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Purpose – To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance. Design/methodology/approach – A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm. Findings – To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact. Practical implications – This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts. Originality/value – The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

Boron is an ambitious fuel in energetic materials since its high heat release values, but its application is prohibited by low combustion efficiency and oxidization during storage. The polydopamine (PDA) was introduced into boron particles, investigating the impact of PDA content on the energetic behavior of boron. The results indicated that the PDA coating formed a fishing net structure on the surface of boron particles. The heat release results showed that the combustion calorific value of B@PDA was higher than that of the raw boron. Specifically, the actual combustion heat of boron powder in B@10%PDA increased by 38.08%. Meanwhile, the DSC peak temperature decreased by 100.65 °C under similar oxidation rate compared to raw boron. Simultaneously, the B@PDA@AP and B@AP composites were prepared, and their combustion properties were evaluated. It was demonstrated that B@10%PDA@AP exhibited superior performance in terms of peak pressure and burning time, respectively. The peak pressure is 12.43 kPa more than B@AP and burning time is 2.22 times higher than B@AP. Therefore, the coating of PDA effectively inhibits the oxidization of boron during storage and enhances the energetic behavior of boron and corresponding composites.

Vibration frequency and vibration duration are disturbance factors for the structural properties of clay. This study investigates how the vibration frequency and vibration duration from construction disturbances affect the mechanical properties of Zhanjiang Formation structural clay. An electric, frequency-adjustable vibration table applied varying frequencies and durations of vibration to undisturbed soil, creating structural clay samples with different disturbance degrees. Unconfined compressive strength tests and one-dimensional consolidation compression tests were conducted on these samples to obtain disturbance degrees RDq and RDS, defined by strength loss values and compression deformation characteristics, respectively. Orthogonal experiments and grey correlation analysis were used to explore the effects of vibration frequency and vibration duration on the mechanical properties of Zhanjiang Formation structural clay. The results indicated that disturbance degrees RDq and RDS increased linearly with increase in vibration frequency and vibration duration. Range analysis was conducted using two-factor three-level orthogonal experiment of disturbance degrees, and a grey relational analysis model was established to determine the primary and secondary effects of vibration duration and vibration frequency on the mechanical properties of Zhanjiang Formation structural clay. The results demonstrated that the findings from orthogonal experiments and grey relational analysis were consistent, showing that vibration duration had a more significant impact than vibration frequency on the mechanical properties of structural clay. The conclusion suggests that vibration disturbance manifests as a “fatigue damage effect”. Continuous vibration disturbance progressively weakens the cementation bonds between soil particles due to “accumulated” energy, leading to gradual fracture and destruction. With constant vibration frequency, longer durations, or with constant duration, higher frequencies intensify the “fatigue damage effect” of vibration disturbance. Furthermore, during vibration disturbance, Zhanjiang Formation structural clay shows a more pronounced “fatigue damage effect” from vibration duration than from vibration frequency, with cementation bonds between soil particles weakening more effectively due to “accumulated” energy. The research findings enhance the understanding of how vibration frequency and vibration duration from disturbance sources impact the mechanical properties of Zhanjiang Formation structural clay, offer theoretical guidance for using construction vibration machinery, and provide a reference for preventing and controlling soil disturbance.

Abstract SARS-CoV-2 Omicron variant infection generally gives rise to asymptomatic to moderate COVID-19 in vaccinated people. The immune cells can be reprogrammed or “imprinted” by vaccination and infections to generate protective immunity against subsequent challenges. Considering the immune imprint in Omicron infection is unclear, here we delineate the innate immune landscape of human Omicron infection via single-cell RNA sequencing, surface proteome profiling, and plasma cytokine quantification. We found that monocyte responses predominated in immune imprints of Omicron convalescents, with IL-1β-associated and interferon (IFN)-responsive signatures with mild and moderate symptoms, respectively. Low-density neutrophils increased and exhibited IL-1β-associated and IFN-responsive signatures similarly. Mild convalescents had increased blood IL-1β, CCL4, IL-9 levels and PI3 + neutrophils, indicating a bias to IL-1β responsiveness, while moderate convalescents had increased blood CXCL10 and IFN-responsive monocytes, suggesting durative IFN responses. Therefore, IL-1β- or IFN-responsiveness of myeloid cells may indicate the disease severity of Omicron infection and mediate post-COVID conditions.

In order to explore the creep characteristics of red clay under different pressures, the stress–strain curve and stress–strain–time curve of red clay under different pressures were obtained, and the triaxial shear and creep characteristics of red clay under different pressures were analysed. A triaxial shear test and creep test were carried out on red clay from Guilin as the research object under different pressures. The results show that: ➀ the cohesion and the angle of internal friction of red clay from Guilin, obtained from a triaxial shear test, are 57.20 kPa and 22.47°, respectively; ➁ according to the physical meaning of the parameters of the Burgers creep model, a simple and practical parameter inversion method is proposed, which can be used to obtain the parameters of the Burgers model for red clay under different perimeter pressures and differing deviatoric stress; and ➂ when comparing the calculation results with the test results, the correlation coefficients R2 of the two are above 0.9, with a good fitting effect, so the model can accurately describe the creep characteristics of red clay from Guilin. This study can provide a theoretical basis and technical support for engineering construction in the red clay area of Guilin.

In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a fault-free pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squares-residuals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.

Nanoplastics in aquatic environments may induce adverse immunotoxicity effects in fish. However, there is insufficient evidence on the visible immunotoxicity endpoints in the larval stages of fish. The liver plays an important role in systemic and local innate immunity in the fish. In this study, the hepatic inflammatory effects of polystyrene (PS) nanoplastic particles (NPs: 100 and 50 nm) and micron PS particles on transgenic zebrafish (Danio rerio) larvae were estimated using fluorescent-labeled neutrophils, macrophages, and liver-type inflammatory binding protein (fabp10a). Particles with smaller size induced higher aggregations of neutrophils and apoptosis of macrophages in the abdomen of the larvae, corresponding to greater hepatic inflammation in the larvae. NPs increased the expression of fabp10a in the larval livers in a dose- and size-dependent manner. PS particles of 50 nm at a concentration of 0.1 mg·L−1 increased the expression of fabp10a in the larval liver by 21.90% (P

In this study, a relatively light and high strength high entropy alloy thermoelectric material (Al2CoCrFeNi) for extreme service conditions was prepared by arc melting method. We find that the microstructure of the high entropy alloy (HEA) can be changed by annealing. By the tuning of proper annealing process, the microstructure evolution of the HEA can be adjusted to improve its mechanical and TE properties. Ultimate compressive strength can reach about 1.3 GPa after 1000 ℃ annealing. Fine-grain and precipitation strengthening are the main reasons for the improvement of the mechanical properties. The conductivity, Seebeck coefficient and thermal conductivity are all affected by grain size, precipitates, structural complexity and so on. The present work provides an effective means of preparing and regulating microstructures for the energy-conversion technique, and also supplies a feasible reference for the future development of lightweight, high-strength HEA TE materials.

Nanoplastics (NPs) are ubiquitous in harvested organisms at various trophic levels, and more concerns on their diverse responses and wide species-dependent sensitivity are continuously increasing. However, systematic study on the toxic effects of NPs with different functional group modifications is still limited. In this review, we gathered and analyzed the toxic effects of NPs with different functional groups on microorganisms, plants, animals, and mammalian/human cells in vitro. The corresponding toxic mechanisms were also described. In general, most up-to-date relevant studies focus on amino (−NH2) or carboxyl (−COOH)-modified polystyrene (PS) NPs, while research on other materials and functional groups is lacking. Positively charged PS-NH2 NPs induced stronger toxicity than negatively charged PS-COOH. Plausible toxicity mechanisms mainly include membrane interaction and disruption, reactive oxygen species generation, and protein corona and eco-corona formations, and they were influenced by surface charges of NPs. The effects of NPs in the long-term exposure and in the real environment world also warrant further study.

It is well documented that Epichloë endophytes can enhance the resistance of grasses to herbivory. However, reports on resistance to pathogenic fungi are limited, and their conclusions are variable. In this study, we chose pathogenic fungi with different trophic types, namely, the biotrophic pathogen Erysiphales species and the necrotrophic pathogen Curvularia lunata, to test the effects of Epichloë on the pathogen resistance of Achnatherum sibiricum. The results showed that, compared to Erysiphales species, C. lunata caused a higher degree of damage and lower photochemical efficiency (Fv/Fm) in endophyte−free (E−) leaves. Endophytes significantly alleviated the damage caused by these two pathogens. The leaf damaged area and Fv/Fm of endophyte−infected (E+) leaves were similar between the two pathogen treatments, indicating that the beneficial effects of endophytes were more significant when hosts were exposed to C. lunata than when they were exposed to Erysiphales species. We found that A. sibiricum initiated jasmonic acid (JA)−related pathways to resist C. lunata but salicylic acid (SA)–related pathways to resist Erysiphales species. Endophytic fungi had no effect on the content of SA but increased the content of JA and total phenolic compounds, which suggest that endophyte infection might enhance the resistance of A. sibiricum to these two different trophic types of pathogens through similar pathways.