Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Cuff-less Continuous Blood Pressure from Fingertip Photoplethysmogram Signals with Deep Neural Network Model

Objective: Blood pressure (BP) is an important physiological index reflecting cardiovascular function. Continuous blood pressure monitoring helps to reduce the prevalence and mortality of cardiovascular diseases. In this study, we aim to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) values continuously based on fingertip photoplethysmogram (PPG) waveforms using deep neural network models. Methods: Two models were proposed and both models consisted of three stages. The only difference between them was the method of extracting features from PPG signals in the first stage. Model 1 adopted Bidirectional Long Short-Term Memory (BiLSTM), while the other used convolutional neural network. Then, the residual connection was applied to multiple stacked LSTM layers in the second stage, following by the third stage with two fully connected layers. Results: Our proposed models outperformed other methods based on similar dataset or framework, while in our proposed models, the model 2 was superior to model 1. It satisfied the standard of Association for the Advancement of the Medical Instrumentation (AAMI) and obtained grade A for SBP and DBP estimation according to the British Hypertension Society (BHS) standard. The mean error (ME) and standard deviation (STD) for SBP and DBP estimations were 0.21 6.40 mmHg and 0.19 4.71 mmHg, respectively. Conclusion: Our proposed models could extract important features of fingertip PPG waveforms automatically and realize cuff-less continuous BP monitoring, which can be helpful in the identification and early treatment of abnormal blood pressure, thus may reduce the occurrence of cardiovascular malignant events.
THREE DIMENSIONAL MHD NANOFLUID STAGNATION POINT FLOW WITH HIGHER ORDER CHEMICAL REACTION

In hot and dry climates, evaporative cooling of the air by water spray can be applied in several requirements, such as evaporative condensers which the airflow is precooled by the water spray before it reaches the condenser. The interaction between water droplets and the air is a complicated two-phase flow that is affected by the several parameters. Here, an Eulerian-Lagrangian 3D model was developed to investigate the influence of important parameters on spray cooling performance in a rectangular duct. The evaluated parameters include the number of nozzles, inlet air flow rate, and spray water flow rate. The results represented that growth in the number of nozzles causes a reduction in the spray cooling efficiency. This is due to decrease of droplets retention time within the duct by increasing the number of nozzles at a constant total spray flow rate in the cases. The maximum and minimum spray cooling efficiency belong to the cases with one nozzle at water flow rate of 20 l/h and four nozzle at water flow rate of 5 l/h, respectively. The difference between spray cooling efficiency at 3 and 4 number of nozzles is less than 1.8%. Moreover, increasing the air flow rate from 0.5 l/h to 2 l/h (by 300%) makes a decrease in the spray cooling efficiency up to 58.6%.
Evaluation of Antioxidant Activity of Obtained Derivatives of Vanillin

Background : Vanillin is a white monoclinic crystalline compound whose chemical nomenclature is p-hydroxy-m-methoxy benzaldehyde. It is a phenolic aldehyde with a pleasant flavor and popularly found in vanilla beans and roasted coffee amongst many other sources. It serves as in addition; it possesses antitumor and particularly antioxidant activity which formed the essence of this study. Objectives : The insidious presence of free oxygenated and nitrogen radicals in the human body has become a worrisome concern. These chemical species continue to plague the human cells, tissues and organs resulting in different pathophysiological conditions such as cancers and neurodegenerative disorders like Alzheimer’s disease and Parkinson’s disease amongst many other ailments. The search for novel pharmacological compounds with the aim of curbing the rising incidence of these radicals led the choice of vanillin in this present study. Methodology : Vanillin was separately subjected to a series of derivatization reactions namely, acetylation, O-demethylation, reduction and oxidation. The melting points, refractive indices and optical rotations of the lead compound and derivatives were obtained. The antioxidant activities of the five compounds were determined using the DPPH (2, 2-diphenyl-1-picrylhydrazyl hydrate) test. Comparison of the obtained antioxidant activities was done to determine if any improvements could be seen in the derivatives. Results : The identities of the derivatives have been revealed to be vanillyl acetate (E-1) (acetyl derivative), 3, 4-dihydroxy benzaldehyde or protocatechui aldehyde (E-2) (demethylated derivative), o-methoxy-p-methyl cyclohexan-1-ol (J-1) (reduced derivative) and vanillic acid (J-2) (oxidized derivative) respectively using the IR spectral technique. Vanillin, E-2 and J-2 derivatives gave marginal antioxidant activity of IC50 of 0.81, 0.84 and 0.0. 85 µg/mL respectively while J-1 and E-1 demonstrated moderately significant IC50 of 0.59 and 0.63 µg/mL which compare favorably with 0.44 µg/mL elicited by Vitamin C (a standard antioxidant drug). It is pertinent to point out that the obtained reduced derivative is a substituted cycloalkanol (a saturated cyclic compound) instead of a substituted phenolic compound as was expected. Conclusion : The results from this study indicate that reduction and acetylation separately enhance the antioxidant activity of vanillin.
Curcumin-incorporated 3D bioprinting gelatin methacryloyl hydrogel reduces reactive oxygen species-induced adipose-derived stem cell apoptosis and improves implanting survival in diabetic wounds

Abstract Background Gelatin methacryloyl (GelMA) hydrogels loaded with stem cells have proved to be an effective clinical treatment for wound healing. Advanced glycation end product (AGE), interacting with its particular receptor (AGER), gives rise to reactive oxygen species (ROS) and apoptosis. Curcumin (Cur) has excellent antioxidant activity and regulates intracellular ROS production and apoptosis. In this study, we developed a Cur-incorporated 3D-printed GelMA to insert into adipose-derived stem cells (ADSCs) and applied it to diabetic wounds. Methods GelMA hydrogels with Cur were fabricated and their in vitro effects on ADSCs were investigated. We used structural characterization, western blot, ROS and apoptosis assay to evaluate the antioxidant and anti-apoptotic activity, and assessed the wound healing effects to investigate the mechanism underlying regulation of apoptosis by Cur via the AGE/AGER/nuclear factor-κB (NF-κB) p65 pathway. Results A 10% GelMA scaffold exhibited appropriate mechanical properties and biocompatibility for ADSCs. The circular mesh structure demonstrated printability of 10% GelMA and Cur-GelMA bioinks. The incorporation of Cur into the 10% GelMA hydrogel showed an inhibitory effect on AGEs/AGER/NF-κB p65-induced ROS generation and ADSC apoptosis. Furthermore, Cur-GelMA scaffold promoted cell survival and expedited in vivo diabetic wound healing. Conclusions The incorporation of Cur improved the antioxidant activity of 3D-printed GelMA hydrogel and mitigated AGE/AGER/p65 axis-induced ROS and apoptosis in ADSCs. The effects of scaffolds on wound healing suggested that Cur/GelMA-ADSC hydrogel could be an effective biological material for accelerating wound healing.
Optimal timing of rectal diclofenac in preventing post-endoscopic retrograde cholangiopancreatography pancreatitis

Background and study aims Rectal nonsteroidal anti-inflammatory drug (NSAID) prophylaxis reduces incidence of post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis. Direct comparisons to the optimal timing of administration, before or after ERCP, are lacking. Therefore, we aimed to assess whether timing of rectal NSAID prophylaxis affects the incidence of post-ERCP pancreatitis. Patients and methods We conducted an analysis of prospectively collected data from a randomized clinical trial. We included patients with a moderate to high risk of developing post-ERCP pancreatitis, all of whom received rectal diclofenac monotherapy 100-mg prophylaxis. Administration was within 30 minutes before or after the ERCP at the discretion of the endoscopist. The primary endpoint was post-ERCP pancreatitis. Secondary endpoints included severity of pancreatitis, length of hospitalization, and Intensive Care Unit (ICU) admittance. Results We included 346 patients who received the rectal NSAID before ERCP and 63 patients who received it after ERCP. No differences in baseline characteristics were observed. Post-ERCP pancreatitis incidence was lower in the group that received pre-procedure rectal NSAIDs (8 %), compared to post-procedure (18 %) (relative risk: 2.32; 95% confidence interval: 1.21 to 4.46, P = 0.02). Hospital stays were significantly longer with post-procedure prophylaxis (1 day; interquartile range [IQR] 1–2 days vs. 1 day; IQR 1–4 days; P = 0.02). Patients from the post-procedure group were more likely to be admitted to the ICU (1 patient [0.3 %] vs. 4 patients [6 %]; P = 0.002). Conclusions Pre-procedure administration of rectal diclofenac is associated with a significant reduction in post-ERCP pancreatitis incidence compared to post-procedure use.
Determinants of maternity waiting home utilization in Sidama Zone, Southern Ethiopia: A cross-sectional study

Objective To estimate the magnitude of maternity waiting home utilization and identify its associated factors in Sidama Zone, Southern Ethiopia. Methods A community-based cross-sectional study was conducted on a total of 748 mothers who gave birth within the last year in the selected woredas (districts) of Sidama Zone. Data were collected from April 1–30, 2019 by using pre-tested and structured questionnaires. Data were coded and entered into EpiData version 3.5. 1 and exported to Stata Version 13 software for analysis. Multivariable logistic regression analysis was performed to identify factors associated with maternity waiting home utilization adjusting for confounders. Results The mean (SD) of the age of the mothers was 31.26(6.42). Utilization of maternity waiting home in Sidama Zone was 67.25% (95% CI: 63.79–70.53). Maternity waiting home utilization was positively associated with protestant religion (AOR = 1.7; 95% CI: 1.00–2.82) and having a spouse who can read and write (AOR = 2.0; 95%CI: 1.11–3.66) while it was negatively associated with maternal age of 31–40 (AOR = 0.4; 95%CI: 0.28–0.64) relative to the age group of 20–30, daily laborer occupation of mothers (AOR = 0.2; 95%CI: 0.06–0.76), monthly income under the poverty level (825-1320EBR) (AOR = 0.6; 95%CI: 0.36–0.92) relative with extreme poverty line (<825 EBR), lack of knowledge about maternity waiting home (AOR = 0.009; 95%CI: 0.002–0.03). Conclusions Women who had knowledge about maternity waiting home, had a husband who can read and write and protestant religion followers have higher probabilities of maternity waiting home utilization, whereas women (31–40 years old), daily laborers and whose family income is below the poverty level have lower probabilities of maternity waiting home utilization. Therefore, Health education about maternity waiting home utilization, spouse education, and women’s economic empowerment is crucial to enhance maternity waiting home utilization.
Incidence and prognostic value of pulmonary embolism in COVID-19: A systematic review and meta-analysis

Background Pulmonary embolisms are frequently and prognostically in individuals infected by coronavirus disease 2019 (COVID-19); the incidence of pulmonary embolisms is varied across numerous studies. This study aimed to assess the pooled incidence of pulmonary embolic events and the prognostic value of such events in intensive care unit (ICU) admissions of patients with COVID-19. Methods The Cochrane Library, PubMed, and EmBase were systematically searched for eligible studies published on or before October 20, 2021. The pooled incidence of pulmonary embolism was calculated using the random-effects model. Moreover, the prognostic value was assessed by measuring the sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Results Thirty-six studies involving 10,367 COVID-19 patients were selected for the final meta-analysis. The cumulative incidence of pulmonary embolism in patients with COVID-19 was 21% (95% confidence interval [95%CI]: 18−24%; P<0.001), and the incidence of pulmonary embolism in ICU and non-ICU patients was 26% (95%CI: 22−31%; P<0.001) and 17% (95%CI: 14−20%; P<0.001), respectively. The predictive role of pulmonary embolism in ICU admission was also assessed, and the sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.31 (95%CI: 0.21−0.42), 0.84 (95%CI: 0.75−0.90), 1.88 (95%CI: 1.45−2.45), 0.83 (95%CI: 0.75−0.91), 2.25 (95%CI: 1.64−3.08), and 0.61 (95%CI: 0.57−0.65), respectively. Conclusion This study found that the incidence of pulmonary embolism was relatively high in COVID-19 patients, and the incidence of pulmonary embolism in ICU patients was higher than that in non-ICU patients.
Synthesizing Invariant Clusters for Polynomial Programs by Semidefinite Programming

In this paper, we present a novel approach to synthesize invariant clusters for polynomial programs. An invariant cluster is a set of program invariants that share a common structure, which could, for example, be used to save the needs for repeatedly synthesizing new invariants when the specifications and programs are evolving. To that end, we search for sets of parameters $R_k$ w.r. t. a parameterized multivariate polynomial $I(a, x)$ (i.e. a template) such that $I(a, x) \leq 0$ is a valid program invariant for all $a \in R_k$. Instead of using time-consuming symbolic routines such as quantifier eliminations, we show that such sets of parameters can be synthesized using a hierarchy of semidefinite programming (SDP). Moreover, we show that, under some standard non-degenerate assumptions, almost all possible valid parameters can be included in the synthesized sets. Such kind of completeness result has previously only been provided by symbolic approaches. Further extensions such as using semialgebraic and general algebraic templates (instead of polynomial ones) and allowing non-polynomial continuous functions in programs are also discussed.
Mineral snowflakes on exoplanets and brown dwarfs. Coagulation and fragmentation of cloud particles with HyLandS

Brown dwarfs and exoplanets provide unique atmospheric regimes that hold information about their formation routes and evolutionary states. Modelling mineral cloud particle formation is key to prepare for missions and instruments like CRIRES+, JWST and ARIEL as well as possible polarimetry missions like {\sc PolStar}. The aim is to support more detailed observations that demand greater understanding of microphysical cloud processes. We extend our kinetic cloud formation model that treats nucleation, condensation, evaporation and settling of mixed material cloud particles to consistently model cloud particle-particle collisions. The new hybrid code, {\sc HyLandS}, is applied to a grid of {\sc Drift-Phoenix} (T, p)-profiles. Effective medium theory and Mie theory are used to investigate the optical properties. Turbulence is the main driving process of collisions, with collisions becoming the dominant process at the cloud base ($p>10^{-4}\,{\rm bar}$). Collisions produce one of three outcomes: fragmenting atmospheres ($\log_{10}(g)=3$), coagulating atmospheres ($\log_{10}(g)=5$, $T_{\rm eff} \leq 1800\, {\rm K}$) and condensational growth dominated atmospheres ($\log_{10}(g\,)=5$, $T_{\rm eff}>1800\, {\rm K}$). Cloud particle opacity slope at optical wavelengths (HST) is increased with fragmentation, as are the silicate features at mid-infrared wavelengths. The hybrid moment-bin method {\sc HyLandS} demonstrates the feasibility of combining a moment and a bin method whilst assuring element conservation. It provides a powerful and fast tool for capturing general trends of particle collisions, consistently with other microphysical processes. Collisions are important in exoplanet and brown dwarf atmospheres but cannot be assumed to be hit-and-stick only. The spectral effects of collisions complicates inferences of cloud particle size and material composition from observational data.
Robust Dynamic Walking for a 3D Dual-SLIP Model under One-Step Unilateral Stiffness Perturbations: Towards Bipedal Locomotion over Compliant Terrain

Bipedal walking is one of the most important hallmarks of human that robots have been trying to mimic for many decades. Although previous control methodologies have achieved robot walking on some terrains, there is a need for a framework allowing stable and robust locomotion over a wide range of compliant surfaces. This work proposes a novel biomechanics-inspired controller that adjusts the stiffness of the legs in support for robust and dynamic bipedal locomotion over compliant terrains. First, the 3D Dual-SLIP model is extended to support for the first time locomotion over compliant surfaces with variable stiffness and damping parameters. Then, the proposed controller is compared to a Linear-Quadratic Regulator (LQR) controller, in terms of robustness on stepping on soft terrain. The LQR controller is shown to be robust only up to a moderate ground stiffness level of 200 kN/m, while it fails in lower stiffness levels. On the contrary, the proposed controller can produce stable gait in stiffness levels as low as 30 kN/m, which results in a vertical ground penetration of the leg that is deeper than 10% of its rest length. The proposed framework could advance the field of bipedal walking, by generating stable walking trajectories for a wide range of compliant terrains useful for the control of bipeds and humanoids, as well as by improving controllers for prosthetic devices with tunable stiffness.
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning

Previous few-shot learning (FSL) works mostly are limited to natural images of general concepts and categories. These works assume very high visual similarity between the source and target classes. In contrast, the recently proposed cross-domain few-shot learning (CD-FSL) aims at transferring knowledge from general nature images of many labeled examples to novel domain-specific target categories of only a few labeled examples. The key challenge of CD-FSL lies in the huge data shift between source and target domains, which is typically in the form of totally different visual styles. This makes it very nontrivial to directly extend the classical FSL methods to address the CD-FSL task. To this end, this paper studies the problem of CD-FSL by spanning the style distributions of the source dataset. Particularly, wavelet transform is introduced to enable the decomposition of visual representations into low-frequency components such as shape and style and high-frequency components e.g., texture. To make our model robust to visual styles, the source images are augmented by swapping the styles of their low-frequency components with each other. We propose a novel Style Augmentation (StyleAug) module to implement this idea. Furthermore, we present a Self-Supervised Learning (SSL) module to ensure the predictions of style-augmented images are semantically similar to the unchanged ones. This avoids the potential semantic drift problem in exchanging the styles. Extensive experiments on two CD-FSL benchmarks show the effectiveness of our method. Our codes and models will be released.
Expert System of Boiler's Four-Tube Crack Prediction Based on Knowledge Base

Expert system of boiler's four-tube crack prediction based on knowledge base in posed according to analysis of characteristic of four-tube crack. General structure of system and object-oriented model are discussed in the paper. Database (DB) is set up according to system demand, case base (CB) founded through accumulating cases; rule base (KB) established after accepting expert experience and knowledge of this field, and model base (MB) built according to the structure of knowledge. The four bases are recombined in the expert system. The predictive methods based on case, on rule and on model wer described. Compared with each other, advantage and disadvantage of these reasoning methods are obtained. The mixed reasoning based on case, rule and model is posed to overcome their shortcoming, retain fully their advantage and find the answer to the problem as possible. The definition, representation and calculation of the degree of similarity and rule representation are discussed. The function of system and way of realization are described in the paper. It is proved by application that expert system of four-tube crack prediction based on knowledge base can achieve the same predictive result as the expert. Figs 3 and refs 8.
Do BERTs Learn to Use Browser User Interface? Exploring Multi-Step Tasks with Unified Vision-and-Language BERTs

Pre-trained Transformers are good foundations for unified multi-task models owing to their task-agnostic representation. Pre-trained Transformers are often combined with text-to-text framework to execute multiple tasks by a single model. Performing a task through a graphical user interface (GUI) is another candidate to accommodate various tasks, including multi-step tasks with vision and language inputs. However, few papers combine pre-trained Transformers with performing through GUI. To fill this gap, we explore a framework in which a model performs a task by manipulating the GUI implemented with web pages in multiple steps. We develop task pages with and without page transitions and propose a BERT extension for the framework. We jointly trained our BERT extension with those task pages, and made the following observations. (1) The model learned to use both task pages with and without page transition. (2) In four out of five tasks without page transitions, the model performs greater than 75% of the performance of the original BERT, which does not use browsers. (3) The model did not generalize effectively on unseen tasks. These results suggest that we can fine-tune BERTs to multi-step tasks through GUIs, and there is room for improvement in their generalizability. Code will be available online.
Learning for Robot Decision Making under Distribution Shift: A Survey

With the recent advances in the field of deep learning, learning-based methods are widely being implemented in various robotic systems that help robots understand their environment and make informed decisions to achieve a wide variety of tasks or goals. However, learning-based methods have repeatedly been shown to have poor generalization when they are presented with inputs that are different from those during training leading to the problem of distribution shift. Any robotic system that employs learning-based methods is prone to distribution shift which might lead the agents to make decisions that lead to degraded performance or even catastrophic failure. In this paper, we discuss various techniques that have been proposed in the literature to aid or improve decision making under distribution shift for robotic systems. We present a taxonomy of existing literature and present a survey of existing approaches in the area based on this taxonomy. Finally, we also identify a few open problems in the area that could serve as future directions for research.