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Warming and wetting on the Qinghai-Tibet Plateau (QTP), together with increasing engineering disturbance, have accelerated permafrost degradation along the Qinghai-Tibet Engineering Corridor (QTEC), inducing surface deformation that threatens infrastructure stability and environmental safety. Interferometric synthetic aperture radar (InSAR) has become an effective tool for large-scale deformation monitoring; however, conventional single-orbit ascending or descending observations measure only one-dimensional line-of-sight (LOS) displacement. Because LOS deformation represents a projection of actual ground motion, it cannot always be directly interpreted as vertical uplift or subsidence, especially where horizontal displacement is significant. In this study, ascending and descending Sentinel-1 InSAR observations from 25 February 2017 to 31 March 2022 were integrated to decompose two-dimensional deformation components, namely east-west and vertical displacement, in the permafrost zone of the QTEC. Based on the decomposed results, surface-deformation types were discriminated, and the interpretation applicability of conventional single-orbit LOS observations was quantitatively evaluated using vertical deformation as a reference. The results show that single-orbit LOS deformation can reasonably represent vertical deformation in 52.84 % of the study area, whereas the remaining 47.16 % is dominated by horizontal displacement and is therefore unsuitable for direct interpretation as uplift or subsidence. These findings highlight the necessity of distinguishing deformation types before interpreting LOS deformation and provide practical guidance for improving the reliability of InSAR-based deformation monitoring and engineering-risk assessment in permafrost corridors.

To achieve the dual improvement of weight reduction and vibration damping of brake discs on the premise that heat transfer performance and strength performance were not attenuated, a multi-objective optimization scheme based on the response surface algorithm was proposed. Firstly, considering the influence of thermal stress on the natural frequency of brake discs, a thermal-structural-modal coupling analysis model of the braking system was established. The transient temperature field, transient stress field and pre-stress modal response characteristics were obtained, and the accuracy of the finite element model was verified through temperature and modal tests. Secondly, experimental design was carried out based on Central Composite Design (CCD), and the quadratic response surface method was introduced to construct a surrogate model, so as to realize the mathematical expression of natural frequency, peak temperature, peak stress and mass. Finally, a multi-dimensional optimization mathematical model was constructed. With the minimum mass as the objective, combined with the Sequential Quadratic Programming (SQP) algorithm, the first-order natural frequency was optimized to the range of 1400-1800 Hz on the premise that the peak thermal stress was not increased. The research results showed that under the condition of meeting the multi-objective optimization requirements, the mass of the brake disc could be reduced by more than 3.5 %. The proposed thermal-structural-modal coupling analysis model solved the problem that the traditional modal calculation had a large deviation from the actual boundary conditions. The applied quadratic response surface surrogate model realized multi-objective collaborative optimization, which had important engineering value for improving the NVH performance of automobiles, alleviating the continuous excitation of the brake disc friction pair and the modal coupling resonance between components, and promoting the development of automotive lightweighting and intelligentization.

This paper addresses the inefficient and error-prone identification of tree hazards near transmission corridors by proposing a recognition method that uses 3D R-tree-integrated point cloud segmentation. LiDAR-equipped UAVs capture the original point cloud data of the corridor. The data are denoised and enhanced using principal component analysis (PCA). A 3D R-tree-integrated octree indexing structure is constructed to rapidly locate potential tree hazards by querying regions within the minimum safe distance. A Euclidean clustering algorithm with cylinder k-point constraints is applied to extract these hazardous point clouds. The spatial location of the transmission corridor is determined by fitting its point cloud via RANSAC-based least squares. Finally, distances between tree crowns and the corridor are calculated to identify hazards. Experiments demonstrate that the proposed method is efficient, accurate, and sensitive, offering an intelligent solution for automated corridor monitoring.

This is an authorial synthesis article, reflecting my personal opinion and critical interpretation as the sole author, built upon and supported by the body of literature cited throughout the text. It is not a systematic review, nor a meta-analysis, nor a primary research report. It is an integrative essay that articulates evidence streams from evolutionary anthropology, sleep medicine, functional orthodontics, nutrition, neurophysiology, and the Ibero-American tradition of jaw functional orthopedics, with the aim of offering a coherent reading of the historical moment the profession is going through. The article argues that the dominant orthodontic “wait-and-see” doctrine is no longer scientifically sustainable, and that the contemporary literature – from Kevin Boyd’s evolutionary anthropology and the Mews’ orthotropics to Christian Guilleminault’s sleep medicine, the 2024 Chinese Expert Consensus on pediatric orthodontics, and Paul Henny's recent philosophical-structural critique – converges on the need for timely, interdisciplinary, airway- and function-centered care. The article also seeks to position the Ibero-American tradition of neuro-occlusal rehabilitation (Pedro Planas) and jaw functional orthopedics (Wilma Alexandre Simões) as the historical and conceptual bedrock of much of what the Anglophone literature is now rediscovering, and to articulate the recently proposed neurostomatological framework as the unifying biological foundation of the new paradigm.

This study introduces the Quantum Becker Model (QBM), a novel quantum-classical hybrid framework that integrates rational choice theory with parameterized quantum neural networks. We created this model to better capture the complex, nonlinear cognitive dynamics that support corrupt decision-making in Indonesia, which conventional economic modeling approaches frequently fail to address. By representing individual choices as evolving qubit states modulated through rotation gates, the QBM naturally accounts for psychological superposition and abrupt behavioral shifts. We calibrated the model using empirical Indonesian data, including the 2024 Anti-Corruption Behavior Index (IPAK = 3.85), low detection probabilities, and sentencing records from 1,768 court decisions. Simulations revealed a near-total collapse of the cognitive state into the corrupt basis (offense probability 99.43 %) under current enforcement conditions. Analysis of the three-dimensional social loss landscape showed that increases in punishment severity yield little deterrence when the probability of apprehension remains low. Optimal policy configurations need significant improvements in detection capabilities rather than relying solely on harsher penalties. The QBM therefore provides policymakers with a robust computational tool for evaluating policy trade-offs and designing more effective, evidence-based anti-corruption strategies. Using quantum machine learning, law and economics, and computational public policy analysis, the proposed approach establishes a novel interdisciplinary connection. Its findings will contribute to quantum economics modeling theory as well as provide practical insights into designing more effective anti-corruption policies based on evidence.

Responsive polymers can generate various self-sustained motions through the tuning of their geometric configurations, external stimuli, and boundary constraints. Research and innovation of novel self-sustained motions can broaden the scope of application for self-sustained active machines. In this paper, a novel dynamic model for the self-excited vibration of a thermally expandable cantilever beam driven by a steady temperature difference is constructed. The governing equation for the self-excited vibration of the beam is derived and solved using the modal superposition method. Numerical calculations reveal that the beam has two typical motion states, namely, the static state and the self-excited vibration state. The self-excitation mechanism is explained by the coupling between beam motion and the periodic switching of the thermally induced bending torque. The effects of each system parameter on the amplitude of the vibration in steady state are further investigated quantitatively, and the critical values for triggering the beam self-excited vibration are identified. Furthermore, the period of the self-excited vibration in steady state is almost unaffected by environmental parameters. The self-excited vibration cantilever beam holds promising potential for applications in soft robotics, energy harvesting, active motors, and self-sustained machinery.

Drawbacks of continuous flexible manipulators, such as insufficient trajectory tracking accuracy and obvious flexible vibration, were targeted, and an integrated hierarchical strategy of trajectory replanning and disturbance rejection control was proposed for cable-driven flexible manipulators. A STO-MPC (Stochastic Trajectory Optimization Model Predictive Control) framework was constructed. Probabilistic obstacle avoidance constraints and gradient-independent solving mechanisms were introduced to tackle the high computational delay and poor dynamic adaptability of traditional planning approaches, while stiffness constraints and residual vibration suppression requirements of flexible structures were fully satisfied. A cooperative control framework of ADO-RITSMC (Adaptive Disturbance Observer-Rapid Integral Terminal Sliding Mode Control) was established. Model-free real-time compensation for multi-source disturbances was realized, finite-time convergence of tracking errors was achieved by the improved terminal sliding mode, and residual vibration of flexible links was effectively suppressed accordingly. Comprehensive comparative tests were conducted on standard O-shaped trajectories and high-curvature V-shaped trajectories, with GO-MPC (Gaussian Observer-based Model Predictive Control), APF-MPC (Artificial Potential Field-based Model Predictive Control) sliding mode algorithms. Experimental results demonstrated that STO-MPC can achieve the lowest peak computation time, completes the convergence of obstacle state estimation within 0.5 s, and yields a steady-state velocity estimation error of 0.0015 m/s. Its trajectory tracking RMSE (Root Mean Square Error) is 18.7 % and 36.8 % lower than that of GO-MPC and APF-MPC respectively, delivering superior real-time performance, estimation stability and tracking accuracy. The proposed ADO-RITSMC reduces vibration amplitude by 16.7 % with a peak vibration acceleration of –1.0 g, and exhibits faster vibration attenuation and slighter trajectory oscillation during dynamic obstacle avoidance, which fully verifies the hierarchical collaborative advantages of STO-MPC trajectory replanning and ADO-RITSMC vibration suppression.

To address issues such as the large number of parameters, high computational complexity, and inadequate real-time performance in existing CNN-Transformer hybrid fault diagnosis models, we propose a lightweight fault diagnosis framework, LWConvFormer. This framework comprises two core innovative modules: a dynamic separable multi-scale convolutional module that employs a gated network for adaptive feature extraction, thereby reducing computational load while enhancing adaptability to complex fault modes; and a broadcast self-attention module that substitutes traditional matrix multiplication with broadcast operations, thereby decreasing computational complexity from a quadratic to a linear level. Experimental results based on the planetary gearbox at Xi'an Jiaotong University and the QPZZ-II type rotating machinery test bench demonstrate that LWConvFormer maintains excellent diagnostic performance across various noise levels. The number of parameters and computational load are reduced by a factor of 6 to 10 compared to mainstream methods, while the training speed increases by nearly 7 times. This framework effectively balances diagnostic accuracy, model lightweighting, and noise resistance, offering an efficient solution for real-time fault diagnosis in industrial settings.