The effects of emotion and COVID-19 context priming on the size and color of drawings: based on human figure drawings and tree drawings

IntroductionThis study aimed to investigate the effects of emotional themes and the COVID-19 pandemic context priming on the size and color of drawings.MethodsA 3 (emotion: peacefulness, gratitude, loneliness) × 2 (context: pandemic, regular) mixed design was used and 113 university students in Shanghai were recruited to draw human figures and trees using 10 marker colors.ResultsThe size of the drawings depicting loneliness was smaller than that of the those depicting peacefulness and gratitude. Drawings depicting loneliness used more cool and non-preferred colors; drawings depicting gratitude used more warm and preferred colors. Drawings in the pandemic context were larger, which may reflect the more significant threat perceived by individuals. Drawings in the pandemic context used more red colors, showing symbolic meanings such as danger.DiscussionThe drawing size and drawing color are influenced by emotional themes and the pandemic context priming

Theoretical and numerical estimation of ship-to-ship hydrodynamic interaction effects

The main objective of this paper is to investigate theoretically and numerically how much interactions are expected between two ships travelling in waves. The theoretical estimation is based on asymptotic far-field wave patterns produced by a translating and oscillating source. The far-field wave pattern is governed by the parameter τ=ωeu0/g; For values of the parameter τ>0.25 there exist a fan-shaped quiescent region in front of the vessel. As τ increases, the range of the fan-shaped quiescent region will be expanded. The critical line between the quiescent and wake region can be estimated by the asymptotic expressions theoretically. It is expected that there is no hydrodynamic interaction if the two ships are located in each other's fan-shaped quiescent region. But due to the near-field local waves produced by the 3-D ships, the critical line could be different from that estimated from asymptotic wave pattern. Therefore, we developed a 3-D panel method based on Rankine-type Green function to investigate the hydrodynamic interaction effects for several combinations of parameters, including oscillation frequency, forward speed and transverse distance between two ships. Finally, the critical line calculated numerically was presented and compared to the theoretical estimation

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

A dynamic simulation model for financing strategy management of infrastructure PPP projects

Strategic management is vital for significant infrastructure public-private partnership (PPP) projects characterised by a heavy and irreversible investment over a long period. In PPP projects, the financing strategy relates to the capital structure of the project and the coordination of the participants’ requirements. In this paper, a system dynamics (SD) model is described to analyse the impacts of two types of financing strategies on the needs of creditors, the government, and private investors, considering the dynamic and complex characteristics of infrastructure PPP projects. The proposed model has been implemented on a PPP highway project. A number of experiments were conducted over a 33-year strategic planning horizon as a means of assessing the long-term effects of different financing strategies. The experimental results reveal that the model is a useful tool that could support decision-makers in identifying the intervals with different management focus of financing risk and comparing different financing strategies to choose the optimal one. It is especially helpful for the government to select a financing strategy for infrastructure PPP projects with capital limitations

Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, both spatial and appearance information will become ambiguous simultaneously due to the high overlap between objects. In this paper, we demonstrate that this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence state and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Furthermore, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, by leveraging both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and occlusion are frequent and severe. The code and models are available at https://github.com/ymzis69/HybirdSORT

The effect of fog on the probability density distribution of the ranging data of imaging laser radar

This paper outlines theoretically investigations of the probability density distribution (PDD) of ranging data for the imaging laser radar (ILR) system operating at a wavelength of 905 nm under the fog condition. Based on the physical model of the reflected laser pulses from a standard Lambertian target, a theoretical approximate model of PDD of the ranging data is developed under different fog concentrations, which offer improved precision target ranging and imaging. An experimental test bed for the ILR system is developed and its performance is evaluated using a dedicated indoor atmospheric chamber under homogeneously controlled fog conditions. We show that the measured results are in good agreement with both the accurate and approximate models within a given margin of error of less than 1%

NanoSIMS analysis of water content in bridgmanite at the micron scale: An experimental approach to probe water in Earth’s deep mantle

Water, in trace amounts, can greatly alter chemical and physical properties of mantle minerals and exert primary control on Earth’s dynamics. Quantifying how water is retained and distributed in Earth’s deep interior is essential to our understanding of Earth’s origin and evolution. While directly sampling Earth’s deep interior remains challenging, the experimental technique using laser-heated diamond anvil cell (LH-DAC) is likely the only method available to synthesize and recover analog specimens throughout Earth’s lower mantle conditions. The recovered samples, however, are typically of micron sizes and require high spatial resolution to analyze their water abundance. Here we use nano-scale secondary ion mass spectrometry (NanoSIMS) to characterize water content in bridgmanite, the most abundant mineral in Earth’s lower mantle. We have established two working standards of natural orthopyroxene that are likely suitable for calibrating water concentration in bridgmanite, i.e., A119(H2O) = 99 ± 13 μg/g (1SD) and A158(H2O) = 293 ± 23 μg/g (1SD). We find that matrix effect among orthopyroxene, olivine, and glass is less than 10%, while that between orthopyroxene and clinopyroxene can be up to 20%. Using our calibration, a bridgmanite synthesized by LH-DAC at 33 ± 1 GPa and 3,690 ± 120 K is measured to contain 1,099 ± 14 μg/g water, with partition coefficient of water between bridgmanite and silicate melt ∼0.025, providing the first measurement at such condition. Applying the unique analytical capability of NanoSIMS to minute samples recovered from LH-DAC opens a new window to probe water and other volatiles in Earth’s deep mantle