Hybridization of Energy Optimization Technique for Cluster Based Routing using Various Computational Intelligence Methods in WSN

Approaches in WSN technology has determined by opportunity of tiny and inexpensive sensor nodes with adequacy of sensing multiple kinds of information processing and wireless communication. Network lifetime and energy efficiency are major indexes of WSN. Several clustering techniques are intended to extend the network lifetime but whereas there is an issue of incompetent Cluster Head (CH) election. To overcome this issue, an Integration of Novel Memetic and Brain Storm Optimization approach with Levy Distribution (IoNM-BSOLyD) has been proposed for clustering using fitness function. In the meanwhile, election of CH is done by utilizing fitness function, which incorporates following amplitude such as energy, distance to adjacent nodes, distance to BS, and network load. After clustering, routing techniques decides the detecting and pursuing the route in WSN. In this proposed work, a Water Wave Optimization with Hill Climbing technique (WWO-HCg) is introduced for routing purpose. This proposed methodology deals with ternary QoS aspect such as network delay, energy consumption, packet delivery ratio, network lifetime and security to select optimal path and enhance QoS as well. This proposed protocol provides better performance result than other contemporary protocols

Artificial Neural Network Based Automatic Number Plate Recognition System

This paper deals with Automatic Number Plate Recognition (ANPR) using Artificial Neural Network. The ANPR system includes steps like pre-processing, localization, character segmentation and character recognition. The developed system first detects the vehicle and then captures the vehicle image. The captured image is pre-processed in order to enhance it for further processing. In localization the license plate region is located and cropped from the complete image. In character segmentation images of individual alpha-numeric characters are extracted from the localized plate. In this paper, we proposed Neural Network based character recognition. Scaled Conjugate Gradient Backpropogation algorithm is used for training the neural network. This system is implemented in MATLAB R2014b

Blackmailing: the keystone in the human mating system

<p>Abstract</p> <p>Background</p> <p>The human mating system is characterized by bi-parental care and faithful monogamy is highly valued in most cultures. Marriage has evolved as a social institution and punishment for extra pair mating (EPM) or adultery is common. However, similar to other species with bi-parental care, both males and females frequently indulge in EPM in secrecy since it confers certain gender specific genetic benefits. Stability of faithful monogamy is therefore a conundrum. We model human mating system using game theory framework to study the effects of factors that can stabilize or destabilize faithful committed monogamy.</p> <p>Results</p> <p>Although mate guarding can partly protect the genetic interests, we show that it does not ensure monogamy. Social policing enabled by gossiping is another line of defense against adultery unique to humans. However, social policing has a small but positive cost to an individual and therefore is prone to free riding. We suggest that since exposure of adultery can invite severe punishment, the policing individuals can blackmail opportunistically whenever the circumstances permit. If the maximum probabilistic benefit of blackmailing is greater than the cost of policing, policing becomes a non-altruistic act and stabilizes in the society. We show that this dynamics leads to the coexistence of different strategies in oscillations, with obligate monogamy maintained at a high level. Deletion of blackmailing benefit from the model leads to the complete disappearance of obligate monogamy.</p> <p>Conclusions</p> <p>Obligate monogamy can be maintained in the population in spite of the advantages of EPM. Blackmailing, which makes policing a non-altruistic act, is crucial for the maintenance of faithful monogamy. Although biparental care, EPM, mate guarding and punishment are shared by many species, gossiping and blackmailing make the human mating system unique.</p

ANTI-INFLAMMATORY AND ANALGESIC ACTIVITY OF ARENGA WIGHTII GRIFF.-AN ENDEMIC PALM OF WESTERN GHATS

Objective: The present study aims to scientifically validate the anti-inflammatory and analgesic activities of Arenga wightii.Methods: The stem pith was excised from mature palm, sliced into small pieces, shade dried and powdered. The powder was extracted with ethanol, concentrated under reduced pressure and the crude extract was referred to as AW. The anti-inflammatory and analgesic activity of AW was analyzed in Wistar rats and Swiss albino mice.Results: The results revealed that the ethanolic extract of the stem pith of A. wightii showed a dose dependent anti-inflammatory and analgesic activity, which was comparable to the standards, indomethacin and acetyl salicylic acid respectively.Conclusion: The results of the current study reveal that A. wightii possesses significant anti-inflammatory and analgesic activity.Â

Neutrophil-lymphocyte ratio as a predictor of response to neoadjuvant chemotherapy and survival in oesophageal adenocarcinoma

Background: Inflammation has an important role in cancer survival, yet whether serum markers of inflammation predict response to potentially curative neoadjuvant chemotherapy (NAC) in oesophageal adenocarcinoma (OAC) is controversial. This study aimed to determine whether systemic inflammatory response (SIR) was associated with response to NAC and survival. Methods: Consecutive patients with OAC planned to undergo surgery with curative intent received blood neutrophil and lymphocyte measurements at diagnosis to calculate Neutrophil-lymphocyte ratio (NLR). Pathological variables including pTNM stage, differentiation, vascular invasion, and Mandard Tumour Regression Grade (TRG) were recorded. TRGs 1&2 were taken to represent good response and primary outcome was overall survival (OS). Results: During follow-up of 136 patients, 36 patients (26.5%) suffered recurrence and 69 patients (50.7%) died. Receiver-Operator-Characteristic (ROC) analysis of NLR before NAC predicted poor TRG (area-under-the-curve (AUC) 0.71 (95% confidence interval (CI) 0.58-0.83, p=0.002). On univariable analysis, pT-stage (p<0.001), pN-stage (p<0.001), poor differentiation (p=0.006), margin positivity (p=0.001), poor TRG (p=0.014), and NLR (dichotomised 2.25, p=0.017) were associated with poor OS. but only NLR (Hazard Ratio (HR) 2.28 95% CI (1.03-4.93), p=0.042) retained independent significance on multivariable analysis. Conclusions: Pre-treatment NLR was associated with OAC pathological response to NAC and OS

SYNTHESIS, CHARACTERIZATION AND QUANTITATION OF REGIOISOMERIC IMPURITY IN NIMODIPINE BULK AND FORMULATION

Objective: The present research work was directed towards the synthesis characterization and quantitation of regioisomeric impurity of Nimodipine i.e. diethyl 1, 4-dihydro-2,6-dimethyl pyridine dicarboxylate in bulk and tablet formulation, by UV,IR,NMR and GC-MS techniques and a RP-HPLC method was developed as per ICH Q2B guidelines for quantitation of 1, 4-Dihydro-2, 6-Dimethyl-4-(p-nitro phenyl) pyridine-3,5 dicarboxylate (NI) from bulk and formulation. Methods: The synthesis of NI was carried out by Hantzch pyridine synthesis, by using p-nitrobenzaldehyde, ethylacetoacetate, in presence of ammonia and methanol as a catalyst. The percentage yield was found to be 89.29%. Recrystallization and purification of NI was done. The preliminary evaluation was done on laboratory scale via melting point, elemental analysis and TLC. Results: The melting point of impurity was found to be 156-1580C. The TLC of impurity was carried by using Chloroform: Methanol (9:1) and the Rf was found to be 0.79. The confirmation of structure of NI was carried out by using sophisticated techniques i.e., FT-IR, NMR (13C and 1H), GC-MS etc. The RP-HPLC method was developed to quantify the NI in Nimodipine bulk and formulation as per ICH Q2B guidelines. The method validation was done as per ICH guidelines. Conclusion: The validated optimized method was found to be linear, précised, robust, rugged and accurate. Finally NI was quantified from bulk Nimodipine and its marketed tablet formulation. It was concluded that the amount of NI, present in tablet was found to be 0.1% and in the bulk 0.067% respectively. Thus it was revealed that the NI was found to be within the limit laid down ICH guidelines (Not more than 0.1 %)

Can Image Enhancement Allow Radiation Dose to Be Reduced Whilst Maintaining the Perceived Diagnostic Image Quality Required for Coronary Angiography?

Objectives: The aim of this research was to quantify the reduction in radiation dose facilitated by image processing alone for percutaneous coronary intervention (PCI) patient angiograms, without reducing the perceived image quality required to confidently make a diagnosis. Methods: Incremental amounts of image noise were added to five PCI angiograms, simulating the angiogram as having been acquired at corresponding lower dose levels (10-89% dose reduction). Sixteen observers with relevant experience scored the image quality of these angiograms in three states - with no image processing and with two different modern image processing algorithms applied. These algorithms are used on state-of-the-art and previous generation cardiac interventional X-ray systems. Ordinal regression allowing for random effects and the delta method were used to quantify the dose reduction possible by the processing algorithms, for equivalent image quality scores. Results: Observers rated the quality of the images processed with the state-of-the-art and previous generation image processing with a 24.9% and 15.6% dose reduction respectively as equivalent in quality to the unenhanced images. The dose reduction facilitated by the state-of-the-art image processing relative to previous generation processing was 10.3%. Conclusions: Results demonstrate that statistically significant dose reduction can be facilitated with no loss in perceived image quality using modern image enhancement; the most recent processing algorithm was more effective in preserving image quality at lower doses. Advances in knowledge: Image enhancement was shown to maintain perceived image quality in coronary angiography at a reduced level of radiation dose using computer software to produce synthetic images from real angiograms simulating a reduction in dose

Assessment of India’s virtual water trade in major food products

This paper analyzes virtual water trade flows through food products between India and its trading partners. It relies on the gravity model of trade and estimates a panel data fixed effect regression to identify drivers of virtual water trade. Our results show that India was the net exporter of virtual water in food products during 1990-2013; however later it turned out to be its net importer. Further our analysis shows distance between trading partners as the primary driver of virtual water trade. India prefers trading with its neighbours to reduce transportation costs. The availability of arable land and water used in crop production are limiting factors for production of food crops and thus act as essential factors in deciding the virtual water trade flows. These findings indicate that resource endowment factors influence bilateral virtual water trade flows

EEG Signal Classification Automation using Novel Modified Random Forest Approach

Digitalization and automation are the two aspects in the medical industry that define compliance with industry 4.0. Automation is essential for speeding up the diagnosis process, while digitalization leads to smart medicine and efficient diagnosis. Epilepsy is one such disease that can use these automation techniques. The automatic monitoring of epilepsy EEG is of great significance in clinical medicine. Aiming at the non-stationary characteristics of EEG signals, the classification of EEG signals is based on the combination of overall empirical mode. It is proposed using the random forest method. The EEG signal data set has an epileptic interval over 200 single-channel signals with a seizure period. A total of 819,400 data are used as samples. First, the overall epileptic EEG signal modal is decomposed into multiple intrinsic modal functions. The effective features are extracted from the first-order intrinsic modal function. Finally, random forest and Least Square SVM (LS-SVM) are considered to classify the EEG signals characteristics. The correct recognition rate of random forest and LS-SVM is compared. The results show that random forest classification method has an ideal classification effect on epilepsy EEG signals during and between seizures. The recognition accuracy is 99% and 60%, which is higher than the accuracy of the LS-SVM. The proposed method improves clinical epilepsy. The efficiency of EEG signals analysis

Modelling dynamics of institutional credit to agriculture in India

Not AvailableCredit is considered as one of the most important and basic input in agricultural production process. The prime source of agricultural credit in India has drastically shifted from non-institutional (money lenders) to institutional source in the last five decades due to various policy initiatives of Government of India. Grass root level analysis of the dynamic helps in further policy framework. Hence in this study based on district wise average outstanding agricultural credit by scheduled commercial banks (SCBs) for the TE ending 2017-18, three districts from each state indicating high, medium and low exposure categories is selected using clustering technique. For these study districts outstanding agricultural credit by SCBs was extracted (1976-2017) and analysed. From the Bai-Perron test years viz., 1983, 1990, 1997, 2004 and 2011 are identified to be most common structural breaks in the time series data of each district owing to various policy reforms in the field of agricultural finance. Based on these breaks the time series further subdivided into six phases viz., phase-I (1976-1982), phase-II (1983-1989), phase-III (1990-1996), phase-IV (1997-2003), phase-V (2004-2010) and phase-VI (2011-2017). Phase-wise CAGR was calculated for all the districts and Garrett ranking technique is employed for further ranking of phases across six regions of the country. Phase-I is identified as the phase with high rate of growth in agricultural advances in selected districts across all regions except southern where it is ranked second. The policy initiatives of that period i.e. setting of priority sector lending targets and establishment of Regional Rural Banks have played crucial role in this growth phenomenon of agricultural advances. Further recent policies like doubling agricultural package and ground level credit policies have also played crucial role in the growth of agricultural advances at grass root level in all regions except eastern and north-eastern regions. Whereas in the eastern and north-eastern region districts the growth in initial phases was relatively better than in the recent phases indicating the effectiveness of initial policy measures in those regions. Institutional credit to agriculture is influenced by various drivers. Hence factors like number of scheduled commercial bank branches, share of GIA in GSA, share of AUC in GSA and annual rainfall are regressed on district wise outstanding agricultural credit by SCBs. To explore the variability panel dataset was created with the above mentioned variables and the impact of these important drivers on institutional credit to agriculture is quantified at different levels (region level, credit exposure category wise and at national level) by employing panel data regression technique. The consistency and suitability of fixed effect model over random effect model is highlighted by Hausman test. Number of operating branches in the district is one of the important variables with positive influence indicates the institutional credit to agriculture is found to be more responsive for branch expansion especially in Andhra Pradesh, Karnataka, Chhattisgarh, Tamil Nadu and Paducherry. In this study, an attempt was made to evaluate the performance of models like ARIMA, ARIMAX and ARIMA intervention on district level agricultural credit series. In the ARIMAX model number of SCB branches in the district is used as explanatory variable and in the ARIMA intervention model year 2004 is used as intervention point. District wise best model was identified and forecasted the institutional credit supply to agriculture at district level for the next five years. We have also made an attempt to estimate the direct credit requirement for agriculture of the district under certain assumptions. Short term and term credit requirement of the district is arrived separately by using the district level data on area under crops, scale of finance and unit cost. Term credit requirement of southern region districts like Guntur and Belgaum is relatively high and in districts of north eastern region viz, West Tripura and Papumpure it is very low. Hence there is need for counterproductive policy of first estimation of agricultural credit requirements depending on crop patterns and later meeting the requirements through effective policies.Not Availabl