Reduced order modeling of convection-dominated flows, dimensionality reduction and stabilization

We present methodologies for reduced order modeling of convection dominated flows. Accordingly, three main problems are addressed. Firstly, an optimal manifold is realized to enhance reducibility of convection dominated flows. We design a low-rank auto-encoder to specifically reduce the dimensionality of solution arising from convection-dominated nonlinear physical systems. Although existing nonlinear manifold learning methods seem to be compelling tools to reduce the dimensionality of data characterized by large Kolmogorov n-width, they typically lack a straightforward mapping from the latent space to the high-dimensional physical space. Also, considering that the latent variables are often hard to interpret, many of these methods are dismissed in the reduced order modeling of dynamical systems governed by partial differential equations (PDEs). This deficiency is of importance to the extent that linear methods, such as principle component analysis (PCA) and Koopman operators, are still prevalent. Accordingly, we propose an interpretable nonlinear dimensionality reduction algorithm. An unsupervised learning problem is constructed that learns a diffeomorphic spatio-temporal grid which registers the output sequence of the PDEs on a non-uniform time-varying grid. The Kolmogorov n-width of the mapped data on the learned grid is minimized. Secondly, the reduced order models are constructed on the realized manifolds. We project the high fidelity models on the learned manifold, leading to a time-varying system of equations. Moreover, as a data-driven model free architecture, recurrent neural networks on the learned manifold are trained, showing versatility of the proposed framework. Finally, a stabilization method is developed to maintain stability and accuracy of the projection based ROMs on the learned manifold a posteriori. We extend the eigenvalue reassignment method of stabilization of linear time-invariant ROMs, to the more general case of linear time-varying systems. Through a post-processing step, the ROMs are controlled using a constrained nonlinear lease-square minimization problem. The controller and the input signals are defined at the algebraic level, using left and right singular vectors of the reduced system matrices. The proposed stabilization method is general and applicable to a large variety of linear time-varying ROMs

Physics-aware registration based auto-encoder for convection dominated PDEs

We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arising from convection-dominated nonlinear physical systems. Although existing nonlinear manifold learning methods seem to be compelling tools to reduce the dimensionality of data characterized by a large Kolmogorov n-width, they typically lack a straightforward mapping from the latent space to the high-dimensional physical space. Moreover, the realized latent variables are often hard to interpret. Therefore, many of these methods are often dismissed in the reduced order modeling of dynamical systems governed by the partial differential equations (PDEs). Accordingly, we propose an auto-encoder type nonlinear dimensionality reduction algorithm. The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs on a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized. We demonstrate the efficacy and interpretability of our approach to separate convection/advection from diffusion/scaling on various manufactured and physical systems.Comment: 10 pages, 6 figure

Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case

Earth system models suffer from various structural and parametric errors in their representation of nonlinear, multi-scale processes, leading to uncertainties in their long-term projections. The effects of many of these errors (particularly those due to fast physics) can be quantified in short-term simulations, e.g., as differences between the predicted and observed states (analysis increments). With the increase in the availability of high-quality observations and simulations, learning nudging from these increments to correct model errors has become an active research area. However, most studies focus on using neural networks, which while powerful, are hard to interpret, are data-hungry, and poorly generalize out-of-distribution. Here, we show the capabilities of Model Error Discovery with Interpretability and Data Assimilation (MEDIDA), a general, data-efficient framework that uses sparsity-promoting equation-discovery techniques to learn model errors from analysis increments. Using two-layer quasi-geostrophic turbulence as the test case, MEDIDA is shown to successfully discover various linear and nonlinear structural/parametric errors when full observations are available. Discovery from spatially sparse observations is found to require highly accurate interpolation schemes. While NNs have shown success as interpolators in recent studies, here, they are found inadequate due to their inability to accurately represent small scales, a phenomenon known as spectral bias. We show that a general remedy, adding a random Fourier feature layer to the NN, resolves this issue enabling MEDIDA to successfully discover model errors from sparse observations. These promising results suggest that with further development, MEDIDA could be scaled up to models of the Earth system and real observations.Comment: 26 pages, 5+1 figure

Learning Closed-Form Equations for Subgrid-Scale Closures From High-Fidelity Data: Promises and Challenges

There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive libraries to learn closures from filtered direct numerical simulations of 2D turbulence and Rayleigh-Bénard convection (RBC). Across common filters (e.g., Gaussian, box), we robustly discover closures of the same form for momentum and heat fluxes. These closures depend on nonlinear combinations of gradients of filtered variables, with constants that are independent of the fluid/flow properties and only depend on filter type/size. We show that these closures are the nonlinear gradient model (NGM), which is derivable analytically using Taylor-series. Indeed, we suggest that with common (physics-free) equation-discovery algorithms, for many common systems/physics, discovered closures are consistent with the leading term of the Taylor-series (except when cutoff filters are used). Like previous studies, we find that large-eddy simulations with NGM closures are unstable, despite significant similarities between the true and NGM-predicted fluxes (correlations >0.95). We identify two shortcomings as reasons for these instabilities: in 2D, NGM produces zero kinetic energy transfer between resolved and subgrid scales, lacking both diffusion and backscattering. In RBC, potential energy backscattering is poorly predicted. Moreover, we show that SGS fluxes diagnosed from data, presumed the “truth” for discovery, depend on filtering procedures and are not unique. Accordingly, to learn accurate, stable closures in future work, we propose several ideas around using physics-informed libraries, loss functions, and metrics. These findings are relevant to closure modeling of any multi-scale system

Integrating rehabilitation services into primary health care:policy options for Iran

BACKGROUND: Providing rehabilitation services in primary health care (PHC) is associated with numerous health, social, and economic benefits. Therefore, low and middle-income countries, such as Iran, should benefit from the advantages of integrating rehabilitation services into PHC. We conducted a qualitative study to determine policy solutions that could facilitate the integration of rehabilitation services into Iran’s PHC network. METHODS: Semi-structured interviews were conducted with 38 participants, including health policymakers, rehabilitation managers, faculty members, and rehabilitation practitioners. Purposive and snowball sampling strategies were adopted to recruit participants. The WHO Health System building blocks framework analysis was applied to analyze the collected data. RESULTS: Participants’ perspectives and experiences outlined potential policy options including: (1) stewardship: increasing political support, strengthening the leadership of the rehabilitation sector, and promoting inter-sectoral collaborations; (2) service delivery: increasing the knowledge of healthcare professionals, using local volunteers, deploying mobile rehabilitation teams, using telerehabilitation, and improving referral pathways; (3) financing: increasing government funding, preparing a package of rehabilitation services, and using appropriate payment mechanisms; (4) human resources: expanding rehabilitation workforce, training rehabilitation assistants, and enhancing employment and social opportunities; (5) information systems: establishing a comprehensive information system and an effective surveillance system; and (6) technologies: facilitating access to a range of rehabilitation equipment and raw materials, especially for prosthetics and orthotics services. CONCLUSION: Based on the WHO six building blocks framework, this study identified several policy options for integrating rehabilitation services into the Iranian PHC Network. Some of the policy options include increasing political support, promoting inter-sectoral collaborations, increasing the skills and knowledge of healthcare workers, establishing effective referral pathways, strengthening team-working, and increasing government funding

Isolation and Identification of an Indigenous Probiotic Lactobacillus Strain: Its Encapsulation with Natural Branched Polysaccharids to Improve Bacterial Viability

Background and Objective: Probiotics have to reach their site of action in certain numbers in order to exhibit positive health effects. Encapsulation has shown remarkable enhancing effects on probiotic survival in simulated gastric conditions compared to free bacteria. The purpose of this study was identification and evaluation of a potential probiotic strain using encapsulation process by new carriers in order to improve probiotic viability during in vitro simulated conditions.Material and Methods: A native Lactobacillus was isolated from yogurt, identified as Lactobacillus casei PM01 (NCBI registered) and analyzed for probiotic properties alongside established probiotic strains of Lactobacillus acidophilus ATCC 43556, and Lactobacillus rhamnosus ATCC 7469. Acid and bile resistance, adhesion to Caco-2 cells and antibiotic resistance were evaluated. Lactobacillus casei PM01 was encapsulated with alginate, chitosan and natural branched polysaccharides (pectin, tragacanth gum and gum Arabic) by using extrusion technique. Encapsulation efficiency, acidification activity and viability of entrapped Lactobacillus casei PM01 in simulated gastric pH were determined. Results and Conclusion: Based on the results, all the three strains could be considered as potential probiotics, and are good candidates for further in vitro and in vivo evaluation. The results showed that the survival of encapsulated Lactobacillus casei PM01 was significantly (p≤0.05) increased when it was incubated in simulated gastric pH. It can be concluded that indigenous Lactobacillus casei PM01 in encapsulated form is introduced as an efficient probiotic strain for using in dairy products.Conflict of interest: The authors declare no conflict of interest

COVID-19 and disabled people: perspectives from Iran

This is a Current Issue because, at the time of writing, COVID-19 has affected many countries and territories worldwide and Iran ranked early on as one of the most seriously affected countries. As a result, this pandemic crisis poses a considerable challenge to people with disabilities in Iran. In this short article we show the different challenges people with disabilities are facing during the COVID emergency in Iran. In addition, we provide several recommendations, based on our perspective and experience in Rehabilitation and Health Policy Centres, to improve the situation in the content of the COVID-19 breakout. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group

Physical rehabilitation financing in Iran: a policy analysis using Kingdon�s multiple streams

Background: Adequate financing is a crucial function, securing that physical rehabilitation services (i.e., physiotherapy, occupational therapy, prosthetics and orthotics) are available with no financial hardship. Like many other countries, despite the adoption of various policies and strategies in recent decades, Iran enjoys no desirable physical rehabilitation financing (PRF). Accordingly, this qualitative study aimed to explore the PRF-related strategies and issues as well as their impacts on relevant policies in Iran. Methods: An analysis of PRF-related policies was conducted in Iran using semi-structured interviews and policy documents review. Purposive and snowball sampling techniques were employed to select key informants, including health-policy makers, civil society, rehabilitation-policy makers, university professors, and practitioners. Thematic analysis was used to analyze the collected data. The analysis was framed within Kingdon�s multiple streams. Results: The hindering factors for desirable financing were weak insurance coverage, lack of sustainable financial resources, fragmented financing, lack of split between provider and financer, high-cost of physical rehabilitation services, low engagement of relevant experts in policy-making processes, and corrupt activities. In the policy stream, the following factors were highlighted: involvement of sustainable financial resources, the use of external revenue sources, allocated resources� earmarking, the integration of the current funds to have better pooling, the use of incentive and timely payment mechanisms, the implementation of strategic purchasing principals, and the employment of effective rationing strategies. Moreover, parliament support, changes in administrations, international effects, pressures from interest campaigns and NGOs, and international sanctions were found as factors affecting the politics stream. Conclusion: The study findings revealed that a variety of national and international factors affect PRF-related issues in Iran. The recently enacted laws indicate that the PRF policies have already been on the national health political agenda. The study reflected the multifaceted nature of barriers to optimal PRF in Iran. © 2021, The Author(s)

Physical rehabilitation in Iran after international sanctions: Explored findings from a qualitative study

Background: Although the main aims of sanctions are the political and economic pressures on governments, literature has demonstrated the harsh effects of sanctions on the general public, especially on the patients, poor and disabled people. Since the international sanctions regime negatively affected almost all dimensions of Iran's health sector, this qualitative study was conducted to investigate the situation of the physical rehabilitation sector after these sanctions. Methods: This qualitative study was conducted from January 2019 to June 2019 in Iran using Skype, telephone, and face-to-face in-depth semi-structured interviews. Purposive and snowball sampling approaches were used to identify the participants. Also, framework analysis approach was applied to analyze the collected data. Results: In total, 38 individuals including health policy-maker, faculty member, rehabilitation expert, Physiotherapist, Occupational therapist, and Orthotist/Prosthetist, were involved in the study. Based on our findings, a number of challenges facing the Iranian physical rehabilitation sector during the international sanctions period included: 1) socioeconomic challenges (inadequate funding, rising inflation rate, high unemployment rate, catastrophic expenditures, and inappropriate employment status of practitioners); 2) education challenges (decreased international collaboration and shortage of training devices and materials); 3) international challenges (rising issues in accessing services for patients from neighborhood countries); and 4) service delivery challenges (shortage of raw materials for producing the orthoses and prostheses, hardening of the importing the needed equipment, inappropriate infrastructures, and impossibility to use external assistance). Conclusion: After international sanctions, the Iranian physical rehabilitation sector has faced considerable multifaceted challenges. Therefore, the international community must be aware of the situation and be concerned about the irreparable consequences. © 2020 The Author(s)

NKG2A inhibits TH2 cell effector function in vitro

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens