Physical Knowledge Research Articles

Pilot Study Assessing the Baseline Physical Activity and Knowledge of Heart Health of Elementary School-Aged Children in Rural South Carolina and the Impact of an Interactive Educational Program.

This study asked whether, if provided with education on heart-healthy habits, elementary school children in Abbeville, Greenwood, and Saluda counties in South Carolina would retain and desire to implement healthy nutrition and increased exercise. We hypothesized that teaching children about heart-healthy habits would increase their activity levels and improve their desire to be active. This was a longitudinal survey study. Children at local after-school programs were given a pre-survey, the Kids' Activity and Nutrition Questionnaire (KAN-Q), to assess their activity levels, diet, and knowledge of heart health. The children had a 15-minute educational period followed by an interactive program. Students completed the same survey at each of the three sessions. A total of 44 children answered the survey questions at the beginning of each session. Out of the 10 behavioral questions, six indicated a favorable shift towards adopting healthier habits. Whole grain consumption increased to 71% from 32% (p<0.01). Vegetable consumption increased from 39% to 88%, and fruit consumption increased from 68% to 92% (p<0.01). There was a 30% decrease in the consumption of sugary drinks (p<0.01), and a reduction in hours watching television (p=0.05). All four knowledge-based questions had an increase in correct responses across the three sessions (p<0.01). To augment behavior, a dual approach of education on nutrition and exercise leads to improved nutrition choices and exercise habits for primary prevention of cardiovascular risk factors in elementary school-aged children. These habits improve adherence to increased physical activity and better nutrition after school.

Determination of Knowledge, Attitudes and Practices of Nurses Working in Surgical Intensive Care Units on the Use of Physical Restraint: A Multicenter Cross-Sectional Study

Background: Research on the knowledge, attitudes, and practices of nurses working in surgical intensive care units regarding the use of physical restraints is limited. Objective: This study aimed to assess nurses’ knowledge, attitudes, and practices regarding physical restraint use in surgical intensive care units (S-ICUs). Method: The research, a descriptive cross-sectional study, involved nurses from three hospitals’ surgical intensive care units between March 15th and June 15th, 2022, with a total of 73 voluntarily participating nurses (n=73). Data were gathered using the Physical Restraint Knowledge, Attitude, and Practice Scale via face-to-face surveys. In the analysis of the data, independent t tests and one-way ANOVA were used. The post hoc Tukey HSD test was utilized to determine the variables contributing to the differences. A p-value of

Indrnn-Based Data-Driven Modeling Integrated With Physical Knowledge for Engine Performance Monitoring

Indrnn-Based Data-Driven Modeling Integrated With Physical Knowledge for Engine Performance Monitoring

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.

A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints

Deck-rib welded joint is an important part of orthotropic steel decks, which are susceptible to fatigue cracks under various load cycles. Therefore, it is essential to use efficient predicting methods to assess the fatigue performance of deck-rib welded joints with new geometry and form in order to ensure a reliable operation of the bridge. A modified physics-informed neural network (PINN) is proposed for the fatigue life prediction of the novel deck-rib double-side welded joints. The modified PINN incorporates implicit physical model as an activation function into the hidden neurons, and explicit physical constraints into the loss function. Additionally, the influence of deck thickness is integrated using limited test sample data based on the observational approach. The 62 experimental data were taken from the literature to compare the performance of the artificial neural network (ANN), traditional PINN and the modified PINN. The results demonstrate that the modified PINN enhances the learning process of the neural network through the joint learning of physical knowledge by activation function and loss function. Furthermore, the fatigue life prediction of the deck-rib double-side welded joints under the modified PINN exhibits physical consistency and accuracy when compared with the traditional ANN and PINN.

Battery Lifetime Prediction and Degradation Analysis with Machine Learning Method

Due to environmental pollution and climate crisis caused by fossil fuels, eco-friendly alternative energy sources are gaining attention. Particularly, electric vehicles (EVs) are being recognized as the next-generation means of transportation. The most crucial technologies in electric vehicles are lithium-ion batteries and Battery Management Systems (BMS). However, the extended lifespan of lithium-ion batteries leads to prolonged assessment periods, making it challenging to measure and improve performance over time. Additionally, the operation of batteries under various conditions makes it difficult to measure state of charge (SOC) and state of health (SOH) in real-time, introducing many variables. With recent advancements in predictive technology through machine learning, accurate predictions can be made without complex physical knowledge, leveraging high-quality data.In this study, machine learning techniques were applied using charge/discharge data from lithium-ion LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite pouch cell batteries. The values measured during the initial charge/discharge cycles were converted into variables that could assess battery degradation modes. Machine learning algorithms were then applied, followed by model tuning to reduce overfitting and enhance accuracy. This research contributes to real-time battery management system data using machine learning technology to predict the remaining useful life (RUL) of lithium-ion batteries with high accuracy, using only initial cycles. This approach enhances the efficiency of BMS data in real-world scenarios and contributes to the overall time efficiency of battery research. Acknowledgement This research was supported by National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT(NRF-2021K1A4A8A01079455).

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable ‘grey-box’ AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale ‘world model’ by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Predicting households’ attitudes and product adoption intentions

PurposeThe present study aims to examine the households’ attitudes and intentions to adopt an indoor air purifier against the smog crisis in India by using a comprehensive theoretical framework based on the combination of the Protective Action Decision Model (PADM) and the Theory of Planned Behavior (TPB). The United Nations Sustainable Development Goals (SDGs) 2030 also emphasized ensuring a healthy and safe life, especially by achieving SDG-3, SDG-11 and SDG-13.Design/methodology/approachUsing purposive sampling, the data were collected through a survey questionnaire distributed to 382 households, and study hypotheses were assessed by using partial least squares structural equation modeling employing SmartPLS.FindingsThe results revealed that mental health risk perception (MHRP) was the most influential determinant of households’ attitudes toward adopting air purifiers, followed by smog knowledge, physical health risk perception (PHRP), information seeking and product knowledge. Notably, results revealed that households’ attitude is a leading determinant of their adoption intention toward the air purifier compared to subjective norms (SN) and perceived behavioral control (PBC).Originality/valueTo the best of the authors’ knowledge, the present study is the first to provide new insights into an individual’s protective behavior response toward ecological hazards by examining the households’ adoption intention toward the air purifier against the smog crisis using PADM and TPB model inclusively. In addition, the present study analyzes the impact of both PHRP and MHRP on individuals’ protective behavior separately. Also, this study provides theoretical contributions and important practical implications for the government, manufacturers and air purifier sellers.

The role of physical therapists in the early detection of skin melanoma: insights from an anonymous survey.

Skin melanoma is the most severe form of skin cancer. Recent years have seen an increase in melanoma incidence. Melanomas often appear on the back, a hidden area, leading to late diagnoses. Physical therapists, who frequently treat patients with lower back pain, could play a crucial role in early melanoma detection. An anonymous online survey was conducted among 254 Israeli physical therapists to assess their perspectives on melanoma detection, their knowledge in identifying suspicious lesions, and their referral patterns. The survey included sections on demographics, professional perspectives, melanoma knowledge, personal or family experiences with melanoma, and referral frequency for dermatological evaluation. The survey revealed that 75.2% of physical therapists viewed melanoma detection as part of their professional duties, yet 59.1% reported insufficient knowledge in identifying suspicious lesions. Despite this, 94.1% expressed a desire to improve their knowledge. However, only 44.1% actively referred patients with suspicious lesions to dermatologists. There was a significant positive correlation between professional experience and referral rates (p-value < 0.001), indicating that more experienced therapists were more likely to refer patients. The study highlights the critical need for incorporating melanoma detection training into physical therapy education and professional development. Enhancing physical therapists' knowledge and skills in this area could improve early detection and patient outcomes. Despite the potential role of physical therapists in melanoma detection, current training programs lack emphasis on this aspect, underscoring the importance of revising educational curricula to include skin cancer detection techniques. The findings suggest that while physical therapists recognize their role in melanoma detection, there is a significant knowledge gap. Addressing this through targeted education and training could enhance early detection efforts and improve patient care.

A hybrid physics-informed machine learning approach for time-dependent reliability assessment of electromagnetic relays

Electromagnetic relays (EMRs) are intricate micro-electromechanical systems characterized by nonlinear behavior and coupling effects between electromagnetic and mechanical forces. Accurately modeling degradation and assessing reliability are crucial yet challenging tasks for ensuring their safe and efficient operation. Current data-driven methods for degradation modeling and reliability assessment often neglect the known physical knowledge regarding EMRs, leading to inaccuracies in modeling and assessment outcomes when data is incomplete. While physics-informed machine learning (PIML) approaches offer a potential solution, common regression models like Gaussian processes (GP) and long short-term memory (LSTM) suffer from underfitting and overfitting, respectively. To address these issues, we presents a hybrid PIML approach for time-dependent reliability assessment based on the emerging variational autoencoder (VAE) framework. This approach combines the advantages of GP-based methods that enable probabilistic representation with deep neural network-based methods that are more flexible and computationally efficient. Finally, we validate our proposed approach using real-world engineering data, demonstrating its superior accuracy and computational efficiency compared to state-of-the-art methods.

Aerodynamic shape optimization using a physics-informed hot-start method combined with modified metric-based proper orthogonal decomposition

Aerodynamic shape optimization based on computational fluid dynamics still has a huge demand for improvement in the optimization effect and efficiency when optimizing the unstable flow of airfoils. This article presents a physics-informed hot-start method combined with modified metric-based proper orthogonal decomposition (MPOD-ML-Phys). The data-based filtering strategy is a core step in the original metric-based proper orthogonal decomposition method (MPOD), but existing filtering strategies generate a significant amount of additional computational consumption. Therefore, this article applies machine learning methods to data-based filtering strategy in MPOD and establishes a modified MPOD method (MPOD-ML). In addition, during the MPOD-ML process, a lot of hidden physical knowledge that is beneficial for optimization will also be generated. This article combines Bayesian optimization to construct an MPOD-ML-Phys method, which fully utilizes the flow physical knowledge in MPOD-ML. The efficiency and effect of MPOD-ML and MPOD-ML-Phys are validated by two typical cases: inverse and direct design for airfoils. The results indicate that both MPOD-ML and MPOD-ML-Phys methods can effectively improve the overall optimization efficiency. However, the intervention of machine learning models has significantly reduced the robustness of the MPOD-ML method, while the embedding of physical knowledge makes MPOD-ML-Phys more robust. Meanwhile, the optimized airfoil obtained by MPOD-ML-Phys has better drag divergence characteristics, a later flow separation point, and better flow stability.

A data augmentation method for multiaxial fatigue life prediction based on physics-informed tabular generative adversarial network

Abstract Currently, in the multiaxial fatigue life prediction problem, machine learning (ML) algorithms are still limited by small samples. For ML tasks requiring large amounts of data, this paper proposes a Conditional Tabular Generative Adversarial Network (CTGAN) model using a physical equation as a generation constraint. The proposed method can make synthetic samples satisfy specific physical knowledge. The method is evaluated using two datasets of different materials. ML-based algorithms verify the feasibility of using synthetic datasets for life prediction. Verification analysis shows that the proposed method successfully synthesizes high-quality multiaxial fatigue datasets and effectively improves the prediction accuracy of ML algorithms.

Family Involvement and Physical Education: A Collaborative Strategy to Enhance Students' Physical Literacy

This study examines the impact of family involvement on students' physical education literacy and proposes effective collaborative strategies. Through a case study of a typical family, interview data from students, parents and physical education teachers as well as observation records of classroom and extracurricular activities were collected. The results show that active family involvement can significantly improve students' physical education interest, skills and knowledge. Based on this, this paper proposes a variety of strategies to promote synergistic co-operation between families and schools in physical education in order to enhance students' physical literacy in a holistic manner.

Analyzing risk influencing factors of ship collision accidents: A data-driven Bayesian network model integrating physical knowledge

The interaction and propagation effects among risk-influencing factors (RIFs) often lead to maritime accidents. Therefore, to support maritime risk management in decision-making, it is imperative to conduct a quantitative risk assessment (QRA) of these accidents, which requires a methodology capable of capturing causal relationships in limited cases. To achieve this goal, this study proposes a data-driven Bayesian network (BN) model that integrates physical knowledge with the QRA. Based on the collected data, a combination of domain knowledge, structure learning, and parameter learning is employed to construct the model. In the data-driven phase, three structural learning algorithms including Bayesian search (BS), Greedy thick thinning (GTT), and Peter-Clark (PC) algorithms, were used in combination with the expectation maximization algorithm of parameter learning. Then, using four indices calculated by the confusion matrix, the most fitting algorithm for structure learning was chosen, and the confusion matrix was obtained by five-fold cross-validation. Finally, network propagation impact (NPI) is introduced to prioritize RIFs. Preventive measures and emergency plans are formulated based on the network resilience metric (NRM) and network vulnerability metric (NVM). This approach leverages the objectivity of a data-driven BN and integrates domain expertise to enhance model validity. A case study of collisions was conducted to demonstrate the applicability of the model. The data were sourced from 327 collision accident reports provided by the China Maritime Safety Administration, and the PC algorithm was chosen. Findings indicate that the “Management system”, “Supervision”, and “Crew training” RIFs are prioritized for preventive measures, whereas “Communication” receives more resources for emergency handling. The results suggest the formulation of risk management strategies for practitioners to improve maritime safety.

Chaotic time series prediction based on physics-informed neural operator

This paper investigates the prediction of chaotic time series using physics-informed neural operator (PINO) with different driven methods, such as data-driven method, physics-driven method, and hybrid data-physics-driven method. Here, the chaotic time series are generated from two classical time delayed chaotic systems, including Mackey–Glass equation (MG) and Optoelectronic Oscillator (OEO). The simulation results from these two chaotic systems or experimental results from Optoelectronic Oscillator demonstrate that when there is enough data with good quality, it is possible to train the model solely using data-driven method. Conversely, when possessing complete mastery of the physical prior knowledge, it is also available to train the model solely using physics-driven method, indicating there is no need to prepare label datasets as in data-driven method. However, when dataset quality is poor, for example, contaminated by noise, and precise physical prior knowledge is not fully acquired, the hybrid method will have a better performance.

Physical Education Teaching Quality Assessment Model Based on Gaussian Process Machine Learning Algorithm

Physical education is an integral component of academic curricula focused on promoting overall health and well-being through physical activity and exercise. It encompasses a range of activities designed to enhance students' physical fitness, motor skills, and knowledge of healthy lifestyle habits. In addition to fostering physical development, physical education contributes to the development of social skills, teamwork, and discipline. Students engage in various sports, fitness routines, and educational modules that encourage a lifelong commitment to an active and healthy lifestyle. This demand for improvement in the teaching quality assessment of physical education among the students. Hence, this paper proposed a novel Gaussian Hidden Chain Probabilistic Machine Learning (GHCP-ML). The proposed GHCP-ML model estimates the features for the teaching quality assessment using the Gaussian Hidden Chain model. With the proposed GHCP-ML model features related to the teaching assessment of the physical education are computed. The proposed GHCP-ML model uses the machine learning model for the assessment and computation of the factors related to the teaching quality of students in physical education. With the Gaussian Chain model, the factors related to physical education are evaluated for the classification of the relationship between physical education and teaching quality assessment. Simulation analysis demonstrated that with the proposed GHCP-ML model physical education is improved significantly with teaching quality by ~12% than the conventional techniques. The student physical education performance is improved by more than 80% with the proposed GHCP-ML model compared with the conventional techniques.

Remaining useful life prediction across operating conditions based on deep subdomain adaptation network considering the weighted multi-source domain

As one of the core contents of transfer learning, domain adaptation (DA) has been widely used to predict of the remaining useful life (RUL) of rolling bearings across operating conditions. However, existing RUL prediction methods often adopt single-source DA and do not consider the sample's drift phenomenon and migration degree in the source domain. At the same time, the distribution of bearing samples in different subdomains (degradation stages) differs, forcing global adaptation to inevitably lead to negative migration. Therefore, a deep subdomain adaptation network considering weighted multi-source domain (DSAN-WM) is proposed to improve the prediction accuracy of RUL across operating conditions. Firstly, a migration metric is developed to measure the migration degree of samples in the source domain, and a high-quality multi-source domain sample set is constructed. Secondly, a fine-grained feature distribution alignment strategy for subdomains based on adaptive degradation stage identification is proposed to reduce the difference in sample distribution in the same subdomain. Thirdly, for each decision boundary obtained by the multi-source domain model, consistent alignment and degradation constraints based on physical knowledge are established to improve the consistency and prediction accuracy of each regressor output. Lastly, the effectiveness of the proposed method is verified using eight migration scenarios of two bearing data sets, and the superiority of the method is proven through a comparison with advanced deep learning and transfer learning approaches.

Minimal model identification of drum brake squeal via SINDy

The industrial standard in the design and development process of NVH(Noise Vibration Harshness) characteristic of brakes is the application of Finite Element(FE) models with a high number of degrees of freedom in the range of one or several millions. Nevertheless, parallel experimental investigations are still indispensable. On the other hand, minimal models with, due to the inclusion of the self-excitation process, at least two degrees of freedom are well known to be capable to explain qualitatively phenomena as instability of the desired non-vibrating solution or limit cycle oscillation but are in general very inaccurate in predicting the dynamics of a specific real brake. This is because the underlying physical assumptions are already too restrictive and model parameters (especially those referring to nonlinearities) are widely unknown. To overcome this problem, the data-driven modeling approach SINDy(Sparse Identification of Nonlinear Dynamics) is applied to identify appropriate nonlinear functions for a brake squeal minimal model. A problem thereby is the limited database. It turns out that the naive implementation of the method yielding the lowest possible residuum does not necessarily provide physically meaningful models and results, respectively. Instead, a constrained model that incorporates physical knowledge is used to robustly identify parameters and reproduce realistic dynamic behavior. Thereby, several appropriate models with coexisting limit cycles and stationary equilibrium are identified. In particular, it was found that the angular position of the brake drum has a significant influence on the model parameters and therefore must be taken into account in a model with long-term validity.

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINNs) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatio-temporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

On-line measurement of COD and nitrate in water against stochastic background interference based on ultraviolet–visible spectroscopy and physics-informed multi-task learning

Traditional ultraviolet–visible spectroscopic quantitative analytical methods face challenges in simultaneous and long-term accurate measurement of chemical oxygen demand (COD) and nitrate due to spectral overlap and the interference from stochastic background caused by turbidity and chromaticity in water. Addressing these limitations, a compact dual optical path spectrum detection sensor is introduced, and a novel ultraviolet–visible spectroscopic quantitative analysis model based on physics-informed multi-task learning (PI-MTL) is designed. Incorporating a physics-informed block, the PI-MTL model integrates pre-existing physical knowledge for enhanced feature extraction specific to each task. A multi-task loss wrapper strategy is also employed, facilitating comprehensive loss evaluation and adaptation to stochastic backgrounds. This novel approach significantly outperforms conventional models in COD and nitrate measurement under stochastic background interference, achieving impressive prediction R2 values of 0.941 for COD and 0.9575 for nitrate, while reducing root mean squared error (RMSE) by 60.89 % for COD and 77.3 % for nitrate in comparison to the conventional chemometric model partial least squares regression (PLSR), and by 30.59 % and 65.96 %, respectively, in comparison to a benchmark convolutional neural network (CNN) model. The promising results emphasize its potential as a spectroscopic instrument designed for online multi-parameter water quality monitoring against stochastic background interference, enabling long-term accurate measurement of COD and nitrate levels.