Search Results (5,392)

Heading is a crucial navigation parameter for high-precision flight guidance. Since the heading changes rapidly while unmanned aerial vehicles (UAVs) track great ellipse routes in polar regions, it is necessary to implement special guidance algorithms. This article presents a high-precision polar flight guidance algorithm for fixed-wing UAVs along great ellipse routes based on heading prediction. Specifically, a globally applicable definition of polar grid frame was proposed. On this basis, a novel flight guidance algorithm based on heading prediction was developed. Therein, the calculation method for grid azimuth on great ellipse routes based on the WGS-84 ellipse model was derived in detail, realizing accurate heading estimation and prediction. Subsequently, the predicted grid heading was utilized to tackle the difficulty of heading changes, enabling the UAV to predict and adjust its heading in advance. Moreover, an adaptive predicted lead-time adjustment strategy based on fuzzy decision-making was introduced to improve the prediction accuracy under challenging situations, and an enhanced particle swarm optimization algorithm was employed to determine the hyperparameters in fuzzy rules. To verify the effectiveness of the proposed algorithm, extensive simulations were operated using the Monte Carlo method, and the proposed algorithm demonstrated 3–4 times higher guidance accuracy compared to conventional algorithms. Full article

Autonomous underwater vehicles (AUVs) play a critical role in underwater remote sensing and monitoring applications. This paper addresses the problem of navigating multiple AUVs to perform sweep video sensing of unknown underwater regions over uneven seafloors, where visibility is limited by the conical field of view (FoV) of the onboard cameras and by occlusions caused by terrain. Coverage is formulated as a feasibility objective of achieving a prescribed target fraction while respecting vehicle kinematics, actuation limits, terrain clearance, and inter-vehicle spacing constraints. We propose an online, occlusion-aware trajectory planning algorithm that integrates frontier-based goal selection, safe viewing depth estimation with clearance constraints, and model predictive control (MPC) for trajectory tracking. The algorithm adaptively guides a team of AUVs to preserve line of sight (LoS) visibility, maintain safe separation, and ensure sufficient clearance while progressively expanding coverage. The approach is validated through MATLAB simulations on randomly generated 2.5D seafloor surfaces with varying elevation characteristics. Benchmarking against classical lawnmower baselines demonstrates the effectiveness of the proposed method in achieving occlusion-aware coverage in scenarios where fixed-pattern strategies are insufficient. Full article

This study examines the relationship between energy efficiency in agricultural production and its determinants, considering technological, economic, political, and social factors. The aim was to determine the impact of the CAP on the energy efficiency of agricultural production, as well as technological, market, and social changes. The impact of time effects was also taken into account. The study focuses on the four Visegrad Group countries over the 2004–2023 period. Both fixed-effects and dynamic panel models were employed to capture structural changes over time. The significance of agriculture, as a result of structural transformations, is relatively small and hovers around 3% in these countries. The CAP was found to have a significant impact on the energy efficiency of agricultural production. However, it was not the amount of support but rather its structure that played a crucial role, particularly environmental support (0.04). The inertia effect was also of fundamental importance (0.41—elasticity in the inertia model). The total value of transfers, especially in the long term, proved to be a discouraging factor for this process. Market conditions, including energy prices (0.456), structural changes in farms (0.016), and labor input (−0.04), were also significant factors. However, it was not so much the size of support but rather the structure of support that was crucial. The total value of transfers, especially in the long term, was a demotivator for this process. Market conditions, including energy prices, structural changes on farms, and labor inputs, were also important factors. A key recommendation for agricultural financial support policy is to focus support more on environmental and low-emission issues, which are linked to improving the energy efficiency of production while maintaining its growth. Transfers related to the growing importance of renewable energy sources and support for rural development, which do not yield beneficial effects in the considered scope, require increased conditionality. Full article

This paper investigates how digital finance affects corporate ESG performance through the following mediation paths. Based on agency theory and a resource-based view, we hypothesize that digital finance benefits ESG performance not only directly but also indirectly through enhancing TFP and analyst coverage. We test our hypotheses using 22,576 firm-year observations of Chinese listed firms from 2011 to 2023 by employing a fixed-effects mediation model. The empirical results support our hypotheses. Digital finance improves ESG performance directly, and part of its effect goes through higher TFP and better analyst monitoring. The results show that digital finance plays dual roles in improving efficiency and market monitoring, which is beneficial to corporate sustainability. By identifying the above two mediation paths, this paper enriches the theoretical understanding of the relationship between financial digitalization and sustainability and provides practical implications for policymakers and managers to improve ESG performance. Full article

High-quality corporate innovation serves as a critical driver for achieving corporate sustainable development. This study bridges the gap between macroeconomic fiscal sustainability and microeconomic innovation quality. Specifically, this paper investigates the influence of local fiscal sustainability on high-quality corporate innovation, examining the underlying mechanisms and heterogeneous effects. Methodologically, data were collected using Python-based retrieval and web-scraping techniques. A multi-dimensional index of local fiscal sustainability was constructed, comprising five key dimensions to quantitatively map provincial fiscal sustainability across China. Corporate innovation quality was measured using patent citation metrics. Employing panel data from A-share listed companies over the 2015–2023 period, we implemented a two-way fixed-effects model for rigorous empirical econometric analysis. The findings indicate a significant positive relationship between local fiscal sustainability and high-quality corporate innovation. This result remains robust after a battery of robustness tests, including the use of instrumental variable (IV) methods. Mechanism analysis reveals that the resource compensation effect is the primary channel. Furthermore, our analysis identifies heterogeneity across varying innovation environments, economic regions, and industry characteristics. The positive influence is particularly pronounced in provinces with stronger intellectual property protection, firms located in the eastern regions, and High-Tech Enterprises. Collectively, the conclusions drawn from this research offer valuable policy implications for strengthening local fiscal sustainability and enhancing high-quality corporate innovation. Full article

Addressing climate change, pursuing green development, and achieving high-quality development are rapidly coalescing into a global strategic consensus. Against this backdrop, this paper empirically examines the impact of green investor entry on the high-quality development of manufacturing enterprises. Using a sample of A-share listed manufacturing companies from 2015 to 2023, it employs fixed-effects and mediation models. The findings reveal: (1) Green investor entry significantly promotes high-quality development in manufacturing enterprises, a conclusion that holds after endogeneity and robustness tests. (2) Mechanism effects indicate that green investors empower manufacturing enterprises to achieve high-quality development through the integration of digital and physical technologies. (3) Heterogeneity tests indicate that in eastern regions and non-heavily polluting industries, the entry of green investors exerts a more pronounced promotional effect on the high-quality development of manufacturing enterprises. (4) Green investor entry significantly promotes high-quality development of manufacturing enterprises under the negative moderation of financing constraints. These findings confirm the catalytic role of green investor entry in advancing high-quality development within manufacturing enterprises, clarify the mechanism of digital–physical integration linking the two, and provide empirical evidence and policy insights to support strategic decisions promoting high-quality development through green investor entry in China’s manufacturing sector. Full article

Background: Breast cancer is the most common cancer diagnosis and the second leading cause of cancer-related death in women. Breast cancer is a major health burden worldwide. Advances in breast cancer detection and treatment have contributed to improving the rate of survival, although mortality rates remain significantly high. Despite all these advances, more efficient diagnostic methods and effective treatments are necessary. Colchicine is a natural alkaloid with strong antimitotic activity, but its potential effects on extracellular matrix components in cancer remain poorly understood. Objective: This study aimed to investigate the influence of colchicine on glycosaminoglycan (GAG) concentrations and cell viability in MCF-7 breast cancer cells cultured in a three-dimensional (3D) hollow fiber bioreactor system. Methods: Magnetic resonance imaging (MRI) was applied as a non-invasive technique to quantify GAG levels through fixed charge density (FCD) and T₁ relaxation mapping. MCF-7 HER-2-overexpressing and HER-2-negative cells were treated with 1000 nM colchicine for 72 h, and cell viability was assessed in parallel with GAG measurements. Results: Colchicine significantly reduced cell viability and altered GAG concentrations. HER-2-overexpressing MCF-7 cells exhibited higher baseline GAG levels than HER-2-negative controls, and colchicine decreased the GAG content in both lines. Conclusions: Colchicine reduces viability and modifies GAG concentrations in 3D cultures of MCF-7 cells. The use of MRI provides a reproducible, non-destructive tool for monitoring extracellular matrix changes, offering a novel methodological approach for studying drug effects in physiologically relevant cancer models. Full article

This paper addresses the issue of existing research that fails adequately capture the spatiotemporal nonstationarity caused by the building of occlusion and flight dynamics in air-to-ground channels from unmanned aerial vehicles (UAVs) in urban scenarios. This study focuses on the angular-altitude correlations of three key metrics: path loss (PL), shadow fading, and the Ricean K-factor. A dynamic path-loss model incorporating the look-down angle is proposed, an exponential decay model for the shadow-fading standard deviation is constructed, and a model for the angle-dependent variation of the Ricean K-factor is established based on line-of-sight probability. Simulations were conducted in two urban-geometry scenarios using WinProp to evaluate the combined effects of flight altitude and elevation angle. The results indicate that path loss decreases and subsequently stabilizes with increasing elevation angle, the shadow-fading standard deviation decreases significantly, and the Ricean K-factor increases with angle and saturates at high angles, in agreement with theoretical predictions. These models are more adaptable to UAV mobility scenarios than traditional fixed exponential models and provide a useful basis for UAV link planning and system optimization in urban environments. Full article

This paper proposes a household energy management system for all-electric households, focusing on the interplay between cost savings and occupant comfort through an implicit demand response programme. A sequential multi-objective optimisation model is developed based on the lexicographic approach, allowing for the effective prioritisation of objectives. The model optimally schedules a diverse range of electricity demands using real-world data from a Norwegian pilot household to evaluate its unique flexibility potential, while remaining adaptable for other regions. This includes integrating thermal and non-thermal demands with electric mobility via vehicle-to-home enabled electric vehicle charger. This approach achieves significant cost savings on energy bills and enhances user comfort across aggregated comfort indicators. Multiple scenarios are designed to evaluate the performance of the proposed demand response under diverse pricing mechanisms. Results indicate that transitioning from variable pricing to fixed pricing can lead to lower average electricity costs and higher average user comfort. The analysis reveals that prioritising occupant comfort can substantially increase electricity demand, resulting in a nearly fourfold rise in average annual expenses, while also leading to a decrease in self-consumption and self-sufficiency. Additionally, the study illustrates how grid tariff adjustments can benefit households and support the development of local renewable energy. Full article

As a systematic project, corporate green innovation involves technological, organizational, and environmental dimensions. Therefore, its effective functioning is contingent on guidance from internal leadership. STEM represents an integration of science, technology, engineering, and mathematics education. A STEM CEO is a chief executive officer holding a degree in science, engineering, agriculture, or medicine. However, research on the impact of STEM CEOs on green innovation is limited. Using data from Chinese listed manufacturing firms from 2010 to 2023, panel fixed effects models reveal that STEM CEOs positively influence corporate green innovation. Further analysis indicates that alleviating financing constraints, fostering external collaboration, increasing R&D investment, and improving the efficiency of innovation resource allocation are key pathways through which STEM CEOs enhance green innovation output. Furthermore, this impact is positively moderated by the level of green finance development and the intensity of market competition. Finally, heterogeneity tests demonstrate that these positive effects are more pronounced for firms with high public environmental concern, in non-heavily polluting industries, with strong ESG performance, and in highly competitive industries. These findings underscore the role of STEM leaders in enhancing the output of green innovation systems, offering actionable insights into the interaction between STEM CEOs and the external environment. Full article

Environmental protection is an essential path to achieving high-quality economic development, and green tax policies are an effective means of achieving environmental protection. This study categorizes green tax policies into environmental protection-oriented green tax policies, resource-oriented green tax policies, and guidance-oriented green tax policies based on the nature of the tax. The fixed-effect model, the system GMM model and the continuous DID model are used to explore the causal relationship between the overall green tax policy, the classified green tax policy and the use of natural resources. The spatial Durbin model is used to explore the spatial spillover effect of the green tax policy and the regional heterogeneity in the east, central, west and northeast of China. Finally, the role of trade openness in the relationship between the green tax policy and natural resource use is explored. The research results show that (1) the green tax policy has a positive effect on natural resource use, but the green tax policy in the previous period has no promoting effect, and the natural resource use in the previous period has a positive impact on the current period. Among them, there is no causal relationship between the resource-occupying green tax and natural resource use. (2) All three types of green tax policies studied in this paper have spatial spillover effects, but the spillover effects of the three types of green tax policies are relatively small in the eastern region. The spillover effects of the three types of green tax policies in the central region are significantly negative. In the western region, only the guiding green tax policy has a spillover effect. In the northeastern region, the environmental protection green tax policy and the resource-based green tax policy are significantly negative, while the guiding green tax spillover effect is significantly positive. (3) In the mechanism test, the guiding green tax policy has an impact on natural resource utilization through trade openness, while the environmental protection green tax policy and the resource-based green tax policy cannot affect natural resource utilization through the level of trade openness. Finally, based on the research conclusions, policy recommendations are proposed from the perspectives of policy timeliness, tax structure adjustment, and trade network optimization to maximize economic benefits. Full article

This paper examines how marketization influences the spatial effects of household consumption upgrading in China, by analyzing provincial panel data from China between 2010 and 2022. The study employs a two-way fixed-effects Spatial Durbin Model to capture both the direct effects of marketization within a region and the spillover effects transmitted to neighboring regions. This model incorporates spatial dependence in both dependent and independent variables, providing a comprehensive assessment of spatial interactions. The results reveal that marketization and consumption upgrading both have the spatial pattern characteristics of significant spatial difference and agglomeration features. Marketization considerably encourages the upgrading of local people’s consumption and has positive spillover effects on the consumption upgrading levels of nearby regions. Mechanism analysis shows that market competition and enterprise innovation play key roles in this process. Heterogeneity analysis shows in eastern regions, areas with high industrial upgrading levels, high financial agglomeration levels, and high house prices, and the promotion effect of marketization on household consumption upgrading is more pronounced. These findings suggest that promoting differentiated regional marketization reforms, amplifying the spillover effects of marketization, reinforcing the dual engine of competition and innovation, and strengthening industrial upgrading and financial agglomeration are key to promote Chinese household consumption upgrading. Full article

Hierarchical Reinforcement Learning (HRL) is effective for long-horizon and sparse-reward tasks by decomposing complex decision processes, but its real-world application remains limited due to instability between levels, inefficient subgoal scheduling, delayed responses, and poor interpretability. To address these challenges, we propose Timed and Bionic Circuit Hierarchical Reinforcement Learning (TBC-HRL), a biologically inspired framework that integrates two mechanisms. First, a timed subgoal scheduling strategy assigns a fixed execution duration $\tau$ to each subgoal, mimicking rhythmic action patterns in animal behavior to improve inter-level coordination and maintain goal consistency. Second, a Neuro-Dynamic Bionic Circuit Network (NDBCNet), inspired by the neural circuitry of C. elegans, replaces conventional fully connected networks in the low-level controller. Featuring sparse connectivity, continuous-time dynamics, and adaptive responses, NDBCNet models temporal dependencies more effectively while offering improved interpretability and reduced computational overhead, making it suitable for resource-constrained platforms. Experiments across six dynamic and complex simulated tasks show that TBC-HRL consistently improves policy stability, action precision, and adaptability compared with traditional HRL, demonstrating the practical value and future potential of biologically inspired structures in intelligent control systems. Full article

Post-activation performance enhancement (PAPE) is an acute performance increase in voluntary exercises induced by a conditioning activity. Due to the scarcity of evidence about the effectiveness of distinct protocols, the aim of this study was to compare the effects of two different flywheel PAPE protocols (half-squat and lunge exercises) on vertical and horizontal jump performance, as well as change-of-direction ability in female amateur footballers (n = 21). Each protocol consisted of 3 sets of 6 repetitions for the half-squat protocol or 10 repetitions for the lunge protocol, with two minutes of passive rest, performed with a conical pulley. Both protocols were followed by rests of two, eight, and twelve minutes for repeated countermovement jump (CMJ), triple hop, and change-of-direction test (modified T-505) testing. The fixed-effect model 2-ways-repeated measures ANOVA showed that there was no significant interaction between time and exercises performed (p > 0.05). There was no significant relationship between exercise specificity and performance in sport-specific tasks. Our results suggest that, within this population, neither flywheel protocol provided measurable PAPE benefits across varied time windows. The findings underscore the importance of strength levels in achieving PAPE benefits and question the specificity of PAPE protocols to targeted sport performance outcomes. Full article

Compaction quality control in earthworks and pavements still relies mainly on density-based acceptance referenced to laboratory Proctor tests, which are costly, time-consuming, and spatially sparse. Lightweight dynamic cone penetrometer (LDCP) provides rapid indices, such as $q_{d0}$ and $q_{d1}$, yet acceptance thresholds commonly depend on ad hoc, site-specific calibrations. This study develops and validates a supervised machine learning framework that estimates $q_{d0}$, $q_{d1}$, and $Z_c$ directly from readily available soil descriptors (gradation, plasticity/activity, moisture/state variables, and GTR class) using a multi-campaign dataset of $n=360$ observations. While the framework does not remove the need for the standard soil characterization performed during design (e.g., W, ${\gamma }_{d,\mathrm{field}}$, and $R{C}_{\mathrm{SPC}}$), it reduces reliance on additional LDCP calibration campaigns to obtain device-specific reference curves. Models compared under a unified pipeline include regularized linear baselines, support vector regression, Random Forest, XGBoost, and a compact multilayer perceptron (MLP). The evaluation used a fixed 80/20 train–test split with 5-fold cross-validation on the training set and multiple error metrics ($R^2$, RMSE, MAE, and MAPE). Interpretability combined SHAP with permutation importance, 1D partial dependence (PDP), and accumulated local effects (ALE); calibration diagnostics and split-conformal prediction intervals connected the predictions to QA/QC decisions. A naïve GTR-average baseline was added for reference. Computation was lightweight. On the test set, the MLP attained the best accuracy for $q_{d1}$ ($R^2=0.794$, RMSE $=5.866$), with XGBoost close behind ($R^2=0.773$, RMSE $=6.155$). Paired bootstrap contrasts with Holm correction indicated that the MLP–XGBoost difference was not statistically significant. Explanations consistently highlighted density- and moisture-related variables (${\gamma }_{d,\mathrm{field}}$, $R{C}_{\mathrm{SPC}}$, and W) as dominant, with gradation/plasticity contributing second-order adjustments; these attributions are model-based and associational rather than causal. The results support interpretable, computationally efficient surrogates of LDCP indices that can complement density-based acceptance and enable risk-aware QA/QC via conformal prediction intervals. Full article