Applying the genetic algorithm for determination electrospinning parameters of poly vinylidene fluoride (PVDF) nano fibers: theoretical & experimental analysis

Poly Vinylidene Fluoride (PVDF) because of its piezoelectric properties has been applied in different applications such as smart textiles, medical application and membranes for energy harvesting. It was declared that nanofibres diameters and electrospinning parameters could be enhanced the piezoelectric properties of these materials. The main objective of this paper is applying the Genetic Algorithm (GA) to determine the optimum condition of solution parameters and processing conditions based on the desired diameter size of PVDF fibers to produce the fibers without any structural faults. In this method, The Fitness function was determined by a simple analytical model presented by Fridrikh. Toward approving the GA results the experimental tests were done. the effect of four parameters such as flow rate of the polymer solution, electrospinning voltage, electrospinning distance and polymer concentration on the fiber formation and fiber diameter size of electrospun PVDF fibers have been explored by Scanning Electron Microscopy (SEM) to attest the accuracy of the model. Assessment of experimental and theoretical results show that electrospinning parameters determined by GA method leads to achieve desire fiber diameters. Because of time and energy consuming of electrospinning process, the GA method may be useful to achieve desired fiber diameter by determining electrospinning parameters for polymers prior to fiber production

EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks

Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure. EF is estimated from cardiac ultrasound videos known as echocardiograms (echo) by manually tracing the left ventricle and estimating its volume on certain frames. These estimations exhibit high inter-observer variability due to the manual process and varying video quality. Such sources of inaccuracy and the need for rapid assessment necessitate reliable and explainable machine learning techniques. In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos. Our model first infers a latent echo-graph from the frames of one or multiple echo cine series. It then estimates weights over nodes and edges of this graph, indicating the importance of individual frames that aid EF estimation. A GNN regressor uses this weighted graph to predict EF. We show, qualitatively and quantitatively, that the learned graph weights provide explainability through identification of critical frames for EF estimation, which can be used to determine when human intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN achieves EF prediction performance that is on par with state of the art and provides explainability, which is crucial given the high inter-observer variability inherent in this task.Comment: Published in MICCAI 202

EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscle. The key challenge of automating this task with machine learning is the sparsity of clinical labels, i.e., only a few landmark pixels in a high-dimensional image are annotated, leading many prior works to heavily rely on isotropic label smoothing. However, such a label smoothing strategy ignores the anatomical information of the image and induces some bias. To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD). Our main contributions are: 1) a hierarchical graph representation learning framework for multi-resolution landmark detection via GNNs; 2) induced hierarchical supervision at different levels of granularity using a multi-level loss. We evaluate our model on a public and a private dataset under the in-distribution (ID) and out-of-distribution (OOD) settings. For the ID setting, we achieve the state-of-the-art mean absolute errors (MAEs) of 1.46 mm and 1.86 mm on the two datasets. Our model also shows better OOD generalization than prior works with a testing MAE of 4.3 mm.Comment: To be published in MICCAI 202

Satisfaction of patients presenting to health insurance offices: using data mining method

Background: The quality of healthcare services can be determined by patient satisfaction as it affects the performance, sustainability, and durability of health services. The aim of this study was to analyze the satisfaction of patients presenting to health insurance offices by using the data mining method. Method: A cross-sectional study was conducted on those who referred to the offices of the Health Insurance Organization (e.g., Kerman, Sistan and Baluchestan, Hormozgan and Yazd provinces) who were selected by non-random cluster sampling. A researcher-made questionnaire consisting of 79 items was used for data collection. Face and content validity of 0.86% was obtained using the views of five academic experts. Cronbach's alpha coefficient of the questionnaire was 0.966. Data were analyzed by SPSS-18 software. Results: The studied variables regarding quality service indicators included speeding up the administration of affairs, non-discrimination between clients, empathy with clients, keeping clients' secrets, politeness and kindness, paying attention to the needs and wishes of clients. Access to information, raising awareness, payment of compensation, attention to the rights of the disabled, rule of law and clarification of matters and criticisms were in a favorable condition, and service quality is the only unfavorable indicator in health insurance offices. Conclusion: Governments are required to respect people's rights regardless of skin color, race, religion, gender, and in the present study, the satisfaction of clients with the performance of insurance service offices was evident

Barriers to support nurses as second victim of medical errors: A qualitative study

BackgroundGiven the inevitability of medical errors and their impact on health workers, providing support to those who suffer is vital for their physical and mental recovery. Identifying the barriers to obtaining support is imperative in this regard.AimsThe current study was conducted to identify the barriers regarding supporting nurses as second victims of nursing errors in clinical settings in Iran.MethodsThis qualitative study was conducted with a sample, which was included 18 nurses. The subjects were selected through the purposive sampling method, and data were collected using in-depth and semi-structured interviews. The data were analysed using methods as described by Graneheim and Lundman (citation needed). The research context included the general and specialized departments of hospitals in Tehran, Iran, during 2017.ResultsAccording to the results, mismanagement, Cultural barriers, inadequate information, and Legal barriers were the main barriers to supporting nurses.ConclusionTraining nurses about the second victim phenomenon is recommended as well as the methods to manage the effects of this phenomenon, the supportive resources, and legal issues

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Graph neural networks and transformers for enhanced explainability and generalizability in medical machine learning

Machine learning frameworks for medical applications must be explainable, generalizable despite the scarcity of training data, and able to tackle various clinical tasks with minimal modifications. Explainability is crucial for safety-critical applications, as users must recognize when human supervision is needed. Additionally, developing strong inductive bias from sparsely labeled data is essential, given that large-scale medical datasets are not widely available. In a clinical setting, numerous metrics are measured daily, making it logistically challenging to maintain separate models for individual metrics. This highlights the need for flexible frameworks that preserve explainability. In this thesis, we address these requirements by proposing three frameworks that harness the representation power of graph neural networks (GNNs) or transformers, improving the state-of-the-art and enhancing the practicality of machine learning in medical applications. Our first framework aims to provide explainability in the prediction pipeline. We demonstrate its effectiveness using the task of left ventricular ejection fraction estimation from echocardiographic videos. This framework employs GNNs to learn a weighted graph between the frames of an input echocardiogram before producing a single ejection fraction estimate. Our results show that the learned latent structure aligns with clinical guidelines for predicting ejection fraction and can serve as a surrogate for the model's confidence in its predictions. The second framework improves model generalizability for sparsely labeled data using GNNs. We apply the framework to the task of clinical landmark detection, where only a small number of frames in a video are labeled. To maximize the use of supervisory signals, we employ a multi-scale objective function and a hierarchical graph structure. Our results indicate that this approach builds better inductive bias and outperforms previous work. Lastly, we propose a flexible framework that offers attention-based explainability on multiple levels, making it suitable for various clinical tasks. This framework utilizes Transformers, a special instance of GNNs, to capture patch-wise, frame-wise, and video-wise interactions in echocardiographic data. This approach aids in identifying pertinent information for a specific clinical metric. To showcase the flexibility of this framework, we consider two critical cardiac tasks: aortic stenosis detection and ejection fraction estimation.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Evaluation of false-twist textured yarns by image processing

A new method has been introduced to determine the crimp features of false twist textured yarns by applying computer vision and image processing method. Hence, the test results, with accuracy, are achieved more quickly than by the other exciting method. The mean angle of filament orientation in false twist textured yarns with different texturizing variables (heater temperature, texturizing speed and twist) is determined. Similarly, the direct tracking algorithms to achieve a good correlation with crimp contraction are also used. The results show that by this new method a correlation coefficient of more than 95% is achieved between mean orientation angle and crimp contraction

Evaluation of Floorcovering Abrasion Resistance by Means of Image Processing Technique

An important factor in material quality is the time duration that materials are used (known as substance lifetime). Lifetime is a function of several factors, and among them wearing and abrasion resistances are more important than the other aspects. In addition, to the physical and mechanical properties, abrasion has a significant effect on the textiles appearance. This phenomenon considerably influences the texture of floor coverings in which continuous abrasion are applied. While the texture retention of the floorcovering during lifetime usage is quite desirable and leads to consumer satisfaction, it is necessary to evaluate the abrasion resistance of floor covering before its application. In this paper, the abrasion resistance for three types of floorcoverings (100% polypropylene(pp), 100% polyester(p) and 50/50% polypropylene-polyester(pp/p)) are tested with four kinds of steel abrasives with different coefficients of friction during three steps of abrasion cycles (1000,3000 and 5000). Their images were assessed by image processing method with two different functions. The results approved the procedure of the abrasion during 5000 cycles in the experimental condition. Weight changes of the floor-covering samples were evaluated as an index to illustrate the abrasion process

Nanofibers-based piezoelectric energy harvester for self-powered wearable technologies

The demands for wearable technologies continue to grow and novel approaches for powering these devices are being enabled by the advent of new energy materials and novel manufacturing strategies. In addition, decreasing the energy consumption of portable electronic devices has created a huge demand for the development of cost-effective and environment friendly alternate energy sources. Energy harvesting materials including piezoelectric polymer with its special properties make this demand possible. Herein, we develop a flexible and lightweight nanogenerator package based on polyvinyledene fluoride (PVDF)/LiCl electrospun nanofibers. The piezoelectric performance of the developed nanogenator is investigated to evaluate effect of the thickness of the as-spun mat on the output voltage using a vibration and impact test. It is found that the output voltage increases from 1.3 V to 5 V by adding LiCl as additive into the spinning solution compared with pure PVDF. The prepared PVDF/LiCl nanogenerator is able to generate voltage and current output of 3 V and 0.5 µA with a power density output of 0.3 µW cm−2 at the frequency of 200 Hz. It is found also that the developed nanogenerator can be utilized as a sensor to measure temperature changes from 30◦C to 90◦C under static pressure. The developed electrospun temperature sensor showed sensitivity of 0.16%/◦C under 100 Pa pressure and 0.06%/◦C under 220 Pa pressure. The obtained results suggested the developed energy harvesting textiles have promising applications for various wearable self-powered electrical devices and systems