Dual level searching approach for solving multi-objective optimisation problems using hybrid particle swarm optimisation and bats echolocation-inspired algorithms

A dual level searching approach for multi objective optimisation problems using particle swarm optimisation and modified adaptive bats sonar algorithm is presented. The concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is integrated with the established particle swarm optimisation algorithm. The proposed algorithm incorporates advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handle the complexity of multi objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi objective optimisation benchmark test functions and problem. The acquired results show that the proposed algorithm perform well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving of multi objective optimisation problems

Modeling and Speed Control for Sensorless DC Motor BLDC Based on Real Time Experiement

This paper presents a modeling of the Brushless DC motor based on the system identification method. The input and output data were collected and simulated based on the real-time experiment. Taking a continues time form for the system model, a transfer function was selected in this work. The potentiometer has been used to send  Pulse Width Modulation (PWM) signals as input signal to the Brushless DC motor to determine the open-loop model of brushless DC motor (BLDC). LM2907 Tachometer attached with Brushless DC motor driver to measure the output speed. The input signal and measured output data were interfaced to plant by C code generation Matlab/Simulink through Arduino Mega controller. System identification toolbox was used for collecting data to obtain the estimates model. The best fit found for the system was 90.2%. The PID controller was developed to control the desired speed based on the given speed to demonstrate the feasibility of the given method.  &nbsp

Comparison of non-mydriatic fundus photography and optical coherence tomography with dilated fundus examination for detecting diabetic retinopathy including diabetic macular edema

Given increasing diabetes rates worldwide, better screening tools for diabetic retinopathy (DR) and macular edema (DME) are needed. The study aim was to compare reliability and predictive values between non-mydriatic fundus photography (NMFP) and spectral-domain optical coherence tomography (OCT) for detection of DR and DME with dilated fundus examination (DFE). This was a non-interventional, comparative study. Diabetics underwent both NMFP and macula OCT, followed by DFE. Images were interpreted by two masked ophthalmologists. The DFE result was considered gold standard. One hundred and fifty-four eyes of 83 patients were recruited. Sensitivity of NMFP for DR was 77.3% and 80.3% for OCT. Specificity for NMFP was 81.8% and 55.7% for OCT. Area under Receiver Operating Characteristics Curve (AROC) for DR was 0.80 for NMFP and 0.68 for OCT. The sensitivity of NMFP for DME was 63.2% and 82.5% for OCT. Specificity for DME was 90.1% by NMFP and 61.5% for OCT. Positive predictive value (PPV) of NMFP and OCT for DR was 76.1% (95% CI: 63.9-85.3%) and 57.6% (46.8-67.7%), respectively. Negative predictive value (NPV) of NMFP and OCT was 82.7% (95% CI: 72.8-89.7%) and 79.0% (66.4-87.9%) respectively. Positive predictive value of NMFP and OCT for DME was 80.0% (95% CI: 67.6- 88.5%) and 57.3% (45.9-68.0%), respectively. Negative predictive value of NMFP and OCT was 79.6% (95% CI:70.3 - 86.7%) and 84.8% (95% CI:73.4 - 92.1%), respectively. Eyes with normal OCT miss 21% of DR. In conclusion, NMFP is better than OCT for DR screening, while OCT is better than NMFP and DFE for detection of DME. Both modalities should be for better DR screening

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019

Background The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. Methods We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively. Funding The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38)

A new bats echolocation-based algorithm for single objective optimisation

© 2016, The Author(s). Bats sonar algorithm (BSA) as a swarm intelligence approach utilises the concept of echolocation of bats to find prey. However, the algorithm is unable to achieve good precision and fast convergence rate to the optimum solution. With this in mind, an adaptive bats sonar algorithm is introduced with new paradigms of real bats echolocation behaviour. The performance of the algorithm is validated through rigorous tests with several single objective optimisation benchmark test functions. The obtained results show that the proposed scheme outperforms the BSA in terms of accuracy and convergence speed and can be efficiently employed to solve engineering problems

Hybridizing invasive weed optimization with firefly algorithm for unconstrained and constrained optimization problems

© 2005 – ongoing JATIT & LLS. This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm for global optimization problems. Unconstrained and constrained optimization problems with continuous design variables are used to illustrate the effectiveness and robustness of the proposed algorithm. The firefly algorithm (FA) is effective in local search, but can easily get trapped in local optima. The invasive weed optimization (IWO) algorithm, on the other hand, is effective in accurate global search, but not in local search. Therefore, the idea of hybridization between IWO and FA is to achieve a more robust optimization technique, especially to compensate for the deficiencies of the individual algorithms. In the proposed algorithm, the firefly method is embedded into IWO to enhance the local search capability of IWO algorithm that already has very good exploration capability. The performance of the proposed method is assessed with four well-known unconstrained problems and four practical constrained problems. Comparative assessments of performance of the proposed algorithm with the original FA and IWO are carried out on the unconstrained problems and with several other hybrid methods reported in the literature on the practical constrained problems, to illustrate its effectiveness. Simulation results show that the proposed HIWFO algorithm has superior searching quality and robustness than the approaches considered