In this article, we classify irregular threefolds with numerically trivial canonical divisors in positive characteristic. For a threefold, if its Albanese dimension is not maximal, then the Albanese morphism will induce a fibration which either maps to a curve or is fibered by curves. In practice, we treat arbitrary dimensional irregular varieties with either one-dimensional Albanese fiber or one-dimensional Albanese image. We prove that such a variety carries another fibration transversal to its Albanese morphism (a “bi-fibration” structure), which is an analog structure of bielliptic or quasi-bielliptic surfaces. In turn, we give an explicit description of irregular threefolds with trivial canonical divisors.

This study was conducted to investigate the effects of blended oils with an balanced n-6/n-3 polyunsaturated fatty acid (PUFA) ratio of 6:1 and unsaturated fatty acid/saturated fatty acid (UFA/SFA) ratio of 2.5:1 on growth performance and intestinal health in LPS-challenged piglets. One hundred and twenty piglets were selected and randomly assigned to two treatments (2% soybean oil or 2% blended oils). On d 28, the experiment was conducted as a 2 × 2 factorial arrangement of treatments including dietary treatment (2% soybean oil vs. 2% blended oil) and LPS challenge (saline vs. LPS). The results showed that the blended oils supplementation increased ADG and ADFI during 1-14 days (P < 0.05), and reduced feed to gain ratio in the whole experimental period (P < 0.05). In addition, the blended oils supplementation improved intestinal morphology, increased maltase and sucrase activities, and alleviated inflammation response in the intestine. Moreover, the blended oils supplementation increased proliferating cell nuclear antigen (PCNA) mRNA expression in jejunum and Ki67 mRNA expression in ileum (P < 0.05) in both saline-treated piglets and LPS-challenged piglets. The blended oils reduced C-myc and caspase-3 mRNA expressions and increased Axin2 and Cyclin d1 mRNA expressions after LPS challenge (P < 0.05). In conclusion, the blended oils can improve growth performance and promote intestinal health in piglets.

Using an administrative nationwide dataset of 1,673 tea producers, this study examines the key factors that drive tea pricing. Empirical results indicate that price levels vary across production regions, tea varieties, altitude, and certification status. On average, black tea commands higher prices, while other teas (green, Oolong, and Baozhong) are typically lower. However, as a special local tea, Oriental Beauty (and other teas) has the highest price. The cultivation altitude and organic certification are significantly associated with price premiums. In summary, this study provides strong evidence to show that regional origin, growing conditions, and certification may greatly influence tea’s market price, offering practical insights for producers and policymakers.

Strategy research has long linked sustained competitive advantage to barriers to imitation. We highlight network effects as an alternative mechanism and adopt a geotemporal perspective to theorize how firms sustain advantage as it unfolds over time in international markets. Our study examines this question through the performance persistence of social platforms, focusing on how institutional and demand-side conditions shape the sustainability of platforms’ competitive advantages. We propose that intellectual property rights protection may restrict the degree of freedom in information dissemination, dampening the role of network effects in sustaining superior performance, whilst demand heterogeneity may enhance the value of sizable network membership for information consumption. Evidence from a cross-country dataset of platforms supports these predictions. These findings enrich our understanding of how geographic variations shape the endurance of a platform’s competitive advantage over time, offering implications for both global strategy and platform governance.

Numerical simulations and theoretical analysis are conducted to investigate the Atwood-number dependence of perturbation evolution in a shocked heavy fluid layer. For layers without reverberating waves, a higher Atwood number of one interface significantly enhances its coupling effect on the perturbation growth at the opposite interface. A theoretical model incorporating the startup, linear and nonlinear stages is developed to predict the interface mixing width. Dimensionless formulae are derived, identifying eight distinct modulation regimes of multi-interface instability. When reverberating waves are present, the individual effects of the upstream ($A_1$) and downstream ($A_2$) Atwood numbers are examined. The model is further modified to account for additional reverberating waves required at higher $A_2$ values for accurate amplitude prediction. Both theory and simulations demonstrate that perturbation growth at one interface can be actively controlled by adjusting the Atwood number of the opposite interface. These findings provide insights for mitigating instabilities in applications such as inertial confinement fusion through appropriate material selection.

A central concept in Global International Relations (IR) is ‘global’ or ‘globality,’ which has been commonly understood as the sum total of all geo-cultural parts of the world. This understanding underpins Global IR’s efforts to make IR more geo-epistemologically representative and inclusive, but it has been criticized for its essentialism trap and geo-epistemology. While many critics problematize Global IR’s conception of globality, few pay attention to an alternative form of existence of globality, namely, its embodiment within each of the ‘geo-cultural’ parts. This article aims to engage with this form of globality by drawing on the conceptual framework of quantum holography, which sees the whole as not just comprising its parts but also being encoded within them. The implications are that if global-local relationality is holographic in nature, globalizing IR should entail not only externally expanding IR’s geo-epistemological horizon but also discovering and appreciating the globality that always already exists within each locale. Thus, this approach may help us tackle the stubborn binary of global vs. local/national in the Global IR debate and mitigate its essentialism trap and geo-epistemology. To illustrate, we apply this framework to problematize ‘Chineseness’ in the Chinese School of IR theory.

System components usually attain marginal lifetimes with stochastic dependence in the context of load-sharing reliability structures. This study deals with the load-sharing parallel systems of two components. We prove that two marginal lifetimes are positively quadrant dependent when component lifetimes have continuous probability distributions, and such a stochastic dependence is upgraded to the total positive of order 2 in the setting of component lifetimes having an exponential distribution. In addition, we discuss how these findings shed light on related results for the load-sharing Ross model, the conditional residual lifetime, and the conditional inactivity time.

This work proposes a data-driven explicit algebraic stress-based detached-eddy simulation (DES) method. Despite the widespread use of data-driven methods in model development for both Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulations (LES), their applications to DES remain limited. The challenge mainly lies in the absence of modelled stress data, the requirement for proper length scales in RANS and LES branches, and the maintenance of a reasonable switching behaviour. The data-driven DES method is constructed based on the algebraic stress equation. The control of RANS/LES switching is achieved through the eddy viscosity in the linear part of the modelled stress, under the $\ell ^2-\omega$ DES framework. Three model coefficients associated with the pressure–strain terms and the LES length scale are represented by a neural network as functions of scalar invariants of velocity gradient. The neural network is trained using velocity data with the ensemble Kalman method, thereby circumventing the requirement for modelled stress data. Moreover, the baseline coefficient values are incorporated as additional reference data to ensure reasonable switching behaviour. The proposed approach is evaluated on two challenging turbulent flows, i.e. the secondary flow in a square duct and the separated flow over a bump. The trained model achieves significant improvements in predicting mean flow statistics compared with the baseline model. This is attributed to improved predictions of the modelled stress. The trained model also exhibits reasonable switching behaviour, enlarging the LES region to resolve more turbulent structures. Furthermore, the model shows satisfactory generalization capabilities for both cases in similar flow configurations.

By generating drag and turbulence away from the bed, aquatic vegetation shapes the mean and turbulent velocity profile. However, the near-bed velocity distribution in vegetated flows has received little theoretical or experimental attention. This study investigated the near-bed velocity profile and bed shear stress using a coupled particle image velocimetry and particle tracking velocimetry system, which enabled the acquisition of flow-field measurements at very high spatial and temporal resolution. A viscous sublayer with a linear velocity profile was present, but this sublayer thickness was much smaller in vegetated flows than in bare flows with the same channel velocity. However, the dimensionless viscous sublayer thickness was the same in vegetated and bare flows, $z_v^+ = z_v \langle u_*\rangle / \nu = 6.1 \pm 0.7$. In addition, in vegetated flow, the horizontally averaged velocity profile above the viscous sublayer did not follow the classic logarithmic law found for bare beds. This deviation was attributed to the violation of two key assumptions in the classic Prandtl mixing length theory. By modifying the mixing length theory for vegetated conditions, a new theoretical power law profile for near-bed velocity was derived and validated with velocity data from both the present and previous studies, with mean percent errors of 4.9 % and 7.8 %, respectively. Using the new velocity law, the spatially averaged bed shear stress (and friction velocity) can be predicted from channel-average velocity, vegetation density and stem diameter, all of which are conveniently measured in the field.