Sathyabama Institute of Science and Technology

This article presents an enhanced Change Detection method for the analysis of Satellite image based on Normalized Difference Vegetation Index (NDVI). NDVI employs the Multi-Spectral Remote Sensing data technique to find Vegetation Index, land cover classification, vegetation, water bodies, open area, scrub area, hilly areas, agricultural area, thick forest, thin forest with few band combinations of the remote sensed data. Land Resources are easily interpreted by computing their Normalized Difference Vegetation Index for Land Cover classification. Remote Sensing data from Landsat TM image along with NDVI and DEM data layers have been used to perform multi-source classification. The Change Detection method used was NDVI differencing. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4 and 0.5. The Simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for policy makers in decision making. The Vegetation analysis can be helpful in predicting the unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies. From the empirical study, the forest or shrub land and Barren land cover types have decreased by about 6% and 23% from 2001 to 2006 respectively, while agricultural land, built-up and water areas have increased by about 19%, 4% and 7% respectively. Curvature, Plan curvature, Profile curvature and Wetness Index areas are also estimated.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise. The new algorithm shows significantly better image quality than a standard median filter (SMF), adaptive median filters (AMF), a threshold decomposition filter (TDF), cascade, and recursive nonlinear filters. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The proposed algorithm (PA) is tested on different images and is found to produce better results in terms of the qualitative and quantitative measures of the image. </para>

Zinc oxide (ZnO) is an adaptable material that has distinctive properties, such as high-sensitivity, large specific area, non-toxicity, good compatibility and a high isoelectric point, which favours it to be considered with a few exceptions. It is the most desirable group of nanostructure as far as both structure and properties. The unique and tuneable properties of nanostructured ZnO shows excellent stability in chemically as well as thermally stable n-type semiconducting material with wide applications such as in luminescent material, supercapacitors, battery, solar cells, photocatalysis, biosensors, biomedical and biological applications in the form of bulk crystal, thin film and pellets. The nanosized materials exhibit higher dissolution rates as well as higher solubility when compared to the bulk materials. This review significantly focused on the current improvement in ZnO-based nanomaterials/composites/doped materials for the application in the field of energy storage and conversion devices and biological applications. Special deliberation has been paid on supercapacitors, Li-ion batteries, dye-sensitized solar cells, photocatalysis, biosensors, biomedical and biological applications. Finally, the benefits of ZnO-based materials for the utilizations in the field of energy and biological sciences are moreover consistently analysed.

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by using small kernels. The weight of the neuron is given as small. Experimental results show that the CNN archives rate of 97.5% accuracy with low complexity and compared with the all other state of arts methods.

studies such as molecular docking, target analysis, toxicity prediction and ADME prediction. Andrographolide was docked successfully in the binding site of SARS-CoV-2 Mpro. Computational approaches also predicts this molecule to have good solubility, pharmacodynamics property and target accuracy. This molecule also obeys Lipinski's rule, which makes it a promising compound to pursue further biochemical and cell based assays to explore its potential for use against COVID-19.Communicated by Ramaswamy H. Sarma.

Addressing heavy metal pollution isone of the hot areas of environmental research.Despite natural existence, various anthropomorphic sources have contributed to an unusually high concentration of heavy metals in the environment.They are characterized by their long persistence in natural environment leading to serious health consequences in humans, animals, and plants even at very low concentrations (1 or 2 μg in some cases). Failure of strict regulations by government authorities is also to be blamed for heavy metal pollution. Several individual treatments, namely, physical, chemical and biological are being implied to remove heavy metals from the environment.But, they all face challenges in terms of expensiveness and in-situ treatment failure.Hence, integrated processes are gaining popularity as it is reported to achieve the goal effectively in various environmental matrices and will overcome a major drawback of large scale implementation.Integrated processes are the combination of two different methods to achieve a synergistic and an effective effort to remove heavy metals. Most of the review articles published so far mainly focus on individual methods on specific heavy metal removal, that too from a particular environmental matrix only.To the best of our knowledge, this is the first review of this kind that summarizes on various integrated processes for heavy metal removal from all environmental matrices. In addition, we too have discussed on the advantages and disadvantages of each integrated process, with a special mention of the few methods that needs more research attention. To conclude, integrated processes areproved as a right remedial option which has been detaily discussed in the present review. However, more research focus on the process is needed to challenge the in-situ operative conditions. We believe, this review on integrated processes will surely evoke a research thrust that could give rise to novel remediation projects for research community in the future.

Superparamagnetic iron oxide nanoparticles (SPIONs) have been recognized in numerous fields including nanobiotechnology, biomedical engineering, and many other fields for its inestimable applications. Superparamagnetic property and the smaller size of SPIONs are the major reasons for its utilization in various fields. In this review, the overall view on work done so far on SPIONS is detailed. Where, it started with different methods of synthesis of SPIONs including various types physical (such as gas-phase deposition, pulsed laser ablation, power ball milling), chemical (chemical co-precipitation, micro-emulsions, hydrothermal synthesis) and biological methods (using bacteria and plant) and are also elaborated. Its properties and characteristics are detailed. The formulation of SPIONs into drug-laden nanocarrier for exhibiting targeted drug delivery and its use in cancer treatment as hyperthermia is emphasised. Its various other applications consist of radiation therapy, environmental remediation, tissue engineering etc., which are also elaborated.

Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.

This paper presented a comparative study of human skin color detection HSV and YCbCr color space. Skin color detection is the process of separation between skin and non-skin pixels. It is difficult to develop uniform method for the segmentation or detection of human skin detection because the color tone of human skin is drastically varied for people from one region to another. Literature survey shows that there is a variety of color space is applied for the skin color detection. RGB color space is not preferred for color based detection and color analysis because of mixing of color (chrominance) and intensity (luminance) information and its non uniform characteristics. Luminance and Hue based approaches discriminate color and intensity information even under uneven illumination conditions. Experimental result shows the efficiency of YCbCr color space for the segmentation and detection of skin color in color images.

OBJECTIVE: To synthesize the ecofriendly nanoparticles, which is viewed as an alternative to the chemical method which initiated the use of microbes like bacteria and fungi in their synthesis. METHODS: The current study uses the endophytic bacterium Bacillus cereus isolated from the Garcinia xanthochymus to synthesize the silver nanoparticles (AgNPs). The AgNPs were synthesized by reduction of silver nitrate solution by the endophytic bacterium after incubation for 3-5 d at room temperature. The synthesis was initially observed by colour change from pale white to brown which was confirmed by UV-Vis spectroscopy. The AgNPs were further characterized using FTIR, SEM-EDX and TEM analyses. RESULTS: The synthesized nanoparticles were found to be spherical with the size in the range of 20-40 nm which showed a slight aggregation. The energy-dispersive spectra of the nanoparticle dispersion confirmed the presence of elemental silver. The AgNPs were found to have antibacterial activity against a few pathogenic bacteria like Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Salmonella typhi and Klebsiella pneumoniae. CONCLUSIONS: The endophytic bacteria identified as Bacillus cereus was able to synthesize silver nanoparticles with potential antibacterial activity.

Dyes (colorants) are used in many industrial applications, and effluents of several industries contain toxic dyes. Dyes exhibit toxicity to humans, aquatic organisms, and the environment. Therefore, dyes containing wastewater must be properly treated before discharging to the surrounding water bodies. Among several water treatment technologies, adsorption is the most preferred technique to sequester dyes from water bodies. Many studies have reported the removal of dyes from wastewater using biochar produced from different biomass, e.g., algae and plant biomass, forest, and domestic residues, animal waste, sewage sludge, etc. The aim of this review is to provide an overview of the application of biochar as an eco-friendly and economical adsorbent to remove toxic colorants (dyes) from the aqueous environment. This review highlights the routes of biochar production, such as hydrothermal carbonization, pyrolysis, and hydrothermal liquefaction. Biochar as an adsorbent possesses numerous advantages, such as being eco-friendly, low-cost, and easy to use; various precursors are available in abundance to be converted into biochar, it also has recyclability potential and higher adsorption capacity than other conventional adsorbents. From the literature review, it is clear that biochar is a vital candidate for removal of dyes from wastewater with adsorption capacity of above 80%.

Supercapacitors (SCs) have received a great deal of attention and play an important role for future self-powered devices, mainly owing to their higher power density. Among all types of electrical energy storage devices, electrochemical supercapacitors are considered to be the most promising because of their superior performance characteristics, including short charging time, high power density, safety, easy fabrication procedures, and long operational life. An SC consists of two foremost components, namely electrode materials, and electrolyte. The selection of appropriate electrode materials with rational nanostructured designs has resulted in improved electrochemical properties for high performance and has reduced the cost of SCs. In this review, we mainly spotlight the non-metallic oxide, especially metal chalcogenides (MX; X = S, Se) based nanostructured electrode materials for electrochemical SCs. Different non-metallic oxide materials are highlighted in various categories, such as transition metal sulfides and selenides materials. Finally, the designing strategy and future improvements on metal chalcogenide materials for the application of electrochemical SCs are also discussed.

Azadirachta indica commonly known as Neem, is an evergreen tree. Since time immemorial it has been used by Indian people for treatment of various diseases due to its medicinal properties. It possesses anti-bacterial, anti-cariogenic, anti-helminthic, anti-diabetic, anti-oxidant, astringent, anti-viral, cytotoxic, and anti-inflammatory activity. Nimbidin, Azadirachtin and nimbinin are active compounds present in Neem which are responsible for antibacterial activity. Neem bark is used as an active ingredient in a number of toothpastes and toothpowders. Neem bark has anti-bacterial properties, it is quite useful in dentistry for curing gingival problems and maintaining oral health in a natural way. Neem twigs are used as oral deodorant, toothache reliever and for cleaning of teeth. The objective of this article is to focus on the various aspects of Azadirachta indica in dentistry in order to provide a tool for future research.

In the fast developing world, the concern for the environmental pollution and the prevention of non-renewable and non-biodegradable resources has attracted researchers seeking to develop new eco-friendly materials and products based on sustainability principles. The fibers from the natural sources provide indisputable advantages over synthetic reinforcement materials such as low cost, low density, non-toxicity, comparable strength, and minimum waste disposal problems. In the present experiment, banana fiber reinforced epoxy composites are prepared and the mechanical properties of these composites are evaluated. The composite samples with different fiber volume fractions were prepared by using the hand lay-up process and apply pressure at room temperature. The samples were subjected to the mechanical testing such as tensile, flexural and impact loading. Scanning electron microscope (SEM) analysis is carried out to evaluate fiber matrix interfaces and analyze the structure of the fractured surfaces.

The grade of irrigation water available to irrigators has a significant impact on crops as well as yields. Therefore, it is a need to better understand irrigation water quality. The present study mainly focuses on the assessment of the suitability of water of forty-four fixed bore wells of Kanchipuram district, Tamil Nadu, India. The groundwater sample datasets of post-monsoon (2005–2013) and pre-monsoon (2006–2013) season were collected for 9 years. Water quality indices, namely sodium adsorption ratio, exchangeable sodium percent (SSP or %Na), residual sodium carbonate (RSC or RA), Kelly’s ratio, permeability index, chloroalkaline indices (CAI1 and CAI2), potential salinity (PS), magnesium hazard, total dissolved solids and total hardness, have been calculated for separate bore wells. The r1 and r2 indices show that groundwater of the study area is Na+–SO42− and deep meteoric percolation type. Majority of the wells are fall under moderate to unsuitable category of water for irrigation purposes. Further, wells water has also been classified on the base of meteoric genesis index.

This critical review summarizes the utilization of algae as the resilient source for biofuel. The paper validates the different stages in generation of biofuels and provides a clarity on III generation biofuels. The microalgae is focused as an incredible source and a detailed discussion has been carried out from the cultivation, extraction and conversion to the final product. An elaborate view on conversion methodologies and troubles involved in the respective techniques are presented. The efficiency of the algal fuel performing in I/C engines derived from major techniques is considered. There exist new challenging barriers in the implementation of microalgae as prospective source in the energy market. In addition, types of pyrolysis for the production of main product from microalgae had been discussed in detail. Besides, some microalgae grow easily from fresh to waste water, make it more feasible source. Although the microalgae are a best alternative, cost of production and the yield of biofuel are still challenging. Further, cultivation of microalgae is very effective by applying two stage cultivation strategies. This comprehensive review provides the useful tool to identify, innovate and operate microalgae as the potential based biofuel.

Tafel-equation-based electrochemical analysis is widely employed in hydrogen evolution reaction (HER) to evaluate and characterize electrocatalysts. Tafel slope and exchange current density are the only two parameters that are invariably obtained and discussed with respect to Tafel equation in the literature. Herein, insights on Tafel constant in the analysis of HER are discussed, and its practical advantage is indicated. It is proposed that Tafel constant can be considered as the onset potential of HER. Tafel constant becomes the defining parameter between two electrocatalysts when other parameters such as Tafel slope or exchange current density become same. The significance of the same Tafel constants is illustrated using CoSe2 and NiSe2. Variation of the Tafel constant within a series of Co(1–x)NixSe2 is examined. The occurrence of the same Tafel constants between two electrocatalysts leads to one electrocatalyst becoming the better catalyst in the lower current regime, whereas the other exhibits higher activity in the higher current regime. Furthermore, concepts developed here are applied to several literature examples, and the significance of Tafel constant in the analysis of HER is established.

Aim/Purpose: The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during COVID-19 pandemic. Background: The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education “e-learning.” The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology: An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students’ perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution: The study aims to explore the quality of e-learning in higher education from the students’ perspective. The study was analyzed based on the student’s data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings: The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students between gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners: The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendation for Researchers: The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society: The study revealed that the positive results of the students’ perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvement. Future Research: There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.

Abstract Zebrafish ( Danio rerio ) is emerging as an increasingly successful model for translational research on human neurological disorders. In this review, we appraise the high degree of neurological and behavioural resemblance of zebrafish with humans. It is highly validated as a powerful vertebrate model for investigating human neurodegenerative diseases. The neuroanatomic and neurochemical pathways of zebrafish brain exhibit a profound resemblance with the human brain. Physiological, emotional and social behavioural pattern similarities between them have also been well established. Interestingly, zebrafish models have been used successfully to simulate the pathology of Alzheimer’s disease (AD) as well as Tauopathy. Their relatively simple nervous system and the optical transparency of the embryos permit real-time neurological imaging. Here, we further elaborate on the use of recent real-time imaging techniques to obtain vital insights into the neurodegeneration that occurs in AD. Zebrafish is adeptly suitable for Ca 2+ imaging, which provides a better understanding of neuronal activity and axonal dystrophy in a non-invasive manner. Three-dimensional imaging in zebrafish is a rapidly evolving technique, which allows the visualisation of the whole organism for an elaborate in vivo functional and neurophysiological analysis in disease condition. Suitability to high-throughput screening and similarity with humans makes zebrafish an excellent model for screening neurospecific compounds. Thus, the zebrafish model can be pivotal in bridging the gap from the bench to the bedside. This fish is becoming an increasingly successful model to understand AD with further scope for investigation in neurodevelopment and neurodegeneration, which promises exciting research opportunities in the future.

Abstract As the nano revolution unfolds, it is imperative to integrate nanoscience and medicine. The secret gleaned from nature have led to the generation of biogenic technologies for the fabrication of advanced nanomaterials. Present investigation discloses the gold nanoparticles biosynthesizing capability of the flower of pharmacologically important tree Couroupita guianensis . Rapid, cost-effective, one-step process of synthesis has been achieved. Newly genre gold nanoparticles were characterized by involving UV–vis spectroscopy, FTIR, XRD, SEM, and TEM analysis. Interestingly, as a result of extensive screening on the application of newly synthesized gold nanoparticles their anticancer potential has been discovered using MTT assay, DNA fragmentation, apoptosis by DAPI staining, and comet assay for DNA damage.