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Nature has a massive range of diverse species of flora. The phytochemical content of each plant may vary due to the diverse climate and different geographical areas. Several plants have been outlined for their reducing properties and highly exploited for the green synthesis of metal nanoparticles. Moreover, phytochemicals present in plant parts (leaf, root, flower, fruit, etc.) act as both reducing and capping agents for the production of nanoparticles. Plant-mediated nanoparticles have gained so much attention in the present era for its green and rational approach and developed as a green substitute to conventional synthesis approaches. This critical review encloses literature, phytosynthesis methods, and factors affecting the synthesis of silver nanoparticles. Also, the main characterization techniques especially, spectroscopic techniques are epigrammatically summarized and discussed here.

Clustering based unsupervised segmentation approaches have become important tools to model data and infer knowledge. Such approaches are often used in various biomedical applications to assist experts in analyzing the data. Segmentation of brain magnetic resonance (MR) images has become a very challenging task due to the presence of Rician noise and intensity inhomogeneity. The existing methods dealing with such issues work in the spatial domain. These methods are prone to losing image details while reducing the effect of noise. The proposed method works in the moment domain, and for this purpose, we have selected Zernike moments (ZMs). The ZMs are orthogonal, rotation invariant and robust to image noise. The proposed method is based on our previous work on Zernike moments-based unbiased nonlocal means (ZM-UNLM) approach for denoising MR images affected by the Rician noise. The ZM-UNLM-based approach uses both the local and nonlocal information while eliminating Rician noise from brain MR images which have fine image details along with the noise components. Keeping in view the superior performance of the ZM-UNLM, we extend it to segment brain MR images and propose a method called local ZMs (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach which provides much-improved segmentation accuracy as compared to the existing spatial domain-based state-of-the-art techniques. It is shown how the LZMs based technique reduces the effect of the Rician noise while retaining the fine structures, edges, and other image details in the moment domain. The complete framework consists of an intelligent knowledge-driven approach that segments tissues from brain MR images using both the local and nonlocal information in the moment domain. The proposed approach has significance in developing future expert and intelligent systems that do not require any human intervention to manually annotate data and saves time for diagnostic related tasks. Detailed experimental results are provided to demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.

Segmentation of brain magnetic resonance (MR) images has a significant impact on the computer-aided diagnosis and analysis. However, due to the presence of noise in medical images, many segmentation methods suffer from limited segmentation accuracy. To reduce the effect of noise and achieve high segmentation accuracy many approaches based on the local and nonlocal information in the spatial domain have been proposed in the past. Recently, the authors have proposed a discrete cosine transform (DCT)-based local and nonlocal fuzzy C-means method (DCT-LNLFCM) which performs much better than the existing methods. However, the method is slow in speed. This paper presents a fast DCT-based nonlocal fuzzy C-means (DCT-NLFCM) segmentation method which is not only very fast than the DCT-LNLFCM, but also provides better segmentation results. The proposed method uses DCT-based MR pre-filtered image to achieve high segmentation accuracy and its histogram enables to achieve very high computation speed. Detailed experiments are conducted to establish the superiority of the proposed method over the state-of-the-art unsupervised methods.

We present novel techniques for realising reliable low overhead logic functions and more complex systems based on the switching characteristics of memristors. Firstly, we show that memristive circuits have inherent properties for realising multiple valued MIN-MAX operations over the post algebra. We then present an efficient hybrid 1T-4M logic architecture for dual XOR/AND and XNOR/OR functionality, which can be seamlessly integrated with the existing CMOS technology. Although memristors are usually considered to operate at lower frequencies, however, recent advances in technology show their potentiality at high frequencies. To this end, we also explore the effects of high frequencies on their performance and thereby propose reliable high frequency design techniques based on our 1T-4M architectures. Experimental results, based on the design of full adders and multipliers over GF, show that the proposed designs require significantly lower power and overhead while maintaining reliable performance at low as well as at high frequencies compared to the existing techniques.

TAG (terbium aluminum garnet, (Tb3Al5O12) is being investigated recently for their applications in the realization of photonic devices because of its broad transmission ranging from 350 to 1500nm. An emission property of any material depends upon electronic transition levels, temperature, excitation wavelength etc. A lot of research had been reported on temperature dependent emission properties of TAG. In this study an attempt had been made to explore the excitation dependent emission properties of TAG. TAG was prepared by sol gel technique followed by sintering at 1100°C. Structural phase was analysis from the XRD spectra. XRD studies confirmed the formation of nano material and has cubic structure. The emission properties were studied by exciting the TAG by UV light and blue light i.e. 280nm, 300nm, 380 nm, 400nm, 480 and 484nm. Spectra power distribution curve was obtained from emission data excited by with UV radiation and blue light. From emission spectra, Colour co-ordinates (CIE), colour correlated temperature (CCT) and colour purity were calculated for TAG at different excitation. Emission properties indicates that TAG is potential nanophosphor for optical devices.

Background: Caesarean delivery is defined as an operative procedure to deliver the fetus or foetuses after the period of viaility through an incision on the adominal wall and uterine wall in an intact uterus. The World Health Organization (WHO) has identified an ideal caesarean section (CS) rate for a nation of around 10-15%1. The objective of the present study was to find the rate of primary caesarean deliveries in and its contribution to total caesarean rate and to analyze the indications of the caesarean sections. Methods: It is a retrospective observational study conducted in the Department of Obstetrics and Gynaecology at Regional hospital,Bilaspur. A total of 90 primary caesarean deliveries were studied. Results: The total deliveries during the study period were 809 and the total caesarean section rate observed was 14.96%. The caesarean section rate among primigravidae was 74.38%. Out of the total number of primicaesarean deliveries, 92.22% were performed in emergency and 7.78% were performed electively. Among the emergency caesarean sections performed, 60.24% of patients had induced labor and 39.76% had spontaneous labor. The most common indication of caesarean section was fetal distress (43.33%) followed by failed induction(18.89%) and malpresentations (16.67%). Conclusions: Caesarean audit should be performed routinely and every case should be scrutinised. Reducing the primary caesarean rate not only decreases total caesarean rate but also many long-term complications associated with previous caesarean sections like adherent placenta ,rupture uterus

Accurate segmentation of brain tissues from magnetic resonance images (MRI) is a crucial requirement for the quantitative analysis of brain images. Due to the presence of noise in brain MRI, many segmentation methods suffer from low segmentation accuracy. The existing methods deal the noise sensitivity of the MRI segmentation in the spatial domain by combining the local and nonlocal information in the fuzzy C-means (FCM) method. These methods are prone to loosing image details while reducing the effect of noise. In this paper, we propose a transform domain approach using the discrete cosine transform (DCT). Working in the transform domain has an advantage over the spatial domain in which the intensity of the image is decorrelated and the image information is represented by the independent frequency bands. The low and middle level frequency bands represent the holistic and fine structures of the image and the high frequency band mostly carries the noise information. In the proposed method, called the DCT-based local and nonlocal FCM (DCT-LNLFCM), the distance function of the FCM is represented as the sum of the local and nonlocal distances which themselves are the weighted values of the Euclidean distance used in the FCM. Since the weights are computed in the transform domain, a good tradeoff is achieved between noise insensitivity and preservation of the image details. This results in the high accuracy of the MRI segmentation. Detailed experimental results are presented and comparison with the state-of-the-art techniques is performed to demonstrate the high performance of the proposed approach. The proposed method provides an improvement in the average segmentation accuracy from 1.10% to 2.03% on simulated images and 1.52% to 1.91% on real images.

Remotely sensed data have been used to extract relevant information on various natural resources and environment. Satellite derived information may be contaminated by a variety of factors. A robust approach for the reconstruction of the temporal profile of NDVI is proposed in this study. The proposed approach consider the crop growth pattern in the Himalayan foothills region so as to make the road map for optimized information extraction. The data mining and the decision tree classification approach has been used to discriminate the crops in the study area. The study has been focused on to extract the information related to crops such as discrimination of different crops at various levels of classification, estimation of the crop yield and monitoring the growth of the crops. A classification model with a good accuracy has been obtained. The results revealed the potential of reconstruction, data mining and decision tree techniques to analyze remote sensing data.

Heterotopic pregnancy is defined as multiple gestation in which intrauterine and extrauterine gestational sacs co-exist. The extra uterine gestational sac is most commonly tubal ectopic pregnancy. We presented case of a 26 years old multigravida who presented to emergency with complaints of pain abdomen and giddiness for 2-3 days. She was at period of gestation (POG) 7 weeks and on clinical examination patient was anxious with mild pallor, mildly tachycardiac and blood pressure (BP) was 90/60 mm of Hg. After thorough clinical examination and sonography diagnosis of heterotopic pregnancy with ruptured tubal ectopic was made. She was taken up for Emergency laparotomy after investigations and consent. Left salpingectomy was done and she was discharged with a single intrauterine live pregnancy on 6th post op day. For early detection of cases of heterotopic pregnancy careful evaluation of adnexa is mandatory in early gestation scan.

Ficus thonningii is a plant that is widely utilised in African ethnomedicine to cure a variety of ailments. a rational examination of F. thonningii's nutritional, phytochemical, and pharmacological characteristics in connection to its medicinal uses.Alkaloids, terpenoids, flavonoids, tannins, and active proteins are among the bioactive components found in Ficus thonningii, all of which contribute to its therapeutic qualities.Continue to discover, isolate, and quantify the active components, as well as establish the processes behind its curative powers.

Memristors are an attractive option for use in future architectures due to their non-volatility, high density and low power operation. Gas sensing is one of the proposed application of memristive devices. In spite of these advantages, memristors are susceptible to defect densities due to the nondeterministic nature of nano-scale fabrication. In this paper, a novel spice memristor model incorporating fault models that emulates the gas sensing behaviour with/without faults is developed for simulation and integration with design automation tools. Our simulation results show that the proposed non-linear model detects the presence of the oxidising/reducing gas and analyses the defects/faults affecting the functionality of the sensor.

An efficient memristor MIN function based activation circuit is presented for memristive neuromorphic systems, using only two memristors and a comparator. The ReLU activation function is approximated using this circuit. The ReLU activation function helps to significantly reduce the time and computational cost of training in neuromorphic systems due to its simplicity and effectiveness in deep neural networks. A multilayer neural network is simulated using this activation circuit in addition to traditional memristor crossbar arrays. The results illustrate that the proposed circuit is able to perform training effectively with significant savings in time and area in memristor crossbar based neural networks.

Brain magnetic resonance images (MRI) suffer from many artifacts such as noise and intensity inhomogeneity. Moreover, they contain an abundant amount of fine image structures, edges, and corners in various areas of the image. These anomalies and structural complexities affect the segmentation process of the brain MRI which is required by physicians for the diagnosis purpose. Recently, we have proposed a local Zernike moment (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach that has dealt with the noise artifact in the moment domain using the LZM approach. The method provides high segmentation results for the MR images corrupted with Rician noise. However, the method does not deal with the intensity inhomogeneity artifact effectively. Moreover, the method uses a regularization parameter that needs to be adjusted to obtain effective segmentation results. This paper presents an unsupervised local Zernike moment and unbiased nonlocal means-based bias corrected fuzzy C-means (LZM-UNLM-BCFCM) approach that deals with both noise and intensity inhomogeneity artifacts. The main concept behind the proposed method is to use the attractive properties of the LZMs to effectively filter the image by determining a large number of similar regions in an MR image which is mostly corrupted by Rician noise and intensity inhomogeneity. The ability of the LZMs to determine such regions in MR images consisting of fine tissue structures in any orientation is well utilized for dealing with the high levels of noise. The intensity inhomogeneity is removed by estimating the bias field pixel-by-pixel during the segmentation process using the filtered image without the use of regularization parameter. The bias field is estimated as a linear combination of the orthogonal polynomials in which the weights are obtained by minimizing the fuzzy objective function. Experimental results on both simulated and real MR images show the superiority of the proposed method as compared to other unsupervised state-of-the-art approaches.

This paper presents a new and efficient approach for recovering surface height from its image gradients. In order to compensate noise effect in image intensities, Gaussian filter is used to smooth the intensity values. Image gradient is calculated from the smoothen image by taking the central difference of intensity values. A minimization problem is formulated in terms of gradient values. This minimization problem is solved in Fourier domain to avoid the iterative minimization. The experiments have been performed on real and synthetic images to demonstrate the accuracy of the approach using Gaussian filter. Several different measures have been used for error estimation.

Memristor is being considered as a game changer for the realization of neuromorphic hardware systems due to its similarity with biological synapse. Recent studies show that memristor crossbar can provide high density and high performance neural network hardware implementation at low power due to its physical layout, nano scale size and low power consumption feature. This paper describes the training method that can be used for the implementation of memristive multi-layer neural network with ex-situ method. We mimic the behavior of memristor crossbar in software training process to achieve more accurate and close computations to hardware. Voltage divider has been used to calculate the dot product in this method. To demonstrate the accuracy and effectiveness of this method, different patterns and non-separable functions using memristor crossbar structures are simulated. The results demonstrate that more accurate computations can be produced using this learning method for ex-situ. It also reduces the learning time of functions.

The most of the memristor based applications which have been proposed so far have not considered the parasitic components. In this paper, we apply a generic memristor model which includes the parasitic effects to our proposed memristive logic architectures. First, we show that the current response of the memristor has the decaying oscillation when the unit step function is applied. Then we demonstrated that our specific memristive logic structure can almost eliminate those effects which are generated by the parasitic components of the memristor. In addition, the propagation delay and the variation of the memristive XOR gate are increased because of the parasitic components. With the delay analysis on cascaded memristive logic design, the experimental results show that our 3T-4M memristive XOR architecture can build the more robust delay based memristive physical unclonable function (PUF) comparing to the existing memristive PUF.

Starch from water chestnuts ( Trapa natans ) was isolated and modified by dry heating and hydrocolloids [carboxy methyl cellulose (CMC) and sodium alginate]. Native and modified starches were evaluated for their physicochemical, pasting, thermal and morphological properties. Pasting and thermal properties were studied using Rapid Visco Analyzer (RVA) and Differential Scanning Calorimeter (DSC) respectively. Morphological properties were studied by Scanning Electron Microscopy (SEM). Modification of the starch by dry heating with and without gums reduced paste clarity and increased the water and oil binding capacity; solubility and swelling power decreased. Dry heating of native starch increased peak viscosity; however, with addition of CMC, peak viscosity decreased. Starch modified with CMC and 4 h heating exhibited lowest gelatinization temperature ( T 0 ). Pasting characteristics of native water chestnut starch were largely affected by the addition of gums and/or heat treatment. Overall onset gelatinization temperature reduced with heat treatment and addition of gums. Morphological studies revealed no significant variation in starch granule size. Starch granules were seen agglomerated because of leaching of amylose and granule interspacing decreased with addition of gums.

This work on elemental and binary nanowires of Cr and Mn, exploring their non-magnetic, ferromagnetic and anti-ferromagnetic states, shows that alloying increases the stability of binary linear chains in comparison to their elemental counterpart. In addition, it also favors not only the linearization of CrAs and MnAs nanowires but also a better magnetic stability for binary linear chains, in general. Furthermore, it is encouraging to note that, besides being more stable, the alloy nanowires are also endowed with a large magnetic moment. Also, the binary nanowires show more feasibility towards tailoring of magnetic state with stretching, in comparison to elemental nanowires. We note that the cohesive energy of studied nanowires is effectively dictated by the X atoms (X = Si, Ge, and As) whereas their magnetic properties are decided by the magnetic M atoms (M = Cr and Mn). Density of states conveys that hybridization of d-states of M atoms and p -states of X atoms plays an important role in higher stability of binary nanowires. The band structure indicates tuning of optical properties of nanowires, while linear chains of MnAs seem suitable for spintronics.

Functional and sensory properties of cookies prepared by supplementing different proportions of cassava flour (CF) and water chestnut flour (WCF) blends (0–100%) to wheat flour (WF) were studied. Seven formulations of cookies were prepared from (a) Control (100% WF), (b) 30% WF, 35% WCF and 35% CF, (c) 27% WF, 37.5% WCF and 37.5% CF, (d) 20% WF, 40% WCF and 40% CF, (e) 15% WF, 42.5% WCF and 42.5% CF, (f) 10% WF, 45% WCF and 45% CF, and (g) 0% WF, 50% WCF and 50% CF. Cookies were subjected to physical analysis (cookie diameter, cookie thickness, spread ratio, bulk volume, bulk density, breaking strength, and color analysis) and evaluated for consumer acceptance by descriptive sensory analysis. Cookies prepared from water chestnut and cassava flour had low moisture content (5.63%), low fat (24.87%), higher spread ratio (8.148), decreased L, a and b values (dark color), and low breaking strength than control ones. Sensory evaluation established that cookies prepared from 50% WCF and 50% CF were more acceptable than cookies prepared from other formulations.

The experiment was laid out in a Completely Randomized Design (factorial) consisting of three cultivars and six calcium nitrate doses (0, 5, 10, 15, 20 and 25 g/m2). Among the cutivars, maximum bulblet multiplication was found in UHFS LA Lily Collection 1 (2.59) followed by Batistero (2.48) and Frans Hals (2.32), respectively. Among the different calcium nitrate doses, 10 g/m2 calcium nitrate dose gave best results for per cent sprouting (76.03%), minimum days taken for sprouting (102.00 days), sprout length (9.60 cm), fresh weight of bulblet/scale (1.69 g), bulblet diameter (9.32 mm) and propagation index (125.69%) when supplied to the scales in ten equal split doses in liquid form. However, for obtaining higher number of leaves/scale (6.38) and maximum multiplication (2.90 bulblets/scale) calcium nitrate application at the rate of 20 g/m2 supplied to these scales proved to be most effective treatment. The interaction between cultivars and calcium nitrate doses has shown variable responses. Highest per cent sprouting was observed in Frans Hals (77.14%) with 15 g/m2 and 25 g/m2 calcium nitrate and in UHFS LA Lily Collection 1 (77.14%) with 10 g/m2, whereas maximum sprout length (10.18 cm), number of bulblets/scale (3.01) and bulblet diameter (9.77 mm) were found in cultivar UHFS LA Lily Collection 1 with 10 g/m2 and number of leaves/scale (7.03) in cultivar Batistero with 25 g/m2 calcium nitrate dose.

Mobile Ad Hoc Network (MANET) consists of a collection of wireless mobile hosts without the required intervention of any existing infrastructure or centralized access point such as base station. The dynamic topology of MANET allows nodes to join and leave the network at any point of time. Wireless MANET is particularly vulnerable due to its fundamental characteristics such as open medium, dynamic topology, distributed cooperation and constrained capability. In this paper we simulate the blackhole attack which is one of the possible attacks on AODV routing protocol in mobile ad hoc networks by the help of network simulator (NS-2). The simulation results show the packet loss, throughput, and end-to-end delay with blackhole and without blackhole on AODV in MANET. We analyzed that the packet loss increases in the network with a blackhole node. We also observed that the throughput and end-to-end delay decreases in the network with a blackhole node.

We report theoretically on the effect of dimensionality on elementary low-dimensional systems of transition metals of groups 3--7 (Sc, Y; Ti, Zr, Hf; V, Nb, Ta; Cr, Mo, W; Mn, Tc, Re). This work completes the exploration on elementary low-dimensional systems of all transition metals, those of groups 8 to 12 being reported earlier. In contrast to the elementary low-dimensional systems of late transition metals, magnetic moment per atom $(\ensuremath{\mu})$ predicted for some cases of early transition metals is very large. An interesting trend is observed for magnetic ordering as we go from group 3 to 7. The elementary low-dimensional systems of groups 3 and 4 prefer ferromagnetic ordering, with only Hf preferring to stay nonmagnetic as in bulk. Antiferromagnetic ordering is preferred by elementary low-dimensional systems of transition metals of groups 5 to 7. The maximum $\ensuremath{\mu}$ for ferromagnetic elementary low-dimensional systems ($1.8\phantom{\rule{0.3em}{0ex}}{\ensuremath{\mu}}_{\mathrm{B}}$ for Sc linear chains) is much smaller than that for antiferromagnetic systems ($\ensuremath{\sim}3.4\phantom{\rule{0.3em}{0ex}}{\ensuremath{\mu}}_{\mathrm{B}}$ for linear chains of Cr and Mn). The antiferromagnetic ordering in two-dimensional systems is invariably accompanied by lattice expansion with respect to bulk. Our results are in accordance with available experimental and theoretical results. Magnetic linear chains of Sc, Ti, Zr (ferromagnetic in nature), and Cr, Re (antiferromagnetic in nature) are predicted to offer relative ease of formation experimentally and could be useful from an application point of view.