African swine fever (ASF) is a highly contagious animal disease caused by African swine fever virus (ASFV). It is listed by the World Organization for Animal Health (WOAH) as an animal disease subject to statutory reporting. ASFV, a large, enveloped double-stranded DNA virus with high genomic complexity, exhibits a case fatality rate of up to 100%, posing a significant threat to the global pig industry and food safety. To date, the absence of a safe commercial ASFV vaccine primarily stems from challenges in identifying immunogenic viral antigens, insufficient characterization of ASFV pathogenesis, and limited understanding of the virus’s immune evasion mechanisms. Here, we review the pathogenic characteristics (morphological structure, clinical symptoms, and epidemiological characteristics), molecular biological characteristics, and infection mechanism of ASFV, as well as the immune response mechanism, vaccine research, and the latest information on ASFV in other areas. This review will be in favour of understanding the current state of knowledge of ASF and developing effective vaccines to control this disease.

Fully resolving turbulent flows remains challenging due to a turbulent systems’ multiscale complexity. Existing data-driven approaches typically demand expensive retraining for each flow scenario and struggle to generalize beyond their training conditions. Leveraging the universality of small-scale turbulent motions (Kolmogorov’s K41 theory), we propose a scale-oriented zonal generative adversarial network (SoZoGAN) framework for high-fidelity, zero-shot turbulence generation across diverse domains. Unlike conventional methods, SoZoGAN is trained exclusively on a single dataset of moderate-Reynolds-number homogeneous isotropic turbulence (HIT). The framework employs a zonal decomposition strategy, partitioning turbulent snapshots into subdomains based on scale-sensitive physical quantities. Within each subdomain, turbulence is synthesized using scale-indexed models pretrained solely on the HIT database. A SoZoGAN demonstrates high accuracy, cross-domain generalizability and robustness in zero-shot super-resolution of unsteady flows, as validated on untrained HIT, turbulent boundary layer and channel flow. Its strong generalization, demonstrated for homogeneous and inhomogeneous turbulence cases, suggests potential applicability to a wider range of industrial and natural turbulent flows. The scale-oriented zonal framework is architecture-agnostic, readily extending beyond generative adversarial networks to other deep learning models.

Let A be an abelian variety defined over a global function field F and let p be a prime distinct from the characteristic of F. Let $F_\infty $ be a p-adic Lie extension of F that contains the cyclotomic $\mathbb {Z}_p$-extension $F^{\mathrm {cyc}}$ of F. In this paper, we investigate the structure of the p-primary Selmer group $\mathrm {Sel}(A/F_\infty )$ of A over $F_\infty $. We prove the $\mathfrak {M}_H(G)$-conjecture for $A/F_\infty $. Furthermore, we show that both the $\mu $-invariant of the Pontryagin dual of the Selmer group $\mathrm {Sel}(A/F^{\mathrm {cyc}})$ and the generalized $\mu $-invariant of the Pontryagin dual of the Selmer group $\mathrm {Sel}(A/F_\infty )$ are zero, thereby proving Mazur’s conjecture for $A/F$. We then relate the order of vanishing of the characteristic elements, evaluated at Artin representations, to the corank of the Selmer group of the corresponding twist of A over the base field F. Assuming the finiteness of the Tate–Shafarevich group, we establish that this corank equals the order of vanishing of the L-function of $A/F$ at $s=1$. Finally, we extend a theorem of Sechi—originally proved for elliptic curves without complex multiplication—to abelian varieties over global function fields. This is achieved by adapting the notion of generalized Euler characteristic, introduced by Zerbes for elliptic curves over number fields. This new invariant allows us, via Akashi series, to relate the generalized Euler characteristic of $\mathrm {Sel}(A/F_\infty )$ to the Euler characteristic of $\mathrm {Sel}(A/F^{\mathrm {cyc}})$.

Shanlan upland rice, as a unique local rice germplasm resource in Hainan comprises abundant genetic diversity. One hundred and sixty two Shanlan upland rice accessions collected from diverse ecological regions in Hainan were systematically characterized based on 11 agronomic traits. The rich genetic diversity was confirmed by phenotypic data from two consecutive years (2023 and 2024). Coefficient of variation ranged from 14.11% to 46.04% in 2023 and from 11.45% to 44.82% in 2024, with panicle-related traits (number of primary branches, grain number of primary branches, number of secondary branches, grain number of secondary branches and grain number per main panicle) exhibiting particularly high variation. Correlation analysis revealed highly significant synergistic effects among yield-related traits. Cluster analysis of the 162 accessions consistently classified them into five major groups across both growing seasons. Through grain number per main panicle and seed setting rate investigation, three excellent resources were selected that demonstrated stable and superior performance in both seasons. Notably, Line 69 exhibited outstanding “large panicles with high seed-setting rate,” producing 261.4 and 305 grains per main panicle in 2023 and 2024, respectively, with seed-setting rates reaching 93.79% and 90.07%. This study presents phenotypic data for Shanlan upland rice, offers high-quality breeding materials for subsequent research, and lays a theoretical groundwork for conserving and exploiting Hainan’s rice resources.

To address the complexity and excessive reliance on expert experience in tuning fuzzy Proportional-Integral-Derivative (PID) controller parameters, this study proposes a variable-rate spraying control system that integrates an improved Beetle Antennae Search (IBAS) algorithm with fuzzy PID control. To evaluate the feasibility of the system, a mathematical transfer function of the variable-rate spraying system was constructed, and a flow control simulation platform was established for simulation analysis. To overcome the limitations of conventional BAS, which is prone to premature convergence and limited search precision, the IBAS algorithm was developed. The improvements include a hybrid disturbance strategy to enhance individual search capability and a simulated annealing mechanism to prevent the algorithm from being trapped in local optima. Using the IBAS algorithm, the proportional and quantization factors of the fuzzy PID controller were optimized offline to obtain the optimal parameters. The IBAS-fuzzy PID controller was then compared in simulation with conventional PID, fuzzy PID, and BAS-optimized fuzzy PID controllers. The simulation results demonstrated that the IBAS-fuzzy PID algorithm achieved higher flow control accuracy than existing methods. To further validate the effectiveness of the improved algorithm under practical conditions, field experiments were conducted. The results indicated that the IBAS-optimized fuzzy PID controller outperformed the three other control methods in terms of flow control accuracy. Overall, both simulation and field results confirm that the proposed IBAS algorithm for fuzzy PID parameter optimization significantly enhances response speed, control precision, and overshoot reduction, providing a novel approach and potential application for variable-rate spraying technology.

The existing intelligent optimization algorithms face challenges related to premature convergence in the synthesis of array antennas, resulting in low solution accuracy and a tendency to get stuck in local optima. In this paper, a logistic chaos and spiral flight dandelion optimizer (LSDO) algorithm is applied to sparse antenna array synthesis with constraints. To optimize the positions of the array elements and reduce sidelobe levels, the logistic chaotic mapping is employed for population initialization, which generates a diverse and uniformly distributed initial population. Additionally, the dandelion optimizer (DO) algorithms utilize a spiral flight strategy to enhance local exploitation capability and escape from the local optimum of the sidelobe level. For algorithm performance, numerical experimental results show the stability and robustness of the LSDO algorithm. For the optimization of planar sparse arrays, the LSDO algorithm significantly outperforms conventional optimization methods, achieving a peak sidelobe level (PSLL) reduction of 15.5% for DO, 9% for PSO, and 14.56% for IWO. These results confirm the effectiveness and superiority of the proposed algorithm.

Recent research on zoonotic diseases has increasingly focused on tick-borne illnesses due to their high prevalence in northwestern China. This study aimed to determine the prevalence of tick-borne pathogens in yaks (Bos grunniens) within Qinghai Province. A total of 299 blood samples were collected from yaks in Xining City of Qinghai Province and analysed using polymerase chain reaction. Results indicated the absence of several significant zoonotic pathogens, including Borrelia burgdorferi sensu lato, Anaplasma spp. and Coxiella burnetii. However, rickettsiae were detected in the sampled yaks. The overall prevalence of spotted fever group rickettsiae was 46·5%, with a significant difference between females (68·3%) and males (9·09%). Age was also identified as a significant factor influencing infection rates. Furthermore, sequencing analysis revealed that the obtained rickettsial sequences shared 99·04–100% nucleotide identity with Rickettsia raoultii, a species endemic to Qinghai, China. Phylogenetic analysis based on the ompA and gltA genes confirmed that these sequences clustered within the R. raoultii clade. This study demonstrates a high prevalence of R. raoultii infection in yaks from Qinghai. Consequently, the implementation of preventive and therapeutic measures for yaks is recommended to mitigate the risk of transmission. This study did not collect tick samples simultaneously, so the transmission vector cannot be identified. Additionally, uneven sample distribution across some age groups may affect the representativeness of the results.

We identify a parsimonious set of factors from a large pool of candidates for explaining hedge fund returns, ranging from equity market, anomaly, and trend-following factors to macroeconomic factors. The resulting 9-factor model, including five anomaly factors, outperforms existing hedge fund models both in sample and out of sample, with a significant reduction in alphas while showing substantial cross sectional performance heterogeneity. Further analysis based on fund holdings confirms the model’s ability to capture returns from arbitrage trading. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and improve fund performance evaluation.

Excellent products often contain profound cultural connotations. To improve the quality of cultural products, it is important to study how typical cultural carriers can be more promptly and efficiently identified and incorporated into products through a detailed and easy-to-use design process. In this article, we propose an approach from three different levels to assist designers in incorporating cultural features into products, including: (1) the integrated framework of the composition and division of cultural carriers, (2) the extraction and translation model from cultural carriers, cultural elements to cultural features and (3) the cultural product design process. The proposed approach was applied in a large and complex cultural product case, that is, inter-city train design. The evaluation of the recognition of culture features indicated that the approach contributed to conferring culture on products through thoughtful design and could ensure that the product schemes reflect cultural features as well as interesting cultural connotations.

High gain greater than 106 is crucial for the preamplifiers of joule-class high-energy lasers. In this work, we present a specially designed compact amplifier using 0.5%Nd,5%Gd:SrF2 and 0.5%Nd,5%Y:SrF2 crystals. The irregular crystal shape enhances the gain length of the laser beam and helps suppress parasitic oscillations. The amplified spontaneous emission (ASE) induced by the high gain is analyzed through ray tracing. The balance between gain and ASE is estimated via numerical simulation. The gain spectral characteristics of the two-stage two-pass amplifier are examined, demonstrating the advantages of using different crystals, with bandwidths up to 8 nm and gains over 106. In addition, the temperature and stress distributions in the Nd,Gd:SrF2 crystal are simulated. This work is expected to contribute to the development of high-peak-power ($\ge$terawatt-class) high-energy (joule-class) laser devices.

Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.

This paper proposes a new surface fitting method based on double model comparison to solve the aspherical surface parameters, allowing for the simultaneous extraction of the surface deviation and the optimal surface fitting parameters for the radio antenna’s main reflector. This method employs the laser tracker to obtain the 3D coordinates of the points on the antenna surface, which can be expressed in terms of Zernike polynomials. Then compare the Zernike polynomial description with the ideal aspherical equation description to establish a discrepancy model in the optical design software. Finally, by optimizing this model, the optimal surface parameters can be obtained. The simulation results show that the method is suitable for high-precision fitting of aspherical surfaces with cone coefficient K in the range of [−4, 0.3], with the maximum deviation percentage of the radius of curvature at 0.036% and the cone coefficient at 0.14%. Experimental research is conducted on the 3.2 m sector sub-aperture spliced radio antenna; the fitted radius of curvature is 2012.3204 mm, the conic coefficient is −1.0476, and the Root Mean Square (RMS) is 0.6232 mm, confirming the adaptability of this method.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis (MA). Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model (BGLMM) can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this article, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios against the BGLMM and conventional two-stage MA that excludes DZS. Through extensive simulation studies and real-world MA case studies, we demonstrate that ZIBGLMM outperforms the BGLMM and conventional two-stage MA that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

Contrafreeloading (CFL) refers to animals’ tendency to prefer obtaining food through effort rather than accessing food that is freely available. Researchers have proposed various hypotheses to explain this intriguing phenomenon, but few studies have provided a comprehensive analysis of the factors influencing this behaviour. In this study, we observed the choice of alternative food containers in budgerigars (Melopsittacus undulatus) to investigate their CFL tendencies and the effects of pre-training, food deprivation, and effort required on the CFL tasks. The results showed that budgerigars did not exhibit significant difference in their first choices or the time interacting with less challenging versus more challenging food containers. Moreover, when evaluating each budgerigar’s CFL level, only half of them were identified as strong contrafreeloaders. Thus, we suggest that budgerigars exhibit an intermediate CFL level that lies somewhere between a strong tendency and the absence of such behaviour. Furthermore, we also found that food-deprived budgerigars tended to select less challenging food containers, and pre-trained budgerigars were more likely to choose highly challenging food containers than moderately challenging food containers, which means that the requirement of only a reasonable effort (access to food from moderately challenging food containers in this study) and the experience of pre-training act to enhance their CFL levels, whereas the requirement of greater effort and the experience of food deprivation act to decrease their CFL levels. Studying animal CFL can help understand why animals choose to expend effort to obtain food rather than accessing it for free, and it also has implications for setting feeding environments to enhance the animal welfare of captive and domesticated animals.