. Key factors that determined the use of EMM services included knowledge of the location of EMM’s house, being aware about EMMs by health workers, trust in services provided by EMMs, and perception that many others mothers in a village also knew about EMM services. Conclusion: EMM seems to be an important mechanism to ensure assistance during home births and postnatal care for ethnic minority groups, who are often resistant to attend health facilities. Building trust and engaging with communities are the key facilitators to increase the utilization of services provided by EMMs. Communication campaigns to raise awareness about EMMs and to promote their services in the village, particularly by other health workers, represent an important strategy to further improve effectiveness of EMM scheme

Towards a service-oriented architecture for knowledge management in big data era

Nowadays, big data is a revolution that transforms conventional enterprises into data-driven organizations in which knowledge discovered from big data will be integrated into traditional knowledge to improve decision-making and to facilitate organizational learning. Consequently, a major concern is how to evolve current knowledge management systems, which are confronted with a various and unprecedented amount of data, resulting from different data sources. Therefore, a new generation of knowledge management systems is required for exploring and exploiting big data as well as for facilitating the knowledge co-creation between the society and its business environment to foster innovation. This article proposes a service-oriented architecture for elaborating a new generation of big data-driven knowledge management systems to help enterprises to promote knowledge co-creation and to obtain more business value from big data. The proposed architecture is presented based on the principles of design science research and its evaluation uses the analytical evaluation method

Identification of nonlinear heat transfer laws from boundary observations

We consider the problem of identifying a nonlinear heat transfer law at the boundary, or of the temperature-dependent heat transfer coefficient in a parabolic equation from boundary observations. As a practical example, this model applies to the heat transfer coefficient that describes the intensity of heat exchange between a hot wire and the cooling water in which it is placed. We reformulate the inverse problem as a variational one which aims to minimize a misfit functional and prove that it has a solution. We provide a gradient formula for the misfit functional and then use some iterative methods for solving the variational problem. Thorough investigations are made with respect to several initial guesses and amounts of noise in the input data. Numerical results show that the methods are robust, stable and accurate

Effect of ciprofloxacin dosages on the performance of sponge membrane bioreactor treating hospital wastewater

© 2018 Elsevier Ltd This study aimed to evaluate treatment performance and membrane fouling of a lab-scale Sponge-MBR under the added ciprofloxacin (CIP) dosages (20; 50; 100 and 200 µg L−1) treating hospital wastewater. The results showed that Sponge-MBR exhibited effective removal of COD (94–98%) during the operation period despite increment of CIP concentrations from 20 to 200 µg L−1. The applied CIP dosage of 200 µg L−1 caused an inhibition of microorganisms in sponges, i.e. significant reduction of the attached biomass and a decrease in the size of suspended flocs. Moreover, this led to deteriorating the denitrification rate to 3–12% compared to 35% at the other lower CIP dosages. Importantly, Sponge-MBR reinforced the stability of CIP removal at various added CIP dosages (permeate of below 13 µg L−1). Additionally, the fouling rate at CIP dosage of 200 µg L−1 was 30.6 times lower compared to the control condition (no added CIP dosage)