Pneumonia detection in chest X-ray images using compound scaled deep learning model

Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper. ResNet50 is a multilayer layer convolution neural network having residual blocks. As it was very difficult to obtain a sufficiently large dataset for detection tasks, data augmentation techniques were used to increase the training dataset. Transfer learning is also used while training the models. The proposed model could help in detecting the disease and can assist the radiologists in their clinical decision-making process. The model was evaluated and statistically validated to overfitting and generalization errors. Different scores, such as testing accuracy, F1, recall, precision and AUC score, were computed to check the efficacy of the proposed model. The proposed model attained a test accuracy of 98.14% and an AUC score of 99.71 on the test data from the Guangzhou Women and Children’s Medical Center pneumonia dataset

Machine vision for the measurement of machining parameters: A review

Machining parameters have significant value in manufacturing and machining industries as they result in quality and dimensional accuracy of the product. The machining parameters are measured using various machine vision systems. In this review, machine vision and its various procedures have been discussed that are used to measure machining parameters, i.e., tool condition monitoring (TCM) tool wear and surface characteristics like surface roughness, surface defects, etc. Nowadays, Tool condition monitor is a significant machining parameter is developed in manufacturing and machining industries. The development of various techniques of machine vision explore in tool condition monitoring is of significant interest because of the improvement of non-tactile applications and computing hardware. The review also discusses the enhancement of machine vision systems in tool condition monitoring

Artificial intelligence techniques for implementation of intelligent machining

For the past few years, the rapid progress and development of artificial intelligence (AI) based technologies have been analyzed for the applications of the intelligent manufacturing industry, i.e., industry 4.0. this has triggered a valuable transformation in means, models, and ecosystems within the manufacturing industry and AI development. With the advancement in manufacturing technology, there is a need to execute these technologies and AI more efficiently and cost-effectively. It can be possible by combining traditional manufacturing and machining technologies with recently developed intelligent manufacturing technologies comprising hardware and software techniques. This review paper discusses various AI implementation-based intelligent manufacturing industries with their architecture and technology systems based on the integration of AI with manufacturing and information communication. Furthermore, AI-based manufacturing application, their implementation, and current development in intelligent manufacturing have also been discussed

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Recent advances in technology and refinement of available computational resources paved the way for the extensive use of computers to model and simulate complex real-world problems difficult to solve analytically. The appeal of simulations lies in the ability to predict the significance of a change to the system under study. The simulated results can be of great benefit in predicting various behaviors, such as the wind pattern in a particular region, the ability of a material to withstand a dynamic load, or even the behavior of a workpiece under a particular type of machining. This paper deals with the mathematical modeling and simulation techniques used in abrasive-based machining processes such as abrasive flow machining (AFM), magnetic-based finishing processes, i.e., magnetic abrasive finishing (MAF) process, magnetorheological finishing (MRF) process, and ball-end type magnetorheological finishing process (BEMRF). The paper also aims to highlight the advances and obstacles associated with these techniques and their applications in flow machining. This study contributes the better understanding by examining the available modeling and simulation techniques such as Molecular Dynamic Simulation (MDS), Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Discrete Element Method (DEM), Multivariable Regression Analysis (MVRA), Artificial Neural Network (ANN), Response Surface Analysis (RSA), Stochastic Modeling and Simulation by Data Dependent System (DDS). Among these methods, CFD and FEM can be performed with the available commercial software, while DEM and MDS performed using the computer programming-based platform, i.e., "LAMMPS Molecular Dynamics Simulator," or C, C++, or Python programming, and these methods seem more promising techniques for modeling and simulation of loose abrasive-based machining processes. The other four methods (MVRA, ANN, RSA, and DDS) are experimental and based on statistical approaches that can be used for mathematical modeling of loose abrasive-based machining processes. Additionally, it suggests areas for further investigation and offers a priceless bibliography of earlier studies on the modeling and simulation techniques for abrasive-based machining processes. Researchers studying mathematical modeling of various micro- and nanofinishing techniques for different applications may find this review article to be of great help

Experimental investigation on magnetorheological finishing process parameters

Magneto-rheological polishing (MRP) fluid was developed by MR fluid using a magnetic field, non-magnetic abrasives such as SiC and Al2O3, and carrier medium like oil. A magnetic polishing tool was developed using a super-strong permanent neodymium magnet (Nd2Fe14B) with 0.5-tesla magnetic intensity. This polishing tool was assembled to the vertical milling machine for the finishing workpieces. In the present research, magnetic materials (steel material) and non-ferromagnetic (copper) content were finishing using a developed MRP setup for experimental investigation. This research also investigated the parametric dependencies of different abrasives on the magneto-rheological finishing process. It determined the effect of magnetic particle concentration and abrasives on the surface roughness of ferromagnetic (stainless steel) and non-ferromagnetic material (copper). The final surface roughness value has reached 30 nm from its initial surface roughness of 800 nm for non-ferromagnetic (copper). For the magnetic material (stainless steel), the value is 50 nm from 1300 nm

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

A comprehensive review on surface post-treatments for freeform surfaces of bio-implants

Surface finish is an essential factor in determining product sustainability and functionality. Most methods have been developed that can be utilized to manufacture optical, mechanical, and electrical devices with a micrometer or submicrometric precision, nanoscale surface roughness, and practically no surface flaws. Finishing technologies are classified into two types: those that use magnetic force and those that do not. These techniques provide flexible finishing tools that may be used efficiently for complicated freeform components. Due to limitations in finishing tool movement over the complex freeform geometry of the components, traditional finishing methods perform relatively badly when finishing sophisticated freeform surfaces. The life and function of the implant are determined by the surface conditions of biomedical components, such as heart valves, dental crowns, knee, elbow, and hip joints. Implants are often made of polymers, metals, ceramics, skin, bone, other human tissues, and other materials. Non-traditional finishing methods using loose abrasives offer greater finishing accuracy, uniformity, performance, and cost-effectiveness. Using abrasive-based finishing technologies like abrasive flow machining, magnetic abrasive finishing, magnetorheological fluid-based finishing, elastic emission machining, heat treatment, surface coating, and laser surface processing, etc., this article critically reviews the published research on fine finishing of freeform surfaces, i.e., biomedical implants, to improve their functionality and surface quality.43 página