With the continuous application and development of marine navigation technology, the accuracy requirements of navigation information for ships are becoming higher and higher. The Inertial Navigation ...System (INS) has been widely used due to their advantages in accuracy and stability. Star sensor, which can provide high-precision carrier attitude quaternion information, has gradually gained more and more applications. With the continuous application and development of various integrated navigation systems, based on the above two sensors, the Star sensor/INS combined attitude positioning system has the advantages in high-precision, anti-interference and autonomy. However, sensor errors and system initial errors will affect the performance of this integrated navigation system significantly. To compensate the above errors is a common way to improved the accuracy of the integrated navigation system, and the error calibration is the premise of error compensation. Different from the traditional Star sensor/INS integrated navigation system which carries out data fusion on the computer, this study is based on the actual installation in which the Star sensor is installed directly and fixedly on the INS. In this case, not only the error sources within the INS, but also the installation error between the INS and the Star sensor should be considered. Thus, on the basis of considering the traditional error terms, introduced the new installation error term and established the extended INS error model, the errors can be estimated and compensated in real time by utilizing the state estimation method, improving the accuracy of the Star sensor/INS integrated navigation system greatly. Simulations are carried out and the results verify the effectiveness and availability of the improved on-line calibration method proposed in this manuscript.
For an underwater Strapdown Inertial Navigation System/Doppler velocity log (SINS/DVL) integrated navigation system, the short-term failure of DVL may lead to the loss of reliable external velocity ...information from DVL, which will cause the SINS errors to accumulate. To circumvent this problem, this paper proposes a velocity predictor based on fuzzy multi-output least squares support vector machine (FMLS-SVM) to predict DVL measurements when DVL malfunctions occur. Firstly, the single-output least squares support vector machine (LS-SVM) model is extended to the multi-output LS-SVM model (MLS-SVM), and the self-adaptive fuzzy membership is introduced to fuzzify the input samples to overcome the over-fitting problem caused by the excessive sensitivity to the outlier points. Secondly, the fuzzy membership function is designed from the idea of the K nearest neighbor (KNN) algorithm. Finally, considering the influence of vehicle maneuver on the prediction model of DVL, the dynamic attitude angles are extended to the input samples of the prediction model to improve the adaptability of the DVL prediction model under large maneuver conditions. The performance of the method is verified by lake experiments. The comparison results show that the velocity predictor based on FMLS-SVM can correctly provide the estimated DVL measurements, effectively prolong the fault tolerance time of DVL faults, and improve the accuracy and reliability of the SINS/DVL integrated navigation system.
Enhanced accuracy and long-term predictions of ship motion during sea operations can effectively mitigate safety risks associated with aircraft takeoff and landing on board. This article proposes a ...transformer-based ship motion attitude prediction model. Our work leverages a novel self-attention mechanism (AM) with adaptive position encoding and learnable attention weights to improve long-term prediction accuracy. Furthermore, we also incorporate a pretraining phase using a random masking strategy to enhance the model's training capability and reduce prediction phase duration. The proposed model is evaluated using data from a ship undergoing constant speed and Z-word motion to predict the roll and pitch angles of the ship. The model is compared with autoregressive moving average (ARMA), EMD-ARMA, long-short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and traditional transformer models. The experimental results demonstrate that the proposed method outperforms these models in multistep prediction scenarios.
•A new seq2seq rainfall-runoff model named LSTM-MSV-S2S is proposed.•LSTM-MSV-S2S has promising performance on the CAMELS data set.•LSTM-MSV-S2S is more appropriate for multi-day-ahead runoff ...predictions.
Rainfall-runoff modeling is a challenging and important nonlinear time series problem in hydrological sciences. Recently, among the data-driven rainfall-runoff models, those ones based on the long short-term memory (LSTM) network show good performance. Furthermore, LSTM-based sequence-to-sequence (LSTM-S2S) models achieve promising performance for multi-step-ahead runoff predictions. In this paper, for multi-day-ahead runoff predictions, we propose a novel data-driven model named LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) rainfall-runoff model, which contains m multiple state vectors for m-step-ahead runoff predictions. It differs from the existing LSTM-S2S rainfall-runoff models using only one state vector and is more appropriate for multi-day-ahead runoff predictions. To show its performance and advantages, we compare it with two LSTM-S2S models by testing them on 673 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set. The results show that our LSTM-MSV-S2S model has better performance in general and thus using multiple state vectors is more appropriate for multi-day-ahead runoff predictions.
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•GO-TiO2 modified membranes presented high rejection and degradation efficiencies.•Membrane filtration enhanced the degradation efficiency and decreased the EEO.•The 300 nm UV-LED ...achieved a high removal efficiency with low energy consumption.•High membrane flux and high degradation rate co-occurred in dead-end filtration.•UV-LED-based photocatalytic ceramic membrane is promising for water treatment.
The development of ultraviolet light-emitting diode (UV-LED) technology provides more potential for the application of photocatalytic membranes. In this study, graphene oxide-titanium dioxide (GO-TiO2) photocatalytic ceramic membranes were prepared and activated by UV-LED. We systematically evaluated the removal of naproxen (NAP), carbamazepine (CBZ) and diclofenac (DCF) under 365 nm, 300 nm and 275 nm UV-LED irradiation in photocatalytic ceramic membrane reactors (PCMRs). Our results showed that this GO-TiO2 modified ceramic membrane achieved simultaneous separation and photocatalytic degradation for tested pharmaceuticals. Through fluence-based and energy-based efficiencies analysis, the 300 nm UV-LED is a good option for achieving a high removal efficiency with low energy consumption. Membrane filtration enhanced the degradation efficiency by four times through enhancing the mass transfer effect in membrane pores, and reduced the electrical energy per order (EEO) by a factor of ten. In addition, high membrane flux, high degradation rate and strong catalytic stability could be simultaneously achieved in closed-loop dead-end filtration. The addition of H2O2 could further increased the removal efficiency. PCMRs equipped with UV-LEDs are promising for pharmaceuticals removal as well as solving the issue of membrane concentrated water.
Context and Objective:
Paraoxonase 1 (PON1), an enzyme associated with high-density lipoprotein (HDL-PON1), is reported to have antioxidant and cardioprotective properties. The aim of the present ...study was to investigate the effects of anthocyanins on the HDL-PON1 activity and cholesterol efflux capacity in hypercholesterolemic subjects.
Design and Participants:
A total of 122 hypercholesterolemic subjects were given 160 mg of anthocyanins twice daily or placebo (n = 61 of each group) for 24 weeks in a double-blind, randomized, placebo-controlled trial. Participants and investigators were masked to treatment allocation.
Results:
Anthocyanin consumption significantly increased HDL cholesterol and decreased low -density lipoprotein cholesterol concentrations compared with placebo (P < .018 and P < .001, respectively). Anthocyanin supplementation also increased the activity of HDL-PON1 compared with placebo (P < .001). Furthermore, cholesterol efflux capacity was increased more in the anthocyanin group (20.0% increase) than in the placebo group (0.2% increase) (P < .001). The negative correlations established between HDL-PON1 activity and the levels of lipid hydroperoxides associated with HDL confirm the relationship between PON1 activity and lipid peroxidation of lipoproteins. Furthermore, a strong positive correlation was noted between increased HDL-PON1 activity and improved cholesterol efflux capacity both before and after adjustment for HDL cholesterol and apolipoprotein AI in anthocyanin-treated subjects (both P < .001). Inhibition of HDL-PON1 activity strongly prevented the antioxidant ability of HDL and attenuated the cholesterol efflux capacity of subjects from anthocyanin group.
Conclusions:
Our observations suggest that the alterations of PON1 activity by anthocyanin observed in hypercholesterolemic HDL reflect a shift to an improvement of cholesterol efflux capacity of HDL and may provide a link between anthocyanin and cardioprotective effects.
•A new framework named LSTM-SS is proposed to deal with rainfall-runoff modeling.•LSTM-SS improves performance significantly on the CAMELS data set.•LSTM-SS achieves good performance for ...multi-day-ahead runoff predictions.•LSTM-SS has good performance with short input sequence length.
Rainfall-runoff modeling, a nonlinear time series process, is challenging and important in hydrological sciences. Among the data-driven approaches, those ones based on the long short-term memory (LSTM) network show their promising performance. In this paper, for rainfall-runoff modeling, we propose a novel data-driven framework named long short-term memory based step-sequence (LSTM-SS) framework, which contains m specific models for m-step-ahead runoff predictions. This model uses the sequential information of runoff series and follows the causality in practice: the current runoff is not affected by the later meteorological data. To show its performance and advantages, we test it on 241 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set and predict the 7-day-ahead runoff. The results show that our rainfall-runoff models outperform the benchmark (physically-based or data-driven) models significantly employing for the CAMELS data set, including the Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine, a two-layer LSTM network, and a LSTM-based sequence-to-sequence network. For 1-day-ahead runoff predictions, the median of Nash–Sutcliffe model efficiency for the 241 basins provided by our model is 0.85, while that provided by the two-layer LSTM network is 0.65. Furthermore, the results also show that our proposed LSTM-SS framework not only can significantly improve the performance of a single daily runoff prediction, but also has good performance for multiple-step-ahead runoff predictions.
7-Ketocholesterol (7-KC), a form of cholesterol oxidation product, plays an essential role in the atherogenesis in animal models.
We sought to determine the association of circulating 7-KC with ...clinical cardiovascular outcomes and total mortality in patients with stable coronary artery disease.
We measured the plasma 7-KC levels by high-performance liquid chromatography in a prospective cohort study of 1016 patients (mean age, 63.2 years; male 61.1%) with stable coronary artery disease who were recruited from December 2008 to December 2011 and followed up for a median of 4.6 years. We adjudicated myocardial infarction, hospitalization of heart failure, cardiovascular death, all-cause death, and composite end points of myocardial infarction/heart failure/death by review of medical records and death certificates. We used multivariable Cox proportional hazards analysis to compare the incidence rate of cardiovascular events and all-cause death according to the quartile of the plasma 7-KC. During the median 4.6 years follow-up, totally 221 participants (21.8%) experienced a cardiovascular event or death. The adjusted risk of the composite end points was higher in the highest 7-KC quartile than in the lowest quartile (hazard ratio, 1.76; 95% confidence interval, 1.42-2.21;
<0.001). After adjustment for demographic and clinical variables and other biomarkers, including high-sensitivity C-reactive protein and NT-proBNP (N-terminal pro-B-type natriuretic peptide), 1 SD increase in the 7-KC level remained associated with a 36% higher rate of composite outcomes (hazard ratio, 1.36; 95% confidence interval, 1.22-1.48;
=0.007). Plasma 7-KC clearly improved various model performance measures, including C statistics, integrated discrimination, and category-free net reclassification.
High 7-KC levels are associated with increased risk of cardiovascular events, total death, and composite outcomes in patients with stable coronary artery disease.
Although a few studies have analyzed the associations between ambient air pollutants and the development of tuberculosis (TB), most have been conducted in the core countries with inconsistent ...results. In this study, we used a distributed lag non-linear model to investigate the associations between the newly diagnosed TB cases and daily exposure to particulate matter with an aerodynamic diameter of <10μm (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2) in Chengdu, a severely polluted city. There were 36,108 newly diagnosed active TB cases from January 1, 2010 to December 31, 2015 in Chengdu. In a single-pollutant model, the cumulative relative risk of active TB cases was 1.06 lag of 0 to 21days, 95% confidence interval (CI): 1.01–1.11 for each 10μg/m3 increase in PM10 above the threshold of 70μg/m3; 1.06 (lag of 0 to 2days, 95% CI: 1.03–1.09) for each 10μg/m3 increase in NO2 above the threshold of 40μg/m3; and 1.07 (lag of 0 to 2days, 95% CI: 1.02–1.12) for each 10μg/m3 increase in SO2 above the threshold of 60μg/m3. Meanwhile, we found a positive association in males after exposure to a 10μg/m3 increase in SO2 above the threshold of 60μg/m3 at a lag of 0 to 2days. Exposure to PM10, NO2, and SO2 was associated with an increment in the incidence of active TB cases.
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•It is important to evaluate the associations between ambient air pollutants and incidence of TB in the polluted city.•Positive associations between PM10, NO2, SO2 and the incidence of TB were observed in Chengdu.•The effect between exposure to PM10, NO2, and SO2 and TB cases was not modified by gender or age.•SO2 demonstrated remarkable effects in males only.
In hydrology, runoff predictions are challenging when the data is lacking (e.g., predictions in un-gauged basins (PUB) and predictions with limited data (PLD)). Here, PLD refers to the case that the ...data is not enough for training or fine-tuning a data-driven model well (e.g., a new-gauged basin). We also name PLD as PNB (predictions in new-gauged basins). The difference between PNB and PUB is that the new-gauged basins can provide some data (e.g., runoff observations) while the un-gauged basins cannot. The long short-term memory (LSTM)-based models have shown good performance in runoff predictions due to their advanced structures. However, those structures have low level of flexibility and two nonadjacent positions cannot communicate directly. For high level of flexibility and better performance, we propose a simple Transformer-based rainfall-runoff model named RRS-Former, and want to show its power and also the power of the previously proposed RR-Former in PNB compared with LSTM-based models. The main part of RRS-Former is attention modules in which two arbitrary positions can be connected directly. Four hydrological units including 241 basins in the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset are used to compare the performance of our Transformer-based models and that of LSTM-based models. Besides using the k-fold validation to test performance for PNB, we propose a new way named unit-fold validation, in which we train the models by using the basins in three hydrological units and then test the performance using basins in the rest one hydrological unit. The results based on both k-fold and unit-fold show that our RRS-Former and RR-Former have better performance and are more reliable compared with the LSTM-based models.
•A data-driven rainfall-runoff model based on Transformer is proposed and named RRS-Former.•A Transformer-based model is more flexible than LSTM and has better transfer ability.•RRS-Former has less parameters and less training time compared with RR-Former.•A Transformer-based model is more reliable than LSTM in the case of data lacking.•Unit-fold has more advantages for testing the transfer ability compared with k-fold.
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