Chemically Radiative MHD Flow of a Micropolar Nanofluid over a Stretching/ Shrinking Sheet with a Heat Source or Sink

This study examines the behavior of a micropolar nanofluidflowing over a sheet in the presence of a transverse magneticfield and thermal effects.In addition,chemical(first-order homogeneous)reactions are taken into account.A similarity transformation is used to reduce the system of governing coupled non-linear partial differ-ential equations(PDEs),which account for the transport of mass,momentum,angular momentum,energy and species,to a set of non-linear ordinary differential equations(ODEs).The Runge-Kutta method along with shoot-ing method is used to solve them.The impact of several parameters is evaluated.It is shown that the micro-rota-tional velocity of thefluid rises with the micropolar factor.Moreover,the radiation parameter can have a remarkable influence on theflow and temperature profiles and on the angular momentum distribution.

Synthesis and evaluation of novel analogues of mangiferin as potent antipyretic

Objective:To screen different analogues of mangiferin pharmacologically for antipyretic activity. Methods:The naturally occurring xanthone glycoside mangiferin was isolated by column chromatography from the elhanolic extract of stem bark of Mangifera indica.Mangiferin was further converted to 5-(N-phenylamino methyleno) mangiferin.5-(N-p-chlorophenylamino methyleno) mangiferin,5-(N-2-methyl phenylamino methyleno) mangiferin.5-(N-p-methoxy phenylamino methyleno) mangiferin.5-(N,N-diphenylamino methyleno) mangiferin,5-(N-α-napthylamino methyleno) mangiferin and 5-(N-4-methyl phenylamino methyleno) mangiferin analogues.The synthesized compounds were further screened for antipyretic activity along with mangiferin at a dose level of 100 and 200 tng/kg.Mangiferin and its analogues were characterized by melting point and R_f value determination and through spectral technique like UV,IR,and NMR spectral analysis.Results:The antipyretic activity of mangiferin as well as all analogues was found to be more significant in at higher dose ie.200 mg/kg which was depicted thmugh a decrease in rectal temperature up to 3 h.Conclusions:The antipyretic activity of mangiferin and its analogues may be attributed to inhibition in synthesis of TNF-αand anti-oxidant activity associated with amelioration of inflammatory actions of cytokines.

Utilization of Swertia chirayita Plant Extracts for Management of Diabetes and Associated Disorders: Present Status, Future Prospects and Limitations

Diabetes mellitus is referred as common metabolic abnormalities characterized as hyperglycemia,mainly caused due to insufficient production of insulin at cellular level or/and defects in insulin action.Such an endocrine disorder is responsible for serious health problems and its worldwide prevalence is rapidly increasing.Common management of diabetes by oral administration of drugs without creating any side effects is still considered a challenging task and increasing cost of con-ventional medicine in developing countries is another matter of concern.To address these issues,traditional preparations of herbal plant extracts in the form of medicines already gained immense attention.Swertia chirayita is one among such plants which is known for its hypoglycemic potential.Numerous chemical constituents with promising pharmacological properties have been identified from such plant extracts but still,such compounds have not been well characterized,specifically against human application.Hence,more research efforts are necessitated to understand exact mechanism of such compounds and to develop overall safety of such plant formulations.Present review clearly represents antidiabetic properties of Swertia chirayita extract,strategies to be taken to strengthen its safety application on humans and biotechnological interventions that ensure conservation of such endangered species to promote its future application in modern medicine.

Lockdown during COVID-19 pandemic:A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PMand NOconcentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis(DFA) during the lockdown period(March 24–April 20, 2020) and the corresponding period during the previous two years(i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PMand NOconcentration in urban cities, causing low concentrations. It is observed that the original PMand NOconcentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PMand NOconcentrations. Hurst exponent of the PMand NOconcentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations(APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.

Effect of benzaldehyde on the electrodeposition and corrosion properties of Ni–W alloys

The effect of different concentrations of benzaldehyde on the electrodeposition of Ni–W alloy coatings on a mild steel substrate from a citrate electrolyte was investigated in this study. The electrolytic alkaline bath(p H 8.0) contained stoichiometric amounts of nickel sulfate, sodium tungstate, and trisodium citrate as precursors. The corrosion resistance of the Ni–W-alloy-coated specimens in 0.2 mol/L H2SO4 was studied using various electrochemical techniques. Tafel polarization studies reveal that the alloy coatings obtained from the bath containing 50 ppm benzaldehyde exhibit a protection efficiency of 95.33%. The corrosion rate also decreases by 21.5 times compared with that of the blank. A higher charge-transfer resistance of 1159.40 ?·cm2 and a lower double-layer capacitance of 29.4 μF·cm-2 further confirm the better corrosion resistance of the alloy coating. X-ray diffraction studies reveal that the deposits on the mild steel surface are consisted of nanocrystals. A lower surface roughness value(Rmax) of the deposits is confirmed by atomic force microscopy.

Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.

Examining the Present and Future Integrated Role of Artificial Intelligence in the Business: A Survey Study on Corporate Sector

In the fast-moving world, it is noticed that every industry is developing gradually, but recently it is identified that the use of AI has become the talk of own. Therefore, this study is focused on gathering data regarding the AI on how it has transformed the entire world’s corporate sector. The essential application AI in the business world helps the business to perform better in the corporate sector. In this paper, the critical role of artificial intelligence is to grow business in different sectors and also address its ethical and unethical issues. The paper has all the initial background and comprehensive literature regarding AI and machine learning. It is discovered how the technological world has been striving to take their business on to new heights, which requires updated technological changes in internal business activities. Companies can now effortlessly interact with their customers in making their application accessible for the end-users through implementing AI and machine learning. Companies are getting higher profitability and enhancing their performance and achieving economic advantages by integrated AI. Moreover, their technological developments will take human jobs in the future, so, it is suggested that humans should work on their skills and competencies so that they can deal with unemployment.

Machines,Tools and Tool Transporter Concurrent Scheduling in Multi⁃machine FMS with Alternative Routing Using Symbiotic Organisms Search Algorithm

This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations for minimization of the makespan(MSN). When tools are expensive, just a single copy of every tool kind is made available for use in the FMS system. Because the tools are housed in a central tool magazine(CTM), which then distributes and delivers them to many machines, because there is no longer a need to duplicate the tools in each machine, the associated costs are avoided. Choosing alternative machines for job operations(jb-ons), assigning tools to jb-ons, sequencing jb-ons on machines, and arranging allied trip activities, together with the TT’s loaded trip times and deadheading periods, are all challenges that must be overcome to achieve the goal of minimizing MSN. In addition to a mixed nonlinear integer programming(MNLIP) formulation for this simultaneous scheduling problem, this paper suggests a symbiotic organisms search algorithm(SOSA) for the problem’s solution. This algorithm relies on organisms’ symbiotic interaction strategies to keep living in an ecosystem. The findings demonstrate that SOSA is superior to the Jaya algorithm in providing solutions and that using alternative machines for operations helps bring down MSN.

Spectral Response and Photoelectrochemical Properties of Cd1-xZnxSe Films

Cd1-xZnxSe films with different zinc contents are deposited by an electron beam evaporation technique onto glass substrates for applications in solid-state photovoltalc devices. The structural, optical and photoelectrochemical （PEC） properties of Cd1-xZnxSe films are studied. The host material Cd1-xZnxSe is prepared by the physical vapor deposition method of electron beam evaporation technique （PVD： EBE） under a pressure of 1 × 10^-5 mbar. The x-ray diffractogram indicates that these alloy films are polycrystalline in nature, hexagonal structure with strong preferential orientation of the crystallites along （002） direction. The optical properties shows that the band gap Eg varies from 2.08 to 2.84eV as zinc content varies from 0.2 to 0.8. A PEC cell of the configuration n-Cd1-xZnxSe/Na2S-S-NaOH is fabricated and the dynamic current-voltage characteristics in the dark atmosphere have been examined at room temperature. It has been found that both Voc and Iac decrease with the photoelectrode composition x. Efficiency η and fill factor （FF） also show similar variations. The material properties would be altered and excellently controlled by controlling the system composition x.

IoT-Based Wrist Band for Women Safety

In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”.Women and girls have been subjected to numerous incidents,including acid throwing,rape,kidnapping,and harassment.It is common to read a lot of news like this in newspapers every day.These incidents make women feel unsafe in this society.Our freedom came a long time ago,but women still lack complete security in this society.All women cannot fight or shout all the time when some danger is happening to them.What can the physically challenged person and Children do?To make women feel safe,we designed“Wrist Band”using IoT for women safety.As the sensors sense information from the body,it will always update the information such as pulse,temperature,and vibration to the well-wishers through the Blynk app.

Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

Processing map for hot working of SiC_p/7075 Al composites

The hot deformation behaviour of 7075 aluminium alloy reinforced with 10%of SiC particles was studied by employing both"processing maps"and microstructural observations.The composite was characterized by employing optical microscope to evaluate the microstructural transformations and instability phenomena.The material investigated was deformed by compression in the temperature and strain rate ranges of 300-500℃and 0.001-1.0 s-1,respectively.The deformation efficiency was calculated by strain rate sensitivity(m)values obtained by hot compression tests.The power dissipation efficiency and instability parameters were evaluated and processing maps were constructed for strain of 0.5.The optimum domains and instability zone were obtained for the composites.The optimum processing conditions are obtained in the strain rate range of 0.1-0.9 s-1and temperature range of 390-440 ℃with the efficiency of 30%.

Development of processing maps for as-cast ZE41A magnesium alloy

The hot deformation behavior of as-cast ZE41A magnesium alloy was studied by employing both processing maps and microstructural observations. To obtain processing map, hot compression tests were performed over a range of strain rates (0.001-1.0 s-1) and temperatures (250-450 ℃). Power dissipation efficiency (η) and instability parameter [ξ (ε)] were evaluated and processing maps were developed for the strain of 0.5. The dynamic recrystallization (DRX) and instable zones were identified and validated through micrographs. The processing maps can be used to select optimum strain rates and temperatures for effective hot working of the material.

Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification

Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas.Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection.However,many deep learning framework based approaches do not consider both spatial and textural details into account.In order to handle this issue,a Convolutional Neural Network(CNN)based multi-feature extraction and fusion is introduced which considers both spatial and textural features.This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features.Then the fused image is classified into change and unchanged regions.The presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification errors.The proposed method was compared with six state-of-theart change detection methods and analyzed.The main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels,the mixed pixel problem is solved.Experiments indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors,higher overall accuracy and kappa coefficient.

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

In this communication we have used Bickley’s method for the construction of a sixth order spline function and apply it to solve the linear fifth order differential equations of the form yx(x)+g (x)y(x)= r(x) where g(x) and r(x) are given functions with the two different problems of different boundary conditions. The method is illustrated by applying it to solve some problems to demonstrate the application of the methods discussed.

Thermal Diffusion Effect on MHD Heat and Mass Transfer Flow past a Semi Infinite Moving Vertical Porous Plate with Heat Generation and Chemical Reaction

The objective of present work is to study the thermo diffusion effect on an unsteady simultaneous convective heat and mass transfer flow of an incompressible, electrically conducting, heat generating/absorbing fluid along a semi-infinite moving porous plate embedded in a porous medium with the presence of pressure gradient, thermal radiation field and chemical reaction. It is assumed that the permeable plate is embedded in a uniform porous medium and moves with a constant velocity in the flow direction in the presence of a transverse magnetic field. It is also assumed that the free stream consists of a mean velocity, temperature and concentration over which are super imposed an exponentially varying with time. The equations of continuity, momentum, energy and diffusion, which govern the flow field, are solved by using a regular perturbation method. The behavior of the velocity, temperature, concentration, Skin-friction, rate of heat transfer and rate of mass transfer has been discussed for variations in the physical parameters. An increase in both Pr and R results a decrease in thermal boundary layer thickness. However, concentration decreases as Kr, Sc increase but it increases with an increase in both So and δ.

Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease

The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications.It does,though,have certain drawbacks.For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome,the ECG signal withMyocardial Infarction is difficult to interpret.These diseases cause variations in the ST portion of the ECG signal.It reduces the clarity of ECG signals,making itmore difficult to diagnose these diseases.As a result,the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic technique.Based on these concepts,this article reviews the different procedures involved in ECG signal pre-processing,feature extraction,feature selection,and classification techniques to diagnose heart disorders such as LeftVentricularHypertrophy,Bundle Branch Block,andMyocardial Infarction.It reveals the flaws and benefits in each segment,as well as recommendations for developing more advanced and robustmethods for diagnosing these diseases,which will increase the system’s accuracy.The current issues and prospective research directions are also addressed.

An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model

Hepatocellular carcinoma(HCC)is one major cause of cancer-related mortality around the world.However,at advanced stages of HCC,systematic treatment options are currently limited.As a result,new pharmacological targetsmust be discovered regularly,and then tailored medicines against HCC must be developed.In this research,we used biomarkers of HCC to collect the protein interaction network related to HCC.Initially,DC(Degree Centrality)was employed to assess the importance of each protein.Then an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top ranked proteins related to HCC.Finally,physio-chemical proteins are used to evaluate the outcome of the top ranked proteins.The proposed graph theory and machine learning techniques have been compared with six existing methods.In the proposed approach,16 proteins have been identified as potential therapeutic drug targets for Hepatic Carcinoma.It is observable that the proposed method gives remarkable performance than the existing centrality measures in terms of Accuracy,Precision,Recall,Sensitivity,Specificity and F-measure.

A combinatorial approach to study hepatoprotective activity of Acanthus ilicifolius leaf extract against hepatocellular carcinoma(HCC)

OBJECTIVE To study the bioactive phytochemicals in the leaves of A.ilicifolius against Hepatocellular carcinoma(HCC) through in silico,in vitro and in vivo studies.METHODS A.ilicifolius leaves were collected from Cuddalore District,Tamil Nadu,India.Authenticated by the Botanical Survey of India.The fresh leaves of A.ilicifolius were washed and shade dried at room temperature(28 ± 2)℃.The dried leaves were powdered by electrical blender.25 gms of A.ilicifolius leaf powder was used for methanol extraction in the Soxhlet apparatus.The Phytochemical compounds were analyzed by GC-MS and the structure was retrieved from PubChem.Totally,seven HCC target proteins were collected from literature,ligand and proteins were prepared for in silico molecular docking.HepG2 cell lines were used for in vitro(MTT assay).BALB/c mice were used for in vivo studies,the biochemical parameters and histopathological studies were carried out with standard procedure.RESULTS The phytochemical26.27-Di(nor)-cholest-5,7,23-trien-22-ol,3-methoxymethoxy exhibited maximum docking score against the HCC target protein C-Jun N-terminal kinase 1(JNK 1)(-6.839798).MTT assay revealed that the extract at a concentration of 200 μg·mL-1,caused 60% cell death in HepG2 cell lines.Further animal studies using to injecting HCC induced BALB/c mice,restoration of haematological parameters and cells to normal was observed after 15 days of oral administration of the extract.These findings suggest the possibility of using A.ilicifolius leaves in the treatment of HCC.CONCLUSION The in silico,in vitro and in vivo studies indicated that the A.ilicifolius leaves had anticancereous activity against Hepatocellular carcinoma.There can be a possibility of synergistic activity of phytochemicals together against HCC.

Computational Microuidic Channel for Separation of Escherichia coli from Blood-Cells

Microuidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells,etc.,in various biochemical diagnosis procedures including urinary tract infections(UTI)and bloodstream infections.This paper presents the multi physics computational model specifically designed to study the effects of design parameters of a microuidics channel for the separation of Escherichia coli(E.coli)from various blood constituents including red blood cells(RBC)and platelets.A standard two inlet and a two outlet microchannel of length 805µm with a channel width of 40µm is simulated.The effect of electrode potentials and the effect of electrode placement along the channel length and also the levitation of electrodes from the channel wall are studied to optimize the selective particle separation throughput.Simulated results show the efcient separation of E-coli with a mean diameter 0.68µm is achieved at low voltages(less than 20 V)when electrodes placed near to the micro channel and also noticed that the applied electric potential is inversely proportional to the number of electrodes placed along the microuidic channel.The computer aided multi physics simulations with multiple governing parameters could be advantage in design optimization of microuidics channels and support precise bioparticle separation for better diagnosis.