Physical Knowledge Research Articles

Physics-informed neural networks for dynamic process operations with limited physical knowledge and data

In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for modeling dynamic processes with incomplete mechanistic semi-explicit differential–algebraic equation systems and scarce process data. In particular, we focus on estimating states for which neither direct observational data nor constitutive equations are available. We propose an easy-to-apply heuristic to assess whether estimation of such states may be possible. As numerical examples, we consider a continuously stirred tank reactor and a liquid-liquid separator. We find that PINNs can infer immeasurable states with reasonable accuracy, even if respective constitutive equations are unknown. We thus show that PINNs are capable of modeling processes when relatively few experimental data and only partially known mechanistic descriptions are available, and conclude that they constitute a promising avenue that warrants further investigation.

Probabilistic framework for strain-based fatigue life prediction and uncertainty quantification using interpretable machine learning

Establishing a unified fatigue life prediction model and quantifying the uncertainty in the mechanical behavior of materials are critical to ensure the structural integrity and equipment performance. For the commonly-used strain-based fatigue methods, existing estimation methods exhibit inevitable deviations, while data-driven methods have shown poor extrapolation ability and interpretability. Therefore, this paper aims to develop a probabilistic framework for strain-based fatigue life prediction and uncertainty quantification (UQ) to provide an indication for fatigue design/assessment using interpretable machine learning (ML) techniques. Based on Shapley additive explanations (SHAP) and symbolic regression (SR), interpretable prediction models with concise expressions and outstanding prediction performance are established and optimized according to the priori physical knowledge. Moreover, accounting for the material variability, the probabilistic assessment with UQ excellently validates the prediction model, and quantifies the variability of ε-N curves. The proposed framework provides valuable reference and shows promising prospects in fatigue design for engineering components.

Physically-guided open vocabulary segmentation with weighted patched alignment loss

Open vocabulary segmentation is a challenging task that aims to segment out the thousands of unseen categories. Directly applying CLIP to open-vocabulary semantic segmentation is challenging due to the granularity gap between its image-level contrastive learning and the pixel-level recognition required for segmentation. To address these challenges, we propose a unified pipeline that leverages physical structure regularization to enhance the generalizability and robustness of open vocabulary segmentation. By incorporating physical structure information, which is independent of the training data, we aim to reduce bias and improve the model’s performance on unseen classes. We utilize low-level structures such as edges and keypoints as regularization terms, as they are easier to obtain and strongly correlated with segmentation boundary information. These structures are used as pseudo-ground truth to supervise the model. Furthermore, inspired by the effectiveness of comparative learning in human cognition, we introduce the weighted patched alignment loss. This loss function contrasts similar and dissimilar samples to acquire low-dimensional representations that capture the distinctions between different object classes. By incorporating physical knowledge and leveraging weighted patched alignment loss, we aim to improve the model’s generalizability, robustness, and capability to recognize diverse object classes. The experiments on the COCO Stuff, Pascal VOC, Pascal Context-59, Pascal Context-459, ADE20K-150, and ADE20K-847 datasets demonstrate that our proposed method consistently improves baselines and achieves new state-of-the-art in the open vocabulary segmentation task.

Deciphering laser shock peening quality monitoring: Wavelet-driven network with interpretability

Quality monitoring of laser shock peening based on acoustic emission technology is a topical multidisciplinary issue that has received much attention in recent years. To acquire complex time-varying information in acoustic emission signals, convolutional neural networks with powerful learning capabilities have shown potential for a wide range of applications. However, the black-box property of the network imposes limitations on its further development and decision credibility. Therefore, this study proposes a wavelet-driven network with theoretical basis and physical significance. This network can cyclically utilize discrete wavelet packet transform to map input features to the wavelet domain during the learning process, thereby obtaining more robust and valuable information. This paper also constructs a novel wavelet attention mechanism that takes into account the difference between low-frequency and high-frequency information, and is able to allocate resources in both the decomposition component and the time-domain dimension. The proposed method can be seen as a multiresolution analysis technique that combines existing physical knowledge with nonlinear feature processing and feature selective enhancement. The results of the two laser shock peening cases show that the proposed method not only outperforms current state-of-the-art models in terms of monitoring performance, but also has better physical interpretability. Importantly, the proposed method has the potential to be further extended to other interpretable structural health monitoring.

Review of approximation formulas for fractional derivatives of variable order

Fractional differential equations of variable order, depending on a time or space variable, are successfully used to study time and/or spatial dynamics. The purpose of this review is to consider the latest research and results related to the basic definitions and approximation formulas for fractional derivatives of variable order. The review begins with an examination of existing definitions based on various physical and practical knowledge. The following are formulas for discretizing fractional derivatives, since they play a key role in numerical modeling, providing an effective means of describing complex systems with long-term memory, heterogeneous parameters and non-local interactions. Their use not only improves the accuracy of models, but also facilitates the numerical solution of differential equations, which is essential for analyzing and predicting the behavior of real processes in various fields of science and engineering. This review is intended to help readers select appropriate definitions and discretization formulas to effectively solve specific physical and engineering problems.

Textile Collage as the Artistic Method to Comprehend Industrial Memory: the Analysis of the Project “Radiant City” by Elena Sharganova

The article is focused on the sociological analysis of the project “Radiant city” of Elena Sharganova, resident of the tenth season of Open Studios at the Center of Contemporary Art Winzavod (2023/24), that is devoted to the artist’s hometown — Nelidovo — and consists of a series of textile collages, made with mixed media techniques including photo printing, cyanotype, machine seam, hand embroidery. Sociological conceptualization of the project is made based on the interview with the artist, analysis of archive publications kindly provided by the artist, and researches on social memory and industrial cities. Based on the interpretation of collages as spaces of representation in terms of Henri Lefebvre that represent a zone of experience, in which images and symbols fill in physical space and knowledge about it, the article shows that the “Radiant city” is the artist’s reflections on Nelidovo’s unrealized development trajectories and destinies of small industrial cities in general. There are three key topics raised by the artist in this project: disappointment in communism and nostalgia for the past, the problem of small industrial cities’ development, interrelation of collective and individual memory. Elena Sharganova’s project significance is defined by experiments with art mediums and a research approach that brings together art and social issues.

A data-and-knowledge-driven WNN modeling approach for the absorption refrigeration system

Given that the cooling capacity of the absorption refrigeration system depends on both the heat supply and the operating conditions, a data-and-knowledge-driven Wavelet Neural Network (WNN) modeling approach is proposed based on the analysis of its working principle and the coupling relationship between variables. This approach utilizes the Simple Particle Swarm Optimization (SPSO) algorithm instead of the gradient descent method to search the network parameters of WNN, which makes up for the defects of the gradient descent method that is easy to fall into the local optimum. Furthermore, the inequality constraints of energy and temperature are integrated into the loss function of model training as prior knowledge, and the model’s parameters are trained using experimental data, which reduces the training burden of the model and the model’s dependence on the training data, and increases the network’s interpretability and the model’s robustness. The experimental results show that the prediction accuracy and generalization of the WNN model are improved after replacing the optimization algorithm and incorporating the physical knowledge under the same working conditions, and its RMSE and MAE are reduced by 2.08% and 1.63%, respectively, compared with the original WNN model.

Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing

Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.

Understanding the social and emotional domains of physical literacy in post‐secondary education: A scoping review

AbstractWhile a variety of definitions persist, a common definition of physical literacy is that it encompasses the motivation, confidence, physical competence, knowledge and understanding to value and take responsibility for engagement in physical activities over the lifespan. While this perspective to physical literacy is broad, a great deal of curricular outcomes still focus on the physical competence, knowledge and understanding aspects of the framework leaving a gap in the holistic nature of the construct. This scoping review explores the construct of physical literacy within post‐secondary education, focusing on how social and emotional dimensions are integrated in teaching, delivery and assessment. This scoping review followed PRISMA‐ScR guidelines. Five online databases were used to identify papers published that used physical literacy in combination with post‐secondary as the educational setting with a focus on curriculum. The findings underscore a substantial gap in the literature. It was identified that the physical aspects of physical literacy were predominant with limited attention to the social and emotional domains. The gap implies that the comprehensive construct of physical literacy, which encompasses non‐physical facets, is often omitted in post‐secondary curricula. The review highlights an unexplored opportunity for post‐secondary institutions to intentionally construct more comprehensive physical literacy programmes. By incorporating social and emotional dimensions into these programmes, students gain a more holistic understanding of physical literacy. Subsequent research can delve into effective integration strategies and the potential benefits of such an approach.

Asuhan Keperawatan pada Tn. A dengan Diagnosa Medis Post Operasi Soft Tissue Tumor (STT) Flank Sinistra di Ruang Mawar 2 RSUD Dr. Soeselo Kabupaten Tegal

Tumor is a disease that is currently one of the health problems that causes death, soft tissue tumor or soft tissue tumor is an abnormal lump that is formed due to the growth of new cells (Irsal et al., 2020). The signs and symptoms in soft tissue tumor patients include pain due to the surgical process. The purpose of this writing is to find out and provide nursing care to Mr. A with a medical diagnosis post-operative soft tissue tumor (STT) left flank in the rose room 2 RSUD dr. Soeselo Tegal Regency in accordance with nursing standards. The methods used are descriptive methods and data collection methods. From the case review, it was found that Mr. A complains of pain. Three diagnoses were found, namely acute pain, impaired physical mobility and knowledge deficit. Interventions are prepared based on the theory of SDKI, SLKI and SIKI as well as on the client's condition and can be implemented.

Asuhan Keperawatan pada Tn. S dengan Post Operasi Hernioraphy pada Hernia Inguinalis Lateralis Dextra di Ruang Mawar 2 RSUD dr. Soeselo Kabupaten Tegal

A hernia is a protrusion or protusion of the contents of a cavity through a defect or weak part of the abdominal wall. A hernia consists of a ring, a sac and the contents of the hernia. The occurrence of a hernia is triggered by an increase in intra-abdominal pressure that is repeated or continuous (Sayuti, et al., 2023). The purpose of this paper is to find out and provide nursing care to Mr. S with post-operative hernioraphy on the right lateral inguinal hernia in the mawar 2 ward of dr. Soeselo Hospital, Tegal Regency according to nursing standards. The method used is the data collection method in the form of interviews, observations and examinations. From the case review, it was found that the client’s main complaint was pain in the surgical wound, P: pain when moving, Q: like being pulled, R: upper right groin, S: 6, T: continuous pain. The nursing problems found were acute pain, impaired physical mobility and knowledge deficit. Interventions were arranged based on the theory in SDKI, SLKI and SIKI as well as on the client’s condition and could be implemented.

The effect of an educational intervention on healthy lifestyle knowledge and behaviour among middle-aged women

Objectives: This study aimed to assess the impact of an educational intervention on middle-aged women’s knowledge and behaviour regarding various aspects of a healthy lifestyle. Design: Quasi-experimental study involving 150 middle-aged women (divided into experimental and control groups). Setting: Participants were selected from health centres of Tehran, Iran. Method: Data collection included demographic information, knowledge of a healthy lifestyle and healthy lifestyle behaviour. Participants in both the intervention and control groups completed the questionnaires before and 2 months after the educational intervention. The intervention involved 10 minutes of face-to-face training followed by distribution of training booklets provided by the Ministry of Health in Iran. Statistical analyses were conducted using SPSS. Results: Within the intervention group, significant differences were observed in total healthy lifestyle knowledge and subscales for physical activity knowledge, anthropometry knowledge, healthy diet knowledge, and smoking knowledge, between the pre- and post-intervention periods. Other than for smoking behaviour, there was no significant difference in healthy behaviours pre- and post-intervention for the experimental and also the control group. Conclusion: While there was an increase in knowledge about healthy lifestyles following the intervention, this knowledge increase was not associated with a change in healthy lifestyle behaviours. Attention should therefore be given to other influences including socio-economic factors, mental health status, environmental factors, cultural influences, duration of the intervention and psychological factors if changes in healthy behaviour are to be achieved.

Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field.

Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field.

Enhancing predictive monitoring of ethylene oxychlorination reactor states through spatiotemporal coupling analysis

The production of polyvinyl chloride (PVC) encounters challenges stemming from the temporal and spatial coupling characteristics inherent in the fixed bed ethylene oxychlorination process. Consequently, the implementation of enhanced safety measures and risk reduction strategies becomes imperative. This study introduces a pioneering methodology leveraging a spectral temporal graph neural network. By leveraging reactor temperature data, spatial variable decoupling facilitated by the Fourier transform, and a self-attentive mechanism within graph neural networks, the proposed approach adeptly forecasts future reactor states. The model's seamless alignment with the physical knowledge of reaction processes, validated through the adjacency matrix and hotspot region identification, underscores its efficacy in ensuring process safety and mitigating operational risks in PVC production. Empirical findings further validate the effectiveness of the approach, with predictions demonstrating an error margin of less than 0.5°C in forecasting future reactor temperatures.

HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting

Abstract. This work proposes a hybrid approach that combines physics and artificial intelligence (AI) for cloud cover nowcasting. It addresses the limitations of traditional deep-learning methods in producing realistic and physically consistent results that can generalise to unseen data. The proposed approach, named HyPhAICC, enforces a physical behaviour. In the first model, denoted as HyPhAICC-1, a multi-level advection dynamics is considered a hard constraint for a trained U-Net model. Our experiments show that the hybrid formulation outperforms not only traditional deep-learning methods but also the EUMETSAT Extrapolated Imagery model (EXIM) in terms of both qualitative and quantitative results. In particular, we illustrate that the hybrid model preserves more details and achieves higher scores based on similarity metrics in comparison to U-Net. Remarkably, these improvements are achieved while using only one-third of the data required by the other models. Another model, denoted as HyPhAICC-2, adds a source term to the advection equation, it impaired the visual rendering but displayed the best performance in terms of accuracy. These results suggest that the proposed hybrid physics–AI architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep-learning models.

Disclosure and Experiences of HIV-Related Stigma among Adolescents and Young Adults Living with HIV in South Africa.

Social networks expand rapidly in adolescence, increasing HIV status disclosure considerations and concerns for young people living with HIV, especially in settings where HIV-related stigma is prevalent. This study examines HIV disclosure and enacted stigma among adolescents and young adults living with HIV in South Africa. This study uses survey data from a sample of 1186 youth living with HIV, aged 14-24, and enrolled in peer support groups led by community-based organizations in KwaZulu Natal and Gauteng provinces, South Africa. Study participants completed a questionnaire on sociodemographic details, physical health, school attendance, who knew the individual's HIV status, and experiences of HIV-related mistreatment. Mixed effects logistic regression examined the association between experiences of HIV-related mistreatment and factors that may inadvertently disclose one's status, such as poor physical health and missed school, and knowledge of an individual's HIV-positive status by their caregiver, household, friends, educators, and most recent sexual partner. Almost a quarter of the sample reported an experience of HIV-related mistreatment in the past six months. After controlling sociodemographic characteristics, missed school due to illness (AOR = 1.75, 95% CI = 1.27-2.43), and knowledge of HIV status by non-family members (AOR = 2.19, 95% CI = 1.60-3.00) were significantly associated with HIV-related mistreatment. Findings suggest that experiences of enacted stigma are common among youth and linked to poor physical health and knowledge of HIV status outside the family. Effective community-level stigma reduction interventions are urgently needed. In the meantime, adolescents need individualized disclosure counseling and support managing their physical health to prevent further inadvertent disclosure and discrimination.

Asuhan Keperawatan pada Keluarga Tn.K dengan Gangguan Sistem Endokrin : Diabetes Melitus di Desa Kalibuntu Rt 04 Rw 02 Kecamatan Losari Kabupaten Brebes

Diabetes Mellitus (DM) is a long-term metabolic disorder caused by the body's insulin hormone which cannot be used properly to regulate blood sugar balance, resulting in hyperglycemia (Zakiudin et al., 2023). There are two factors that can affect a person's glucose levels, the first is endogenous factors such as insulin, glucagon, and cortisol which act as receptor systems in liver and muscle cells. The second factor is the amount and type of food consumed, which is an exogenous factor. Blood glucose levels are also influenced by a person's character (gender, family history of diabetes, and age), dietary factors (high energy, high carbohydrates, high fat, high protein, and low fiber), lack of physical activity, nutritional status, hypertension and knowledge about nutrition (Viera Valencia & Garcia Giraldo, 2019).

Form-finding of tensegrity structures based on graph neural networks

Tensegrity structures, characterized by enhanced stiffness, slender struts, and superior buckling resistance, have found wide-ranging applications in fields such as engineering, architecture, art, biology, and robotics, attracting extensive attention from researchers. The form-finding process, a critical step in the design of tensegrity structures, aims to discover the self-equilibrated configuration that satisfies specific design requirements. Traditional form-finding methods based on force density often require repeated steps of eigenvalue decomposition and singular value decomposition, making the process complex. In contrast, this paper introduces a new intelligent form-finding algorithm that uses the force density method and combines the Coati optimization algorithm with Graph Neural Networks. This algorithm avoids the complex steps of eigenvalue and singular value decomposition and integrates the physical knowledge of the structure, making the form-finding process faster and more accurate. In this algorithm, various force densities are initially randomized and input into a trained Graph Neural Networks to predict a fitness function’s value. Through optimizing the constrained fitness function, the algorithm determines the appropriate structural force density and coordinates, thereby completing the form-finding process of the structure. The paper presents seven typical tensegrity structure examples and compares various form-finding methods. The results of numerical examples show that the method proposed in this paper can find solutions that align with the super-stable line more quickly and accurately, demonstrating its potential value in practical applications.

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation.

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

HCP-PIGN: Efficient heat conduction prediction by physics-informed graph convolutional neural network

This work proposes a novel surrogate model (noted as HCP-PIGN) combining two groups of neural networks: i.e., the physics-informed and the graph convolutional neural networks (noted as PINN and GCN). It aims to tackle the existing challenges: pixelated pre-processing of data and large amounts of training data. For predicting 2D steady-state heat conduction, the GCN acting as the prediction module, considering the interdependence between unstructured and neighboring nodes. The PINN serving as the physical constraint module, embeds governing equations into the neural network’s loss function. The HCP-PIGN model obtains precise predictions with diverse geometries and within milliseconds. The predictive performance of HCP-PIGN was further compared with three network structures: i.e., the physics-informed fully connected neural network (noted as FNN), purely data-driven based FNN, and GCN. The results indicate that HCP-PIGN has the lowest error of temperature field predictions, which are below 3 % and 1.3 % for the max and mean relative errors, respectively. The improvements of 28.1% and 34.6% in accuracy are achieved over the pure data-driven GCN, and the physics-driven FNN, respectively. Therefore, the proposed HCP-PIGN model improves the physical prior knowledge and model’s adaptabilities to geometry variations, resulting in superior performances.