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Inflammation or swelling of the tissue lining on the sinuses is known as sinusitis, due to an inflammatory response, or an infection brought on by a virus, bacteria, or fungus. An unchecked sinus infection can spread to the eyes and brain, causing blindness and brain abscess respectively. In this work, a non-invasive technique developed for the detection of sinusitis from thermal images has been presented. CNN, KNN, and SVM have been used as classifiers. For KNN and SVM features were extracted from cropped thermal face images by taking the output of the first fully connected layer of Alexnet and VGGNet. The fusion of these classifiers has been done to improve the detection performance of the individual classifiers. The algorithm has been tested on a publicly available dataset. The performance evaluation has been carried out on the basis of five indices. Based on the results, fusion with neural network illustrates the highest accuracy of 99.59% irrespective of the feature extraction method, followed by the majority voting technique with an accuracy of 99.18% for features extracted by Alexnet and 98.78% for features extracted by VGGNet.

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an IoT and edge computing based framework is proposed to automatically diagnose COVID-19 from CT scans of the patients using Deep Learning techniques. The proposed method requires less computational power and uses ensemble learning to increase the models’ overall predictive performance. In the simulation, it was found that each model performs better in some areas than the other. The proposed scheme uses ensemble learning to take advantage of such an occurrence and achieved an accuracy of 86.2% and an AUC score of 89.8% on the COVIDCT-Dataset. This accuracy is achieved keeping the hardware accessibility in mind by training the models using a labeled dataset of CT-scans of the patients. Unlike other works, we were able to train models on a single enterprise-level GPU. It can easily be provided on the edge of the network, which reduces communication overhead and latency. This work aims to demonstrate a less hardware-intensive approach for COVID19 detection with excellent performance combined with medical equipment and help ease the examination procedure.

In spite of innumerable benefits of multi-biometry authentication, a couple of imaging sensors are required to capture multi-biometric samples, but that increase the overall cost as well as degree of user cooperation. This work presents a single sensor-based multimodal biometric identification system by fusing major finger knuckle, minor finger knuckle, palm print and handprint features of the human hand for enhancing the security and privacy of any consumer device. A virtual imaging device has been suggested to capture palmer and dorsal view of hand with single-shot multi-trait acquisition mechanism. The hand images captured from digital camera are first preprocessed to get the major knuckle, minor knuckle and palm ROI’s. The finger knuckles and palm print ROI’s are then enhanced and transformed to illumination invariant representation using robust encoding techniques, over which ray tracing features are emphasized predominantly. A non-rigid multi-scale approach, namely deep matching, has been employed to obtain the matching score between the corresponding correlation maps for finger knuckle or palm print recognition. Apart from that, we present a new scheme to extract shape, and geometrical features of handprint and employ metric learning-based L2-norm for feature matching in $$2D^{2}{} \textit{PCA}+2D^{2}{} \textit{LDA}$$ space. Five publicly available databases are used to evaluate the effectiveness of proposed approach. Finally, the score-level weighted sum rule fusion has been adopted to combine matching scores of four traits which show that the proposed method outperforms other unimodal and state-of-the-art multimodal identification methods in terms of EER (0.01%), DI (3.64) and CRR (100%).

In recent years bioelectric signals like electrocardiogram, electroencephalogram, and photoplethysmogram based person authentication has received tremendous attention of the biometric community. This is due to the inherent advantages offered by these traits in comparison to the traditional biometrics like fingerprint, face, and iris. The unique qualities possessed by these traits ensure entire population coverage and alleviate the need of vitality check. An overview of biometrics with emphasis on ECG-based biometric recognition has been presented in this chapter. One non-fiducial feature extraction approach, which uses combination of autocorrelation and discrete cosine transform, has been discussed. The classification task has been carried out by employing two neural network architectures, namely, multilayer perceptron and radial basis function. Another set of experiments have been carried out using support vector machine as classifier in the multi-class classification mode. Two set of datasets have been used in these experiments, first one is Lead II ECG database and second dataset is Lead I database. Both the datasets have a population size of more than 100 subjects with multiple session recordings having sufficient gap between them. The experiments have been conducted for both within and across-session settings. The results obtained depict the efficacy of ECG as a trait which can be utilised for biometric recognition especially in the multimodal mode.

Electrocardiogram (ECG) recording is a multi-channel signal that results due to the traversing of electrical impulse through a human heart. The amplitude and time intervals of the characteristic points, P-QRS-T, of an ECG waveform that results due to different events occurred during a cardiac cycle will contain vital information related to the functioning of cardiovascular system. Accurate detection of these fiducial points ensures the accurate diagnosis of various types of arrhythmia. In this paper, an approach employing Dual Window Fourier Transform (DWFT) for QRS complex detection has been suggested. For extracting the QRS complex from ECG waveform, thresholding was done on the normalized energy signal obtained on transformation of ECG signal with DWFT. The testing of suggested approach was carried out on the records of MIT-BIH Arrhythmia database and the achieved detection accuracy has been found to be more satisfactory.

Transformers are one of the most valuable equipment in substation. The operation of entire power system is strongly linked to reliable and accurate operation of this device. Therefore, the life assessment of transformers and their maintenance schedule is critical for efficient transmission and distribution of electric power. In this study an on-line predictive maintenance approach for transformers using thermal images has been suggested. Four segmentation approaches have been compared for hotspot detection in thermal images of transformers. The Modified Kmeans algorithm provides the best segmentation performance amongst the four algorithms studied in this work.

Due to distinct advantages offered by biomedical signals oflate biomelectric signals like ECG, EEG and PPG have been examined to determine their efficacy for biometric application. This study explores the ability of using Photoplethysmogram (PPG) signal as biometric identifier. Three feature vectors were obtained by computing autocorrelation of PPG signal and its first and second derivatives. In this work, two fusion schemes have been employed, feature level fusion and score level fusion. The experiments were conducted on PPG signals collected from fingertips of 38 healthy subjects. The results obtained show PPG has a potential to be used as biometric and higher recognition rate is achieved by using score level fusion.

In the present globalized society, people from all over the world are coming and working together. The biggest hurdle in this process is the medium of interaction, as all individuals do not speak a common language. A way to circumvent this problem is to develop a machine based system for recognition of different languages and subsequently employing automatic machine translation mechanism for communication between people from diverse language or dialect groups. A MFCC and GMM based approach for recognition of dialects of Himachal Pradesh has been presented in this paper. Around seven dialects spoken in different regions of Himachal Pradesh have been used in this study. The results obtained shows that dialects spoken in adjacent regions have significant overlap and are difficult to distinguish. Accuracy for offline and online tests was respectively around 80% and 70%.

A field experiment was conducted at Punjab Agricultural University, Regional Research Station, Gurdaspur (Punjab) during kharif 2016-17, to study the effect of split application of potassium on yield and yield attributes of soybean. The experiment was conducted in randomized complete block design with seven treatments applied at sowing and as split application of potassiumT1: Recommended fertilizer dose (12.5 kg N, 32 kg P2O5and no potassium application) , T2: 15 kg K2O ha-1(at sowing), T3: 30 kg K2O ha-1(at sowing), T4:60 kg K2O ha-1(at sowing), T5: 7.5 kg K2O ha-1 (at sowing) + 7.5 kg K2O ha-1 (at flowering), T6: 15 kg K2O ha-1(at sowing) +15 kg K2O ha-1(at flowering), T7: 30 kg K2O ha-1(at sowing) +30 kg K2O ha-1(at flowering). Results revealed that significantly higher grain yield (25.2 q ha-1) was obtained in T7 (30 kg K2O ha-1 (at sowing) + 30 kg K2O ha-1 (at flowering) as compared to all other treatments but it was statistically at par with the split application of potassium @ 15 kg K2O ha-1 (at sowing) + 15 kg K2O ha-1 (at flowering) (T6) with grain yield of (22.6 q ha-1). Split application of potassium significantly increased soybean grain yield over the basal application with highest benefit cost ratio (7.1). Among the split application of potassium, grain yield of soybean was increased significantly by 15.6 % in T7 treatment over T5. Split application of potassium was more beneficial than applying full dose of potassium at the time of sowing due to higher number of pods and pod weight plant-1.

Fundus image, picture of posterior portion of eye, facilitates non-intrusive diagnosis of various eye diseases. Glaucoma is progressive neurodegenerative ocular disorder, second leading cause of blindness in the world. For diagnosis of Glaucoma using fundus image, cup to disc ratio is the method preferred by researchers. Various methods like Active Contour, K-mean Clustering and Thresholding method are used to segment cup and disk from fundus images. In this paper a comparative study of the above mentioned methods has been presented. These methods were independently tested on two public databases namely DRISHTI-GS, and DRIONS-DB. Based upon the results obtained it was found that active contour technique is best among these methods.

The ongoing pandemic of COVID-19 has shownthe limitations of our current medical institutions. Thereis a need for research in the field of automated diagnosisfor speeding up the process while maintaining accuracyand reducing computational requirements. In this work, anautomatic diagnosis of COVID-19 infection from CT scansof the patients using Deep Learning technique is proposed.The proposed model, ReCOV-101 uses full chest CT scans todetect varying degrees of COVID-19 infection, and requiresless computational power. Moreover, in order to improvethe detection accuracy the CT-scans were preprocessed byemploying segmentation and interpolation. The proposedscheme is based on the residual network, taking advantageof skip connection, allowing the model to go deeper.Moreover, the model was trained on a single enterpriselevelGPU such that it can easily be provided on the edge ofthe network, reducing communication with the cloud oftenrequired for processing the data. The objective of this workis to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that canbe combined with medical equipment and help ease theexamination procedure. Moreover, with the proposed modelan accuracy of 94.9% was achieved.

Numerous behavioral or physiological biometric traits, including iris, signature, hand geometry, speech, palm print, face, etc. have been used to discriminate individuals in a number of security applications over the last 30 years. Among these, hand-based biometric systems have come to the attention of researchers worldwide who utilize them for low- to medium-security applications such as financial transactions, access control, law enforcement, border control, computer security, time and attendance systems, dormitory meal plan access, etc. Several approaches for biometric recognition have been summarized in the literature. The survey in this article focuses on the interface between various hand modalities, summary of inner- and dorsal-knuckle print recognition, and fusion techniques. First, an overview of various feature extraction and classification approaches for knuckle print, a new entrant in the hand biometrics family with a higher user acceptance and invariance to emotions, is presented. Next, knuckle print fusion schemes with possible integration scenarios, and traditional capturing devices have been discussed. The economic relevance of various biometric traits, including knuckle print for commercial and forensic applications is debated. Finally, conclusions related to the scope of knuckle print as a biometric trait are drawn and some recommendations for the development of hand-based multimodal biometrics have been presented.

A field investigation was carried out to characterize the growth and development of Bt cotton hybrids by detopping and use of plant growth retardants during the rainy (kharif) season of 2011 and 2012. The experimental site had loamy sand soils with normal in reaction. The experiment (split plot) involved three Bt cotton hybrids (MRC 7017, MRC 7031 and RCH 314) in main plots and growth regulation treatments (Mepiquat chloride (MC) @ 300 ppm, 2, 3, 5-tri iodo benzoic acid (TIBA) @ 100 ppm and Maelic hydrazide (MH) @ 250 ppm) in sub plots with four replications. Hybrid MRC 7017 produced significantly higher (p

Developing a multimodal biometric system based on single-shot imaging (SSI) has recently grown interested in researchers worldwide. A palm region basically enriches with most discriminative features like lines, shape, and geometry which can be easily clubbed and captured together. In this work, feature-level fusion of hand shape, geometry, and palm print features has been performed. The extracted palm ROI samples undergo certain rotation and illumination effects that limit the matching performance. ROI samples are first geometrically aligned and then transformed into illumination-invariant form using CS-LBP. Further, local key points of transformed ROI images are extracted using SURF descriptor. In addition to this, a set of novel geometrical and shape features have also been computed from the hand registered image. All the three set of features are concatenated, and then, the highly uncorrelated features are selected from the fused feature set using sub-pattern PCA for classification. The performance of the proposed multimodal system is found to be superior to each of individual modality as well as reported state-of-the-art systems.

The protozoan parasite Leishmania has evolved several strategies to undermine host defense mechanisms by inducing Th2-type adaptive immunity and suppressing effector functions of Th1 phenotype. In our earlier studies, using centrin gene-deleted Leishmania (LdCen−/−) parasites as an immunogen, we have shown induction of an effective Th1-type immunity and robust memory responses that mediate protection against virulent challenge. However, role of inhibitory signals in Leishmania vaccine induced immunity in general, and LdCen−/− in particular has not been studied. Herein, we report that immunization with LdCen−/− parasites produces more functional Th1-type CD4+ T cells via downregulation of CD200–CD200R immune inhibitory axis compared to wild-type infection. We found that expression of CD200 and CD200R was significantly reduced in LdCen−/− infection compared to wild-type infection. Diminished CD200–CD200R signaling in LdCen−/− infection enabled proliferation of CD4+ T cells and resulted in the induction of pro-inflammatory cytokines and suppression of anti-inflammatory response. The effects of diminished CD200–CD200R signaling by LdCen−/− were most evident in the suppression of IL-10-producing CD4+ T cells that helped enhance more Th1 cytokine producing and multi-functional T cells compared to wild-type infection. In vivo blocking of CD200 expression with anti-CD200 treatment in wild-type infected mice limited Th2 response as indicated by reduction of IL-10-producing Tr1 cells and reduced parasite burden. On the other hand, treatment with anti-CD200 improved the LdCen−/− vaccine-induced multifunctional response and reduction in splenic parasite load upon challenge. Taken together, these studies demonstrate the role of CD200–CD200R signals in the protection induced by LdCen−/− parasites.

The protozoan parasite

In this work, the hand-based multimodal biometric system is presented using score-level fusion of hand geometry and local palmprint features. Initially, a palm ROI of fixed size has been cropped on the basis of finger base points. However, these images are not well aligned and reduce the matching accuracy. To better align them, L-K tracking-based palm image alignment method has been presented. Following this, the poor contrast ROI image is enhanced using novel fractional G-L filter. Then, local keypoints of aligned ROI images are extracted using Block–SIFT descriptor. Secondly, a set of novel geometrical features has been computed from Palmer region of hand image. Further, the highly uncorrelated features are selected from palm and hand geometry using Dia-FLD. In order to handle robust classification, a high-performance method Linear SVM has been used. Finally, score-level fusion rule has been employed which has shown the increased performance of combined approach in terms of Correct Recognition Rate (99.34%), Equal Error Rate (2.16%), and Computation Time (2084 ms). The proposed system has been tested on largest publicly available contact based and contactless databases: Bosphorus hand database, CASIA, and IITD palmprint databases to validate the results.

Personal Authentication using biometrie traits has emerged as a powerful technology in various scenarios including criminal and civilian applications. A large number of studies related to Finger Knuckle Image recognition have been proposed recently. It is evident that an accurate segmentation of region of interest is very crucial stage to achieve superior recognition results. In this article, we have proposed a novel ROI segmentation algorithm for extracting a fixed size major knuckle ROI from finger dorsal image using Watershed Transformation Algorithm (WTA). At first, the finger dorsal image is aligned by constructing a local coordinate system to control the spatial variation occurred during acquisition. For this, we have used the magnitude of the finger image to obtain the center knuckle line of the phalangeal joint as one of the ROI coordinate. A Random Decision Forest classifier has been used to measure the performance of proposed FKI recognition algorithm. The algorithm has been evaluated over publicly available PolyU FKP database. The results have also been compared with state-of-art algorithm. It has been observed that proposed algorithm outperforms with EER drop close to 23%. It clarifies that the proposed algorithm has been extracting FKP ROI more consistently and hence assist to improve the performance of conventional finger knuckle image biometric system.

Currently, there is no vaccine against visceral leishmaniasis (VL). Toward developing an effective vaccine, we have reported extensively on the immunogenicity of live attenuated LdCentrin−/− mutants in naive animal models. In VL endemic areas, asymptomatic carriers outnumber symptomatic cases of VL and are considered to be a reservoir of infection. Vaccination of asymptomatic cases represents a viable strategy to eliminate VL. Immunological correlates of protection thus derived might have limited applicability in conditions where the immunized host has prior exposure to virulent infection. To examine whether LdCen−/− parasites can induce protective immunity in experimental hosts that have low-level parasitemia from a previous exposure mimicking an asymptomatic condition, we infected C57Bl/6 mice with wild-type Leishmania donovani parasites expressing LLO epitope (LdWTLLO 103, i.v.). After 3 weeks, the mice with low levels of parasitemia were immunized with LdCen−/− parasites expressing 2W epitope (LdCen−/−2W 3 × 106 i.v.) to characterize the immune responses in the same host. Antigen experienced CD4+ T cells from the asymptomatic (LdWTLLO infected) LdCen−/−2W immunized, and other control groups were enriched using LLO- and 2W-specific tetramers, followed by Flow cytometric analysis. Our analysis showed that comparable CD4+ T cell proliferation and CD4+ memory T cell responses (TCM) represented by CD62Lhi, CCR7+, and IL-7R+ T cell populations were induced with LdCen−/−2W in both asymptomatic and naive animals that received LdCen−/− immunization. Upon restimulation with peptide, TCM cells differentiated into effector T cells and there was no significant difference in the recall response in animals with asymptomatic infection. Following virulent challenge, comparable reduction in splenic parasite burden was observed in both asymptomatic and naive LdCen−/− immunized animals concomitant with the development of multifunctional CD4+ and CD8+ T cells. Further, LdCen−/−2W immunization resulted in complete clearance of the preexisting asymptomatic infection (LdWTLLO). Our results demonstrate that LdCen−/−2W immunization could be efficacious for use in asymptomatic VL individuals. Further, immunization with LdCen−/− could help in reducing the parasite burden in the asymptomatic cases and aid in controlling the VL in endemic areas.

Hand based biometrie authentication is becoming so popular in the field of information security because it is an accurate and easily accessible procedure to legalize the human identity. Many studies related to palm print recognition have been proposed recently. To achieve superior recognition results, an accurate segmentation of region of interest is very crucial. In this article, a novel palm print ROI extraction algorithm has been presented which extracts a fixed size region from a full hand image. The quadrangular shape ROI covers the maximum possible area over palm and thus consists of more features. The basic coordinate system is modified to find accurate base points at fingers bottom so that a square with longest side can be drawn using Bresenham's line algorithm. The publically available CASIA, PolyU and IIT Delhi Contactless palm print databases have been used for testing. Addressing to the matching problem, a novel Deep-Matching algorithm has been used. The results have been compared with two state-of-art algorithms. It has been observed that proposed algorithm outperforms with EER drop not more than 20%. With this it is clear that the proposed algorithm has been extracting palm ROI more consistently and hence assist to improve the performance of traditional palm print biometric system.

No licensed human vaccines are currently available against leishmaniasis. Several anti-leishmanial vaccines are currently undergoing testing, including genetically modified live-attenuated parasite vaccines. Studies with live attenuated Leishmania vaccines such as centrin deleted Leishmania donovani parasites (LdCen−/−) showed protective immunity in animal models. Such studies typically examined the biomarkers of protective immunity however the biomarkers of attenuation in the parasite preparations have not received adequate attention. As several candidate vaccines enter clinical trials, a more complete product characterization to enable maintenance of product quality will help meet regulatory requirements. Towards this goal, we have determined the complete genome sequence of LdCen−/− and its parent strain Ld1S-2D (LdWT) and characterized the LdCen−/− vaccine strain using bioinformatics tools. Results showed that the LdCen−/− parasites, in addition to loss of the centrin gene, have additional deletions ranging from 350 bp to 6900 bp in non-contiguous loci on several chromosomes, most commonly in untranslated regions. We have experimentally verified a subset of these adventitious deletions that had no impact on the attenuation of the LdCen−/− parasites. Our results identified hitherto unknown features of attenuation of virulence that could be used as markers of product quality in production lots and highlight the importance of product characterization in parasitic vaccines.

To develop a biometrie system using finger knuckle patterns has recently grown interest of researchers worldwide. Compared with its hand counterparts, skin patterns lie nearby the joints of finger surface has several unique merits. Knuckle print based biometric system is highly acceptable because these patterns do not disturb with injury and wound on the finger surface. However, geometrical and textural features have been used widely in most of the existing knuckle print recognition methods where an individual is verified by extraction of lines, creases, and shape features. This paper presents a score level fusion scheme for personal recognition using novel inner knuckle print based features i.e, knuckle distance based geometrical features, and local direction pattern based texture features. The proposed method is tested on our in-house Hand database (720 images) collected over a span of 3 months. Finally, the test and training images are compared in terms of using a K-NN classifier. Experimental results reveal the performance of the proposed FKP system in terms of CRR (96%), EER (3.45%), and computation time.

This paper presents a hand biometric system by fusing information of palmprint and finger knuckle to check the loopholes that are present in transfer of payments through various levels of bureaucratic financial inclusion projects. Initially, a novel, fixed size ROIs of palm and finger knuckle has been extracted. The poor contrast ROI images are enhanced using modified CLAHE algorithm. To minimize the pose and illumination effects, Line Ordinal Pattern (LOP) based transformation scheme has been applied. The generation of dense feature representation by using dual tree complex wavelet transform can increase the discrimination power of independent local features. Then, the original feature space is mapped into high dimensional sub feature set, where K2DPCA is performed on each subset to extract high order statistics. Addressing to the matching problem, a high-performance Random Forest method has been employed. Finally, the two modalities are combined at weighted sum score level fusion rule which has shown the increased performance (CRR (100%), EER (0.68%), and (computation time (2130 ms)) of combined approach. The proposed method is evaluated using a virtual combination of publicly available PolyU palm print and PolyU FKP databases.

Single-incision laparoscopic surgery (SIS) is less invasive than standard laparoscopic surgery; however, it is more difficult due to restriction of motion and the impossibility to use assistants. To overcome these obstacles, we developed a self-sustaining multipurpose internal retractor by attaching a Lone Star retractor hook to a laparoscopic bulldog clamp. Herein, we report our SIS experience using our novel retractor. Between October 2008 and April 2011, 104 patients underwent SIS using the internal retractor: 67 bandings (43% simultaneous hiatal hernia repair), 8 sleeve gastrectomies, 27 cholecystectomies, and 2 Nissen fundoplication. Mean age was 40 (range, 21 to 85) and mean body mass index was 40 kg/m (range, 20 to 64 kg/m). No intraoperative complications were observed from the use of the retractor but 2 cases required additional retraction due to liver size. This retractor has been successfully used for different SIS procedures showing to be safe, adaptable, and easy to use, lessening some challenges of SIS.

Splenic artery aneurysms (SAA) are a rare entity most commonly diagnosed incidentally. Their association with pregnancy increases the risk of rupture resulting in a disproportionately high maternal and fetal mortality. Accordingly, elective surgical treatment is recommended in asymptomatic patients with aneurysms less than 2 cm. In this case, we present a patient during her third trimester of pregnancy with a SAA who was treated by laparoscopic aneurysm resection and splenectomy. The patient is a 38-year-old multiparous woman, with an incidental diagnosis of a SAA in 2010. Subsequently, the patient became pregnant and at 27 weeks started to develop abdominal pain. Failed embolization was attempted with worsening of the patient’s symptoms. A CT angiogram revealed a 1.6 cm distal third SAA without any evidence of rupture. Due to the localization of the lesion, the patient was offered a laparoscopic aneurysm resection and splenectomy. Operating time was 90 min and estimated blood loss was 5 cc. Postoperative fetal monitoring was normal. No perioperative complications were observed. The patient was discharged on postoperative day 3. Two months after laparoscopic splenectomy, the patient delivered a male infant in perfect health. Although this is a rare disease, the risk of aneurysmal rupture is increased during pregnancy. As a result of high maternal and fetal mortality, elective surgery should be performed. Laparoscopic surgery is the technique of choice.

The concentration of greenhouse gases has increased as a result of anthropogenic activities, which resulted in the warming of the earth atmosphere. The agriculture sector contributes to emission of greenhouse gases and is also worst affected from the global warming. The increasing concentration of carbon dioxide may have fertilization effect on the productivity of rice-wheat system, but this effect will be negated by the simultaneous increase of temperature. The rise in the day-time maximum temperature has positive effect on the rice yield but negatively affected the wheat yield. While rise in the night-time minimum temperature decreased the yield of rice. Our goal here is to discuss the possible effects of shift in the climatic variables on the productivity of rice-wheat system that will provide important insights for adapting to it.Various options to mitigate the negative impacts of climatic shift on the productivity of rice-wheat system have been discussed.

This article investigates a new approach based on combination of Autocorrelation based method and wavelet transform method used in parallel in single system. The proposed approach utilizes the fusion at decision level. This approach has been tested on the normal PTB database and a larger in-house data set of 100 subjects recorded using BIOPAC MP-150 system with a gap of 2 months between training and testing samples. The results obtained have shown improvement in performance in comparison to the system's performance where single approach was used individually. Moreover, the results on in-house database also validate the permanence characteristic of Electrocardiogram (ECG).

The introduction of laparoscopic surgery, and more recently of robotics, has increased the number of living donor kidney transplants. This approach has already improved living donor acceptance rates. Even newer developments in the field have now been introduced with the purpose of further reducing postoperative pain and length of hospital stay, while offering better cosmetic results. In particular, single-incision surgery has gained popularity by improving the well-known benefits of minimally invasive surgery. In this case report, we present the first single-incision robotic-assisted living donor nephrectomy.

In the quest of enhancing nitrogen (N) use efficiency in irrigated transplanted rice beyond that observed with blanket recommendation, leaf colour chart (LCC) is being used to apply N whenever colour of the first fully opened leaf from the top is less green than a critical colour shade. So as to avoid frequent (every 7–10 days) monitoring of leaf colour, criteria were developed to apply fertilizer N at critical growth stages of rice but by adjusting the dose of N as per colour of the leaf measured with LCC. A series of experiments were carried out at Ludhiana and Gurdaspur locations from 2007 to 2010 with treatments refined progressively to work out appropriate combination of fixed and adjustable rates of fertilizer N at critical stages of transplanted rice. A dose of 30 kg N ha −1 at transplanting as prescriptive N management proved to be adequate for achieving high yields of rice. Corrective N management consisting of adjustable N doses was worked out as application of 45, 30 or 0 kg N ha −1 depending upon leaf colour to be −1 only if leaf colour is less green than LCC shade 4 at initiation of flowering. A combination of these prescriptive and corrective N management strategies resulted in optimum rice grain yield and high N use efficiency with less fertilizer N application than the blanket recommendation. For some rice cultivars, particularly in years with favorable climate, fixed date adjustable dose N management produced yield levels higher than those achieved by applying blanket recommendation for fertilizer N and resulted in agronomic efficiency higher than 25 kg grain/kg N.

Hand biometric systems have been adopted in a variety of access control application because hands are rich with unique line, point, vein, and texture patterns which even low resolution scanners can easily capture. In this article, finger dorsal surface is utilized to extract the local texture information by SIFT and further modified by LDA algorithm. Finally, the test and training images are compared in terms of employing a KNN classifier. The performance of proposed algorithm is tested over a standard benchmark FKP databases (PolyU) with different number of training samples. The obtained results clarify the effectiveness of proposed method with the other state-of-the-art algorithms in terms of the Correct Recognition Rate, Equal error rate, and Computation Time.

Response of Bt cotton to square removal was studied to determine the impact of loss of fruiting forms due to insect pest attack or physiological damage on phenology, growth and yield of cotton at Punjab Agricultural University, Ludhiana, India. Three Bt cotton hybrids were assessed for the removal of floral buds for consecutive four weeks @ 25% and 50% from the day squares start initiation. The vegetative growth (plant height) in 25% and 50% square removal treatments increased significantly (˂0.0001) over that of undamaged control treatment among all the hybrids. In contrast, the reproductive growth (number of squares, flowers, bolls per plant and seed cotton yield) significantly decreased in 50% simulation treatments over that of undamaged control treatment and 25% square removal treatment in different hybrids. The decrease in seed cotton yield was 8.6 and 10.4% as compared to 0 and 25% square removal treatments, respectively. The results indicate that Bt cotton can tolerate at least modest levels (25%) of square removal without yield reduction.