A new deck arch bridge and a study on its mechanical properties by FE and experiment methods

In this study, a new type of deck arch bridge was proposed based on the triangle stability principle, and its mechanical properties were tested. The new deck arch bridge—named the superposed truss arch bridge—consists of main arches, auxiliary arches, a girder, and web members. These components adopt steel structures and are connected into a series of triangles to form a superposed truss structure. The new structural system design retains the advantages of the truss and arch structure. Additionally, the rise-span ratio of the main arch can be smaller in the new system design, so the rise height is smaller, which can decrease construction difficulty. The underlying mechanical principles of the new bridge were explained. A new type of railway deck arch bridge with a 650 m span was designed, and the finite element method was used to analyze its stiffness, strength, stability, and dynamic properties and the corresponding effects of the arch-axis coefficient, rise-span ratio, and span length on the mechanical properties. An experiment was carried out on a new deck arch bridge and a conventional deck arch bridge with the same span (10 m) to compare their performance. The results showed that the new deck arch bridge exhibited good mechanical properties while being inexpensive and easy to construct, which makes it suitable for high-speed railway bridges.

Study on the Stability Principle of Mechanical Structure of Roadway with Composite Roof

With the typical composite roof roadway and roof fall accidents in the Guizhou Province of China as the research background, the expression of damage parameters of composite roof was deduced according to Weibull statistical distribution, generalized Hooke’s law and Mises yield criterion, and the influence of shape and scale parameters of Weibull on damage characteristic was discussed. Based on the infinite slab theory, the expressions of deflection and layer separation of each layer of the composite roof were obtained, the critical load expression of each delamination was determined, and the influence of roadway width, overlying strata load, elastic modulus, shape parameters and scale parameters on the stability of composite roof was explored. The research shows that the bolt support can effectively reduce the layer separation between the composite roofs and enhance the stability of the composite roof. On this basis, it is proposed that for the surrounding rock control problem of roadways with composite roof, the active support technology with bolts as the core should be adopted.

Concise Robust Control of the Nonlinear Ship in Course-Keeping Control

Most of the existing nonlinear ship course-keeping control systems are designed with the Nomoto model, which solely considers the yawing of the ship with only one Degree of Freedom (DOF), and it does not consider the coupling between the longitudinal and the lateral velocity of the ship. In this paper, a nonlinear ship course controller design method that can be used in a nonlinear coupled model was proposed. A stable nonlinear ship course controller with anti-wind and anti-wave interference was constructed based on the Lyapunov stability principle and robust control theory, which can be used in the course control of autopilot in the case of wind and waves. In this method, the coupling among the longitudinal and lateral velocity as well as yawing of the ship was considered. The simulation results showed that the method can not only effectively control the ship’s course but also can track the dynamic course effectively. At the same time, compared with the PID control method based on backstepping, the steering angle of the rudder angle of our method is smaller and the wear and tear of steering gear will be smaller.

Ranking the Regimes in Aristotle's Politics: The Four-Principles Approach

There is a long-standing debate over which constitution Aristotle regards as best in the Politics. I attempt to clarify his view by reconstructing four principles he uses to assess constitutions, in both ideal and more ordinary circumstances: (i) the supremacy-of-virtue principle, (ii) the more-virtuous-citizens-are-better-than-fewer principle, (iii) the equality principle, and (iv) the stability principle. I apply these principles to defend a rank ordering of constitutions, which situates the ideal aristocracy of books 7 and 8 at the top, and tyranny, along with unmixed forms of democracy and oligarchy, at the bottom.

Adaptive Learning in Macroeconomics

While rational expectations (RE) remains the benchmark paradigm in macro-economic modeling, bounded rationality, especially in the form of adaptive learning, has become a mainstream alternative. Under the adaptive learning (AL) approach, economic agents in dynamic, stochastic environments are modeled as adaptive learners forming expectations and making decisions based on forecasting rules that are updated in real time as new data become available. Their decisions are then coordinated each period via the economy’s markets and other relevant institutional architecture, resulting in a time-path of economic aggregates. In this way, the AL approach introduces additional dynamics into the model—dynamics that can be used to address myriad macroeconomic issues and concerns, including, for example, empirical fit and the plausibility of specific rational expectations equilibria. AL can be implemented as reduced-form learning, that is, the implementation of learning at the aggregate level, or alternatively, as discussed in a companion contribution to this Encyclopedia, Evans and McGough, as agent-level learning, which includes pre-aggregation analysis of boundedly rational decision making. Typically learning agents are assumed to use estimated linear forecast models, and a central formulation of AL is least-squares learning in which agents recursively update their estimated model as new data become available. Key questions include whether AL will converge over time to a specified RE equilibrium (REE), in which cases we say the REE is stable under AL; in this case, it is also of interest to examine what type of learning dynamics are observed en route. When multiple REE exist, stability under AL can act as a selection criterion, and global dynamics can involve switching between local basins of attraction. In models with indeterminacy, AL can be used to assess whether agents can learn to coordinate their expectations on sunspots. The key analytical concepts and tools are the E-stability principle together with the E-stability differential equations, and the theory of stochastic recursive algorithms (SRA). While, in general, analysis of SRAs is quite technical, application of the E-stability principle is often straightforward. In addition to equilibrium analysis in macroeconomic models, AL has many applications. In particular, AL has strong implications for the conduct of monetary and fiscal policy, has been used to explain asset price dynamics, has been shown to improve the fit of estimated dynamic stochastic general equilibrium (DSGE) models, and has been proven useful in explaining experimental outcomes.

Optimal Simulation of Sandcastle Life in Dynamic Environment Based on Stability Principle

This paper mainly studies to explore a three-dimensional geometric model including three modules of a sandcastle foundation with optimal stability. Firstly, based on the knowledge of streamline structure, structural mechanics, and fluid mechanics, the most stable sand pile foundation model under the action of tidal current and wave is established. Secondly, by limiting the degree of allowable aggregation, the discrete global optimization algorithm based on the continuous descent method is adopted to find out the optimal water-sand ratio. Finally, we apply the above results to verify the reliability of the optimal model by comprehensively considering the influence of rainfall factors on sandcastles.

Adaptive Control for Dual-Arm Robotic System Based on Radial Basis Function Neural Network

The paper has developed an adaptive control using neural network for controlling a dual-arm robotic system in moving a rectangle object to the desired trajectories. Firstly, the overall dynamics of the manipulators and the object have been derived based on Euler-Lagrangian principle. And then based on the dynamics, a controller has been proposed to achieve the desired trajectories of the grasping object. A radial basis function neural network has been applied to compensate uncertainties of dynamic parameters. The adaptive algorithm has been derived owning to the Lyapunov stability principle to guarantee asymptotical convergence of the closed dynamic system. Finally, simulation work on MatLab has been carried out to reconfirm the accuracy and the effectiveness of the proposed controller.

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

Quantum Correction for Newton’s Law of Motion

A description of the motion in noninertial reference frames by means of the inclusion of high time derivatives is studied. Incompleteness of the description of physical reality is a problem of any theory, both in quantum mechanics and classical physics. The “stability principle” is put forward. We also provide macroscopic examples of noninertial mechanics and verify the use of high-order derivatives as nonlocal hidden variables on the basis of the equivalence principle when acceleration is equal to the gravitational field. Acceleration in this case is a function of high derivatives with respect to time. The definition of dark metrics for matter and energy is presented to replace the standard notions of dark matter and dark energy. In the Conclusion section, problem symmetry is noted for noninertial mechanics.