History effects play a significant role in determining the velocity in boundary layers with pressure gradients, complicating the identification of a velocity scaling. This work pivots away from traditional velocity analysis to focus on fluid acceleration in boundary layers with strong adverse pressure gradients. We draw parallels between the transport equation of the velocity in an equilibrium spatially evolving boundary layer and the transport equation of the fluid acceleration in temporally evolving boundary layers with pressure gradients, establishing an analogy between the two. To validate our analogy, we show that the laminar Stokes solution, which describes the flow immediately after the application of a pressure gradient force, is consistent with the present analogy. Furthermore, fluid acceleration exhibits a linear scaling in the wall layer and transitions to logarithmic scaling away from the wall after the initial period, mirroring the velocity in an equilibrium boundary layer, lending further support to the analogy. Finally, by integrating fluid acceleration, a velocity scaling is derived, which compares favourably with data as well.

The phenomenon of focusing of microwave beams in a plasma near a turning-point caustic is discussed by exploiting the analytical solution to the Gaussian beam-tracing equations in the two-dimensional (2-D) linear-layer problem. The location of maximum beam focusing and the beam width at that location are studied in terms of the beam initial conditions. This focusing must be taken into account to interpret Doppler backscattering (DBS) measurements. We find that the filter function that characterises the scattering intensity contribution along the beam path through the plasma is inversely proportional to the beam width, predicting enhanced scattering from the beam focusing region. We show that the DBS signal enhancement for decreasing incident angles between the beam path and the density gradient is due to beam focusing and not due to forward scattering, as was originally proposed by (Gusakov et al., (Plasma Phys. Contr. Fusion, vol. 56, 2014, p. 0250092014, 2017); Plasma Phys. Rep. vol. 43(6), 2017, pp. 605–613). The analytic beam model is used to predict the measurement of the $k_y$ density-fluctuation wavenumber power spectrum via DBS, showing that, in an NSTX-inspired example, the spectral exponent of the turbulent, intermediate-to-high $k_y$ density-fluctuation spectrum might be quantitatively measurable via DBS, but not the spectral peak corresponding to the driving scale of the turbulent cascade.

The well-known quadratic temperature–velocity (TV) relation is significant for physical understanding and modelling of compressible wall-bounded turbulence. Meanwhile, there is an increasing interest in employing the TV relation for laminar modelling. In this work, we revisit the TV relation for both laminar and turbulent flows, aiming to explain the success of the TV relation where it works, improve its accuracy where it deviates and relax its limitation as a wall model for accurate temperature prediction. We show that the general recovery factor defined by Zhang et al. (J. Fluid. Mech., vol. 739, 2014, pp. 392–440) is not a wall-normal constant in most laminar and turbulent cases. The effective Prandtl number $Pr_e$ is more critical in determining the shape of temperature profiles. The quadratic TV relation systematically deviates for laminar boundary layers irrespective of Mach number and wall boundary conditions. We find a universal distribution of $Pr_e$, based on which the TV relation can be notably improved, especially for cold-wall cases. For turbulent flows, the TV relation as the wall model can effectively improve the near-wall temperature prediction for cold-wall boundary layer cases, but it involves boundary-layer-edge quantities used in the Reynolds analogy scaling, which hinders the application of the wall model in complex flows. We propose a transformation-based temperature wall model by solving inversely the newly developed temperature transformation of Cheng and Fu (Phy. Rev. Fluids, vol. 9, 2024, no. 054610). The dependence on edge quantities is thus removed in the new model and the high accuracy in turbulent temperature prediction is maintained for boundary layer flows.

Asian corn borer, Ostrinia furnacalis Guenée (Lepidoptera: Crambidae), is a major pest in corn production, and its management remains a significant challenge. Current control methods, which rely heavily on synthetic chemical pesticides, are environmentally detrimental and unsustainable, necessitating the development of eco-friendly alternatives. This study investigates the potential of the entomopathogenic nematode Steinernema carpocapsae as a biological control agent for O. furnacalis pupae, focusing on its infection efficacy and the factors influencing its performance. We conducted a series of laboratory experiments to evaluate the effects of distance, pupal developmental stage, soil depth, and light conditions on nematode attraction, pupal mortality and sublethal impacts on pupal longevity and oviposition. Results demonstrated that S. carpocapsae exhibited the highest attraction to pupae at a 3 cm distance, with infection declining significantly at greater distances. Younger pupae (<12 h old), were more attractive to nematodes than older pupae, and female pupae were preferred over males. Nematode infection was highest on the head and thorax of pupae, with a significant reduction in infection observed after 24 h. Infection caused 100% mortality in pupae within 2 cm soil depth, though efficacy was reduced under light conditions. Sublethal effects included a significant reduction in the longevity of infected adults and a decrease in the number of eggs laid by infected females compared to controls. These findings underscore the potential of S. carpocapsae as an effective biocontrol agent for sustainable pest management in corn production, offering a viable alternative to chemical pesticides.

Fuel pre-injection in the inlet of a hypersonic engine has been proven to be advantageous in the range of the very high flight Mach numbers. In this paper, a rapid inlet performance analysis model with fuel pre-injection is proposed. The modelling process is divided into two stages. Firstly, the baseline inlet model is provided based on the working principle of the inlet. Then, the newly proposed fuel injection and heat release model is added to the baseline inlet model. Among them, the fuel injection and heat release model is equivalent to increasing the compression angle in the cold state. And in the hot state the effect of the fuel heat release will be considered in addition to the effect of cold state. The research results show as the equivalence ratio increases, the equivalent compression angle also increases, but the two are not in a linear relationship. Based on this pattern of effect, fuel injection can be used to regulate the shock wave position and accurately control the flow rate of the inlet. In addition, by comparing to numerical simulation, it is found that the analysis model can almost reasonably predict the performance of the pre-injection inlet. However, the calculation of drag coefficient has some deviation compared to numerical simulation, which is probably due to the lack of consideration of friction drag and the interaction between the shock wave and boundary layer in the model analysis. Overall, the modelling method proposed in this paper can reflect the effect of fuel injection on inlet performance, which can be used to optimise injection strategy in the future.

The oriental armyworm, Mythimna separata (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges for timely and accurate monitoring, as conventional methods are labour-intensive and prone to errors. To address these limitations, this study investigates the use of machine learning for automated and precise identification of M. separata larval instars. A total of 1577 larval images representing different instar were analysed for geometric, colour, and texture features. Additionally, larval weight was predicted using 13 regression models. Instar identification was conducted using Support Vector Classifier (SVC), Random Forest, and Multi-Layer Perceptron. Key feature contributing to classification accuracy were subsequently identified through permutation feature importance analysis. The results demonstrated the potential of machine learning for automating instar identification with high efficiency and accuracy. Predicted larval weight emerged as a key feature, significantly enhancing the performance of all identification models. Among the tested approaches, BaggingRegressor exhibited the best performance for larval weight prediction (R2 = 98.20%, RMSE = 0.2313), while SVC achieved the highest instar identification accuracy (94%). Overall, the integration of larval weight with other image-derived features proved to be a highly effective strategy. This study demonstrates the efficacy of machine learning in enhancing pest monitoring systems by providing a scalable and reliable framework for precise pest management. The proposed methodology significantly improves larval instar identification accuracy and efficiency, offering actionable insights for implementing targeted biological and chemical control strategies.

While the innovation behaviors of family firms (FFs) have attracted burgeoning scholarly attention, few studies have investigated how intergenerational succession, one of the most critical aspects of family dynamics changes among FFs, affects innovation behaviors. Based on the socioemotional wealth perspective (SEW), we have introduced a concept of innovation decoupling that refers to the tendency of prioritizing the symbolic disclosures over substantive changes of innovation and proposed that FFs that have experienced intergenerational succession would exhibit a greater extent of innovation decoupling. By tracking a sample of Chinese publicly listed FFs from 2012 to 2021 while applying the machine learning approach, we have confirmed the proposition and further unveiled that such inclination becomes weaker when the focal FF is influenced by the family affective endowment and the successor with ascribed bureaucratic connections. Overall, this study brings new nuances to the knowledge of the innovation behaviors of FFs by highlighting the inter-firm heterogeneities and impacts of family dynamics.

Given the growing trend of using digital platforms for exporters' internationalization, the management of exporters' online internationalization has become a critical issue. However, academic research in this area remains sparse. Specifically, little is known about when and under what conditions exporters may consider discontinuing the use of a digital platform for exporting, i.e., online de-internationalization. This study develops and tests a theoretical framework for these determinants and the contingencies for exporters' online de-internationalization. Specifically, drawing on the de-internationalization literature, we identify sets of internal and external antecedents of exporters' intention to discontinue the use of digital platforms for exporting. Furthermore, we examine the moderating effect of technological opportunism. Based on a unique sample of Chinese exporters registered on Alibaba.com, the world's largest business-to-business platform, the empirical findings support our proposed determinants of online de-internalization. This article ultimately discusses the theoretical and managerial implications.

We investigate the statistical properties of kinetic and thermal dissipation rates in two-dimensional/three-dimensional vertical convection of liquid metal ($Pr = 0.032$) within a square cavity. Two situations are specifically discussed: (i) classical vertical convection with no external forces and (ii) vertical magnetoconvection with a horizontal magnetic field. Through an analysis of dissipation fields and a reasonable approximation of buoyancy potential energy sourced from vertical heat flux, the issue of the ‘non-closure of the dissipation balance relation’, which has hindered the application of the GL theory in vertical convection, is partially resolved. The resulting asymptotic power laws are consistent with existing laminar scaling theories and even show certain advantages in validating simulations with large Prandtl number ($Pr$). Additionally, a full-parameter model and prefactors applicable to low-$Pr$ fluids are provided. The extension to magnetoconvection naturally introduces the approximate expression for total buoyancy potential energy and necessitates adjustments to the contributions of kinetic dissipation in both the bulk and boundary layer. The flow dimensionality and boundary layer thickness are key considerations in this analysis. The comprehension of Joule dissipation has been updated: the Lorentz force generates positive dissipation in the bulk by suppressing convection, while in the Hartmann layer, shaping the exponential boundary layer requires the fluid to perform positive work to accelerate, leading to negative dissipation. Finally, the proposed transport equations for magnetoconvection are supported by current direct numerical simulation (DNS) and literature data, and the applicability of the model is discussed.

On both global and local levels, one can observe a trend toward the adoption of algorithmic regulation in the public sector, with the Chinese social credit system (SCS) serving as a prominent and controversial example of this phenomenon. Within the SCS framework, cities play a pivotal role in its development and implementation, both as evaluators of individuals and enterprises and as subjects of evaluation themselves. This study engages in a comparative analysis of SCS scoring mechanisms for individuals and enterprises across diverse Chinese cities while also scrutinizing the scoring system applied to cities themselves. We investigate the extent of algorithmic regulation exercised through the SCS, elucidating its operational dynamics at the city level in China and assessing its interventionism, especially concerning the involvement of algorithms. Furthermore, we discuss ethical concerns surrounding the SCS’s implementation, particularly regarding transparency and fairness. By addressing these issues, this article contributes to two research domains: algorithmic regulation and discourse surrounding the SCS, offering valuable insights into the ongoing utilization of algorithmic regulation to tackle governance and societal challenges.

In 2023 the Supreme Court of Mauritius cited human rights and public health arguments to strike down a colonial-era law criminalizing consensual same-sex sex. The parliament of Singapore recently did the same through legislative means. Are these aberrations or a shifting global consensus? This article documents a remarkable shift international legal shift regarding LGBTQ+ sexuality. Analysis of laws from 194 countries across multiple years demonstrates a clear, ongoing trend toward decriminalization globally. Where most countries criminalized same-sex sexuality in the 1980s, now two-thirds of countries do not criminalize under law. Additionally, 28 criminalizing countries in 2024 demonstrate a de facto policy of non-enforcement, a milestone towards legal change that all of the countries that have fully decriminalized since 2017 have taken. This has important public health effects, with health law lessons for an era of multiple pandemics. But amidst this trend, the reverse is occurring in some countries, with a counter-trend toward deeper, harsher criminalization of LGBTQ+ sexuality. Case studies of Angola, Singapore, India, Botswana, Mauritius, Cook Islands, Gabon, and Antigua and Barbuda show many politically- and legally-viable pathways to decriminalization and highlight actors in the executive, legislative, and judicial arenas of government and civil society engaged in legal change.