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Brain tumor is an anomalous growth of brain cells. Segmentation of brain tumors is currently the most important surgical and pharmaceutical procedure. However, manually segmenting the brain tumor is a challenging task due to the complex structure of brain. In recent years, artificial intelligence techniques with the fuzzy logic have shown better results in the field of medicine. In this work, a novel deep learning classification network with fuzzy hexagonal membership function (DLC-FHMF) model has been proposed for accurately segmenting brain tumors. The different MRI modalities namely T1, T1-c, T2 and Flair images are preprocessed using a fuzzy hexagonal trilateral and median filter to eliminate the Rician noise. Afterwards, the DLC-FHMF model is used for segmenting the tumor portion by using the multimodal composition of MRI as input. The fuzzy weights are determined with hexagonal membership functions and convoluted with the corresponding MRI images. The quantitative examination is carried out using the performance metrics namely accuracy, specificity, precision, sensitivity, incorrect segmentation, under-segmentation, and over-segmentation. In addition to the above metrics, the pre-processing metrics include PSNR, RMSE, and SSIM. The experimental fallout portrayals that the proposed DLC-FHMF approach attains a better accuracy range of 99% for detecting brain tumors using the BRATS 2013 dataset. The proposed DLC-FHMF model improves the overall accuracy by 15.1%, 11.1%, 3.0%, 21.2% and 0.5% better than ANN, SVM, NB, DNN and DAE respectively.

The Internet of Things (IoT) has remained extensively to connect accessible medical resources and provide smooth, dependable, and in effect of healthcare services to patients, and it has the potential to disrupt healthcare innovation. Health monitoring system using IoT application is the recent trend in medical field, by discovering the potential of the technology. Humans are incrassating the several issues and precocious death due to numerous illness, and by deficiency in providing medical treatment to the patients. To obtain the solution to this issue, a real time health monitoring system is proposed based on the recent technology such as IoT. Enhanced and intelligent healthcare system is the representation of developed and prosperous nation. The proposed method improves the monitoring system by shrinking the use of sensors, which have been attempted to exploit the new technology to obtain the solution for the healthcare problem currently society is facing, and a remote healthcare system is designed. The proposed automated and intelligent system involved with the different phases such as revealing the vitality of patient’s using sensors, sends patients data to cloud storage and thus providing it to the doctors to utilize it, which will help to access and observe the patient’s well-being improvement away from hospital locations. The proposed automated and intelligent system is used to interconnect the accessible medical resources and offer smart, reliable, and effective healthcare system to the people. An IoT architecture with customized healthcare applications have been developed to support remote health monitoring system.

We demonstrate a functionalization strategy to improve the basic fluorescence properties and to systematically customize the bandgap properties of Graphene quantum dots (GQD) by functionalization with a well-known fluorophore called Dithienopyrrole derivative (DTPAN) whereby making them adequately acceptable for the suitable promising applications. We observed that the functionalization with DTPAN not only shifts the photoluminescence spectrum but also enhances the other inherent fluorescence properties. The functionalized material (DTPAN-fn-GQD) is used for the sensitive detection of Pb2+ ions in aqueous media. The detection of Pb2+ within the linear range of 0.5–40 nM was obtained with a limit of detection of 0.25 nM. Stern–Volmer analysis suggests that both static and dynamic mechanisms are active for the quenching process and further the Cyclic Voltammetry measurements along with DFT calculations of DTPAN-fn-GQD recommend the possibility of electron transfer from the HOMO of DTPAN to the LUMO of GQD upon photoexcitation. However, the presence of Pb2+ metal ions will perturbate this charge transfer process and lead to quenching of fluorescence. The sensor was applied efficiently to the determination of Pb2+ ions in real water samples. Paper strips based on the developed fluorescent probe were shown to be useful for rapid visual sensing, which can expand its potential ability for developing simple analytical devices.

Recently graphene quantum dot (GQD) based nanohybrid materials have received much attention. Herein, a highly fluorescent biocompatible GQD and N-functionalized dithienopyrrole (DTP-PPD) modified nanohybrid system was fabricated (DTP-PPD-fn-GQD) for the first time. Modification resulted in stable fluorescence with a quantum yield of approximately 22% and 36 nm redshift compared to unmodified GQD. The apparent bandgap tuning along with fluorescence property changes was investigated by cyclic voltammetry measurements and density functional theory studies. This water-soluble system was then applied for the sensitive and selective detection of Pb2+ ions. As a result of a specific interaction towards Pb2+ ions, the fluorescence intensity was quenched. The detection limit is found to be 1.02 nM within the linear range of 3 to 30 nM. Finally, the feasibility of the developed probe was tested with real samples of water. Chemically modified GQDs are already widely exploited for Pb2+ sensing, whereas GQD/thiophene-based nanohybrid systems are less utilized. This newly developed nanohybrid of GQD (DTP-PPD-fn-GQD) has excellent fluorescence properties and bandgap tunability. Moreover, effective fluorometric sensing of Pb2+ ions in an aqueous medium is well investigated. This gives more insight into developing GQD-based highly promising nanohybrid colorimetric as well as fluorescent sensors.

Let [Formula: see text] be a simple graph of order [Formula: see text]. A set [Formula: see text] is said to be a restrained set if each vertex in [Formula: see text] is adjacent to a vertex in [Formula: see text]. The restrained polynomial of a graph [Formula: see text] is [Formula: see text] where [Formula: see text] is the number of restrained sets of [Formula: see text] of cardinality [Formula: see text]. A set [Formula: see text] is said to be a restrained dominating set if each vertex in [Formula: see text] is adjacent to a vertex in [Formula: see text] and to a vertex in [Formula: see text]. The restrained domination polynomial of a graph [Formula: see text] is defined by [Formula: see text] where [Formula: see text] is the number of restrained dominating sets of [Formula: see text] of cardinality [Formula: see text]. In this paper, we derive restrained domination polynomial of join and corona of any two graphs.

Background: Accurate detection of brain tumor and its severity is a challenging task in the medical field. So there is a need for developing brain tumor detecting algorithms and it is an emerging one for diagnosis, planning the treatment and outcome evaluation. Materials and Methods: Brain tumor segmentation method using deep learning classification and multi-modal composition has been developed using the deep convolutional neural networks. The different modalities of MRI such as T1, flair, T1C and T2 are given as input for the proposed method. The MR images from the different modalities are used in proportion to the information contents in the particular modality. The weights for the different modalities are calculated blockwise and the standard deviation of the block is taken as a proxy for the information content of the block. Then the convolution is performed between the input image of the T1, flair, T1C and T2 MR images and corresponding to the weight of the T1, flair, T1C, and T2 images. The convolution is summed between the different modalities of the MR images and its corresponding weight of the different modalities of the MR images to obtain a new composite image which is given as an input image to the deep convolutional neural network. The deep convolutional neural network performs segmentation through the different layers of CNN and different filter operations are performed in each layer to obtain the enhanced classification and segmented spatial consistency results. The analysis of the proposed method shows that the discriminatory information from the different modalities is effectively combined to increase the overall accuracy of segmentation. Results: The proposed deep convolutional neural network for brain tumor segmentation method has been analysed by using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). The complete, core and enhancing regions are validated with Dice Similarity Coefficient and Jaccard similarity index metric for the Challenge, Leaderboard, and Synthetic data set. To evaluate the classification rates, the metrics such as accuracy, precision, sensitivity, specificity, under-segmentation, incorrect segmentation and over segmentation also evaluated and compared with the existing methods. Experimental results exhibit a higher degree of precision in the segmentation compared to existing methods. Conclusion: In this work, deep convolution neural network with different modalities of MR image are used to detect the brain tumor. The new input image was created by convoluting the input image of the different modalities and their weights. The weights are determined using the standard deviation of the block. Segmentation accuracy is high with efficient appearance and spatial consistency. The assessment of segmented images is completely evaluated by using well-established metrics. In future, the proposed method will be considered and evaluated with other databases and the segmentation accuracy results should be analysed with the presence of different kind of noises.

Background: The aim was to study maternal and fetal outcomes in pre-eclamptic mothers and to study sociodemographic distribution, severity of preeclampsia.Methods: This was a prospective observational study conducted in the department of obstetrics and gynaecology at Government head quarters hospital, Cuddalore, Tamil Nadu, for 12 months (October 2019 to September 2020). 108 preeclamptic mothers between 32-39 weeks of gestational age who met the inclusion and exclusion criteria were studied consecutively.Results: Among 108 participants, 52.7% were primi, 69.4% belonged to SE class-IV, 45.4% were between 21-25 years, 44.4% were referral cases, 60.1% had BMI >25 kg/m2, 79.6% had late onset preeclampsia, 11.1% had preterm delivery. LSCS rate was 81.5% with most common indication fetal distress 22.7% then oligohydraminos 18.2%. Recurrent preeclampsia among patients with previous childbirth was 48.6%. Maternal complications were 17.6% severe preeclampsia, 13% PPH, 3.7% abruption, 3.7% antepartum eclampsia, 1.9% postpartum eclampsia. 25.9% patients got admitted in maternal ICU. 68.4% of the severe preeclamptic patients were treated with MgSO4 regimen. 6.48% of patients had imminent symptoms. 11.1% neonates had APGAR score 4-6. Neonatal complications 30.5% LBW, 33.3% SGA, 14.8% FGR, 1.9% still birth and 0.9% IUFD. 48.6% neonates were admitted in NICU. There was no maternal mortality in this study.Conclusions: Fetomaternal morbidities of preeclampsia is reduced by emphasising early registration and admission, providing adequate nutrition through existing government schemes, educating women on significance of routine antenatal check up at PHCs with BP monitoring, albuminuria evaluation and clinical evaluation of fundal height at each visit. Screening Doppler and growth scan done for timely intervention.

Let R be a commutative ring with identity. The nilpotent graph of R , denoted by Γ N ( R ) , is a graph with vertex set Z N ( R ) ∗ , and two vertices x and y are adjacent if and only if x y is nilpotent, where Z N ( R ) = { x ∈ R : x y is nilpotent, for some y ∈ R ∗ } . In this paper, we characterize finite rings (up to isomorphism) with identity whose nilpotent graphs can be embedded in the projective plane or Klein bottle. Also, we classify finite rings whose nilpotent graphs are ring graph or outerplanarity index 1,2.

Background: Irreversible visual impairment and morbidity are associated with pregnancy induced hypertension. It causes pathological changes in vascularity of placenta, kidney and brain along with two major pathological types of changes in fundus namely arteriolar vasospasm and permeability changes in vascular endothelium. The aim of our study was to analyse the relationship between fundus changes in pregnancy induced hypertension with visual impairment and its reversibility.Methods: A prospective observational study done on pregnant women with any grade of pregnancy induced hypertension with recent visual impairment from 24 completed weeks of pregnancy.Results: Out of 75 patients with PIH, all the patients had varying degree of fundus changes in one or both eyes. In 150 eyes of the 75 patients, 86 (57.30%) eyes had isolated arteriolar vasospasm, 14 (9.33%) had grade III hypertensive retinopathy, 4 (2.66%) had grade IV hypertensive retinopathy, 30 (20%) had macular oedema, 4 (2.66%) had central serous chorioretinopathy, 2 (1.33%) had vascular occlusion, 2 (2.66%) eyes had normal fundus with cortical blindness, 2 (2.66%) had exudative retinal detachment, 6 (4%) eyes had normal fundus with changes in the other eye.Conclusions: Out of 75 patients, 7 (9.3%) patients had irreversible loss of vision, 3 (42.85%) due to arteriolar vasospasm, and 4 (57.15%) due to choroidal ischemia. Among the 4 patients with choroidal ischemia, 3 (75%) were in the group of eclampsia and 1 (25%) in gestational hypertension.

Background: The aim is to study the prevalence of prelabour rupture of the membranes (PROM), to identify risk factors, mode of delivery, and its maternal and fetal effects.Methods: This was a cross-sectional study conducted in the department of obstetrics and gynaecology at Government Head Quarters Hospital, Cuddalore, Tamil Nadu, with a duration of 6months (January 2020 – June 2020). The study was conducted on 800 pregnant women between 28-42 weeks of gestational age consecutively and those who met the inclusion and exclusion criteria were taken into study.Results: The prevalence of PROM was 27.9% (tPROM 24.6% and PPROM 3.2%). Most of the cases were primigravida (74%). Risk factors associated with PROM were low socioeconomic state (63.2%), urinary tract infection (UTI) (7.2%), vaginal infections (5.8%), and previous history of PROM (3.1%). Most of the patients were delivered by lower segment caesarean section (LSCS) (55.2%), normal vaginal delivery (39.9%) and forceps delivery (4.9%). The most common indication for LSCS was fetal distress (43.9%). Misoprostol induction was associated with more failed induction (2 times) than syntocinon. Maternal complications were post-partum haemorrhage (PPH) (8%), fever (6.7%), wound infection (6.2%), manual removal of placenta (4.4%), and puerperal sepsis (0.9%). Neonatal complications were neonatal intensive care unit (NICU) admissions (14%), respiratory distress syndrome (RDS) (11%), neonatal sepsis (2.6%). Maternal (54.5%) and neonatal (90%) morbidity were more in prolonged PROM >24 hours.Conclusions: Antenatal screening for genitourinary infections especially in cases of the previous history of abortions and PROM should be done. Oxytocin is the preferred method of induction over misoprostol in this study. Active management in term PROM cases can reduce the cesarean section rate.

Let R be a commutative ring and I be a non-zero ideal of R . Let R ⋈ I be the subring of R × R consisting of the elements ( r , r + i ) for r ∈ R and i ∈ I . In this paper we characterize all isomorphism classes of finite commutative rings R with identity and ideal I such that Γ ( R ⋈ I ) is planar. We determine the number of vertices of Γ ( R ⋈ I ) , a necessary and sufficient condition for the graph Γ ( R ⋈ I ) to be outerplanar and the domination number of Γ ( R ⋈ I ) .

In this paper, we studied the 4-total prime cordial labeling of Cn2, butterfly and double wheel.

Let G be a (p, q) graph. Let f : V (G) → {1, 2,…, k} be a map where k ∈ ℕ is a variable and k>1. For each edge uv, assign the label gcd(f (u), f (v)). f is called k-total prime cordial labeling of G if |tf(i) − tf (j)| ≤1, i, j ∈ {1, 2, …, k} where tf (x) denotes the total number of vertices and the edges labelled with x. A graph with a k-total prime cordial labeling is called k-total prime cordial graph. In this paper we investigate the 4-total prime cordial labeling of G ⋃ Pn where G is a 4-total prime cordial graph.

Let G be any simple graph . In this paper, we define a labeling called group S3 cordial prime labeling and prove that the splitting graph of paths, combs and star K1n are group S3 cordial prime. We further give condition for a Splitting graph of Fan graphs to be group S3 cordial prime.

Magnetic Resonance (MR) images are exaggerated with noise leads to the limitations in achieving better restoration results. So there is a need for developing efficient restoration algorithms in the medical images to avoid crucial problems for diagnosis and treatment of diseases. In this present work, a trilateral based fuzzy filter is developed for better restoration results. To preserve the structural information, edges trilateral based fuzzy trapezoidal membership function along with median filter has been used. The weights are obtained for the trilateral filter and median filter using fuzzy trapezoidal membership function. The weights are convoluted with the filtered image of the trilateral and median filter. The results obtained using this method are improved and it is analyzed with the simulated images and real images. The results are compared with the median, wiener, trilateral, fuzzy NLM with trapezoidal.

Benign papillary cystadenofibroma of fallopian tube is very rare tumor of female genital tract. Usually asymptomatic and incidental finding. Worldwide literature only 18 cases were found. On account of its rarity and best of our knowledge, is the first case of benign papillary cystadenofiboma presented as posterior fornix cyst, so we are presenting this case. We report a rare case of benign papillary cystadenofibroma of fallopian tube in a 30-years old female P3L3 presenting with abdominal pain. On examination abdomen was soft, per vaginally mobile non-tender cystic mass of 5×4 cm was noted. Laparotomy was planned. Intraoperatively 5x4 cm cyst arising from serosal surface of left fallopian tube near fimbrial end noted with bilateral ovaries normal. Left fimbrial cystectomy with salpingectomy was done with sparing both ovaries. Histopathology suggestive of benign papillary cystadenofibroma of fallopian tube. Patient had uneventful recovery in follow up period. Benign papillary cystadenofibroma of fallopian tube is rare tumor found incidentally. Tumor seems to have benign course only cystectomy is required for treatment.

We hereby report the results of a prospective cohort study where we compared the association of various demographic factors and gestational diabetes mellitus (GDM), determined the prevalence of GDM and its maternal and fetal outcomes.

Heart defects have remained one of the top causes of death in the world. As much as sporting activities and exercises are considered beneficial for the health of an individual, there are also a few but significant demerits as huge populations of athletes suffer from heart defects. This is especially pronounced among those who participate in strenuous activities for long periods of time. One of the oldest and most useful diagnostic tools for heart disease is the electrocardiograph. However, they remain bulky, heavy and not wearable. They also often require the help of medical professionals to interpret the electrocardiogram. As a result, the preliminary results of the implementation of a prototype ECG Smart Jersey using Next Generation Computing (which include IoT, Machine Learning and Android App) for the automated detection of heart defects among athletes is presented in this paper. The prototype is a proof of concept, which could be further enhanced to ensure that a wearable and automated ECG monitoring, analysis and interpretation is accomplished for athletes in order to reduce the burden of sudden deaths among them.

With the increasing effect of globalization and technology, Human Resource Information Technology plays a key role in today’s modern enterprise management. However, developing countries such as Cameroon are facing challenges in its implementation. The aim of this research is to explore factors influencing the adoption of HRIS by SMEs. To understand this issue, this paper integrates the unified theory of acceptance and use of technology (UTAUT) model. Primary data have been collected through survey by administrating structured questionnaire to the employees (HR department) of a number of organizations. We found that Performance Expectancy and Internal Social Influence have a significant effect on the intention to adopt HRIS. In contrast, the specific groups—age and education—are significantly different in the relationship between Adoption Intention and Use. Of course, these findings open the door for future research issues, including explaining why SMEs organizations are sluggish to respond to HR innovation.

A Radio Mean labeling of a connected graph G is a one to one map f from the vertex set V ( G ) to the set of natural numbers N such that for two distinct vertices u and v of G , d ( u , v ) + ⌈ f ( u ) + f ( v ) 2 ⌉ ≥ 1 + diam ( G ) . The radio mean number of f , r m n ( f ) , is the maximum number assigned to any vertex of G .The radio mean number of G , r m n ( G ) is the minimum value of r m n ( f ) taken over all radio mean labelings f of G . In this paper we find the radio mean number of graphs with diameter three, lotus inside a circle, Helms and Sunflower graphs.

In this paper we investigate 4-prime cordial labeling behavior of shadow graph of a path, cycle, star, degree splitting graph of a bistar, jelly fish, splitting graph of a path and star.

This paper presents the result of experimental comparison of the video quality of streamed videos using streaming devices and streaming platforms. The objective metrics considered for the comparison of these media are the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Visual Information Fidelity (VIF), Information Fidelity Criterion (IFC), and Visual Signal-to-Noise Ratio (VSNR). To determine the better platform for effective multimedia applications especially for educational purposes, videos were obtained and streamed via Data video, YouTube and Facebook. In the first two scenarios, Data video at two different settings were engaged at the encoding end and PotPlayer was used for decoding and recording of the transmitted videos. In the third and fourth scenarios, Videos were streamed via YouTube and Facebook. The aforementioned objective metrics were implemented in MATLAB R2017b for the experimental analyses and our results showed that YouTube produced streamed videos of the best quality with PNSR of 37.98, SIMM of 0.98 and VSNR of 33.12.

Welding process with trailing heat sink for 2 mm mild steel plates has been analyzed to estimate distortion and residual stresses using a finite element modeling software Sysweld. The material properties used for the analysis are both temperature dependent and phase dependent. A transient thermal analysis is carried out using Goldak double ellipsoidal heat source model and heat sink as Gaussian model with negative heat flux. The finite element analysis (FEA) is conducted by considering the material properties of all phases of steel as well as without phase transformation i.e. by considering properties of only ferrite phase. Temperature distribution, distortion and residual stresses are calculated and compared for four cases: without phase without cooling, without phase with cooling, with phase without cooling and with phase with cooling. It is found that FEA without phase transformation effects overestimates the residual stresses in the fusion zone (FZ) and heat affected zone (HAZ). It is also found that a trailing heat sink reduces transverse compressive residual stresses thus minimizing the possibilities of buckling.

Let G be a (p;q) graph. Let f : V (G) → {1; 2:::;p} be a function. For each edge uv, assign the label |f (u) − f (v)|. f is called a difference cordial labeling if f is a one to one map and |ef (0) − ef (1)| ≤ 1 where ef (1) and ef (0) denote the number of edges labeled with 1 and not labeled with 1 respectively. A graph with a difference cordial labeling is called a difference cordial graph. In this paper, we investigate the difference cordial labeling behavior of subdivision of some snake graphs.

Magnetic resonance images (MRI) plays a crucial role in neuroscience and medical diagnosis. Denoising MRI images is an important preprocessing step required in many of the automatic computed aided-diagnosis systems in neuroscience. Rician noise occurs in the MRI image during acquisition. Non local mean filter is used for denoising. But the parameter selection is not optimized. The proposed method removal of rician noise in MRI images using bilateral filter by fuzzy trapezoidal membership function improves the denoising efficiency at various noise variances, preserves the fine structures and edges. The fuzzy weights were obtained with the statistical features such as local mean (μ i ) and global mean (μ g ) by constructing trapezoidal membership function. Bilateral filter is used to preserve the edges by smoothening the noises in MRI image and preserves the structural information. Local filter preserves the edges. MRI images are restored by multiplying its corresponding fuzzy weight with the restored image of local order filter and bilateral filter. Experiments on simulated and real MRI data were done at different noise levels by the proposed method and the existing methods. The result shows that the proposed method restores the image in better visual quality and can be well utilized for diagnostic purpose at both low and high densities of rician noise.

Tomatoes are amongst the most consumed vegetables in India. Due to various issues in cultivation and sale of tomatoes, it is not preferred by most farmers in Tamilnadu, India. This paper showers light on various problems faced by the farmers involved in the tomato supply chain process. Around 20 different issues were identified from literature, brainstorming with experts and direct interactions with farmers. These issues were categorized into three main categories namely Avoidable, Unavoidable and Manageable issues. Further the same are divided into five sub - categories namely finance, crop related, nature, labor and miscellaneous issues. It was identified that majority of the farmers faced labor shortage, knowledge of Post Harvesting Techniques (PHT), and transportation issues and basic amenities. Focusing on selective avoidable and manageable issues a conceptual model named “FArmers and COnsumers Only” (FACOO) has been designed such that it would reduce the impact on the tomato farmers and provide a better way to manage their produce at various stages and acquire profit.

Weld distortion and residual stresses are two major issues in the fabrication process. Numerical techniques are being tried out to accurately predict the structural integrity of the welding. Interpass time in the multipass welding is an important parameter which influences the weld distortion and residual stresses. In this study two pass tungsten inert gas (TIG) welding of 6 mm mild steel plates has been analyzed using Finite element analysis (FEA) software Sysweld and parametric study is conducted with different interpass time. The temperature distribution, distortion and residual stresses are calculated using three dimensional finite element model (FEM) considering phase transformations in the material. The transient thermo-metallurgical analysis followed by elasto-plastic analysis is carried out using temperature dependent and phase dependent material properties. The material deposition in the multipass welding is numerically simulated using chewing gum method, where dummy phase and dummy material are assigned for the element activation. The phase proportions are calculated by assigning suitable phase kinetics parameter extracted from continuous cooling transformation (CCT) diagram of a given material. Experiments are conducted for validation after given edge preparation and using same material as filler wire. The FEM analysis is carried out for eight cases with different time interval between passes, starting from 30 s to 240 s in the steps of 30 s. FEM results are verified with experimentally measured values. It is found that the time interval between passes has less influence on the residual stresses but significantly affects the distortion and phase proportion due to the first pass preheating effect on second pass and second pass postheating effect on first pass.

Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.

Lanthanum carbonate incorporated chitosan microparticles (LCCM) is proposed for collection of phosphate from 0.9% saline and human plasma serum samples. Loading of ∼4.1 mg of La 2 (CO 3 ) 3 ·3H 2 O onto 0.1 g of chitosan has been achieved. The optimal glutaraldehyde (cross-linker), acetic acid concentration and lanthanum carbonate to chitosan weight ratio for preparation of LCCM were found to be 84.6–85.7%, 0.3–0.4 mol l −1 , and 1:1 respectively. Moreover, the percent collection of phosphate is higher with LCCM compared to lanthanum carbonate (LC) at all weights in a weight variation study. Under optimal conditions, the LCCM was found to be a better phosphate collector with a percent efficiency of >99% compared to 85% with lanthanum carbonate and less than 4% with glutaraldehyde cross-linked chitosan employing 0.1 g of respective materials.

The restrained bondage number of a graph G denoted by br (G) is the least positive integer k such that there exists F C E(G) with |F| = k and γ r (G \ F) > γ r (G). In this paper we define restrained bondage number br (G) and initiate a study of this parameter. We obtain br (G) for various classes of graphs, certain bounds for br (G) and attain the sharpness for these bounds. We also define uniform restrained bondage number bur (G) and derive few results for bur (G).

The response of peripheral venous plasma angiotensin II (A-II) to upright posture combined with an injection of furosemide was higher in patients with hypertension and associated renal parenchymal disease, renal artery stenosis or malignant hypertension than in patients with essential hypertension and normotensive control patients. Very low responses to the same stimuli were observed in primary aldosteronism. Markedly increased A-II levels were measured in the renal veins of the affected kidneys in hypertensive patients with associated unilateral renal parenchymal disease or renal artery stenosis, suggesting that human kidneys contain considerable converting enzyme activity. Plasma A-II levels were normalized in three patients with primary aldosteronism after removal of an adrenal adenoma and in one patient with malignant hypertension after bilateral nephrectomy and renal transplantation.

The degree prime graph DP(G) of a graph G is a graph having the same vertex set as G and two vertices are adjacent in DP(G) if and only if their degrees are unequal and relatively prime in G. In this paper, we obtain several properties of DP(G) and characterise graphs G which are isomorphic to DP(G).