Integrating community level transmission geographical networks into a dynamical system for better epidemic control.

Aqueous zinc ion batteries have emerged as promising candidates for next-generation energy storage systems due to their inherent advantages of cost-effectiveness, operational safety, and environmental compatibility. Nevertheless, critical challenges including zinc dendrite formation, parasitic corrosion reactions, hydrogen evolution, and unsatisfactory Zn2+ diffusion kinetics still hinder their commercial viability. To address these limitations systematically, recent research efforts have focused on developing comprehensive mechanistic analyses through advanced characterization methodologies. This review presents a critical evaluation of state-of-the-art analytical techniques for investigating aqueous zinc ion batteries, encompassing fundamental principles, operational protocols, and practical applications across various research scenarios, thereby establishing a robust methodological framework for future studies. The discussion commences with an examination of conventional characterization approaches that provide essential baseline information regarding electrode morphology and electrochemical behavior. Subsequently, we introduced in situ analytical platforms combining three-dimensional visualization techniques, multimodal spectroscopic characterization, and dynamic electrochemical monitoring systems. These advanced operando characterization tools enable real-time observation of interfacial evolution and transient reaction processes, offering unprecedented insights into battery failure mechanisms at multiple scales.

ABSTRACT Reliability studies in several engineering domains have recently made extensive use of the general family of inverted exponentiated distributions. Using this family as a baseline model, in this work, we have obtained a number of statistical inferences on the power-trend mechanism-based composite dynamic system. In particular, the maximum likelihood estimates of the unknown parameters and baseline reliability function are computed. The asymptotic and bootstrapped confidence intervals of the baseline reliability function are proposed. A parametric hypothesis test is provided to ascertain, whenever the failed components change the hazard rate function. The Bayes estimates of the unknown model parameters and baseline reliability functions with respect to the squared error and generalized entropy loss functions are obtained. Further, the Metropolis–Hastings algorithm is used to generate Markov chain Monte Carlo samples for the computation of the Bayes estimates. The highest posterior density credible interval of the baseline reliability function is also calculated. For illustration reasons, two real data sets are considered and then analysed. Finally, a simulation study is carried out to examine the behaviour of the proposed estimates.

This study proposes a sustainable urban planning strategy that enhances building energy self-sufficiency through photovoltaic-based renewable energy generation. This research focused on high-rise residential complexes and introduces an adaptive facade system designed to efficiently utilize the extensive solar-exposed surfaces of building facades. The system was programmed to automatically adjust according to the optimal tilt angle, which reflects monthly variations in solar altitude and azimuth, thereby maximizing energy generation. To further improve the effectiveness of the adaptive facade system, a “bridge-type apartment complex” layout was developed, in which building orientations varied around a south-facing axis to include southeast and southwest orientations. The proposed urban configuration was developed using a parametric design within the building information modeling software Revit 2023, and energy generation simulations were conducted for Seoul, South Korea, using Insight (a Revit plug-in) during 2024. Simulation results revealed that bridge-type apartment complexes achieved higher levels of renewable energy generation than conventional slab-type apartment complexes. These findings suggest that a three-dimensional urban design incorporating diverse facade orientations, combined with a dynamic adaptive facade system, not only enhances energy efficiency but also offers the potential to create creative and flexible urban landscapes that contrast with conventional, uniform cityscapes.

Resilience broadly describes the ability to withstand perturbations. Measures of system resilience have gathered increasing attention across applied disciplines; yet, existing metrics often lack computational accessibility and generalizability. In this work, we review the literature on resilience measures through the lens of dynamical systems theory and numerical methods. In this context, we reformulate pertinent measures into a general form and introduce a resource-efficient algorithm designed for their parallel numerical estimation. By coupling these measures with a global continuation of attractors, we enable their consistent evaluation along system parameter changes. The resulting framework is modular and easily extendable, allowing for the incorporation of new resilience measures as they arise. We demonstrate the framework on a range of illustrative dynamical systems, revealing key differences in how resilience changes across systems. This approach offers a more global and comprehensive perspective compared to traditional linear stability metrics used in local bifurcation analysis, which can overlook inconspicuous but significant shifts in system resilience. This work opens the door to genuinely novel lines of inquiry, such as the development of new early warning signals for critical transitions or the discovery of universal scaling behaviors. The presented exemplary analyses can serve as blueprints for further system-specific investigations and comparative studies on different measures of resilience. All code and computational tools are provided as an open-source contribution to the DynamicalSystems.jl software library.

Multi-view learning meets state-space model: A dynamical system perspective.

Effects of environmental factors on host-microbiota interactions in the guts of aquatic organisms: A review.

Study on the dynamic response and system reliability of curved beam bridge under multi-dimensional non-uniform random earthquake excitation

An adaptive multi-head attention dynamic integration system for interval-valued wind speed prediction based on joint feature extraction and optimal model selection

AlchemyNavigation: Comparative evaluation of a dynamic navigation system for unity virtual worlds

Causal graph inference with adaptive dynamic structure learning for mechanism-oriented fault diagnosis in dynamic industrial systems

Pedestrian localization based on kinematic dynamic constraints and adaptive Neuro-Fuzzy inference system

Tidal intensity and suspended sediment concentration drive microplastic distribution in the Pearl River Estuary: Insights from remote sensing retrieval.

A dual dynamic shutter system for accelerating ion irradiation sample throughput via lateral gas implantation gradients

Synergistic modulation of in-plane N-doping and intrinsic vacancy defects in activated carbon for enhanced formaldehyde adsorption.

Image multi-party secure mixing scheme based on dynamic cross-coupled spatiotemporal chaotic system

Integrated optimization approach to cell formation, cell layout, and group scheduling for dynamic cellular manufacturing system

Continual learning (CL) focuses on learning non-stationary data distribution without forgetting previous knowledge. The most widely used memory-replay approaches are often prone to memory overfitting due to the limited memory diversity and hardness. Existing work mitigating memory overfitting either lacks data diversity or hardness or is hard to train. To address the above limitations and release the memory buffer potential, we view the memory buffer transformation from a new dynamic system perspective and propose a continuous and reversible memory transformation method. We introduce an adversarial optimization objective that jointly learns the CL model and memory transformer. Specifically, we present a deterministic continuous memory transformer (DCMT) to generate diverse memory data. Furthermore, we inject uncertainty into the transformation function and develop a stochastic continuous memory transformer (SCMT), which substantially enhances the diversity of the transformed memory buffer. The presented neural transformation approaches have significant advantages over existing ones: (1) they significantly increase the memory buffer diversity and hardness to overfit; (2) they are memory efficient without needing to make a replica of the memory data. Extensive experiments show a significant improvement with our approach compared to strong baselines.

Recursive estimators and hybrid Cramér–Rao bounds for discrete-time Markovian dynamic systems

Discretization of invariant measures for stochastic lattice dynamical systems