Dynamical behavior of a time-delayed infectious disease model with a non-linear incidence function under the effect of vaccination and treatment

When an infectious disease propagates throughout society, the incidence function may rise at first due to an increase in pathogenicity and then decrease due to inhibitory effects until it reaches saturation. Effective vaccination and treatment are very helpful for controlling the effects of such infectious diseases. To analyze the impacts of these diseases, we proposed a new compartmental model with a generalized non-linear incidence function, vaccination function, and treatment function, along with time delays in the respective functions, which show how its monotonic features influence the stability of the model. Fundamental properties of a model, such as positivity, boundedness, and the existence of equilibria, are examined in this work. The basic reproduction number has been computed, and correlative studies for local stability in view of the basic reproduction number have been examined at the disease-free and endemic equilibrium points. A delay-independent global stability result has been established, and to be more precise, we explicitly derived the result on global stability by restricting delay parameters within a very specific range. Furthermore, numerical simulations and some examples based on COVID-19 real-time data are pointed out to emphasize the significance of how the disease's dynamical behavior is characterized by various functions for controlling the spread of disease in a population and to justify the mathematical conclusions.Comment: 25 pages, 19 figure

Regression and Classification of Alzheimer’s Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image

Because of headways in deep learning and clinical imaging innovation, a few specialists are presently utilizing convolutional neural networks (CNNs) to extricate profound level properties from clinical pictures to all the more exactly classify Alzheimer's disease (AD) and expect clinical scores. A limited scale profound learning network called PCANet utilizes principal component analysis (PCA) to make multi-facet channel banks for the incorporated learning of information. Blockwise histograms are made after binarization to get picture ascribes. PCANet is less versatile than different frameworks since the multi-facet channel banks are made involving test information and the produced highlights have aspects during the many thousands or even many thousands. To conquer these issues, we present in this study a PCANet-based, information free organization called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet). To deliver the last picture highlights, we first form higher-request tensors and utilize tensor decomposition (TD) to achieve information dimensionality decrease. Specifically, we foster staggered channel banks for test getting the hang of utilizing nonnegative matrix factorization(NMF) as opposed to PCA. These properties serve as input to the support vector machine (SVM) that our technique employs to diagnose AD, forecast clinical score, and categorise AD

EFFECT OF MOMORDICA CHARANTIA AND SYZYGIUM CUMINI EXTRACT ON SERUM ELECTROLYTES IN ALLOXAN INDUCED DIABETIC RATS

Objective: Diabetes is a group of disorders characterized by high blood glucose levels. Disturbances in serum electrolytes like sodium (Na+) and potassium (K+) are found in diabetes. The purpose of the study was to investigate the disturbances in concentrations of serum electrolytes in hyperglycemic crisis and the effect of syzygium cumini and momordica charantia standardized aqueous extracts on serum electrolytes (Na+and K+) in normal and diabetic rats.Methods: Diabetes is induced by intraperitoneal injection of alloxan at a dose of 120 mg/kg b. w in rats. Rats were divided into 5 groups (normal control, disease control, metformin, test 1 and test 2). In test groups 1 and 2, SASESC (standardized aqueous seed extract of syzygium cumini) and SAFEMC (standardized aqueous fruit extract of momordica charantia) were respectively administered orally to alloxan induced diabetic rats, and their serum electrolyte levels were observed at 1st, 4th, 7th and 14th days.Results: By the 14thday, the Na+ and K+ levels in groups 4 and 5 were almost normal. However, in group 3 (standard), Na+levels were relatively lower and K+ levels were relatively higher than groups 4 and 5 (test). In group 2 (disease control) as compared to group 1 (normal control), a decrease in Na+ and increase in K+ levels was observed even on day 14.Conclusion: Treatment with anti diabetic drugs like metformin, syzygium cumini (test-1), momordica charantia (test-2) restored the electrolyte levels almost back to normal over a period of study (14 d). There was significant (**P<0.01, *P<0.05) normalization of electrolyte levels in diabetic rats. It was concluded that syzygium cumini and momordica charantia showed better efficiency in restoring the electrolyte imbalance as compared to metformin during our study

Hybrid Approach for Alzheimer’s Disease Diagnosis For 3D Brain MR Image

Because of headways in deep learning and clinical imaging innovation, a few specialists are presently utilizing convolutional neural networks (CNNs) to extricate profound level properties from clinical pictures to all the more exactly classify Alzheimer's disease (AD) and expect clinical scores. A limited scale profound learning network called PCANet utilizes principal component analysis (PCA) to make multi-facet channel banks for the incorporated learning of information. Blockwise histograms are made after binarization to get picture ascribes. PCANet is less versatile than different frameworks since the multi-facet channel banks are made involving test information and the produced highlights have aspects during the many thousands or even many thousands. To conquer these issues, we present in this study a PCANet-based, information free organization called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet). To deliver the last picture highlights, we first form higher-request tensors and utilize tensor decomposition (TD) to achieve information dimensionality decrease. Specifically, we foster staggered channel banks for test getting the hang of utilizing nonnegative matrix factorization(NMF) as opposed to PCA. These properties serve as input to the support vector machine (SVM) that our technique employs to diagnose AD, forecast clinical score, and categorise AD

TCSC-STATCOM Controller for the Voltage Stability Improvement of the Wind Farm Connected to the Grid

The project is about the improvement of voltage stability of the system which has a wind farm connected to the Grid. A combination of TCSC and STATCOM is used in the controller. The controller ensures that the system receives enough reactive power to maintain stability. The simulation model is going to be inbuilt MATLAB/SIMULINK. output voltages of the system without the controller, with each component acting separately and the collaborative control effect of TCSC and STATCOM, will be compared and studied in the simulated model to prove the efficiency of the controller

Quality Control in Soil Testing Using the Internal Standards: Temporal Variability in Organic Carbon and Extractable Nutrient Elements

Internal soil standards are used in an analytical laboratory as one of the tools for providing feedback on the quality of soil testing service provided. The objective of this study is to provide results on the temporal variability in soil test values of the internal soil standards used for soil organic carbon (C), extractable phosphorus (P), potassium (K), sulfur (S), boron (B) and zinc (Zn) determination in our analytical service laboratory. The range, mean, standard deviation (SD) and coefficient of variation (CV) values for the internal standards for various fertility parameters varied according to the parameter, and were generally of acceptable quality, except for the extractable P (Olsen-P) in the lower range of extractable P. Our results show that the use of internal soil standards in an analytical service laboratory is a simple, inexpensive and effective tool for providing a feedback on the quality of soil testing service

Heavy-Metal Concentrations in Sediments Collected from ICRISAT Lake, Patancheru, India

To determine and characterize the initial background concentrations of heavy metals, a total of 50 sediment samples were collected from the largest lake at the International Crops Research Institute for the Semi-arid Tropics (ICRISAT) in Patancheru, India. The finely ground sediment samples were digested using a microwave-assisted digestion method and analyzed for 15 heavy metals using inductively coupled plasma–optical emission spectrometry (ICP-OES). The results showed that the concentrations of the heavy metals varied greatly with metal and sediment sample, but in general the concentrations were low. Our results suggest that the sediments from this lake (15 ha in area) at the ICRISAT center do not appear contaminated with the heavy metals evaluated, and they indeed reflect normal background concentrations of these metals released through the natural process of weathering

Simple and accurate method for routine analysis of heavy metals in soil, plant, and fertilizer

With environmental issues relating to heavy-metal contamination of natural resources becoming of increasing concern, there is an obvious need to have a method that can be used for routine analysis of a range of materials including soil, sediment, sewage sludge, plant, mineral and organic fertilizers, and other miscellaneous materials for heavy metals of concern. A single-step microwave digestion method was developed using aqua regia solution for digestion of finely ground samples for determining 15 heavy metals in soil, plant, and fertilizer materials using inductively coupled plasma–optical emission spectrometry (ICP-OES). Results on the recoveries of 15 heavy metals added via certified standard reference sample to soil, plant, or fertilizer samples showed that the results varied with the metal and the substrate, and with few exceptions, the results were satisfactory. The method is simple, rapid, and accurate and seems ideal for the routine analyses of a range of materials. Using microwave-assisted digestion, an analyst can perform more than 100 analyses in a working da

Comparative Evaluation of ICP-AES and Turbidimetric Methods for Determining Extractable Sulfur in Soils

The deficiency of sulfur (S) as a constraint to crop productivity in irrigated, intensified systems has long been recognized (Kanwar 1972; Pasricha and Fox 1993; Singh 2001). A recent survey of farmers’ fields in the Indian semi-arid tropics (SAT) demonstrated that the deficiency of S, as a constraint to crop production and productivity, is also equally widespread in the rainfed production systems (Rego et al. 2007; Sahrawat et al. 2007). The results of this research further showed that soil testing was effective in diagnosing S deficiency; and the crops grown on farmers’ fields with calcium chloride extractable-S levels of less than 8-10 mg kg-1 soil responded positively to the application of sulfur (Rego et al. 2007)

Comparative Evaluation of Inductively Coupled Plasma–Atomic Emission Spectroscopy and Colorimetric Methods for Determining Hot-Water-Extractable Boron in Soils

Frequency of boron (B) deficiency is increasing in rainfed systems, and hence there is a need to diagnose the deficiency. Colorimetric methods are still widely used in soil-testing laboratories in India for measuring B. Little information is available on the comparative evaluation of the colorimetric and inductively coupled plasma (ICP) methods for determining extractable B in soils. We describe results of the comparative evaluation of these methods for measuring extractable B in 57 soil samples with pH values ranging from 5.3 to 9.5. There was a significant correlation between B values determined by the two methods, and the correlation coefficient was greater for soil samples with pH in the neutral to alkaline range. Interaction between soil samples and methods (ICP or colorimetric) was significant except for soil samples in the pH range of 8.0 to 9.5. Precision for B determination was greater with the ICP than with the colorimetric method