Dynamic Principle Research Articles

Star point positioning for large dynamic star sensors in near space based on capsule network

In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a star point positioning algorithm based on the capsule network whose input and output are both vectors. First, a PCTL (Probability-Coordinate Transformation Layer) is designed to represent the mapping relationship between the probability output of the capsule network and the star point sub-pixel coordinates. Then, Coordconv Layer is introduced to implement explicit encoding of space information and the probability is used as the centroid weight to achieve the conversion between probability and star point sub-pixel coordinates, which improves the network’s ability to perceive star point positions. Finally, based on the dynamic imaging principle of star sensors and the characteristics of near-space environment, a star map dataset for algorithm training and testing is constructed. The simulation results show that the proposed algorithm reduces the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the star point positioning by 36.1% and 41.7% respectively compared with the traditional algorithm. The research results can provide important theory and technical support for the scheme design, index demonstration, test and evaluation of large dynamic star sensors in near space.

Neural Dynamic Principles for an Intentional Embodied Agent.

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.

Opportunities and challenges of a dynamic consent-based application: personalized options for personal health data sharing and utilization

BackgroundThe principles of dynamic consent are based on the idea of safeguarding the autonomy of individuals by providing them with personalized options to choose from regarding the sharing and utilization of personal health data. To facilitate the widespread introduction of dynamic consent concepts in practice, individuals must perceive these procedures as useful and easy to use. This study examines the user experience of a dynamic consent-based application, in particular focusing on personalized options, and explores whether this approach may be useful in terms of ensuring the autonomy of data subjects in personal health data usage.MethodsThis study investigated the user experience of MyHealthHub, a dynamic consent-based application, among adults aged 18 years or older living in South Korea. Eight tasks exploring the primary aspects of dynamic consent principles–including providing consent, monitoring consent history, and managing personalized options were provided to participants. Feedback on the experiences of testing MyHealthHub was gathered via multiple-choice and open-ended questionnaire items.ResultsA total of 30 participants provided dynamic consent through the MyHealthHub application. Most participants successfully completed all the provided tasks without assistance and regarded the personalized options favourably. Concerns about the security and reliability of the digital-based consent system were raised, in contrast to positive responses elicited in other aspects, such as perceived usefulness and ease of use.ConclusionsDynamic consent is an ethically advantageous approach for the sharing and utilization of personal health data. Personalized options have the potential to serve as pragmatic safeguards for the autonomy of individuals in the sharing and utilization of personal health data. Incorporating the principles of dynamic consent into real-world scenarios requires remaining issues, such as the need for powerful authentication mechanisms that bolster privacy and security, to be addressed. This would enhance the trustworthiness of dynamic consent-based applications while preserving their ethical advantages.

Overview of steam turbines and efficiency

Thermal power plants, which produce energy from the burning of fossil fuels that in turn generate turbine-turning steam, contribute a large proportion of energy in the world. Research on how we could apply fluid dynamic and thermodynamic principles in the design of turbines is crucial to improving their efficiency and fuel consumption. This paper mainly aims to explore and summarize how the power efficiency of turbines is maximized in thermal power plants and analyze the designs of turbines that increase proficiency. Literature and past paper review will be the primary method of this research. The main findings of this research are that efficiency in thermal turbines can be increased in three principal ways: a multi-stage expansion mechanism, the application of alternating blades, and in implementation of a reheating system. Using these methods, efficiency has been increased almost a million-fold in steam turbines. The purpose of this paper is to gain insights into how energy output in thermal power plant turbines is maximized and the specific designs of turbines that improve its efficiency.

Determining the aerodynamic performance of a high-speed unmanned marine wig craft

The study object of this paper is the aerodynamic processes during the movement of an unmanned aerial vehicle flying low over the underlying surface. The ground effect is known to enhance the aerodynamic performance of low-flying aircraft, particularly larger ones. However, this effect is most noticeable for large objects. Unmanned vehicles are typically characterized by their relatively compact geometry. This study explores the aerodynamic processes involved in the flight of a small-sized vehicle using the principle of dynamic support over a surface. The particular prototype of the unmanned aerial vehicle suggested by the authors has been examined herein. The aim of the study is to evaluate the aerodynamic forces affecting a small-sized unmanned high-speed vehicle that employs the dynamic principle of support over the surface (WIG craft) by using CFD modeling. In contrast to most available studies on the ground effect, a 3D problem statement was used in this work. Computational experiments have visualized the physical fields surrounding an aircraft in flight over the ground. This study determines how the distance from the surface affects the aerodynamic properties of a small-sized aircraft, as well as the height of the effective zone where ground effect influences the small-sized WIG craft within . It has been shown that approaching the surface leads to a shift in the center of pressure of the vehicle, which leads to a change in aerodynamic momentum. This phenomenon must be taken into account when designing a control system to provide a stable flight. The study's findings are directly relevant for the development of a type of unmanned vehicles that use the dynamic principle of support over the surface.

Life-cycle planning with CEV model and time-inconsistent preferences

In this paper, we investigate an optimization problem for a wage earner seeking to maximize expected utilities until retirement by choosing optimal consumption, investment, and life insurance purchase strategies. The constant elasticity of variance (CEV) model is adopted to describe the price process of the risky asset. Additionally, we assume that the wage earner has time-inconsistent preferences. This makes the wage earner discount her payoff by a non-constant discount rate. Applying the dynamic programming principle, we have derived the Hamilton-Jacobi-Bellman (HJB) equation corresponding to the optimization problem. Furthermore, we present semi-analytical expressions for optimal strategies and value functions in three cases: the benchmark model with time-consistent preferences, the naive and sophisticated wage earners with time-inconsistent preferences. Finally, illustrations of the optimal solutions and some economic insights are provided in the numerical examples.

Study on the Gas Phase Liquid Carrying Velocity of Deep Coalbed Gas Well with Atomization Assisted Production

In order to clarify the gas-phase carrying capacity after the atomization of water from the bottom of deep coalbed wells, considering characteristics of atomization-assisted production and the dynamic equilibrium principle of gas–liquid two-phase flow in the wellbore, the gas-phase liquid-carrying drop model was established, and the solution method of the upstream and downstream driving force of liquid drop flow was studied. We also verified the theoretical model through physical simulation. Then, the law for the influence of droplet size, wellbore inclination, wellbore diameter, and wellhead back pressure of the critical liquid-carrying velocity in the gas phase is analyzed using the model. The results show the following: ① the larger the diameter of atomized droplets, the greater the gravity force applied to it, the worse the ability to be carried by the gas phase, a onefold increase in droplet diameter corresponds to the increase in the minimum critical velocity of the gas phase by 1.45 times; ② with the increase in wellbore inclination, the liquid-carrying capacity of the gas phase decreases, and the minimum critical liquid-carrying velocity of equal diameter droplets increases by 0.01438 m/s or 1.27 times for the increase in wellbore inclination by 10°; ③ with the increase in wellbore diameter, both the driving force of a droplet of equal diameter and the flow resistance through the gas phase in the wellbore decrease within the range of a driving pressure difference of 0.2 Mpa; the decrease in liquid-carrying velocity caused by the decrease in received flow resistance can reach the maximum value of 0.0473 m/s; ④ with the increase in wellhead back pressure, the driving force of equal-diameter droplets decreases, the resistance against passing through the high-concentration gas phase increases, and the gas-phase-carrying droplets start the game; ⑤ the atomization-assisted production has the function of drainage gas recovery, and the adoption of atomization-assisted production technology can increase the production time of a coalbed gas flowing well, enabling the wells to have a good transition time interval for the conversion of flowing wells to pumping ones, which provides a precise production dynamic basis for the efficient design and implements the overall strategy of drainage gas recovery by deep-well pumping. In short, this technology has the high-efficiency liquid-carrying function of “water atomization to help liquid-phase flow and increase gas production”, as well as obvious technical advantages, which can provide a new idea for the development of deep coalbed methane wells and other types of gas wells with water.

A novel functional index, aortic-valve-coefficient, for assessing aortic-stenosis in patients undergoing TAVR: A prospective-pilot study

BackgroundEvaluating the severity of aortic stenosis (AS) can be challenging, particularly in patients with low-gradient (LG, Δp < 40 mmHg) AS. Objective: This study aims to improve the accuracy of assessing severity of AS using a novel functional index- Aortic Valve Coefficient (AVC). The AVC is defined as ratio of mean transvalvular pressure-drop (Δp) to the proximal dynamic pressure (1/2 × blood density × VLVOT2; VLVOT: left ventricular outflow tract peak velocity). Hypothesis: AVC, developed from fundamental fluid dynamic principles, is a better index for accessing AS severity as it incorporates square of VLVOT and downstream pressure recovery. MethodsThis pilot prospective study enrolled 47 patients undergoing TAVR for AS. Using cardiac-catheterization-measured Δp and echocardiography-Doppler-derived VLVOT, AVC was evaluated. Pre- and post-TAVR pressure-velocity measurements were obtained, resulting in a dataset with 78 data points, including 32 data points specifically linked to LG AS. Linear regression analysis was performed to correlate AVC with Δp, VLVOT and aortic-valve-area. Welch 2-sample t-test was carried out to compare the means of AVC against aortic-valve-area. ResultsModerate correlation (r = 0.85) was observed between AVC and aortic-valve-area indicating AVC could be a prospective index. However, correlation decreased (r = 0.75) in LG AS patients, indicating increased discordancy. Comparing AVC and aortic-valve-area in LG AS patients with left ventricular ejection fraction (LVEF) < 50 % and LVEF ≥50 %, t-test showed that AVC values are significantly different (p < 0.05) as compared to aortic-valve-area (p = 0.48). ConclusionAVC, a novel index, has the potential to improve assessment of AS severity and clinical decision making for treating patients with AS. Condensed abstractComplex hemodynamics, such as paradoxical “low-flow low-gradient (LG)” Aortic stenosis (AS) can be difficult to diagnose. Currently, mean transvalvular pressure-drop and flow-derived aortic-valve-area assess AS severity. Aortic valve coefficient (AVC) is a novel index which combines both pressure-drop and flow measurements to assess the severity of AS. A total of 47 patients (72 data points) were studied undergoing TAVR. In LG AS patients, t-test comparing left ventricular ejection fraction (LVEF) < 50 % and LVEF ≥50 % showed that AVC are significantly different (p < 0.05) as compared to aortic-valve-area (p = 0.48). Therefore, AVC could be a better index.

A Systematic Review of Modeling and Simulation for Precision Diamond Wire Sawing of Monocrystalline Silicon.

Precision processing of monocrystalline silicon presents significant challenges due to its unique crystal structure and chemical properties. Effective modeling and simulation are essential for advancing the understanding of the manufacturing process, optimizing design, and refining production parameters to enhance product quality and performance. This review provides a comprehensive analysis of the modeling and simulation techniques applied in the precision machining of monocrystalline silicon using diamond wire sawing. Firstly, the principles of mathematical analytical model, molecular dynamics, and finite element methods as they relate to monocrystalline silicon processing are outlined. Subsequently, the review explores how mathematical analytical models address force-related issues in this context. Molecular dynamics simulations provide valuable insights into atomic-scale processes, including subsurface damage and stress distribution. The finite element method is utilized to investigate temperature variations and abrasive wear during wire cutting. Furthermore, similarities, differences, and complementarities among these three modeling approaches are examined. Finally, future directions for applying these models to precision machining of monocrystalline silicon are discussed.

Mechanistic insights into condensate formation of human liver-type phosphofructokinase by stochastic modeling approaches

Human liver-type phosphofructokinase 1 (PFKL) has been shown to regulate glucose flux as a scaffolder arranging glycolytic and gluconeogenic enzymes into a multienzyme metabolic condensate, the glucosome. However, it has remained elusive of how phase separation of PFKL is governed and initiates glucosome formation in living cells, thus hampering to understand a mechanism of glucosome formation and its functional contribution to human cells. In this work, we developed a stochastic model in silico using the principle of Langevin dynamics to investigate how biological properties of PFKL contribute to the condensate formation. The significance of an intermolecular interaction between PFKLs, an effective concentration of PFKL at a region of interest, and its own self-assembled filaments in formation of PFKL condensates and control of their sizes were demonstrated by molecular dynamics simulation using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). Such biological properties that define intracellular dynamics of PFKL appear to be essential for phase separation of PFKL, which may represent an initiation step for the formation of glucosome condensates. Collectively, our computational study provides mechanistic insights of glucosome formation, particularly an initial stage through the formation of PFKL condensates in living human cells.

Fluid dynamics and venous hemodynamics in the lower extremities.

Chronic venous disease is a vascular disorder characterized by impaired venous return and a progressive dysfunction of the venous system. Pathological reflux can occur due to abnormal dilation and weakening of the vein wall. The circulatory system is a natural structure in which physical laws, such as the law of closed containers and gravity, operate. The malfunctions in the system also adhere to these laws of nature. This article explains how the principles of fluid dynamics apply to the flow of blood in the veins of the legs. I am discussing the principles of Pascal's law, Torricelli's law, Bernoulli's law, and Poiseuille's law, and how they are relating to the anatomy and physiology of the venous system.

Spatiotemporal cerebral blood flow dynamics underlies emergence of the limbic-sensorimotor-association cortical gradient in human infancy.

Infant cerebral blood flow (CBF) delivers nutrients and oxygen to fulfill brain energy consumption requirements for the fastest period of postnatal brain development across the lifespan. However, organizing principle of whole-brain CBF dynamics during infancy remains obscure. Leveraging a unique cohort of 100+ infants with high-resolution arterial spin labeled MRI, we found the emergence of the cortical hierarchy revealed by the highest-resolution infant CBF maps available to date. Infant CBF across cortical regions increased in a biphasic pattern with initial rapid and sequentially slower rate, with break-point ages increasing along the limbic-sensorimotor-association cortical gradient. Increases in CBF in sensorimotor cortices were associated with enhanced language and motor skills, and frontoparietal association cortices for cognitive skills. The study discovered emergence of the hierarchical limbic-sensorimotor-association cortical gradient in infancy, and offers standardized reference of infant brain CBF and insight into the physiological basis of cortical specialization and real-world infant developmental functioning.

Force, inertia and motion from Aristotle to nowadays didactics

This study delves into the historical development of the core principles of dynamics, namely the interplay between forces and motion. We explore the intricate and nonlinear transition from the Aristotelian framework, which held sway for eighteen centuries, to the Newtonian paradigm. We posit that the complexity of this transition largely stems from what we perceive as an inadequate understanding of force. Our focus lies particularly on the seminal contributions of Galileo and the subsequent remarkable advancements made by Newton. However, we also note that, somewhat surprisingly, the transition does not conclude with Newton himself. What is commonly known as Newtonian dynamics in modern textbooks diverges significantly from the original theory outlined in the Principia, and we endeavour to elucidate these disparities. In the concluding sections, we scrutinise the implications of these concepts for contemporary teaching methodologies. Specifically, we delve into various interpretations of the Principle of Inertia, the Second Law, and their interrelationship, pinpointing what we perceive as weak points in current didactic approaches and proffering some suggestions for effectively imparting these concepts.

Machine learning-assisted discovery of flow reactor designs

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterizations of reactor geometries are low dimensional with expensive optimization, limiting more complex solutions. To address this challenge, we have established a machine learning-assisted approach for the design of new chemical reactors, combining the application of high-dimensional parameterizations, computational fluid dynamics and multi-fidelity Bayesian optimization. We associate the development of mixing-enhancing vortical flow structures in coiled reactors with performance and used our approach to identify the key characteristics of optimal designs. By appealing to the principles of fluid dynamics, we rationalized the selection of design features that lead to experimental plug flow performance improvements of ~60% compared with conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with ‘augmented intelligence’ approaches can give rise to reactor designs with enhanced performance.

Four-dimensional hemodynamic prediction of abdominal aortic aneurysms following endovascular aneurysm repair combining physics-informed PointNet and quadratic residual networks

Hemodynamic parameters can provide surveillance for the risk of complication of abdominal aortic aneurysms following endovascular aneurysm repair (EVAR). However, obtaining hemodynamic parameters through computational fluid dynamics (CFD) has disadvantages of complex operation and high computational costs. Recently proposed physics-informed neural networks offer novel solutions to solve these issues by leveraging fundamental physical conservation principles of fluid dynamics. Based on cardiovascular point datasets, we further propose an integration algorithm combining physics-informed PointNet and quadratic residual networks (PIPN-QN) that is capable of mapping sparse point clouds to four-dimensional hemodynamic parameters. The implemented workflow includes generating point cloud datasets through CFD simulation and dynamically reproducing the three-dimensional flow field in the spatial and temporal dimensions through deep learning. Compared with physics-informed PointNet (PIPN), the PIPN-QN reduces the mean square error of pressure and wall shear stress by around 32.1% and 33.1% and anticipates hemodynamic parameters in less than 2 s (14 400 times faster than CFD). To address the challenge of big data requirements, we quantify the universal flow field using a reduced number of supervision points, as opposed to the large number of point clouds generated from the CFD simulation. The PIPN-QN can meet the real-time hemodynamic parameters obtained from patients with abdominal aortic aneurysms following EVAR with higher accuracy, faster speed, and lower training costs.

Carbon emission warning method for machine tool manufacturing process based on dynamic adaptive EWMA control chart.

Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.

Enhanced Dynamic Programming Approaches for Efficient Solutions to the Traveling Salesman Problem

This study aims to enhance dynamic programming techniques for efficiently solving the Traveling Salesman Problem, a fundamental combinatorial optimization challenge. Given its NP-hard classification, traditional exact algorithms become computationally infeasible as the problem size increases. The research revisits foundational dynamic programming principles, notably the Held-Karp algorithm, and identifies existing limitations. The study begins with a comprehensive literature review, followed by an analysis of the dynamic programming challenges specific to TSP. Novel algorithms are then developed, implemented, and rigorously tested against benchmark instances. Performance evaluation is conducted using metrics such as execution time, memory usage, and solution optimality across different problem sizes. Results demonstrate significant improvements in efficiency and scalability, with enhanced algorithms achieving optimal solutions in reduced time and computational resource usage. However, the exponential growth in complexity remains a challenge for larger instances. The study concludes with recommendations for future research, focusing on further algorithmic refinements and exploring hybrid approaches to address large-scale TSPs.

Vehicle Turning Carbon Emissions and Highway Planar Alignment Design Indicators

The carbon emitted by vehicles traveling on curved roads is greatly affected by the alignment of the route, yet the mechanism behind this is not yet clear, leading to current horizontal alignment designs being unable to avoid this problem. To clarify the principles and indicator thresholds of low-carbon design for planar geometry, this study takes the carbon emission of traveling on curved routes as the research object, and establishes a relationship model between carbon emissions and design indicators based on the principles of vehicle dynamics and kinematics. Field tests were conducted to validate the quantitative relationship model. The model shows that both radius and superelevation are negatively correlated with carbon emissions, while the lateral force coefficient is positively correlated with carbon emissions. The contribution of radius to carbon emissions is greater than that of superelevation. This study clarifies the recommended values of low-carbon design indicators by assessing carbon emissions according to the current route design specification, outlines the principles of superelevation settings, and proposes a methodology to deal with the relationship between superelevation and the lateral friction coefficient. The research findings promote the quantification and standardization of low-carbon highway design, contributing to the early mitigation of high-carbon emissions from curved traffic during the design phase.

Optimization on manifold injection in DI diesel engine fuelled with acetylene

Researchers demonstrated that implementing new combustion technology and optimising fuel quantity results in a significant reduction in traditional fossil fuel usage and emission levels. The Reactivity Controlled Compression Ignition (RCCI) combustion strategy is one of the low temperature combustion technologies, and it is used to reduce the overall combustion temperature while also providing better combustion control. This study looks into RCCI combustion technology, which uses conventional diesel fuel as the high reactivity fuel (HRF) injected through the injector and acetylene gas as the low reactivity fuel (LRF) injected into the cylinder via a modified inlet manifold alongside air. The modified engine setup was tested for performance, emissions, and combustion under various load conditions, as well as different mass flow rates of acetylene gas, a low reactivity fuel that is injected with air. The flow field of the low reactivity fuel at the inlet manifold is analysed using the Computational Fluid Dynamics principle, which is used to determine the best flow rate for improving combustion quality. According to the simulation results, the optimal acetylene flow rate is 3 Litres Per Minute (LPM), and experimentation shows that at 3 LPM acetylene injection, the brake thermal efficiency (BTE) improves by about 3.2%, and emissions such as carbon monoxide (CO), hydrocarbon (HC), smoke intensity, and oxides of nitrogen (NOx) are reduced by about 35%, 17%, 10%, and 21%, respectively.

Risk-Averse Markov Decision Processes Through a Distributional Lens

By adopting a distributional viewpoint on law-invariant convex risk measures, we construct dynamic risk measures (DRMs) at the distributional level. We then apply these DRMs to investigate Markov decision processes, incorporating latent costs, random actions, and weakly continuous transition kernels. Furthermore, the proposed DRMs allow risk aversion to change dynamically. Under mild assumptions, we derive a dynamic programming principle and show the existence of an optimal policy in both finite and infinite time horizons. Moreover, we provide a sufficient condition for the optimality of deterministic actions. For illustration, we conclude the paper with examples from optimal liquidation with limit order books and autonomous driving. Funding: This work was supported by Natural Sciences and Engineering Research Council of Canada [Grants RGPAS-2018-522715 and RGPIN-2018-05705].