K.S. Rangasamy College of Technology

ABSTRACT A novel approach to study the effect of nanosilica on maize crop improvement was proposed. Nanosilica powders were mixed with soil at different concentrations along with control and conventional silica under in vitro and in vivo conditions. In in vitro, the nanosilica increases seed germination (2–11%), water usage efficiency (up to 53%), and total chlorophyll content (13–17%) of maize crop. In in vivo, influence of nanosilica was analyzed on basic parameters such as stem height, stem width, number of leaves, and silica content. The effect of nanosilica on maize crop was found to be enhanced in all aspects. KEYWORDS: nanosilica powdersmaize seed germinationleaf transpirationtotal chlorophyll Acknowledgments We acknowledge the financial support of Defense Research and Development Organization, New Delhi to carry out this project (ERIP/ER/0705076/M/01/1/1016 dt. 11.02.08).

BACKGROUND: Diabetes is often connected with significant morbidity, mortality and also has a pivotal role in the development of cardiovascular diseases. Diet intervention, particularly naturaceutical antioxidants have anti-diabetic potential and avert oxidative damage linked with diabetic pathogenesis. The present study investigated the effects of diosgenin, a saponin from fenugreek, on the changes in lipid profile in plasma, liver, heart and brain in high-fat diet-streptozotocin (HFD-STZ)-induced diabetic rats. Diosgenin was administered to HFD-STZ induced diabetic rats by orally at 60 mg kg(-1) body weight for 30 days to assess its effects on body weight gain, glucose, insulin, insulin resistance and cholesterol, triglycerides, free fatty acids and phospholipids in plasma, liver, heart and brain. RESULTS: The levels of body weight, glucose, insulin, insulin resistance, cholesterol, triglycerides, free fatty acids, phospholipids, VLDL-C and LDL-C were increased significantly (P < 0.05) whereas HDL-C level decreased in the HFD/STZ diabetic rats. Administration of diosgenin to HFD-STZ diabetic rats caused a decrease in body weight gain, blood glucose, insulin, insulin resistance and also it modulated lipid profile in plasma and tissues. CONCLUSION: The traditional plant fenugreek and its constituents mediate its anti-diabetic potential through mitigating hyperglycaemic status, altering insulin resistance by alleviating metabolic dysregulation of lipid profile in both plasma and tissues.

In this study, we prepared MgO nanoparticles using a hot-air spray pyrolysis method. The prepared nanoparticles were characterised using X-ray diffraction (XRD) and the crystallite size was found to be 24 nm. Scanning electron microscopy (SEM) imaging showed needle-like morphology, which was also confirmed by transmission electron microscopy. Specific surface area (24 m2 g−1) of the MgO nanoparticles was analysed using the Barrett–Emmett–Teller method. Colloidal methyl silicate and MgO nanoparticle-embedded methyl silicate solutions were prepared using the sol–gel method. Cotton fabrics were separately functionalised with silica and MgO/methyl silicate composite using an optimised pad-dry-cure method. The phase and functional group of the coated and uncoated fabrics were analysed by XRD and Fourier transform infrared spectroscopy. The surface morphology of the coated fabrics was analysed using SEM. Elemental analysis, which was carried out using energy-dispersive spectroscopy, confirmed the presence of methyl silicate and MgO nanoparticles along with cellulose on the surface of the fabric. The washing durability of the coated fabrics after 5, 10 and 15 washes was assessed using SEM, confirming the adherence of nanoparticles on the surface of the fabric. The burning performance of the coated fabrics was in the order of MgO/methyl silicate (21.4 s) > methyl silicate (17.6 s) before and after washing. The cotton fabrics coated with MgO/methyl silicate composite showed a better antibacterial activity against Staphylococcus aureus and Escherichia coli than methyl silicate-coated and uncoated fabrics. In addition, the methyl silicate- and MgO/methyl silicate composite-coated cotton fabrics showed a significant water-repellent property with water contact angles of 135.2° and 138.6° for a 5 μl water droplet.

Under natural and greenhouse conditions we found a significant reduction in the physiological and biochemical constituents in leaves of five disease types when compared to healthy ones. The growth characteristics such as height, dry mass, photosynthetic and transpiration rates, stomatal conductance, and water use efficiency were reduced significantly more in susceptible cv. TRI-2024 than in tolerant cv. TRI-2025. Also contents of total sugars, nitrogen, amino acids, proteins, polyphenols, and catechin were reduced in diseased plant leaves. However, the reduction was more prominent in susceptible than tolerant cultivar. Canker size and barker moisture content were larger in the susceptible cultivar than in the tolerant cultivar.

Abstract This study deals with artificial neural network modeling to predict the performance and exhaust emissions of the diesel engine using hybrid fuel. A single cylinder, four-stroke diesel engine was fueled with hybrid fuel and operated at different load conditions to acquire data for training and testing the proposed artificial neural network model. About 70% of the acquired experimental data were used in the view of training while the other 30% was used for testing the proposed model. The artificial neural network model was developed on the basis of standard back propagation algorithm. The developed artificial neural network model predicts the performance and exhaust emissions of the diesel engine with a correlation coefficient of 0.975–0.999 and a low root mean square error. The present study reveals that the artificial neural network approach could be confidently used to predict the performance and emissions of the diesel engine accurately.

An artificial neural network (ANN) model is developed to predict the engine performance of fish oil biodiesel blended with diethyl ether. Engine performance and emission characteristics such as brake thermal efficiency, hydrocarbon, exhaust gas temperature, oxides of nitrogen (NOx), carbon monoxide (CO), smoke and carbon dioxide (CO2) were considered. Experimental investigations on single-cylinder, constant speed, direct injection diesel engine are carried out under variable load conditions. The performance and emission characteristics are measured using an exhaust gas analyser, smoke metre, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. In this model, a back propagation algorithm is used to predict the performance. Computational results clearly demonstrated that the developed ANN models produced less deviations and exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.97–1 and mean relative error of 0–3.061% with experimental values. The root mean square errors were found to be low. The developed model produces the idealised results and it has been found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

Diabetic retinopathy is a vascular disorder caused by changes in the blood vessels of the retina. The proposed work uses an Extreme Learning Machine (ELM) approach for blood vessel detection in digital retinal images. This approach is based on pixel classification using a 7-D feature vector obtained from preprocessed retinal images and given as input to an ELM. Classification results categorizes each pixel into two classes namely vessel and non-vessel. Finally, post processing is done to fill pixel gaps in detected blood vessels and removes falsely-detected isolated vessel pixels. The proposed technique was assessed on the publicly available DRIVE and STARE datasets. The approach proves vessel detection is accurate for both datasets.

The aim of this study is to select the best blend using multi-criteria decision-making (MCDM) technique. The six alternative fuel blends diesel, B20, B40, B60, B80 and B100 are prepared by varying the amount of diesel in biodiesel. Brake thermal efficiency (BTE), exhaust gas temperature (EGT), oxides of nitrogen (NOx), smoke, hydrocarbon (HC), carbon monoxide (CO) and carbon dioxide (CO2) are considered as evaluation criteria. A single cylinder, constant speed, direct injection diesel engine (4.4 kW) was used for exploratory analysis of evaluation criteria at different load conditions. Two models fuzzy analytical hierarchy process-technique for order preference by similarity to ideal solution (FAHP-TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-VIKOR, in Serbian) are proposed to evaluate the best blend. Here, the FAHP is used to analyse the structure of best blend selection and to determine the weights of the criteria. The TOPSIS and VIKOR are used to obtain the final ranking of the blend.

VANETs (vehicular ad hoc networks) are highly mobile wireless ad hoc networks and will play an important role in public safety communications and commercial applications. Routing of data in VANETs is a challenging task due to rapidly changing topology and high speed mobility of vehicles. Conventional routing protocols in MANETs (mobile ad hoc networks) are unable to fully address the unique characteristics in vehicular networks. In this paper, we propose EBGR (edge node based greedy routing), a greedy position based routing approach to forward packet to the node present in the edge of the limited transmission range of forwarding node as most suitable next hop, with consideration of nodes moving in the direction of the destination. We evaluate the performance of our solutions via Manhattan mobility model. This paper presents a detailed description of our approach and simulation results show that end to end delay in packet transmission is minimized considerably compared to current routing protocols of VANET.

Segmentation is an important task for image analysis. Region based segmentation methods are best suited for images taken in noisy environment. Selecting a seed pixel is a challenging task in region growing methods. To overcome this drawback, Genetic based Fuzzy Seeded Region Growing Segmentation (GFSRGS) algorithm is proposed in this paper. The proposed algorithm optimizes the selection of multiple seed pixels using genetic based fuzzy approach. It is experimented with diabetic retinopathy images to find out the exudates regions. The results of the proposed algorithm are compared with the ground truth data. It achieves better accuracy when compared to the existing methods.

Cascaded H-Bridge multilevel inverters synthesize a medium voltage output based on a series connection of power cells which use standard low-voltage component configurations. This characteristic allows one to achieve high-quality output voltages and input currents and also outstanding availability due to their intrinsic component redundancy. Due to these features, the cascaded multilevel inverter has been recognized as an important alternative in the medium-voltage inverter market. This paper presents a cascaded H-bridge multilevel boost inverter for electric vehicle (EV) and hybrid EV (HEV) applications implemented without the use of inductors. Traditionally, each H-bridge needs a dc power supply. The proposed design uses a standard three-leg inverter (one leg for each phase) and an H-bridge in series with each inverter leg which uses a capacitor as the dc power source. The Hybrid Cascaded H-Bridge Multilevel inverter is implemented using the Selective Harmonic Elimination PWM (SHEPWM) technique. The proposed topology offers an intuitive method for minimizing the total harmonic distortion (THD) of the output voltage of the inverter which has been verified using the MATLAB simulation. A fundamental switching scheme is used to do modulation control and to produce a five-level phase voltage.

Botnets are emerging as the most serious cyber threat among different forms of malware. Today botnets have been facilitating to launch many cybercriminal activities like DDoS, click fraud, phishing attacks etc. The main purpose of botnet is to perform massive financial threat. Many large organizations, banks and social networks became the target of bot masters. Botnets can also be leased to motivate the cybercriminal activities. Recently several researches and many efforts have been carried out to detect bot, C&C channels and bot masters. Ultimately bot maters also strengthen their activities through sophisticated techniques. Many botnet detection techniques are based on payload analysis. Most of these techniques are inefficient for encrypted C&C channels. In this paper we explore different categories of botnet and propose a detection methodology to classify bot host from the normal host by analyzing traffic flow characteristics based on time intervals instead of payload inspection. Due to that it is possible to detect botnet activity even encrypted C&C channels are used.

Malignant melanoma is the deadliest form among all skin cancers. Fortunately, if detected early, even malignant melanoma may be treated successfully. In this paper, a new intelligent method of classifying benign and malignant melanoma lesions is used. As the first step of the image analysis, preprocessing techniques are used to remove noise and undesired structures from the images using filter such as median filtering. Segmentation is one of the important steps in cancer automatic detection, because it can greatly affect on the results of detection. In the second step, a simple thresholding method is used to segment and localize the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. In the third step, the different features are extracted from a segmented image and classified by using Stolz algorithm.

The proposed system mainly concentrates on the diagnosis of brain tumor from the CT-Scan (Computerized Tomography) brain images. This work gives the neuroradiologist a second option for the easy identification of tumor cells from the brain image. The important data mining concept that has been included in the proposed work consists of pre-processing of the CT-Scan brain image. The method used for pre-processing includes Shape priori technique. The feature selection from the brain image has been done using the association rule mining. The rules generated for extracted features are stored in the transactional database have been classified using the data mining concept called Decision Tree Classification. The combination of both the association rule mining and the decision tree classification gives the high degree of accuracy and efficiency for the proposed system.

Wireless sensor networks are catching up as the primary mode for monitoring and collecting data in physically challenging environments. They find applications in various fields varying from environment monitoring, military applications to monitoring patients in hospitals. The constraints due to their inherent features make routing in wireless sensor networks a big challenge. This paper covers Genetic algorithm (GA) based simple straight forward, address based shortest path routing in Wireless ad hoc sensor networks (WASN). The routing in packet switched multi-hop networks can be described as a classical combinatorial optimization problem i.e. a shortest path routing problem in graphs. The stress is on energy efficient routing where the network optimizes between efficient routing and maximizing life of the network. The results show that the GA is efficient for energy constrained WASNs due to their fastness in computation comparing to all other algorithms.

Phosphate‐based glass systems with different Ag 2 O contents 45 P 2 O 5 –30 CaO –(25‐ x ) Na 2 O – x Ag 2 O ( x = 0, 0.25, 0.5, 0.75, and 1.0, hereafter termed, respectively, as PCNA 0, PCNA 0.25, PCNA 0.5, PCNA 0.75, and PCNA 1) are prepared by keeping the ratio of P/Ca as 3.0. The influence of Ag 2 O on phosphate glass system is studied in terms of ultrasonic parameters, pH and in vitro studies. The structural role of Ag 2 O , i.e., loose packing of glass network is noticed up to 0.5 mol% of Ag 2 O content and then by an increase in packing density with further addition of Ag 2 O . The hydroxyapatite ( HAp ) forming ability of prepared glasses is carried through in vitro studies in simulated body fluid. The scanning electron microscopy images before and after in vitro studies show the formation of a low concentration of HAp in all glass surfaces except PCNA 1, whereas a higher rate of formation of HAp is evidenced on PCNA 1 glass sample. The Fourier Transform Infrared spectra and X‐ray Diffraction patterns that are observed support higher bioactivity on PCNA 1 glass sample.

Abstract In this study, heat transfer performance of nanofluids (Al2O3/water and CuO/water nanofluid) is experienced by using the condensing unit of an air conditioner. Nanoparticles at 30 nm are suspended at various volume concentrations (1%, 2%, 3%, and 4%) in the base fluid are produced for this current work. The nanofluids, considered as a cooling fluid, flow in the outer side of the tube of condenser, and general working condition of the air conditioner is applied for the investigation. Experimental results highlight the enhancement of heat transfer rate because of the existence of nanoparticles in the fluid. Two nanofluids show better heat transfer rate than does the base fluid. The Nusselt numbers for CuO/water and Al2O3/water nanofluids are enhanced up to 39.48% and 33.86%, respectively. The findings show that CuO/water nanofluids exhibit better heat transfer rate than Al2O3/water nanofluids.

A series of phosphate based glasses of composition 45 P 2 O 5 –(40− x ) CaO –15 Na 2 O – x ZnO ( x = 0, 3, 6, 8 10, and 12 mol%) were prepared employing melt‐quenching techniques. Based on the thermal analysis data of glasses, the schedule of thermal treatments have been designed and employed to obtain their glass ceramic derivatives. The increase in thermal stability and decrease in crystallization tendency was observed from differential thermal analysis traces. The durability of phosphate glasses were improved as a result of the addition of ZnO content. The increasing ZnO content alters the phosphate glass structure and enhances the cross‐link formation between different network chains. A broad‐based characterization approach combining different techniques was used to investigate the in vitro properties of glasses and their glass‐ceramic equivalents against the addition of ZnO content into the glass composition. The in vitro results revealed the decreasing nature of apatite forming ability of phosphate glasses with increase in ZnO content before and after thermal treatments. Both the glass and glass‐ceramics containing ZnO content above 10 mol% exhibited poor in vitro performance mainly because of their dissolution nature. The weight loss analysis revealed that the decreasing solubility of glass and glass‐ceramics was observed as increasing of ZnO content.

Magnesium-doped NBG composites (SiO₂–CaO–P₂O₅–MgO) coated implant is found to be a potential nanocomposite for high load-bearing applications with better anticorrosive property and long-term stability.

Multilevel inverter is one of the most recent and popular type of advances in power electronics. It synthesizes desired output voltage waveform from several dc sources used as input for the multilevel inverter. This paper presents the two techniques for improving the quality of the output voltage and efficiency. First, new novel topology for multilevel inverter is introduced which reduces the number of switches compared to other for the same level of output voltage. Second, Selective Harmonics Elimination Stepped Waveform (SHESW) method is implemented to eliminate the lower order harmonics. Fundamental switching scheme is used to control the power electronics switches in the inverter. The proposed topology is suitable for any number of levels. When the levels are increased the number of switches used is reduced compared to the conventional cascaded H-bridge multilevel inverter. This paper is particularly focuses on seven level inverter. In the proposed topology only 7 switches were used. The harmonic reduction is achieved by selecting appropriate switching angles. It shows hope to reduce initial cost and complexity hence it is apt for industrial applications. In this paper third and fifth level harmonics have been eliminated. Simulation work is done using the MATLAB software which validates the proposed method and finally THD comparison is presented for analysis.