Qinghai Normal University

In multi-robot systems, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>-winners-take-all (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>-WTA) problem serves as a competitive selection mechanism to identify the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula> most suitable robots from a group of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math> </inline-formula> (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{n} &gt; \boldsymbol{k}$</tex-math> </inline-formula>) ones for a given task. Existing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>-WTA networks either fail to eliminate lagging errors or involve a complex modeling process due to the introduction of additional Lagrangian variables to deal with bounded constraints. To address these challenges, a primal-dual-based differential <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>-WTA (PDD-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>WTA) network is proposed in this article. This network incorporates time-derivative information to mitigate lagging errors and leverages linear variational inequalities (LVIs) to streamline the network structure. Theoretical analyses and comparative simulations validate the effectiveness and superiority of the PDD-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>WTA network. Furthermore, a multi-robot competitive coordination (MCC) model, which integrates the PDD-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol{k}$</tex-math> </inline-formula>WTA network, a consensus estimator, and a robot control law, is developed to achieve the tracking of a mobile target.

Carbon dynamics in alpine inland basins are jointly regulated by hydrothermal conditions, yet the elevational threshold at which hydrothermal drivers shift remains unquantified in the Qinghai Lake Basin. Using multi-source remote sensing data (2003-2023) with piecewise linear regression and structural equation modeling, we examined spatiotemporal patterns and drivers of gross primary productivity (GPP), net primary productivity (NPP), and carbon use efficiency (CUE) during the growing season. The results are as follows: (1) GPP and NPP were low in the northwest and high in the southeast, with multi-year means of 307.9 and 260.23 g C m⁻2, respectively, both increasing significantly; (2) a clear ecological threshold at ~3526 m was detected (GPP, 3524.80 ± 7.11 m; NPP, 3527.38 ± 7.21 m), suggesting a shift in the relative importance of hydrothermal drivers across the identified elevation threshold, with productivity transitioning from primarily moisture-constrained to temperature-sensitive; (3) CUE regulation was decoupled from carbon fixation: normalized difference vegetation index (NDVI) had a negative effect (β = -0.28) above 3526 m, suggesting increased vegetation may reduce CUE via enhanced respiration; (4) zoning based on this threshold showed that high and medium carbon sequestration potential areas were almost entirely (> 99%) above the threshold, whereas 99.6% of low-potential areas occurred below it, supporting differentiated basin management. This study quantifies a pivotal elevational threshold, reveals the decoupling between carbon fixation and utilization processes in response to shifts in hydrothermal drivers, and provides a practical framework for carbon cycle prediction and management in alpine inland basins.

Introduction Understanding the spatiotemporal dynamics and drivers of habitat quality in the Huangshui River Basin is crucial for ecological conservation and restoration efforts in the cold and arid regions of the Qinghai-Tibet Plateau. Methods Utilizing land-use change data spanning from 2000 to 2020, we integrated the PLUS–InVEST model with the Geodetector method to quantify the trajectories of habitat quality and identify the key driving factors under four distinct scenarios: The Natural Development Scenario (NDS), Economic Development Scenario (EDS), Cultivated land Protection Scenario (CPS), and Ecological Protection Scenario (EPS). Results Our findings reveal the following: (1) Grassland remained the predominant land-use type from 2000 to 2020, with an average habitat quality index of 0.65. While overall habitat quality remained relatively high, a slight decline was observed, exhibiting a spatial gradient with lower values in the central basin and higher values toward the periphery. (2) By 2035, both NDS and EDS are projected to result in significant habitat fragmentation, primarily driven by the expansion of construction land, while CPS and EPS are expected to enhance habitat quality through the preservation of cultivated and ecological lands. (3) Land-use type emerged as the most influential factor driving spatial heterogeneity in habitat quality, with the interaction between land-use and temperature demonstrating the highest explanatory power. Discussion These results provide robust projections of future land-use patterns and their impacts on habitat quality in the Huangshui River Basin, offering a scientific foundation for sustainable basin management and ecological protection.

To investigate the differences in microclimatic and eco-environmental effects of centralized photovoltaic (PV) power stations under diverse climatic backgrounds, high-altitude desert PV stations in Qinghai Province representing hyper-arid, arid, and semi-arid climates were selected. Micro-meteorology factors and vegetation evolution characteristics inside and outside the PV arrays were analyzed by employing paired inside-outside observations and long-time-series NDVI retrieval. It is indicated that the micro-meteorology and eco-environmental effects exhibit differential responses along the aridity gradient, with water availability identified as the core regulatory factor. A significant “heat island effect” with no vegetation recovery was observed in the hyper-arid zone; nocturnal warming and slight humidification with a trend of vegetation recovery were exhibited in the arid zone; while positive ecosystem feedback was demonstrated in the semi-arid zone, where the shading and wind-blocking effects of PV modules facilitated soil moisture conservation, leading to rapid vegetation recovery that offset physical warming through transpiration cooling. The evolutionary pattern of PV ecological effects transitioning from physical disturbance to ecological regulation is elucidated, and the feasibility of synergy between PV development and ecological restoration under suitable water conditions is confirmed.

Raising people’s disaster awareness is a prerequisite for enhancing their disaster-coping capacities. Although “disaster awareness” has long been a very common term in academic circles, disaster management communities, and even society at large, there still remains a notable lack of sufficient discussion regarding what this awareness of the general public specifically refers to, and how it can be concretely measured across different types of hazards and disasters, varied population groups, and diverse physical and socio-economic contexts. This gap is particularly acute in China. First, after reviewing the existing international understanding of the term of Public Disaster Awareness (PDA), this study newly defined this concept by drawing upon concepts of environmental awareness and environmental literacy in the field of environmental studies. Next, based on this new PDA definition, UNISDR’s terminology on public awareness of disaster, the authors’ extensive relevant research experience, and other valuable factors, this study proposed an education objective-oriented dimensional construct for PDA concept by fully incorporating UNESCO-UNEP’s classic understanding of the categories of environmental education objectives. Specifically, the proposed PDA dimensional construct comprises five specific dimensions: 1) disaster sensitivity and risk perception, 2) acquisition of disaster knowledge, 3) mastery of disaster-coping measures and skills, 4) attitudes and values, and 5) participation in disaster reduction. Subsequently, utilizing this novel PDA dimensional construct, the study developed several sets of disaster awareness measurement questionnaires that focus on people’s earthquake disasters awareness (EDA) to target different public groups in China: primary and high school students, their teachers, students’ parents, and the general public. Using these questionnaires and stratified sampling processes, the current EDA state and associated disaster education issues across several regions in China were surveyed over the past few years. Data analysis of high school students in Weinan, Shaanxi province revealed the following key findings. (i) Students’ overall EDA level was largely acceptable, but their performance in disaster sensitivity and local risk perception aspects was apparently unsatisfactory. (ii) Girls performed better than boys in attitudes, values, and participation, while boys were better in the knowledge dimension. (iii) As grade levels increased, students’ attitude and values, and participation became increasingly passive and indifferent. (iv) Extracurricular activities contributed much more to students’ EDA development than curricular education. (v) The EDA-building effects of different extracurricular activities varied; specifically, the overall effects of several activities were more significant than others, and each activity had its own distinct impact patterns across the five disaster awareness dimensions. Finally, based on these findings, education objective-specific policy recommendations for improving disaster education in local primary and high schools were provided. The novel construction of the PDA concept holds significant theoretical value for exploring both disaster awareness and disaster education. Furthermore, the central logic and specific strategies (e.g., approaches for PDA indicator development) that derived from measuring the seismic disaster awareness of China’s population have promising transferable practical implications for addressing similar issues regarding both other types of disasters and the disasters in other countries.

Large-scale agricultural remote sensing monitoring is challenged by pronounced spatial heterogeneity arising from fragmented terrain, complex climatic backgrounds, and diverse cropping structures. However, existing agricultural zoning schemes generally lack an integrated consideration of remote sensing imaging mechanisms and key variable conditions such as atmospheric interference and crop phenology, limiting their direct utility in guiding region-specific sensor selection and classification algorithm calibration. To address this limitation, this study integrates multi-source earth observation data and agricultural statistical information to construct an Agricultural Remote-sensing Classification Difficulty Index (ARCDI) from multiple dimensions, including image availability, cropping structure, cropland fragmentation, and topographic environment. On this basis, a graph theory-based spatially constrained Skater clustering algorithm is introduced to establish a two-tier “cropland–major cereal crops” zoning framework oriented toward remote sensing applications. The results indicate that the proposed framework delineates five distinct first-tier cropland classification difficulty zones across China. This zoning scheme effectively quantifies the regional heterogeneities in monitoring challenges. Building upon this first-tier zoning, the framework is further refined into 50 second-tier major cereal crop classification difficulty zones, including 13 winter wheat zones, 21 maize zones, and 16 rice zones. Statistical tests and spatial analyses demonstrate that the proposed zoning scheme significantly outperforms conventional clustering approaches in balancing within-zone homogeneity and spatial continuity. This advantage is quantitatively reflected by consistently lower residual spatial autocorrelation (residual Moran’s I ≈ 0.10–0.11) and an approximately 20% reduction in within-zone variance compared with other spatially constrained methods. Extensive field-sample validation provides preliminary evidence of an inverse relationship between crop-type classification difficulty and accuracy. These results confirm the framework’s reliability in identifying regional difficulty and its decision-support value for selecting remote sensing strategies. Overall, this study systematically elucidates the spatial differentiation patterns of remote sensing classification difficulty for cropland and major cereal crops across China. The proposed framework provides robust scientific support for data selection, algorithm optimization, and differentiated strategy formulation in national-scale agricultural monitoring, thereby facilitating the operationalization of regional agricultural remote sensing applications.