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There are few putative macroevolutionary trends or rules that withstand scrutiny. Here, we test and verify the purported tendency for animal clades to reach their maximum morphological variety relatively early in their evolutionary histories (early high disparity). We present a meta-analysis of 98 metazoan clades radiating throughout the Phanerozoic. The disparity profiles of groups through time are summarized in terms of their center of gravity (CG), with values above and below 0.50 indicating top- and bottom-heaviness, respectively. Clades that terminate at one of the “big five” mass extinction events tend to have truncated trajectories, with a significantly top-heavy CG distribution overall. The remaining 63 clades show the opposite tendency, with a significantly bottom-heavy mean CG (relatively early high disparity). Resampling tests are used to identify groups with a CG significantly above or below 0.50; clades not terminating at a mass extinction are three times more likely to be significantly bottom-heavy than top-heavy. Overall, there is no clear temporal trend in disparity profile shapes from the Cambrian to the Recent, and early high disparity is the predominant pattern throughout the Phanerozoic. Our results do not allow us to distinguish between ecological and developmental explanations for this phenomenon. To the extent that ecology has a role, however, the paucity of bottom-heavy clades radiating in the immediate wake of mass extinctions suggests that early high disparity more probably results from the evolution of key apomorphies at the base of clades rather than from physical drivers or catastrophic ecospace clearing.

The height of the center of gravity (ZCG) is a critical parameter for evaluating vehicle safety and performance. Systematic errors arise in ZCG measurement via the tilt-table test method due to unlocked suspension systems and variable sprung mass conditions, which compromise accuracy. To address this limitation, a CNN–LSTM–Attention model integrating convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and an attention mechanism is proposed. The CNN extracts spatial correlations among vehicle load transfer, suspension stiffness, and tilt angles. The LSTM captures temporal dependencies in tilt angle sequences, while the attention mechanism amplifies critical load-transfer features near the 0° region. Simulations of vehicles with unlocked suspension and variable sprung mass were conducted in Adams using tilt-table protocols. The CNN–LSTM–Attention model was trained on simulation data and validated with real-world tilt-test data under identical suspension conditions. Results demonstrate that the CNN–LSTM–Attention model achieves at least a 6.9% improvement in computational speed and at least a 0.1% reduction in prediction error compared to CNN, CNN-LSTM, and Transformer baselines. The CNN–LSTM–Attention model demonstrates valid predictive capability for ZCG at 0° tilt angle. This novel approach provides a robust solution for the tilt-table test method ZCG measurement, enhancing practical accuracy in vehicle dynamics parameter quantification.

This paper presents a model predictive control (MPC) approach based on the extended disturbance observer (EDOB) for trajectory tracking of a coaxial octorotor unmanned aerial vehicle (UAV). First, the system dynamic model is derived using Newton–Euler relations in the presence of time‐varying centre of gravity (COG); then, a two‐loop cascade structure is presented to perform the trajectory tracking task. Both loops are controlled using MPC with feedforward compensation based on the EDOB to improve disturbance rejection abilities. When the mass changes, the moment of inertia and COG are affected. The EDOB simultaneously estimates the effects of time‐varying mass, external disturbances, and parametric uncertainties in six degrees of freedom. After obtaining virtual control inputs using designed controllers, constrained control allocation is used to obtain rotors speed in a valid range. The proposed control scheme is evaluated using simulation. The simulation results show the ability of the developed control strategy in accurate trajectory tracking and stable flight in different conditions and being robust to uncertainty and disturbance.

Many studies have reported that the human body-balance ability was essential in the early detection and self-management of chronic diseases. However, devices to measure balance, such as motion capture and force plates, are expensive and require a particular space for installation as well as specialized knowledge for analysis. Therefore, this study aimed to propose and verify a new algorithm to score the human body-balance ability on the wobble board (HBBAWB), based on a geometric solution using a cheap and portable device. Although the center of gravity (COG), the projected point of the center of mass (COM) on the fixed ground, has been used as the index for the balance ability, generally, it was not proper to use the COG under the condition of no fixed environment. The reason was that the COG index did not include the information on the slope for the wobble. Thus, this study defined the new index as the perpendicular-projection point (PPP), which was the projected point of the COM on the tilted plane. The proposed geometric solution utilized the relationship among three points, the PPP, the COM, and the middle point between the two feet, via linear regression. The experimental results found that the geometric solution, which utilized the relationship between the three angles of the equivalent model, enabled us to score the HBBAWB.

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

This paper presents a novel nonlinear dynamic inversion-based L1 adaptive control (NDI-L1) for fixed-wing aircraft with strong nonlinearity, model uncertainties, and center of gravity variations. This adaptive structure can decouple the system’s fast adaptation and robustness, thus reducing the oscillations caused by high adaptation gain. Moreover, compared to existing L1 adaptive control, NDI-L1 control enlarges its application range, providing satisfactory dynamic performance as well as robustness within the flight envelope. Additionally, this paper also improves the existing piecewise-constant adaptation (PCA), addressing the contradiction between estimation accuracy and sampling time. The modified PCA achieves the desired estimation accuracy while effectively reducing the computational burden. The dynamic performance of the proposed adaptive structure under disturbance is theoretically analyzed. Importantly, the robust flight controller is designed based on the proposed NDI-L1 to eliminate the influences of parameter perturbations and sudden changes in the center of gravity on flight dynamics. Simulations and hardware-in-the-loop experimental results clearly confirm the effectiveness, advantages, and robustness of the proposed NDI-L1 with modified PCA, and compare it to other existing methods.

This paper presents a center of gravity (CG) control strategy to enhance tailsitter transitions and maneuvers using a robust incremental nonlinear dynamic inversion (INDI) controller. Tailsitters, which use a flying wing configuration without tail surfaces, typically have lower pitching moments than conventional aircraft. By adjusting the CG during flight based on static stability analysis, the proposed method improves transition performance. Static stability generates restoring moments that can assist or hinder maneuvers. The CG adjustment controls the restoring pitching moment relative to the angle of attack, avoiding the need for additional actuators or motors found in other configurations like tilt-rotors, tilt-wings and other tailsitter variations. The INDI controller compensates for CG shifts using a feedforward term and time-delay approximation. Simulation results show improved trajectory tracking over fixed CG configurations, enabling more efficient transitions. This work extends the TIGPD INDI studies, with relative stability analysis confirming the TIGPD-INDI controller meets the gain and phase margin requirements of military specifications.

The act of balancing simultaneous rapid development of urbanization and ecological environmental protection is an increasingly important strategic agenda of national importance. While focusing on the principle of sustainable development, this paper calculates and analyzes the extent of coupling coordination degree (CCD) between the new urbanization quality (NUQ) and eco-environmental carrying capacity (EECC) of 246 prefecture level and above cities in China. The gravity center moving trajectory, Moran's I and LISA are employed to perform exploratory spatial analysis, and the differences among cities are analyzed by variation coefficient (CV) method. Based on simulation by the NUQ and CCD, results obtained show a continuous growth trend at similar rate among the cities. Meanwhile, the mean value of EECC showed a fluctuating growth trend. The coordinated development type cities were largely concentrated in the coastal areas of the east. In comparison, other areas are dominated by disorders recession type cities and the coordination transition type cities. The center of gravity CCD was found moving from the northeast to the southwest. The situation of HH (CDD main city—high, CDD surrounding cities—high) and LL (CDD main city—low, CDD surrounding cities—low) always accounted for more than seventy percent. The cluster locations of HH and LH (CDD main city—low, CDD surrounding cities—high) are largely concentrated around mega-urban agglomerations comprising the following three major urban agglomerations, i.e., (i) the Yangtze River Delta; (ii) the Pearl River Delta; and (iii) the Beijing-Tianjin-Hebei. CV of CCD shows that the gap among cities is gradually narrowing. In summary, the results conclude that most of the evaluated cities have not achieved coordinated development. As such, enhancing ecological environment protection awareness, improving the utilization effectiveness of natural resources and gradual elimination of regional differences should be the way forward in facilitating the process of new urban construction. The significance of this research not only helps increase the focus on ecological environmental governance, but also enrich the theoretical system of urbanization, both of which have important theoretical significance and application values in the building of an ecological civilization.

In recent years, carbon emissions have become a hot spot issue, and countries have made efforts to control the increasing rate of CO2 concentration. Prior studies have mainly focused on the national total carbon emissions, but per capita carbon emissions are still poorly known. Here, we used multiple economic development indices to investigate the dynamics of per capita carbon emissions. Additionally, we used the Mann–Kendall test to assess the directions and magnitudes of trends and to investigate abrupt changes in per capita carbon emissions. Our results showed the highest positive growth rate of 0.439 mts/yr in Oman, and the highest negative growth rate of −0.462 mts/yr in the United Arab Emirates. Hurst Index analysis showed that about 86% of countries will keep the current trends of carbon emissions if current mitigation measures remain unchanged. Furthermore, we analyzed the shift in the center of gravity for per capita carbon emissions and used the contribution decomposition method to identify the drivers for the shift, which changed direction in 2004. The main driver behind the westward shift in the gravity center before 2004 was the fact that carbon emissions grew more strongly in the west than in the east before 2004, while the driver for behind the eastward shift in the gravity center after 2004 was a combination of emission reductions in the west and emission increases in the east. Our results highlighted the importance of understanding that the per capita CO2 emissions are clearly defined within the context of global carbon neutrality, which can help policymakers set more reasonable targets with which to better achieve carbon neutrality goals.

In automated sorting and grasping of livestock meat cuts, the ideal assumption of symmetric mass distribution is often violated due to irregular morphology and soft tissue deformation. Under the combined effects of gripping forces and gravity, the originally balanced configuration evolves into an asymmetric state, resulting in dynamic shifts of the center of gravity (CoG) that undermine the stability and accuracy of robotic grasping. To address this challenge, this study proposes a CoG trajectory prediction method tailored for meat-cut grasping tasks. First, a dynamic model is established to characterize CoG displacement during grasping, quantitatively linking gripping force to CoG shift. Then, the prediction task is reformulated as a nonlinear state estimation problem, and a Small-Target Bayesian–Probability Hypothesis Density (STB-PHD) algorithm is developed. By incorporating historical error feedback and adaptive covariance adjustment, the proposed method compensates for asymmetric perturbations in real time. Extensive experiments validated the effectiveness of the proposed method: the Optimal Sub-Pattern Allocation (OSPA) metric reached 4.82%, reducing the error by 4.35 percentage points compared to the best baseline MGSTM (9.17%). The task completion time (TC Time) was 6.15 s, demonstrating superior performance in grasping duration. Furthermore, the Average Track Center Distance (ATCD) reached 8.33%, outperforming the TPMBM algorithm (8.86%). These results demonstrate that the proposed method can accurately capture CoG trajectories under deformation, providing reliable control references for robotic grasping systems. The findings confirm that this approach enhances both stability and precision in automated grasping of deformable objects, offering valuable technological support for advancing intelligence in meat processing industries.