LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry

We present a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-informed neural networks (PINNs). In our experiments, existing versions of PINNs are seen to learn poorly in many problems, especially for complex geometries, as it becomes increasingly difficult to establish appropriate sampling strategy at the near boundary region. Overly dense sampling can adversely impede training convergence if the local gradient behaviors are too complex to be adequately modelled by PINNs. On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution. To prevent such issues, we propose a new Boundary Connectivity (BCXN) loss function which provides linear local structure approximation (LSA) to the gradient behaviors at the boundary for PINN. Our BCXN-loss implicitly imposes local structure during training, thus facilitating fast physics-informed learning across entire problem domains with order of magnitude sparser training samples. This LSA-PINN method shows a few orders of magnitude smaller errors than existing methods in terms of the standard L2-norm metric, while using dramatically fewer training samples and iterations. Our proposed LSA-PINN does not pose any requirement on the differentiable property of the networks, and we demonstrate its benefits and ease of implementation on both multi-layer perceptron and convolutional neural network versions as commonly used in current PINN literature.Comment: 11 pages, 7 figure

A qualitative study on healthcare professionals’ perceived barriers to insulin initiation in a multi-ethnic population

Background: Nationwide surveys have shown that the prevalence of diabetes rates in Malaysia have almost doubled in the past ten years; yet diabetes control remains poor and insulin therapy is underutilized. This study aimed to explore healthcare professionals’ views on barriers to starting insulin therapy in people with type 2 diabetes. Methods: Healthcare professionals consisting of general practitioners (n = 11), family medicine specialists (n = 10), medical officers (n = 8), government policy makers (n = 4), diabetes educators (n = 3) and endocrinologists (n = 2) were interviewed. A semi-structured topic guide was used to guide the interviews by trained facilitators. The interviews were transcribed verbatim and analysed using a thematic analysis approach. Results: Insulin initiation was found to be affected by patient, healthcare professional and system factors. Patients’ barriers include culture-specific barriers such as the religious purity of insulin, preferred use of complementary medication and perceived lethality of insulin therapy. Healthcare professionals’ barriers include negative attitudes towards insulin therapy and the ‘legacy effect’ of old insulin guidelines; whilst system barriers highlight the lack of resources, language and communication challenges. Conclusions: Tackling the issue of insulin initiation should not only happen during clinical consultations. It requires health education to emphasise the progressive nature of diabetes and the eventuality of insulin therapy at early stage of the illness. Healthcare professionals should be trained how to initiate insulin and communicate effectively with patients from various cultural and religious backgrounds

CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method

In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy. The computation of differential operators required for PINNs loss evaluation at collocation points are conventionally obtained via AD. Although AD has the advantage of being able to compute the exact gradients at any point, such PINNs can only achieve high accuracies with large numbers of collocation points, otherwise they are prone to optimizing towards unphysical solution. To make PINN training fast, the dual ideas of using numerical differentiation (ND)-inspired method and coupling it with AD are employed to define the loss function. The ND-based formulation for training loss can strongly link neighboring collocation points to enable efficient training in sparse sample regimes, but its accuracy is restricted by the interpolation scheme. The proposed coupled-automatic-numerical differentiation framework, labeled as can-PINN, unifies the advantages of AD and ND, providing more robust and efficient training than AD-based PINNs, while further improving accuracy by up to 1-2 orders of magnitude relative to ND-based PINNs. For a proof-of-concept demonstration of this can-scheme to fluid dynamic problems, two numerical-inspired instantiations of can-PINN schemes for the convection and pressure gradient terms were derived to solve the incompressible Navier-Stokes (N-S) equations. The superior performance of can-PINNs is demonstrated on several challenging problems, including the flow mixing phenomena, lid driven flow in a cavity, and channel flow over a backward facing step. The results reveal that for challenging problems like these, can-PINNs can consistently achieve very good accuracy whereas conventional AD-based PINNs fail.Agency for Science, Technology and Research (A*STAR)This research is supported by A*STAR under its AME Programmatic programme: Explainable Physics-based AI for Engineering Modelling & Design (ePAI) [Award No. A20H5b0142]

Comparison of T-Spot.TB and tuberculin skin test among silicotic patients

In the present study, T-Spot.TB and the tuberculin skin test (TST) were compared in the screening of latent tuberculosis infection among silicotic patients. A conditional probability model was used to compare the potential clinical utilities of T-Spot.TB and TST performed on 134 silicotic subjects from December 1, 2004 to January 31, 2007. Data from a historical cohort were also reanalysed for further comparison. Agreement with T-Spot.TB was best using a TST cut-off of 10 mm. Age ≥65 yrs independently predicted a tuberculin reaction <10 mm (odds ratio=3), but not a negative T-Spot.TB response. Lower measures of agreement were observed among current smokers and those aged ≥65 yrs. Tuberculin reaction size was well correlated with both early secretary antigenic target 6 and culture filtrate protein 10 spot counts, except among current smokers. Within the current estimates of sensitivity (88-95%) and specificity (86-99%) for T-Spot.TB, the positive likelihood ratio for T-Spot.TB test would be substantially higher (6.29-95.0 versus 1.65-1.94) and negative likelihood ratio substantially lower (0.05-0.14 versus 0.32-0.41) than the corresponding ratios for the tuberculin test. A low tuberculosis risk differential was similarly observed between tuberculin-negative and untreated tuberculin-positive subjects in the historical cohort. T-Spot.TB is likely to perform better than tuberculin test in the screening of latent tuberculosis infection among silicotic subjects. Copyright©ERS Journals Ltd 2008.link_to_subscribed_fulltex

Occurrence and risk assessment of multiple classes of antibiotics in urban canals and lakes in Hanoi, Vietnam

10.1016/j.scitotenv.2019.07.092Science of The Total Environmen