Excavations at Aketala reveal traces of human activity at the oases of the western Tarim Basin, north-western China, by at least 2200 BC. The recovered artefacts indicate that, by 1800 BC, the Andronovo culture had reached this region, bringing agropastoralism and developing the earliest regional evidence of bronze manufacturing techniques.

In the field of parafoil airdrop path planning, the inherent complexity and time-sensitive nature of mission requirements necessitate rapid path generation through low-order mathematical models that approximate the system’s true dynamics. This study presents a novel sparse identification framework for constructing a parafoil path planning approximate model. Leveraging high-fidelity 9-degree-of-freedom (9 DOF) dynamic simulation data as training inputs, our method identifies simple nonlinear relationships between 3D positional coordinates (for spatial targeting) and yaw angle (for directional control), which are critical path planning parameters. Compared to conventional 4 DOF models, experimental validation using field airdrop data reveals that the proposed sparse model achieves enhanced predictive accuracy while maintaining computational efficiency. Quantitative analysis demonstrates reductions in root mean square error (RMSE) by approximately 12.96% (horizontal position), 54.44% (height) and 37.96% (yaw angle). The efficacy is further confirmed through successful fixed-point homing across diverse initial deployment scenarios, underscoring its potential for parafoil path planning.

Previous studies highlighted the health benefits of coffee and tea, but they only focused on the comparisons between different consumptions. Consequently, the association estimate lacked a clear interpretation, as the substitution of beverages and distribution of doses were not explicitly prescribed. We focused on the ‘relative association’ to ascertain the optimal consumption strategy (including total intake and optimal allocation strategy) for coffee, tea and plain water associated with decreased mortality. Self-reported coffee, tea and plain water intake were used from the UK Biobank. Within a compositional data analysis framework, a multivariate Cox model was used to assess the relative associations after adjusting for a range of potential confounders. The lower mortality risk was observed with at least approximately 7–8 drinks/d of total consumption. When the total intake > 4 drinks/d, substituting plain water with coffee or tea was linked to reduced mortality; nevertheless, the benefit was not seen for ≤ 4 drinks/d. Besides, a balanced consumption of coffee and tea (roughly a ratio of 2:3) associated with the lowest hazard ratios of 0·55 (95 % CI 0·47, 0·64) for all-cause mortality, 0·59 (95 % CI 0·48, 0·72) for cancer mortality, 0·69 (95 % CI 0·49, 0·99) for CVD mortality, 0·28 (95 % CI 0·15, 0·52) for respiratory disease mortality and 0·35 (95 % CI 0·15, 0·82) for digestive disease mortality than other combinations. These results highlight the importance of the rational combination of coffee, tea and plain water, with particular emphasis on ensuring adequate total intake, offering more comprehensive and explicit guidance for individuals.

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.

Eclipta [Eclipta prostrata (L.) L.] is an important tropical weed that has recently emerged as a problematic weed in dry direct-seeded rice (Oryza sativa L.) (DSR) fields in China. Understanding its seed germination biology and ecology is crucial for developing integrated weed management strategies in the DSR system. Laboratory experiments were conducted to investigate seed germination of E. prostrata seeds under varying environmental conditions. Germination was greatest under alternating temperature regimes of 25/15 to 40/30 C, whereas it wa-s significantly reduced at 20/10 C and completely inhibited at 15/5 C. Germination was also fully suppressed under continuous darkness, indicating strong light dependency. Eclipta prostrata seeds tolerated a broad range of pH values (4 to 10) with germination rates consistently greater than 95%. However, germination declined sharply under osmotic potentials, falling below 2% at −0.6 MPa, and being completely inhibited at −0.7 MPa. Seeds also showed moderate salt tolerance, with 50% inhibition at 150 mM NaCl and no germination at 300 mM NaCl. Exposure to radiant heat (>90 C for 5 min) prevented germination, suggesting residue burning may be an effective control measure. Seedling emergence was highest (100%) on the soil surface but declined steeply with increasing burial depth, with no emergence observed beyond 0.5 cm. Similarly, surface application of wheat (Triticum aestivum L.) straw residue (2 to 6 Mg ha−1) significantly reduced seedling emergence and biomass. These findings provide essential insights into E. prostrata germination ecology and offer practical implications for its integrated management in DSR systems.

Within the broad context of design research, joint attention within co-creation represents a critical component, linking cognitive actors through dynamic interactions. This study introduces a novel approach employing deep learning algorithms to objectively quantify joint attention, offering a significant advancement over traditional subjective methods. We developed an optimized deep learning algorithm, YOLO-TP, to identify participants’ engagement in design workshops accurately. Our research methodology involved video recording of design workshops and subsequent analysis using the YOLO-TP algorithm to track and measure joint attention instances. Key findings demonstrate that the algorithm effectively quantifies joint attention with high reliability and correlates well with known measures of intersubjectivity and co-creation effectiveness. This approach not only provides a more objective measure of joint attention but also allows for the real-time analysis of collaborative interactions. The implications of this study are profound, suggesting that the integration of automated human activity recognition in co-creation can significantly enhance the understanding and facilitation of collaborative design processes, potentially leading to more effective design outcomes.

The heating effect of electromagnetic waves in ion cyclotron range of frequencies (ICRFs) in magnetic confinement fusion device is different in different plasma conditions. In order to evaluate the ICRF heating effect in different plasma conditions, we conducted a series of experiments and corresponding TRANSP simulations on the EAST tokamak. Both simulation and experimental results show that the effect of ICRF heating is poor at low core electron density. The decrease in electron density changes the left-handed electric field near the resonant layer, resulting in a significant decrease in the power absorbed by the hydrogen fundamental resonance. However, quite a few experiments must be performed in plasma conditions with low electron density. It is necessary to study how to make ICRF heating best in low electron density plasma. Through a series of simulation scans of the parallel refractive index (n//) of the ICRF antenna, it is concluded that the change of the ICRF antenna n// will lead to the change of the left-handed electric field, which will change the fundamental absorption of ICRF power by the hydrogen minority ions. Fully considering the coupling of ion cyclotron wave at the tokamak boundary and the absorption in the plasma core, optimizing the ICRF antenna structure and selecting appropriate parameters such as parallel refractive index, minority ion concentration, resonance layer position, plasma current and core electron temperature can ensure better heating effect in the ICRF heating experiments in the future EAST upgrade. These results have important implications for the enhancement of the auxiliary heating effect of EAST and other tokamaks.

The incorporation of trace metals into land snail shells may record the ambient environmental conditions, yet this potential remains largely unexplored. In this study, we analyzed modern snail shells (Cathaica sp.) collected from 16 sites across the Chinese Loess Plateau to investigate their trace metal compositions. Our results show that both the Sr/Ca and Ba/Ca ratios exhibit minimal intra-shell variability and small inter-shell variability at individual sites. A significant positive correlation is observed between the shell Sr/Ca and Ba/Ca ratios across the plateau, with higher values being recorded in the northwestern sites where less monsoonal rainfall is received. We propose that shell Sr/Ca and Ba/Ca ratios, which record the composition of soil solution, may be controlled by the Rayleigh distillation in response to prior calcite precipitation. Higher rainfall amounts may lead to a lower degree of Rayleigh distillation and thus lower shell Sr/Ca and Ba/Ca ratios. This is supported by the distinct negative correlation between summer precipitation and shell Sr/Ca and Ba/Ca ratios, enabling us to reconstruct summer precipitation amounts using the Sr/Ca and Ba/Ca ratios of Cathaica sp. shells. The potential application of these novel proxies may also be promising for other terrestrial mollusks living in the loess deposits globally.

Emission line galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN–MLP that combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN–MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN–MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, and Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, and Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

Regenerative involution is crucial for renewing the mammary gland and maximizing milk production. However, the temporal profiles indicators of oxidative status during this phase are still unclear. In this study, Experiment 1 aimed to investigate the dynamic changes in indicators of oxidative status in plasma during regenerative involution. The dairy goats were dried off at 8 weeks (wk) before kidding (−8 wk, n = 14) or −12 wk (n = 6). The blood samples taken at −8, −7, −6, −5, −4, −3, −2, −1 wk, on the day for kidding (0 wk) and the first week after kidding (+1 wk, milk production 1.28 ± 0.31 kg per day). Experiment 2 aimed to investigate the dynamic changes in indicators of oxidative status in mammary cells. Seven selected goats were biopsied for tissue collection and cell isolation at −8, −4, −1, +1 wk (milk production 1.28 ± 0.31 kg per day), respectively. Plasma analysis in Experiment 1 showed an increase in reactive oxygen species (ROS) levels, peaking at −4 wk (P < 0.01). No significant differences were observed between the dry-off treatments (P = 0.36). The activity of superoxide dismutase (SOD) in plasma remained stable from −7 wk to the first week after kidding (+1 wk), while glutathione peroxidase (GSH-Px) activity peaked at −4 wk. An increased catalase activity was observed at +1 wk (P < 0.01), indicating its response to lactation. In Experiment 2, an increase in ROS levels in isolated mammary cells was observed at −4 wk, while SOD, GSH-Px, and malondialdehyde levels in tissue homogenates rose around kidding (P < 0.01). The dynamic change of the oxidative status suggests that targeted antioxidant strategies would be helpful for regenerative involution of mammary gland in ruminants.

This paper presents an investigation of the secondary saturation characteristics of a HfTe2 saturable absorber. Pulse energies of 5.85 and 7.4 mJ were demonstrated with a high-order Hermite–Gaussian (HG) laser and a vortex laser, respectively, using alexandrite as the gain medium. To the best of our knowledge, these are the highest pulse energies directly generated with HG and vortex lasers. To broaden the applications of high-energy pulsed HG and vortex lasers, wavelength tuning in the region of 40 nm was achieved using an etalon.

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.

Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems.

Schubert polynomials are polynomial representatives of Schubert classes in the cohomology of the complete flag variety and have a combinatorial formulation in terms of bumpless pipe dreams. Quantum double Schubert polynomials are polynomial representatives of Schubert classes in the torus-equivariant quantum cohomology of the complete flag variety, but no analogous combinatorial formulation had been discovered. We introduce a generalization of the bumpless pipe dreams called quantum bumpless pipe dreams, giving a novel combinatorial formula for quantum double Schubert polynomials as a sum of binomial weights of quantum bumpless pipe dreams. We give a bijective proof for this formula by showing that the sum of binomial weights satisfies a defining transition equation.