Dynamic Rules Research Articles

Learning Optimal Dynamic Treatment Regimes from Longitudinal Data.

Studies often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone (BUP-NX) dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy.

First Order Methods for Geometric Optimization of Crystals: Experimental Analysis

AbstractThe geometric optimization of crystal structures is a procedure widely used in computational chemistry that changes the geometrical placement of the particles inside a structure. It is called structural relaxation and constitutes a local minimization problem with a non‐convex objective function whose domain complexity increases according to the number of particles involved. This work studies the performance of the two most popular gradient methods in structural relaxation, Steepest Descent and Conjugate Gradient. Although frequently employed, there is a lack of their study in this context from an algorithmic point of view. The algorithms are initially benchmarked on the basis of a constant step size. Three concepts for designing dynamic step size rules are then examined in detail and analyzed. Results show that there is a trade‐off between convergence rate and the possibility of an experiment to succeed. In order to address this, a function is proposed as a formal means for assigning utility to each method based on preference. The function is built according to a recently introduced model of preference indication concerning algorithms with deadline and their run time. It introduces the quantification of the optimization algorithms' performance according to convergence speed and success rate, thus enabling the appointment of a specific algorithmic recipe as the best choice for balanced preferences.

Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics.

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey-Glass or Stuart-Landau-like systems, but also to reservoirs whose dynamical model is not available.

Evacuation simulation considering pedestrian risk perception under toxic gas diffusion

Toxic gas leakage in densely populated public areas can easily endanger people's safety and social stability. This paper establishes a pedestrian evacuation model based on cellular automata that considers the pedestrians' perception of casualties and environmental risks under the real-time spread of toxic gas. The model simulates the emergency evacuation process of pedestrians and their exit selection behavior in the toxic gas leakage incident at a passenger station. The simulation results indicate that evacuees' dynamic exit selection behavior regarding the risk perception of toxic gas incidents positively affects pedestrian evacuation. An optimal perception radius and toxic gas alarm conditions exist that minimize pedestrian casualties while maximizing evacuation efficiency. An increase in the exit change probability keeps pedestrians away from risk but may lead to congestion and reduce evacuation efficiency when the pedestrian density is high. The location of the toxic gas source has different impacts on casualties, and the dynamic exit selection rules prove more effective when the gas source is closer to one of two exits rather than being located between them.

A Comprehensive Ad-Blocking and VPN Solution for Secure, Responsible and Ethical Online Browsing

A Comprehensive Ad-Blocking and VPN Solution for Secure, Responsible and Ethical Online Browsing

Numerical Study on the Dynamic Response and Damage Cumulative of Bolt-Supported Cavern under Adjacent Cyclic Explosion

Adjacent cyclic explosions significantly impact the stability of underground anchored caverns. Based on the similar model test of the vault explosion of the anchored cavern, the dynamic analysis finite element software ANSYS/LSDYNA(18.0) was used to establish a model of the straight wall side explosion of the underground anchored cavern and conduct a numerical simulation. When the total amount of explosion load is the same, we compared the stress time history curve, displacement time history curve, tunnel wall displacement, and circumferential strain curve of the surrounding rock in the underground anchored cavern (under both a high-level single-side blast and a low-level cyclic side blast). We obtained the dynamic response rules of the surrounding rock. By comparing the damage evolution process of the surrounding rock in the two situations, the damage accumulation law of the surrounding rock was analyzed. At the same time, the axial stress distribution characteristics of underground anchor cavern anchors under the action of cyclic explosion were studied. The findings demonstrate that when the total level of blast load adjacent to the cavern is the same, the displacement and circumferential peak strain of surrounding rock and the axial stress of rock bolt in the high-level single explosion are greater than those in the low-level cyclic explosion. However, compared to a single explosion, the rock mass suffers more damage in the cyclic explosion. This study will provide engineers with information that will assist them with a better understanding of the cumulative damage mechanisms of surrounding rock, as well as the stress characteristics of rock bolts under dynamic loads near the explosion site, which will be used to design underground caves with anti-blast features.

Tracking of car body vibration performance of centralized power EMU

The centralized power EMU is a new type of passenger transport vehicle in the process of upgrading the transportation equipment of China’s ordinary speed railways. It can significantly improve the transportation efficiency and passenger comfort of ordinary-speed railways. Detailed research on its dynamic performance during service can guide the widespread use and maintenance of this type of EMU. This paper conducted long-term tracking tests on a centralized power EMU. It analyzes the dynamic performance and evolution rule of the centralized power EMU during long-term service from the perspectives of wheel wear and car body vibration. It compares the performance differences between the control and the trailer car. In the tracking experiment, it was found that the car body exhibits vertical low-frequency vibration. Many experimental studies have shown that the frequency at which the vehicle passes through a bridge that stretches across a distance of 32 meters on the bridge pier at 160 km/h is 1.3 Hz, which excites the nodding mode and causes significant vertical harmonic vibration of the car body.

Color image encryption algorithm based on novel dynamic DNA encoding and chaotic system* *This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant no. KJZD-M202000501), Sponsored by Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0139) and National Natural

To enhance the security of image data, prevent unauthorized access, tampering, and leakage, maintain personal privacy, protect intellectual property rights, and ensure the integrity of images during transmission and storage. This study introduces an innovative color image encryption scheme based on dynamic DNA encoding operations and chaotic systems. By simulating a quantum random walk, a random key is generated to enhance the security of the confidential system. In addition, we integrated the enhanced Josephus problem into DNA coding rules to create dynamic DNA coding rules. At the same time, we proposed a dynamic double-loop DNA XOR operation, which fully utilizes the random sequence generated by the generalized Hamiltonian chaos system to precisely control the loop direction, starting point, and number of operations. This enhances the complexity of the encryption algorithm. After sufficient experimental verification and in-depth research and analysis, our innovative design not only enhances the difficulty of cracking while ensuring image quality but also provides reliable protection for the security of image data.

Mechanical behavior and performance evolution of railway ballast track under dynamic stabiliser based on the hybrid MBD-DEM simulation

The uncertainty in local structure and time-variable compaction of ballast aggregates poses challenges in selecting suitable stabilization methods and parameters for ballast tracks in practical engineering, often resulting in suboptimal operational outcomes. To address this issue comprehensively, the spatial hybrid model of the double dynamic stabilisers-ballast track (MBD-DEM) is proposed in this paper. Subsequent numerical simulations of stabilisation operations are conducted to elucidate ballast vibration, migration behavior and the evolution of ballast track resistance under varying initial conditions of the ballast bed. Initially, leveraging multibody dynamics theory (MBD) and discrete element method (DEM), a MBD model of double stabilisers and a DEM ballast track model containing 6 sleepers were established respectively. Using the coupling interface, the real-time hybrid calculation of these two models is achieved through 6 sleepers in the middle of the ballast bed. Subsequently, the model's accuracy was validated by comparing test data from stabilisation vehicle operations on a Kunming Railway Bureau test line with simulation data from the dynamic double stabilisers-ballast track spatial hybrid model. Finally, utilizing the proposed hybrid model, the dynamic behavior, contact states, and movement rules of ballast particles during stabilisation operations on both loose and initially stable ballast beds (LBB and ISBB) were investigated at the mesoscopic scale. Concurrently, an in-depth analysis of lateral resistance changes in the ballast bed before and after the stabilisation operation is presented. The relevant findings effectively unveil the evolution of dynamic behavior in the ballast bed during stabilisation operations, concluding with practical suggestions for on-site operations. Simulation results illustrate that the rotation of ballast particles in the XY plane beneath each sleeper is predominantly characterized by harmonic rotation. With an increase in the depth of the ballast bed, the range of the rotation axis for combined angular velocity diminishes, leading to the phenomenon of rotation axis deflection. To ensure operational safety and efficiency, a minimum of two-times stabilization operations is recommended for loose ballast beds (LBB), with three-times operations considered optimal. For initially stable ballast beds (ISBB), one-time stabilization operation is basically sufficient.

Electric Energy Metering Error Evaluation Method Based on Deep Learning

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified.

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

AbstractGlobal food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner.

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

Consequence of anisotropy on flocking: the discretized Vicsek model

We numerically study a discretized Vicsek model (DVM) with particles orienting in q possible orientations in two dimensions. The study investigates the significance of anisotropic orientation and microscopic interaction on macroscopic behavior. The DVM is an off-lattice flocking model like the active clock model (ACM; Chatterjee et al 2022 Europhys. Lett. 138 41001) but the dynamical rules of particle alignment and movement are inspired by the prototypical Vicsek model (VM). The DVM shows qualitatively similar properties as the ACM for intermediate noise strength where a transition from macrophase to microphase separation of the coexistence region is observed as q is increased. But for small q and noise strength, the liquid phase appearing in the ACM at low temperatures is replaced in the DVM by a configuration of multiple clusters with different polarizations, which does not exhibit any long-range order. We find that the dynamical rules have a profound influence on the overarching features of the flocking phase. We further identify the metastability of the ordered liquid phase subjected to a perturbation.

Adaptive asynchronous output feedback control for a class of switched nonlinear systems with a novel aperiodic dynamic sampling rule

Adaptive asynchronous output feedback control for a class of switched nonlinear systems with a novel aperiodic dynamic sampling rule

Deep-learning reconstruction of complex dynamical networks from incomplete data.

Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM2.5 dataset covering 184 cities in China.

A novel dynamic risk assessment method for hazardous chemical warehouses based on improved SVM and mathematical methodologies

Effective dynamic risk assessment is crucial for identifying process hazards and preventing accidents. The rapid development of modern technology urgently requires the development of new dynamic risk assessment methods to address current societal needs, especially in situations where a large amount of real-time data is available. Consequently, this manuscript presents an improved support vector machine model (SVM), integrated mathematical modeling, to facilitate dynamic risk assessment for hazardous chemical warehouses (HCWs). Firstly, an indicator framework and a quantitative risk factor table are crafted. Then a mathematical model using knowledge graph and DEMATEL-variable weight theory (VWT) is established, which combines accident information with VWT to optimize the weight calculation and provides reliable prior knowledge for the improved SVM model. Ultimately, the improved SVM model is crafted by the establishment of an improved hybrid kernel of polynomial and radial basis function kernel, fine-tuning its hyperparameters with particle swarm optimization, and utilizing sensitivity analysis to simplify the model. The model is formulated to accommodate the intricate non-linear relationships within indicators and dynamic risk, thereby establishing dynamic risk assessment rules. Through a case study, the improved SVM model demonstrates higher precision in dynamic risk prediction compared to original models. The results substantiate that the model can provide innovative insight and methodology for the precise evaluation of dynamic risk in HCWs.

Numerical simulation of thermal transfer and flow behavior of Ni60AA formed by laser cladding

The thermal behavior of the cladding layer and the flow behavior of the molten pool significantly affect the geometry, temperature distribution and microstructure of the cladding track, thus affecting the mechanical properties of the cladding. Based on the interaction between the laser and powders, a calculation method of the cross-sectional area of the cladding track is proposed. The droplet energy method and the moving grid method are first combined to establish the surface growth model of the cladding track, to calculate the geometry of the cladding track. Then, the temperature field and flow field of the cladding track are studied. The driving forces of the molten pool flow including shear viscosity, gravity, buoyancy, surface tension at the gas-liquid boundary and pressure between liquids are considered to carry out the dynamics study of the molten pool. According to the force condition of the infinitesimal in the molten pool, the dynamic rules and boundary rules of the molten pool are established, so as to solve the flow field of the molten pool. In addition, the calculated geometry of the cladding track is compared with the experimental result to verify the accuracy of the model, and the microhardness of the cladding track is measured. The results show that the relative error between the simulation and the experiment is less than 6%, and the microhardness of the cladding layer is significantly improved. This work can provide a method for studying the morphology, temperature field, and flow field of laser cladding.

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

Midpoint geometric integrators for inertial magnetization dynamics

We consider the numerical solution of the inertial version of Landau-Lifshitz-Gilbert equation (iLLG), which describes high-frequency nutation on top of magnetization precession due to angular momentum relaxation. The iLLG equation defines a higher-order nonlinear dynamical system with very different nature compared to the classical LLG equation, requiring twice as many degrees of freedom for space-time discretization. It exhibits essential conservation properties, namely magnetization amplitude preservation, magnetization projection conservation, and a balance equation for generalized free energy, leading to a Lyapunov structure (i.e. the free energy is a decreasing function of time) when the external magnetic field is constant in time. We propose two second-order numerical schemes for integrating the iLLG dynamics over time, both based on implicit midpoint rule. The first scheme unconditionally preserves all the conservation properties, making it the preferred choice for simulating inertial magnetization dynamics. However, it implies doubling the number of unknowns, necessitating significant changes in numerical micromagnetic codes and increasing computational costs especially for spatially inhomogeneous dynamics simulations. To address this issue, we present a second time-stepping method that retains the same computational cost as the implicit midpoint rule for classical LLG dynamics while unconditionally preserving magnetization amplitude and projection. Special quasi-Newton techniques are developed for solving the nonlinear system of equations required at each time step due to the implicit nature of both time-steppings. The numerical schemes are validated on analytical solution for macrospin terahertz frequency response and the effectiveness of the second scheme is demonstrated with full micromagnetic simulation of inertial spin waves propagation in a magnetic thin-film.

Package Assembly Design Kits - The Future of Advanced Package Design

HDFO design layout and system-level performance require a range of dynamic design rule definitions. Package Assembly Design Kits can capture and implement these design rule contraints. These PADKs offer a special type of implementation by validating the design of layout with a benchmark that supports the manufacturing and assembly processes performed by the OSATs. Partnering with an OSAT to utilize PADKs early in the life cycle of product development is critical to reducing the number of design layout spins sometimes required to meet manufacturing and assembly best practices. As the HDFO signal I/O counts increase proportionally with the package complexity, the only practical solution is to leverage the strength of a PADK to drive the package design layout from day one. Establishing a PADK into an existing desgin workflow does take some investment; howeverk the rewards can be exponential, with sequential or parallel projects commencing. Areas requiring an investment will depend on the current design environment and established workflows. The following are the four pillars that lead to a successful PADK implementation: high-end computer hardware, compatible software, advanced training, and a qualified support team. The scale of each will additionally depend on the scope of each package being developed. One especially positive feature of the elements that make up the package assembly design kit is that each item can be implemented into existing design flows. The impact on the design flow is minimal, but the results are immense. Pulling all the benefits together is like opening the doors into tomorrow, and they remain open with the potential and opportunities offered by the advancements in Package Assembly Design Kits.