Context. Traditionally, supersonic turbulence is considered to be one of the most likely mechanisms slowing the gravitational collapse in dense clumps, thereby enabling the formation of massive stars. However, several recent studies have raised differing points of view based on observations carried out with sufficiently high spatial and spectral resolution. These studies call for a re-evaluation of the role turbulence plays in massive star-forming regions. Aims. Our aim is to study the gas properties, especially the turbulence, in a sample of massive star-forming regions with sufficient spatial and spectral resolution, which can both resolve the core fragmentation and the thermal line width. Methods. We observed NH 3 metastable lines with the Very Large Array (VLA) to assess the intrinsic turbulence. Results. Analysis of the turbulence distribution histogram for 32 identified NH 3 cores reveals the presence of three distinct components. Furthermore, our results suggest that (1) sub- and transonic turbulence is a prevalent (21 of 32) feature of massive star-forming regions and those cold regions are at early evolutionary stage. This investigation indicates that turbulence alone is insufficient to provide the necessary internal pressure required for massive star formation, necessitating further exploration of alternative candidates; and (2) studies of seven multi-core systems indicate that the cores within each system mainly share similar gas properties and masses. However, two of the systems are characterized by the presence of exceptionally cold and dense cores that are situated at the spatial center of each system. Our findings support the hub-filament model as an explanation for this observed distribution.
Ascorbic acid (AsA) is an important antioxidant for human health. The concept of “oil-vegetable-duel-purpose” can significantly enhance the economic benefits of the rapeseed industry. Rapeseed, when utilized as a vegetable, serves as a valuable food source of AsA. In this study, we integrated transcriptome and metabolome analyses, along with substrate feeding, to identify the L-galactose pathway as the primary source for AsA production, which is primarily regulated by light. Through seven different photoperiod treatments from 12 h/12 h (light/dark) to 24 h/0 h, we found that AsA content increased with longer photoperiods, as well as chlorophyll, carotenoids, and soluble sugars. However, an excessively long photoperiod led to photooxidative stress, which negatively affected biomass accumulation in rapeseed seedlings and subsequently impacted the total accumulation of AsA. Furthermore, different enzymes respond differently to different photoperiods. Analysis of the correlation between the expression levels of AsA biosynthesis-related genes and AsA content highlighted a dynamic balancing mechanism of AsA metabolism in response to different photoperiods. The study revealed that the 16 h/8 h photoperiod is optimal for long-term AsA accumulation in rapeseed seedlings. However, extending the photoperiod before harvest can enhance AsA content without compromising yield. These findings offer novel insights into an effective strategy for the biofortification of AsA in rapeseed.
Abstract Ultrasonic guided wave (UGW) detection is widely used in pipeline monitoring but faces challenges from weak flaw echo signals within the detection data, making weak UGW signals difficult to recognize. It is essential to denoise the UGW detection signals to identify weak echo signals. This paper proposes an improved denoising autoencoder (DAE) based on the fusion of one-dimensional convolution neural network (1DCNN) and full connection (FC). The model expands the amount of training data by adding noise in batches and uses 1DCNN to enhance the ability of extracting UGW signal features. The model was validated using Several numerical simulation signals. Numerical simulation results show that the signal-to-noise ratio (SNR) of the UGW signals can be improved from -20 dB to 8 dB; it has a strong improved SNR, and the mean square error is greatly reduced while maintaining the original phase almost unchanged. The improved DAE method has significant advantages in denoising effect, and it can effectively reduce the noise of the UGW detection signal and realize the identification of small defects of the simulation pipeline.
Spin–orbit torque (SOT) magnetic random access memory is envisioned as an emerging nonvolatile memory due to its ultrahigh speed and low power consumption. The field-free switching scheme in SOT devices is of great interest to both academia and industry. Here, we propose a novel field-free deterministic magnetization switching scheme in a regular magnetic tunnel junction by using two currents sequentially passing interlaced paths, with less requirements of the manufacturing process or additional physical effects. The switching is bipolar since the final magnetization state depends on the combination of current paths. The functionality and robustness of the proposed scheme are validated through both macrospin and micromagnetic simulation. The influences of field-like torque and the Dzyaloshinskii–Moriya interaction effect are further researched. Our proposed scheme shows good scalability and is expected to realize novel digital logic and even computing-in-memory platforms.
True random number generator (TRNG) is an important component for modern information security technologies. Among the candidates, TRNG with spin-orbit torque (SOT)-induced probabilistic magnetization switching is expected to be competitive for its advantages in anti-radiation, unlimited endurance, robust stability, and broad temperature range. However, specific mechanism of the randomness in SOT-induced magnetization switching are still not clear, which limits the demonstration of applicable SOT-TRNG. Here, we performed micromagnetic simulation of the SOT-induced probabilistic magnetization switching by using MuMax3. When various thermal noise seeds were introduced, not only stochastic precession trails within the current pulses, but also stochastic precession tendency after removing the current pulse were obtained, together resulting in random final magnetization states. Particularly, the consistency between mz orientations at the current withdraw moment and at the final stabilized magnetization state shows a positive correlation with the fluctuation range of mz during the current pulse duration, the latter of which narrows with the increasing of channel current density. Our work suggests the importance of thermal noise-related magnetization precession variations on the SOT-induced probabilistic magnetization switching, and suggesting the MuMax3 to be a practical tool for simulating the SOT-TRNG.
The development of renewable energy is an effective avenue for achieving net zero goals. It requires many energy storage systems (ESSs) for adjusting the unstable power generated by renewable energy. To date, PSH is the most technically mature, economically reasonable, and reliable ESS. Currently, various countries have developed PSH. As of 2022, the global installed capacity of PSH has reached 175,060 MW, with an annual increase of 10,300 MW. This paper addresses several technical considerations in the preliminary design of PSH systems, drawing on extensive design experience. Key factors such as the selection of dam sites, installed capacity, and characteristic water levels are thoroughly discussed. These design choices are influenced by a range of factors, including geological and topographical conditions, hydrological parameters, environmental impacts, sedimentation, submersion areas, and resettlement issues. PSH is highly effective in meeting power demands, regulating frequency and phase, serving as an emergency power reserve, and improving the power factor of electrical networks. It enhances the quality of renewable energy sources such as wind, photovoltaic, and tidal power, which are characterized by intermittent supply. Beyond its technical advantages, PSH also contributes to local employment and tourism and supports pollutant reduction efforts. Compared to other energy storage systems, PSH has a more significant environmental impact and requires a longer construction period. Thus, exploring new forms of PSH is crucial. Innovative approaches such as utilizing constructed reservoirs, lakes, seas, and abandoned pits can reduce both investment and construction time while minimizing the environmental impact. This paper aims to provide some technical references and feasible plans to governments, owners, and engineers during the planning and preliminary design stages of a PSH project.
Solving partial differential equations (PDEs) is essential in scientific forecasting and fluid dynamics. Traditional approaches often incur expensive computational costs and trade-offs in efficiency and accuracy. Recent deep neural networks improve accuracy but require quality training data. Physics-informed neural networks (PINNs) effectively integrate physical laws, reducing data reliance in limited sample scenarios. A novel machine-learning framework, Chebyshev physics-informed Kolmogorov-Arnold network (ChebPIKAN), is proposed to integrate the robust architectures of Kolmogorov-Arnold networks (KAN) with physical constraints to enhance calculation accuracy of PDEs for fluid mechanics. We explore the fundamentals of KAN, emphasis on the advantages of using the orthogonality of Chebyshev polynomial basis functions in spline fitting, and describe the incorporation of physics-informed loss functions tailored to specific PDEs in fluid dynamics, including Allen-Cahn equation, nonlinear Burgers equation, two-dimensional Helmholtz equations, two-dimensional Kovasznay flow and two-dimensional Navier-Stokes equations. Extensive experiments demonstrate that the proposed ChebPIKAN model significantly outperforms standard KAN architecture in solving various PDEs by embedding essential physical information more effectively. These results indicate that augmenting KAN with physical constraints can not only alleviate overfitting issues of KAN but also improve extrapolation performance. Consequently, this study highlights the potential of ChebPIKAN as a powerful tool in computational fluid dynamics, proposing a path toward fast and reliable predictions in fluid mechanics and beyond.
In China, adverse events following immunization (AEFI) are reported by the China AEFI Surveillance System (CNAEFIS). Serious AEFI, including deaths, are mandatorily reported and are evaluated for causality by province-or prefecture-level panels of experts. Yeast-derived HepB is the most widely used HepB in China for infants. However, the information about the death of infants caused by HepB is unclear. The CNAEFIS data on deaths following HepB from 2013 to 2020 were used for analyses. Descriptive analysis of epidemiologic characteristics was used to report death cases following HepB. We used administered doses to calculate denominators to estimate the risk of death after vaccination. During 2013–2020, there were 161 deaths following the administration of 173 million doses of HepB, for an overall incidence of 0.9 deaths per million doses. One hundred fifty-seven deaths were categorized as coincidental, and four deaths were accompanied by an abnormal reaction determined to be unrelated to the cause of death. The most common causes of death were neonatal pneumonia and foreign body asphyxia. These data provide reliable evidence on the safety of HepB among infants in China and can enhance public confidence in HepB immunization. To ensure public confidence in infants’ HepB vaccination, monitoring and scientifically evaluating AEFI-related deaths of HepB is necessary.
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