No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

Solution‐Processable Photonic Inks of Mie‐Resonant Hollow Carbon–Silica Nanospheres

Hollow carbon–silica nanospheres that exhibit angle‐independent structural color with high saturation and minimal absorption are made. Through scattering calculations, it is shown that the structural color arises from Mie resonances that are tuned precisely by varying the thickness of the shells. Since the color does not depend on the spatial arrangement of the particles, the coloration is angle independent and vibrant in powders and liquid suspensions. These properties make hollow carbon–silica nanospheres ideal for applications, and their potential in making flexible, angle‐independent films and 3D printed films is explored.     

A write-friendly approach to manage namespace of Hadoop distributed file system by utilizing nonvolatile memory

The Journal of Supercomputing - With the emergence of the big data era, various technologies have been proposed to cope with the exascale of data. For a considerably large volume of data, a single...     

An investigation of the drivers,barriers, and incentives for environmental management systems in the Malaysian food and beverage industry

Clean Technologies and Environmental Policy - Food production and consumption is one of the major causes of global environmental degradation. One way to address environmental impacts in the food...     

Toxoplasma gondii GRA9 Regulates the Activation of NLRP3 Inflammasome to Exert Anti-Septic Effects in Mice

Dense granule proteins (GRAs) are essential components in Toxoplasma gondii, which are suggested to be promising serodiagnostic markers in toxoplasmosis. In this study, we investigated the function of GRA9 in host response and the associated regulatory mechanism, which were unknown. We found that GRA9 interacts with NLR family pyrin domain containing 3 (NLRP3) involved in inflammation by forming the NLRP3 inflammasome. The C-terminal of GRA9 (GRA9C) is essential for GRA9–NLRP3 interaction by disrupting the NLRP3 inflammasome through blocking the binding of apoptotic speck-containing (ASC)-NLRP3. Notably, Q200 of GRA9C is essential for the interaction of NLRP3 and blocking the conjugation of ASC. Recombinant GRA9C (rGRA9C) showed an anti-inflammatory effect and the elimination of bacteria by converting M1 to M2 macrophages. In vivo, rGRA9C increased the anti-inflammatory and bactericidal effects and subsequent anti-septic activity in CLP- and E. coli- or P. aeruginosa-induced sepsis model mice by increasing M2 polarization. Taken together, our findings defined a role of T. gondii GRA9 associated with NLRP3 in host macrophages, suggesting its potential as a new candidate therapeutic agent for sepsis.     

Gait event detection algorithm based on smart insoles

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel‐strike and toe‐off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel‐strike and toe‐off detection algorithm using a smart insole with inertial measurement units. This method detects heel‐strike and toe‐off events through a time‐frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel‐strike and toe‐off detection algorithm yielded the largest error of 0.03 seconds for running toe‐off events, and an average of 0–0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single‐support time, and double‐support time can all be estimated using the proposed heel‐strike and toe‐off detection algorithm.     

Optimization of Pathogen Capture in Flowing Fluids with Magnetic Nanoparticles

Magnetic nanoparticles have been employed to capture pathogens for many biological applications; however, optimal particle sizes have been determined empirically in specific capturing protocols. Here, a theoretical model that simulates capture of bacteria is described and used to calculate bacterial collision frequencies and magnetophoretic properties for a range of particle sizes. The model predicts that particles with a diameter of 460 nm should produce optimal separation of bacteria in buffer flowing at 1 L h⁻¹. Validating the predictive power of the model, Staphylococcus aureus is separated from buffer and blood flowing through magnetic capture devices using six different sizes of magnetic particles. Experimental magnetic separation in buffer conditions confirms that particles with a diameter closest to the predicted optimal particle size provide the most effective capture. Modeling the capturing process in plasma and blood by introducing empirical constants (c_e), which integrate the interfering effects of biological components on the binding kinetics of magnetic beads to bacteria, smaller beads with 50 nm diameters are predicted that exhibit maximum magnetic separation of bacteria from blood and experimentally validated this trend. The predictive power of the model suggests its utility for the future design of magnetic separation for diagnostic and therapeutic applications.     

Condition monitoring scheme via one-class support vector machine and multivariate control charts

A condition-based maintenance (CBM) has been widely employed to reduce maintenance cost by predicting the health status of many complex systems in prognostics and health management (PHM) framework. Recently, multivariate control charts used in statistical process control (SPC) have been actively introduced as monitoring technology. In this paper, we propose a condition monitoring scheme to monitor the health status of the system of interest. In our condition monitoring scheme, we first define reference data set using one-class support vector machine (OC-SVM) to construct the control limit of multivariate control charts in phase I. Then, parametric control chart or non-parametric control chart is selected according to the results from multivariate normality tests. The proposed condition monitoring scheme is applied to sensor data of two anemometers to evaluate the performance of fault detection power.