Mamba-PINN Resilience Assessment Model: A Data–Physics Integrated Approach for Typhoon-Resistant Adaptation of Zhuhai Coastal Sewage Treatment Systems

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration.However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model.Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs. Figure~\\ref{fig:spm_2d} illustrates the absolute relative errors of trained PINN networks using several different training losses and neural network architectures.After determining a consistent training strategy for both the SPM and P2D PINN surrogate models, the PINNs are extended to determine additional internal state-of-health parameters. As more and more parameters were introduced, the PINN training suffered from ``the curse of dimensionality", which was mitigated by using a hierarchical training approach (where a PINN trained with fewer variable model parameters was used to train a PINN with more variable model parameters).Next, the high-dimensionality PINN surrogates are then integrated into Bayesian calibration schemes to identify internal Li-ion battery properties from experimentally measured voltages. Interpreting the high-dimensional parameter posteriors is discussed with respect to model error, parameter prior choices, and experimental errors. Figure 1

Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.

Discretization-based methods like the finite element method have proven to be effective for solving the Helmholtz equation in computational acoustics. However, it is very challenging to incorporate measured data into the model or infer model input parameters based on observed response data. Machine learning approaches have shown promising potential in data-driven modeling. In practical applications, purely supervised approaches suffer from poor generalization and physical interpretability. Physics-informed neural networks (PINNs) incorporate prior knowledge of the underlying partial differential equation by including the residual into the loss function of an artificial neural network. Training the neural network minimizes the residual of both the differential equation and the boundary conditions and learns a solution that satisfies the corresponding boundary value problem. In this contribution, PINNs are applied to solve the Helmholtz equation within a two-dimensional acoustic duct and mixed boundary conditions are considered. The results show that PINNs are able to solve the Helmholtz equation very accurately and provide a promising data-driven method for physics-based surrogate modeling.

Memristor devices have diverse physical models depending on their structure. In addition, the physical properties of memristors are described using complex differential equations. Therefore, it is necessary to integrate the various models of memristor into an unified physics-based model. In this paper, we propose a physics-informed neural network (PINN)-based compact memristor model. PINNs can solve complex differential equations intuitively and with ease. This methodology is used to conduct memristor physical analysis. The weight and bias extracted from the PINN are implemented in a Verilog-A circuit simulator to predict memristor device characteristics. The accuracy of the proposed model is verified using two memristor devices. The results show that PINNs can be used to extensively integrate memristor device models.

Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where "practical" refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset. However, collecting such a high-quality, noise-free, and dense dataset remains challenging. Under the practical hurdle posed by the above approaches, the emergence of physics-informed neural network (PINN) presents an alternative to integrate physics and data but with limited representation capacity. In this work, a framework, termed spatial domain decomposition-based physics-informed neural networks (SPINNs), is proposed to enhance the representation capacity in spatially dependent oceanic scenarios and effectively learn from incomplete and biased prior physics and noisy dataset. Experiments demonstrate SPINNs' advantages over PINN in practical acoustic propagation estimation. The learning capacity of SPINNs toward physics and dataset during training is further analyzed. This work holds promise for practical applications and future expansion.

This article presents a novel framework for onsite estimation of lithium-ion battery electrochemical parameters by combining physics-informed neural networks (PINNs) with transfer learning (TL). Unlike conventional physics-based models that require costly offline parameterization or extensive experimental tests, the proposed two-phase strategy first trains a PINN using only the governing equations of the single particle model, and then fine-tunes selected electrochemical parameters with field voltage measurements while keeping most network weights frozen. This enables efficient adaptation to ageing and cell-to-cell variability with minimal data requirements. The approach achieves high accuracy in estimating key parameters such as diffusion coefficients and active material volume fractions under different degradation conditions. Under a simulation environment, the method reconstructs voltage responses with a root-mean-square error of 8.94 mV and estimates active material fractions with an average relative error below 2.2% across 400 study cases, significantly outperforming classical optimization-based estimators. Experimental validation on an aged NMC cell (82.09% nominal capacity) implemented on a Raspberry Pi 5, demonstrates practical feasibility, yielding a relative error of 3.89% in estimating volume fractions with a computational cost of 106 ms per iteration. These results confirm that the proposed TL-based PINN framework offers an accurate, computationally efficient, and easily deployable solution for real-time battery management systems.

In the aerospace industry, modern aircraft are increasingly equipped with a growing number of sensors, which enable the development of predictive maintenance solutions utilizing data-driven diagnostic and prognostic (D&P) techniques to enhance operational availability and reduce maintenance costs. However, constructing a purely data-driven D&P solution requires a substantial amount of run-to-fail sensor data, which is often unavailable for highly reliable and safety-critical aircraft systems. This limitation restricts the applicability of purely data-driven D&P solutions for aircraft subsystems. To address this limitation, we developed a novel Hybrid Digital Twin framework that integrates physics-based subsystem models with sensor data, enabling enhanced feature generation for improved fault diagnostics and prognostics. Our approach simultaneously estimates both design and health-related parameters, facilitating accurate model calibration even when some of design data is not available. Sensor features enhanced with estimated health-related parameters enable more accurate data-driven diagnostics and prognostics solutions of a sub-system or a component. The framework is demonstrated on key subsystems of the aircraft Environment Control System (ECS), including the Heat Exchanger and Centrifugal Compressor. Various parameter estimation techniques including nonlinear least squares, particle swarm optimization, and extended Kalman filter, Unscented Kalman filter, Physics-Informed Neural Networks, etc., are evaluated. This Hybrid Digital Twin approach offers a promising pathway for more accurate, robust and scalable health management of aircraft subsystems having limited operational data.

The escalating frequency and severity of extreme environmental events underscores the critical need for a paradigm shift from reactive to proactive management strategies. This perspective article argues that artificial neural networks (ANNs) represent a transformative tool for environmental forecasting, capable of capturing the non-linear, high-dimensional dynamics that define complex Earth systems. While ANNs demonstrate superior predictive performance across domains such as hydrology, air quality, and ecology, their integration into decision-making workflows remains hindered by challenges related to data quality, model interpretability, and a lack of interdisciplinary collaboration. We synthesize current advancements, highlighting the pivotal role of physics-informed neural networks (PINNs) and explainable AI (XAI) in bridging the gap between data-driven insights and physical plausibility. Finally, we propose a concrete interdisciplinary roadmap, encompassing curated benchmarks, hybrid modeling, educational initiatives, and institutional co-design, to translate computational potential into trustworthy, actionable tools for building environmental resilience.

With the rapid development of wireless communication technology, the efficient utilization of spectrum resources, optimization of communication quality, and intelligent communication have become critical. Radio map reconstruction is essential for enabling advanced applications, yet challenges such as complex signal propagation and sparse observational data hinder accurate reconstruction in practical scenarios. Existing methods often fail to align physical constraints with data-driven features, particularly under sparse measurement conditions. To address these issues, we propose Physics-Aligned Radio Map Diffusion Model (PhyRMDM), a novel framework that establishes cross-domain representation alignment between physical principles and neural network features through dual learning pathways. The proposed model integrates Physics-Informed Neural Networks (PINNs) with a representation alignment mechanism that explicitly enforces consistency between Helmholtz equation constraints and environmental propagation patterns. Our architecture employs two synergistic U-Nets: the first ensures physical consistency by minimizing PDE residuals and boundary conditions through latent space alignment, while the second refines predictions via diffusion-based denoising with attention-guided feature fusion. This dual alignment strategy enables simultaneous satisfaction of wave propagation laws and data distribution characteristics. Experimental results demonstrate significant improvements over state-of-the-art methods, achieving NMSE of 0.0031 and RMSE of 0.0125 under Static Radio Map (SRM) conditions, and NMSE of 0.0047 with RMSE of 0.0146 in Dynamic Radio Map (DRM) scenarios. The proposed representation alignment paradigm provides 37.2% accuracy enhancement in ultra-sparse cases (1% sampling rate), confirming its effectiveness in bridging physics-based modeling and deep learning for radio map reconstruction. These advancements establish a new framework for sparse signal environment characterization, with direct applications in 5G/6G network optimization and intelligent spectrum management. The code can be found on the website: https://github.com/Hxxxz0/RMDM

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.

With the arrival of the “5G” era and the digital economy era, the development of the city has permeated all aspects of the city, and with the emergence of “smart city”, it is likely to have “smart” everywhere in the future city. At the same time, “Future Smart Transportation” was proposed as a discussion topic at the “2019 (8th) International Smart City Summit and Smart Ecology Expo”. The research and development of the driverless bus stops will spur the driverless bus stops to take on the past with a brand-new look, welcome the future, and contribute to the sustainable development of the smart city. Taking the driverless bus station in Zhengdong New District as an example, this paper explores the sustainable development of a smart city through the creation of manned (open)-shared (transition space)-driverless (semi-enclosed space) space and the introduction of modular design approach. To connect people’s work, life and transportation, and derive a new public transportation environment based on manned bus stops, namely, intelligence, sustainability, environmental friendliness and safety, thus create a smart transportation pilot to embrace the bright future of sustainable smart cities.

The purpose of this work is to develop ecological and economic indicators for assessing the sustainable development of small cities. The authors use methods of collecting and processing scientific material, systematization, as well as logical and project-based approaches. In accordance with the purpose, the authors solve the following problems in their work: the formation of indicators and models for assessing the ecological and economic development of the city; testing the model on the example of a specific city. The paper proposes an algorithm for the formation of a model for an integral assessment of the sustainable development of the city, reflecting the dynamics of the economic, social and environmental development of the city. The object of the study is a complex of relationships that arise during the assessment of the state of the city in order to ensure its sustainable development. The article presents a comprehensive assessment of the ecological, social and economic factors of Almetyevsk (Republic of Tatarstan) and Salavat (Republic of Bashkortostan).

In today's world, facing various global challenges such as climate change, poverty, inequality, and environmental degradation, the United Nations proposed a global action plan in 2015 - the Sustainable Development Goals, with SDG1 aiming to eradicate all forms of poverty. The rapid pace of global urbanization in the 21st century is bringing new challenges. Among them, poverty is a major issue with serious implications for sustainable urban development. In China, poverty has always been a focal point of government and societal attention. By the end of 2020, absolute poverty in rural areas of China had been effectively eliminated, but urban poverty has increasingly become prominent, affecting the sustainable development of Chinese cities. The purpose of this research is to collect information from literature, identify research hotspots and trends in urban poverty alleviation in China, and analyze the challenges faced by sustainable development in Chinese cities and how to address these issues. This study analyzes the research hotspots and trends in urban poverty alleviation in China through the use of CiteSpace software and bibliometrics in order to promote sustainable urban development in China.

The objectives of this study are as follows: 1) Investigating past research methodologies for evaluating various city functions and its activities on sustainable city development, and examining innovative viewpoints of city evaluation considering concepts of compact city as a sustainable one. 2) Constructing a city profile database, developing comprehensive framework for city evaluation considering environmental aspects and compactness, and examining advantage of compact city characteristics and possibilities of achievement on macro perspective integrating social, economical and environmental index. 3) Investigating city characteristics transition from developing to developed country on macro perspective index. The preliminary regression analysis on city compactness using statistical data showed the results as follows: 1) In order to establish comfortable life environment, economic growth is necessary to some extent. 2) In order to establish sustainable city focused on energy use, it is necessary to restrict total city population. 3) In order to reduce air pollutant and alleviant severe traffic condition, high density is necessary to some extent but excess high density leads rapid increase of pollutant load and serious traffic condition. In addition to above socio-economic analysis, we examined spatial city density transition from developing to developed countries and derived the possibilities of comprehensive city evaluation including socio-economic, environmental and spatial density index.

This study takes 22 megacities and super-large cities in China as research subjects, systematically exploring the coupled system relationship between digital maturity and resilient cities. Using methods such as information entropy theory, coupling coordination degree model, and grey relational coefficient measurement, it conducts comprehensive evaluation analysis, coupling coordination analysis, and factor contribution analysis. The results indicate that digital maturity and resilient cities exhibit a mutually reinforcing relationship. The positive interaction and coupling between digital maturity and resilient cities development have further promoted innovation in China’s urban governance and contributed to the long-term sustainable development of cities. Although the digital maturity and resilience levels of these cities show a fluctuating upward trend, they have not yet reached an ideal state, and significant differences exist among different urban clusters and regions. Megacities and super-large cities demonstrate high coupling characteristics among their internal subsystems, but their coordination level remains relatively low. Key factors such as digital governance policies, digital economy, and disaster-resistant infrastructure play a crucial role in advancing the sustainable development of resilient cities. Therefore, it is urgent to improve the coupling coordination mechanism to enhance sustainable development capacity.