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.

This paper investigates the flow past a flexible splitter plate attached to the rear of a fixed circular cylinder at low Reynolds number 150. A systematic exploration of the plate length ($L/D$), flexibility coefficient ($S^{*}$) and mass ratio ($m^{*}$) reveals new laws and phenomena. The large-amplitude vibration of the structure is attributed to a resonance phenomenon induced by fluid–structure interaction. The modal decomposition indicates that resonance arises from the coupling between the first and second structural modes, where the excitation of the second structural mode plays a critical role. Due to the combined effects of added mass and periodic stiffness variations, the two modes become synchronised, oscillating at the same frequency while maintaining fixed phase difference $\pi /2$. This further results in the resonant frequency being locked at half of the second natural frequency, which is approximately three times the first natural frequency. A reduction in plate length and an increase in mass ratio are both associated with a narrower resonant locking range, while a higher mass ratio also shifts this range towards lower frequencies. A symmetry-breaking bifurcation is observed for cases with $L/D\leqslant 3.5$, whereas for $L/D=4.0$, the flow remains in a steady state with a stationary splitter plate prior to the onset of resonance. For cases with a short flexible plate and a high mass ratio, the shortened resonance interval causes the plate to return to the symmetry-breaking stage after resonance, gradually approaching an equilibrium position determined by the flow field characteristics at high flexibility coefficients.

The current study aimed to investigate the effects of different iron sources on growth performance and small intestinal health in weaned piglets. Two hundred and forty piglets (Duroc × Large White × Landrace, 9.52 ± 1.60 kg, 40 ± 2 d) were assigned to four treatments including control group, a basal diet without iron supplemented in mineral premix; ferrous sulfate (FeSO4) group, 100 mg Fe/kg dry matter (DM); ferrous glycinate (Fe-Gly) group, 80 mg Fe/kg DM; amino acid-Fe(II)-chelator complexes group, 30 mg Fe/kg DM. There were four pens for each treatment, and each pen had fifteen piglets. The experiment lasted for 28 days. Compared to the control group, three iron sources increased average daily feed intake (P < 0.05). Fe-Gly and amino acid-Fe(II)-chelator complexes increased average daily gain (P < 0.05). Amino acid-Fe(II)-chelator complexes increased villus height in jejunum (P < 0.05). In addition, Fe-Gly increased Ki67 and leucine rich repeat containing G protein-coupled receptor 5 (Lgr5) mRNA expression in duodenum (P < 0.05). Amino acid-Fe(II)-chelator complexes increased claudin-1 mRNA expression, and both amino acid-Fe(II)-chelator complexes and Fe-Gly increased Lgr5 mRNA expression (P < 0.05) in jejunum. These results suggest that organic iron is more effective than FeSO4 in improving growth performance, and has a positive effect on intestinal health in weanling piglets.

The study presents a novel cable-driven serial robot based on flexible joints and tensegrity structures, which features a rapid response capability in complex dynamic environments. This makes it particularly suitable for human–robot interaction scenarios. Compared to traditional rigid serial robots, the design’s compliance demonstrates significant advantages in addressing complex demands. The study delves into kinematic and dynamic modeling methods and verifies their effectiveness through simulations. The kinematic model transforms the local coordinate system to the global one using general kinematic equations. First, the static and dynamic model of the robot is derived based on the torque balance equation, and then the dynamic model of the robot is constructed. By simplifying the robot model, the relationship between tension values from driving cables and the robot’s workspace is analyzed under the constraints of tensegrity structures and flexible joints. Additionally, trajectory simulations validate the kinematic and dynamic models. The kinetic energy variation curves based on the trajectories confirm the accuracy of the theoretical analysis. This method demonstrates broad applicability and can be applied to other serial robots with flexible structures, offering effective solutions for use in complex dynamic environments.

In this study, changes in physiological characteristics of Coridius chinensis (Hemiptera: Dinidoridae) during diapause and post-diapause development period were determined. The moisture content of C. chinensis at the beginning of diapause was significantly lower than that at any other stage, and the moisture content during post-diapause development period was significantly higher than that from October to December 2021 and in February and April of the following year. The fat content gradually declined over time. The glycogen content remained at lower levels during diapause but rose sharply during the post-diapause development period, when it became significantly higher than that during diapause. The trehalose content gradually declined in the early stages of diapause but rose greatly in the middle stage, followed by a gradual decline in the late stages and a significant increase during the post-diapause development period. The protein content was at lower levels in the early stages of diapause, significantly lower than that in the middle and late stages of diapause and that during post-diapause development period. The results indicated significant differences in changes in the moisture, fat, carbohydrate, and protein contents between the diapause and post-diapause development periods, with obvious stage characteristics. This study provides a scientific basis for further research on the diapause physiology of C. chinensis.

As international exploration of the Meso-Neoproterozoic continues, these layers have become a key target for deep oil and gas field exploration. The Ordos Basin exhibits considerable sedimentary thicknesses within the Meso-Neoproterozoic. However, significant hydrocarbon discoveries have not been forthcoming, primarily due to the complex tectonic evolution. This paper focuses on the southern Ordos Basin, utilizing logging-seismic calibration to interpret seismic data and elucidate Meso-Neoproterozoic tectonic features. By comparing ancient and modern tectonic patterns, based on palaeotectonic maps retrieved through the impression method and combining these with tectonic evolution profiles, the study clarifies the history of tectonic modification. Under the control of two fracture systems – basin-controlling fractures at the margin and trough-controlling fractures – the Changchengian exhibits two categories (single-fault and double-fault) and five sub-categories of fault depression combinations. The study highlights significant differences between ancient and modern tectonics in the Meso-Neoproterozoic, which are attributed to various tectonic stages, including the trough-uplift depositional differentiation stage during the early rift-late depression of the Changchengian, the basin-margin subsidence stage of the southwestern depression of the Jixianian, the uplift and denudation stage of the Sinian basin’s main body and the four-stage tectonic remodelling stage of differential uplift-subsidence in the Palaeoproterozoic. This study employs the ancient-present tectonic pattern as a point of departure, thereby enhancing the theoretical understanding of deep-seated tectonics in the Ordos Basin. It offers novel insights into the exploration of Meso-Neoproterozoic gas reservoirs from a tectonic remodelling perspective.

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set = 2977, test set = 1277). There were 2783 men and 1471 women (median age = 61 years). RM was identified in 754 (17·7 %) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable performance to predict RM in the test set (AUC = 0·924, 95 % CI = 0·904, 0·944) and an external validation set (n 798, AUC = 0·909, 95 % CI = 0·876, 0·943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all P < 0·05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimise patient outcomes in multidisciplinary cancer care.

This study introduces a low-profile, broadband antenna with filtering features and tunable radiation nulls. The antenna consists of an arc-shaped slot, a sawtooth square slot, a Y-shaped filtering branch, two rectangular metal cavities, and curved current loops. High-frequency current balancing technology is used in this research, two rectangular metal cavities are added above the slot to balance the current strength and reduce cross-polarization. By introducing a Y-shaped filtering branch based on the reverse diversion technique, the filtering capability of the antenna can be significantly enhanced. The electric and magnetic field intensity in the specific area is enhanced through arc-shaped slot tuning technology, and the bandwidth is effectively broadened. The radius adjustment of the sector-shaped feeding network controls the position of the high-frequency radiation null, and the curved current loops control the low-frequency radiation null, the two modulate to regulate the roll-off rate of the radiation characteristic. Experimental tests demonstrate an impedance matching bandwidth greater than 55%, a peak gain of 4.5 dBi, and out-of-band suppression of 25 and 21 dB in the low and high-frequency bands, respectively. Moreover, the cross-polarization level obtained in the xoz plane is lower than –35 dB. The designed antenna demonstrates considerable potential for broadband filtering applications.

Ultra-thin liquid sheets generated by impinging two liquid jets are crucial high-repetition-rate targets for laser ion acceleration and ultra-fast physics, and serve widely as barrier-free samples for structural biochemistry. The impact of liquid viscosity on sheet thickness should be comprehended fully to exploit its potential. Here, we demonstrate experimentally that viscosity significantly influences thickness distribution, while surface tension primarily governs shape. We propose a thickness model based on momentum exchange and mass transport within the radial flow, which agrees well with the experiments. These results provide deeper insights into the behaviour of liquid sheets and enable accurate thickness control for various applications, including atomization nozzles and laser-driven particle sources.

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.

The World Cancer Research Fund and the American Institute for Cancer Research recommend a plant-based diet to cancer survivors, which may reduce chronic inflammation and excess adiposity associated with worse survival. We investigated associations of plant-based dietary patterns with inflammation biomarkers and body composition in the Pathways Study, in which 3659 women with breast cancer provided validated food frequency questionnaires approximately 2 months after diagnosis. We derived three plant-based diet indices: overall plant-based diet index (PDI), healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI). We assayed circulating inflammation biomarkers related to systemic inflammation (high-sensitivity C-reactive protein [hsCRP]), pro-inflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) and anti-inflammatory cytokines (IL-4, IL-10, IL-13). We estimated areas (cm2) of muscle and visceral and subcutaneous adipose tissue (VAT and SAT) from computed tomography scans. Using multivariable linear regression, we calculated the differences in inflammation biomarkers and body composition for each index. Per 10-point increase for each index: hsCRP was significantly lower by 6·9 % (95 % CI 1·6%, 11·8%) for PDI and 9·0 % (95 % CI 4·9%, 12·8%) for hPDI but significantly higher by 5·4 % (95 % CI 0·5%, 10·5%) for uPDI, and VAT was significantly lower by 7·8 cm2 (95 % CI 2·0 cm2, 13·6 cm2) for PDI and 8·6 cm2 (95 % CI 4·1 cm2, 13·2 cm2) for hPDI but significantly higher by 6·2 cm2 (95 % CI 1·3 cm2, 11·1 cm2) for uPDI. No significant associations were observed for other inflammation biomarkers, muscle, or SAT. A plant-based diet, especially a healthful plant-based diet, may be associated with reduced inflammation and visceral adiposity among breast cancer survivors.

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

Cartographer is an algorithm that was open sourced by Google in 2016 and adapted to multiple sensors. To address issues of the original algorithm, such as the negative impact of outlier point cloud on the scan matching, and low accuracy of position fusion. This paper preprocesses the sensor data and presents HT-Carto, an improved hybrid point-cloud filtering system, and a tightly coupled LiDAR/IMU framework based on Cartographer’s front-end. The inertial measurement unit (IMU) provides initial values for the point cloud, and the IMU pre-integration combines the scan-matched pose to construct the factors, which are added as constraints to the factor graph. The result is used to update the current pose and work as odometer residuals at the back-end. The optimization of the selected strategy during point cloud preprocessing, PassThrough, and RadiusOutlierRemoval are combined to ensure quality. An actual vehicle is used in complex indoor environment to verify the stability and robustness of HT-Carto. Compared to the Cartographer, Karto, Hector, and GMapping, HT-Carto demonstrates better localization and mapping, it can obtain a more precise trajectory.

The betatron radiation source features a micrometer-scale source size, a femtosecond-scale pulse duration, milliradian-level divergence angles and a broad spectrum exceeding tens of keV. It is conducive to the high-contrast imaging of minute structures and for investigating interdisciplinary ultrafast processes. In this study, we present a betatron X-ray source derived from a high-charge, high-energy electron beam through a laser wakefield accelerator driven by the 1 PW/0.1 Hz laser system at the Shanghai Superintense Ultrafast Laser Facility (SULF). The critical energy of the betatron X-ray source is 22 ± 5 keV. The maximum X-ray flux reaches up to 4 × 109 photons for each shot in the spectral range of 5–30 keV. Correspondingly, the experiment demonstrates a peak brightness of 1.0 × 1023 photons·s−1·mm−2·mrad−2·0.1%BW−1, comparable to those demonstrated by third-generation synchrotron light sources. In addition, the imaging capability of the betatron X-ray source is validated. This study lays the foundation for future imaging applications.

For binary plug nozzle, the plug cone is exposed to high-temperature mainstream flow, making it one of the nozzle’s high-temperature components. This paper uses the Realizable k-ε turbulence model and the reverse Monte Carlo method to numerically investigate the aerodynamic and infrared radiation characteristics of the plug nozzle. Various slot cooling configurations were adopted to study the nozzle’s infrared radiation in detail. Results indicate that compared to the baseline nozzle, the plug nozzle’s performance is slightly reduced due to the decrease in effective area of flow over the plug cone. Introducing slot cooling at the rear edge provides significant infrared suppression benefits at low detection angles and notably reduces infrared radiation discrepancy with baseline nozzle at high detection angles. The cooling air from slots causes the nozzle jet to exhibit a ‘thermal layered’ feature. With the same total coolant mass flow, the ‘leading edge + trailing edge’ cooling configuration can lower the area-averaged wall temperature of the plug cone by 5.5% – 12.3%. However, its infrared radiation intensity at each detection angle on the pitch detection plane is higher than that of the ‘trailing edge’ configuration. The significance of leading-edge cooling is focused more on thermal protection for the plug. Thus, it is essential to balance coolant mass flow distribution between infrared radiation suppression and thermal protection.

Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

The Central Asian Orogenic Belt is the world’s largest accretionary orogenic belt, associated with the closure of the Paleo-Asian Ocean (PAO). However, the final closure timing of the eastern PAO remains contentious. The Permian-Triassic sedimentary sequences in the Wangqing area along the Changchun-Yanji suture zone offer important clues into this final closure. New data on petrology, whole-rock geochemistry, zircon U-Pb geochronology and zircon Hf isotopes of sedimentary rocks from the Miaoling Formation and Kedao Group in Wangqing area provide new insights into the final closure of the eastern end of the PAO. The maximum deposition ages of the Miaoling Formation and Kedao Group have been constrained to the Late Permian (ca. 253 Ma) and early Middle Triassic (ca. 243 Ma), respectively. These sedimentary rocks exhibit similar geochemical characteristics, showing low textural and compositional maturities, implying short sediment transport, with all detrital zircons suggesting their origins from felsic igneous rocks. The εHf(t) values of the Miaoling Formation range from −6.09 to 12.43 and from −2.20 to 7.59 for the Kedao Group, implying these rocks originated from NE China. Considering our new data along with previously published data, we propose that a reduced remnant ocean remained along the Changchun-Yanji suture zone in the early Middle Triassic (ca. 243 Ma), suggesting the final closure of the eastern PAO likely occurred between the latest Middle Triassic and early Late Triassic.