Dynamic Principle Research Articles

Complexity Analysis and Control of Output Competition in a Closed-Loop Supply Chain of Cross-Border E-Commerce Under Different Logistics Modes Considering Chain-to-Chain Information Asymmetry

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product–market (PM) chains, based on the e-commerce platform’s information-sharing strategy and the manufacturer’s selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state.

Dynamic principles of the microbiome and the bovine vagina: a review

The role of microbes inhabiting various body sites in supporting host physiology and health is substantial, and recent advancements in DNA sequencing technology have facilitated a more in-depth understanding of these microbial contributions. The influence of microbiota within a given organ can be broadly categorized as having two main functions: (1) promoting organ homeostasis and (2) creating conditions that inhibit the growth of pathogenic microorganisms, thereby protecting the host from diseases. In livestock production, numerous phenotypes critical to industry outcomes are affected by the microbiome, which has sparked considerable academic interest in recent years. This review aims to analyze the extensive data available on the microbiomes of humans and other mammalian species, examining microbiome ecology to elucidate principles that may assist in interpreting data on livestock microbiomes. Additionally, the review will discuss techniques available for investigating various microbiome aspects and will examine existing data on the reproductive microbiome, with a particular focus on the bovine vaginal microbiome.

Ultrasonic dissolution of solid Al in liquid Sn during soldering: Modeling and equation, trend prediction, accelerating effect

Soldering of ceramics/metals using an inactive commercial solder with the advantage of low cost has wide application prospects. The dissolution behavior of base metal could not be quantified, which has been a basic issue for the joining design. This work investigated the dissolution of the solid Al in liquid Sn with and without the ultrasound. The physical model for the dissolving process was established based on the experiments. The relationship equation of the average concentration of Al (Ca) and soldering time was derived by using the Nernst-Brunner principle. The key parameters of limiting solubility (C0L) and dissolution rate constant (K) were obtained through dissolving experiments of liquid Sn to solid Al. The calculation results show that the effect of soldering temperature, and thickness Sn layer on the Al concentration (Ca) and dissolution rate (dCa/dt) under ultrasound is weaker than that without ultrasound. Compared to the condition without ultrasound, the value K, the maximum value of Ca and dCa/dt was increased by 5.8 times, 4.5 times, and 52 times at 250 °C under the ultrasound action, respectively, and the activation energy of dissolution was reduced by 41 %. The mechanism of ultrasonically accelerating dissolution of the solid Al by the liquid Sn has been revealed by using the bubble dynamics principle. It will provide a guideline for the design of soldering ceramics/metals using an inactive commercial solder.

Contemporary and Future Secondary Copper Reserves of Southeast Asian Countries

This study employed dynamic material flow analysis (MFA) and mass balance principles to examine copper flows in Indonesia, Malaysia, the Philippines, Thailand, and Vietnam from 1960 to 2020, with projections extending to 2050 using five shared socioeconomic pathway (SSP) scenarios. We applied the secondary resources classification framework to assess secondary copper resources and their recoverability in these countries. The results indicated that total copper stocks across these countries would continue to rise, with Indonesia’s copper stock projected to reach around 5000–12,000 kt by 2050, the highest among the five nations. In 2022, Malaysia had the highest per capita copper stock at 100 kg/person, although all countries were expected to remain below the per capita stock levels seen in major copper-consuming developed countries by 2050. Copper demand was projected to increase by 118–238 kt annually from 2023 to 2050, leading to a significant rise in end-of-life copper scrap. By 2050, secondary copper reserves in Indonesia were estimated to reach 4096 kt, with similar growth trends observed in other countries (3898 kt in Thailand, 3290 kt in Vietnam, 3096 kt in Malaysia, and 2564 kt in the Philippines). This highlights both the potential for resource recovery and the need for improved waste management. If recycling rates increase to 80–90%, secondary reserves could meet up to 42–65% of the copper demand in 2050. However, current recycling rates remain well below this potential, underscoring the urgent need for better waste management systems. This study emphasizes the balance between economic development and resource sustainability, offering critical insights for policymakers to improve recycling efficiency and reduce reliance on primary copper sources.

Optimal Portfolio and Retirement Decisions with Costly Job Switching Options

In this paper, we consider the utility maximization problem of an agent regarding optimal consumption-investment, job-switching strategy, and the optimal early retirement date. The agent can switch between two jobs or job categories at any time before retirement, but incurs a cost when switching to a position offering higher labor income. The agent's utility maximization involves a combination of stochastic control for consumption and investment, switching control for job-switching, and optimal stopping for early retirement decisions, making it a non-trivial and highly challenging problem. By utilizing the dynamic programming principle, we can derive the nonlinear Hamilton-Jacobi-Bellman (HJB) equation in the form of a system of variational inequalities with obstacle constraints, which arises from the agent's optimization problem. We employ guess and verify methods based on economic intuition to derive the closed-form solution of this HJB equation and demonstrate, through a verification theorem, that this solution aligns with the solution to the agent's utility maximization problem.

Optimal Attitude Determination for the CR200 Underwater Walking Robot

The Crabster CR200 is an underwater walking robot inspired by crabs and lobsters, designed for precise seabed inspection and manipulation. It maintains stability and position on the seafloor, even in strong currents, by adjusting its posture through six legs, each with four degrees of freedom. The key advantage of the CR200 lies in its ability to resist drifting in strong currents by adapting its posture to maintain its position on the seafloor. However, information is still lacking on which specific posture generates the maximum downforce to ensure optimal stability in the presence of currents and the seabed. This study aims to determine the fluid forces acting on the CR200 in various postures using Computational Fluid Dynamics (CFD) and identify the posture that generates the maximum downforce. The posture is defined by two parameters: angle of attack and seafloor clearance, represented by the combination of the robot’s pitch angle and distance to the seabed. By varying these parameters, we identified the posture that produces the greatest downforce. Through a series of analyses, we identified two main fluid dynamic principles affecting the downforce on a robot close to the seabed. First, an optimal pitch angle exists that generates the maximum downward lift on the robot’s body. Secondly, there is an ideal distance from the seabed that produces maximum suction on the bottom surface, thereby creating a strong Venturi effect. Based on these principles, we determined the optimal robot posture to achieve maximum downforce in strong current conditions. The optimal underwater robot posture identified in this study could be applied to similar robots operating on the seafloor. Furthermore, the methodology adopted in this study for determining the optimal posture can serve as a reference for establishing operational postures for similar underwater robots.

Simulation of Fluid Flow through Orifice Meter

The modeling of fluid flow using an orifice meter is an engineering endeavor aimed at thoroughly investigating and optimizing fluid flow measurement in various industrial applications. Orifice meters are highly regarded for their accurate measurement of fluid flow rates as they pass through a specifically designed orifice plate, utilizing the principles of fluid dynamics. The primary objective of this project is to develop a computational fluid dynamics (CFD) model capable of simulating the intricate fluid flow through an orifice meter. The simulation encompasses detailed geometric features of the orifice meter, including the precise dimensions of the orifice plate, pipe diameter, and any relevant taper angles. Additionally, the model takes into account various fluid parameters, boundary conditions, and factors such as velocity and pressure at different points within the system. The comprehensive study visualizes and quantifies key fluid properties, such as velocity profiles, pressure differentials, and flow velocities, across the entire length of the orifice meter using CFD analysis. This in-depth analysis provides crucial insights into the dynamic performance of the orifice meter. The obtained information holds significant potential to revolutionize flow rate measurement accuracy, efficiency, and cost-effectiveness in diverse industries, including water management, chemical processing, and oil and gas. The project's methodology for simulating fluid flow through orifice meters can bring about substantial improvements in the understanding and optimization of fluid flow in industrial processes.

Hybrid CFD PINN FSI Simulation in Coronary Artery Trees

This paper presents a novel hybrid approach that integrates computational fluid dynamics (CFD), physics-informed neural networks (PINN), and fluid–structure interaction (FSI) methods to simulate fluid flow in stenotic coronary artery trees and predict fractional flow reserve (FFR) in areas of stenosis. The primary objective is to utilize a 1D PINN model to accurately predict outlet flow conditions, effectively addressing the challenges of measuring or estimating these conditions within complex arterial networks. Validation against traditional CFD methods demonstrates strong accuracy while embedding physics-based training to ensure compliance with fundamental fluid dynamics principles. The findings indicate that the hybrid CFD PINN FSI method generates realistic outflow boundary conditions crucial for diagnosing stenosis, requiring minimal input data. By seamlessly integrating initial conditions established by the 1D PINN into FSI simulations, this approach enables precise assessments of blood flow dynamics and FFR values in stenotic regions. This innovative application of 1D PINN not only distinguishes this methodology from conventional data-driven models that rely heavily on extensive datasets but also highlights its potential to enhance our understanding of hemodynamics in pathological states. Ultimately, this research paves the way for significant advancements in non-invasive diagnostic techniques in cardiology, improving clinical decision making and patient outcomes.

Comparing freshwater mussel responses to stress using life-history and dynamic energy budget theory

Freshwater mussels are experiencing severe population declines, affecting their critical role in freshwater ecosystems. A thorough assessment of threats posed by various stressors is needed; however, the large number of species to be considered and significant data gaps, especially for listed species, hinder the process. We combined a traits-based approach to represent multiple species grouped into three life-history categories – Equilibrium, Opportunistic, and Periodic – with the Dynamic Energy Budget modeling principles to capture the physiological mechanisms driving individual-level responses. We used the DEB model to simulate individual life cycles and explore relationships between underlying energetics and emerging individual traits of 47 freshwater mussel species and the common toxicity test surrogate, the Eastern oyster (Crassostrea virginica), under control and stressed conditions. Stress was introduced via physiological modes of action related to four key metabolic pathways: energy assimilation, maintenance, growth, and reproduction. We recorded maximum length, age at maturity, and fecundity and compared these endpoints and their stress-induced changes among life-history categories. The life-history differences among freshwater mussels directly emerged from underlying energetics, with high assimilation and maintenance supporting opportunistic traits. Stress imposed on energy assimilation had the strongest effect on all life-history traits, and a 25 % reduction in assimilation rate resulted in an average 25 % and 60 % decrease in maximum length and fecundity, respectively, and a 24 % increase in age at maturity. Equilibrium species suffered the greatest negative effects overall, indicating that this life-history strategy might be the most susceptible to stressors. The Eastern oyster displayed extreme opportunism in its life-history traits, but its responses to stress were generally within the range observed for freshwater mussels. The study provides a much-needed general understanding of stress responses across freshwater mussel life-history categories and contributes to the foundation for developing life-history-driven population models.

Exploration on the application of dynamics principles in police physical education curriculum teaching

The police physical education curriculum focuses on the integration of physical training and police skills training, aiming to enhance students’ physical fitness and police skill levels. By reasonably applying the principles of dynamics, teachers can develop more scientific and reasonable training plans, optimizing aspects such as strength training, speed training, endurance training, and skill instruction. The application of dynamics principles not only helps improve students’ physical fitness but also assists teachers in better understanding the mechanical principles involved when students perform movements, thereby enabling more effective skill instruction. Therefore, in the teaching of police physical education, emphasis should be placed on the integration and practice of dynamics principles to improve teaching quality and effectiveness, providing strong support for cultivating high-quality police talents.

Study for Hydrogen Migration Characteristics in the Elbow of a Hydrogen-Doped Natural Gas Pipeline under Normal Transport and Shutdown Conditions.

During the transportation of hydrogen-doped natural gas (HCNG), there is a risk of uneven distribution of hydrogen at the elbow, causing hydrogen damage to the pipeline. Therefore, based on the basic principles of computational fluid dynamics, this paper uses a hybrid model to describe the flow process of HCNG in an elbow. The results show that under normal transportation, the hydrogen volume fraction varies within 0.2% with the influence of pressure, flow velocity, temperature, hydrogen volume fraction, and undulating angle, and the uneven distribution of hydrogen in the elbow can be ignored. However, in the shutdown state, the hydrogen slip rate gradually slows down, the hydrogen volume fraction is distributed in a horizontal concentration gradient along the vertical direction, and it gradually accumulates at the height of the undulating tube. The difference in hydrogen volume fraction reaches 50%, and the stratification phenomenon is obvious.

Ostensible Refusals in Mandarin Chinese

This study investigates ostensible refusals in response to four types of stimulus speech acts (request, suggestion, invitation and offer). Based on the collected data, the study aims to make an overall analysis of ostensible refusal speech acts in Chinese language and explores the basic guiding principle that underlies the interactional process of ostensible refusing. Consequently, it briefly revisits the ostensible refusals in Chinese context. The results show that ostensible refusals can exist in all four types of initiating speech acts, among which the ostensible refusals to request and suggestion are rarely investigated. Overall, ostensible refusals rely on both linguistic and non-linguistic (prosodic and paralinguistic features) devices. In terms of manner and pragmatic efforts of ostensible refusing, they vary slightly from type to type. Underlying the ostensible refusal-negotiation is the dynamic balance principle which guides interactants to employ different pragmatic strategies in ostensible refusing.

Optimal Consumption and Investment with Income Adjustment and Borrowing Constraints

In this paper, we address the utility maximization problem of an infinitely lived agent who has the option to increase their income. The agent can increase their income at any time, but doing so incurs a wealth cost proportional to the amount of the increase. To prevent the agent from infinitely increasing their income and borrowing against future income, we additionally consider a non-negative wealth constraint that prohibits borrowing based on future income. This utility maximization problem is a mixture of stochastic control, where the agent chooses consumption and investment, and singular control, where the agent chooses a non-decreasing income process. To solve this non-trivial and challenging problem, we derive the Hamilton–Jacobi–Bellman (HJB) equation with a gradient constraint using the dynamic programming principle (DPP). Then, using the guess-and-verify method and a linearization technique, we obtain a closed-form solution to the HJB equation and, based on this, find the optimal strategy.

Research on Virtual Simulation Experiment Course for Enterprise Decision-Making based on Multilateral Dynamic Game

Virtual simulation experiment serves as a bridge that organically connects innovation and entrepreneurship classes with professional quality education, fostering a talent cultivation ecosystem that supports high-quality economic development and exerts significant social influence. Currently, there are many shortcomings and bottlenecks in both virtual simulation experiment and the teaching process. This paper discusses the necessity and objectives of the virtual simulation experiment course for enterprise decision-making based on multilateral dynamic game, experimental principles, experimental characteristics, and experimental steps under the principle of "being real and not fictitious, simulating reality with virtuality, and grounding in reality". The experimental platform utilizes real business case that participants can freely choose. Through online human-computer interaction and interaction, as well as offline team discussions, participants are empowered to make independent decisions throughout the experiment. With the assistance of system prompts and task-driven guidance, participants can autonomously complete the experiment. This paper aims to promote the establishment of a virtual simulation experiment platform for enterprise decision-making based on multilateral dynamic game, specifically designed for business students, contributing to the construction of a strong higher education nation.

Enhanced and Efficient Predictions of Dynamic Ionization through Constant-pH Adiabatic Free Energy Dynamics.

Dynamic or structurally induced ionization is a critical aspect of many physical, chemical, and biological processes. Molecular dynamics (MD) based simulation approaches, specifically constant pH MD methods, have been developed to simulate ionization states of molecules or proteins under experimentally or physiologically relevant conditions. While such approaches are now widely utilized to predict ionization sites of macromolecules or to study physical or biological phenomena, they are often computationally expensive and require long simulation times to converge. In this article, using the principles of adiabatic free energy dynamics, we introduce an efficient technique for performing constant pH MD simulations within the framework of the adiabatic free energy dynamics (AFED) approach. We call the new approach pH-AFED. We show that pH-AFED provides highly accurate predictions of protein residue pKa values, with a MUE of 0.5 pKa units when coupled with driven adiabatic free energy dynamics (d-AFED), while reducing the required simulation times by more than an order of magnitude. In addition, pH-AFED can be easily integrated into most constant pH MD codes or implementations and flexibly adapted to work in conjunction with enhanced sampling algorithms that target collective variables. We demonstrate that our approaches, with both pH-AFED standalone as well as pH-AFED combined with collective variable based enhanced sampling, provide promising predictive accuracy, with a MUE of 0.6 and 0.5 pKa units respectively, on a diverse range of proteins and enzymes, ranging up to 186 residues and 21 titratable sites. Lastly, we demonstrate how this approach can be utilized to understand the in vivo performance engineered antibodies for immunotherapy.

Numerical Simulation of Corrosive Flow in the Production End of Carbon Capture, Utilization, and Storage-Enhanced Oil Recovery Backflow Wastewater Pipeline

In a carbon capture, utilization, and storage-enhanced oil recovery (CCUS-EOR) system process, the high carbon dioxide content and high acid value of the fluid at the extraction end are treated at the wastewater treatment station and then flow through the transfer station of the gathering system. The reinjection system’s pipelines at the transfer station are prone to corrosion and leakage risks. A steady-state nonisothermal flow and corrosion model was established to examine this corrosive phenomenon by utilizing the principles of fluid dynamics and electrochemical corrosion. The study examined how flow velocity, temperature, pressure, and pH affected the pipeline’s corrosion. Simulation results showed flow velocity increased in specific places of both straight and curved pipeline sections. The velocity near the inner wall of curved portions was higher, making them more susceptible to cavitation damage. Faster flow rates, lower pH values, higher temperatures, and smaller pipeline diameters all contributed to increased corrosion on the inner walls of wastewater pipelines. The highest simulated corrosion rate was 8.3162 mm/a corresponding to the smallest pipeline diameter (100 mm), lowest pH (4), highest temperature (60°C), and highest flow rate (36.847 m/s). When designing the CCUS-EOR system, the nonmetallic pipeline should be given priority. If carbon steel metal pipelines are utilized, it is best to use larger-diameter materials in the engineering design. Additionally, procedures such as adding corrosion inhibitors to regulate pH values and lowering the temperature of wastewater conveyance might be explored to reduce corrosion on the pipeline’s inner walls.

A dynamic modelling approach to explore zero emission building stock opportunities towards 2050 – Case study of a university campus

The building sector has a significant influence on energy use and environmental impact. When observing a university campus, reduction of energy use is a complex issue due to the variety of building types, usage, building construction age, and different implementation of improvement measures. To investigate how energy efficiency measures on a single building may influence the entire campus, a combination of building energy simulation and a scenario-based aggregation method using dynamic material flow analysis principles was implemented. This study investigated possible energy efficiency measures at a university campus in Trondheim, Norway. Scenario analysis was used to identify the most critical factors for future development and to evaluate to what extent the campus might develop towards a net zero-emission campus during the period 2017–2050. Four scenarios were introduced including two aspects: renovation of the existing building stock and changes in campus energy supply systems. The results on energy efficiency packages highlighted that saving potentials were highly dependent on the construction period of the buildings. The package combining envelope renovation and operation improvements showed the highest savings. The findings indicated that advanced renovation, including extensive use of heat pumps, might be the most promising strategy for reducing energy demand by 26 %.

Feedback Stabilization of Chain-Like MDOF Nonlinear Structural Systems Under Purely Parametric Gaussian White Noises

A feedback control method is proposed to asymptotically stabilize the chain-like multi-degree-of-freedom (MDOF) nonlinear structural system under purely parametric Gaussian white noises. First, the motion equation of a chain-like nonlinear structural system with [Formula: see text] coupled elements and an undetermined control law is derived. It is reduced to a one-dimensional partially averaged Itô stochastic differential equation of the controlled Hamiltonian by applying the stochastic averaging method, which bypasses the challenge of calculating multiple Lyapunov exponents. Next, based on the dynamical programming principle, the dynamical programming equation of the averaged system with an undetermined cost function is established and addressed to obtain the optimal control law. Then, the Lyapunov exponent of the completely averaged Itô equation is derived. For the given requirement of stabilizing the system, the Lyapunov exponent is entirely fixed and used to analyze the stability of the originally chain-like MDOF nonlinear structural system. A controlled chain-like 8-DOF nonlinear structural system is taken as an example to illustrate the application and effectiveness of the proposed procedure and the effect of control forces on stabilizing the system.

An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality

Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R2 = 0.987 and root mean square error (RMSE) = 0.15 mg/m3, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R2 values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.

The separation theorem for mean-field linear quadratic optimal control problem with partial information

Abstract We investigate the separation principle for a class of mean-field stochastic optimal control problems with partial information, where the linear stochastic dynamic system is observed through a linear noisy channel. In our model, the time inconsistency caused by the mean-field terms makes the dynamic programming principle (DPP) inapplicable, which immediately leads to difficulties in deriving the separation principle. Utilizing the optimal filtering equations in several dimensions, we can transform the stochastic optimal control problem with partial information into one with complete details. Subsequently, the Hamilton-Jacobi-Bellman (HJB) equation that can solve the optimal control is obtained with the support of the dynamic programming principle. The separation principle for solving the optimal control of the original problem is derived.