The white mullet, Mugil curema (Mugilidae), is a catadromous euryhaline fish with an omnivorous diet, and is distributed mainly along the Pacific and Atlantic coasts of the Americas. Mullets represent an important economic resource for the artisanal fisheries in Mexico. In this study, 73 individuals of M. curema were analysed; specimens were sampled in 4 coastal lagoons of the Yucatán Peninsula, Mexico, between 2022 and 2024. Parasite identification was accomplished by using DNA sequences. Nineteen parasite taxa were found, including 1 monopisthocotylan, 1 copepod, 1 acanthocephalan, 1 nematode, and 15 trematodes. Specimens were sequenced for a nuclear or mitochondrial molecular marker. Ten taxa are reported for the first time in this host species, including the adult trematodes Saccocoelioides olmecae; Schikhobalotrema sp. 1 and sp. 2, Hemiuridae gen. sp., the larval trematodes Mesostephanus microbursa, M. cubaensis, Cardiocephaloides medioconiger, Saccularina sp., Bucephalus sp., as well as the larval nematode Contracaecum fagerholmi. Of the 21 metazoan parasites recorded, 58% were adults and 42% were larval stages. The checklist of the metazoan parasites of M. curema was updated. Our study contributes to the understanding of the parasite diversity of an economically important fish species with a wide distribution range and corroborates the usefulness of combining morphological and molecular data for species identification and for linking larval forms with adults to complete parasite life cycles. Our results will be useful in further studies of parasites as bioindicators of ecosystem health, and studies of the role of parasites in food webs in coastal lagoons.

This study proposes a radial basis function neural network disturbance observer- (RBFNNDO) based anti-saturation backstepping controller for hypersonic vehicles with input saturations and multiple disturbances. Firstly, in response to the problem of ‘exploding complexity’ in backstepping controller, we adopt finite-time tracking differentiators (FTD), which realise higher tracking accuracy and tracking speed than those of the existing methods. Secondly, we develop multivariable neural network disturbance observers to estimate the lumped disturbances involving aerodynamic uncertainties and external disturbances, thereby improving the robustness of the proposed controller. Thirdly, in order to alleviate the input saturation and minimise the duration time, we use an adaptive fixed-time anti-saturation compensator (AFAC). The simulation results have proven that our proposed backstepping controller outperforms other existing methods in terms of control performance and saturation time.

The problem of fair representation is studied in the case of cleavage according to one or two criteria. In the latter case we show that with minimal requirements of equity, a solution exists only if the two criteria are binary. This might explain an observed tendency to political bipolarization.

In his seminal work on partitions and divisor functions, MacMahon introduced the following two q-series:

\begin{align*}A_k(q):= \sum_{0 \lt s_1 \lt s_2 \lt \cdots \lt s_k} \frac{q^{s_1+s_2+\cdots+s_k}}{(1-q^{s_1})^2(1-q^{s_2})^2\cdots(1-q^{s_k})^2},\qquad\quad\quad\quad\\[6pt]C_k(q):= \sum_{0 \lt s_1 \lt s_2 \lt \cdots \lt s_k}\frac{q^{2(s_1+s_2+\cdots+s_k)-k}}{(1-q^{2s_1-1})^2(1-q^{2s_2-1})^2\cdots(1-q^{2s_k-1})^2}.\end{align*}

In 2013, Andrews and Rose proved that $A_k(q)$ and $C_k(q)$ are quasimodular forms of weight $\leq 2k$. Recently, Ono and Singh proved two interesting identities involving $A_k(q)$ and $C_k(q)$ and showed that the generating functions for the three-coloured partition function $p_3(n)$ and the overpartition function $\overline{p}(n)$ have infinitely many closed formulas in terms of MacMahon’s quasimodular forms $A_k(q)$ and $C_k(q)$. In this paper, we introduce the finite forms $A_{k,n}(q)$ and $C_{k,n}(q)$ of MacMahon’s q-series $A_k(q)$ and $C_k(q)$ and prove two identities which generalize Ono–Singh’s identities. We also prove some new identities involving $A_{k,n}(q)$, $C_{k,n}(q)$ and certain infinite products based on two Bailey pairs. Those identities are analogous to Ono–Singh’s identities.

Trichostrongylus spp. are globally distributed gastrointestinal nematodes that affect ruminants and humans, posing significant veterinary and public health challenges. Despite their zoonotic potential, the temporal dynamics of Trichostrongylus infection remain poorly understood globally. This study aimed to estimate long-term trends in Trichostrongylus prevalence in humans, ovines, and bovines using time series modelling. A systematic review identified 240 eligible studies with annual prevalence data across 60 countries. Following Kalman smoothing, annual prevalence time series were constructed for each host species covering 1947–2024 for humans, 1966–2024 for ovines, and 1962–2024 for bovines. ARIMA models were fitted separately: ARIMA(0,1,1) for humans, ARIMA(3,0,0) for ovines, and ARIMA(0,1,1) for bovines. Model selection was based on Stationary R2, RMSE, MAPE, and the Ljung-Box Q test for residual independence. Forecast 95% confidence intervals were reported to convey uncertainty in the projected trends. All three models demonstrated good in-sample fit and adequate residual diagnostics. Infection rates in humans and bovines are projected to decline, from 4.64% to 3.73% in humans and from 20.11% to 11.76% in bovines by 2034. In contrast, the ovine model forecasts an increase in infection rates, from 6.50% to 15.56%. This increase in ovines may reflect greater pasture exposure and environmental persistence of infective larvae, while improvements in hygiene and livestock management likely contribute to the declining trends observed in humans and bovines. The rising infection rate in ovines, coupled with sustained zoonotic risk, underscores the need for integrated One Health surveillance and control efforts.

China accounts for over 90% of global alveolar echinococcosis (AE) cases caused primarily by Echinococcus multilocularis. If left untreated, AE can have a 10-year mortality rate of 94%. Understanding its epidemiological patterns is essential for targeted control strategies. Surveillance data from 2006 to 2020 were obtained from the Public Health Scientific Data Center, and spatial and temporal trends were analyzed using spatial autocorrelation, hot spot analysis, and centroid migration techniques. A total of 51,403 echinococcosis cases were reported from 2006 to 2020, with an average annual incidence of 0.25 per 100,000. Most cases (71.33%) occurred in individuals aged 20–60 years. High-incidence areas centred in western/northwestern provinces, including Qinghai, Xinjiang, Xizang, Gansu, and Ningxia. Incidence and cases increased until 2017, then declined steadily. Spatial autocorrelation revealed persistent High-High clusters in Gansu and Ningxia (2008–2018) and Xizang (2010–2020), while Low-Low clusters persisted in central/eastern China. Hotspot analysis confirmed sustained high-risk zones in western/northwestern regions. Trend surface and centroid migration showed a southward disease shift within Qinghai. Echinococcosis remains endemic in western/northwestern China, with a trend of southward expansion. Strengthened, tailored interventions are urgently needed, particularly in high-burden areas like Xizang and Qinghai.

In the process of utilising machine vision-assisted large aircraft component docking assembly, due to the occlusion induced by process equipment such as assembly tooling, the features on the calibration board cannot be extracted by each camera at the same time, resulting in calibration difficulties or calibration failure. This paper aims to propose a stereo calibration method for multi-cameras in large aircraft component assembly to improve calibration accuracy. Firstly, the sub-pixel edge extraction method based on Canny-Zernike is proposed to accurately extract the circular edges and circle centres of the calibration board, and the Zernike moment model is improved. The circle centre sorting method based on the triangular markers is introduced to realise the sorting of circle centres on the calibration board. Secondly, the intrinsic and extrinsic parameter models of multi-cameras and the visual parameter models between cameras are constructed, and Zhang’s calibration method and indirect calibration method are integrated to solve the parameters. Subsequently, the distortion correction model is optimised by Levenberg-Marquardt. Finally, experiments are performed to test the proposed method. The results show that the proposed method, compared with uncalibration and Zhang’s calibration method, the proposed method achieves stereo calibration of the multi-cameras under complex working conditions, enhances the calibration accuracy and improves the quality of the large aircraft component docking assembly.

Social scientists have quickly adopted large language models (LLMs) for their ability to annotate documents without supervised training, an ability known as zero-shot classification. However, due to their computational demands, cost, and often proprietary nature, these models are frequently at odds with open science standards. This article introduces the Political Domain Enhanced BERT-based Algorithm for Textual Entailment (DEBATE) language models: Foundation models for zero-shot, few-shot, and supervised classification of political documents. As zero-shot classifiers, the models are designed to be used for common, well-defined tasks, such as topic and opinion classification. When used in this context, the DEBATE models are not only as good as state-of-the-art LLMs at zero-shot classification, but are orders of magnitude more efficient and completely open source. We further demonstrate that the models are effective few-shot learners. With a simple random sample of 10–25 documents, they can outperform supervised classifiers trained on hundreds or thousands of documents and state-of-the-art generative models. Additionally, we release the PolNLI dataset used to train these models—a corpus of over 200,000 political documents with highly accurate labels across over 800 classification tasks.

The improvement of the accuracy and real-time performance of sector traffic flow prediction is of great significance to air traffic management decision-making. Sectors operate under complex spatial structures and time dimensions. Some neural network methods adopt sequence order to gradually transmit information, which makes it difficult to achieve complete parallel training. Not only does it take too long to train, resulting in low training efficiency, but it is also easy to lose the effective correlation information of long sequence data. To this end, a sector traffic flow prediction method based on attention-improved graph convolutional transformer (AGC-T) network is proposed to improve the current traffic prediction problem for sectors. First, the graph structure information and historical traffic data of the sector are input into the graph convolutional network improved based on the attention mechanism to fully capture the spatial relationship with sectors as nodes. Combined with the transformer’s multi-head self-attention mechanism, it can directly focus on the sequence data at any position without gradually transmitting information. Not only does it improve efficiency through parallel training, but the encoder-decoder structure can also mine the information features in the traffic data, focus on the traffic data features of key nodes and more accurately predict sector traffic. Finally, the operation traffic data of sectors in typical areas in central and southern China are taken as an example to analyse the model. The results show that compared with other prediction models, the AGC-T model $RSME$, $MAE$ and ${R^2}$ are 45.16%, 46.78% and 2.63% higher than the GCN model in the 15-min single-day traffic prediction task, and 41.74%, 35.27% and 1.20% higher than the GRU model. In the single-week traffic prediction task, $RSME$, $MAE$ and ${R^2}$ are 37.12%, 40.54% and 3.55% higher than the GCN model, and 35.15%, 35.17% and 0.65% higher than the GRU model, respectively, showing better prediction performance. This study will help air navigation service providers (ANSP) to make sector traffic predictions more accurately, thereby implementing more scientific and reasonable traffic management measures.

We determine the conditions for the reducibility of some parametrised families of quadratic and cubic polynomials over finite fields, and count the number of irreducible trinomials. The existence of a factorisation of these polynomials plays an important role in studying the finite groups of exceptional Lie types.

To investigate the stall mechanisms of a multi-stage axial compressor under different rotational speeds and identify the initial stall stages, this study focuses on a high-load nine-stage axial compressor, validated through experimental data. The results reveal that at 100% corrected rotational speed, flow instability is primarily triggered by corner separation in the front four stators (S1–S4). At 80% corrected rotational speed, the instability stems from the interaction between the first rotor (R1) tip leakage vortex and the main flow, coupled with the front four stators’ corner separation. Precise identification of initial stall locations in multi-stage axial compressors is imperative. The study first employs qualitative flow-field analysis to identify initial stall locations by comparing meridional mass flux variation contour maps and axial velocity iso-surfaces. The results show that the stall inception occurs at the S2 root under 100% corrected rotational speed, while at 80% corrected rotational speed, stall initiates simultaneously at both the S2 root and the R1 tip. Furthermore, an innovative three-dimensional flow blockage quantification method was established to systematically evaluate blockage severity within multi-stage blade passages. This approach utilises relative blockage variation metrics to quantitatively identify regions of rapid flow deterioration, achieving remarkable consistency with qualitative flow-field analysis. The qualitative and quantitative analysis results have been mutually corroborated. The proposed blockage quantification approach enables precise evaluation across stages without complex flow fields comparisons, allowing rapid identification of stall-initiating locations and supporting subsequent stability enhancement optimization.

The practice of anthropology is based on the ethnographer “being there” in time and space. And the act of writing is the reenactment of “presence” for the reader. “The field” is a romanticized space for empirical exploration. However, technological innovation and connectivity have enabled easy access to new “fieldsites” and vicarious participant-observation without being “present.” The entertainment media ecosystem is now more heterogeneous than ever and is more relevant in everyday life. The depth with which we immerse ourselves in these imaginary worlds speaks volumes about our withdrawal from other forms of engagement with the people, communities, and social problems around us. Romance and fantasy are a means to escape vulnerability and hopelessness, as well as serving as an outlet for the frustrations of failed social mobility. This essay posits that romance is a method for living today, and enjoyment is empiricism for a public anthropology. Romance is more than a genre; it is a guide to understanding how society functions. There is something deeply human about living through our imaginations to escape our present. Enjoying romance as a method to engage with the world offers insight into political infrastructures, social hierarchies, and elite intrigue. Life is full of afflictions, and romance is more than a salve; it offers a strategy for navigating social relations.

This study proposes a geometric solution to the norm differential game design problem in target-attacker-defender (TAD) engagements, addressing key limitations of conventional zero-effort-miss approaches. By leveraging the geometric analogy between guidance-law-generated trajectories and Dubins paths, we reformulate the derivation of zero-effort-miss-based guidance laws as a Nash equilibrium optimisation problem, with optimal strategies determined through reachable set analysis of Dubins path frontier. The resulting model is a non-convex optimisation problem, which prevents the derivation of traditional state-feedback control laws. To overcome this limitation and enable real-time implementation, we develop a custom back propagation neural network, enhanced with a relaxation factor method for output filtering, a Holt linear trend model for outlier compensation and a saturation function for oscillation suppression. Extensive simulations demonstrate that the proposed framework significantly outperforms baseline methods. These results validate the effectiveness and robustness of our approach for high-performance TAD applications.

This paper studies a distributed fixed-time dynamic event-triggered formation control framework for a group of hypersonic gliding vehicles (GHGVs) suffering from internal uncertainties and non-affine properties. The main challenge is strong coupling of non-affine nonlinear dynamic with hypervelocity characteristics and multi-source uncertainties make it difficult to design the control protocol. Firstly, by integrating the distributed consensus control strategy, fractional order control theory and dynamic event-triggered mechanism, a framework of fixed-time formation control for GHGVs system is constructed. Secondly, to mitigate the issue of ‘explosion of complexity’ (EI), a fixed-time command filter (FCF) is proposed and a compensative strategy is formulated to tackle the impact of filtering errors. Thirdly, an additional auxiliary differential equation (ADE) is developed to decouple the control input from the status variable. Several radial base function neural networks (RBFNN) are utilised to handle the unknown internal uncertainties. Furthermore, a unique dynamic event-triggered mechanism (DTEM) is introduced for each follower, facilitating seamless transitions between two distinct dynamic threshold strategies. Analysis based on Lyapunov function illustrates that the output tracking error of followers exponentially converges to a small range within a fixed time, and Zeno behaviour is prevented. Finally, several numerical simulations are presented to demonstrate the practicability and meliority of the suggested approach.

This paper provides a preliminary investigation into the use of a novel passive aircraft flight loads alleviation device called the Superelastic Monostable Spoiler. The work focuses primarily on understanding the behaviour of such a device and the related loads alleviation performance during dynamic gust events. A number of different design parameters are explored, such as the trigger condition and the activation speed. The main aim of the paper is to define the preliminary operational requirements of such a device in order to guide the future detailed design, which is not addressed here. It was found that the Superelastic Monostable Spoiler could potentially provide loads alleviation performance comparable with typical gust loads alleviation technologies currently used in modern civil aviation based on the use of ailerons and spoilers.