Phosphorus(p) migration behavior in the process of converter slag gasification dephosphorization

The high P content in steel slag limits its recycling during the smelting process, and the P can be effectively removed from the steel slag by gasification dephosphorization. In this experiments, the effects of temperature, basicity, and FeO for gasification dephosphorization rate are studied through thermodynamic calculations. The Scanning Electron Microscope (SEM) and Energy Dispersive Spectrometer (EDS) analysis that microscopic morphology of slag before and after reduction. In addition, a model is established to describe the phosphorus migration behavior of gasification dephosphorization process

Effect of iron oxide content on dephosphorization behavior of slag gasification

The coke reduction gasification dephosphorization experiments were conducted on converter slag with FeO contents of 15 %, 20 %, 30 % and 35 %, respectively. Thermodynamic calculations , Scanning Electron Eicroscope (SEM) and Energy Dispersive Spectrometer (EDS) showed that the actual reduction Gibbs free energy of P2O5 was less than that of FeO for the coke reduction converter slag product of P2, and the reactive driving force of P2O5 was greater under high FeO conditions. With the increase of FeO content, the gasification dephosphorization rate showed a trend of increasing first and then decreasing. After gasification dephosphorization, the presence of the Fe phase will adsorb more P elements, so the high FeO content is beneficial to increase the P2O5 activity, which is conducive to the gasification dephosphorization reaction

Spectra of Baryons Containing Two Heavy Quarks in Potential Model

In this work, we employ the effective vertices for interaction between diquarks (scalar or axial-vector) and gluon where the form factors are derived in terms of the B-S equation, to obtain the potential for baryons including a light quark and a heavy diquark. The concerned phenomenological parameters are obtained by fitting data of $B^{(*)}-$mesons instead of the heavy quarkonia. The operator ordering problem in quantum mechanics is discussed. Our numerical results indicate that the mass splitting between $B_{3/2}(V), B_{1/2}(V)$ and $B_{1/2}(S)$ is very small and it is consistent with the heavy quark effective theory (HQET).Comment: 16 page

Reconstruction of Gas Temperature and Density Profiles of the Galaxy Cluster RX J1347.5-1145

We use observations of Sunyaev-Zel'dovich effect and X-ray surface brightness to reconstruct the radial profiles of gas temperature and density under the assumption of a spherically symmetric distribution of the gas. The method of reconstruction, first raised by Silk & White, depends directly on the observations of the Sunyaev-Zel'dovich effect and the X-ray surface brightness, without involving additional assumptions such as the equation of state of the gas or the conditions of hydrostatic equilibrium. We applied this method to the cluster RX J1347.5-1145, which has both the Sunyaev-Zel'dovich effect and X-ray observations with relative high precision. It is shown that it will be an effective method to obtain the gas distribution in galaxy clusters. Statistical errors of the derived temperature and density profiles of gas were estimated according to the observational uncertainties.Comment: 6 pages, 1 figure. The published version, 2008, Chin. J. Astron. Astrophys., 8, 67

Investigating the relationship between momentary emotion self-reports and Head and Eye Movements in HMD-based 360 VR video watching

Inferring emotions from Head Movement (HM) and Eye Movement (EM) data in 360◦ Virtual Reality (VR) can enable a low-cost means of improving users' Quality of Experience. Correlations have been shown between retrospective emotions and HM, as well as EM when tested with static 360◦ images. In this early work, we investigate the relationship between momentary emotion self-reports and HM/EM in HMD-based 360◦ VR video watching. We draw on HM/EM data from a controlled study (N=32) where participants watched eight 1-minute 360◦ emotion-inducing video clips, and annotated their valence and arousal levels continuously in real-time. We analyzed HM/EM features across fine-grained emotion labels from video segments with varying lengths (5-60s), and found significant correlations between HM rotation data, as well as some EM features, with valence and arousal ratings. We show that fine-grained emotion labels provide greater insight into how HM/EM relate to emotions during HMD-based 360◦ VR video watching

A social VR clinic for knee arthritis patients with haptics

Social virtual reality (VR) invites multiple users to interact in a shared immersive environment, which can be potentially useful for remote personalized healthcare. This demo presents a social VR clinic that allows patients to consult a nurse represented as a virtual avatar. It offers a "walk-in" virtual surgery room, enables patients to interact with animated virtual 3D artifacts, and train the patient to use an injection tool wearing a pair of mechanical VR gloves that provide haptic feedback (SenseGlove). The demo shows the potential of social VR as a new tool to help patients receive remote personalized medical care

Two-dose Covid-19 vaccination and possible arthritis flare among patients with rheumatoid arthritis in Hong Kong

Objectives: To investigate the relationship between COVID-19 full vaccination (two completed doses) and possible arthritis flare. Methods: Patients with rheumatoid arthritis (RA) were identified from population-based electronic medical records with vaccination linkage and categorised into BNT162b2 (mRNA vaccine), CoronaVac (inactive virus vaccine) and non-vaccinated groups. The risk of possible arthritis flare after vaccination was compared using a propensity-weighted cohort study design. We defined possible arthritis flare as hospitalisation and outpatient consultation related to RA or reactive arthritis, based on diagnosis records during the episode. Weekly prescriptions of rheumatic drugs since the launch of COVID-19 vaccination programme were compared to complement the findings from a diagnosis-based analysis. Results: Among 5493 patients with RA (BNT162b2: 653; CoronaVac: 671; non-vaccinated: 4169), propensity-scored weighted Poisson regression showed no significant association between arthritis flare and COVID-19 vaccination ((BNT162b2: adjusted incidence rate ratio 0.86, 95% Confidence Interval 0.73 to 1.01); CoronaVac: 0.87 (0.74 to 1.02)). The distribution of weekly rheumatic drug prescriptions showed no significant differences among the three groups since the launch of the mass vaccination programme (all p values >0.1 from Kruskal-Wallis test). Conclusions: Current evidence does not support that full vaccination of mRNA or inactivated virus COVID-19 vaccines is associated with possible arthritis flare

CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR videos

Watching 360 videos using Virtual Reality (VR) head-mounted displays (HMDs) provides interactive and immersive experiences, where videos can evoke different emotions. Existing emotion self-report techniques within VR however are either retrospective or interrupt the immersive experience. To address this, we introduce the Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 Videos (CEAP-360VR). We conducted a controlled study (N=32) where participants used a Vive Pro Eye HMD to watch eight validated affective 360 video clips, and annotated their valence and arousal (V-A) continuously. We collected (a) behavioral (head and eye movements; pupillometry) signals (b) physiological (heart rate, skin temperature, electrodermal activity) responses (c) momentary emotion self-reports (d) within-VR discrete emotion ratings (e) motion sickness, presence, and workload. We show the consistency of continuous annotation trajectories and verify their mean V-A annotations. We find high consistency between viewed 360 video regions across subjects, with higher consistency for eye than head movements. We furthermore run baseline classification experiments, where Random Forest classifiers with 2s segments show good accuracies for subject-independent models: 66.80% (V) and 64.26% (A) for binary classification; 49.92% (V) and 52.20% (A) for 3-class classification. Our open dataset allows further experiments with continuous emotion self-reports collected in 360 VR environments, which can enable automatic assessment of immersive Quality of Experience (QoE) and momentary affective states