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Efficient and accurate extreme short-term prediction is of great significance for the safety of ship and marine structures in actual sea waves. Due to the stochastic of actual sea waves, short-term prediction always uses time series analysis. The neural networks, particularly long short-term memory (LSTM) neural networks, have received increasing attention for their powerful forecasting capability in time series analysis. Based on this, an improved form of LSTM combining generative adversarial ideas is proposed, in which the frequency domain characteristics are embedded in the neural network to achieve coupled time-frequency domain information forecasting. The experimental test shows that the forecasting accuracy of this method is better than the results of traditional time series analysis methods and the LSTM neural network, and it is suitable for extreme short-term time series prediction for better ship maneuvering.

(1) Background: Mengshan cattle from the Yimeng mountainous region in China stand out as a unique genetic resource, known for their adaptive traits and environmental resilience. However, these cattle are currently endangered and comprehensive genomic characterization remains largely unexplored. This study aims to address this gap by investigating the genomic features and selection signals in Mengshan cattle. (2) Methods: Utilizing whole-genome resequencing data from 122 cattle, including 37 newly sequenced Mengshan cattle, we investigated population structure, genetic diversity, and selection signals. (3) Results: Our analyses revealed that current Mengshan cattle primarily exhibit European taurine cattle ancestry, with distinct genetic characteristics indicative of adaptive traits. We identified candidate genes associated with immune response, growth traits, meat quality, and neurodevelopment, shedding light on the genomic features underlying the unique attributes of Mengshan cattle. Enrichment analysis highlighted pathways related to insulin secretion, calcium signaling, and dopamine synapse, further elucidating the genetic basis of their phenotypic traits. (4) Conclusions: Our results provide valuable insights for further research and conservation efforts aimed at preserving this endangered genetic resource. This study enhances the understanding of population genetics and underscores the importance of genomic research in informing genetic resources and conservation initiatives for indigenous cattle breeds. [ABSTRACT FROM AUTHOR]

In this paper, we theoretically and experimentally reported a lead‐free pyroelectric infrared (PIR) detector using (Bi1/2Na1/2TiO3)‐BaTiO3(BNT‐BT) ferroelectric ceramics as the sensitive material. The variation of noise density, voltage response rate (RV), and specific detection rate (D*) with the modulation frequency under the current mode amplification circuit was investigated, and it was found that the lead‐free PIR detector showed high RV in the low frequency band. The RV and D* reached 1.51 × 105 V/W and 2.02 × 108 cmHz1/2W−1 at 10 Hz, respectively. The results were much superior to the PIR based on traditional commercial pyroelectric ceramics, indicating that BNT‐BT lead‐free ceramics have great potential in application to PIR detectors. [ABSTRACT FROM AUTHOR]

In this study, ternary ferroelectric 0.06Pb(Mn1/3Nb2/3)O3–0.94Pb(Zr0.48Ti0.52)O3 (PMN–PZT) thin film with high piezoelectric coefficient were grown on La0.6Sr0.4CoO3‐buffered Pt/Ti/SiO2/Si substrate by RF magnetron sputtering method. The phase and domain structure along with the macroscopic electrical properties were obtained. Under the optimized temperature of 550°C and sputtering pressure 0.9 Pa, the PMN–PZT film owned large remnant ferroelectric polarization of 62 μC/cm2. In addition, the PMN–PZT film had polydomain structures with fingerprint‐type nanosized domain patterns and typical local piezoelectric response. Through piezoelectric force microscopy, the PMN–PZT thin film at nanoscale exhibited obvious domain reversal when subjected to in situ poling field. It was further found that the quasi‐static piezoelectric coefficient of the PMN–PZT thin film reached 267 pC/N, which was about twice to that of the commercial PbZrO3–PbTiO3 (PZT) thin film. The optimized relaxor ferroelectric thin film PMN–PZT on silicon with global electrical properties shows great potential in the piezoelectric micro‐electro‐mechanical systems applications. [ABSTRACT FROM AUTHOR]

Herein, we firstly present the (K,Bi)(Nb,Yb)O3 inorganic ferroelectric photovoltaic (FPV) film, in which a nearly ideal bandgap of ∼1.45 eV in the center of the solar spectrum and the co-existence of oxygen vacancies as well as ferroelectric polarization were confirmed. Furthermore, a novel cell structure is successfully fabricated by combining charge-transporting TiO2 nanoparticles, the perovskite sensitizer and a light-absorbing oxide hole p-type NiO conductor to realize a 1 V open circuit voltage, which can be increased to 1.56 V by adjusting the test bias near the coercive voltage. Additionally, under simulated standard AM 1.5G illumination, a fill factor of 86% and a power conversion efficiency of 0.85% are achieved via oxygen vacancy electromigration and polarization switching modulation. It is shown that the obtained power conversion efficiency is one to three orders of magnitude higher than those of pure BiFeO3 and Pb(Zr,Ti)O3. The enhanced PV effects are well elucidated using the transformation from a Schottky-like barrier to Ohmic contacts caused by polarization switching and oxygen vacancies. Building upon the above studies, deep insights into the bandgap tunability and PV effects in ferroelectric films with high oxygen vacancy concentration are provided and will facilitate a new versatile route for exploring high PV performance based on inorganic ferroelectric films. [ABSTRACT FROM AUTHOR]

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

Herein, we firstly present the (K,Bi)(Nb,Yb)O 3 inorganic ferroelectric photovoltaic (FPV) film, in which a nearly ideal bandgap of ∼1.45 eV in the center of the solar spectrum and the co-existence of oxygen vacancies as well as ferroelectric polarization were confirmed. Furthermore, a novel cell structure is successfully fabricated by combining charge-transporting TiO 2 nanoparticles, the perovskite sensitizer and a light-absorbing oxide hole p-type NiO conductor to realize a 1 V open circuit voltage, which can be increased to 1.56 V by adjusting the test bias near the coercive voltage. Additionally, under simulated standard AM 1.5G illumination, a fill factor of 86% and a power conversion efficiency of 0.85% are achieved via oxygen vacancy electromigration and polarization switching modulation. It is shown that the obtained power conversion efficiency is one to three orders of magnitude higher than those of pure BiFeO 3 and Pb(Zr,Ti)O 3 . The enhanced PV effects are well elucidated using the transformation from a Schottky-like barrier to Ohmic contacts caused by polarization switching and oxygen vacancies. Building upon the above studies, deep insights into the bandgap tunability and PV effects in ferroelectric films with high oxygen vacancy concentration are provided and will facilitate a new versatile route for exploring high PV performance based on inorganic ferroelectric films.