The first catalytic and enantioselective C-H direct acylation of ferrocene derivatives has been developed. A series of 2-acyl-1-dimethylaminomethylferrocenes with planar chirality were provided under highly efficient and concise one-pot conditions with up to 85% yield and 98% ee. The products obtained could be easily converted to various chiral ligands via diverse transformations.

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This study developed a new cross-platform instrument for microstructure turbulence measurement (CPMTM) and evaluated its performance. The CPMTM is designed as an “all-in-one” payload that can be easily integrated with a variety of marine instrumentation platforms. The sensors in the CPMTM include two shear probes, a fast-response temperature probe, and an accelerometer for monitoring vibrations. In addition, a custom-designed flexible connection vibration-damping device is used to isolate platform vibrations. To validate the CPMTM performance, a direct comparison was carried out with a reference acoustic Doppler velocimeter in a controlled flume for four background turbulence levels. The results of the comparison show that the velocity spectra measured by the CPMTM and ADV w components are in agreement, which demonstrates the ability of the CPMTM to acquire accurate turbulence data. Furthermore, the CPMTM was integrated into the long-range Sea-Whale 2000 AUV and tested in the northern South China Sea in September 2020. The data collected by the CPMTM show that the measured shear spectrum of the noise reduction agrees well with the empirical Nasmyth spectrum. Turbulent kinetic energy dissipation rates as low as 7 × 10−10 W kg−1 can be resolved. Laboratory and field experiments illustrate that the CPMTM has an extraordinarily low noise level and is validated for turbulence measurements.

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BackgroundEarly to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.MethodWe enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.ResultOur results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378–0.816)], having cigarette smoking [2.121 (1.191–3.779)], higher positive syndrome [1.016 (1.002–1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026–1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599–0.7748) had better prediction ability than that of other algorithms.ConclusionML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

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An autonomous Moored Reciprocating Vertical Profiler (MRVP) has been developed and tested for measuring ocean turbulence. The MRVP is designed to combine the advantages of long-term moored measurements at specified depths with those of short-term ship-supported continuous profiling performed at high vertical resolution. The profiler is programmed to repeat vertical motions autonomously along the mooring cable based on a buoyancy-driven mechanism. A sea trial has been conducted in the South China Sea to evaluate the performance of the profiler. The shear probe data are unreliable when the flow past sensors is not sufficiently greater than an estimate of turbulent velocity. For 65% of the dataset, turbulence measurements are of high quality and the magnitude of dissipation rates is up to O(10−10) W kg−1. To minimize the contamination induced by instrument vibration and improve the estimation of turbulent kinetic energy terms, an advanced cross-spectrum algorithm is implemented to the measured shear data. The corrected spectra agreed well with the empirical Nasmyth spectrum, and dissipation rates had averagely decreased a factor of 2 and 8 times lower than the raw spectra. The autonomous MRVP is proven to be a stable platform, and the novel upward measurement provides a new perspective for measuring long-term time series of turbulence mixing.

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