Bio-effects of 5th generation electromagnetic waves on organs of human beings

Objectives:The uses of devices of electromagnetic waves(EMWs)are increasing day by day.Similarly,the generation of the waves is increasing.The frequency spectrum of the generation of waves is also increased.In this manuscript,an analysis of the high frequency EMWs has been made by the electric fields generated due to the exposure of 5th generation(5 G)of mobile phones.Methods:Due to the emission of waves from the towers,the electric field is generated around the transmission tower of mobile phones.The electric fields are computed by the power of the transmission tower.The electric fields across the biological tissues/cells are also computed when the EMWs penetrate inside the body.The electric fields are made across the organs of different depths inside the body.Results:The induced electric fields inside the organs of the human beings are responsible for the absorption of energy from high frequency EMWs.The absorbed amount of energy from high frequency waves may become the cause of harmful effects on the life of organs of human beings.Conclusion:In this manuscript,after analysis of the computed electric fields inside the organs of human beings,it is concluded that the EMWs of 5 G spectrum of mobile phone towers may more harmful for the life of organs as 4th generation(4 G)spectrum of mobile phone waves.The energy absorption by the 4 G spectrum is lower than 5 G spectrum due to the range of frequency of waves.The effects on the pancreas,retina,skin,intestine,spleen,stomach and uterus are more than low water content organs like nails,bone,teeth etc.

Evolving spectrum of diabetic wound: Mechanistic insights and therapeutic targets

Diabetes mellitus is a chronic metabolic disorder resulting in an increased blood glucose level and prolonged hyperglycemia,causes long term health consequences.Chronic wound is frequently occurring in diabetes patients due to compromised wound healing capability.Management of wounds in diabetic patients remains a clinical challenge despite many advancements in the field of science and technology.Increasing evidence indicates that alteration of the biochemical milieu resulting from alteration in inflammatory cytokines and matrix metalloproteinase,decrease in fibroblast and keratinocyte functioning,neuropathy,altered leukocyte functioning,infection,etc.,plays a significant role in impaired wound healing in diabetic people.Apart from the current pharmacotherapy,different other approaches like the use of conventional drugs,antidiabetic medication,antibiotics,debridement,offloading,platelet-rich plasma,growth factor,oxygen therapy,negative pressure wound therapy,low-level laser,extracorporeal shock wave bioengineered substitute can be considered in the management of diabetic wounds.Drugs/therapeutic strategy that induce angiogenesis and collagen synthesis,inhibition of MMPs,reduction of oxidative stress,controlling hyperglycemia,increase growth factors,regulate inflammatory cytokines,cause NO induction,induce fibroblast and keratinocyte proliferation,control microbial infections are considered important in controlling diabetic wound.Further,medicinal plants and/or phytoconstituents also offer a viable alternative in the treatment of diabetic wound.The focus of the present review is to highlight the molecular and cellular mechanisms,and discuss the drug targets and treatment strategies involved in the diabetic wound.

Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

Security systems are the need of the hour to protect data from unauthorized access.The dissemination of confidential information over the public network requires a high level of security.The security approach such as steganography ensures confidentiality,authentication,integrity,and non-repudiation.Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver.Therefore,we present an efficient hybrid security model that provides multifold security assurance.To this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high computations.The modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret data.The enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)algorithm.The experiments are conducted on the images of the USC-SIPI standard image database.The experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

Influence of tool rotation speeds on mechanical and morphological properties of friction stir processed nano hybrid composite of MWCNT-Graphene-AZ31 magnesium

The ever-increasing demand for light weighted hard materials for transportation industries encouraged researchers to develop composites with excellent mechanical properties which can transform it into more economical and eco-friendly.Reinforcing the metals with carbonaceous nanomaterials are progressively in focus due to their excellent capability to inculcate and tailor the properties of MMCs.In the present research,a hybrid nanocomposite of MWCNT-Graphene-AZ31 Mg alloy has been developed by using variable tool rotation speeds with friction stir processing(FSP).Optimized reinforcement ratio of 1.6%vol.MWCNT and 0.3%vol.of graphene have been used with variable tool rotation speeds,whereas other processing parameters are kept constant.The developed specimens were investigated using standard testing equipment for evaluating and comparing the mechanical properties on the basis of the microstructure of the processing regions and their morphological analysis,according to the ASTM standards.The obtained results revealed an improvement of 19.72%in microhardness and 77.5% of compressive strength in comparison with the base metal AZ 31 Magnesium alloy,with a tool rotational speed of 1400rpm.The values of tensile stress and percentage area reduction were recorded as less than that of the base metal matrix,but an increasing trend has been observed in the values of both with the improvement on rotational speeds of the tool.The effectual strengthening mechanisms are analyzed on the bases of SEM images and observed that discussed and found that grain refinement strengthening is the major contributor to the strength of the nanocomposite.

Deep Learning Approach for Analysis and Characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively.In a recent pandemic,laboratories perform diagnostics manually,which requires a lot of time and expertise of the laboratorial technicians to yield accurate results.Moreover,the cost of kits is high,and well-equipped labs are needed to perform this test.Therefore,other means of diagnosis is highly desirable.Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19.The radiography observes change in Computed Tomography(CT)chest images of patients,developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing.The proposed work suggests an Artificial Intelligence(AI)based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module.The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects.The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases,while 397 belong to negative cases of COVID-19.Our experiment resulted in an accuracy of 98.4%,sensitivity of 98.5%,specificity of 98.3%,precision of 97.1%,and F1-score of 97.8%.The additional parameters of classification error,mean absolute error(MAE),root-mean-square error(RMSE),and Matthew’s correlation coefficient(MCC)are used to evaluate our proposed work.The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.

Ordering Cost Depletion in Inventory Policy with Imperfect Products and Backorder Rebate

This study presents an inventory model for imperfect products with depletion in ordering costs and constant lead time where the price discount in the backorder is permitted.The imperfect products are refused or modified or if they reached to the customer,returned and thus some extra costs are experienced.Lately some of the researchers explicitly present on the significant association between size of lot and quality imperfection.In practical situations,the unsatisfied demands increase the period of lead time and decrease the backorders.To control customers'problems and losses,the supplier provides a price discount in backorders during shortages.Also,an order’s policies may result in including some imperfect products in arrival lots.A discount on price may be offered by the supplier on the out-of-stock products to manage the backorder problems.The study aims to develop a model with imperfect products by permitting the price discount in backorders,and the cost of ordering is considered a decision variable.First,it is assumed that the demand for lead time is followed by a normal distribution and then stops it and assumed that the first two moments of demand for lead time are known.Further,the minimax distribution method is used to solve this model,and a separate algorithm is designed.In this study,two models are discussed with and without a normally distributed rate of demand.The current study verified with the help of some numerical examples over various model parameters.

Secure Cloud Data Storage System Using Hybrid Paillier–Blowsh Algorith

Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically recongured in order to deliver optimum resource utilization by exploiting the pay-per-use model.However,concerns around security have been an impediment in the extensive adoption of the cloud computing model.In this regard,advancements in cryptography,accelerated by the wide usage of the internet worldwide,has emerged as a key area in addressing some of these security concerns.In this document,a hybrid cryptographic protocol deploying Blowsh and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption Standard(AES)and Rivest Shamir Adleman(RSA)techniques.Algorithms for secure data storage protocol in two phases have been presented.The proposed hybrid protocol endeavors to improve the power of cloud storage through a decrease in computation time and ciphertext size.Simulations have been carried out with Oracle Virtual Box and Fog server used on an Ubuntu 16.04 platform.This grouping of asymmetric and homomorphic procedures has demonstrated enhanced security.Compression usage has helped in decreasing the storage space and computation time.Performance analysis in terms of computation overhead and quality of service parameters like loads of parameters with and without attacks,throughput,and stream length for different modes of block cipher mode has been carried out.Security analysis has been carried out by utilizing the Hardening Index as an audit parameter using Lynis 2.7.1.Similarly,for halting the aforementioned approaches and for regulating trafc,rewall protection has been generated in the chosen hybrid algorithms.Finally,enhancements in the performance of the Paillier and Blowsh hybrid scheme with and without compression compared to the existing schemes using RSA and AES procedures have been demonstrated.

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the world.There are several risk factors for causing heart diseases like sedentary lifestyle,unhealthy diet,obesity,diabetes,hypertension,smoking and consumption of alcohol,stress,hereditary factory etc.Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being.At present,the highly stressful life with bad lifestyle activities causes heart disease at a very young age.The main aim of this research is to predict the premature heart disease based on machine learning algorithms.This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself.Support Vector Machine(SVM)is used for segregating the CVD patients based on their symptoms and medical observation.The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time.A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.

Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

We live in an age where everything around us is being created.Data generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery processes.MapReduce model functionality is used for creating a cluster parallel,distributed algorithm,and large datasets.The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results.Expected results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution times.Mapper output and proponents have been implemented,and the map has been used to reduce the function.The execution input key/value pair and output key/value pair have been set.This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data.The technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous environments.The results show that the proposed work reduces the data processing time by 30%.

Spatiotemporal dynamics of immunogenic tumors

A mathematical model,consisting of a system of two coupled reaction-diffusion partial differential equations describing the interaction between solid tumor and immune system(termed as effector cells),is proposed here.The main focus is on the analysis of the spa-tiotemporal dynamics of tumor cells and immune cells.The resulting systerm is analyzed and numerical simulations are presented.Diferent types of spatial patterns with respect to different initial conditions,and time are observed.Their analysis and mechanism of spatiotemporal pattern formation in immunogenic tumor are studied.Spatiotemporal perturbation around non-spatial steady state beyond the linear regime is obtained based on the analysis of higher-order perturbation terms.

Fixed Point Theorems in Relational Metric Spaces with an Application to Boundary Value Problems

In this paper,we establish fixed point theorems for generalized nonlinear contractive mappings using the concept of w-distance in relational metric spaces.Thus we generalize the recent results of Senapati and Dey[J.Fixed Point Theory Appl.19,2945-2961(2017)]and many other important results relevant to this literature.In order to revel the usefulness of such investigations,an application to first order periodic boundary value problem are given.Moreover,we furnish a non-trivial example to demonstrate the validity of our generalization over previous existing results.