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Uncrewed Wave Gliders (UWGs) are capable of harnessing energy from ocean waves and photovoltaic sources to enable long-duration voyages. Since the float’s yaw motion relies on the rudder of the submersible for control, this introduces many unknown nonlinear and time-delay factors into the control system. Moreover, the susceptibility of UWGs to waves influences results in limited maneuverability and necessitates energy efficiency considerations, complicating the task of following a designated path to a specific point for observations. To address these challenges, this paper first introduces a differential model-free adaptive control (DMFAC) approach for managing the float’s heading control, along with a proof of its stability. Furthermore, an improved attractive force line-of-sight (IAFLOS) guidance strategy for overall mooring control is proposed. The integration of the DMFAC heading controller and the IAFLOS strategy forms a comprehensive mooring control system, which is validated through simulation studies in typical maritime conditions. This control system ensures that, while considering energy conservation strategies, the distance between the wave glider and the mooring point remains within 20 m during mooring.

Previous studies have primarily focused on the extraction of highland barley crops using single phenological images, which ignored the selection of the optimal phenological period for classification. Utilizing the multiple phenological images from Sentinel-2 to construct 25 features, including spectral, red edge, vegetation, and texture features, the recursive feature elimination algorithm and the random forest algorithm (RF) were employed to optimize feature datasets for different phenological stages, which were then used for the identification and classification of high-land barley by RF. The main results were as follows: (1) Information extraction based on feature optimization combinations yielded good overall classification accuracy, with classification accuracies for highland barley being 92.56% (jointing stage), 90.90% (heading stage), 90.74% (flowering stage), 91.55% (milk ripening stage), and 90.51% (maturity stage), respectively. (2) NDVIre1 had the highest importance score (0.1792) in the feature selection combination, indicating that the red edge index contributed significantly to crop information extraction and classification. (3) The five feature variables—GLCM_Mean, RVI, homogeneity, MAX, and GLCM_Correlation—showed stability and universality in the extraction of highland barley. These results demonstrated that the images that derived from the jointing and milk ripening phenological stages had the best applicability for highland barley extraction, and the optimized feature datasets that composed of NDVIre1 were conductive to detect and monitor of highland barley crops in the mountainous regions of northwest China.

More efficient use of soil resources, such as nitrogen (N) and phosphorus (P), can improve plant community resistance and resilience against drought in arid and semi-arid lands. Intercropping of legume and non-legumes can be an effective practice for enhancing P mineralization uptake, and plant nutrient status. However, it remains unclear how intercropping systems using desert plant species impact soil-plant P fractions and how they affect N and water uptake capacity. Alhagi sparsifolia (a legume) and Karelinia caspia (a non-legume) are dominant plant species in the Taklamakan Desert in Xinjiang Province, China. However, there is a lack of knowledge of whether these species, when intercropped, can trigger synergistic processes and mechanisms that drive more efficient use of soil resources. Thus, in a field experiment over two years, we investigated the impact of monoculture and intercropping of these plant species on soil-plant P fractions and soil-plant nutrients. Both plant species’ foliar nutrient (N, P, and K) concentrations were higher under monoculture than intercropping (except K in K. caspia). Nucleic acid P was higher in the monoculture plots of A. sparsifolia, consistent with higher soil labile P, while metabolic P was higher in monoculture K. caspia, associated with higher soil moderately labile Pi. However, both species had a higher residual P percentage in the intercropping system. Soils from monoculture and intercropped plots contained similar microbial biomass carbon (MBC), but lower microbial biomass N:microbial biomass phosphorus (MBN:MBP) ratio associated with reduced N-acetylglucosaminidase (NAG) activity in the intercropped soils. This, together with the high MBC:MBN ratio in intercropping and the lack of apparent general effects of intercropping on MBC:MBP, strongly suggest that intercropping improved microbe N- but not P-use efficiency. Interestingly, while EC and SWC were higher in the soil of the K. caspia monoculture plots, EC was significantly lower in the intercropped plots. Plants obtained better foliar nutrition and soil P mineralization in monocultures than in intercropping systems. The possible positive implications of intercropping for reducing soil salinization and improving soil water uptake and microbial N-use efficiency could have advantages in the long term and its utilization should be explored further in future studies.

The use of phosphorus (P) to alleviate soil nutrient deficiency alters resources in plant and microbial communities, but it remains unknown how mixed and monospecific planting of forest tree species shape soil microbial structure and functions in response to drought and its interplay with phosphorus addition. We investigated the microbial structure and chemical properties of forest soils planted with P. zhennan monoculture, A. cremastogyne monoculture, and their mixed cultures. The three planting systems were exposed to drought (30–35% water reduction) and the combination of drought with P. A well-watered treatment (80–85% water addition) of similar combinations was used as the control. Planting systems shaped the effects of drought on the soil microbial properties leading to an increase in nitrate nitrogen, urease activity, and microbial biomass carbon in the monocultures, but decrease in mixed cultures. In the monoculture of P. zhennan, addition of P to drought-treated soil increased enzyme activities, the concentration of dissolved organic nitrogen, and carbon, leading to increase in the total bacteria, G+ bacteria, and arbuscular mycorrhizal fungi. Except in the drought with P addition treatment, the impact of admixing on total phospholipid fatty acids (PLFAs), bacterial PLFA, and fungi PLFA was synergistic in all treatments. Our findings indicated that in monoculture of P. zhennan and its mixed planting with A. cremastogyne, greater biological activities could be established under drought conditions with the addition of P.

The wave-driven catamaran is a small surface vehicle driven by ocean waves. It consists of a hull and hydrofoils, and has a multi-body dynamic structure. The process of moving from static state to autonomous navigation driven by ocean waves is called “self-propulsion”, and reflects the ability of the wave-driven catamaran to absorb oceanic wave energy. Considering the importance of the design of the wave-driven catamaran, its self-propulsion performance should be comprehensively analysed. However, the wave-driven catamaran’s multi-body dynamic structure, unpredictable dynamic and kinematic responses driven by waves make it difficult to analyse its self-propulsion performance. In this paper, firstly, a multi-body dynamic model is established for wave-driven catamaran. Secondly, a two-phase numerical flow field containing water and air is established. Thirdly, a numerical simulation method for the self-propulsion process of the wave-driven catamaran is proposed by combining the multi-body dynamic model with a numerical flow field. Through numerical simulation, the hydrodynamic response, including the thrust of the hydrofoils, the resistance of the hull and the sailing velocity of the wave-driven catamaran are identified and comprehensively analysed. Lastly, the accuracy of the numerical simulation results is verified through a self-propulsion test in a towing tank. In contrast with previous research, this method combines multi-body dynamics with computational fluid dynamics (CFD) to avoid errors caused by artificially setting the motion mode of the catamaran, and calculates the real velocity of the catamaran.

Climate change is an important driver of biodiversity loss and extinction of endemic montane species. In China, three endemic Juniperus spp. (Juniperuspingii var. pingii, J.tibetica, and J.komarovii) are threatened and subjected to the risk of extinction. This study aimed to predict the potential distribution of these three Juniperus species under climate change and dispersal scenarios, to identify critical drivers explaining their potential distributions, to assess the extinction risk by estimating the loss percentage in their area of occupancy (AOO), and to identify priority areas for their conservation in China. We used ensemble modeling to evaluate the impact of climate change and project AOO. Our results revealed that the projected AOOs followed a similar trend in the three Juniperus species, which predicted an entire loss of their suitable habitats under both climate and dispersal scenarios. Temperature annual range and isothermality were the most critical key variables explaining the potential distribution of these three Juniperus species; they contribute by 16–56.1% and 20.4–38.3%, respectively. Accounting for the use of different thresholds provides a balanced approach for species distribution models’ applications in conservation assessment when the goal is to assess potential climatic suitability in new geographical areas. Therefore, south Sichuan and north Yunnan could be considered important priority conservation areas for in situ conservation and search for unknown populations of these three Juniperus species.

Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms’ richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern.

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

This study aims to investigate the critical state of the saddle-shaped unstable region of the axial-flow pump and propose a suitable criterion for identifying this state. The bifurcation SST k–ω model considered the rotation effect is used in the present work and verified in the numerical calculation of a water jet pump. Then, it is used to simulate the critical state of the axial-flow pump. Results show that the leading-edge separation vortex generates at 0.6Qd, while the head declines only at 0.55Qd. Therefore, using the inflection point of the head-flow curve as the critical state criterion is unsuitable. In addition, the fixed monitoring point is unsuitable for identifying the critical state due to the insensitivity to the amplitude, main frequency, and periodicity changes at the critical state. Finally, to identify the critical state, it is essential to arrange a monitoring point at the leading edge of the blade suction near the shroud, which should rotate with the impeller. The critical state criterion is that the main frequency position of the pressure fluctuation signal is offset at the monitoring point, and the amplitude is increased by 10 times.

Abstract: This study aims to research the prediction performance of a bifurcated adaptive DDES (BADDES) model in different turbulent flows with rotation and curvature [...]