Characterizations of fuzzy fated filters of R0-algebras based on fuzzy points

More general form of the notion of quasi-coincidence of a fuzzy point with a fuzzy subset is considered, and generalization of fuzzy fated of R0-algebras is discussed. The notion of an (∈,∈∨ qk)-fuzzy fated filter in a R0-algebra is introduced, and several properties are investigated. Characterizations of an (∈, ∈ ∨ qk)-fuzzy fated filter in an R0-algebra are discussed. Using a collection of fated filters, a (∈,∈∨ qk)-fuzzy fated filter is established

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations

Hepatitis C Treatment: current and future perspectives

Hepatitis C virus (HCV) is a member of Flaviviridae family and one of the major causes of liver disease. There are about 175 million HCV infected patients worldwide that constitute 3% of world's population. The main route of HCV transmission is parental however 90% intravenous drug users are at highest risk. Standard interferon and ribavirin remained a gold standard of chronic HCV treatment having 38-43% sustained virological response rates. Currently the standard therapy for HCV is pegylated interferon (PEG-INF) with ribavirin. This therapy achieves 50% sustained virological response (SVR) for genotype 1 and 80% for genotype 2 & 3. As pegylated interferon is expensive, standard interferon is still the main therapy for HCV treatment in under developed countries. On the other hand, studies showed that pegylated IFN and RBV therapy has severe side effects like hematological complications. Herbal medicines (laccase, proanthocyandin, Rhodiola kirilowii) are also being in use as a natural and alternative way for treatment of HCV but there is not a single significant report documented yet. Best SVR indicators are genotype 3 and 2, < 0.2 million IU/mL pretreatment viral load, rapid virological response (RVR) rate and age <40 years. New therapeutic approaches are under study like interferon related systems, modified forms of ribavirin, internal ribosome entry site (HCV IRES) inhibitors, NS3 and NS5a inhibitors, novel immunomodulators and specifically targeted anti-viral therapy for hepatitis C compounds. More remedial therapies include caspase inhibitors, anti-fibrotic agents, antibody treatment and vaccines

Frequency of psychiatric illnesses and opinion/vantage of patients attending neurology out-patient department

Objective: To determine the Frequency of psychiatric illnesses in patients with neurological conditions, and to take their opinion about psychiatric disorders. Method: The cross-sectional study was conducted from June 1 to August 30, 2021, at the Neurology Outpatient Department of Allied Hospital, Faisalabad, Pakistan, and comprised patients of either gender aged 12-70 years from among those visiting the outpatient clinic. Data was collected through interviews and the 41-item Depression Anxiety Stress Scale. Data was analysed using SPSS 21. Results: Of the 201 patients, 160(79.6%) were females and 41(20.4%) were males. The overall mean age was 34.5+/-9.38 years. Primary neurological problem was headache 119(59.2%). Overall, 155(77.2%) patients met the criteria of psychiatric disorders; 55(27.4%) anxiety, 37(19.4%) had depressive disorder, 42(20.8%) mixed anxiety depressive disorder, and 19(9.5%) had other psychiatric illnesses. Also, 101(50.2%) patients lacked awareness about psychiatry illnesses, 35(17.4%) had fear of stigma, and 28(13.9%) had misconceptions. Conclusion: The Frequency of psychiatric disorders among those visiting the neurology outpatient department was high, and was associated with negative views about such illnesses. Key Words: Comorbidity, Neurology outpatient, Psychiatric illness, Opinion

Energy Management of Microgrids for Smart Cities: A Review

Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities

Development and characterization of symbiotic microcapsules to enhance the viability of probiotic under stressed conditions

ABSTRACTIn the present research, the survival and sustainability of a Lactobacillus rhamnosus probiotic has been investigated with regard to the prebiotic impact of introducing two different kinds of onion (Allium cepa L.) peel extract to probiotic microcapsules. Effective inclusion of red and white onion peel extract featuring good prebiotic action into the microcapsules enhanced probiotic survival. The structure, distribution of size, zeta potential, and encapsulation efficacy of probiotics and substances in the extract were evaluated along with the probiotics capability to persist under simulated gastrointestinal circumstances. Fourier Transform Infrared Spectroscopy (FTIR) was employed to investigate the molecular structure and internal framework. The wall and core components possess adhesive relationships, as demonstrated by FTIR spectra. Probiotics that were free and those that were in capsules were evaluated as well in vitro in undesirable persistence performance (>90%). Probiotics with encapsulation exhibited substantially (p > .05) greater vitality compared free cells, in accordance with an in-vitro experiment. Under simulated gastrointestinal situations, free cells continued diminishing their vitality, but encapsulated cells maintained their viability count beyond the suggested level (107 cfu/g). SEM photographs indicated that probiotics have been effectively encapsulated inblends. The microcapsules were around 191 ± 2 and 176 ± 2 μm in size. These outcomes reveal that these kinds of microcapsules may encourage the probiotic L. rhamnosus prolonged viability and consistency under multiple conditions