Turbidity currents (TCs) are a common kind of particle-laden flow in underwater natural environments. This work employs a Eulerian–Lagrangian model to investigate the dynamic regimes of lock-exchange TC in a moderate flow Reynolds number range (${Re} = 1716-3836$) as well as the formation and evolution mechanisms of interfacial Kelvin–Helmholtz (KH) billows composed of a fluid–particle mixture. The results demonstrate that a fluid streak with high stretching at the interface, which twists and takes on a braided structure, is the key to the onset of KH instability. An increase in ${\textit{Re}}$ results in a higher interfacial fluid velocity gradient that intensifies the shear instability, and an increase in the convergent fluid force acting on the particles. This provides an explanation for the significant increases both in quantity and strength of KH vortices as ${\textit{Re}}$ rises. The enhanced KH vortices contribute to particle suspension and streamwise transport at larger ${\textit{Re}}$, leading to an extension in the duration of the slumping stage, which exhibits a constant forward velocity regime. The spatially continuous braided structure in the vorticity sheet region is responsible for the intriguing merging phenomenon of interfacial vortices. Furthermore, TC kinetic energy increases with the increasing ${\textit{Re}}$, and the system dissipation rate decreases in the early and middle stages of the TC. This behaviour may be correlated to the reducing shear between the TC and ambient fluid by interfacial KH billows. Regarding the turbulent kinetic energy dissipation of interfacial vortices, normal strain predominates in the middle stage, while shear deformation is most prevalent in the early and later stages.

To meet the demands of laser-ion acceleration at a high repetition rate, we have developed a comprehensive diagnostic system for real-time and in situ monitoring of liquid sheet targets (LSTs). The spatially resolved rapid characterizations of an LST’s thickness, flatness, tilt angle and position are fulfilled by different subsystems with high accuracy. With the help of the diagnostic system, we reveal the dependence of thickness distribution on collision parameters and report the 238-nm liquid sheet generated by the collision of two liquid jets. Control methods for the flatness and tilt angle of LSTs have also been provided, which are essential for applications of laser-driven ion acceleration and others.

Here, we report the generation of MeV alpha-particles from H-11B fusion initiated by laser-accelerated boron ions. Boron ions with maximum energy of 6 MeV and fluence of 109/MeV/sr@5 MeV were generated from 60 nm-thick self-supporting boron nanofoils irradiated by 1 J femtosecond pulses at an intensity of 1019 W/cm2. By bombarding secondary hydrogenous targets with the boron ions, 3 × 105/sr alpha-particles from H-11B fusion were registered, which is consistent with the theoretical yield calculated from the measured boron energy spectra. Our results demonstrated an alternative way toward ultrashort MeV alpha-particle sources employing compact femtosecond lasers. The ion acceleration and product measurement scheme are referential for the studies on the ion stopping power and cross section of the H-11B reaction in solid or plasma.

Multigrain/polydispersity has a significant impact on turbidity current (TC). Despite the fact that several researches have looked into this effect, the impact of the fluid–particle interactions is not fully understood. Motivated by this, we employ the Eulerian–Lagrangian computational fluid dynamics–discrete element method model to investigate the dynamics of the bidisperse lock-exchange TC. Results show that, because the coarse particles will settle faster and stop moving forward, the two phases of bidisperse transport and fine component transport can be distinguished in the evolution of the bidisperse TC. During the bidisperse transport stage, the upper interface of each component is primarily determined by their own settling and transport characteristics and does not strongly depend on the relative fine particle volume fraction $\phi _F$. Fine particles are primarily responsible for the vortical structures near the upper interface of the TC head, and the increase of $\phi _F$ promotes their streamwise development. In comparison, fragmented vortical coherent structures are closely related to the presence of coarse particles, which can be seen in the lower layers. Bidisperse segregation alters the collision process between dispersed phases, which differs from monodisperse TC. The collisions and segregation-induced flow establish interconnections between the two dispersed phases. In the latter stage, the transport of fine particles is inhibited by both the lift force and the contact force produced by the collision with the deposited materials. As $\phi _F$ rises, the negative contact force weakens, and its change is essentially balanced by the rise in negative lift force.

Post-acceleration of protons in helical coil targets driven by intense, ultrashort laser pulses can enhance ion energy by utilizing the transient current from the targets’ self-discharge. The acceleration length of protons can exceed a few millimeters, and the acceleration gradient is of the order of GeV/m. How to ensure the synchronization between the accelerating electric field and the protons is a crucial problem for efficient post-acceleration. In this paper, we study how the electric field mismatch induced by current dispersion affects the synchronous acceleration of protons. We propose a scheme using a two-stage helical coil to control the current dispersion. With optimized parameters, the energy gain of protons is increased by four times. Proton energy is expected to reach 45 MeV using a hundreds-of-terawatts laser, or more than 100 MeV using a petawatt laser, by controlling the current dispersion.

In strong-field physics experiments with ultraintense lasers, a single-shot cross-correlator (SSCC) is essential for fast optimization of the pulse contrast and meaningful comparison with theory for each pulse shot. To simultaneously characterize an ultrashort pulse and its long pedestal, the SSCC device must have both a high resolution and a large temporal window. However, the resolution and window in all kinds of single-shot measurement contradict each other in principle. Here we propose and demonstrate a novel SSCC device with two separate measurement channels: channel-1 for the large-window pedestal measurement has a moderate resolution but a large window, while channel-2 for the ultrashort pulse measurement has a small window but a high resolution; this allows the accurate characterization of the pulse contrast in a single shot. A two-channel SSCC device with a 200-fs resolution and 114-ps window has been developed and tested for its application in ultraintense lasers at 800 nm.

In recent years, the controlling nutritional status (CONUT) score has increasingly became an effective indicator associated with tumor prognosis. This study was conducted to synthesise data on the prognostic value of CONUT score on patients with upper tract urothelial carcinoma (UTUC) or renal cell carcinoma (RCC) undergoing nephrectomy. We designed and performed a systematic analysis of studies that verified the correlation between preoperative CONUT score and prognosis for UTUC and RCC using PubMed, Web of Science and Embase. The conclusion was clarified by pooled hazard ratios (HR) and 95% confidence intervals (95% CI). Subgroup analysis were further conducted in accordance with different primary tumor. Six studies involving 3529 patients were included in this evidence synthesis, which revealed that the CONUT score had a potential role to predict the survival of UTUC and RCC patients accepting surgery. Pooled analysis showed that the overall survival (OS, HR 2·32, p < 0·0001), cancer-specific survival (CSS, HR 2·68, p < 0·0001) and disease-free survival (DFS, HR 1·62, p < 0·00001) were inferior in the high CONUT score group when compared with low score group. Subgroup analysis revealed that this result was in line with UTUC (OS: HR 1·86, p = 0·02; CSS: HR 2·24, p = 0·01; DFS: HR 1·54, p < 0·00001) and RCC (OS: HR 3·05, p < 0·00001; CSS: HR 3·47, p < 0·00001; DFS: HR 2·21, p = 0·0005) patients respectively. Consequently, the CONUT score is a valuable preoperative index to predict the survival of patients with UTUC or RCC undergoing nephrectomy.

Extreme cavitation scenarios, such as water column separations in hydraulic systems during transient processes caused by large cavitation bubbles, can lead to catastrophic destruction. In the present paper, we study the onset criteria and dynamics of large cavitation bubbles in a tube. A new cavitation number $Ca_2 = {l^*}^{-1} Ca_0$ is proposed to describe the maximum length $L_{max}$ of the cavitation bubble, where $l^*$ is a non-dimensional length of the water column indicating its slenderness, and $Ca_0$ is the classic cavitation number. Combined with the onset criteria for acceleration-induced cavitation ($Ca_1<1$, Pan et al., Proc. Natl Acad. Sci. USA, vol. 114, 2017, pp. 8470–8474), we show that the occurrence of large cylindrical cavitation bubbles requires both $Ca_2<1$ and $Ca_1<1$ simultaneously. We also establish a Rayleigh-type model for the dynamics of large cavitation bubbles in a tube. The bubbles collapse at a finite end speed, and the time from the maximum bubble size to collapse is $T_c=\sqrt {2}\sqrt {lL_{max}}\sqrt {{\rho }/{p_\infty }}$, where $l$ is the length of the water column, $L_{max}$ is the maximum bubble length, $\rho$ is the liquid density and $p_{\infty }$ is the reference pressure in the far field. The analytical results are validated against systematic experiments using a modified ‘tube-arrest’ apparatus, which can decouple acceleration and velocity. The results in the current work can guide design and operation of hydraulic systems encountering transient processes.

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

In the process of composing a double-differenced positioning model, it is difficult to separate different frequency signals between code division multiple access (CDMA) systems, the single-difference ambiguity of the pivot satellite and phase differential inter-system biases (PDISBs). Hence it is difficult to calibrate in advance the bias between systems in order to build an inter-system model which only needs one pivot satellite. Based on analysis of the stability of PDISB parameters for non-overlapping frequency CDMA systems, this study adopts a particle filter to estimate the fractional part of the PDISBs (F-PDISBs) between the systems and proposes a particle filter-based inter-system positioning model. Results show that the F-PDISBs and code DISBs for the baselines with the same receiver types and some with different receiver types are rather stable over time and for these baselines it is feasible to use a particle filter to estimate the F-PDISB parameters in the initial stage. Having attained the F-PDISBs, the inter-system model can be constructed to improve positioning accuracy in complex operational environments.

Chinese sprangletop [Leptochloa chinensis (L.) Nees] is one grass weed severely affecting rice (Oryza sativa L.) growth in paddies in China. Cyhalofop-butyl is the main herbicide used to control grass weeds in Chinese paddy fields, especially for controlling L. chinensis; however, L. chinensis has evolved resistance to cyhalofop-butyl due to continuous and extensive application. To investigate cyhalofop-butyl resistance levels and mechanisms in L. chinensis in some of the Chinese rice areas, 66 field populations were collected and treated with cyhalofop-butyl. Of these tested populations, 10 showed a high level of resistance to cyhalofop-butyl; the 50% effective dose ranged within 108.4 to 1,443.5 g ai ha−1 with resistance index values of 9.1 to 121.8 when compared with the susceptible population. Acetyl-coenzyme A carboxylase genes (ACCase) of susceptible and all 10 resistant populations were amplified and sequenced. Among them, Ile-1781-Leu, Trp-2027-Cys, Trp-2027-Ser, and Ile-2041-Asn mutations were found in five resistant populations. No known resistance-related mutations were found in the other five resistant populations, indicating that resistance to cyhalofop-butyl in these populations was likely to be endowed by non–target site resistance mechanisms. Notably, the Ile-1781-Leu and Trp-2027-Cys substitutions have previously been reported, but this is the first report of Trp-2027-Ser and Ile-2041-Asn mutations in L. chinensis. Furthermore, three derived cleaved amplified polymorphic sequence methods were developed to rapidly detect these mutations in L. chinensis.

The present study aimed to investigate whether dietary choline can regulate lipid metabolism and suppress NFκB activation and, consequently, attenuate inflammation induced by a high-fat diet in black sea bream (Acanthopagrus schlegelii). An 8-week feeding trial was conducted on fish with an initial weight of 8·16 ± 0·01 g. Five diets were formulated: control, low-fat diet (11 %); HFD, high-fat diet (17 %); and HFD supplemented with graded levels of choline (3, 6 or 12 g/kg) termed HFD + C1, HFD + C2 and HFD + C3, respectively. Dietary choline decreased lipid content in whole body and tissues. Highest TAG and cholesterol concentrations in serum and liver were recorded in fish fed the HFD. Similarly, compared with fish fed the HFD, dietary choline reduced vacuolar fat drops and ameliorated HFD-induced pathological changes in liver. Expression of genes of lipolysis pathways were up-regulated, and genes of lipogenesis down-regulated, by dietary choline compared with fish fed the HFD. Expression of nfκb and pro-inflammatory cytokines in liver and intestine was suppressed by choline supplementation, whereas expression of anti-inflammatory cytokines was promoted in fish fed choline-supplemented diets. In fish that received lipopolysaccharide to stimulate inflammatory responses, the expression of nfκb and pro-inflammatory cytokines in liver, intestine and kidney were all down-regulated by dietary choline compared with the HFD. Overall, the present study indicated that dietary choline had a lipid-lowering effect, which could protect the liver by regulating intrahepatic lipid metabolism, reducing lipid droplet accumulation and suppressing NFκB activation, consequently attenuating HFD-induced inflammation in A. schlegelii.

Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.

Hepatitis B constitutes a severe public health challenge in China. The Community-based Collaborative Innovation hepatitis B (CCI-HBV) project is a national epidemiological study of hepatitis B and has been conducting a comprehensive intervention in southern Zhejiang since 2009.

The comprehensive intervention in CCI-HBV areas includes the dynamic hepatitis B screening in local residents, the normalised treatment for hepatitis B infections and the upcoming full-aged hepatitis B vaccination. After two rounds of screening (each round taking for 4 years), the initial epidemiological baseline of hepatitis B in Qinggang was obtained, a coastal community in east China. By combining key data and system dynamics modelling, the regional hepatitis B epidemic in 20 years was predicted.

There were 1041 HBsAg positive cases out of 12 228 people in Round 1 indicating HBV prevalence of 8.5%. Of the 13 146 people tested in Round 2, 1171 people were HBsAg positive, with a prevalence of 8.9%. By comparing the two rounds of screening, the HBV incidence rate of 0.192 per 100 person-years was observed. By consulting electronic medical records, the HBV onset rate of 0.533 per 100 person-years was obtained. We generated a simulated model to replicate the real-world situation for the next two decades. To evaluate the effect of interventions on regional HBV prevalence, three comparative experiments were conducted.

In this study, the regional hepatitis B epidemic in 20 years was predicted and compared with HBV prevalence under different interventions. Owing to the existing challenges in research methodology, this study combined HBV field research and simulation to provide a system dynamics model with close-to-real key data to improve prediction accuracy. The simulation also provided a prompt guidance for the field implementation.

In this paper, we will consider derived equivalences for differential graded endomorphism algebras by Keller's approaches. First, we construct derived equivalences of differential graded algebras which are endomorphism algebras of the objects from a triangle in the homotopy category of differential graded algebras. We also obtain derived equivalences of differential graded endomorphism algebras from a standard derived equivalence of finite dimensional algebras. Moreover, under some conditions, the cohomology rings of these differential graded endomorphism algebras are also derived equivalent. Then we give an affirmative answer to a problem of Dugas (A construction of derived equivalent pairs of symmetric algebras, Proc. Amer. Math. Soc. 143 (2015), 2281–2300) in some special case.